Predictive Analytics For Retail Banking .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Analytics for Retail Banking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analytics is helping the banking industry become smarter in managing the myriad challenges.\n",
    "\n",
    "Challenges:\n",
    "- What is a suitable product to recommend to a customer?\n",
    "- What is the best time to market the product?\n",
    "- Which is the most effective channel to contact a customer?\n",
    "\n",
    "The data is related with direct marketing campaigns of a banking institution. The marketing campaigns were based on phone alls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed.\n",
    "The goal is to predict if the client will subscribe a term deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all required libraries for reading data, analysing and visualizing data\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the training & test data\n",
    "# data = pd.read_csv(\"bank.csv\")\n",
    "\n",
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "client_6f3a3d497e084ad9aa44caf3297f8b8a = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='RdKF5a8Kq9yy4FrND9hxAL6GXSlvWlGwM_IAAcmp4DOd',\n",
    "    ibm_auth_endpoint=\"https://iam.eu-gb.bluemix.net/oidc/token\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3.eu-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_6f3a3d497e084ad9aa44caf3297f8b8a.get_object(Bucket='retailbanking-donotdelete-pr-qepbmndlibmkie',Key='bank.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "data = pd.read_csv(body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2343</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>45</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>1270</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2476</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>184</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education default  balance housing loan  contact  \\\n",
       "0   59      admin.  married  secondary      no     2343     yes   no  unknown   \n",
       "1   56      admin.  married  secondary      no       45      no   no  unknown   \n",
       "2   41  technician  married  secondary      no     1270     yes   no  unknown   \n",
       "3   55    services  married  secondary      no     2476     yes   no  unknown   \n",
       "4   54      admin.  married   tertiary      no      184      no   no  unknown   \n",
       "\n",
       "   day month  duration  campaign  pdays  previous poutcome deposit  \n",
       "0    5   may      1042         1     -1         0  unknown     yes  \n",
       "1    5   may      1467         1     -1         0  unknown     yes  \n",
       "2    5   may      1389         1     -1         0  unknown     yes  \n",
       "3    5   may       579         1     -1         0  unknown     yes  \n",
       "4    5   may       673         2     -1         0  unknown     yes  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 rows of the dataset\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['default'].replace(['yes','no'],[1,0],inplace=True)\n",
    "data['housing'].replace(['yes','no'],[1,0],inplace=True)\n",
    "data['loan'].replace(['yes','no'],[1,0],inplace=True)\n",
    "data['deposit'].replace(['yes','no'],[1,0],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['may', 'jun', 'jul', 'aug', 'oct', 'nov', 'dec', 'jan', 'feb',\n",
       "       'mar', 'apr', 'sep'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"month\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education  default  balance  housing  loan  \\\n",
       "0   59      admin.  married  secondary        0     2343        1     0   \n",
       "1   56      admin.  married  secondary        0       45        0     0   \n",
       "2   41  technician  married  secondary        0     1270        1     0   \n",
       "3   55    services  married  secondary        0     2476        1     0   \n",
       "4   54      admin.  married   tertiary        0      184        0     0   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome  deposit  \n",
       "0  unknown    5   may      1042         1     -1         0  unknown        1  \n",
       "1  unknown    5   may      1467         1     -1         0  unknown        1  \n",
       "2  unknown    5   may      1389         1     -1         0  unknown        1  \n",
       "3  unknown    5   may       579         1     -1         0  unknown        1  \n",
       "4  unknown    5   may       673         2     -1         0  unknown        1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.rename(columns={'default':'defaulter','previous':'prev'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['admin.', 'technician', 'services', 'management', 'retired',\n",
       "       'blue-collar', 'unemployed', 'entrepreneur', 'housemaid', 'unknown',\n",
       "       'self-employed', 'student'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"job\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>562</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>1201</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>545</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>1030</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>608</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>services</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>5090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>1297</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>may</td>\n",
       "      <td>786</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>309</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>may</td>\n",
       "      <td>1574</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>may</td>\n",
       "      <td>1689</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>7</td>\n",
       "      <td>may</td>\n",
       "      <td>1102</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>943</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>3837</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>1084</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>0</td>\n",
       "      <td>611</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>541</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>1119</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>41</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>49</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>513</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>442</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>2067</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>43</td>\n",
       "      <td>management</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>2087</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>0</td>\n",
       "      <td>-192</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>985</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>617</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>43</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>929</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>759</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11132</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>-325</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>may</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11133</th>\n",
       "      <td>37</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>1594</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>apr</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11134</th>\n",
       "      <td>58</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>2596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>18</td>\n",
       "      <td>nov</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11135</th>\n",
       "      <td>36</td>\n",
       "      <td>services</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>6</td>\n",
       "      <td>jun</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11136</th>\n",
       "      <td>31</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>sep</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11137</th>\n",
       "      <td>49</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>15</td>\n",
       "      <td>may</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11138</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>28</td>\n",
       "      <td>aug</td>\n",
       "      <td>688</td>\n",
       "      <td>6</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11139</th>\n",
       "      <td>30</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>2593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>27</td>\n",
       "      <td>may</td>\n",
       "      <td>662</td>\n",
       "      <td>7</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11140</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>28</td>\n",
       "      <td>may</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11141</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>6</td>\n",
       "      <td>may</td>\n",
       "      <td>302</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>52</td>\n",
       "      <td>admin.</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11143</th>\n",
       "      <td>38</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>jul</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11144</th>\n",
       "      <td>60</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>15</td>\n",
       "      <td>may</td>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11145</th>\n",
       "      <td>60</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>-134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>12</td>\n",
       "      <td>may</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>4</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11146</th>\n",
       "      <td>30</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>4</td>\n",
       "      <td>feb</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11147</th>\n",
       "      <td>44</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>nov</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>32</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11149</th>\n",
       "      <td>46</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>3317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>nov</td>\n",
       "      <td>231</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11150</th>\n",
       "      <td>34</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>aug</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11151</th>\n",
       "      <td>40</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>917</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>20</td>\n",
       "      <td>may</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11152</th>\n",
       "      <td>34</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>15</td>\n",
       "      <td>jul</td>\n",
       "      <td>659</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11153</th>\n",
       "      <td>43</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>telephone</td>\n",
       "      <td>9</td>\n",
       "      <td>nov</td>\n",
       "      <td>208</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11154</th>\n",
       "      <td>52</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>jul</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11155</th>\n",
       "      <td>35</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>21</td>\n",
       "      <td>nov</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11156</th>\n",
       "      <td>34</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>-72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>7</td>\n",
       "      <td>jul</td>\n",
       "      <td>273</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>single</td>\n",
       "      <td>primary</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>20</td>\n",
       "      <td>apr</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>16</td>\n",
       "      <td>jun</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>19</td>\n",
       "      <td>aug</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>cellular</td>\n",
       "      <td>8</td>\n",
       "      <td>may</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>failure</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cellular</td>\n",
       "      <td>9</td>\n",
       "      <td>jul</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age            job   marital  education  defaulter  balance  housing  \\\n",
       "0       59         admin.   married  secondary          0     2343        1   \n",
       "1       56         admin.   married  secondary          0       45        0   \n",
       "2       41     technician   married  secondary          0     1270        1   \n",
       "3       55       services   married  secondary          0     2476        1   \n",
       "4       54         admin.   married   tertiary          0      184        0   \n",
       "5       42     management    single   tertiary          0        0        1   \n",
       "6       56     management   married   tertiary          0      830        1   \n",
       "7       60        retired  divorced  secondary          0      545        1   \n",
       "8       37     technician   married  secondary          0        1        1   \n",
       "9       28       services    single  secondary          0     5090        1   \n",
       "10      38         admin.    single  secondary          0      100        1   \n",
       "11      30    blue-collar   married  secondary          0      309        1   \n",
       "12      29     management   married   tertiary          0      199        1   \n",
       "13      46    blue-collar    single   tertiary          0      460        1   \n",
       "14      31     technician    single   tertiary          0      703        1   \n",
       "15      35     management  divorced   tertiary          0     3837        1   \n",
       "16      32    blue-collar    single    primary          0      611        1   \n",
       "17      49       services   married  secondary          0       -8        1   \n",
       "18      41         admin.   married  secondary          0       55        1   \n",
       "19      49         admin.  divorced  secondary          0      168        1   \n",
       "20      28         admin.  divorced  secondary          0      785        1   \n",
       "21      43     management    single   tertiary          0     2067        1   \n",
       "22      43     management  divorced   tertiary          0      388        1   \n",
       "23      43    blue-collar   married    primary          0     -192        1   \n",
       "24      37     unemployed    single  secondary          0      381        1   \n",
       "25      35    blue-collar    single  secondary          0       40        1   \n",
       "26      31     technician    single   tertiary          0       22        1   \n",
       "27      43    blue-collar    single  secondary          0        3        1   \n",
       "28      31         admin.   married  secondary          0      307        1   \n",
       "29      28    blue-collar    single  secondary          0      759        1   \n",
       "...    ...            ...       ...        ...        ...      ...      ...   \n",
       "11132   32    blue-collar   married  secondary          0     -325        1   \n",
       "11133   37     management   married   tertiary          0     1594        1   \n",
       "11134   58  self-employed   married   tertiary          0     2596        0   \n",
       "11135   36       services  divorced  secondary          0      115        0   \n",
       "11136   31     management    single   tertiary          0        0        1   \n",
       "11137   49        retired   married    unknown          0      247        0   \n",
       "11138   32     technician   married  secondary          0      235        1   \n",
       "11139   30  self-employed    single   tertiary          0     2593        0   \n",
       "11140   55       services  divorced  secondary          0     2166        1   \n",
       "11141   32    blue-collar   married  secondary          0      130        1   \n",
       "11142   52         admin.  divorced  secondary          0       48        0   \n",
       "11143   38     unemployed  divorced  secondary          0      189        1   \n",
       "11144   60        retired   married  secondary          0        0        1   \n",
       "11145   60        retired  divorced   tertiary          0     -134        0   \n",
       "11146   30         admin.   married  secondary          1       23        0   \n",
       "11147   44     unemployed   married  secondary          0        0        0   \n",
       "11148   32    blue-collar   married  secondary          0        1        1   \n",
       "11149   46   entrepreneur   married  secondary          0     3317        0   \n",
       "11150   34     management   married  secondary          0      355        0   \n",
       "11151   40     management   married   tertiary          0      917        1   \n",
       "11152   34      housemaid   married  secondary          0      390        1   \n",
       "11153   43         admin.    single  secondary          0       35        0   \n",
       "11154   52     technician   married   tertiary          0      523        1   \n",
       "11155   35    blue-collar   married  secondary          0       80        1   \n",
       "11156   34    blue-collar    single  secondary          0      -72        1   \n",
       "11157   33    blue-collar    single    primary          0        1        1   \n",
       "11158   39       services   married  secondary          0      733        0   \n",
       "11159   32     technician    single  secondary          0       29        0   \n",
       "11160   43     technician   married  secondary          0        0        0   \n",
       "11161   34     technician   married  secondary          0        0        0   \n",
       "\n",
       "       loan    contact  day month  duration  campaign  pdays  prev poutcome  \\\n",
       "0         0    unknown    5   may      1042         1     -1     0  unknown   \n",
       "1         0    unknown    5   may      1467         1     -1     0  unknown   \n",
       "2         0    unknown    5   may      1389         1     -1     0  unknown   \n",
       "3         0    unknown    5   may       579         1     -1     0  unknown   \n",
       "4         0    unknown    5   may       673         2     -1     0  unknown   \n",
       "5         1    unknown    5   may       562         2     -1     0  unknown   \n",
       "6         1    unknown    6   may      1201         1     -1     0  unknown   \n",
       "7         0    unknown    6   may      1030         1     -1     0  unknown   \n",
       "8         0    unknown    6   may       608         1     -1     0  unknown   \n",
       "9         0    unknown    6   may      1297         3     -1     0  unknown   \n",
       "10        0    unknown    7   may       786         1     -1     0  unknown   \n",
       "11        0    unknown    7   may      1574         2     -1     0  unknown   \n",
       "12        1    unknown    7   may      1689         4     -1     0  unknown   \n",
       "13        0    unknown    7   may      1102         2     -1     0  unknown   \n",
       "14        0    unknown    8   may       943         2     -1     0  unknown   \n",
       "15        0    unknown    8   may      1084         1     -1     0  unknown   \n",
       "16        0    unknown    8   may       541         3     -1     0  unknown   \n",
       "17        0    unknown    8   may      1119         1     -1     0  unknown   \n",
       "18        0    unknown    8   may      1120         2     -1     0  unknown   \n",
       "19        1    unknown    8   may       513         1     -1     0  unknown   \n",
       "20        0    unknown    8   may       442         2     -1     0  unknown   \n",
       "21        0    unknown    8   may       756         1     -1     0  unknown   \n",
       "22        0    unknown    8   may      2087         2     -1     0  unknown   \n",
       "23        0    unknown    8   may      1120         2     -1     0  unknown   \n",
       "24        0    unknown    8   may       985         2     -1     0  unknown   \n",
       "25        0    unknown    9   may       617         4     -1     0  unknown   \n",
       "26        0    unknown    9   may       483         3     -1     0  unknown   \n",
       "27        0    unknown    9   may       929         3     -1     0  unknown   \n",
       "28        0    unknown    9   may       538         1     -1     0  unknown   \n",
       "29        0    unknown    9   may       710         1     -1     0  unknown   \n",
       "...     ...        ...  ...   ...       ...       ...    ...   ...      ...   \n",
       "11132     1    unknown   21   may       171         1     -1     0  unknown   \n",
       "11133     0   cellular   17   apr       110         1    260     5  failure   \n",
       "11134     0  telephone   18   nov        15         2     -1     0  unknown   \n",
       "11135     0    unknown    6   jun       201         1     -1     0  unknown   \n",
       "11136     0   cellular    8   sep        76         1     -1     0  unknown   \n",
       "11137     0    unknown   15   may       213         2     -1     0  unknown   \n",
       "11138     0  telephone   28   aug       688         6     -1     0  unknown   \n",
       "11139     0    unknown   27   may       662         7     -1     0  unknown   \n",
       "11140     0    unknown   28   may        23        17     -1     0  unknown   \n",
       "11141     0   cellular    6   may       302         2     -1     0  unknown   \n",
       "11142     1   cellular   17   nov       591         1     -1     0  unknown   \n",
       "11143     1   cellular   21   jul       105         2     -1     0  unknown   \n",
       "11144     0    unknown   15   may       229         1     -1     0  unknown   \n",
       "11145     0   cellular   12   may       243         1    271     4  failure   \n",
       "11146     1   cellular    4   feb       149         2     -1     0  unknown   \n",
       "11147     0   cellular   21   nov       175         4     -1     0  unknown   \n",
       "11148     0    unknown    8   may       102         1     -1     0  unknown   \n",
       "11149     0   cellular   19   nov       231         2     -1     0  unknown   \n",
       "11150     0   cellular   21   aug       314         3     -1     0  unknown   \n",
       "11151     0    unknown   20   may       292         1     -1     0  unknown   \n",
       "11152     0   cellular   15   jul       659         3     -1     0  unknown   \n",
       "11153     0  telephone    9   nov       208         1     -1     0  unknown   \n",
       "11154     1   cellular    8   jul       113         1     -1     0  unknown   \n",
       "11155     1   cellular   21   nov        38         2    172     2  failure   \n",
       "11156     0   cellular    7   jul       273         5     -1     0  unknown   \n",
       "11157     0   cellular   20   apr       257         1     -1     0  unknown   \n",
       "11158     0    unknown   16   jun        83         4     -1     0  unknown   \n",
       "11159     0   cellular   19   aug       156         2     -1     0  unknown   \n",
       "11160     1   cellular    8   may         9         2    172     5  failure   \n",
       "11161     0   cellular    9   jul       628         1     -1     0  unknown   \n",
       "\n",
       "       deposit  \n",
       "0            1  \n",
       "1            1  \n",
       "2            1  \n",
       "3            1  \n",
       "4            1  \n",
       "5            1  \n",
       "6            1  \n",
       "7            1  \n",
       "8            1  \n",
       "9            1  \n",
       "10           1  \n",
       "11           1  \n",
       "12           1  \n",
       "13           1  \n",
       "14           1  \n",
       "15           1  \n",
       "16           1  \n",
       "17           1  \n",
       "18           1  \n",
       "19           1  \n",
       "20           1  \n",
       "21           1  \n",
       "22           1  \n",
       "23           1  \n",
       "24           1  \n",
       "25           1  \n",
       "26           1  \n",
       "27           1  \n",
       "28           1  \n",
       "29           1  \n",
       "...        ...  \n",
       "11132        0  \n",
       "11133        0  \n",
       "11134        0  \n",
       "11135        0  \n",
       "11136        0  \n",
       "11137        0  \n",
       "11138        0  \n",
       "11139        0  \n",
       "11140        0  \n",
       "11141        0  \n",
       "11142        0  \n",
       "11143        0  \n",
       "11144        0  \n",
       "11145        0  \n",
       "11146        0  \n",
       "11147        0  \n",
       "11148        0  \n",
       "11149        0  \n",
       "11150        0  \n",
       "11151        0  \n",
       "11152        0  \n",
       "11153        0  \n",
       "11154        0  \n",
       "11155        0  \n",
       "11156        0  \n",
       "11157        0  \n",
       "11158        0  \n",
       "11159        0  \n",
       "11160        0  \n",
       "11161        0  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "defaulter    0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "prev         0\n",
       "poutcome     0\n",
       "deposit      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "      <td>11162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.231948</td>\n",
       "      <td>0.015051</td>\n",
       "      <td>1528.538524</td>\n",
