EnsembleTechniques.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1912,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing all required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer  #DT does not take strings as input for the model fit step....\n",
    "from IPython.display import Image  \n",
    "from sklearn import tree\n",
    "from os import system\n",
    "from sklearn.metrics import accuracy_score,f1_score,recall_score,precision_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1913,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'Target'],\n",
      "      dtype='object')\n",
      "(45211, 17)\n"
     ]
    },
    {
     "data": {
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       "<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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>231</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>447</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>divorced</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>121</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>593</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   age           job   marital  education default  balance housing loan  \\\n",
       "0   58    management   married   tertiary      no     2143     yes   no   \n",
       "1   44    technician    single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur   married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar   married    unknown      no     1506     yes   no   \n",
       "4   33       unknown    single    unknown      no        1      no   no   \n",
       "5   35    management   married   tertiary      no      231     yes   no   \n",
       "6   28    management    single   tertiary      no      447     yes  yes   \n",
       "7   42  entrepreneur  divorced   tertiary     yes        2     yes   no   \n",
       "8   58       retired   married    primary      no      121     yes   no   \n",
       "9   43    technician    single  secondary      no      593     yes   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome Target  \n",
       "0  unknown    5   may       261         1     -1         0  unknown     no  \n",
       "1  unknown    5   may       151         1     -1         0  unknown     no  \n",
       "2  unknown    5   may        76         1     -1         0  unknown     no  \n",
       "3  unknown    5   may        92         1     -1         0  unknown     no  \n",
       "4  unknown    5   may       198         1     -1         0  unknown     no  \n",
       "5  unknown    5   may       139         1     -1         0  unknown     no  \n",
       "6  unknown    5   may       217         1     -1         0  unknown     no  \n",
       "7  unknown    5   may       380         1     -1         0  unknown     no  \n",
       "8  unknown    5   may        50         1     -1         0  unknown     no  \n",
       "9  unknown    5   may        55         1     -1         0  unknown     no  "
      ]
     },
     "execution_count": 1913,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the data as a data frame using pandas.\n",
    "BankRaw = pd.read_csv('bank-full.csv')\n",
    "print(BankRaw.columns)\n",
    "#Shape of the data\n",
    "print(BankRaw.shape)\n",
    "BankRaw.head(10)\n",
    "#Inference: The data set has 45211 data records spread across 17 column types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1914,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 17 columns):\n",
      "age          45211 non-null int64\n",
      "job          45211 non-null object\n",
      "marital      45211 non-null object\n",
      "education    45211 non-null object\n",
      "default      45211 non-null object\n",
      "balance      45211 non-null int64\n",
      "housing      45211 non-null object\n",
      "loan         45211 non-null object\n",
      "contact      45211 non-null object\n",
      "day          45211 non-null int64\n",
      "month        45211 non-null object\n",
      "duration     45211 non-null int64\n",
      "campaign     45211 non-null int64\n",
      "pdays        45211 non-null int64\n",
      "previous     45211 non-null int64\n",
      "poutcome     45211 non-null object\n",
      "Target       45211 non-null object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 5.9+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "job          object\n",
       "marital      object\n",
       "education    object\n",
       "default      object\n",
       "balance       int64\n",
       "housing      object\n",
       "loan         object\n",
       "contact      object\n",
       "day           int64\n",
       "month        object\n",
       "duration      int64\n",
       "campaign      int64\n",
       "pdays         int64\n",
       "previous      int64\n",
       "poutcome     object\n",
       "Target       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 1914,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for Data type of each attribute\n",
    "#If attribute types are not correct appropriate actions needs to be taken.\n",
    "BankRaw.info()\n",
    "BankRaw.dtypes\n",
    "#Inference: \n",
    "# job,marital,education,default,housing,loan,contact,month,poutcome,target are categorical variables of data type 'object' \n",