       "      <td>0.473123</td>\n",
       "      <td>0.130801</td>\n",
       "      <td>15.658036</td>\n",
       "      <td>371.993818</td>\n",
       "      <td>2.508421</td>\n",
       "      <td>51.330407</td>\n",
       "      <td>0.832557</td>\n",
       "      <td>0.473840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.913369</td>\n",
       "      <td>0.121761</td>\n",
       "      <td>3225.413326</td>\n",
       "      <td>0.499299</td>\n",
       "      <td>0.337198</td>\n",
       "      <td>8.420740</td>\n",
       "      <td>347.128386</td>\n",
       "      <td>2.722077</td>\n",
       "      <td>108.758282</td>\n",
       "      <td>2.292007</td>\n",
       "      <td>0.499338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6847.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>550.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1708.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>81204.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age     defaulter       balance       housing          loan  \\\n",
       "count  11162.000000  11162.000000  11162.000000  11162.000000  11162.000000   \n",
       "mean      41.231948      0.015051   1528.538524      0.473123      0.130801   \n",
       "std       11.913369      0.121761   3225.413326      0.499299      0.337198   \n",
       "min       18.000000      0.000000  -6847.000000      0.000000      0.000000   \n",
       "25%       32.000000      0.000000    122.000000      0.000000      0.000000   \n",
       "50%       39.000000      0.000000    550.000000      0.000000      0.000000   \n",
       "75%       49.000000      0.000000   1708.000000      1.000000      0.000000   \n",
       "max       95.000000      1.000000  81204.000000      1.000000      1.000000   \n",
       "\n",
       "                day      duration      campaign         pdays          prev  \\\n",
       "count  11162.000000  11162.000000  11162.000000  11162.000000  11162.000000   \n",
       "mean      15.658036    371.993818      2.508421     51.330407      0.832557   \n",
       "std        8.420740    347.128386      2.722077    108.758282      2.292007   \n",
       "min        1.000000      2.000000      1.000000     -1.000000      0.000000   \n",
       "25%        8.000000    138.000000      1.000000     -1.000000      0.000000   \n",
       "50%       15.000000    255.000000      2.000000     -1.000000      0.000000   \n",
       "75%       22.000000    496.000000      3.000000     20.750000      1.000000   \n",
       "max       31.000000   3881.000000     63.000000    854.000000     58.000000   \n",
       "\n",
       "            deposit  \n",
       "count  11162.000000  \n",
       "mean       0.473840  \n",
       "std        0.499338  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        1.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11162 entries, 0 to 11161\n",
      "Data columns (total 17 columns):\n",
      "age          11162 non-null int64\n",
      "job          11162 non-null object\n",
      "marital      11162 non-null object\n",
      "education    11162 non-null object\n",
      "defaulter    11162 non-null int64\n",
      "balance      11162 non-null int64\n",
      "housing      11162 non-null int64\n",
      "loan         11162 non-null int64\n",
      "contact      11162 non-null object\n",
      "day          11162 non-null int64\n",
      "month        11162 non-null object\n",
      "duration     11162 non-null int64\n",
      "campaign     11162 non-null int64\n",
      "pdays        11162 non-null int64\n",
      "prev         11162 non-null int64\n",
      "poutcome     11162 non-null object\n",
      "deposit      11162 non-null int64\n",
      "dtypes: int64(11), object(6)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# To get a concise summary of the dataframe\n",
    "\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>141.928366</td>\n",
       "      <td>-0.016573</td>\n",
       "      <td>4.315184e+03</td>\n",
       "      <td>-1.003487</td>\n",
       "      <td>-0.126211</td>\n",
       "      <td>-0.076486</td>\n",
       "      <td>0.782547</td>\n",
       "      <td>-0.171159</td>\n",
       "      <td>3.593995</td>\n",
       "      <td>0.550713</td>\n",
       "      <td>0.207618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>defaulter</th>\n",
       "      <td>-0.016573</td>\n",
       "      <td>0.014826</td>\n",
       "      <td>-2.393849e+01</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.003138</td>\n",
       "      <td>0.017781</td>\n",
       "      <td>-0.412504</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>-0.480468</td>\n",
       "      <td>-0.009844</td>\n",
       "      <td>-0.002473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>4315.183833</td>\n",
       "      <td>-23.938489</td>\n",
       "      <td>1.040329e+07</td>\n",
       "      <td>-124.152669</td>\n",
       "      <td>-91.998857</td>\n",
       "      <td>284.299484</td>\n",
       "      <td>25120.222216</td>\n",
       "      <td>-121.985317</td>\n",
       "      <td>6107.664002</td>\n",
       "      <td>227.733044</td>\n",
       "      <td>130.663269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>-1.003487</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>-1.241527e+02</td>\n",
       "      <td>0.249300</td>\n",
       "      <td>0.012924</td>\n",
       "      <td>-0.062099</td>\n",
       "      <td>6.075051</td>\n",
       "      <td>0.009052</td>\n",
       "      <td>3.502385</td>\n",
       "      <td>-0.000962</td>\n",
       "      <td>-0.050833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>-0.126211</td>\n",
       "      <td>0.003138</td>\n",
       "      <td>-9.199886e+01</td>\n",
       "      <td>0.012924</td>\n",
       "      <td>0.113702</td>\n",
       "      <td>0.049034</td>\n",
       "      <td>-0.223992</td>\n",
       "      <td>0.031870</td>\n",
       "      <td>-1.115437</td>\n",
       "      <td>-0.017519</td>\n",
       "      <td>-0.018619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.076486</td>\n",
       "      <td>0.017781</td>\n",
       "      <td>2.842995e+02</td>\n",
       "      <td>-0.062099</td>\n",
       "      <td>0.049034</td>\n",
       "      <td>70.908854</td>\n",
       "      <td>-54.110259</td>\n",
       "      <td>3.140457</td>\n",
       "      <td>-70.730655</td>\n",
       "      <td>-1.138351</td>\n",
       "      <td>-0.236838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.782547</td>\n",
       "      <td>-0.412504</td>\n",
       "      <td>2.512022e+04</td>\n",
       "      <td>6.075051</td>\n",
       "      <td>-0.223992</td>\n",
       "      <td>-54.110259</td>\n",
       "      <td>120498.116170</td>\n",
       "      <td>-39.268069</td>\n",
       "      <td>-1034.115689</td>\n",
       "      <td>-21.255941</td>\n",
       "      <td>78.333097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>-0.171159</td>\n",
       "      <td>0.010267</td>\n",
       "      <td>-1.219853e+02</td>\n",
       "      <td>0.009052</td>\n",
       "      <td>0.031870</td>\n",
       "      <td>3.140457</td>\n",
       "      <td>-39.268069</td>\n",
       "      <td>7.409704</td>\n",
       "      <td>-30.411886</td>\n",
       "      <td>-0.310076</td>\n",
       "      <td>-0.174092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>3.593995</td>\n",
       "      <td>-0.480468</td>\n",
       "      <td>6.107664e+03</td>\n",
       "      <td>3.502385</td>\n",
       "      <td>-1.115437</td>\n",
       "      <td>-70.730655</td>\n",
       "      <td>-1034.115689</td>\n",
       "      <td>-30.411886</td>\n",
       "      <td>11828.363897</td>\n",
       "      <td>126.450007</td>\n",
       "      <td>8.232549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prev</th>\n",
       "      <td>0.550713</td>\n",
       "      <td>-0.009844</td>\n",
       "      <td>2.277330e+02</td>\n",
       "      <td>-0.000962</td>\n",
       "      <td>-0.017519</td>\n",
       "      <td>-1.138351</td>\n",
       "      <td>-21.255941</td>\n",
       "      <td>-0.310076</td>\n",
       "      <td>126.450007</td>\n",
       "      <td>5.253297</td>\n",
       "      <td>0.160076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <td>0.207618</td>\n",
       "      <td>-0.002473</td>\n",
       "      <td>1.306633e+02</td>\n",
       "      <td>-0.050833</td>\n",
       "      <td>-0.018619</td>\n",
       "      <td>-0.236838</td>\n",
       "      <td>78.333097</td>\n",
       "      <td>-0.174092</td>\n",
       "      <td>8.232549</td>\n",
       "      <td>0.160076</td>\n",
       "      <td>0.249338</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   age  defaulter       balance     housing       loan  \\\n",
       "age         141.928366  -0.016573  4.315184e+03   -1.003487  -0.126211   \n",
       "defaulter    -0.016573   0.014826 -2.393849e+01    0.000673   0.003138   \n",
       "balance    4315.183833 -23.938489  1.040329e+07 -124.152669 -91.998857   \n",
       "housing      -1.003487   0.000673 -1.241527e+02    0.249300   0.012924   \n",
       "loan         -0.126211   0.003138 -9.199886e+01    0.012924   0.113702   \n",
       "day          -0.076486   0.017781  2.842995e+02   -0.062099   0.049034   \n",
       "duration      0.782547  -0.412504  2.512022e+04    6.075051  -0.223992   \n",
       "campaign     -0.171159   0.010267 -1.219853e+02    0.009052   0.031870   \n",
       "pdays         3.593995  -0.480468  6.107664e+03    3.502385  -1.115437   \n",
       "prev          0.550713  -0.009844  2.277330e+02   -0.000962  -0.017519   \n",
       "deposit       0.207618  -0.002473  1.306633e+02   -0.050833  -0.018619   \n",
       "\n",
       "                  day       duration    campaign         pdays        prev  \\\n",
       "age         -0.076486       0.782547   -0.171159      3.593995    0.550713   \n",
       "defaulter    0.017781      -0.412504    0.010267     -0.480468   -0.009844   \n",
       "balance    284.299484   25120.222216 -121.985317   6107.664002  227.733044   \n",
       "housing     -0.062099       6.075051    0.009052      3.502385   -0.000962   \n",
       "loan         0.049034      -0.223992    0.031870     -1.115437   -0.017519   \n",
       "day         70.908854     -54.110259    3.140457    -70.730655   -1.138351   \n",
       "duration   -54.110259  120498.116170  -39.268069  -1034.115689  -21.255941   \n",
       "campaign     3.140457     -39.268069    7.409704    -30.411886   -0.310076   \n",
       "pdays      -70.730655   -1034.115689  -30.411886  11828.363897  126.450007   \n",
       "prev        -1.138351     -21.255941   -0.310076    126.450007    5.253297   \n",
       "deposit     -0.236838      78.333097   -0.174092      8.232549    0.160076   \n",
       "\n",
       "              deposit  \n",
       "age          0.207618  \n",
       "defaulter   -0.002473  \n",
       "balance    130.663269  \n",
       "housing     -0.050833  \n",
       "loan        -0.018619  \n",
       "day         -0.236838  \n",
       "duration    78.333097  \n",
       "campaign    -0.174092  \n",
       "pdays        8.232549  \n",
       "prev         0.160076  \n",
       "deposit      0.249338  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.011425</td>\n",
       "      <td>0.112300</td>\n",
       "      <td>-0.168700</td>\n",
       "      <td>-0.031418</td>\n",
       "      <td>-0.000762</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>-0.005278</td>\n",
       "      <td>0.002774</td>\n",
       "      <td>0.020169</td>\n",
       "      <td>0.034901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>defaulter</th>\n",
       "      <td>-0.011425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.060954</td>\n",
       "      <td>0.011076</td>\n",
       "      <td>0.076434</td>\n",
       "      <td>0.017342</td>\n",
       "      <td>-0.009760</td>\n",
       "      <td>0.030975</td>\n",
       "      <td>-0.036282</td>\n",
       "      <td>-0.035273</td>\n",
       "      <td>-0.040680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.112300</td>\n",
       "      <td>-0.060954</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077092</td>\n",
       "      <td>-0.084589</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.022436</td>\n",
       "      <td>-0.013894</td>\n",
       "      <td>0.017411</td>\n",
       "      <td>0.030805</td>\n",
       "      <td>0.081129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>-0.168700</td>\n",
       "      <td>0.011076</td>\n",
       "      <td>-0.077092</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.076761</td>\n",
       "      <td>-0.014770</td>\n",
       "      <td>0.035051</td>\n",
       "      <td>0.006660</td>\n",
       "      <td>0.064497</td>\n",
       "      <td>-0.000840</td>\n",
       "      <td>-0.203888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan</th>\n",
       "      <td>-0.031418</td>\n",
       "      <td>0.076434</td>\n",
       "      <td>-0.084589</td>\n",
       "      <td>0.076761</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.017269</td>\n",
       "      <td>-0.001914</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>-0.030416</td>\n",
       "      <td>-0.022668</td>\n",
       "      <td>-0.110580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.000762</td>\n",
       "      <td>0.017342</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>-0.014770</td>\n",
       "      <td>0.017269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.018511</td>\n",
       "      <td>0.137007</td>\n",
       "      <td>-0.077232</td>\n",
       "      <td>-0.058981</td>\n",
       "      <td>-0.056326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.000189</td>\n",
       "      <td>-0.009760</td>\n",
       "      <td>0.022436</td>\n",
       "      <td>0.035051</td>\n",
       "      <td>-0.001914</td>\n",
       "      <td>-0.018511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.041557</td>\n",
       "      <td>-0.027392</td>\n",
       "      <td>-0.026716</td>\n",
       "      <td>0.451919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>-0.005278</td>\n",
       "      <td>0.030975</td>\n",
       "      <td>-0.013894</td>\n",
       "      <td>0.006660</td>\n",
       "      <td>0.034722</td>\n",
       "      <td>0.137007</td>\n",
       "      <td>-0.041557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102726</td>\n",
       "      <td>-0.049699</td>\n",
       "      <td>-0.128081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>0.002774</td>\n",
       "      <td>-0.036282</td>\n",
       "      <td>0.017411</td>\n",
       "      <td>0.064497</td>\n",
       "      <td>-0.030416</td>\n",
       "      <td>-0.077232</td>\n",
       "      <td>-0.027392</td>\n",
       "      <td>-0.102726</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.507272</td>\n",
       "      <td>0.151593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prev</th>\n",
       "      <td>0.020169</td>\n",
       "      <td>-0.035273</td>\n",
       "      <td>0.030805</td>\n",
       "      <td>-0.000840</td>\n",
       "      <td>-0.022668</td>\n",
       "      <td>-0.058981</td>\n",
       "      <td>-0.026716</td>\n",
       "      <td>-0.049699</td>\n",
       "      <td>0.507272</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.139867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deposit</th>\n",
       "      <td>0.034901</td>\n",
       "      <td>-0.040680</td>\n",
       "      <td>0.081129</td>\n",
       "      <td>-0.203888</td>\n",
       "      <td>-0.110580</td>\n",
       "      <td>-0.056326</td>\n",
       "      <td>0.451919</td>\n",
       "      <td>-0.128081</td>\n",
       "      <td>0.151593</td>\n",
       "      <td>0.139867</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  defaulter   balance   housing      loan       day  \\\n",
       "age        1.000000  -0.011425  0.112300 -0.168700 -0.031418 -0.000762   \n",
       "defaulter -0.011425   1.000000 -0.060954  0.011076  0.076434  0.017342   \n",
       "balance    0.112300  -0.060954  1.000000 -0.077092 -0.084589  0.010467   \n",
       "housing   -0.168700   0.011076 -0.077092  1.000000  0.076761 -0.014770   \n",
       "loan      -0.031418   0.076434 -0.084589  0.076761  1.000000  0.017269   \n",
       "day       -0.000762   0.017342  0.010467 -0.014770  0.017269  1.000000   \n",
       "duration   0.000189  -0.009760  0.022436  0.035051 -0.001914 -0.018511   \n",
       "campaign  -0.005278   0.030975 -0.013894  0.006660  0.034722  0.137007   \n",
       "pdays      0.002774  -0.036282  0.017411  0.064497 -0.030416 -0.077232   \n",
       "prev       0.020169  -0.035273  0.030805 -0.000840 -0.022668 -0.058981   \n",
       "deposit    0.034901  -0.040680  0.081129 -0.203888 -0.110580 -0.056326   \n",
       "\n",
       "           duration  campaign     pdays      prev   deposit  \n",
       "age        0.000189 -0.005278  0.002774  0.020169  0.034901  \n",
       "defaulter -0.009760  0.030975 -0.036282 -0.035273 -0.040680  \n",
       "balance    0.022436 -0.013894  0.017411  0.030805  0.081129  \n",
       "housing    0.035051  0.006660  0.064497 -0.000840 -0.203888  \n",
       "loan      -0.001914  0.034722 -0.030416 -0.022668 -0.110580  \n",
       "day       -0.018511  0.137007 -0.077232 -0.058981 -0.056326  \n",
       "duration   1.000000 -0.041557 -0.027392 -0.026716  0.451919  \n",
       "campaign  -0.041557  1.000000 -0.102726 -0.049699 -0.128081  \n",
       "pdays     -0.027392 -0.102726  1.000000  0.507272  0.151593  \n",
       "prev      -0.026716 -0.049699  0.507272  1.000000  0.139867  \n",
       "deposit    0.451919 -0.128081  0.151593  0.139867  1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>campaign</th>\n",
       "      <th>day</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>deposit</th>\n",
       "      <th>duration</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>39.374063</td>\n",
       "      <td>1195.866567</td>\n",
       "      <td>2.376312</td>\n",
       "      <td>15.340330</td>\n",
       "      <td>0.008246</td>\n",
       "      <td>0.473013</td>\n",
       "      <td>347.929535</td>\n",
       "      <td>0.554723</td>\n",
       "      <td>0.176912</td>\n",
       "      <td>64.338831</td>\n",
       "      <td>0.973763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>39.505144</td>\n",
       "      <td>1203.926440</td>\n",
       "      <td>2.541152</td>\n",
       "      <td>15.235597</td>\n",
       "      <td>0.021091</td>\n",
       "      <td>0.364198</td>\n",
       "      <td>394.658951</td>\n",
       "      <td>0.679527</td>\n",
       "      <td>0.173354</td>\n",
       "      <td>44.061214</td>\n",
       "      <td>0.633230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>42.887195</td>\n",
       "      <td>1621.942073</td>\n",
       "      <td>2.725610</td>\n",
       "      <td>15.442073</td>\n",
       "      <td>0.030488</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>370.182927</td>\n",
       "      <td>0.506098</td>\n",
       "      <td>0.213415</td>\n",
       "      <td>32.746951</td>\n",
       "      <td>0.536585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>47.445255</td>\n",
       "      <td>1366.160584</td>\n",
       "      <td>2.689781</td>\n",
       "      <td>16.364964</td>\n",
       "      <td>0.029197</td>\n",
       "      <td>0.397810</td>\n",
       "      <td>348.346715</td>\n",
       "      <td>0.277372</td>\n",
       "      <td>0.072993</td>\n",
       "      <td>33.540146</td>\n",
       "      <td>0.503650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>40.218239</td>\n",
       "      <td>1793.663679</td>\n",
       "      <td>2.588075</td>\n",
       "      <td>15.792284</td>\n",
       "      <td>0.015199</td>\n",
       "      <td>0.507015</td>\n",
       "      <td>361.482073</td>\n",
       "      <td>0.418161</td>\n",
       "      <td>0.101325</td>\n",
       "      <td>54.456352</td>\n",
       "      <td>0.925175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>65.424165</td>\n",
       "      <td>2417.250643</td>\n",
       "      <td>2.131105</td>\n",
       "      <td>15.519280</td>\n",
       "      <td>0.006427</td>\n",
       "      <td>0.663239</td>\n",
       "      <td>389.993573</td>\n",
       "      <td>0.158098</td>\n",
       "      <td>0.070694</td>\n",
       "      <td>53.003856</td>\n",
       "      <td>0.994859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>39.809877</td>\n",
       "      <td>1865.372840</td>\n",
       "      <td>2.533333</td>\n",
       "      <td>15.938272</td>\n",
       "      <td>0.019753</td>\n",
       "      <td>0.461728</td>\n",
       "      <td>396.155556</td>\n",
       "      <td>0.429630</td>\n",
       "      <td>0.138272</td>\n",
       "      <td>45.039506</td>\n",
       "      <td>0.804938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>38.141928</td>\n",
       "      <td>1081.171181</td>\n",
       "      <td>2.576381</td>\n",
       "      <td>15.728061</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>0.399783</td>\n",
       "      <td>385.955580</td>\n",
       "      <td>0.613218</td>\n",
       "      <td>0.166847</td>\n",
       "      <td>51.546046</td>\n",
       "      <td>0.684724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>26.102778</td>\n",
       "      <td>1500.783333</td>\n",
       "      <td>2.169444</td>\n",
       "      <td>15.175000</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.747222</td>\n",
       "      <td>330.672222</td>\n",
       "      <td>0.141667</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>68.683333</td>\n",
       "      <td>1.297222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>39.001646</td>\n",
       "      <td>1556.294569</td>\n",
       "      <td>2.642348</td>\n",
       "      <td>16.173341</td>\n",
       "      <td>0.015908</td>\n",
       "      <td>0.460779</td>\n",
       "      <td>363.791552</td>\n",
       "      <td>0.480527</td>\n",
       "      <td>0.136039</td>\n",
       "      <td>47.247394</td>\n",
       "      <td>0.843116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>40.997199</td>\n",
       "      <td>1314.719888</td>\n",
       "      <td>2.134454</td>\n",
       "      <td>15.535014</td>\n",
       "      <td>0.022409</td>\n",
       "      <td>0.565826</td>\n",
       "      <td>422.843137</td>\n",
       "      <td>0.313725</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>55.985994</td>\n",
       "      <td>0.812325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>46.857143</td>\n",
       "      <td>1945.457143</td>\n",
       "      <td>2.785714</td>\n",
       "      <td>15.457143</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>330.371429</td>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>55.728571</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age      balance  campaign        day  defaulter  \\\n",
       "job                                                                     \n",
       "admin.         39.374063  1195.866567  2.376312  15.340330   0.008246   \n",
       "blue-collar    39.505144  1203.926440  2.541152  15.235597   0.021091   \n",
       "entrepreneur   42.887195  1621.942073  2.725610  15.442073   0.030488   \n",
       "housemaid      47.445255  1366.160584  2.689781  16.364964   0.029197   \n",
       "management     40.218239  1793.663679  2.588075  15.792284   0.015199   \n",
       "retired        65.424165  2417.250643  2.131105  15.519280   0.006427   \n",
       "self-employed  39.809877  1865.372840  2.533333  15.938272   0.019753   \n",
       "services       38.141928  1081.171181  2.576381  15.728061   0.007584   \n",
       "student        26.102778  1500.783333  2.169444  15.175000   0.002778   \n",
       "technician     39.001646  1556.294569  2.642348  16.173341   0.015908   \n",
       "unemployed     40.997199  1314.719888  2.134454  15.535014   0.022409   \n",
       "unknown        46.857143  1945.457143  2.785714  15.457143   0.014286   \n",
       "\n",
       "                deposit    duration   housing      loan      pdays      prev  \n",
       "job                                                                           \n",
       "admin.         0.473013  347.929535  0.554723  0.176912  64.338831  0.973763  \n",
       "blue-collar    0.364198  394.658951  0.679527  0.173354  44.061214  0.633230  \n",
       "entrepreneur   0.375000  370.182927  0.506098  0.213415  32.746951  0.536585  \n",
       "housemaid      0.397810  348.346715  0.277372  0.072993  33.540146  0.503650  \n",
       "management     0.507015  361.482073  0.418161  0.101325  54.456352  0.925175  \n",
       "retired        0.663239  389.993573  0.158098  0.070694  53.003856  0.994859  \n",
       "self-employed  0.461728  396.155556  0.429630  0.138272  45.039506  0.804938  \n",
       "services       0.399783  385.955580  0.613218  0.166847  51.546046  0.684724  \n",
       "student        0.747222  330.672222  0.141667  0.002778  68.683333  1.297222  \n",
       "technician     0.460779  363.791552  0.480527  0.136039  47.247394  0.843116  \n",
       "unemployed     0.565826  422.843137  0.313725  0.058824  55.985994  0.812325  \n",
       "unknown        0.485714  330.371429  0.042857  0.028571  55.728571  0.700000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(data,index=\"job\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>campaign</th>\n",
       "      <th>day</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>deposit</th>\n",
       "      <th>duration</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apr</th>\n",
       "      <td>40.247021</td>\n",
       "      <td>1734.264355</td>\n",
       "      <td>1.833153</td>\n",
       "      <td>18.630553</td>\n",
       "      <td>0.007584</td>\n",
       "      <td>0.625135</td>\n",
       "      <td>398.721560</td>\n",
       "      <td>0.517876</td>\n",
       "      <td>0.087757</td>\n",
       "      <td>80.005417</td>\n",
       "      <td>0.980498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aug</th>\n",
       "      <td>42.826860</td>\n",
       "      <td>1580.872943</td>\n",
       "      <td>3.514812</td>\n",
       "      <td>15.494404</td>\n",
       "      <td>0.013825</td>\n",
       "      <td>0.452930</td>\n",
       "      <td>352.072416</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.078999</td>\n",
       "      <td>32.930217</td>\n",
       "      <td>0.624753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dec</th>\n",
       "      <td>45.745455</td>\n",
       "      <td>2735.209091</td>\n",
       "      <td>2.181818</td>\n",
       "      <td>14.481818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>397.054545</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.027273</td>\n",
       "      <td>113.172727</td>\n",
       "      <td>2.218182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feb</th>\n",
       "      <td>41.943299</td>\n",
       "      <td>1568.166237</td>\n",
       "      <td>2.155928</td>\n",
       "      <td>8.541237</td>\n",
       "      <td>0.006443</td>\n",
       "      <td>0.568299</td>\n",
       "      <td>343.125000</td>\n",
       "      <td>0.324742</td>\n",
       "      <td>0.117268</td>\n",
       "      <td>72.426546</td>\n",
       "      <td>1.304124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jan</th>\n",
       "      <td>42.345930</td>\n",
       "      <td>1151.093023</td>\n",
       "      <td>1.729651</td>\n",
       "      <td>25.773256</td>\n",
       "      <td>0.017442</td>\n",
       "      <td>0.412791</td>\n",
       "      <td>349.482558</td>\n",
       "      <td>0.235465</td>\n",
       "      <td>0.136628</td>\n",
       "      <td>62.619186</td>\n",
       "      <td>1.290698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jul</th>\n",
       "      <td>41.123514</td>\n",
       "      <td>1061.122193</td>\n",
       "      <td>3.294584</td>\n",
       "      <td>18.407530</td>\n",
       "      <td>0.030383</td>\n",
       "      <td>0.414135</td>\n",
       "      <td>418.867239</td>\n",
       "      <td>0.440555</td>\n",
       "      <td>0.268164</td>\n",
       "      <td>24.017834</td>\n",
       "      <td>0.363276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jun</th>\n",
       "      <td>41.539280</td>\n",
       "      <td>1601.206219</td>\n",
       "      <td>2.853519</td>\n",
       "      <td>11.293781</td>\n",
       "      <td>0.021277</td>\n",
       "      <td>0.446809</td>\n",
       "      <td>364.742226</td>\n",
       "      <td>0.364975</td>\n",
       "      <td>0.114566</td>\n",
       "      <td>21.918167</td>\n",
       "      <td>0.505728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mar</th>\n",
       "      <td>43.427536</td>\n",
       "      <td>2118.605072</td>\n",
       "      <td>2.003623</td>\n",
       "      <td>13.438406</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.898551</td>\n",
       "      <td>291.369565</td>\n",
       "      <td>0.224638</td>\n",
       "      <td>0.047101</td>\n",
       "      <td>66.322464</td>\n",
       "      <td>1.242754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>may</th>\n",
       "      <td>38.728754</td>\n",
       "      <td>1192.357649</td>\n",
       "      <td>2.328258</td>\n",
       "      <td>15.510623</td>\n",
       "      <td>0.014873</td>\n",
       "      <td>0.327550</td>\n",
       "      <td>378.363669</td>\n",
       "      <td>0.839589</td>\n",
       "      <td>0.134207</td>\n",
       "      <td>56.211402</td>\n",
       "      <td>0.734065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nov</th>\n",
       "      <td>42.051962</td>\n",
       "      <td>2349.909862</td>\n",
       "      <td>1.816543</td>\n",
       "      <td>17.381760</td>\n",
       "      <td>0.014846</td>\n",
       "      <td>0.427359</td>\n",
       "      <td>363.215270</td>\n",
       "      <td>0.495228</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>47.084836</td>\n",
       "      <td>0.959703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>45.415816</td>\n",
       "      <td>2037.581633</td>\n",
       "      <td>1.512755</td>\n",
       "      <td>19.186224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.823980</td>\n",
       "      <td>346.801020</td>\n",
       "      <td>0.224490</td>\n",
       "      <td>0.073980</td>\n",
       "      <td>93.691327</td>\n",
       "      <td>1.502551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>44.031348</td>\n",
       "      <td>1930.614420</td>\n",
       "      <td>1.727273</td>\n",
       "      <td>12.109718</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>0.843260</td>\n",
       "      <td>350.968652</td>\n",
       "      <td>0.219436</td>\n",
       "      <td>0.040752</td>\n",
       "      <td>117.774295</td>\n",
       "      <td>2.072100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age      balance  campaign        day  defaulter   deposit  \\\n",
       "month                                                                     \n",
       "apr    40.247021  1734.264355  1.833153  18.630553   0.007584  0.625135   \n",
       "aug    42.826860  1580.872943  3.514812  15.494404   0.013825  0.452930   \n",
       "dec    45.745455  2735.209091  2.181818  14.481818   0.000000  0.909091   \n",
       "feb    41.943299  1568.166237  2.155928   8.541237   0.006443  0.568299   \n",
       "jan    42.345930  1151.093023  1.729651  25.773256   0.017442  0.412791   \n",
       "jul    41.123514  1061.122193  3.294584  18.407530   0.030383  0.414135   \n",
       "jun    41.539280  1601.206219  2.853519  11.293781   0.021277  0.446809   \n",
       "mar    43.427536  2118.605072  2.003623  13.438406   0.000000  0.898551   \n",
       "may    38.728754  1192.357649  2.328258  15.510623   0.014873  0.327550   \n",
       "nov    42.051962  2349.909862  1.816543  17.381760   0.014846  0.427359   \n",
       "oct    45.415816  2037.581633  1.512755  19.186224   0.000000  0.823980   \n",
       "sep    44.031348  1930.614420  1.727273  12.109718   0.003135  0.843260   \n",
       "\n",
       "         duration   housing      loan       pdays      prev  \n",
       "month                                                        \n",
       "apr    398.721560  0.517876  0.087757   80.005417  0.980498  \n",
       "aug    352.072416  0.183673  0.078999   32.930217  0.624753  \n",
       "dec    397.054545  0.181818  0.027273  113.172727  2.218182  \n",
       "feb    343.125000  0.324742  0.117268   72.426546  1.304124  \n",
       "jan    349.482558  0.235465  0.136628   62.619186  1.290698  \n",
       "jul    418.867239  0.440555  0.268164   24.017834  0.363276  \n",
       "jun    364.742226  0.364975  0.114566   21.918167  0.505728  \n",
       "mar    291.369565  0.224638  0.047101   66.322464  1.242754  \n",
       "may    378.363669  0.839589  0.134207   56.211402  0.734065  \n",
       "nov    363.215270  0.495228  0.146341   47.084836  0.959703  \n",
       "oct    346.801020  0.224490  0.073980   93.691327  1.502551  \n",
       "sep    350.968652  0.219436  0.040752  117.774295  2.072100  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(data,index='month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>campaign</th>\n",
       "      <th>day</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>deposit</th>\n",
       "      <th>duration</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>47.365043</td>\n",
       "      <td>1371.835267</td>\n",
       "      <td>2.457077</td>\n",
       "      <td>15.689869</td>\n",
       "      <td>0.021655</td>\n",
       "      <td>0.481052</td>\n",
       "      <td>392.481825</td>\n",
       "      <td>0.483372</td>\n",
       "      <td>0.155452</td>\n",
       "      <td>46.651199</td>\n",
       "      <td>0.664346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>44.533617</td>\n",
       "      <td>1599.927570</td>\n",
       "      <td>2.621477</td>\n",
       "      <td>15.678791</td>\n",
       "      <td>0.014328</td>\n",
       "      <td>0.433790</td>\n",
       "      <td>361.063769</td>\n",
       "      <td>0.488899</td>\n",
       "      <td>0.143757</td>\n",
       "      <td>49.505432</td>\n",
       "      <td>0.822233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>33.017339</td>\n",
       "      <td>1457.255259</td>\n",
       "      <td>2.323195</td>\n",
       "      <td>15.608869</td>\n",
       "      <td>0.013928</td>\n",
       "      <td>0.543491</td>\n",
       "      <td>384.195566</td>\n",
       "      <td>0.440875</td>\n",
       "      <td>0.098351</td>\n",
       "      <td>56.344798</td>\n",
       "      <td>0.913019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age      balance  campaign        day  defaulter   deposit  \\\n",
       "marital                                                                      \n",
       "divorced  47.365043  1371.835267  2.457077  15.689869   0.021655  0.481052   \n",
       "married   44.533617  1599.927570  2.621477  15.678791   0.014328  0.433790   \n",
       "single    33.017339  1457.255259  2.323195  15.608869   0.013928  0.543491   \n",
       "\n",
       "            duration   housing      loan      pdays      prev  \n",
       "marital                                                        \n",
       "divorced  392.481825  0.483372  0.155452  46.651199  0.664346  \n",
       "married   361.063769  0.488899  0.143757  49.505432  0.822233  \n",
       "single    384.195566  0.440875  0.098351  56.344798  0.913019  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(data,index='marital')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>campaign</th>\n",
       "      <th>day</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>deposit</th>\n",
       "      <th>duration</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>primary</th>\n",
       "      <td>48.220667</td>\n",
       "      <td>1523.031333</td>\n",
       "      <td>2.646000</td>\n",
       "      <td>15.510667</td>\n",
       "      <td>0.019333</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>383.803333</td>\n",
       "      <td>0.494667</td>\n",
       "      <td>0.136667</td>\n",
       "      <td>39.976667</td>\n",
       "      <td>0.688000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary</th>\n",
       "      <td>40.086012</td>\n",
       "      <td>1296.480278</td>\n",
       "      <td>2.470051</td>\n",
       "      <td>15.568846</td>\n",
       "      <td>0.014974</td>\n",
       "      <td>0.447407</td>\n",
       "      <td>373.344960</td>\n",
       "      <td>0.533419</td>\n",
       "      <td>0.158145</td>\n",
       "      <td>53.302045</td>\n",
       "      <td>0.821768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tertiary</th>\n",
       "      <td>39.513147</td>\n",
       "      <td>1845.869070</td>\n",
       "      <td>2.487395</td>\n",
       "      <td>15.782597</td>\n",
       "      <td>0.013012</td>\n",
       "      <td>0.541068</td>\n",
       "      <td>368.627812</td>\n",
       "      <td>0.391434</td>\n",
       "      <td>0.098401</td>\n",
       "      <td>53.120629</td>\n",
       "      <td>0.924370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>45.523139</td>\n",
       "      <td>1746.605634</td>\n",
       "      <td>2.672032</td>\n",
       "      <td>16.160966</td>\n",
       "      <td>0.018109</td>\n",
       "      <td>0.507042</td>\n",
       "      <td>346.448692</td>\n",
       "      <td>0.350101</td>\n",
       "      <td>0.052314</td>\n",
       "      <td>50.585513</td>\n",
       "      <td>0.706237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age      balance  campaign        day  defaulter   deposit  \\\n",
       "education                                                                     \n",
       "primary    48.220667  1523.031333  2.646000  15.510667   0.019333  0.394000   \n",
       "secondary  40.086012  1296.480278  2.470051  15.568846   0.014974  0.447407   \n",
       "tertiary   39.513147  1845.869070  2.487395  15.782597   0.013012  0.541068   \n",
       "unknown    45.523139  1746.605634  2.672032  16.160966   0.018109  0.507042   \n",
       "\n",
       "             duration   housing      loan      pdays      prev  \n",
       "education                                                       \n",
       "primary    383.803333  0.494667  0.136667  39.976667  0.688000  \n",
       "secondary  373.344960  0.533419  0.158145  53.302045  0.821768  \n",
       "tertiary   368.627812  0.391434  0.098401  53.120629  0.924370  \n",
       "unknown    346.448692  0.350101  0.052314  50.585513  0.706237  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(data,index='education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5873\n",
       "1    5289\n",
       "Name: deposit, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['deposit'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'balance')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa20470e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['loan']==0]['balance'].plot.hist(bins=30,color='blue', label='loan=0')\n",
    "data[data['loan']==1]['balance'].plot.hist(bins=30, color='green', label='loan=1')\n",
    "plt.legend(fontsize=15)\n",
    "plt.xlabel(\"balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'age')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd493c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['housing']==0]['age'].plot.hist(bins=30,color='blue', label='housing=0')\n",
    "data[data['housing']==1]['age'].plot.hist(bins=30, color='green', label='housing=1')\n",
    "plt.legend(fontsize=15)\n",
    "plt.xlabel(\"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'duration')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd4fa7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[data['deposit']==0]['duration'].plot.hist(bins=30,color='blue', label='deposit=0')\n",
    "data[data['deposit']==1]['duration'].plot.hist(bins=30, color='green', label='deposit=1')\n",
    "plt.legend(fontsize=15)\n",
    "plt.xlabel(\"duration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'balance')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd93cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.scatter(data[\"age\"],data[\"balance\"],color=\"c\")\n",
    "plt.xlabel(\"age\")\n",
    "plt.ylabel(\"balance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0d895f550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd5d14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(data.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'month')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d48bd7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='month',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of last contact month colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"month\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'contact')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d47f8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='contact',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of last contact month colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"contact\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'marital')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d4803c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='marital',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of Marial status colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"marital\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'poutcome')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d47e36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='poutcome',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of poutcome colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"poutcome\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'education')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d4724a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='education',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of educational status colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"education\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:1508: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'job')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d47198d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "sns.countplot(x='job',hue='deposit', data=data,palette='Set1')\n",
    "plt.title(\"Bar chart of last contact month colored by deposit status\", fontsize=17)\n",
    "plt.xlabel(\"job\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/seaborn/categorical.py:462: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  box_data = remove_na(group_data)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Duration')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d478f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data['loan'],y=data['duration'])\n",
    "plt.xlabel(\"Loan\")\n",
    "plt.ylabel(\"Duration\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age         job  marital  education  defaulter  balance  housing  loan  \\\n",
       "0   59      admin.  married  secondary          0     2343        1     0   \n",
       "1   56      admin.  married  secondary          0       45        0     0   \n",
       "2   41  technician  married  secondary          0     1270        1     0   \n",
       "3   55    services  married  secondary          0     2476        1     0   \n",
       "4   54      admin.  married   tertiary          0      184        0     0   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  prev poutcome  deposit  \n",
       "0  unknown    5   may      1042         1      0     0  unknown        1  \n",
       "1  unknown    5   may      1467         1      0     0  unknown        1  \n",
       "2  unknown    5   may      1389         1      0     0  unknown        1  \n",
       "3  unknown    5   may       579         1      0     0  unknown        1  \n",
       "4  unknown    5   may       673         2      0     0  unknown        1  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"pdays\"].replace(-1,int(0),inplace=True)\n",
    "data.head()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>deposit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>562</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>545</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>608</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1297</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>309</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1574</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1689</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>943</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3837</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1084</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>611</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>541</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>513</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>442</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2067</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2087</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-192</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>985</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>617</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>929</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>759</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11132</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-325</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>8</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11133</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1594</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11134</th>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11135</th>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11136</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11137</th>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11138</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>688</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11139</th>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>662</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11140</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11141</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11143</th>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11144</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11145</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11146</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11147</th>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11149</th>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>231</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11150</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11151</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>917</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11152</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>659</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11153</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11154</th>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11155</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11156</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>273</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  defaulter  balance  housing  loan  \\\n",
       "0       59    0        1          1          0     2343        1     0   \n",
       "1       56    0        1          1          0       45        0     0   \n",
       "2       41    9        1          1          0     1270        1     0   \n",
       "3       55    7        1          1          0     2476        1     0   \n",
       "4       54    0        1          2          0      184        0     0   \n",
       "5       42    4        2          2          0        0        1     1   \n",
       "6       56    4        1          2          0      830        1     1   \n",
       "7       60    5        0          1          0      545        1     0   \n",
       "8       37    9        1          1          0        1        1     0   \n",
       "9       28    7        2          1          0     5090        1     0   \n",
       "10      38    0        2          1          0      100        1     0   \n",
       "11      30    1        1          1          0      309        1     0   \n",
       "12      29    4        1          2          0      199        1     1   \n",
       "13      46    1        2          2          0      460        1     0   \n",
       "14      31    9        2          2          0      703        1     0   \n",
       "15      35    4        0          2          0     3837        1     0   \n",
       "16      32    1        2          0          0      611        1     0   \n",
       "17      49    7        1          1          0       -8        1     0   \n",
       "18      41    0        1          1          0       55        1     0   \n",
       "19      49    0        0          1          0      168        1     1   \n",
       "20      28    0        0          1          0      785        1     0   \n",
       "21      43    4        2          2          0     2067        1     0   \n",
       "22      43    4        0          2          0      388        1     0   \n",
       "23      43    1        1          0          0     -192        1     0   \n",
       "24      37   10        2          1          0      381        1     0   \n",
       "25      35    1        2          1          0       40        1     0   \n",
       "26      31    9        2          2          0       22        1     0   \n",
       "27      43    1        2          1          0        3        1     0   \n",
       "28      31    0        1          1          0      307        1     0   \n",
       "29      28    1        2          1          0      759        1     0   \n",
       "...    ...  ...      ...        ...        ...      ...      ...   ...   \n",
       "11132   32    1        1          1          0     -325        1     1   \n",
       "11133   37    4        1          2          0     1594        1     0   \n",
       "11134   58    6        1          2          0     2596        0     0   \n",
       "11135   36    7        0          1          0      115        0     0   \n",
       "11136   31    4        2          2          0        0        1     0   \n",
       "11137   49    5        1          3          0      247        0     0   \n",
       "11138   32    9        1          1          0      235        1     0   \n",
       "11139   30    6        2          2          0     2593        0     0   \n",
       "11140   55    7        0          1          0     2166        1     0   \n",
       "11141   32    1        1          1          0      130        1     0   \n",
       "11142   52    0        0          1          0       48        0     1   \n",
       "11143   38   10        0          1          0      189        1     1   \n",
       "11144   60    5        1          1          0        0        1     0   \n",
       "11145   60    5        0          2          0     -134        0     0   \n",
       "11146   30    0        1          1          1       23        0     1   \n",
       "11147   44   10        1          1          0        0        0     0   \n",
       "11148   32    1        1          1          0        1        1     0   \n",
       "11149   46    2        1          1          0     3317        0     0   \n",
       "11150   34    4        1          1          0      355        0     0   \n",
       "11151   40    4        1          2          0      917        1     0   \n",