    "#and hence needs to be represented in binary/integer format.We will take the necessary action needed for these categorical variables in subsequent steps\n",
    "#age, balance, day,duration,campain,pdays,previous are continuous variables holding intiger values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1915,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.936210</td>\n",
       "      <td>10.618762</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>-8019.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>102127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>871.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>2.303441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std     min    25%    50%     75%  \\\n",
       "age       45211.0    40.936210    10.618762    18.0   33.0   39.0    48.0   \n",
       "balance   45211.0  1362.272058  3044.765829 -8019.0   72.0  448.0  1428.0   \n",
       "day       45211.0    15.806419     8.322476     1.0    8.0   16.0    21.0   \n",
       "duration  45211.0   258.163080   257.527812     0.0  103.0  180.0   319.0   \n",
       "campaign  45211.0     2.763841     3.098021     1.0    1.0    2.0     3.0   \n",
       "pdays     45211.0    40.197828   100.128746    -1.0   -1.0   -1.0    -1.0   \n",
       "previous  45211.0     0.580323     2.303441     0.0    0.0    0.0     0.0   \n",
       "\n",
       "               max  \n",
       "age           95.0  \n",
       "balance   102127.0  \n",
       "day           31.0  \n",
       "duration    4918.0  \n",
       "campaign      63.0  \n",
       "pdays        871.0  \n",
       "previous     275.0  "
      ]
     },
     "execution_count": 1915,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.describe().transpose()\n",
    "#Inference: All numeric columns are described in below table. I could make below observations:\n",
    "#1.\"balance\" has -ve values, which ideally means customer owns loan from bank. This needs further analysis\n",
    "#2. As there is huge difference between 75% and max w.r.t \"campaign\" column, I could sense presence of outliers here. This needs further analysis\n",
    "#3.\"pdays\" column displays -ve value which cannot be the case in real scenario. Also 75% and max indicates presence of outliers.\n",
    "#This needs further analysis as well.\n",
    "#4. difference between 75% and max is huge w.r.t \"previous\" column, could anticipate outliers here. This needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1916,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False, age          0\n",
       " job          0\n",
       " marital      0\n",
       " education    0\n",
       " default      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",
       " previous     0\n",
       " poutcome     0\n",
       " Target       0\n",
       " dtype: int64)"
      ]
     },
     "execution_count": 1916,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the presence of missing values.\n",
    "BankRaw.isnull().values.any(),BankRaw.isna().values.any(),BankRaw.isna().sum()\n",
    "# Below data indicates that there are no null and NAN values. All values are present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1917,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bb52e3ad0>"
      ]
     },
     "execution_count": 1917,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's reconfirm the understanding with heatmap\n",
    "sns.heatmap(BankRaw.isnull(),yticklabels=False,cbar=False,cmap='viridis')\n",
    "#So based on below outcome and previous analysis, we can conclude that there is no null or NAN values present in the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1918,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plots and checking for class imbalance in below steps and printing insights\n",
    "# We will also check for the presence of outliers based on the visualization and simultaneously print the \n",
    "#5 point summary for each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1919,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b4fd39610>"
      ]
     },
     "execution_count": 1919,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Analyzing the distribution of independent variables one by one below. \n",
    "#Let's analyze numeric variables first\n",
    "sns.distplot(BankRaw['age'], rug=True)\n",
    "#Inference: Age has multiple modes and it is slightly right skewed which indicates outliers. \n",
    "#Data has heavy representation of customers with age between 20-60 years and less spread across other age groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1920,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.0, count    45211.000000\n",
       " mean        40.936210\n",
       " std         10.618762\n",
       " min         18.000000\n",
       " 25%         33.000000\n",
       " 50%         39.000000\n",
       " 75%         48.000000\n",
       " max         95.000000\n",
       " Name: age, dtype: float64)"
      ]
     },
     "execution_count": 1920,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.age.median(),BankRaw.age.describe()\n",
    "#Mean and Median for age are almost same. So we can summarize as below:\n",