       "11152   34    3        1          1          0      390        1     0   \n",
       "11153   43    0        2          1          0       35        0     0   \n",
       "11154   52    9        1          2          0      523        1     1   \n",
       "11155   35    1        1          1          0       80        1     1   \n",
       "11156   34    1        2          1          0      -72        1     0   \n",
       "11157   33    1        2          0          0        1        1     0   \n",
       "11158   39    7        1          1          0      733        0     0   \n",
       "11159   32    9        2          1          0       29        0     0   \n",
       "11160   43    9        1          1          0        0        0     1   \n",
       "11161   34    9        1          1          0        0        0     0   \n",
       "\n",
       "       contact  day  month  duration  campaign  pdays  prev  poutcome  deposit  \n",
       "0            2    5      8      1042         1      0     0         3        1  \n",
       "1            2    5      8      1467         1      0     0         3        1  \n",
       "2            2    5      8      1389         1      0     0         3        1  \n",
       "3            2    5      8       579         1      0     0         3        1  \n",
       "4            2    5      8       673         2      0     0         3        1  \n",
       "5            2    5      8       562         2      0     0         3        1  \n",
       "6            2    6      8      1201         1      0     0         3        1  \n",
       "7            2    6      8      1030         1      0     0         3        1  \n",
       "8            2    6      8       608         1      0     0         3        1  \n",
       "9            2    6      8      1297         3      0     0         3        1  \n",
       "10           2    7      8       786         1      0     0         3        1  \n",
       "11           2    7      8      1574         2      0     0         3        1  \n",
       "12           2    7      8      1689         4      0     0         3        1  \n",
       "13           2    7      8      1102         2      0     0         3        1  \n",
       "14           2    8      8       943         2      0     0         3        1  \n",
       "15           2    8      8      1084         1      0     0         3        1  \n",
       "16           2    8      8       541         3      0     0         3        1  \n",
       "17           2    8      8      1119         1      0     0         3        1  \n",
       "18           2    8      8      1120         2      0     0         3        1  \n",
       "19           2    8      8       513         1      0     0         3        1  \n",
       "20           2    8      8       442         2      0     0         3        1  \n",
       "21           2    8      8       756         1      0     0         3        1  \n",
       "22           2    8      8      2087         2      0     0         3        1  \n",
       "23           2    8      8      1120         2      0     0         3        1  \n",
       "24           2    8      8       985         2      0     0         3        1  \n",
       "25           2    9      8       617         4      0     0         3        1  \n",
       "26           2    9      8       483         3      0     0         3        1  \n",
       "27           2    9      8       929         3      0     0         3        1  \n",
       "28           2    9      8       538         1      0     0         3        1  \n",
       "29           2    9      8       710         1      0     0         3        1  \n",
       "...        ...  ...    ...       ...       ...    ...   ...       ...      ...  \n",
       "11132        2   21      8       171         1      0     0         3        0  \n",
       "11133        0   17      0       110         1    260     5         0        0  \n",
       "11134        1   18      9        15         2      0     0         3        0  \n",
       "11135        2    6      6       201         1      0     0         3        0  \n",
       "11136        0    8     11        76         1      0     0         3        0  \n",
       "11137        2   15      8       213         2      0     0         3        0  \n",
       "11138        1   28      1       688         6      0     0         3        0  \n",
       "11139        2   27      8       662         7      0     0         3        0  \n",
       "11140        2   28      8        23        17      0     0         3        0  \n",
       "11141        0    6      8       302         2      0     0         3        0  \n",
       "11142        0   17      9       591         1      0     0         3        0  \n",
       "11143        0   21      5       105         2      0     0         3        0  \n",
       "11144        2   15      8       229         1      0     0         3        0  \n",
       "11145        0   12      8       243         1    271     4         0        0  \n",
       "11146        0    4      3       149         2      0     0         3        0  \n",
       "11147        0   21      9       175         4      0     0         3        0  \n",
       "11148        2    8      8       102         1      0     0         3        0  \n",
       "11149        0   19      9       231         2      0     0         3        0  \n",
       "11150        0   21      1       314         3      0     0         3        0  \n",
       "11151        2   20      8       292         1      0     0         3        0  \n",
       "11152        0   15      5       659         3      0     0         3        0  \n",
       "11153        1    9      9       208         1      0     0         3        0  \n",
       "11154        0    8      5       113         1      0     0         3        0  \n",
       "11155        0   21      9        38         2    172     2         0        0  \n",
       "11156        0    7      5       273         5      0     0         3        0  \n",
       "11157        0   20      0       257         1      0     0         3        0  \n",
       "11158        2   16      6        83         4      0     0         3        0  \n",
       "11159        0   19      1       156         2      0     0         3        0  \n",
       "11160        0    8      8         9         2    172     5         0        0  \n",
       "11161        0    9      5       628         1      0     0         3        0  \n",
       "\n",
       "[11162 rows x 17 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le=LabelEncoder()\n",
    "data.iloc[:,1]=le.fit_transform(data.iloc[:,1])\n",
    "data.iloc[:,2]=le.fit_transform(data.iloc[:,2])\n",
    "data.iloc[:,3]=le.fit_transform(data.iloc[:,3])\n",
    "data.iloc[:,8]=le.fit_transform(data.iloc[:,8])\n",
    "data.iloc[:,10]=le.fit_transform(data.iloc[:,10])\n",
    "data.iloc[:,15]=le.fit_transform(data.iloc[:,15])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  9,  7,  4,  5,  1, 10,  2,  3, 11,  6,  8])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['job'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['marital'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 3])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 0, 1])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['contact'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  6,  5,  1, 10,  9,  2,  4,  3,  7,  0, 11])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 1, 0, 2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['poutcome'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop(columns=[\"day\",\"deposit\"])\n",
    "Y=data[\"deposit\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2343</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1042</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1467</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1270</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1389</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2476</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>579</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>673</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>562</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>545</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1030</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>608</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1297</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>309</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1574</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>199</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1689</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1102</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>703</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>943</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3837</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1084</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>611</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>541</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1119</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>513</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>442</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2067</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>756</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>388</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2087</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-192</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1120</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>381</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>985</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>617</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>929</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>307</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>538</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>759</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11132</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-325</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11133</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1594</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>260</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11134</th>\n",
       "      <td>58</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2596</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11135</th>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11136</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11137</th>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>213</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11138</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>235</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>688</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11139</th>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2593</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>662</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11140</th>\n",
       "      <td>55</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2166</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11141</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>302</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>591</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11143</th>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>105</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11144</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>229</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11145</th>\n",
       "      <td>60</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "      <td>271</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11146</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11147</th>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>175</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11148</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11149</th>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>231</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11150</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>314</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11151</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>917</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11152</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>659</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11153</th>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>208</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11154</th>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>523</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11155</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11156</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>273</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11157</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>257</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11158</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>733</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>83</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11159</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11160</th>\n",
       "      <td>43</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11161</th>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>628</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11162 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  defaulter  balance  housing  loan  \\\n",
       "0       59    0        1          1          0     2343        1     0   \n",
       "1       56    0        1          1          0       45        0     0   \n",
       "2       41    9        1          1          0     1270        1     0   \n",
       "3       55    7        1          1          0     2476        1     0   \n",
       "4       54    0        1          2          0      184        0     0   \n",
       "5       42    4        2          2          0        0        1     1   \n",
       "6       56    4        1          2          0      830        1     1   \n",
       "7       60    5        0          1          0      545        1     0   \n",
       "8       37    9        1          1          0        1        1     0   \n",
       "9       28    7        2          1          0     5090        1     0   \n",
       "10      38    0        2          1          0      100        1     0   \n",
       "11      30    1        1          1          0      309        1     0   \n",
       "12      29    4        1          2          0      199        1     1   \n",
       "13      46    1        2          2          0      460        1     0   \n",
       "14      31    9        2          2          0      703        1     0   \n",
       "15      35    4        0          2          0     3837        1     0   \n",
       "16      32    1        2          0          0      611        1     0   \n",
       "17      49    7        1          1          0       -8        1     0   \n",
       "18      41    0        1          1          0       55        1     0   \n",
       "19      49    0        0          1          0      168        1     1   \n",
       "20      28    0        0          1          0      785        1     0   \n",
       "21      43    4        2          2          0     2067        1     0   \n",
       "22      43    4        0          2          0      388        1     0   \n",
       "23      43    1        1          0          0     -192        1     0   \n",
       "24      37   10        2          1          0      381        1     0   \n",
       "25      35    1        2          1          0       40        1     0   \n",
       "26      31    9        2          2          0       22        1     0   \n",
       "27      43    1        2          1          0        3        1     0   \n",
       "28      31    0        1          1          0      307        1     0   \n",
       "29      28    1        2          1          0      759        1     0   \n",
       "...    ...  ...      ...        ...        ...      ...      ...   ...   \n",
       "11132   32    1        1          1          0     -325        1     1   \n",
       "11133   37    4        1          2          0     1594        1     0   \n",
       "11134   58    6        1          2          0     2596        0     0   \n",
       "11135   36    7        0          1          0      115        0     0   \n",
       "11136   31    4        2          2          0        0        1     0   \n",
       "11137   49    5        1          3          0      247        0     0   \n",
       "11138   32    9        1          1          0      235        1     0   \n",
       "11139   30    6        2          2          0     2593        0     0   \n",
       "11140   55    7        0          1          0     2166        1     0   \n",
       "11141   32    1        1          1          0      130        1     0   \n",
       "11142   52    0        0          1          0       48        0     1   \n",
       "11143   38   10        0          1          0      189        1     1   \n",
       "11144   60    5        1          1          0        0        1     0   \n",
       "11145   60    5        0          2          0     -134        0     0   \n",
       "11146   30    0        1          1          1       23        0     1   \n",
       "11147   44   10        1          1          0        0        0     0   \n",
       "11148   32    1        1          1          0        1        1     0   \n",
       "11149   46    2        1          1          0     3317        0     0   \n",
       "11150   34    4        1          1          0      355        0     0   \n",
       "11151   40    4        1          2          0      917        1     0   \n",
       "11152   34    3        1          1          0      390        1     0   \n",
       "11153   43    0        2          1          0       35        0     0   \n",
       "11154   52    9        1          2          0      523        1     1   \n",
       "11155   35    1        1          1          0       80        1     1   \n",
       "11156   34    1        2          1          0      -72        1     0   \n",
       "11157   33    1        2          0          0        1        1     0   \n",
       "11158   39    7        1          1          0      733        0     0   \n",
       "11159   32    9        2          1          0       29        0     0   \n",
       "11160   43    9        1          1          0        0        0     1   \n",
       "11161   34    9        1          1          0        0        0     0   \n",
       "\n",
       "       contact  month  duration  campaign  pdays  prev  poutcome  \n",
       "0            2      8      1042         1      0     0         3  \n",
       "1            2      8      1467         1      0     0         3  \n",
       "2            2      8      1389         1      0     0         3  \n",
       "3            2      8       579         1      0     0         3  \n",
       "4            2      8       673         2      0     0         3  \n",
       "5            2      8       562         2      0     0         3  \n",
       "6            2      8      1201         1      0     0         3  \n",
       "7            2      8      1030         1      0     0         3  \n",
       "8            2      8       608         1      0     0         3  \n",
       "9            2      8      1297         3      0     0         3  \n",
       "10           2      8       786         1      0     0         3  \n",
       "11           2      8      1574         2      0     0         3  \n",
       "12           2      8      1689         4      0     0         3  \n",
       "13           2      8      1102         2      0     0         3  \n",
       "14           2      8       943         2      0     0         3  \n",
       "15           2      8      1084         1      0     0         3  \n",
       "16           2      8       541         3      0     0         3  \n",
       "17           2      8      1119         1      0     0         3  \n",
       "18           2      8      1120         2      0     0         3  \n",
       "19           2      8       513         1      0     0         3  \n",
       "20           2      8       442         2      0     0         3  \n",
       "21           2      8       756         1      0     0         3  \n",
       "22           2      8      2087         2      0     0         3  \n",
       "23           2      8      1120         2      0     0         3  \n",
       "24           2      8       985         2      0     0         3  \n",
       "25           2      8       617         4      0     0         3  \n",
       "26           2      8       483         3      0     0         3  \n",
       "27           2      8       929         3      0     0         3  \n",
       "28           2      8       538         1      0     0         3  \n",
       "29           2      8       710         1      0     0         3  \n",
       "...        ...    ...       ...       ...    ...   ...       ...  \n",
       "11132        2      8       171         1      0     0         3  \n",
       "11133        0      0       110         1    260     5         0  \n",
       "11134        1      9        15         2      0     0         3  \n",
       "11135        2      6       201         1      0     0         3  \n",
       "11136        0     11        76         1      0     0         3  \n",
       "11137        2      8       213         2      0     0         3  \n",
       "11138        1      1       688         6      0     0         3  \n",
       "11139        2      8       662         7      0     0         3  \n",
       "11140        2      8        23        17      0     0         3  \n",
       "11141        0      8       302         2      0     0         3  \n",
       "11142        0      9       591         1      0     0         3  \n",
       "11143        0      5       105         2      0     0         3  \n",
       "11144        2      8       229         1      0     0         3  \n",
       "11145        0      8       243         1    271     4         0  \n",
       "11146        0      3       149         2      0     0         3  \n",
       "11147        0      9       175         4      0     0         3  \n",
       "11148        2      8       102         1      0     0         3  \n",
       "11149        0      9       231         2      0     0         3  \n",
       "11150        0      1       314         3      0     0         3  \n",
       "11151        2      8       292         1      0     0         3  \n",
       "11152        0      5       659         3      0     0         3  \n",
       "11153        1      9       208         1      0     0         3  \n",
       "11154        0      5       113         1      0     0         3  \n",
       "11155        0      9        38         2    172     2         0  \n",
       "11156        0      5       273         5      0     0         3  \n",
       "11157        0      0       257         1      0     0         3  \n",
       "11158        2      6        83         4      0     0         3  \n",
       "11159        0      1       156         2      0     0         3  \n",
       "11160        0      8         9         2    172     5         0  \n",
       "11161        0      5       628         1      0     0         3  \n",
       "\n",
       "[11162 rows x 15 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "5        1\n",
       "6        1\n",
       "7        1\n",
       "8        1\n",
       "9        1\n",
       "10       1\n",
       "11       1\n",
       "12       1\n",
       "13       1\n",
       "14       1\n",
       "15       1\n",
       "16       1\n",
       "17       1\n",
       "18       1\n",
       "19       1\n",
       "20       1\n",
       "21       1\n",
       "22       1\n",
       "23       1\n",
       "24       1\n",
       "25       1\n",
       "26       1\n",
       "27       1\n",
       "28       1\n",
       "29       1\n",
       "        ..\n",
       "11132    0\n",
       "11133    0\n",
       "11134    0\n",
       "11135    0\n",
       "11136    0\n",
       "11137    0\n",
       "11138    0\n",
       "11139    0\n",
       "11140    0\n",
       "11141    0\n",
       "11142    0\n",
       "11143    0\n",
       "11144    0\n",
       "11145    0\n",
       "11146    0\n",
       "11147    0\n",
       "11148    0\n",
       "11149    0\n",
       "11150    0\n",
       "11151    0\n",
       "11152    0\n",
       "11153    0\n",
       "11154    0\n",
       "11155    0\n",
       "11156    0\n",
       "11157    0\n",
       "11158    0\n",
       "11159    0\n",
       "11160    0\n",
       "11161    0\n",
       "Name: deposit, Length: 11162, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preprocessing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.53246753,  0.        ,  0.5       , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.49350649,  0.        ,  0.5       , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.2987013 ,  0.81818182,  0.5       , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       ..., \n",
       "       [ 0.18181818,  0.81818182,  1.        , ...,  0.        ,\n",
       "         0.        ,  1.        ],\n",
       "       [ 0.32467532,  0.81818182,  0.5       , ...,  0.20140515,\n",
       "         0.0862069 ,  0.        ],\n",
       "       [ 0.20779221,  0.81818182,  0.5       , ...,  0.        ,\n",
       "         0.        ,  1.        ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "minmax=preprocessing.MinMaxScaler(feature_range=(0,1))\n",
    "minmax.fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the dataset into Training set and Test set\n",
    "from sklearn import model_selection, neighbors\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7618</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>282</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3814</th>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>651</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-395</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10484</th>\n",
       "      <td>51</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2682</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8872</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>663</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>62</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8291</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>775</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>59</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>935</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>523</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10910</th>\n",
       "      <td>48</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>197</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>222</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>567</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>421</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10202</th>\n",
       "      <td>55</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1498</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>340</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1818</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>524</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>554</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>44</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3094</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>720</th>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>809</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1549</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>133</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8948</th>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3494</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6155</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>99</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6506</th>\n",
       "      <td>38</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1221</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4272</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>384</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7447</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1453</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>303</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9481</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>937</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9378</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4221</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>864</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>659</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6452</th>\n",
       "      <td>27</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>740</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>294</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>779</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>184</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4869</th>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6468</th>\n",
       "      <td>47</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1405</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1790</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5872</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>762</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>489</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8831</th>\n",
       "      <td>46</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6205</th>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>371</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2351</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9959</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>155</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7227</th>\n",
       "      <td>31</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4310</th>\n",
       "      <td>49</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>412</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2765</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>847</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3614</th>\n",
       "      <td>68</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2027</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>619</td>\n",
       "      <td>2</td>\n",
       "      <td>92</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6929</th>\n",
       "      <td>53</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>673</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3431</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1672</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>462</td>\n",
       "      <td>1</td>\n",
       "      <td>648</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>29</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>339</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1576</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4502</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5359</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>119</td>\n",
       "      <td>1</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5730</th>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6739</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>68</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3559</th>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1727</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>500</td>\n",
       "      <td>4</td>\n",
       "      <td>358</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6545</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>237</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11099</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>78</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>369</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>741</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3409</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>116</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2391</th>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>495</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10234</th>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>322</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9096</th>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>223</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9800</th>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1937</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>86</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>35</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3837</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1084</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3856</th>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2325</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7697</th>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2595</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>398</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6815</th>\n",
       "      <td>54</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>159</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3021</th>\n",
       "      <td>81</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>452</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>443</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>248</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4779</th>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2885</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1422</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8929 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  defaulter  balance  housing  loan  \\\n",
       "7618    26    1        1          1          0      557        1     1   \n",
       "3814    68    5        1          2          0     2812        0     0   \n",
       "42      45    2        0          2          0     -395        1     0   \n",
       "7300    28    8        2          1          0      154        1     0   \n",
       "10484   51    9        1          1          0     2682        0     0   \n",
       "8872    32    4        1          2          0      663        0     1   \n",
       "8291    26    4        1          2          0      775        0     0   \n",
       "1123    59    5        2          1          0        2        0     0   \n",
       "872     44    4        1          2          0     1138        0     0   \n",
       "10910   48    9        1          1          0      197        0     1   \n",
       "2113    26    1        2          2          0      567        0     0   \n",
       "10202   55    4        2          2          0     1498        0     0   \n",
       "1818    28    1        2          1          0      524        1     0   \n",
       "153     44    7        0          1          0       51        1     1   \n",
       "720     44    9        2          1          0     7138        0     0   \n",
       "1549    44    1        2          1          0        0        0     0   \n",
       "8948    39    9        1          1          0     3494        1     0   \n",
       "6155    55    5        0          1          0      282        1     0   \n",
       "6506    38    9        0          1          0     1221        1     1   \n",
       "4272    30    9        2          2          0      655        0     0   \n",
       "7447    47    3        0          1          0     1453        0     0   \n",
       "9481    32    4        1          2          0      937        0     0   \n",
       "9378    50    4        0          0          0       -6        1     0   \n",
       "4221    32    4        2          2          0        0        1     0   \n",
       "2025    26    8        2          1          0      864        1     0   \n",
       "6452    27    9        2          1          0      740        1     0   \n",
       "4861    19    8        2          3          0      779        0     0   \n",
       "4869    73    5        1          2          0      448        0     0   \n",
       "6468    47    3        0          0          0     1405        0     0   \n",
       "1790    50    1        1          0          0     5872        1     0   \n",
       "...    ...  ...      ...        ...        ...      ...      ...   ...   \n",
       "7579    48    0        1          1          0      476        0     0   \n",
       "8831    46    9        1          2          0        0        0     0   \n",
       "6205    34    2        0          2          0      262        0     0   \n",
       "1334    47    4        1          2          0     2351        0     0   \n",
       "9959    29    4        2          2          0        0        1     0   \n",
       "7227    31    9        2          1          0       40        0     0   \n",
       "4310    49    7        0          1          0        0        0     0   \n",
       "2765    77    5        1          2          0        0        0     0   \n",
       "3614    68    5        0          0          0     2027        0     0   \n",
       "6929    53    9        1          2          0      673        1     0   \n",
       "3431    39    1        2          1          0     1672        1     0   \n",
       "5006    29    7        2          0          0      339        0     0   \n",
       "4502    30    4        2          2          0     5359        0     0   \n",
       "5730    50    4        1          2          0     6739        1     0   \n",
       "3559    59    4        1          0          0     1727        0     0   \n",
       "6545    34    0        2          1          0       66        1     0   \n",
       "11099   31    4        2          2          0       78        1     0   \n",
       "143     28    0        2          1          0      460        1     0   \n",
       "3409    45    1        2          0          0      116        0     0   \n",
       "2391    27    8        2          1          0      111        0     0   \n",
       "10234   53    0        2          0          0        0        1     0   \n",
       "9096    36    7        0          1          0      223        1     1   \n",
       "9800    40    7        0          0          0     1937        1     0   \n",
       "15      35    4        0          2          0     3837        1     0   \n",
       "3856    30    9        2          2          0     2325        0     0   \n",
       "7697    59    9        0          1          0     2595        1     0   \n",
       "6815    54    5        1          0          0        0        1     0   \n",
       "3021    81    5        0          1          0      452        0     0   \n",
       "930     48    4        1          2          0        1        0     0   \n",
       "4779    38    1        2          2          0     2885        1     0   \n",
       "\n",
       "       contact  month  duration  campaign  pdays  prev  poutcome  \n",
       "7618         2      8       282         2      0     0         3  \n",
       "3814         0      1       651         1      1     7         2  \n",
       "42           2      8       470         1      0     0         3  \n",
       "7300         2      8        21         9      0     0         3  \n",
       "10484        2      6        56         1      0     0         3  \n",
       "8872         0      5        62         3      0     0         3  \n",
       "8291         2      8        35         3      0     0         3  \n",
       "1123         1      3       935         1      0     0         3  \n",
       "872          0      1       523         3      0     0         3  \n",
       "10910        0      3        47         2    222     3         1  \n",
       "2113         0      8       421         1      0     0         3  \n",
       "10202        0      1       340         5      0     0         3  \n",
       "1818         0      8       554         2      0     0         3  \n",
       "153          2      8      3094         2      0     0         3  \n",
       "720          0      1       809         2      0     0         3  \n",
       "1549         0      0       133         1      0     0         3  \n",
       "8948         0      9       139         1     92     4         0  \n",
       "6155         2      6        99         2      0     0         3  \n",
       "6506         0      1        73         7      0     0         3  \n",
       "4272         0     10       384         2      0     0         3  \n",
       "7447         2      6       303         1      0     0         3  \n",
       "9481         0      1        90         1     94     2         0  \n",
       "9378         2      8        92         2      0     0         3  \n",
       "4221         0      0       370         3      0     0         3  \n",
       "2025         0      8       659         1      0     0         3  \n",
       "6452         0      8       294         1      0     0         3  \n",
       "4861         0      0       184         4      0     0         3  \n",
       "4869         0      7       136         1      0     0         3  \n",
       "6468         2      6       127         1      0     0         3  \n",
       "1790         0      8       762         3      0     0         3  \n",
       "...        ...    ...       ...       ...    ...   ...       ...  \n",
       "7579         2      6       489         3      0     0         3  \n",
       "8831         2      6       159         1      0     0         3  \n",
       "6205         0      9       371         1      0     0         3  \n",
       "1334         0      7        80         1      0     0         3  \n",
       "9959         0      8       155         1    351     1         0  \n",
       "7227         0      1       123         2      0     0         3  \n",
       "4310         0      5       412         2      0     0         3  \n",
       "2765         0     10       847         1     60     1         2  \n",
       "3614         0      6       619         2     92     2         2  \n",
       "6929         0      5       182         1      0     0         3  \n",
       "3431         0      0       462         1    648     1         0  \n",
       "5006         2      5      1576         3      0     0         3  \n",
       "4502         0      8       119         1     98     1         2  \n",
       "5730         0      5        68         3      0     0         3  \n",
       "3559         1      8       500         4    358     2         0  \n",
       "6545         2      8       237         1      0     0         3  \n",
       "11099        0      5       369         2      0     0         3  \n",
       "143          2      8       741         6      0     0         3  \n",
       "3409         2      0       122         1      0     0         3  \n",
       "2391         0      5       495         2      0     0         3  \n",
       "10234        0      8       176         1    322     5         1  \n",
       "9096         0      8        80         1      0     0         3  \n",
       "9800         2      6        86         1      0     0         3  \n",
       "15           2      8      1084         1      0     0         3  \n",
       "3856         0      1       263         1    127     7         0  \n",
       "7697         0      1       398         2      0     0         3  \n",
       "6815         0      5       159         2      0     0         3  \n",
       "3021         1      2       443         1      0     0         3  \n",
       "930          1      9       248         1      0     0         3  \n",
       "4779         2      6      1422        10      0     0         3  \n",
       "\n",
       "[8929 rows x 15 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7618     0\n",
       "3814     1\n",
       "42       1\n",
       "7300     0\n",
       "10484    0\n",
       "8872     0\n",
       "8291     0\n",
       "1123     1\n",
       "872      1\n",
       "10910    0\n",
       "2113     1\n",
       "10202    0\n",
       "1818     1\n",
       "153      1\n",
       "720      1\n",
       "1549     1\n",
       "8948     0\n",
       "6155     0\n",
       "6506     0\n",
       "4272     1\n",
       "7447     0\n",
       "9481     0\n",
       "9378     0\n",
       "4221     1\n",
       "2025     1\n",
       "6452     0\n",
       "4861     1\n",
       "4869     1\n",
       "6468     0\n",
       "1790     1\n",
       "        ..\n",
       "7579     0\n",
       "8831     0\n",
       "6205     0\n",
       "1334     1\n",
       "9959     0\n",
       "7227     0\n",
       "4310     1\n",
       "2765     1\n",
       "3614     1\n",
       "6929     0\n",
       "3431     1\n",
       "5006     1\n",
       "4502     1\n",
       "5730     0\n",
       "3559     1\n",
       "6545     0\n",
       "11099    0\n",
       "143      1\n",
       "3409     1\n",
       "2391     1\n",
       "10234    0\n",
       "9096     0\n",
       "9800     0\n",
       "15       1\n",
       "3856     1\n",
       "7697     0\n",
       "6815     0\n",
       "3021     1\n",
       "930      1\n",
       "4779     1\n",
       "Name: deposit, Length: 8929, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>defaulter</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>prev</th>\n",
       "      <th>poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8244</th>\n",
       "      <td>60</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>875</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10398</th>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>2</td>\n",
       "      <td>199</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6916</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>223</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3083</th>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>366</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10815</th>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1786</th>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>429</td>\n",
       "      <td>4</td>\n",
       "      <td>164</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2842</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>914</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>524</td>\n",
       "      <td>3</td>\n",
       "      <td>272</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10334</th>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7747</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>103</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5002</th>\n",
       "      <td>73</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>338</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>989</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10490</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1744</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3700</th>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>251</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>537</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6430</th>\n",
       "      <td>48</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>263</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>350</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10667</th>\n",
       "      <td>51</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>730</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>287</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560</th>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4766</th>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>296</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2966</th>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1925</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>487</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>201</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>582</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7479</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>222</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8747</th>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>134</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>246</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>683</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10650</th>\n",
       "      <td>41</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>169</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10869</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>881</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5248</th>\n",
       "      <td>37</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>268</td>\n",
       "      <td>2</td>\n",
       "      <td>182</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7078</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>388</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8622</th>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2996</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10734</th>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-663</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>351</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3397</th>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>230</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>2</td>\n",
       "      <td>210</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9180</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2968</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>265</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>414</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>323</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>332</td>\n",
       "      <td>2</td>\n",
       "      <td>181</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4371</th>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7320</th>\n",
       "      <td>32</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7985</th>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2766</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4476</th>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>344</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3078</th>\n",
       "      <td>59</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4198</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>309</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8814</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6111</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>285</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6508</th>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9058</th>\n",
       "      <td>41</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>336</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7607</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3730</th>\n",
       "      <td>64</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>588</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>574</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8241</th>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>260</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4064</th>\n",