    "#5 point summary: \n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#age: min-18, Q1-33,median-39, Q3-48, max-95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1921,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bbab04750>"
      ]
     },
     "execution_count": 1921,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['balance'], rug=True)\n",
    "#Inference: balance right skewed which indicates outliers. Also it has negetive values. This needs further action\n",
    "#Most of the data is spread across the customers who has balance within 5000 (euros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1922,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(448.0, count     45211.000000\n",
       " mean       1362.272058\n",
       " std        3044.765829\n",
       " min       -8019.000000\n",
       " 25%          72.000000\n",
       " 50%         448.000000\n",
       " 75%        1428.000000\n",
       " max      102127.000000\n",
       " Name: balance, dtype: float64)"
      ]
     },
     "execution_count": 1922,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary: \n",
    "BankRaw.balance.median(),BankRaw.balance.describe()\n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#balance: min -8019.00, Q1-72,median-448, Q3-1428, max-102127\n",
    "# Balance >5000 can be considered as outliers in the above graph which will be replaced in later steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1923,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bbe6e6cd0>"
      ]
     },
     "execution_count": 1923,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['day'], rug=True)\n",
    "#Inference: day has multiple modes and data is somewhat symmetrically distributed across all days of month\n",
    "#No outliers present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1924,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16.0, count    45211.000000\n",
       " mean        15.806419\n",
       " std          8.322476\n",
       " min          1.000000\n",
       " 25%          8.000000\n",
       " 50%         16.000000\n",
       " 75%         21.000000\n",
       " max         31.000000\n",
       " Name: day, dtype: float64)"
      ]
     },
     "execution_count": 1924,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.day.median(),BankRaw.day.describe()\n",
    "#5 point summary: \n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#balance: min 1, Q1-8,median-16, Q3-21, max-31.\n",
    "#No outliers or negetive values are found hence no further action is needed on this variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1925,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bbe6ecbd0>"
      ]
     },
     "execution_count": 1925,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['duration'], rug=True)\n",
    "#Inference: duration is highly right skewed and shows the presence of outliers. \n",
    "#Data is mostly spread across duration of between 0-800(secs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1926,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180.0, count    45211.000000\n",
       " mean       258.163080\n",
       " std        257.527812\n",
       " min          0.000000\n",
       " 25%        103.000000\n",
       " 50%        180.000000\n",
       " 75%        319.000000\n",
       " max       4918.000000\n",
       " Name: duration, dtype: float64)"
      ]
     },
     "execution_count": 1926,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary: \n",
    "BankRaw.duration.median(),BankRaw.duration.describe()\n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#duration: min 0, Q1-103,median-180, Q3-319, max-4918.\n",
    "#Shows the presence of outliers as max value is much more greater than 75%. Needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1927,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc1e7f950>"
      ]
     },
     "execution_count": 1927,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['campaign'], rug=True)\n",
    "#Inference: campaign is highly right skewed and shows the presence of outliers. \n",
    "#Data is mostly spread across campain ranging between 0-8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1928,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0, count    45211.000000\n",
       " mean         2.763841\n",
       " std          3.098021\n",
       " min          1.000000\n",
       " 25%          1.000000\n",
       " 50%          2.000000\n",
       " 75%          3.000000\n",
       " max         63.000000\n",
       " Name: campaign, dtype: float64)"
      ]
     },
     "execution_count": 1928,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary: \n",
    "BankRaw.campaign.median(),BankRaw.campaign.describe()\n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#campaign: min 1, Q1-1,median-2, Q3-3, max-63.\n",
    "#Shows the presence of outliers as max value is much more greater than 75%. Needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1929,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc1e7fc10>"
      ]
     },
     "execution_count": 1929,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['pdays'])\n",
    "#Inference: pdays is highly right skewed and shows the presence of outliers.\n",