       "      <td>54</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5475</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>1032</td>\n",
       "      <td>1</td>\n",
       "      <td>91</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4829</th>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9393</th>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>519</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6360</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>129</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9040</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>340</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3786</th>\n",
       "      <td>77</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>426</td>\n",
       "      <td>1</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2128</th>\n",
       "      <td>29</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>315</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3196</th>\n",
       "      <td>61</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5267</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>226</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1852</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>371</td>\n",
       "      <td>1</td>\n",
       "      <td>204</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1357</th>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>187</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9662</th>\n",
       "      <td>55</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1821</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>224</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4945</th>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1181</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>650</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7222</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>239</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>785</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>442</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>52</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>195</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>220</td>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>432</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2233 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  defaulter  balance  housing  loan  \\\n",
       "8244    60    9        1          2          0       91        0     0   \n",
       "1526    40    7        2          1          0      224        1     0   \n",
       "10398   29    4        2          2          0      502        0     0   \n",
       "6916    33    1        2          1          0      -14        1     0   \n",
       "3083    32    0        1          1          0      366        0     0   \n",
       "10815   38   10        1          1          0      995        0     0   \n",
       "1786    35    2        1          1          0     2971        0     0   \n",
       "2842    30    0        2          1          0      914        1     0   \n",
       "10334   56    4        1          0          0     7747        0     0   \n",
       "5002    73    5        1          0          0     5539        0     0   \n",
       "2410    59    1        0          0          0      989        0     0   \n",
       "10490   48    4        1          0          0     1744        0     0   \n",
       "3700    30    6        2          2          0      251        0     0   \n",
       "6430    48    4        1          2          0      263        1     0   \n",
       "10667   51    5        2          2          0      730        1     0   \n",
       "1560    50    6        0          2          0       13        1     0   \n",
       "4766    34    0        1          2          0       69        0     1   \n",
       "2966    28    8        2          1          0     1925        0     0   \n",
       "490     51    1        1          0          0      201        1     0   \n",
       "7479    30    4        2          2          0      222        1     0   \n",
       "8747    24    6        2          2          0       91        1     0   \n",
       "331     41    1        1          0          0      246        0     0   \n",
       "10650   41    9        1          1          0      335        0     0   \n",
       "10869   50    1        0          1          0      881        1     1   \n",
       "5248    37    4        1          2          0        0        0     0   \n",
       "7078    51    1        1          3          0     1432        0     0   \n",
       "8622    32   10        1          1          0     2996        1     0   \n",
       "10734   57    5        0          1          0     -663        1     0   \n",
       "3397    57    5        1          3          0      230        0     0   \n",
       "9180    52    5        1          0          0        6        0     0   \n",
       "...    ...  ...      ...        ...        ...      ...      ...   ...   \n",
       "2968    29    0        2          1          0      265        1     0   \n",
       "2412    44    2        2          3          0      323        0     0   \n",
       "4371    42    9        2          3          0    11115        0     0   \n",
       "7320    32    9        2          2          0      127        1     0   \n",
       "7985    30    4        2          2          0     2766        1     1   \n",
       "4476    45    9        1          1          0        0        1     0   \n",
       "3078    59    9        1          0          0     4198        0     0   \n",
       "8814    33    4        2          2          0      349        0     0   \n",
       "6111    49    4        1          1          0      104        1     0   \n",
       "6508    61    4        1          1          0      967        0     0   \n",
       "9058    41    7        1          3          0       88        1     0   \n",
       "7607    55    1        1          3          0       23        0     0   \n",
       "3730    64    5        1          1          0      588        0     0   \n",
       "8241    44    4        1          2          0        0        1     0   \n",
       "4064    54    4        0          2          0     5475        0     0   \n",
       "4829    30    3        2          2          0       19        0     0   \n",
       "9393    44    2        1          1          0      887        1     0   \n",
       "6360    34    4        1          2          0        0        1     1   \n",
       "9040    36    0        1          1          0      162        1     1   \n",
       "3786    77    5        1          1          0     4112        0     0   \n",
       "2128    29    9        2          1          0        6        0     0   \n",
       "3196    61    5        1          2          0     5267        0     0   \n",
       "9914    33    7        2          1          0     1852        0     0   \n",
       "1357    31    4        2          2          0      115        0     0   \n",
       "9662    55    5        1          0          0     1821        0     0   \n",
       "4945    39    1        1          3          0     1181        1     0   \n",
       "7222    28    4        2          2          0      320        1     0   \n",
       "20      28    0        0          1          0      785        1     0   \n",
       "3239    52    9        1          1          0      195        1     0   \n",
       "825     33    4        2          2          0      432        0     0   \n",
       "\n",
       "       contact  month  duration  campaign  pdays  prev  poutcome  \n",
       "8244         0      1       105         4      0     0         3  \n",
       "1526         0      0       875         2      0     0         3  \n",
       "10398        0      0       366         2    199     3         1  \n",
       "6916         0      8       223         2      0     0         3  \n",
       "3083         0      4       366         2      0     0         3  \n",
       "10815        2      5        84         1      0     0         3  \n",
       "1786         0      8       429         4    164     2         0  \n",
       "2842         0     10       524         3    272     1         0  \n",
       "10334        2      6       103         2      0     0         3  \n",
       "5002         1      1       338         1      0     0         3  \n",
       "2410         0      5       125         2      0     0         3  \n",
       "10490        2      6       181         1      0     0         3  \n",
       "3700         0      5       537         1      0     0         3  \n",
       "6430         2      8       350         2      0     0         3  \n",
       "10667        2      9       287         2      0     0         3  \n",
       "1560         0      0        76         1      0     0         3  \n",
       "4766         0      9       296         1    205     2         0  \n",
       "2966         0      9       487         1     97     2         0  \n",
       "490          0      5       582         2      0     0         3  \n",
       "7479         0      1       112         4      0     0         3  \n",
       "8747         0      8       134         2      0     0         3  \n",
       "331          2      6       683         3      0     0         3  \n",
       "10650        0      1       169         3      0     0         3  \n",
       "10869        0      0       162         1      0     0         3  \n",
       "5248         0      5       268         2    182     3         2  \n",
       "7078         0      5       388         1      0     0         3  \n",
       "8622         2      8        68         1      0     0         3  \n",
       "10734        0      8       210         1    351     2         0  \n",
       "3397         0      0       177         2    210     3         0  \n",
       "9180         0      1        82         2      0     0         3  \n",
       "...        ...    ...       ...       ...    ...   ...       ...  \n",
       "2968         0      9       414         1    184     1         0  \n",
       "2412         0      5       332         2    181     2         0  \n",
       "4371         0      0       328         1      0     0         3  \n",
       "7320         0      1        20        17      0     0         3  \n",
       "7985         0      9       244         1      0     0         3  \n",
       "4476         0      2       344         1      0     0         3  \n",
       "3078         0      4       309         1     91    10         2  \n",
       "8814         0      1       182         1      0     0         3  \n",
       "6111         2      8       285         1      0     0         3  \n",
       "6508         0      1       180         1      0     0         3  \n",
       "9058         0      8       105         1    336     2         0  \n",
       "7607         0      4       143         1      2     3         1  \n",
       "3730         0      5       574         1    179     3         2  \n",
       "8241         2      8       260         1      0     0         3  \n",
       "4064         0     10      1032         1     91     4         2  \n",
       "4829         0      1       139         1     92     5         2  \n",
       "9393         0      0       519         1     14     2         0  \n",
       "6360         0      5       129         1      0     0         3  \n",
       "9040         0      0       340         2      0     0         3  \n",
       "3786         1      5       426         1    184     3         2  \n",
       "2128         0      8       315         1      0     0         3  \n",
       "3196         0      3       226         3      0     0         3  \n",
       "9914         1      3       371         1    204     3         1  \n",
       "1357         0      7       187         1      0     0         3  \n",
       "9662         0      1       224         2      0     0         3  \n",
       "4945         0      5       650         2      0     0         3  \n",
       "7222         0      0       239         1      0     0         3  \n",
       "20           2      8       442         2      0     0         3  \n",
       "3239         0      3       220         1     63     5         0  \n",
       "825          1      1       588         6      0     0         3  \n",
       "\n",
       "[2233 rows x 15 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8244     0\n",
       "1526     1\n",
       "10398    0\n",
       "6916     0\n",
       "3083     1\n",
       "10815    0\n",
       "1786     1\n",
       "2842     1\n",
       "10334    0\n",
       "5002     1\n",
       "2410     1\n",
       "10490    0\n",
       "3700     1\n",
       "6430     0\n",
       "10667    0\n",
       "1560     1\n",
       "4766     1\n",
       "2966     1\n",
       "490      1\n",
       "7479     0\n",
       "8747     0\n",
       "331      1\n",
       "10650    0\n",
       "10869    0\n",
       "5248     1\n",
       "7078     0\n",
       "8622     0\n",
       "10734    0\n",
       "3397     1\n",
       "9180     0\n",
       "        ..\n",
       "2968     1\n",
       "2412     1\n",
       "4371     1\n",
       "7320     0\n",
       "7985     0\n",
       "4476     1\n",
       "3078     1\n",
       "8814     0\n",
       "6111     0\n",
       "6508     0\n",
       "9058     0\n",
       "7607     0\n",
       "3730     1\n",
       "8241     0\n",
       "4064     1\n",
       "4829     1\n",
       "9393     0\n",
       "6360     0\n",
       "9040     0\n",
       "3786     1\n",
       "2128     1\n",
       "3196     1\n",
       "9914     0\n",
       "1357     1\n",
       "9662     0\n",
       "4945     1\n",
       "7222     0\n",
       "20       1\n",
       "3239     1\n",
       "825      1\n",
       "Name: deposit, Length: 2233, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting Classifier to the Training set\n",
    "clf = neighbors.KNeighborsClassifier()\n",
    "clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Deposit \n",
      "[0 1 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Actual Deposit ')\n",
    "print(Y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Deposit\n",
      "[0 1 1 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted Deposit')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy score: 76.533811\n",
      "Recall score : 74.095238\n",
      "ROC score : 76.396731\n",
      "\n",
      "[[931 252]\n",
      " [272 778]]\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score on Test and Train\n",
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, confusion_matrix\n",
    "\n",
    "knn_acc=accuracy_score(Y_test,Y_pred) * 100\n",
    "print(\"\\nAccuracy score: %f\" %(accuracy_score(Y_test,Y_pred) * 100))\n",
    "print(\"Recall score : %f\" %(recall_score(Y_test, Y_pred) * 100))\n",
    "print(\"ROC score : %f\\n\" %(roc_auc_score(Y_test, Y_pred) * 100))\n",
    "print(confusion_matrix(Y_test, Y_pred)) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Predict the probability of each test data point\n",
    "#Get the probability distribution\n",
    "probas = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAJBCAYAAACOI8ACAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAXEQAAFxEByibzPwAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xu8XFV9///XhwQCCSFE5KaIYERUEBQFAQVBoIpiQaVatRWk9dcbFUEr9isoX6DWaqWCtdVWTWirVg1UfhK0FSQggqDIRUHDxYaAKJeESxJIAuHz/WPtSSbDzJyTc/Y5k7Pzej4e89hn9l5rr7XnzEnmPWvvtSMzkSRJkqQm2WTQHZAkSZKkuhl0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUfShBMRh0RERkQOui8jFRHzq2M4o8f2qRFxVkT8IiIebx1vRLy02r6wen78ePZ7NCJil7bj2GXQ/WmSiDi+el0XDqDtUf099qs/0m3r0fYZ1T7mj3QfkjZckwfdAUkbr4iYBLwVOArYH9gOmAo8DNwG/AD4Smb+fGCdHJyvU14XgMeB+6qfnxhMd/prC2xzMnPhALsyJqoA8dwum5YCi4ArgM9l5q3j2S91V30hcAzwcGZ+ZtD9kTQYBh1JAxER+wPnAy9oW/0E5YPjNsCrqseHI+JC4B2ZuWrcOzp2FgELgAc7N0TEC1kbcn4/M7/epf6dwArgkTHr4fr5WLWcDyzsUeYJyjG3fp6I2l/zTYBnAntUj/dGxJ9l5pcG1bkJ6jHWvi/qqvdSynvyLqBf0Hmw2seiEbQvaQNn0JE07iLiTcA3gSnAYuDvgQsy8/Zq+yTgZZTRnj8H3kIZ6WlM0MnMd/fZ/JJqubhHyCEzD6u/V2MrM38NvHDQ/Rilr2fm8a0nEbEFJZR+Ftge+EJE/Dgzbx5Q/yaczLyOEbwvRlqvYx//CPzjaPYhacPlNTqSxlVE7Ab8ByXk3Aq8NDM/0Qo5AJm5OjN/kpl/DewKXDSY3g7M1Gq5bKC90JAy8/HM/CbwB9WqScCfDbBLkqSKQUfSeDsb2IpyCtCbM/OefoUzc0lmHsMwT9GKiE0i4lUR8YmI+FFE3BMRqyJicURcERF/GhGb9qk/MyLOjIifRsSjVd3fRsTNEfH5iHjaSEpEbBERH4yIayLioYh4IiIeiIhbI+L8iHhrlzpPm4ygdWE0MKda9dy2i/czIua0lR1yMoKIeGVEzI6IOyJieXU8t0bElyPid7qU3yciPhoRV0bEXRGxIiIerl7HUyNiyy515nRcDH55R58XtpUdcjKCiJhR9aH1+j8eEbdHxD9HxPP6HGtrv4dExPSIODsiflnVXxwRF0fEK3vVH63MvBT4TfV03z592y4izomI2yLisR4X2b8sIv6t7XfwUERcHRHvj4gpw+lPRBwREd+p3oePR8QtEXFaRGzeo/ymVZ3zIuInEfGb6r1/f0T8d0S8IyJimG2/IiLmVvtYUb3/PhURW/coP6JJBXrVq57Prp52/g11/ZuLPpMRVO/Jj0TEtdXvYmVE3B0RX4tyCm6veuv9b4mkmmWmDx8+fIzLg3Jqz2oggS+OYj+HVPvILtt2aW2rHk9QQlL7uiuBLbrU3YlyTn+r3GpgCfBk27r5HXWmAze2bX8KeKhqt7VuYZe25lfbzmhb90Hgt239XV09bz3ObSu7sCpzfJd9TwLO7TjmZcDytucPd6nXXn51dRzt624Btuuoc27Vt1aZJR19/nGP380uXdrfA7i7rczjwKNtz1cAb+3xnmiVeQdwe1v99mNeBbxuhO+51us9p0+Z66oyt/Xo2x+3vVZrjq2j7Pur99Ca31PV79bzm4Adu7R9fOu9BvxF2z4634s/BWb2+5tqe62Xdqz7BrDJEHWPBlZWPz/S9nOrb91+72vq17GN/n9DvwU+2Fb2DLr8XbdtfyXrvr+f7HhPPgX8dR3/lvjw4aP+hyM6ksbToawdSf6vMWrjScqpbm8Hng1MycwZlEDyHuBe4CDgb7rUPQPYmfKB7HBgs8x8BuU0u10opyT9qKPOScDelA8xb6UEqJlVnWcD7wb+Zzgdz8y/z8wdqn0C3J2ZO7Q9TupXv83HgfdVP38Z2D0zt8zMaZSweQzw3S71LgVOoMwuNqU6jqmUa6QWAC8GPt/R55OqPre8paPP64xu9BIR04FvUz4g/hp4IzAtM7eiXFj+I8pr+pWI2LvPrj5HCQavBaYBWwL7Vf3flHINzVj937dLtVzSY/s/UILLYaw9tt1bGyPiqKpMUN7Dz8vMrSnH8G5K8NgLmBvlOrZutq32MRfYufodTgf+lBI6XgZ0myzhceCrlNd9B8r7eDplYpCTKB/ufw84se8rUCYYuRp4cfV3N43yt/gQ5X31jT59r8UQf0M7ZObfD2c/1ajjdyl/M3OBlwObV7+37YGzKAHm4xFxTEf1M1j/f0sk1W3QScuHDx8bz4PywaD1beazRrGfQ+jxLe8w6r6CtSMcm3dsu7Xa9o712N8lVZ2nfas7RL35dIzotG07nh4jQW1lFtJlRIcyi11r1OzvavzdPZvyLf9TlA/Qndtbv9dD+uxjl7Zyu3RsO5W1oy57dqk7HfjfqszFfdq/n45Rp2r7S9rKvGoEx996vef02H5s2/4/06NvjwA79WnjlqrcD4BJXba/qW1fx/Z4z2T13uo28vJHbWX2Xc/jbx3fHV22HdK23wV0Hy09vK3M7/Wq32/f67ltyL+hqtwZrdesy7ZvVtv+rU/9k6syN3asX+9/S3z48FH/wxEdSeNpm7afe33rPaYy8yeUD8PTKCMF7R6uljuuxy5HUmcsHUcZNVvM2imfRy3LjGk3UUYbDqxrv23eXi3nZpf7JmXmUuCT1dMjI2JGj/38S2be36X+zyhBCcqoyKhF8dyI+EvWjpKsoowqdfPv2eOatIjYizJiBnBWZq7uLJOZ36acHgflFL1ezs7Mp7qsnw202v/9PvW7mVctZ0VEv/f6pzLz8c6VWa5hunqEbY+7iHgGZSQT4BN9iv5btdw7IrZvW7+h/bsgbZQMOpLG07AuZh51IxGbRZl04H8i4t7qgug1FyNTbkwK5TSpdhdXy09ExL9ExOsjYqshmmvVObG6OPmYiHhmXccyAq0Q8r3MXLE+FaNM5PDOiPj/I2JRdRF7++u2X1W083UblYjYjLXh49I+Rb9XLTcB9ulR5to+9e+tls8Yfu+e5ri21+MpykjPeZQJNpYD78y2GQQ7/LDPfl9RLZ+k3Hy0l9Zr8Ioe25+kjAg9TRV+5veqX03i8FdRJu24v7p4vnWsj7UVfXaf/n1/GNt69X1DcgBrPyN9v5pE4GkPyihcy3Pbfh7JvyWSauZ9dCSNp/abYz6DtR88axMR21E+LL+kbfWKqu3Wt+TbUj7ETOuo/inK9TZvA95bPTIibqGcq/+vmXlbe4XM/GpE7Af8JeWb6t+v+nEH5dqcL2fm9bUd4NBa18vctT6VImIq5cPZoW2rV1FG3lo393wG5TqXztdttJ5BmUAByvU5vbSPhmzXo8zSPvWfrJY9Z90bhvYbhiYl3CyiTHDxxV4jNpWnjTS1aR3Pg5m5sk+51v57Hf9Q9Vuv7zr1I+IFwGWsG2Ifo4xMtEaHWiMW/X7//X5/XdveQD2r7efte5Za19S2n9f73xJJ9XNER9J4av/282Vj1MY/UELOYsqF9Ttm5haZuW1WFyOzNmCtM8KUmU9k5tspp7SdSfkG+jFgT8qMaLdGxAc6G8zM91MuKv8/wHcoHw6fT7nZ6U8iot+d2cdKrmf5j1BCzuOU6w6eS7mGaZu21601WjKWI3P9+p09fh5PX8+1F7XvmJnPz8zXZuYZQ4QcWBu0+xnucfUqN9LXZTYl5CykTDqwTWZOy8ztqt99+yjOuIzMDlgreD+emTHMx/xW5ZH+WyKpXgYdSePpctZ+O/zmunce5f44rfPqT8zM2Zn5244yk4C+p5Zl5k2Z+bHMPAzYmnIh9ZWUDz+f6jbrV2bekZl/m5lvoFyLdADwrWrzSRHxu6M5tvXQupfLLutZr3XdxJmZ+ZnMXJSZnR+ad+isVJMlrA0Bz+lTrn3bA2PUl0FpjfZsG/3vldMacel1/EPVbwWWNaNLEfEc1p7y+I7MnJuZndfQDfd33++0tqe1vQFr/buxRUQ8f6Q7Gcm/JZLqY9CRNG4y8z7ggurpO6vTZYZlmDcr3BZo3RDxhh5lXt1WZkiZ+WRmXkaZdncl5dvsw4eo81Rm/ogyU9WiavURw21zlFoXfB8RPW4O2UMrRHR93aqpdvt94GuFovX+tj8zVwE3V0/73USx9bo/RbkfTJP8pFpOBl7Tp1zrNfhxj+2TKe/xp6n+hg7uaA/WDZC9/m76vufbHDqMbT/pU6YurS9URjr6dDVr39O1TJ4wkn9LJI2OQUfSeDuNMrXzFsCFEdHvG+DW3cUvAHrNstWudSM/KOfHd+5rMt3vn9Pa3u+b8JWsHXVYcwpSvzrVzFmrOuuMsTlVW9sA/3c96rWuO+n1DXO/maegvPZQvrUeif+slsdGxJ6dGyNiS+BD1dNLMvORzjITWWbeTJmSGOC0bveaiYg3UG5gCfC1Prv7SI97BR1HubcLwNfb1re/lt3+bqZT/m6H44PdAnZEHAq8qkvbY2VU78dq5r6Lqqd/NdSXMtUsbe3P1/vfEkn1M+hIGlfVBbh/SAkAewA3RsSp7aeHRMSkiHhZRJwJ/Iq1p6MNte9lrJ3Z6pyIeG3rA1/14fkSyoxPy3vs4q6I+NuI2L/9g0rVt69QLjZ+CvjvtjrXRsR5EXFIRExrq/OsiPgsa0dBLhnOMYxWZt5BuRAa4EMR8cWI2K2tX9tGxNsjovOGra0biJ4WEW+pQiERsWtEfJVyUfVDfZpuTQn9rmpig/X1z5TpnzcFvhMRR7b97l5Cec13pbxvhvuhe6I5tVoeRLkp6K5QTsmMiHexNtxczdrTIjs9RhnR+WpE7FTV3zwi3kt5jQEuyszr2urcytqRxy9HxMtbGyLiAMpMbTOHeQw7AvMiYveq/uSIOJZyw00oI3EXDnNfo9F6P24VEW8b4T4+QLnWbyvgqog4IdqmNY+IZ1Z/Kxfy9OA5kn9LJNVt0Dfy8eHDx8b5oHy7eztrbyKYlG86F7P2hpetKXy/CmzaVveQ1vYu+305ZcSoVX8Fa0d6nqCErIV0v9lme19WU64debyjL+/vqLOwY/tDHe0ncE6Xfs5nDG4YWm2bBPxjRx+WUgJe6/nDHXWeS7kuobX9CcqkCq3nfz1En/+grewqyuxgC4Gr2srs0lZmly772LOq1yrzOGW0of13eWyP16NV5pA+r1nP/g/j/dp6veeMoO6QfWsre3L1PmrVeYjyd9F6fjNdbrbb/p4B/qJtH0uq30er/o2UiQY66x9V/c5b5Za3vV+WU04p7HocrHvD0KPb2nu4+p21tt0F7Nql7TX169pWbb+0re1Hq9dmIW1/w/S5YWi1/WWsvVFt6298CeXvqf3v63uj/bfEhw8f9T8c0ZE0EJn5Q+CFlBsffgW4g/KhaDrlQ8FVlNPMXpSZ78zMJ3rtq2O/11Pu9/INypTSm1A+lHwDODAz/71P9d8B/pZyH5K7KafXUfVtNuVu8p0zqP0+5cacl1E+EG1GGZW4i3KKzmGZecpw+l6XzFydmSdSvtn/CuXb+k0pH0Bvodzc8q0dde6ijHZ9ibWz0q2gTDn9usz82yHa/A9KiLyKMqqwIyU8DfueO1luFLoH5cPnjZTpoKcAdwKfB/bIzLk9d9AAmfkPlN/Df1Deg1MpH5B/BJwC7JeZfadlz8zPAa+jjNI9VT1+CXwUOCAzF3epczHl+p15lIAymfL3MxvYJ8u1JcPp/0WUiQ0uoLx/gvJ38WngpZn5v8PZT02OpczCeBvl/f/c6jHs09ky8wbKjVxPpASnByn/Rm1C+aLmq5R/AzpHnUfyb4mkmkVm56Q6kiRJkjSxOaIjSZIkqXEMOpIkSZIax6AjSZIkqXEMOpIkSZIax6AjSZIkqXEMOpIkSZIax6AjSZIkqXEMOpIkSZIax6AjSZIkqXEMOpIkSZIaZ/KgOyCIiN8CU4G7B90XSZIkqSbPAR7LzB0G0Xhk5iDaVZuIeHTKlCnTZ82aNeiuSJIkSbW48847Wbly5dLM3GoQ7Tuis2G4e9asWS++5ZZbBt0PSZIkqRZ77LEHt95668DOWPIaHUmSJEmNY9CRJEmS1DgGHUmSJEmNY9CRJEmS1DgGHUmSJEmNY9CRJEmS1DgGHUmSJEmN4310JrDMxBu+jk5EEBGD7oYkSZJqZtCZYFavXs3ixYtZunQpq1atGnR3GmGzzTZj+vTpbLPNNkyaNGnQ3ZEkSVINDDoTyOrVq1m0aBErVqwYdFcaZdWqVSxevJjly5ez8847G3YkSZIawKAzgSxevJgVK1YwadIktt9+e6ZNm8Ymm3iZ1Wg89dRTLF++nPvuu48VK1awePFitttuu0F3S5IkSaNk0JlAli5dCsD222/PjBkzBtybZthkk03WvJb33nsvS5cuNehIkiQ1gMMBE0RmrrkmZ9q0aQPuTfO0XtNVq1Y5wYMkSVIDGHQmiPYP356uVr/219SgI0mSNPH5iVmSJElS4xh0JEmSJDWOQUeSJElS4xh01AgrVqzgYx/7GC94wQvYfPPNedaznsUJJ5zAPffcM+iuSZIkaQCcXrphdvnwvEF3YVgWfuKNte1rxYoVHHbYYVx99dXsuOOOHH300SxcuJDZs2dz8cUXc8011zBr1qza2pMkSdKGzxEdTXgf//jHufrqqznggAO47bbb+PrXv861117Lpz/9aR544AFOOOGEQXdRkiRJ48wRHU1oTzzxBJ/97GcB+NznPseWW265Ztspp5zC+eefz5VXXsn111/Py1/+8kF1U5IkbcTG+4ybOs+cmcgc0dGEdtVVV/Hwww8za9YsXvaylz1t+7HHHgvAt7/97fHumiRJkgbIoKMJ7aabbgJgn3326bq9tb5VTpIkSRsHg44mtEWLFgGw0047dd3eWt8qJ0mSpI2DQUcT2rJlywCYOnVq1+3Tpk1bp5wkSZI2DgYdTWiZCUBE9N0uSZKkjYtBRxPa9OnTAVi+fHnX7Y899hjAOrOxSZIkqfkMOprQdt55ZwDuueeerttb61vlJEmStHEw6GhC23vvvQH46U9/2nV7a/1ee+01bn2SJEnS4Bl0NKG96lWvYsaMGdx5553ccMMNT9s+d+5cAI466qjx7pokSZIGyKCjCW2zzTbjxBNPBODEE09c51qdc845h5tvvplXv/rV7LvvvoPqoiRJkgZg8qA7II3WaaedxqWXXsrVV1/NbrvtxkEHHcRdd93FtddeyzbbbMPs2bMH3UVJkiSNM0d0NOFtvvnmXH755Zx++ulMnTqVb33rWyxcuJDjjjuOG264gec///mD7qIkSZLGmSM6DbPwE28cdBcGYosttuDMM8/kzDPPHHRXJEmStAFwREeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDXO5EF3QBqt66+/nu9973tcd911XHvttdx7771MmTKFFStWDLprkiRJGhCDTtOcMWPQPRieMx6pbVdnnXUWF110UW37kyRJ0sRn0NGEd8ABB7D33nuz7777su+++7LDDjsMukuSJEkaMIOOJrxTTz110F2QJEnSBsbJCCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFHkiRJUuNMHnQHpNGaN28eZ5111jrrVq1axf7777/m+emnn84b3/jG8e6aJEmSBsSg0zRnPDLoHoy7Bx54gGuvvXaddZm5zroHHnhgvLslSZKkATLoaMI7/vjjOf744wfdDUmSJG1AvEZHkiRJUuMYdCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFHkiRJUuMYdCRJkiQ1jkFngoiINT9n5gB70kztr2n7ay1JkqSJyaAzQUQEkyZNAmDlypUD7k3ztF7TSZMmGXQkSZIawKAzgUydOhWApUuXDrgnzdN6TadNmzbgnkiSJKkOkwfdAQ3fVlttxdKlS1myZAmTJ09mq622WjPKo5FZvXo1jz76KEuWLAFg+vTpA+6RJEmS6mDQmUCmT5/OjBkzeOSRR7j//vu5//77B92lRtl6660NOpIkSQ1h0JlAIoIddtiBLbbYgoceeshrdWoyZcoUZs6cyYwZM7w+R5IkqSEMOhPMJptswsyZM5k5cyaZ6QxsoxQRhhtJkqQGMuhMYH5IlyRJkrprxKxrEfGMiLg/IjIifjlE2XdHxHURsSwilkTEJRFx4BB1DqzKLanqXRcRx9V7FJIkSZLq0oigA5wDPHOoQhFxDnA+sCdwKXAdcARwZUS8uUedNwNXAq8Hbga+C+wGzKn2J0mSJGkDM+GDTkQcBhwH/OsQ5V4LnAwsBvbOzGMy8/XAwcBqYHZEzOyoMxOYDUwCjs3MQzLzWOCFwB3AyRFxaN3HJEmSJGl0JnTQiYgtgM8DtwJ/P0TxD1TLszPz9tbKzLym2scM4ISOOn9crb8oMy9sq3Mf8KHq6SkjPgBJkiRJY2JCBx3gY8As4M+AJ3oViojNgcOqp3O7FGmte1PH+qP61JkHrAAOr/YvSZIkaQMxYYNOROxFGaWZnZlXDlH8hcAU4IHMvKfL9p9Wy7061u/VsX2NzFwF/BzYHNh9uP2WJEmSNPYmZNCJiE0o1+Q8zNpTyPrZuVp2Czlk5vJqXzMjYnrVxlbA1v3qta3fucd2SZIkSQMwUe+j85fAfsB7MnPxMMpvWS0f61NmOSXYbAksbavTr97yjv33FRG39Ng0azj1JUmSJA3PhBvRiYjnAGcDV2TmnOFWq5Y5jDK9ng+njiRJkqQNwEQc0fknYDPKBATDtbRaTutTZmq1XNZRp7Xt0WHU6Ssz9+i2vhrpefFw9iFJkiRpaBMx6BxFuZ7mnyPWGVBpzXy2c0TMb5XNzGXAour5Tt12GBHTKKetPZyZSwEy89GIeIQyvfROlCmsO7X2t6jLNkmSJEkDMhGDDpRQ8poe27Zo29Y6vgXASmDbiNipy8xr+1TLmzvW30S5oeg+dASdiNgU2LPa74L1PQBJkiaKXT48b1zbW7j5O8e1Pc54ZHzbkzQuJtw1OpkZ3R7ArlWRBW3rH67qPA58v9p+bJfdttZd3LF+Xsf2dkdRRpEuy8wVIz4gSZIkSbWbcEFnFM6plqdFxG6tlRFxAPAnlGtwvtRR54vV+qMj4i1tdbYDPtmxX0mSJEkbiI0m6GTmpcC5wDbAjRHxrYi4BLgS2BQ4ITOXdNRZApwAPAXMjYjLI+KblFPVng+cl5mXjedxSJIkSRraRhN0ADLz/cB7gF8ARwAHApcBr8nMC3rUuYBync5/Ay8F3gDcSQlGJ41HvyVJkiStn4k6GcHTZOZChnFfm+reO3PWc98/BI4cSb8kSZIkjb+NakRHkiRJ0sbBoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcSYPugOSJEmSanTGjHFu75HxbW+YHNGRJEmS1DgGHUmSJEmNY9CRJEmS1DgGHUmSJEmN42QEmpB2+fC8cW1v4SfeOK7tSZIkaXQc0ZEkSZLUOAYdSZIkSY1TW9CJiO0j4uCI2L5j/a4R8bWI+HlEzIuI/epqU5IkSZK6qXNE58PA5cDWrRURsSVwFfA24MXAkcBlEfG8GtuVJEmSpHXUGXQOAX6RmQva1h0P7Ah8DdgdOBmYBnywxnYlSZIkaR11Bp1nA7/qWHcU8CRwUmbenpnnAjcCh9bYriRJkiSto86gMx1Y2noSEQG8Erg+Mxe3lVsA7FRju5IkSZK0jjqDzq+BXduevwKYAczvKDcZWFVju5IkSZK0jjqDzjXAfhFxdERsBZwGJPDtjnIvooQiSZIkSRoTdQadvwFWAhcCDwFvAuZn5tWtAhGxC2X2tWtrbFeSJEmS1jG5rh1l5i8j4tXAScC2wPXApzqKvQ64CfhWXe1KkiRJUqfagg5AZt5AmVK61/YvAF+os01JkiRJ6lTnqWuSJEmStEGoPehExJ4R8ZmI+GFELIiIT7Zte1VEvC8inlF3u5IkSZLUUuupaxHxIeDstv0m8My2IlOBf6BMWuApbJIkSZLGRG0jOhFxNPAJ4C7gGMqEBNFR7FLgwWq7JEmSJI2JOkd0TgaWAUdk5kKAiHVzTmZmRCwAXlBju5IkSZK0jjqv0XkZcE0r5PTxa2DHGtuVJEmSpHXUGXQmA48No9y2wKoa25UkSZKkddQZdO4EXh4Rk3oViIhpwEuBW2tsV5IkSZLWUWfQmQvsBJzVp8xZwEzg66NtLCJOiYgLI+L2iHgkIlZGxF0RcX5E7NGn3rsj4rqIWBYRSyLikog4cIi2DqzKLal2wxYeAAAgAElEQVTqXRcRx432GCRJkiSNjTqDzqeBXwCnRsSVEfHBav3zIuLEiLgUeD9wM/D5Gtr7P8CRwBLgMmAesAJ4N/DTiDiys0JEnAOcD+xJmQHuOuAI4MqIeHO3Rqr1VwKvr/r+XWA3YE61P0mSJEkbmNpmXcvM5RFxKDCHEgpeVW06GDiIMtX0ZcC7MnNlDU0eDVyfmSvaV0bEnwH/BHwxInbOzNXV+tdSZoZbDByQmbdX6w8A5gOzI2J+Zj7Utq+ZwGxgEvDWzLywWr89cBVwckR8OzMvr+F4JEmSJNWkzhEdMvP+zHwDZQa2UymB4wvARynh4ojMvL+mtn7YGXKq9f8M3AE8C9i9bdMHquXZrZBTlb+GMsI0AzihY3d/XK2/qBVyqjr3AR+qnp4yykORJEmSVLM676OzRmbeBNw0FvseptXVchVARGwOHFatm9ul/FzgfcCbKKfgtRzVp07rVLnDI2LzbqFLkiRJ0mDUOqKzIYiId1NGcm4DflWtfiEwBXggM+/pUu2n1XKvjvV7dWxfIzNXAT8HNmfdkSNJkiRJA1Zb0ImI90XE6oh4Q58yR1Zl/rzGdv8qIuZExDcj4ueUyQbuBd6ZmU9VxXault1CDpm5HHgYmBkR06v9bgVs3a9e2/qde2yXJEmSNAB1nrr2VuDezLykT5nvAr8BjqVcv1OH17H2tDSAu4E/zMzr29ZtWS373dB0OSXYbAksbavTr97yjv33FRG39Ng0azj1JUmSJA1Pnaeu7U45launzEzgZ5RTyWqRmYdnZlDuz3MwsACYHxEfaSsWreJ9dhVDPB9OHUmSJEkbgDpHdLam3NNmKA8Bz6ixXQAy82HgB9Wpc9cAZ0XE/2TmjykjNADT+uxiarVcVi2Xdmx7dBh1hupj1xuZViM9Lx7OPiRJkiQNrc4Rnd8CLxlGuT2BB2tsdx2Z+QTwdcpoy5uq1Yuq5U7d6kTENEpQezgzl1b7eRR4pF+9tvWLemyXJEmSNAB1Bp3LgT0i4q29CkTEWyhBZ6xvsNkKUttWywXASmDbiOgWWvapljd3rL+pY/saEbEp5VhWVvuXJEmStIGo89S1TwK/D3wlIg4C/oUyvXNSLrb//4A/pdzb5pM1ttvNa6rlnQCZ+XhEfB84kjIRwmc6yh9bLS/uWD+Pct3PscB/dGw7ijK19CXeQ2cjcMaMcW7vkaHLSJIkqafaRnQy8xfAu4GngL+kTDqwvHr8jHJDzqeA4zPzZ6NpKyIOioi3R8TkjvWbRsRfAn8IPE45ha3lnGp5WkTs1lbnAOBPKNfgfKmjqS9W64+uRqNadbZjbVg7B0mSJEkblDpHdMjMb0bEDcAplCmfn1Ntuhu4FPhMZt5eQ1OzgNnAgxFxPbAYeCblGqEdgRWUQHV3W98ujYhzgZOAGyPie8BmwBGUwPeuzFxnMoXMXBIRJwDfAOZGxBWU0+IOp1zTc15mXlbD8UiSJEmqUa1BByAz7wBquyFoD1cAH6ecorYXJeSsAhYCcykB5I4ufXt/RNwInEgJOE8AlwFnZ+ZV3RrKzAsi4mDgNGB/Sjj6BfC5zJxd83FJkiRJqkHtQWc8ZOb/Ah8ZsmD3unOAOetZ54eU63skSZIkTQBjEnSqa2e2Aab0KpOZTsksSZIkaUzUGnQi4nDWnuK1aZ+iWXfbkiRJktRSW9iIiKOA/wImAQ9RppZeVtf+JUmSJGm46hxV+Rhl9rL3Uy7UX13jviVJkiRp2OoMOnsA12TmeTXuU5IkSZLWW203DKWcpnZfjfuTJEmSpBGpM+hcCuwTEXXuU5IkSZLWW52h5FRgC+DTETGpxv1KkiRJ0nqp8xqd9wDfAd4HHBUR84F7KFNJd8rMPKvGtiVJkiRpjTqDzhmUUBPArOrRSwIGHUmSJEljou4RHUmSJEkauNqCTmaeX9e+JEmSJGk0nCFNkiRJUuMYdCRJkiQ1Tp3X6BARAbwLOBrYDZhOmZygU2Zmv8kKJEmSJGnEags6EbEZMA94Ld3DDaydlU2SJEmSxkydp659ADgMuJgymvPvlGAzBXgRZfrp5cCnMtNT5iRJkiSNmTpPXXs7sAR4Z2Yuj4inADLzCWABcGZEXA5cHhELMvPLNbYtSZIkSWvUObLyfOC6zFxePX8KICImtQpk5g+AHwJ/XmO7kiRJkrSOOoPOauDRtuetwLNtR7lfA7vX2K4kSZIkraPOoPNrYOe253dUy/07yu0FLKuxXUmSJElaR51B50fAHhGxRfX8kmp5bkQcGREviYjPUiYmuLbGdiVJkiRpHXUGnQuAx4AjADLzDuAzwHMoM7HdCPxFVebUGtuVJEmSpHXUNutaZs4DduxY94GI+DFwDDATuA04LzNvr6tdSZIkSepU5/TSXWXmfwL/OdbtSJIkSVJLbaeuRcRHI+J3h1HuTRHx0bralSRJkqROdV6jcwblFLWh/C7wsRrblSRJkqR11Bl0hmsS1c1EJUmSJGksDCLo7AE8NIB2JUmSJG0kRjUZQUR8uWPVq7usa29rd+AVwLdG064kSZIk9TPaWdeOb/s5gedXj35uBv5qlO1KkiRJUk+jDTqHVssAvg98F/i7HmVXAfdm5l2jbFOSJEmS+hpV0MnMK1o/R8T5wA/a10mSJEnSINR2w9DMfE9d+5IkSZKk0ajzhqHbR8TBEbF9x/pdI+JrEfHziJgXEfvV1aYkSZIkdVPn9NIfBi4Htm6tiIgtgauAtwEvBo4ELouI59XYriRJkiSto86gcwjwi8xc0LbueGBH4GuUqaVPBqYBH6yxXUmSJElaR51B59nArzrWHQU8CZyUmbdn5rnAjaydrU2SJEmSaldn0JkOLG09iYgAXglcn5mL28otAHaqsV1JkiRJWkedQefXwK5tz18BzADmd5SbTLmnjiRJkiSNiTqDzjXAfhFxdERsBZwGJPDtjnIvooQiSZIkSRoTdQadvwFWAhcCDwFvAuZn5tWtAhGxC2X2tWtrbFeSJEmS1lHnDUN/GRGvBk4CtgWuBz7VUex1wE3At+pqV5IkSZI61RZ0ADLzBsqU0r22fwH4Qp1tSpIkSVKnOk9dkyRJkqQNQq0jOi0RsTPlRqFTepXJzCvHom1JkiRJqjXoRMQJwOnAzsMoPqnOtiVJkiSppbagExHvAb5YPf0ZcBuwrK79S5IkSdJw1TmicwrwJPDWzOy8d44kSZIkjZs6JyPYDbjSkCNJkiRp0OoMOkvwVDVJkiRJG4A6g85FwH4RsUWN+5QkSZKk9VZn0Pk/wKPAnIjYusb9SpIkSdJ6qXMygk8DtwLHAr8TET8B7gGyS9nMzD+qsW1JkiRJWqPOoHN8288zgMP6lE3AoCNJkiRpTNQZdA6tcV+SJEmSNGK1BZ3MvKKufUmSJEnSaNQ5GYEkSZIkbRAMOpIkSZIaZ8SnrkXEr0bRbmbmrFHUlyRJkqSeRnONzi6jqNttymlJkiRJqsVogs6utfVCkiRJkmo04qCTmXfV2RFJkiRJqouTEUiSJElqHIOOJEmSpMYx6EiSJElqHIOOJEmSpMYx6EiSJElqHIOOJEmSpMYZcdCJiC9HxAltz3eOiGfU0y1JkiRJGrnRjOgcD7y67fn/Ap8aVW8kSZIkqQajCTpPAJu3PY/qIUmSJEkDNZqgczdwUEQ8t67OSJIkSVIdRhN0vgo8G/hVRKyu1h0XEauH8Xhy9F2XJEmSpO5GE3TOAD4IXAUsrNY9BiwaxuPuUbRLREyNiGMi4ksRcXNEPBoRyyPipoj4aERs2afuuyPiuohYFhFLIuKSiDhwiPYOrMotqepdFxHHjeYYJEmSJI2dySOtmJlPAedUDyLiKeCbmXlC34r1eCfwr9XPtwDfBbYCDgT+L/COiHhNZt7fXikizgFOBh4H/odyjdERwO9ExO9l5n91NhQRbwa+SQmFVwIPAocBcyJi78w8ZQyOT5IkSdIo1HkfnfMpozvjYRXwz8ALMnPPzHxbZr4e2B24AXgh8Jn2ChHxWkrIWQzsnZnHVHUOBlYDsyNiZkedmcBsYBJwbGYekpnHVvu/Azg5Ig4dywOVJEmStP5qCzqZ+Z7M/HJd+xuirX/LzD/PzNs71v8G+Ivq6VsiYrO2zR+olme318vMa4DPAzOAztGoP67WX5SZF7bVuQ/4UPXUER1JkiRpA1PniA4AEbFpRLwtIj4bERdExNzq57dFxKZ1t9fFTdVyCrBN1afNKaebAcztUqe17k0d64/qU2cesAI4vNq/JEmSpA3EiK/R6SYiXkWZjW0nnn5PnT8H7o6Id2bm1XW22+F51fIJYEn18wspweeBzLynS52fVsu9Otbv1bF9jcxcFRE/B15BOWXups4ykiRJkgajtqATES8AvgNsCVwP/AdrZ2N7LvAHlFDwnYh4RedpZzU6qVp+NzNXVj/vXC27hRwyc3lEPAzMjIjpmbk0IrYCtu5Xr1r/imr/Bh1JkiRpA1HniM5HKCHn5Mw8t8v28yLifZRJAj4CHF9j2wBExBuAP6KM5pzetqk13fRjfaovpwSbLYGlbXX61Vvesf+h+ndLj02zhlNfkiRJ0vDUeY3OYcANPUIOAJl5HmVWtMNrbBeAiHgRZRQpgL/KzPYRltZpdNlvF0M8H04dSZIkSRuAOkd0tgWuGEa5XwJ71NguEbET5V46M4FzuoStpdVyWp/dTK2WyzrqtLY9Oow6fWVm1+OuRnpePJx9SJIkSRpanSM6i4EXDKPcC1g7ScCoRcQzge9RrpOZDXywS7FF1XKnHvuYRjlt7eHMXAqQmY8Cj/Sr17Z+UY/tkiRJkgagzqBzObBPRLy3V4Fq28uB79fRYERMp0yA8ELgQuC9mdnt9LQFwEpg22r0p9M+1fLmjvU3dWxvb3tTYM9qvwvWv/eSJEmSxkqdQedsyn1lPh8Rl0fEeyPiiIg4vPp5PuXGnI8BfzPaxiJiCnARZdaz/wbekZmru5XNzMdZG66O7VKkte7ijvXz+tQ5CtgcuCwzV6xH1yVJkiSNsdqCTmb+Avhd4AHgNZRQ811KCPk8cDBwP/C7VdkRi4hJwNeAQ4EfAG/JzFVDVDunWp4WEbu17esA4E8o1+B8qaPOF6v1R0fEW9rqbAd8smO/kiRJkjYQtd4wNDMvi4jnAW8DDgKeVW26lxJIvpGZ/aZ4Hq4TgTdXPz8I/FNE1wnQPpiZD1Z9uzQizqXcZ+fGiPgesBlwBCXwvSsz17l2KDOXRMQJwDeAuRFxRdXe4ZRres7LzMtqOB5JkiRJNao16ABUQWZO9RgrM9t+fnPPUnAGJZgAkJnvj4gbKUHpCMr9di4Dzs7Mq7rtIDMviIiDgdOA/Snh6BfA5zJz9mgOQpIkSdLYqD3ojIfMPIMSYkZSdw7rGcIy84fAkSNpT5IkSdL4q3MyAkmSJEnaIBh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS49QadCLiVxHxN3XuU5IkSZLWV90jOrsA27aviIjvR8SHam5HkiRJknqaPNKKEfEN4Drgx8D1mbmsR9FDgIUjbUeSJEmS1teIgw7wOuBYIIGMiF9W658dETtm5m9G3TtJkiRJGoHRBJ2tgRcBr2x7ALweuCcifgVcWUM7kiRJkrReRhxAMjOBW6vHbICIeAq4gnJK2yHAH1JGfN4VEQdRgs+VwJWZefuoei5JkiRJPYzmGp0te1yXc0dmfrhVBngUWAA8ALyNteFn0kjbliRJkqR+RnNK2cMRcStwLfAjyijOOjJzWUQAXJOZJ0TEFGB/4KBRtCtJkiRJfY0m6Pw78HLgeOCPqCYlAA6tppO+HLi+vUJmrqSc2nbFKNqVJEmSpL5Gc43OewAiYiqwD7Af8PfA84BPUELPsmq5e0QcCPw4M58YbaclSZIkqZ9R3zA0Mx/LzKsy85xq1ZeBvYFTKKM6ARwA/IByutvlEXHGaNuVJEmSpF5GHXS6yMz8WWaem5nHVOsuAf4M+BZlxOf0MWhXkiRJkoDxu7/N/Zn5L8C/AETELuPUriRJkqSNUN1B51DgN0MVysyFNbcrSZIkSWvUGnQys9tsau8B7qizHUmSJEnqZ8xPXcvM88e6DUmSJElqNxaTEUiSJEnSQBl0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS4xh0JEmSJDWOQUeSJElS40zYoBMRL4+ID0fEhRHx64jIiFgxjHrvjojrImJZRCyJiEsi4sAh6hxYlVtS1bsuIo6r72gkSZIk1WnyoDswCqcDR69PhYg4BzgZeBz4H2Bz4AjgdyLi9zLzv7rUeTPwTUoovBJ4EDgMmBMRe2fmKaM6CkmSJEm1m8hB5xrgJuDH1eO3/QpHxGspIWcxcEBm3l6tPwCYD8yOiPmZ+VBbnZnAbGAS8NbMvLBavz1wFXByRHw7My+v+dgkSZIkjcKEPXUtM/8uMz+WmRdn5n3DqPKBanl2K+RU+7kG+DwwAziho84fV+svaoWcqs59wIeqp47oSJIkSRuYCRt01kdEbE453QxgbpcirXVv6lh/VJ8684AVwOHV/iVJkiRtIDaKoAO8EJgCPJCZ93TZ/tNquVfH+r06tq+RmauAn1Ou89m9pn5KkiRJqsHGEnR2rpbdQg6ZuRx4GJgZEdMBImIrYOt+9drW79xjuyRJkqQBmMiTEayPLavlY33KLKcEmy2BpW11+tVb3rH/viLilh6bZg2nviRJkqTh2VhGdKJa5jDK9Ho+nDqSJEmSNgAby4jO0mo5rU+ZqdVyWUed1rZHh1Gnr8zco9v6aqTnxcPZhyRJkqShbSwjOouq5U7dNkbENMppaw9n5lKAzHwUeKRfvbb1i3pslyRJkjQAG0vQWQCsBLaNiG6hZZ9qeXPH+ps6tq8REZsCe1b7XVBTPyVJkiTVYKMIOpn5OPD96umxXYq01l3csX5enzpHUaaWviwzV4y6k5IkSZJqs1EEnco51fK0iNittTIiDgD+hHINzpc66nyxWn90RLylrc52wCc79itJkiRpAzFhJyOIiDcCp3es3iwiftT2/KzMnAeQmZdGxLnAScCNEfE9YDPgCErge1dmLmnfWWYuiYgTgG8AcyPiCuBB4HDKNT3nZeZlY3B4kiRJkkZhwgYdYFvglR3romPdtu0bM/P9EXEjcCIl4DwBXAacnZlXdWskMy+IiIOB04D9KeHoF8DnMnN2HQciSZIkqV4TNuhk5hxgznjUy8wfAkeub1uSJEmSBmNjukZHkiRJ0kbCoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCNJkiSpcQw6kiRJkhrHoCP9v/buPGqSqjzA+POyyKosioqCQAYFHcM2bCoEhIgSQPZMBBcQ9EiMHgPHQ4xRg3riFjVRNCITcFTABYEoKkpQdkEQBISDyJBhGVYRUAZmBuHNH3Ubmqb7W6uXr+b5ndPnfl23uupt5lLdb9ddJEmS1DgmOpIkSZIax0RHkiRJUuOY6EiSJElqHBMdSZIkSY1joiNJkiSpcUx0JEmSJDWOiY4kSZKkxjHRkSRJktQ4JjqSJEmSGsdER5IkSVLjmOhIkiRJahwTHUmSJEmNY6IjSZIkqXFMdCRJkiQ1jomOJEmSpMYx0ZEkSZLUOCY6kiRJkhrHREeSJElS45joSJIkSWocEx1JkiRJjWOiI0mSJKlxTHQmICJWjYjjIuKmiFgSEXdGxEkRscGwY5MkSZL0TCY644iIVYHzgA8DawL/A9wOHA5cFRGzhhieJEmSpC5MdMb3z8CrgV8AL8vMuZm5A3AMsB5w0jCDkyRJkvRMJjpjiIiVgfeUp+/OzIdbdZn5OeBa4K8iYs4w4pMkSZLUnYnO2HYC1gYWZObVXepPL+U+gwtJkiRJ0nhMdMa2ZSmv6lF/Vcd+kiRJkkbASsMOYMS9pJR39Ki/o2O/MUXE9T2qNl+wYAGzZ8+eTGzLtTvveXj8nWo0e4XBno/v2hYkjQ6vudL0LK//Dy1YsABgw4HG0sZEZ2xrlvKRHvWLO/abqhWWLl36xA033HDjNI+jPrlh0Ce8r+cZW7P8LRhQJJrZbC+aqJFqKyN0zVV3I9Ve9Ewj9P/Q5sBqA4zkaUx0xhalzHHqJyQzu6a7rTs9veqlFtuKJsP2oomyrWgybC+aqDF6Mw2EY3TG9qdSrtGjfvVSDvj+oCRJkqSxmOiM7bZSbtCjfoOO/SRJkiSNABOdsV1Tym161Le2XzuAWCRJkiRNkInO2C4BHgJmRcTWXeoPKuXZgwtJkiRJ0nhMdMaQmcuA48vT4yPiybE6EXE0sAVwcWZeMYz4JEmSJHUXmb0mFBNARKwKnA/sANwFXARsVJ7fD+yYmTcPLUBJkiRJz2CiMwERsRrwAeAQqkWPHgDOAT6UmbcPMzZJkiRJz2SiI0mSJKlxHKMjSZIkqXFMdCRJkiQ1jomOJEmSpMYx0ZEkSZLUOCY6kiRJkhrHRKdmEbFqRBwXETdFxJKIuDMiToqIDSZ5nIURkWM8Nu/Xe9Dg1NVe2o63aUScWNrPkoi4LyIujYj31x27BquOthIRh41zXWk93trP96L+q/PaEhFviIgfR8TvI+KxiLg3Is6OiN37EbsGq+a2smdEnBsRD0bEIxFxXUS8PyJW6kfsGqyImBMR/xQRZ0TEovJ5sWQax1s7Iv4jIm6NiKWl/M+IWLu2mJ1euj5lcdHzgFfz1OKiGwPbA/cBr8rMBRM81kKqhUnn99jlA5l51zRD1hDV2V7K8fYHTgVWAa4GbgKeC/wlsDgzN60zfg1OXW0lInYCjuxRvRawX/l7VmbeMs2wNSQ1fxYdDXwWSOASYBHwF8B2ZZejMvMrdcavwam5rRwLfBJ4Ari8vH5H4PnAT4G9MvPPNb8FDVBEnAXs27F5aWauOoVjPRf4BfBS4BbgSmB2edwM7JiZ908vYiAzfdT0AD5K9WFwKbBm2/ajy/YLJnGshdU/z/Dfl48Z0V62BJYCvwd26qhbAdh22O/Xx2i0lTHOcVQ51sXDfr8+RqO9AOuV68rSLteVA6m+0C5uP4ePmfWosa1sV9rDMmCPtu1rAReUYx077PfrY9rt5VjgOGBv4AXl33XJFI/19fL67wErtW3/Qtk+v46YvaNTk4hYGbgXWBvYJjOv7qi/BtiC6gvnryZwvIXARpkZfQhXQ9aH9nIhsDOwT2ae3YeQNSR1t5UxznMJ1a+678rME6YRsoaozvYSEXsDPwDOycw9u9T/mupHlh0y85c1vQUNSM1tZR5wBHBiZr6zo2428BuqOzzrZ+bj9b0LDVNEJFO4oxMRL6S6O/w4sGFm3tNWtwpwO7Au8OL2uqlwjE59dqK6WCzovFgUp5dyn8GFpBFWW3uJiJdTJTk3meQ0Ut+vLRGxCVWSswz4zlSPo5FQZ3tZOsFz/mGC+2m01NlW5pTy/M6KzLyeqrfBelTXGWlPqhzkws5EJjOXUv3AsmLZb1ocHFafLUt5VY/6qzr2m5AyiHwW1QfO9cCZmXnflCLUKKmzvbQGBJ9b+lvPBbaluvV7LfCdzPzjVAPV0PXl2tLhzaX8YWY+MI3jaPjqbC9XAA8Bu0XETpl5casiIg6g+rX/0sy8earBaqjqbCtrlLLX9eMPwPPKsS6aUHRqsom0vbczvc81wESnTi8p5R096u/o2G+iPt3x/PMR8d7M/O9JHkejpc72MruUjwK/BjbrqP9ERByYmRdOLkSNiH5dW9odWspvTOMYGg21tZfMfDAijgROAS4s3RsXAZtQjck4BzhsWtFqmOq8ttxHNah8o86KiFgB2LA83XgS8am5BvG5Bth1rU5rlvKRHvWLO/Ybz/eBA6guGqsDrwQ+RzWj1ryI2G+M12r01dle1inl+6j6tB5A1R1hM6pZ2J4HnBUR608tVA1Z3deWp4mI7anaygPAD6dyDI2UWttLZp5O1X3kfqquTnOpZuS6F/hZ2a6Zqc62ckEp39albi6wWvn72RMLTQ3X18+1diY69WlNGtBrdodJTSqQme/NzDMz87bMfO/Uc10AAArRSURBVDQzr8/MY4C/L7t8aqqBaiTU2V5WLOVKwJtLu3koM2/KzEOpup+sA7x7aqFqyGq9tnTR6rb27cxcNs1jafhqbS8RcQxwLnAhVVe1NUv5C+AzwLenFqZGQJ1t5UtU3Rx3jIivlTXd1o6IuaWuNa30E1MLVQ3T78+1J5no1OdPpVyjR/3qpXx4mueZR/VL2svKAGLNTHW2l9axFmXmT7vUn1zKXScWmkZM364tZRG/ueWp3daaobb2EhG7AP9O1SX24My8LjMXZ+Z1wEFU63UdGBF7TDNmDUdtbSUzFwH7U43FeRvwO6q7xN+imkHrpLKrYwAFg/vObKJTo9tK2Wsl4Q069puSzHwCaC3eZVekmavO9rKwlLeOU//8CRxLo6ef15Y9qNrFLZl56RRer9FTZ3t5aynPKJ89TypTBJ9Rnu46mQA1Mmq9tmTmz6kmT3oX8GXgeOAtVF0dWyvdXz+lSNU0A/nODE5GUKdrSrlNj/rW9mtrOFdrTMa0M10NTZ3tpTUt6Lo96p9bStvLzNTPa0ur29o3p/BajaY620vry0avWRtb23tdezTaar+2ZOaDwNPW4Sp3jneh6rbmpDiCAX5n9o5OfS6h6p86KyK27lJ/UCmntc5JWXhrM6oBXDdO51gaqjrby3lUA/dmRcSGXep3LWWvaRw12vpybYmINYF9y1MTneaos73cXcpte9RvV8qFE45Oo2Qg31uoZnV8AdXCs7dP81hqhnOoEt+dI+JpvU3KgqH7lPofT/dEJjo1KYN4jy9Pj4+IJ/sdRsTRVIM3L87MK9q2/0NE3BgRn2g/VkS8PiLm0CEitgC+SzVIa54Dh2euOttLZj4CfBFYGfivjmO9gaq/dAJf7df7Uf/U2VY6HEDVD/qyzPxdH0LXENTcXs4q5aER8bRFIyNiX+AQqi8jZ9b8NjQAdV9bImJORETHttdRfT4tAY7uw9vQCBvje8tdwGnAs4Avl7t+LZ+mWlz21My8m2my61q9Pg78NdXKv7+LiIuopofegWoKzsM79n8e1d2ZzrE2rwI+EhG3Uo3HuY9q3YJtqP7NLgA+0Kf3oMGpq70AHAfsDOxVjnU51diLHal+0PhgZv6yH29CA1FnW2lpdVtzEoLmqau9nEX149rBwPcj4krg/6g+j1p3eT6Ymb/tx5vQQNR5bfkesGJEXEd1p2gzYGuqNd4Osp3MfBGxF/Chjs3PiojL2p5/LDNbSxWM1V7eR/Ud5UDgxnJ9mU21nMoC4B/riNk7OjXKzCXAa4GPUXUt249qcaz5wNaTWD36J1QzlPyRalXYA4FNgYuBdwC7l1/xNYPV2F5ax9oN+CDwINW6F7OBnwN7Z+a/1Rq8BqrOtgJQ1lTaDXgMpwdunLraS2Ym1ax8R1CNrdiUamatjYEfAXt6bZnZar62fIVqQdkdqO4Yr0vVk+CVbV98NbOtR/Xv23pA1cuofdt6EzlQZv6eqvvrF6nu7OwPrEV1l3H7Uj9tUV3HJEmSJKk5vKMjSZIkqXFMdCRJkiQ1jomOJEmSpMYx0ZEkSZLUOCY6kiRJkhrHREeSJElS45joSJIkSWocEx1JkiRJjWOiI0mSJKlxTHQkSZIkNY6JjiRJkqTGMdGRJPUUEdnxeCIiHoyIiyLiyIiIAcSwazn310bxPBGxMCKyy/aMiIUd2zYu28+fVrCSpHGZ6EiSJmJ+eZwC3AC8BjgROHWYQTVFRBxWEqB/HXYsktQUKw07AEnS6MvMw9qfR8TrgB8BfxcRp2Tm2UMJbDTsDqw8wX0XAS8HHulfOJIk8I6OJGkKMvNc4Bvl6X7DjGXYMnNBZt44wX0fy8wbM/O2fsclScs7Ex1J0lRdXcoNWxsi4mulC9auEfH6iPh5GdOTEbF2236viIhTIuKuiFgWEYsi4usRsdlYJ4yI9cs57omIRyPiqoh4a499d46I4yPi2oh4oOx/Y0R8sj2WGs7TdYxOj32fMUan/H1yefqRjjFRh0XEweXvU8Y47sllnzdPJA5JWh7YdU2SNFXPLuXSLnWHAEcCVwI/BmYBCRARuwM/AFYDrgLOBzYH3gLsHxF/k5kXdTnmusBlwCrlNesArwXmR8QmmXlcx/6fAbYCfgP8rLxuG+BYYO+I2DEzH67hPNN1DtXn8WuAa4Bft9XdDFwO3A0cGBHvycw/tL84Ip4DHAw8CJxec2ySNGN5R0eSNGlltrW9y9Nru+zyDuBNmbl9ZrbKhyJiDaoJDVYDjsrMOaV+a+BoYE3g1IhYpcsx9wF+C8zKzLmZuQfwauBh4MMRsVXH/h8F1s/MbTPzwMzcG9gE+Cowu5yvm8meZ1oy85PAvPL0rMw8rO1xcWY+BpxElXi9pcshDgHWAL6RmUvqjE2SZjITHUnShEXEihHxUqov3q+iuptzcpddf5iZ3+6y/W+BFwAXZeZX2isy8/PAr4ANgP27vDaB92Tm4rbXXAF8ierz7KiO4/0oMx/o2LYUeB/wZ2DfHm9zUucZkBOBJ6juknVqbZvXpU6Sllt2XZMkjavHGJQ/AW/LzAVd6r7f41A7l7LXeJNvAnPKft/qqLs6M3/b5TWnUXVH26mzIiJeTHWHZnPgOTz1A98y4KU9Ypj0efotMxdGxE+APUuXu8sAImJrqv9el2dmtztrkrTcMtGRJE3E/FI+AfwRuA44o/OOSZtes4q9qJQLe9S3tr+oS92tk3lNRBwNfAJ4Vo/X9TKp8wzQCcCeVN0CLyvb3lHKE4cSkSSNMBMdSdK4OtfRmYDxxoqMN0vZhGYx6yUidgQ+CzwEvJNqUoG7S9c1IuJOYP3pnGMIzgbuAOZGRKv73SFUd9a6dROUpOWaY3QkSYN0Zyk36VG/USnvGqOu1/Y727a1xvj8S2bOz8xb25Kc1YAXjhHjZM4zMJn5ONU4nDWAN1GNd1oLOK3H7HGStFwz0ZEkDVJr2uhDe9Qf2rFfu60i4mVdtr+plJe0bVunlLd32f9gIMaIcTLnqcuyUo7X02Ie8DhVlzW7rUnSGEx0JEmD9B3gHmDniHhne0VEvBfYjqp71pldXrsC8IWIWL3tNXOAd1ONHTqhbd+bSnlERKzctv8rgE+NE+NkzlOX1l2iMRdMzcxFVF3YtqWsu5OZV/YhHkma8RyjI0kamMxcHBGHUi0YekJJdm6imhVta2AxcEirm1mHs4EtgAURcSFVt63dgJWBj2fmr9r2PRk4hrImTkRcQbUQ6C7AWcD29O6iNpnz1OUy4F7goIg4H7iFKqk6KTMv7dj3BJ6aGvurfYhFkhrBOzqSpIHKzPOo7tycRrVmzkFUY2a+CczJzG7d1gDup1q753+B1wK7AjcAh2fmhzrOcX85x6lUs669EXgx8GGe6oLWy4TPU5ey0OdewLnAVsBhwBFAty50F1B1X3uU3tN0S9JyLzKnNbGNJEkaoIg4hCrBmT+F2fAkablhoiNJ0gxRxhtdAWwJbJ+ZVww5JEkaWY7RkSRpxEXEG4H9qMYWzQbONMmRpLE5RkeSpNG3DXA48CKqbmtvH244kjT67LomSZIkqXG8oyNJkiSpcUx0JEmSJDWOiY4kSZKkxjHRkSRJktQ4JjqSJEmSGsdER5IkSVLjmOhIkiRJahwTHUmSJEmNY6IjSZIkqXFMdCRJkiQ1jomOJEmSpMYx0ZEkSZLUOCY6kiRJkhrHREeSJElS4/w/UCXJfHr+k/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0d2a660f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the probabilities\n",
    "# plot\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(dpi=150)\n",
    "plt.hist(probas, bins=20)\n",
    "plt.title('Classification Probabilities')\n",
    "plt.xlabel('Probability')\n",
    "plt.ylabel('# of Instances')\n",
    "plt.xlim([0.5, 1.0])\n",
    "plt.legend(Y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std = minmax.fit_transform(X_train)\n",
    "X_test_std = minmax.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluation Metrics\n",
    "from sklearn.model_selection import cross_val_score,cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print cross validation score for kNN\n",
    "clf_acc = cross_val_score(clf, X_train_std, Y_train, cv=3, scoring=\"accuracy\",  n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_proba = cross_val_predict(clf, X_train_std, Y_train, cv=3, method='predict_proba')\n",
    "clf_scores = clf_proba[:, 1] # get the score of the positive class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.7430299 ,  0.73790323,  0.74025538])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build a Logistic Regression Model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train,Y_train)\n",
    "lr_acc = cross_val_score(lr, X_train_std, Y_train, cv=3, scoring='accuracy', n_jobs=-1)\n",
    "lr_proba = cross_val_predict(lr, X_train_std, Y_train, cv=3, method='predict_proba')\n",
    "lr_scores = lr_proba[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.7897212 ,  0.79200269,  0.78729839])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict on test data\n",
    "Y_pred=lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Deposit \n",
      "[0 1 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Actual Deposit ')\n",
    "print(Y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Deposit\n",
      "[0 1 1 ..., 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted Deposit')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy score: 79.982087\n",
      "Recall score : 78.666667\n",
      "ROC score : 79.908143\n",
      "\n",
      "[[960 223]\n",
      " [224 826]]\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score on Test and Train\n",
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, confusion_matrix\n",
    "acc_lr=accuracy_score(Y_test,Y_pred) * 100\n",
    "print(\"\\nAccuracy score: %f\" %(accuracy_score(Y_test,Y_pred) * 100))\n",
    "print(\"Recall score : %f\" %(recall_score(Y_test, Y_pred) * 100))\n",
    "print(\"ROC score : %f\\n\" %(roc_auc_score(Y_test, Y_pred) * 100))\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "def ROC_curve(title, Y_train, scores, label=None):\n",
    "    # calculate the ROC score\n",
    "    fpr, tpr, thresholds = roc_curve(Y_train, scores)\n",
    "    print('AUC Score ({}): {:.2f} '.format(title, roc_auc_score(Y_train, scores)))\n",
    "    \n",
    "    # plot the ROC curve\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label, color='b')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('ROC Curve: {}'.format(title), fontsize=16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.77729258,  0.7718414 ,  0.77318548])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Decision Tree Classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtc_clf = DecisionTreeClassifier()\n",
    "dtc_clf.fit(X_train,Y_train)\n",
    "\n",
    "#Print cross validation score for Decision Tree Calssifier\n",
    "dtc_clf_acc = cross_val_score(dtc_clf, X_train_std, Y_train, cv=3, scoring=\"accuracy\",  n_jobs=-1)\n",
    "dtc_proba = cross_val_predict(dtc_clf, X_train_std, Y_train, cv=3, method='predict_proba')\n",
    "dtc_clf_scores = dtc_proba[:, 1]\n",
    "dtc_clf_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict on test data\n",
    "Y_pred=dtc_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Deposit \n",
      "[0 1 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Actual Deposit ')\n",
    "print(Y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Deposit\n",
      "[0 1 1 ..., 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted Deposit')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy score: 77.877295\n",
      "Recall score : 77.428571\n",
      "ROC score : 77.852071\n",
      "\n",
      "[[926 257]\n",
      " [237 813]]\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score on Test and Train\n",
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, confusion_matrix\n",
    "acc_dt=accuracy_score(Y_test,Y_pred) * 100\n",
    "print(\"\\nAccuracy score: %f\" %(accuracy_score(Y_test,Y_pred) * 100))\n",
    "print(\"Recall score : %f\" %(recall_score(Y_test, Y_pred) * 100))\n",
    "print(\"ROC score : %f\\n\" %(roc_auc_score(Y_test, Y_pred) * 100))\n",
    "print(confusion_matrix(Y_test, Y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.77729258,  0.7718414 ,  0.77318548])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtc_clf_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build Random Forest Calssifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rmf = RandomForestClassifier(max_depth=2, random_state=0)\n",
    "rmf_clf = rmf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.75680215,  0.74798387,  0.72950269])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print cross validation score for Random Forest Calssifier\n",
    "rmf_clf_acc = cross_val_score(rmf_clf, X_train_std, Y_train, cv=3, scoring=\"accuracy\",  n_jobs=-1)\n",
    "rmf_proba = cross_val_predict(rmf_clf, X_train_std, Y_train, cv=3, method='predict_proba')\n",
    "rmf_clf_scores = rmf_proba[:, 1]\n",
    "rmf_clf_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict on test data\n",
    "Y_pred=rmf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual Deposit \n",
      "[0 1 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Actual Deposit ')\n",
    "print(Y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Deposit\n",
      "[0 1 1 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted Deposit')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy score: 75.145544\n",
      "Recall score : 74.476190\n",
      "ROC score : 75.107918\n",
      "\n",
      "[[896 287]\n",
      " [268 782]]\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score on Test and Train\n",
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, confusion_matrix\n",
    "acc_rmf=accuracy_score(Y_test,Y_pred) * 100\n",
    "print(\"\\nAccuracy score: %f\" %(accuracy_score(Y_test,Y_pred) * 100))\n",
    "print(\"Recall score : %f\" %(recall_score(Y_test, Y_pred) * 100))\n",
    "print(\"ROC score : %f\\n\" %(roc_auc_score(Y_test, Y_pred) * 100))\n",
    "print(confusion_matrix(Y_test, Y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.145544111061355"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_rmf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "def ROC_curve(title, Y_train, scores, label=None):\n",
    "    # calculate the ROC score\n",
    "    fpr, tpr, thresholds = roc_curve(Y_train, scores)\n",
    "    print('AUC Score ({}): {:.2f} '.format(title, roc_auc_score(Y_train, scores)))\n",
    "    \n",
    "    # plot the ROC curve\n",
    "    plt.figure(figsize=(8,6))\n",
    "    plt.plot(fpr, tpr, linewidth=2, label=label, color='b')\n",
    "    plt.xlabel('False Positive Rate', fontsize=16)\n",
    "    plt.ylabel('True Positive Rate', fontsize=16)\n",
    "    plt.title('ROC Curve: {}'.format(title), fontsize=16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Support Vector Classification\n",
    "from sklearn.svm import SVC\n",
    "svc = SVC(kernel='rbf',probability = True)\n",
    "svc_classifier = svc.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print cross validation score for SVC\n",
    "svc_acc = cross_val_score(svc_classifier, X_train_std, Y_train, cv=3, scoring=\"accuracy\",  n_jobs=-1)\n",
    "svc_proba = cross_val_predict(svc_classifier, X_train_std, Y_train, cv=3, method='predict_proba')\n",
    "svc_scores = svc_proba[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.78535438,  0.78864247,  0.78998656])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict on test data\n",
    "Y_pred=svc_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual deposit\n",
      "[0 1 0 ..., 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "print('Actual deposit')\n",
    "print(Y_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted deposit\n",
      "[0 0 0 ..., 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print('Predicted deposit')\n",
    "print(Y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Accuracy score: 52.843708\n",
      "Recall score : 0.285714\n",
      "ROC score : 49.889265\n",
      "\n",
      "[[1177    6]\n",
      " [1047    3]]\n"
     ]
    }
   ],
   "source": [
    "#Accuracy score on Test and Train\n",
    "from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, confusion_matrix\n",
    "acc_svm=accuracy_score(Y_test,Y_pred) * 100\n",
    "print(\"\\nAccuracy score: %f\" %(accuracy_score(Y_test,Y_pred) * 100))\n",
    "print(\"Recall score : %f\" %(recall_score(Y_test, Y_pred) * 100))\n",
    "print(\"ROC score : %f\\n\" %(roc_auc_score(Y_test, Y_pred) * 100))\n",