    "#Data is heavily concentrated around 0 value of pdays. Needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1930,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, count    45211.000000\n",
       " mean        40.197828\n",
       " std        100.128746\n",
       " min         -1.000000\n",
       " 25%         -1.000000\n",
       " 50%         -1.000000\n",
       " 75%         -1.000000\n",
       " max        871.000000\n",
       " Name: pdays, dtype: float64)"
      ]
     },
     "execution_count": 1930,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary: \n",
    "BankRaw.pdays.median(),BankRaw.pdays.describe()\n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#pdays: min -1, Q1 -1,median -1, Q3 -1, max 871.\n",
    "#Shows the presence of extreme outliers. \n",
    "#Also this variable indicates that days completed after the previous contact has been made with the customer, \n",
    "#the  presence of -ve values does not make any sense. \n",
    "#In other way I feel we can assume that -ve value indicates that no contact has been made in previous campaign\n",
    "#This assumption will contradict the note provided in assignment: '999 means client was not previously contacted'.\n",
    "#But value 999 does not exist in the data set against this variable which means that we can proceed with our assumption\n",
    "# \"\"-ve value indicates that no contact has been made in previous campaign\"\n",
    "#Later we will analyze the relatioship of this column with another column 'previous' \n",
    "#which indicates if customer has been contacted previously or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1931,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc3e80590>"
      ]
     },
     "execution_count": 1931,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(BankRaw['previous'])\n",
    "#Inference: previous is right skewed and shows the presence of outliers. \n",
    "#Data is heavily concentrated around 0 value of previous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1932,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, count    45211.000000\n",
       " mean         0.580323\n",
       " std          2.303441\n",
       " min          0.000000\n",
       " 25%          0.000000\n",
       " 50%          0.000000\n",
       " 75%          0.000000\n",
       " max        275.000000\n",
       " Name: previous, dtype: float64)"
      ]
     },
     "execution_count": 1932,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 point summary: \n",
    "BankRaw.previous.median(),BankRaw.previous.describe()\n",
    "#Based on below result five point summary would look as below:(Considered Q1 is 25% and Q3 is 75%)\n",
    "#previous: min 0, Q1 0,median 0, Q3 0, max 275.\n",
    "#Shows the presence of outliers as max value is much more greater than 75%. Needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1933,
   "metadata": {},
   "outputs": [],
   "source": [
    "#5-point summary of numeric variables are complated above\n",
    "#Analyzing categorical variables one by one below to check the distribution pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1934,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'management'),\n",
       " Text(0, 0, 'technician'),\n",
       " Text(0, 0, 'entrepreneur'),\n",
       " Text(0, 0, 'blue-collar'),\n",
       " Text(0, 0, 'unknown'),\n",
       " Text(0, 0, 'retired'),\n",
       " Text(0, 0, 'admin.'),\n",
       " Text(0, 0, 'services'),\n",
       " Text(0, 0, 'self-employed'),\n",
       " Text(0, 0, 'unemployed'),\n",
       " Text(0, 0, 'housemaid'),\n",
       " Text(0, 0, 'student')]"
      ]
     },
     "execution_count": 1934,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart=sns.countplot(BankRaw['job'])\n",
    "chart.set_xticklabels(chart.get_xticklabels(), rotation=90, horizontalalignment='right')\n",
    "# Inference: Data is heavily distributed across job types-management,technician,blue-collar, \n",
    "# moderately distributed across job types-admin,services and least distributed across job types- entrepreneur,unknown,\n",
    "#retired,self-employed,unemployed,housemaid,student.\n",
    "# Job has 'unknown' as one of category. \n",
    "#As this is very negligible when compared to other categories, we will retain it as it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1935,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4118090>"
      ]
     },
     "execution_count": 1935,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['marital'])\n",
    "#Inference: Data has more representation of married customers, moderate distribution of single customers and \n",
    "#least representation of divorced customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1936,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc40cd690>"
      ]
     },
     "execution_count": 1936,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['education'])\n",
    "#Inference: Data has more representation of customers with secondary degree, moderate distribution of customers with teriary degree\n",
    "#and less representation of customers with primary degree and least customers with unknown degree. \n",
    "#As the number of unknown is least, we will retain it as it is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1937,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc439da10>"
      ]
     },
     "execution_count": 1937,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['default'])\n",
    "#Inference: Data set has more non-defaulters and very least defaulters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1938,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc448fd50>"
      ]
     },
     "execution_count": 1938,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['housing'])\n",
    "#Inference: Data set has somewhat equal representation of customers with housing loan and without housing loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1939,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc45c8550>"
      ]
     },
     "execution_count": 1939,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['loan'])\n",
    "#Inference: Majority of customers does not have personal loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1940,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc44dc810>"
      ]
     },
     "execution_count": 1940,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['contact'])\n",
    "#Inference: Majority of customers can be contacted with cellular mode in this data set while \n",
    "#moderate amount of customers does not have any mode of contact (\"Unknown\") and least number of customers \n",
    "#can be reached over telephone\n",
    "#We will keep the 'unknown' category as it is since it is more of mode of contact and \n",
    "#customers can be contacted through other modes as well if needed(Ex: email,postal address)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1941,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc46cc0d0>"
      ]
     },
     "execution_count": 1941,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['month'])\n",
    "#Inference: Most of the customers in data set were previously contacted in the month of may. \n",
    "#Average number of customers were contacted in the month of jun, jul,aug,nov \n",
    "#Least number of customers were contacted in the month of oct,dec,jan,feb,mar,apr,sep\n",
    "#No unknowns found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1942,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4971a50>"
      ]
     },
     "execution_count": 1942,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['poutcome'])\n",
    "#Inference: poutcome (outcome of previous campaign) is unknown across majority of data set and \n",
    "#has smaller presence among failure,other and success categories. \n",
    "#This data may not add much value from analysis perspective as for most of the customers it is unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1943,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4ad9550>"
      ]
     },
     "execution_count": 1943,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(BankRaw['Target'])\n",
    "#Inference: Data set has majority of the customers who has not opted for term deposit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1944,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4aaec10>"
      ]
     },
     "execution_count": 1944,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['age'])\n",
    "#Inference: Target column is heavily spread between age group 30-50. \n",
    "#Also there is equal representation of customers who opted for Term deposit and did not opt for it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1945,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4d00410>"
      ]
     },
     "execution_count": 1945,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['balance'])\n",
    "#Inference: There is equal distribution of Customers who opted for 'Term deposit' and who has not opted\n",
    "# and this distribution can be found between salary range less that ~15000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1946,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4e45510>"
      ]
     },
     "execution_count": 1946,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['day'])\n",
    "#Inference: There is equal distribution of Customers who opted for 'Term deposit' and who has not opted\n",
    "# and distribution is concentrated between 8th-22nd day of month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1947,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc4f7b310>"
      ]
     },
     "execution_count": 1947,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['duration'])\n",
    "#Inference: The customer who opted for Term deposit and the one's who slightly spend more duration in last call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1948,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc50aa790>"
      ]
     },
     "execution_count": 1948,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['campaign'])\n",
    "#Inference:There is equal distribution of customers who opted for Term Deposit and not opted w.r.t campaign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1949,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc51e6d50>"
      ]
     },
     "execution_count": 1949,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['pdays'])\n",