    "print(confusion_matrix(Y_test, Y_pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (kNN): 0.81 \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0c2e82588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (Logistic Regression): 0.86 \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0c2e39fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (Support Vector Classification): 0.86 \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0c2e6da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (Decision Tree Classifier): 0.77 \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0c2e28ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC Score (Random Forest Classifier): 0.82 \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAGKCAYAAADkN4OIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XmYFNXVx/HvYVgVBRFQEBCNgqLihgsGFXfELbiiYiRqTGI0btG4vcY97muMcQnBgGtcCQJGEXdRcA2iREQURBYBURYZlvP+caszTdM9UzPT3TXT8/s8Tz1VXXW7+nR1z5y+t27dMndHRERESlOjpAMQERGRwlGiFxERKWFK9CIiIiVMiV5ERKSEKdGLiIiUMCV6ERGREqZE38CY2WAz87Sp3Mw+N7PrzKx5jufsYmZPmtkcM1tuZtPN7C9mtkmO8k3M7Awze8PMvoue84WZDTGznWLGebCZjTSzuWa2InrtEWY2oDbvv64wsysyPoflZjbZzC4ws0T+Ls1sqJlNT+K1M2U5PunTFknHl8nMzjGzI6v5nB5m9ncz+zL6/BeZ2Wtm9rvU36KZ9Y3ec9+CBF55fKn/FV3T1q1rZsOiv0s3s9uTjFHiaZx0AJKYY4CZwHrAAODiaPms9EJmdhLwd+B14GxgFrA1cCFwtJnt7+4fpZVfFxgN7AL8FbgOWAxsAQwCxgIbVBaYmd0CnAc8AZwJzAY2Ag4DHjezXu7+YS3ee13SB1gFtAEGAzcCq4FbEoypLkkdn3QzkgikCucQ/kaeilPYzI4BhgGTgKuBz4B1gb2BKwED7ihIpPE9B/QGvklb91vgeOAU4L/RtoVRucnFDlBicndNDWgiJBMHtshY/wKwFGiUtq478CMh4TbKKL8hMJXwx94kbf0DwHKgd47XH1BFfIOi+M7PsX1noEsejkOzhD+HK6L32ThtXSPgU+DThGIaCkxP8rhUdnzyvP+8fv7AdGB4zLJbAsuAp7O9P6Ad8NNouW90HPom/ZlE8fwd+KqA+zegadLvs9QmNd1LyntAC6Bt2rpzgDLgLHdfnV7Y3ecDlxD+aR0JYGYdCD8k7nf3t7K9iLs/XUUclwCT3D1rjdbd33X3r6LXy9rUbGYvm9nLaY9TTYtHmtn9ZjYPmGNmx0bre2bZx2gz+yDtcWMzu9jMPo2aWWeZ2S25TnfURHSMPwS6ZMSyRdRc+oWZLTOzaWZ2j5ltkFFuqJnNNLMdoybgpWb2mZn9Osv728/M3jOzH6NTN7/KFpOZdTCzf5jZt9H7/sjMBmWUSTXx7mFmj5vZD9Gplouj7f3M7H0zW2JmE8xs59oeq7TX3tXMXjSzxdH+x5rZrjmOS28ze9PMlhFaTlLbf2lmH0bH4lsz+5uZtcnYx9lm9kl0/Bea2USLTiNF38FNgRPTTi8MrSTscwmtqWe4+8rMje4+z93fqOQ9H2hmo8zsm+gznmRm55tZWUa5E6Ljvjg6LfCf9M/Zwim5F8xsfrSfaWb2l7TtazTdm5kT/r47p73Pvrma7qO/t/HRvr8zs3+aWeZ3e7qZDTezU8zsU6AcOKSSYyc1oEQvKV2BRcD8tHX7ARPd/ZuszwhNe6uBfaPH+xB+GIyoSQBm1pFwWuBfNXl+DHcRagwnEf5hjSC858zEtRGwP6FpNWU4cBnwMOEf0Z+AU4GHMp77crYfH9XQFfg8Y11HwmmWc4CDgKsIn82oLM9fP4pxOHAEMAG4x8z2SYtx6+i5y4CBhB9X50T7TH8v6wKvAAdHZX4G/AcYZmanZ3ntB6PtA4BngOvM7AbgJuAG4DhC8/QzZta0yiMRlEU/slLT//5nRT/QXiGcChoM/Dx6/6+Y2fYZ+2kFPAo8Er2fh6N9XA/8BXgROBy4AOgHjE4lTjM7kXAq5RGgP3AioZUr9WNgAOH00vOEJuzehOb4XPYHJlTyd1WVzQmnwE4hfBcfJLSAXJsqYGZ9CN+BVwif2zHA/UDraHvLKN5VhGPXn/C9qux0bu/oObOpeJ/vZSsY/bh8ktCcfzTwK2BbwmezXkbxfQin6q4kHPuPkPxKuklBU3EnKpruuxP+qDcg/MNYCZyZUXYZ8EgV+5sNjIqW/5Dadw1j2y16/q9ilh9KlqZm4GXg5bTHfaP9Pp2l7P2EJJp+yuKc6Hh0iB7vGT3/5xnPPTFav0PaurHA1BixXxE9t1n0ObQj9JNYCfysiuc2Jpy7dmDHjOPhwD5p65oB3wL3pa17KFq3btq6zoTa1PS0dWeSpdmYkBTnAmUZ36nLM2KcC6wANktbf3hUdu+YxydzGp5W5gngO6B12rr1gQXAU1mOyxEZr9GVkOguz1j/06j8z6LHfwbeqyLe6cRvuq/y7yrLd7dvju0WHetLCefKG0Xrfw8sqGS/vaL99qykTOpz7Zq2bjgZf3OZMQItCT+gh2Q53uXAORnHbSmwcZzjoalmk2r0DdenhH/CC4C/Afe6+59rsB/La1SFle20wTBgEypaJSDU+F/0ihpXP8I/qCfTa5fAv6Pte6We6O77uXt1eoX/SPgc5hI6Ll7s7s+kFzCzpmZ2iYXTBsui8q9Fm7tn7G+pu49Li2c5oaNXepNpb8KPsyVp5WYAmc3FewFfu/vLGeuHE36Y9MhYPzptfyuJ+nC4+xdpZT6N5p2JZ3dCx87U9H8Z8Y109+/SXvd7QkvN3hn7WQmMzFh3AKFV86GMz/Vt4HsqPtcJwA5mdpeZ7W9m68SMvSCi0yn3mtmXhO/lCuAaQm29fVRsArBB1Cx+qJm1ztjNZ4QfSfea2SAzi/t5xNGb8IMr87jOJHz+e2WUH+/us/P4+pJBib7hGkD4x9mfUEM7w8x+nlFmJuFXeFZR025bKnpBp+ab1jCm2j6/KtmaSl8j1CpOgv81a+/Ems327YGmhKsHVqRNc6PtG9Yipt2BXQmfx3vA9ZnnOgmnCa4gJNhDovKpS7ky+wgszPIayzPKdQDmZCmXua4N2Y/Z7LTtlb12eY51sHbcubzr7hPTpvQfDZXFl3llx1x3z+y9n0qKU1nzc11BSFSpz/UfwG8ILU7PAwvM7ClLu+ysmmZQw+94dOpiBHAoIbnvS/g7TjXbNwdw91cIzfWdCT9w50V9GXpG2xcRmsxnEU5dfBWd6z+qhu8pXeq4vsjax3U71v57qekpDIlJl9c1XJPcfSqAmb1EOC92k5k9mVbTGwucamYdPPv5xEMIPxZfih6/TGgKPYyK2m5s7j7LzD6Jnn9JjKf8SEjAmTZkzb4G/3uJLK/pZjYcOMfMfkNI+ItZs/Y/P3qtPXPEMStGrLm8G9V+J5jZa8AU4C4z294rOkAOBP7h7teknhSdY62pbwiXK2bKXLeAtVsMADaO5tmOcTEtoCKWdBtH29Jlux93Kv4Dyf4DaT6E7whwL6H2u0FU/hbgMULyr64XgdPMbOMa1GR/Qmh2P8ndh6dWmtlhmQXd/Qngiei70pfQT2KMmXVy99Xu/gFwVFTb7kU4dfR49N2bVIP3lZI6roOBj7Ns/yEz1Fq8lsSgGr2kmncvIPwSPyNt0x2EznZ3WcYgLlGv5OsItaGnov3MIpwPPd3Memd7LTP7WRXhXAdsa2bn5Xj+jmk9d78ENjKztmnbf0L25FSZYYTzikcSzrs/6e5L07aPIdSUWmXULlNTbRL9/3i4kuEqQqel9JrVOoTaULpf1OKl3gL6Ry0yAERNtz/NKPcK0MnMMtefQGjN+KQWMeTDK8Ah6Z27ouXDom1VeYHw/e6S43P9IvMJ7r7Q3R8DHid8TinLCVetxHEb4QfxXzJ7ykfvoW2WY56SOm2wIq18E8L3Nit3X+zuIwk/VjqQUaN295XuPp5wWqQRoUNsbbxJSOZb5DiuU2q5f6km1egFAHcfYWYTgN+b2Z/dfZm7fxJdjvMAMNbM/kqoDW5FGDCnNXCAu6cnoXOAbmnlXyTUkDcn/DPqReiRnSuO4RZGz7sl+rHwOKEptj2hBeGkaB9fAf8k9G5+yMxuJZxGuJjQ0aw67/2/ZvY2cD3hfP2wjO0vm9kjhNrRrcA7hATRlXDq4w/u/l8AMxsLbFrN8/Tp7iX86LrMzJ6IapNjgJPN7D+EH1ZHAnvUcP8QmnyPAf5tZjcRWkWuZO2m+6GEQZKeMrNLCadyTiSc2/5VlqbwYrua0IQ9Nurd74QOoesQfjBVyt0/j573ZzPrTvhx8COhufsA4AF3H2dm9xES11uEHzjdCN/D9FarycCeZnYo4fv6rbtPz/G6n0WnyYYD46O/k9SAOXsSeqhfxdp9JiD8uPoSuNbMVhES/rmZhczsKkILzThCi1Mn4HfAB+4+L4rzdMLf4hfRa/8u7X3WmLt/b2YXAHebWTtC341FhL+tvQkdZR+uzWtINSXdG1BTcSdyDJgTbTsw2nZuxvrdic7zEc6xfkkY9a5zjtdoQhhB601Cp6Zywj+TB6ikl2/GPvoTLt+bR/hnNgd4Fjgso9zPCKOLLSNcg34guXvd71/J6/02KrNGD/y07Y0ISe9DQjJYFC3fSKjpp8q9TIxBZ6hkQBjCP2AnGlyI8APmUULz8kJCr/ldojKD0543FJiZZX9rHI9o3f7A+4Sa6DRCchmaGTuhBjiM8ONpOeEUz6A436nodV/PWNc1KntaTY9PRrndqPgxuYRwumnXjDJZj0va9pOA8dHzFxOS6Z+BTtH2k6P3Mjc6Bl8QauXrp+1jK0J/j6VR3ENjfAe2iWL7ivA3sijaxxlEA/qQpdc9sANhFL6lhO/rVcBppPWQJ/wofp7ww3w5oV/A34CO0fbuhFMPXxC+z/MIl1zuluVz7Zq2rspe9xl/w+MI/wOWEX6kDgF6pJWZTsyrFTTVfLLoYIuIiEgJ0jl6ERGREqZELyIiUsKU6EVEREqYEr2IiEgJU6IXEREpYSVxHX3btm29a9euSYchIiJSFO++++637t4uTtmSSPRdu3Zl4sSJSYchIiJSFNFNjWJR072IiEgJU6IXEREpYUr0IiIiJUyJXkREpIQp0YuIiJQwJXoREZESpkQvIiJSwpToRURESpgSvYiISAkraqI3syFmNtfMJuXYbmZ2p5lNNbOPzGynYsYnIiJSaopdox8K9Ktk+8HAltF0OnBPEWISEREpWUUd697dXzWzrpUUOQL4h7s7MN7MWptZB3f/pigBiohIneMeplWrYPXqMF+1CpYvhx9/zD0tXx7Kpyb3NefZ1sXZVpOyAGefDR07Fv/41bWb2mwCzEh7PDNat1aiN7PTCbV+unTpUpTgRERKwYoVsHBhxfT997ByZUXSSiXUFSvgv/+F1q3hhx/CVF5ekWTTE+2KFWsm1cx9rVoVnpt63vLl4XG2103NU8vuSR+x/DjhBCV6AMuyLutH7O73AfcB9OrVq0S+BiIiNbdkCcycGZLzlClhmjEjJNRUkp0xA+bMSTrS6jODRo2grKxi3rx55VPTphXlU89PzTPXpa/PVj7XPG5ZM+jQIZljV9cS/Uygc9rjTsCshGIRESm61atDzfm778K0eHHF9MMPMG8ezJoFy5aF8kuWwPTp8MUXYX2c2m+jRrDBBhXT+utDkyZrJ9JGjcIPhCVLYNddYb31oFmzkLRSybRZszBv0qTiOdmmsrKQeJs1q5iaNoXGjdd8Xq5ly1YNlFjqWqIfAZxpZo8CuwGLdH5eROqzVatC03gqcadPixZVLH/zDbz3HkybVnFOt7rKyqBzZ+jWDbp3D/PNNquo3TZpEpqON9kklJWGoaiJ3sweAfoCbc1sJvBHoAmAu/8VGAX0B6YCS4FfFDM+EZHqKi+HMWPgqadg0qSQtFNN5UuXhlp4dbVsGc6Lt2oVatstW4Zp3XWhXbvQBLzeeqFss2aw6aYhoXfqFJK5SLpi97o/vortDvy2SOGIiNSIO7z9NgwbBo89BvPn5y5rFhJ2q1YheaemzMcbbgjbbw9bbx1q3yL5Utea7kVE6qzPP4fhw8M0dWrF+m23hUGDoE8f2GijkKibNoUWLULNu5HGIJUEKdGLiKSZNQs++2zNy89eey30Vn/33YpyHTqEy6VOOgl69lRnMam7lOhFpMFauhQ++QQ+/hj+85+QyMeNq/w5J50Upn33VYc2qR+U6EWkQZk+Hf75T3j88ZDYc12Odthha16CtvHGoQaf6gQnUl8o0YtIyZsxoyK5v/12xfqysnAZ2rbbwjbbhCb47baDzTdXU7yUDiV6ESkJs2eHc+tLllRMX38NI0fCm29WlFtnnVBbP/ZY6NcvPBYpZUr0IlKnlZeHDnGpsdZTI8QtXhwGmRk/Ht56C778Mvc+WrSAQw4Jyf2QQ5TcpWFRoheROmf2bPjgA/jlL8PY7XG0bBma4NdbLwwskxpgZscd4cQTw2ORhkiJXkQS5R6uSX/ttTC9/vqa16inbL55SOItW1bM27SBnXeG3r2hRw/1ghfJRoleRIpq1Sr48MOQ0FOJffbsNcu0bAk77QS77x6a2vfaK5lYRUqBEr2I5J176Ok+Y0a4T/ny5TBxYkjsb7659vjv7drBnntWTNtvH+5qJiK1pz8lEcmb1avhllvgtttCR7lcNt88JPQ+fcK8WzddziZSKEr0IlJtc+fC88+H3u4ffRSa3r/4Ys3bq7ZpA1tuGW6RahauU08l9002SS52kYZGiV5EYnEP909//fVwHXou7drBTTeFm7yoc5xI8pToRSSrxYvhmWfg4YfDWPBz5oRz7emuvx569QqXsfXoEeaNGqkZXqQuUaIXkTW8/DI88AA8/XS46Uu6li3DmO/HHw//93/QpEkiIYpINSjRiwgQesI//jicdlrFuj32CHdq23//cFvWdddNLj4RqRklepEGyj1czz56dJjefDNc4w7QqhW8917oHS8i9ZsSvUgDMWdO6Ck/ejS8/z7MmwcLFlRsLysL169vtx1ccYWSvEipUKIXKWHucOON4Rat77679vaOHeHgg8O0//6hJi8ipUWJXqSELFoUEvvkyeG69g8/rNjWvDn07RuSet++0LZtOO+uHvIipU2JXqQEfPMNvPpq6An/2WdrbmvZMtwF7tprw+1aRaRhUaIXqafmz4dRo+Df/4YnnoAffwzrmzcPw9DusgtsthlsuKFq7SINmRK9SD2yZEkYxOaRR0LHupUrK7btsgscc0wYka5Dh+RiFJG6RYlepB6YMAF23XXNdWVloQPdwQfDQQeFseRFRDIp0YvUYd9+C5deCvfdV7GuXbtw+dvRR0P79omFJiL1hBK9SB20ZAkMGQJ//CMsXBjuzX788bDPPjB4sM65i0h8SvQidcxzz8HJJ4fOdhCa5++8E7beOtm4RKR+apR0ACISlJfDBRfAoYeGJL/FFqE3/b//rSQvIjWnGr1IHfDVV7DDDqGZvqwMrrsOfv/7cMtXEZHa0L8RkQQtXx7Ow3frFpJ869Zh4JsLL1SSF5H8UI1eJCHz58OAAfDaa+Fxr17hPvDbb59sXCJSWpToRRIwdSr07x+Gq+3YMfSwP+AA1eJFJP+U6EWK7NVX4cgjQ41+hx3gX/+CTp2SjkpESpUSvUgBLVsGv/gFzJwZbhn75psV2w45JAxlu956ycUnIqVPiV6kQFavhpNOgiefXHvbOefATTeFgXBERApJ/2ZE8uTrr2Hp0nDefcIEGDMGxo8PNfbzz4d994VmzaB7d2jVKuloRaShUKIXqaEVK+CLL+Dzz8NtYceOXbtMs2bw+OPQr1/x4xMRASV6kRoZOzYMTZuurAy6dg0j3LVuHW48s/vuoVe9iEhSlOhFYvrhBxg2LHSge/31ivW77BJuNnPhhbDhhsnFJyKSjRK9SBXcYehQOOWUtbfNmKFL40SkbtPwHCKVmDkTBg1aM8kPHx4S/LJlSvIiUvepRi+SxerV8Otfw/33V6wbOBBuuw023ji5uEREqkuJXiTDl1/CQQfBlCkV6wYMgAcfhKZNk4tLRKQm1HQvkmbVqjDIzZQpoef8Aw+Ec/RPPaUkLyL1k2r0IpGlS+HMM8Pd5DbYAD76SOfgRaT+U6IXAZYsga23Dp3sIFxGpyQvIqVATffS4C1aFG4wk0ryv/tdeCwiUgpUo5cGqbwcJk6Ejz+G008P6zp2hNGjoWfPZGMTEcknJXppcN5/H3baac11TZrAK6/AFlskE5OISKEUvenezPqZ2RQzm2pmF2XZ3sXMxpnZ+2b2kZn1L3aMUrpeegn23rvi8QEHwO23w6xZSvIiUpqKWqM3szLgbuAAYCYwwcxGuPvktGKXAY+7+z1m1gMYBXQtZpxSmq69Fi67LCwfeWQYs16XzIlIqSt2jX5XYKq7T3P3cuBR4IiMMg6sHy23AmYVMT4pUU8/XZHkTz0VHn1USV5EGoZiJ/pNgBlpj2dG69JdAQwys5mE2vxZxQlNStUjj4QaPIT7xj/wQDgnLyLSEBQ70VuWdZ7x+HhgqLt3AvoDw8xsrTjN7HQzm2hmE+fNm1eAUKUUfP89/PKXYXmffeDcc5ONR0Sk2Iqd6GcCndMed2LtpvlTgccB3P0toDnQNnNH7n6fu/dy917t2rUrULhSn33/fbh0bskS6NYNnn8eLNtPTRGRElbsRD8B2NLMNjOzpsBAYERGma+A/QDMbGtColeVXapl8mRo1Qoeeyw00998s5rrRaRhKmqve3dfaWZnAs8DZcAQd//YzK4CJrr7COB84H4zO5fQrD/Y3TOb90VycofDDgvLG2wAr74K226bbEwiIkkp+oA57j6K0Mkufd3lacuTgZ8WOy4pHX/7G0ybFpbHjVOSF5GGTWPdS0lxhxtvDMsXXADbb59sPCIiSVOil5Ly6qvw2WewySZw3XVJRyMikjwleikZ339fkdxPOQUa604OIiK6qY2UhunTw1j1q1bBOuuE0e9EREQ1eikBo0bBZpuFJL/xxvDGG7DppklHJSJSN6hGL/XasmXwi19UPB43DrbaKrl4RETqGtXopd768MNwy9m5c6FFC/j6ayV5EZFMqtFLvfTBB7DjjmF5gw1CTb5jx2RjEhGpi1Sjl3pn3LhQk0/54ANdLy8ikosSvdQrn30G++4bLqXr0wcWLIAuXZKOSkSk7lLTvdQb7uEudBDOxb/0km5UIyJSFdXopd4YN65i+YUXlORFROKoVo3ezLoDewIbAkPdfY6ZdQbmu/vSQgQoknLNNRXzTp2SjUVEpL6IlejNrAkwBDgBMMLtY18A5gB/Bj4GLilQjCJ8+mmo0a+3Hpx5ZtLRiIjUH3Gb7q8GDgd+CWxKSPYpo4CD8hyXyP+sWAG/+lVYPvJIaNUq2XhEROqTuE33JwL/5+5DzKwsY9s0YLP8hiVS4Q9/CHel69AB/vSnpKMREalf4tbo2wGTKtnePA+xiKzl66/hzjuhrAyefDIkexERiS9uov8S2CXHtl7AZ/kJR6TC6tVw6KHhZjX77Qe9eycdkYhI/RM30Q8HLjWzo4BU072bWW/gPGBoAWKTBuzqq8Md6T74IDw++uhk4xERqa/inqP/E7AT8E9gcbRuHLAe8DRwe/5Dk4Zo2DC4/XZ4773wuGVLeOwx6N8/2bhEROqrWIne3VcCA8zsAEIP+/bAfGCMuz9fwPikgfjxRzjsMHjxxYp1Rx8Nf/97SPYiIlIzca+jb08YFOcFwvXz6dsaAW3dfW4B4pMGYOFC2G23MI49QLNm8M470LNnsnGJiJSCuOfovwF2zrFtx2i7SI0MGlSR5IcPh2XLlORFRPIl7jl6q2RbY2B1HmKRBujTTyua66+7Dk48Mdl4RERKTc5Eb2YtgfXTVrU1s44ZxVoQhsWdU4DYpMStXAk//zmUl8PgwXDxxUlHJCJSeiqr0Z8PXB4tO/CvHOUMuDafQUnDcP31MGECdO4cetqLiEj+VZboRwKzCYn8L8CNwBcZZZYDk939ncKEJ6Vq8WK44YawPGSIxq8XESmUnIne3d8F3gUwMweedPdvixWYlLbLLgvJvk8f2H//pKMRESldca+jv7fQgUjDsXgx3HFHWD7++GRjEREpdXF73WNm3YBfAN1Z+yY27u6H5DMwKU3ucMklYblTJzjllGTjEREpdXEHzNkZeI3Qu74LMAVoQxghbxbwVaEClNJy5ZVw113QuDE89BA0130PRUQKKu6AOdcDzwFbEjrnDXL3jYFDo338oTDhSSm5886Q6Bs1gkcegb32SjoiEZHSFzfRb0+4Q11qYJwyAHcfBVxH6JEvkpU7vPUWnH12ePz3v+tudCIixRI30TcDfnD31cACYKO0bZMBDVgqWZWXQ9++sMce4fHBB4dBckREpDjiJvppQGpUvI+BwWnbBgG6oY1kNXw4vPpqWO7ZE25U24+ISFHFTfSjgQOi5T8BR5jZAjObC5wM3FmI4KT+e+ihML/1VvjwQ9h222TjERFpaOJeR39J2vIYM9sTOBpYh3BP+hEFik/qKXd44w146SVo2hR+8YukIxIRaZhiX0efzt3HA+PzHIuUkLvvhrPOCsuHHgqtWycbj4hIQxW36T4nM+thZo/kIxgpDStWVIxjv+mmYbhbERFJRqU1ejMzYDvCIDmfu/snadu2I9zdbgCwrJBBSv3y1FMwcyZ07w6TJ4fr5kVEJBk5/wWb2cbAG8D7wLPAJDN70Mwam9mfo/WHEu5st0UxgpW6b/VquPnmsHzOOUryIiJJq6xGfz2wA+Fe8+8BmwEXAq8AvYHHgAvcfWahg5T64803YeJEaNtW18uLiNQFlSX6A4Cr3P361AozmwQ8D/zV3c8odHBSf/z4I5xxRhj1DqBfP1hnnWRjEhGRyhN9e0LTfbrUY3W+k/9ZvBjWW2/Ndeefn0wsIiKypsrOoJYByzPWpR4vKUw4Uh898UTF8p57wnffwQ47JBePiIhUqOo6+gPNLL2jXSPAgX5mtlV6QXd/ON/BSf0wZkyY33lnxbXzIiJSN1SV6K/Ksf6ajMcOKNE3QKtWwQsvhOV+/ZKNRURE1lZZot+6aFFIveMO33wTavELFkDXrrCFLrIUEalzciZ6d59SzECk/nCHrbaC//63Yt0ee4BZcjGJiEh2NRrrXhq2Dz+sSPK77w477wwXXphsTCIikp0SvVRLeTmcfXZY7t8fnnsSMdgkAAAgAElEQVQu2XhERKRyRR+g1Mz6mdkUM5tqZhflKHOsmU02s4/NTJ386og5c2DffeHVV6FjR3jggaQjEhGRqhS1Rm9mZcDdhFH3ZgITzGyEu09OK7MlcDHwU3dfaGbtixmj5Na7N3zxRVh+5hno0CHZeEREpGrFrtHvCkx192nuXg48ChyRUeaXwN3uvhDA3ecWOUbJYubMiiQ/Zgzsskuy8YiISDzVTvRmtoWZ7WZmNRnJfBNgRtrjmdG6dN2Abmb2hpmNN7OsV2eb2elmNtHMJs6bN68GoUh13H57mPftCwcdlGgoIiJSDbETvZmdamYzgSnAm8BW0fonzOzXcXeTZZ1nPG4MbAn0BY4HHjCz1ms9yf0+d+/l7r3atWsX8+WlJsrLYdiwsKyhbUVE6pdYid7MBgP3AS8BJ7Nmwn4bOC7m680EOqc97gTMylLmWXdf4e5fEH5YbBlz/1IAY8fC3LnQpg3ccEPS0YiISHXErdFfANzh7j9n7TvXfUJUu49hArClmW1mZk2BgcCIjDLPAPsAmFlbQlP+tJj7lzybObPiTnTnnANNmyYbj4iIVE/cXvc/AXJdMf0DsEGcnbj7SjM7k3BP+zJgiLt/bGZXARPdfUS07UAzmwysAi5w9/kx45Q82203mBW1uZx4YrKxiIhI9cVN9AtYs8k9XTfgm7gv6O6jgFEZ6y5PW3bgvGiSBL39dkWSf+op2HzzZOMREZHqi9t0/xxwmZmlJ3uPOsmdAzyb98gkUW+/DfvvH5bPPRcGDEg2HhERqZm4if7SqOxkYCShp/zN0eMmwJUFiU4SMX9+GMN+8WL4yU8qztGLiEj9EyvRR4PW7ATcCbQDvgbaAA8Cu6UGt5HScGX0s61rV5g0CTbJHOlARETqjdhD4Lr7d4Sa/aWFC0eStGwZXHEF3HVXuOXss89C8+ZJRyUiIrUR9zr668ws7iV0Uk8NGwY33hiWjzoKevZMNh4REam9uOfozwI+joacPcvMNBRdCfrXv8L88svh0UeTjUVERPIjbqJvD/wcmAfcCnxtZiPN7Bgza1aw6KRoJk6EkSOhWTM44wwoK0s6IhERyYe4nfGWuftD7n4wYdjai4AOwGPAHDO7v4AxSoGNH19xN7rBg2GjjRINR0RE8qjad69z9znufqu77wzsRxgZ75S8RyYFt3IlXHwx/PSnFesuuSS5eEREJP9qcpvaZmZ2nJn9CxgDbETu4XGlDhswAK6/HlavhhNOgDlzoEuXpKMSEZF8qs5tavua2d+AOYQb22wE/B7o6O6HFyg+KZD//Ceckwf405/goYegfftkYxIRkfyLdR29mX0FbALMAO4G/uHuUwoZmBTWO++EeZs2cMEFycYiIiKFE3fAnBcIyf2VQgYjxfP222F+/vnqYS8iUspiJXp3P7XQgUhxTZgQ5nvumWwcIiJSWDkTvZntCkxy96XRcqXc/Z28RiYFs2xZOEffqBHstFPS0YiISCFVVqMfD+wOvBMte45yFm1TA3A9MGQI3H47rFoF22wD666bdEQiIlJIlSX6g4FPouX+5E70Uk/MnAmnpp2Eufji5GIREZHiyJno3f35tOUxxQlHCunmm8O8WTN4991QoxcRkdIW9+51k81suxzbepjZ5PyGJfm2ahU8/nhYfvVVJXkRkYYi7oA5WwEtcmxbB+ien3CkUF5/Hb75BjbbrGJcexERKX3VGQI31zn6nsCiPMQiBZS67exxx4FZsrGIiEjxVHZ53VmE+9BDSPJPmNnyjGItgI7AE4UJT/Jh5Up48smwfNxxycYiIiLFVVmv+1nAu9HyFsAUYH5GmeXAZOCe/Icm+TJuHMybB926wfbbJx2NiIgUU2W97p8EngSw0NZ7qbtPK1JckkepZvuBA9VsLyLS0MQdAvf4QgcihVFeDk89FZbVbC8i0vBUdo7+QsKNbGZHy5Vxd78pv6FJPrzwAnz3HWy7LfTokXQ0IiJSbJXV6K8HXgZmR8uVcUCJvg7661/DfODAZOMQEZFkVJboW7h7qpd9rmvopQ778EMYORJatIDTT086GhERSUJlnfGWZ1uW+iM15O3pp0O7dsnGIiIiyYg7BO7mZrZD2uNmZvZHM/unmZ1WuPCkphYuhMceC8uDBiUbi4iIJCdWr3vgL4Tr5T+IHl8NnAv8FxhgZmXufm8B4pMaWLwYLr0UVqyAPn1g552TjkhERJISdwjcHYBXASxcVD8YuMTdtyF01Pt1QaKTanGHBx8MA+PcEw1h9Pvf69p5EZGGLG6ibw18Gy3vAGwIRPdC4wXgJ3mOS2rguutg8OBw85pddgk3sjniiKSjEhGRJMVtup8LbA68DhwAfOHuX0bb1gVWFSA2qabhw8P8wANh9GhoVJ1bFomISEmKm+hHAteaWTfgdGBI2rZtgC/yHZhUz4wZ8OmnIbk/+6ySvIiIBHET/UXAesBxwIvANWnbjgVeynNcUk133RXmhx0GzZsnG4uIiNQdcce6/x44Kce2XfIakVTb2LFwUzQu4VZbJRuLiIjULXFr9ACY2XrArkAbwi1rJ7j7D4UITOL5+mv42c/CcqdOcPXVycYjIiJ1S+wzuWZ2GfAN8G/gMUIT/jdmdmmBYpNKlJfDrbfCNtuE6+bbtYMJE6BJk6QjExGRuiTuyHi/Ba4Cngb6AzsCB0ePrzKz3xQsQlmDOzz8MGy3HZx/PixaFHrZv/subLxx0tGJiEhdE7fp/kzgL+5+Ztq6D4HnzWwRcBZwT76Dk7U9/TSceGJY7tYt1Or799egOCIikl3cpvvNgWdzbHs22i5FcNttYX744TBpEhxyiJK8iIjkFjfRLwC659jWPdouBTZxYhjtDsIQtzofLyIiVYmb6J8hDJhzTDTWPQBmNoBwg5tnChGcrGnatDA/4ADo2DHZWEREpH6ozoA5OxF62y83s7lAO6AZMCHaLgX21lth3qtXsnGIiEj9EXfAnEVmtgcwANiTcB39AuAV4Fl311j3RTBmTJgffHCycYiISP0Re8CcKJk/EU1SZNOnh7Hs118fdt896WhERKS+qPQcvZkNNLPxZvatmU01s2vNrFqj6Ul+pGrzBxygTngiIhJfzkRvZscADwMbA28ASwnn4q/J9RwpnFSi79cv2ThERKR+qaxGfx7wHLClux/h7j2BG4CzzEw3QS2i8vJw4xpQohcRkeqpLGF3B+5x9xVp6+4EWgCb1vQFzayfmU2JTgXk7K1vZkebmZtZg+9jPm5cGM++R49w4xoREZG4Kkv0rYFvM9bNi+Yb1OTFzKwMuJswTn4P4Hgz65Gl3HrA74C3a/I6pea558J8n32SjUNEROqfqprgvZrrq7IrMNXdp7l7OfAocESWclcDNwI/1vB1SsYPP8Az0XBEuqxORESqq6pE/4aZlacmYFm0/u309Wa2PObrbQLMSHs8M1r3P2a2I9DZ3UdWtiMzO93MJprZxHnz5lVWtF478kiYMQPatIE+fZKORkRE6pvKLpW7oQCvl+32K/9rHYg6+d0GDK5qR+5+H3AfQK9evWrawlCnzZ0LL74YlseNg1atko1HRETqn5yJ3t0vLsDrzQQ6pz3uBMxKe7wesC3wcjSk/sbACDM73N0nFiCeOu1PfwrznXeGnj2TjUVEROqnYl8mNwHY0sw2M7OmwEBgRGqjuy9y97bu3tXduwLjgQaZ5FevhqeeCsuDBiUbi4iI1F9FTfTuvhI4E3ge+AR43N0/NrOrzOzwYsZS1111FXz1FXTuDL/7XdLRiIhIfVX04WzdfRQwKmPd5TnK9i1GTHVNeTncf39Y7t8fGml4IhERqSGlkDpm9erQ037WLGjbFu66K+mIRESkPlOir2NGj64YIGfMGN3ARkREakeJvo459NAwv+CC0NteRESkNmInejPbyMyuM7PXzWxyauhaMztD49HnxyuvVCyff35ycYiISOmIlejNbCvgP8BvCLer7Q40jzZ3B84pSHQNzI03hvnJJ8NGGyUbi4iIlIa4NfqbgS+AzYD+rDnC3RtA7zzH1eAsXhxuRWsGN92UdDQiIlIq4l5etzcwyN2/i+5Al2420CG/YTU8V1wBy5fDHntAu3ZJRyMiIqWiOp3xVuVYvyEVN7uRGhoVjSxw5ZXJxiEiIqUlbqKfCJyUY9tRhKFqpYZ+/BH++9/QbL/77klHIyIipSRu0/21wBgz+xfwEOGOc3uZ2a+AY4F9ChRfg/DUU7BqFWyzDbRsmXQ0IiJSSmLV6N39RUJC3x54mNAZ71bgEOBYd3+jYBE2AL/+dZifemqycYiISOmJPda9uz9lZk8D2wDtgfnAf9x9daGCawjmzw+d8KBisBwREZF8qdZNbdzdgUkFiqVBevbZcBObgw6CLbdMOhoRESk1sRK9mR1bVRl3f7z24TQ8r70W5n36JBuHiIiUprg1+kdzrPe0ZSX6avrmG3j44dDb/tgqf0qJiIhUX9xEv3WWdRsChwJHAyfnLaIG5I47QrP9kUdCt25JRyMiIqUoVqJ39yk5Nr1pZqsIY+C/lbeoGoBVq2DIkLB8wQXJxiIiIqUrH7epHQccnof9NCjPPAPz5sFPfgK77ZZ0NCIiUqrykeh7Ee5oJzG5w9ChYfm448I5ehERkUKI2+v+wiyrmwLbAgOA+/MZVKl77TUYOTIsH3VUsrGIiEhpi9sZ7/os61YBXwO3AboVS0zl5XDuuWF5t91gp52SjUdEREpb3ETfIsu6FRoVr/pGjoT33oNWreDBB5OORkRESl2V5+jNrClwBbCtuy9Pm5Tka2DcuDA/7zzo3j3ZWEREpPRVmejdvRw4G1i38OGUtvJyePLJsLyP7vcnIiJFELfX/YdAj0IG0hA8/XQYDW+LLWCPPZKORkREGoK4if5C4A9mtn8hgyl1550X5mefDWVlycYiIiINQ9zOeEOA1sDzZrYUmM2a49y7u+uMcyU++wxmzQrLGtdeRESKJW6if5c1E7tU07/+Feabbw7t2ycbi4iINBxxx7ofWOhAStkPP8C114blG25INhYREWlYcp6jN7NpZrZ9MYMpVZ98AgsWQOfOMGBA0tGIiEhDUllnvK5AsyLFUdIWLgzz7t3VCU9ERIorHze1kSrMmBHmbdokG4eIiDQ8VSV6dcDLg4cfDvN99002DhERaXiq6ox3pZl9G2M/7u4n5yOgUjNtWhj2tkULGKgujSIiUmRVJfodgOUx9qOafw533BHmRx8dbmQjIiJSTFUl+p+5+ztFiaQEucOdd4blk05KNhYREWmY1BmvgL5NO+mh8/MiIpIEJfoCmjAhzPv00WV1IiKSDCX6AnrjjTD/6U+TjUNERBqunOfo3V0/Amopleh1S1oREUmKknmBrFgB70TdGHv3TjYWERFpuJToC2T8eFi2LAx7265d0tGIiEhDpURfICNHhvkhhyQbh4iINGxK9AWwciXcd19Y3muvZGMREZGGTYm+AG68Eb77Lgx727Nn0tGIiEhDpkSfZzNmwOWXh+VHH4XNNks2HhERadiU6PPsoYdg1So49lg4/PCkoxERkYZOiT7PXnwxzI86Ktk4REREQIk+ryZNgrFjoUmTMOytiIhI0pTo82jSpDA/+GDo2DHZWERERCCBRG9m/cxsiplNNbOLsmw/z8wmm9lHZjbWzDYtdow19f33Yb7hhsnGISIiklLURG9mZcDdwMFAD+B4M+uRUex9oJe79wSeAG4sZoy1kTo/361bsnGIiIikFLtGvysw1d2nuXs58ChwRHoBdx/n7kujh+OBTkWOsUaWLoVx48LynnsmG4uIiEhKsRP9JsCMtMczo3W5nAqMLmhEefLHP8K338K228KuuyYdjYiISJDzNrUFYlnWedaCZoOAXsDeObafDpwO0KVLl3zFVyPffQdDh4blG24Ive5FRETqgmLX6GcCndMedwJmZRYys/2BS4HD3X15th25+33u3svde7VL+PZw994bavM77xx63IuIiNQVxU70E4AtzWwzM2sKDARGpBcwsx2BewlJfm6R46u21asrbmBz1VVg2dosREREElLURO/uK4EzgeeBT4DH3f1jM7vKzFIDxt4EtAT+aWYfmNmIHLurE554AqZNgy5d4KCDko5GRERkTcU+R4+7jwJGZay7PG15/2LHVFOffw7HHReWTzgBysqSjUdERCSTRsarhYceqli++OLk4hAREclFib4WZs8O81tvhfXXTzYWERGRbJToa+HTT8N8882TjUNERCQXJfoaWr0aPvggLO+4Y7KxiIiI5KJEX0NvvgkLF8Kmm0KnejFIr4iINERK9DX06KNh3rcvNNJRFBGROkopqga++iqMhmcGv/td0tGIiIjkpkRfA/fdBytXhmvod9op6WhERERyU6KvpvJyeOCBsHzGGcnGIiIiUhUl+mp6+mmYMwe22Qb69Ek6GhERkcop0VfTPfeE+Rln6AY2IiJS9ynRV8OCBfDKK9C8OQwalHQ0IiIiVVOir4bRo8O8Vy8NeSsiIvWDEn01DBkS5ieemGwcIiIicSnRxzR9Orz0Umi2Hzgw6WhERETiUaKPaejQMD/ySGjdOtFQREREYlOij2H16opEf8opiYYiIiJSLUr0MYwbB19+GW5gs88+SUcjIiISnxJ9DMOHh/ngwbqBjYiI1C9KWzFMnhzmBxyQbBwiIiLVpUQfw1dfhXmXLsnGISIiUl1K9FWYPTtMZWXQoUPS0YiIiFSPEn0V/v73MD/4YGjcONlYREREqkuJvgoffRTmAwYkG4eIiEhNKNFX4ZNPwrxHj2TjEBERqQkl+kqsWgVTpoTlrbdONhYREZGaUKKvxPTp8OOP0LEjtGqVdDQiIiLVp0RfCTXbi4hIfadEX4nUQDlqthcRkfpKib4S770X5kr0IiJSXynR5+AebmYDsMsuycYiIiJSU0r0OcyZA3Pnhk54O++cdDQiIiI1o0Sfw5w5Yd6lC5glG4uIiEhNKdHnMH9+mLdpk2wcIiIitaFEn8OXX4a5bmQjIiL1mRJ9Dh9/HObbbZdsHCIiIrWhRJ/D7Nlh3qlTsnGIiIjUhhJ9DnPnhnm7dsnGISIiUhtK9DnMmxfm7dsnG4eIiEhtKNHn8O23Yd62bbJxiIiI1IYSfRbusGRJWG7ePNlYREREakOJPouZM2HhwnANvZruRUSkPlOiz+Kdd8K8Vy+NiiciIvWbEn0WEyaE+a67JhuHiIhIbSnRZ5Gq0euudSIiUt8p0WdYvRrefTcsK9GLiEh9p0SfYdky+P57aNFC49yLiEj9p0SfYfnyMNdldSIiUgqU6DOkEn3TpsnGISIikg9K9Bl++CHM11032ThERETyQYk+w/z5Ya6hb0VEpBQUPdGbWT8zm2JmU83soizbm5nZY9H2t82sazHjSw1927JlMV9VRESkMIqa6M2sDLgbOBjoARxvZj0yip0KLHT3LYDbgBuKGeNXX4W5hr4VEZFSUOwa/a7AVHef5u7lwKPAERlljgAejJafAPYzK95AtB9+GObbb1+sVxQRESmcYif6TYAZaY9nRuuylnH3lcAiYMPMHZnZ6WY20cwmzkvdPD4PFi2CsjIlehERKQ3FTvTZauZegzK4+33u3svde7Vr1y4vwQEMHQqLF8N+++VtlyIiIokpdqKfCXROe9wJmJWrjJk1BloBC4oSXaR5c11HLyIipaHYiX4CsKWZbWZmTYGBwIiMMiOAk6Plo4GX3H2tGr2IiIhUrXExX8zdV5rZmcDzQBkwxN0/NrOrgInuPgL4GzDMzKYSavIDixmjiIhIKSlqogdw91HAqIx1l6ct/wgcU+y4RERESpFGxhMRESlhSvQiIiIlTIleRESkhCnRi4iIlDAlehERkRKmRC8iIlLClOhFRERKmBK9iIhICVOiFxERKWFWCsPIm9k84Ms87rIt8G0e99dQ6TjWno5h7ekY1p6OYe3l+xhu6u6xbt1aEok+38xsorv3SjqO+k7HsfZ0DGtPx7D2dAxrL8ljqKZ7ERGREqZELyIiUsKU6LO7L+kASoSOY+3pGNaejmHt6RjWXmLHUOfoRURESphq9CIiIiWsQSd6M+tnZlPMbKqZXZRlezMzeyza/raZdS1+lHVbjGN4nplNNrOPzGysmW2aRJx1WVXHMK3c0WbmZqbez1nEOY5mdmz0ffzYzB4udox1XYy/5y5mNs7M3o/+pvsnEWddZWZDzGyumU3Ksd3M7M7o+H5kZjsVJTB3b5ATUAZ8DmwONAU+BHpklDkD+Gu0PBB4LOm469IU8xjuA6wTLf9Gx7D6xzAqtx7wKjAe6JV03HVtivld3BJ4H9ggetw+6bjr0hTzGN4H/CZa7gFMTzruujQBewE7AZNybO8PjAYM2B14uxhxNeQa/a7AVHef5u7lwKPAERlljgAejJafAPYzMytijHVdlcfQ3ce5+9Lo4XigU5FjrOvifA8BrgZuBH4sZnD1SJzj+EvgbndfCODuc4scY10X5xg6sH603AqYVcT46jx3fxVYUEmRI4B/eDAeaG1mHQodV0NO9JsAM9Iez4zWZS3j7iuBRcCGRYmufohzDNOdSvg1KxWqPIZmtiPQ2d1HFjOweibOd7Eb0M3M3jCz8WbWr2jR1Q9xjuEVwCAzmwmMAs4qTmglo7r/M/OicaFfoA7LVjPPvAQhTpmGLPbxMbNBQC9g74JGVP9UegzNrBFwGzC4WAHVU3G+i40Jzfd9CS1Lr5nZtu7+XYFjqy/iHMPjgaHufouZ9QaGRcdwdeHDKwmJ5JSGXKOfCXROe9yJtZuh/lfGzBoTmqoqa5ZpaOIcQ8xsf+BS4HB3X16k2OqLqo7hesC2wMtmNp1wXm+EOuStJe7f87PuvsLdvwCmEBK/BHGO4anA4wDu/hbQnDCGu8QT639mvjXkRD8B2NLMNjOzpoTOdiMyyowATo6WjwZe8qhHhQAxjmHU7HwvIcnrnOjaKj2G7r7I3du6e1d370ro53C4u09MJtw6K87f8zOEzqGYWVtCU/60okZZt8U5hl8B+wGY2daERD+vqFHWbyOAn0e973cHFrn7N4V+0QbbdO/uK83sTOB5Qm/TIe7+sZldBUx09xHA3whNU1MJNfmByUVc98Q8hjcBLYF/Rv0Yv3L3wxMLuo6JeQylCjGP4/PAgWY2GVgFXODu85OLum6JeQzPB+43s3MJTc6DVfmpYGaPEE4NtY36MfwRaALg7n8l9GvoD0wFlgK/KEpc+oxERERKV0NuuhcRESl5SvQiIiIlTIleRESkhCnRi4iIlDAlehERkRKmRC+ShZkNju4Ul23av5r7Oi16XlHG+TezazLiXRjdfTHvl4eaWePoNS5LW3ekmZ2Tpez+Udk++Y6jkvi2yDgWq8zsGzMbZmY1GnrUzHYysyvMrHW+4xUphAZ7Hb1ITMcQRrNKNzmJQGqgdzTfEPgV8IiZNXX3f+TrBaJrr3uz5vjdRwJ9gNszir8TxfRxvl6/Gq4BngOaRTFcDmxlZr2j+1hUx06E66OHAho+V+o8JXqRyn3g7lOTDqImortjAWBm/yYM+XoOkLdEn/k6VZT7njCyXxI+T4vzFTNrRrhByw6ARhmUkqame5EaMrMWZnaHmX1sZkuiJuERZtY9xnNPMrMPouctMrOPzOy0jDL7mNlLZrY4mkabWY+axOruK4APgC3S9t/KzP4SxV1uZlPM7OyMGNY3sz+b2QwzW25mc8zsBTPrFm1fo+nezIYDJwKbpjWXT422rdF0b2b3mdksMyvLeM3m0TG5OW1dezO7NypfbmafmNmpNTkWkfeieZeM177GzN43s+/N7FszG2tmu6ZtPw24P3r4Rdp77JR2PC6NjuVyM/vazG6KfliIJEI1epHKlVm4oVGKu/uqaLlFNF0FzCY0kf8WeMvMtso1tr+Z7Q08SGjaPp8w3GgPYIO0MkcATxLGxj6B8KP8IsId13q6+9c1eC+bETU1R8l1NNAT+D9Cc/rhwO1mtqG7Xx495w6gH+GmRFMJNzDpQ7jBUzZ/jMpsDwyI1v2Yo+w/CPeI3w/4d9r6Iwj3PB8WxdoaeIMwlOjlwHTCMKL3R6ci7on17tfUNZp/nrG+I3AL4XRNS8K9Ll4zs53c/WPgWWBz4GLCKYrUOOWpz/oR4GDgekLrxTaE70cX4LgaxClSe+6uSZOmjIlwW1jPMr1eyXPKgHUJY1iflbb+tOi5naLHFwFzK9mPEZLZ8xnrWxPuuXBzFbFfE71e42jaCLg6WndzVOZn0eNBGc8dSkjMbaLHnwI3VvJajaP9XJa2bjgwPUvZ/aOyfdLe5zRgWEa5kcBHaY+vBJYBP8ko93dgDlBWSXxbRK95ShTruoQfFrOAR6s4jmWEHxefA7dk+Ty7ZpTfJ1p/Qsb6k6P12yX9vdbUMCc13YtUbgCwS9q0RnOxmQ00s3fMbBGwElhMqOVX1nw/AWhnZv8ws0PMLLN2vBWwKfBQ1BTcOGpVWAy8DewVM/YV0TQbuAC4lVAzJ9rHSuDRjOcMJ3RY2y0t1lPN7CIz29nM8vY/w909er0BZrYugJm1Aw5izX4E/YA3gS8zjsfzQHsqP9YpfyMci8XAi4Qa+8mZhczsQDN72czmE45POaEGH+c1+hF+JD2dEWeqtWLPGPsQyTslepHKTXL3iWnTlNQGMxtAaKqdBBxPSI67EGrdzXPt0N3HEppxuxJunfqtmf3bzLaNirSP5g9SkaxTUz/CKYI4Uj9OtgDWc/fz3X15tK0N8K2v3eN8dtp2gDMI56R/Sei0NtfMbjGzFjFjqMo/CLXsI6PHxxP+Lz2cVqY9sC9rH4tHou1xjseVhGPRF7gnWr4rvYCZ7ULomb+I0AKwe1RuEpV8nhlxNie06KTHmbrfeNzPTSSvdI5epOYGAp+6+ympFWbWnNDEXil3fxx43MxaEpLYDcBoM+sCpG6deiEwLsvTl2dZl+01Kv82NSUAAAMUSURBVOtNvoBwK83GGcl+42g+P9rHD4RTDReZWVfC5YZ/ItRcL6WW3H2qmY0HBhHOyQ8Cxrr7rLRi8wmX752XYzdTcqxPNz3teLxiZusDp5nZX9091THvaML7Oir9mJhZG8IpgqrMJyT5vXNsn5VjvUhBKdGL1Nw6hObddD+nGi1l7r4YGGFmWxA6gW1AuE5/BtDD3W/KU6yZXgHOBY4CHktbfyIh2b2dJdbpwE1mdhKwbeb2NMsJpy/iGgbcaWb7EGrQJ2VsH0MYB2C6u39bjf1W5g+E9/5HQuc/qPg8/3fvbjM7kNBB75O056Z+aGW+xzGEzpXruvsreYpTpNaU6EVqbgzw5+gysNGEJPVb4PvKnmRm1xKacccRem13Ac4EJrr7gqjMmcBTUQvBPwm1xY2BPf6/nTtmjSII4zD+vKSOgoXkA9hZaCla2QYR/AZiYXVgwNZKRRCCIY0oCKKVIJrGMpUKYqUGsRBRQUSwOS2SQERei3ei55pcEJHI+vxgmpu53Tn24GVn/rvA68yc/8O53wMeUcn1KaqQHaFCiOcyc9jm8Ri4Sy1fL1OBs73A1THHfgGciIiTwBNgNTOfjxl/C5ijCv4ysNDpn6VWEh5ExBzwEpiksgwHM/MYvykz30fEFWAmIvZn5lPqeg6A6xFxox3/DL/eia+/MGnQHif8AjzLzMWIuE3t0V+iXhAEtUUzDZzOzG7KX/r7tjsNaLP9i40fqfs9Y8ZMABeoQrBCFe59VNDr2si4bur+KBXQ+kDdHb6j9sGnOsc/RO0ZD6m77DfUvvSBLeZ+npZ122LcTuBym8catQR+qjNmlirWn6kg2xIwGOnfKHU/Sa0SDFvfq/b5T6n7znkWWt/NTea6i3rU722b60fgPiNPN2zyvfXU/fEN+na333Rn5LOZdo5VqlAfBh4Ci53vnm3X/Wvn2k5QKyVL7Zp9ot5fcBHYsd3/a9v/2SLz+yqVJEnqGVP3kiT1mIVekqQes9BLktRjFnpJknrMQi9JUo9Z6CVJ6jELvSRJPWahlySpxyz0kiT12DdFi865bX+pSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0c2e28588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot ROC Curve for each algorithm\n",
    "ROC_curve('kNN', Y_train, clf_scores)\n",
    "ROC_curve('Logistic Regression', Y_train, lr_scores)\n",
    "ROC_curve('Support Vector Classification',Y_train,svc_scores)\n",
    "ROC_curve('Decision Tree Classifier',Y_train,dtc_clf_scores)\n",
    "ROC_curve('Random Forest Classifier',Y_train,rmf_clf_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "ename": "ApiRequestFailure",
     "evalue": "Error during getting ML Token. (GET https://eu-gb.ml.cloud.ibm.com/v3/identity/token)\nStatus code: 400, body: {\"message\": \"Use IAM token as ML token was discontinued\"}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mApiRequestFailure\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-94-8063b34521e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m }\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mclient\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mWatsonMachineLearningAPIClient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwml_credentials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m model_props = {client.repository.ModelMetaNames.AUTHOR_NAME: \"Abhishek\", \n\u001b[1;32m     12\u001b[0m                \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepository\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelMetaNames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAUTHOR_EMAIL\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"abhishek@gmail.com\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_machine_learning_client/client.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, wml_credentials, project_id)\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproject_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproject_id\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwml_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mservice_instance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mServiceInstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     50\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mICP\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     51\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mservice_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mservice_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_details\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_machine_learning_client/instance.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, client)\u001b[0m\n\u001b[1;32m     30\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wml_credentials\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mu'instance_id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_href_definitions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHrefDefinitions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wml_credentials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwml_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'Successfully prepared token: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwml_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;31m# ml_repository_client is initialized in repo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/watson_machine_learning_client/instance.py\u001b[0m in \u001b[0;36m_create_token\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    154\u001b[0m                     \u001b[0mtoken\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'token'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    155\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mApiRequestFailure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'Error during getting ML Token.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    157\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mApiRequestFailure\u001b[0m: Error during getting ML Token. (GET https://eu-gb.ml.cloud.ibm.com/v3/identity/token)\nStatus code: 400, body: {\"message\": \"Use IAM token as ML token was discontinued\"}"
     ]
    }
   ],
   "source": [
    "from watson_machine_learning_client import WatsonMachineLearningAPIClient\n",
    "\n",
    "wml_credentials = {\n",
    "   \"instance_id\": \"3281aeea-4dc6-41cb-9f39-1f1d2b5761b8\",\n",
    "  \"password\": \"2492ccc8-7ed1-434d-af69-0a170fe6a4aa\",\n",
    "  \"url\": \"https://eu-gb.ml.cloud.ibm.com\",\n",
    "  \"username\": \"16752374-b3a2-4623-b3f1-a91af0ff5806\"\n",
    "}\n",
    "\n",
    "client = WatsonMachineLearningAPIClient(wml_credentials)\n",
    "model_props = {client.repository.ModelMetaNames.AUTHOR_NAME: \"Abhishek\", \n",
    "               client.repository.ModelMetaNames.AUTHOR_EMAIL: \"abhishek@gmail.com\", \n",
    "               client.repository.ModelMetaNames.NAME: \"Bank Model\"\n",
    "              }\n",
    "\n",
    "model=client.repository.store_model(lr, meta_props=model_props)\n",
    "\n",
    "published_model_uid = client.repository.get_model_uid(model)\n",
    "published_model_uid\n",
    "\n",
    "deployment = client.deployments.create(published_model_uid, name=\"Retail Banking\")\n",
    "\n",
    "scoring_endpoint = client.deployments.get_scoring_url(deployment)\n",
    "scoring_endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.deployments.list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client.deployments.delete(\"\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}