    "#Inference: As per the below distribution, the customers who has opted for Term deposit were contacted recently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1950,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc530d8d0>"
      ]
     },
     "execution_count": 1950,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bivariate Analysis\n",
    "sns.boxplot(BankRaw['Target'],BankRaw['previous'])\n",
    "#Inference: Customers who opted for Term deposit and not opted have equal distribution w.r.t previous contact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1951,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Take appropriate actions for outliers and -ve values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1952,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc5436310>"
      ]
     },
     "execution_count": 1952,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Continuing with outliers analysis further\n",
    "sns.boxplot(BankRaw['pdays'])\n",
    "#From below graph, the data does not seems to be correct. Needs further analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1953,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          36954\n",
       "job          36954\n",
       "marital      36954\n",
       "education    36954\n",
       "default      36954\n",
       "balance      36954\n",
       "housing      36954\n",
       "loan         36954\n",
       "contact      36954\n",
       "day          36954\n",
       "month        36954\n",
       "duration     36954\n",
       "campaign     36954\n",
       "pdays        36954\n",
       "previous     36954\n",
       "poutcome     36954\n",
       "Target       36954\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1953,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['pdays']<0].count()\n",
    "#Inference: There are 36954 rows where the pdays is -ve. Let's analyse it further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1954,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>40.932430</td>\n",
       "      <td>10.430218</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>1318.788846</td>\n",
       "      <td>3039.557077</td>\n",
       "      <td>-8019.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>1358.0</td>\n",
       "      <td>102127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>16.145424</td>\n",
       "      <td>8.372554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>257.726119</td>\n",
       "      <td>262.256406</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>318.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>2.921957</td>\n",
       "      <td>3.325791</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>36954.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std     min    25%    50%     75%  \\\n",
       "age       36954.0    40.932430    10.430218    18.0   33.0   39.0    49.0   \n",
       "balance   36954.0  1318.788846  3039.557077 -8019.0   55.0  414.0  1358.0   \n",
       "day       36954.0    16.145424     8.372554     1.0    9.0   17.0    22.0   \n",
       "duration  36954.0   257.726119   262.256406     0.0  101.0  177.0   318.0   \n",
       "campaign  36954.0     2.921957     3.325791     1.0    1.0    2.0     3.0   \n",
       "pdays     36954.0    -1.000000     0.000000    -1.0   -1.0   -1.0    -1.0   \n",
       "previous  36954.0     0.000000     0.000000     0.0    0.0    0.0     0.0   \n",
       "\n",
       "               max  \n",
       "age           95.0  \n",
       "balance   102127.0  \n",
       "day           31.0  \n",
       "duration    4918.0  \n",
       "campaign      63.0  \n",
       "pdays         -1.0  \n",
       "previous       0.0  "
      ]
     },
     "execution_count": 1954,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['pdays']<0].describe().transpose()\n",
    "#Inference: From below data we can see that for all -ve value of 'pdays', the 'previous' value is 0, which means \n",
    "#customers was not previously contacted. Hence my previous assumption around -1 value of pdays holds good \n",
    "# i.e., 'pdays' = '-ve' means customers was not previously contacted. \n",
    "#We will replace this -ve value with 0 for now so that we can proceed with assumption that these are the customers \n",
    "# not contacted previusly and hence pdays is also 0 \n",
    "#Note: We cannot replace it with 25%, median and 75% value as all are -1 in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1955,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.0, count    45211.000000\n",
       " mean        40.197828\n",
       " std        100.128746\n",
       " min         -1.000000\n",
       " 25%         -1.000000\n",
       " 50%         -1.000000\n",
       " 75%         -1.000000\n",
       " max        871.000000\n",
       " Name: pdays, dtype: float64)"
      ]
     },
     "execution_count": 1955,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.pdays.median(),BankRaw.pdays.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1956,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.000000\n",
       "mean        41.015195\n",
       "std         99.792615\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max        871.000000\n",
       "Name: pdays, dtype: float64"
      ]
     },
     "execution_count": 1956,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[\"pdays\"]=np.where(BankRaw[\"pdays\"]<0, 0,BankRaw['pdays'])\n",
    "BankRaw.pdays.describe().transpose()\n",
    "#Inference: 45211 records of data having pdays <0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1957,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.000000\n",
       "mean        28.440579\n",
       "std         62.910455\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%          0.000000\n",
       "max        182.000000\n",
       "Name: pdays, dtype: float64"
      ]
     },
     "execution_count": 1957,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Outliers treatment for 'pdays'\n",
    "#In general, a reputed bank will maintain the relationship between customers by contacting them atleast once in 6 months.\n",
    "#So we can consider anything > 6 months (182 days) as outlier and let's replace those values with this 180 days \n",
    "BankRaw[\"pdays\"]=np.where(BankRaw[\"pdays\"]>182, 182,BankRaw['pdays'])\n",
    "BankRaw.pdays.describe().transpose()\n",
    "# Inference: We can see that after replacement, std has reduced and replacement is successful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1958,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc54eafd0>"
      ]
     },
     "execution_count": 1958,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/VkrZARARXwF+vdo+Fzi19bYFABwdEUeVUt4YyYGlwRhg1V5ENGhF9exSyp6IWEfrzbbf6ah2XHXZvSMzofT+eA5Yo8ExwOtVfE+h9fagE4HfiYgp1Vsi/mGvy38D+GzPQkTMHNFppffIAGs0WAO0RcSLwN8C/wlsAW6j9dtUnqb1Tlg7q8svBmZFxIsR8T3gz0Z+ZGlw/iecRq2ImFxK2VUdAT8M/HMp5eHsuaT3yiNgjWbLqt/dt57WL9x8JHkeaUg8ApakJB4BS1ISAyxJSQywJCUxwJKUxABLUpL/A9Q9Tb9Vpc4TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the presence of outliers\n",
    "sns.boxplot(BankRaw['age'])\n",
    "#From boxplot, we can see the presence of outliers ie.,age>70 needs to be replaced with median value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1959,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing the outliers with median value ie., 39 in this case\n",
    "BankRaw[\"age\"]=np.where(BankRaw[\"age\"]>70, 39,BankRaw['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1960,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.528876</td>\n",
       "      <td>9.925626</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>-8019.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>102127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>28.440579</td>\n",
       "      <td>62.910455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>2.303441</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean          std     min    25%    50%     75%  \\\n",
       "age       45211.0    40.528876     9.925626    18.0   33.0   39.0    48.0   \n",
       "balance   45211.0  1362.272058  3044.765829 -8019.0   72.0  448.0  1428.0   \n",
       "day       45211.0    15.806419     8.322476     1.0    8.0   16.0    21.0   \n",
       "duration  45211.0   258.163080   257.527812     0.0  103.0  180.0   319.0   \n",
       "campaign  45211.0     2.763841     3.098021     1.0    1.0    2.0     3.0   \n",
       "pdays     45211.0    28.440579    62.910455     0.0    0.0    0.0     0.0   \n",
       "previous  45211.0     0.580323     2.303441     0.0    0.0    0.0     0.0   \n",
       "\n",
       "               max  \n",
       "age           70.0  \n",
       "balance   102127.0  \n",
       "day           31.0  \n",
       "duration    4918.0  \n",
       "campaign      63.0  \n",
       "pdays        182.0  \n",
       "previous     275.0  "
      ]
     },
     "execution_count": 1960,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the outliers is replaced correctly\n",
    "BankRaw[BankRaw['age']<=70]. describe().transpose()\n",
    "# We can conclude that  after replacing the outliers, all 45211 records have age <=70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1961,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc585e850>"
      ]
     },
     "execution_count": 1961,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the presence of outliers\n",
    "sns.boxplot(BankRaw['balance'])\n",
    "#From boxplot, we can see the presence of outliers. Also this has -ve values.\n",
    "#A balance of negetive value may also be indication of existence of loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1962,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>39.480351</td>\n",
       "      <td>9.068517</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>-317.677642</td>\n",
       "      <td>368.994983</td>\n",
       "      <td>-8019.0</td>\n",
       "      <td>-422.0</td>\n",
       "      <td>-230.0</td>\n",
       "      <td>-93.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>16.439724</td>\n",
       "      <td>8.578670</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>252.096389</td>\n",
       "      <td>250.280003</td>\n",
       "      <td>4.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>3253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>2.926713</td>\n",
       "      <td>3.474046</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>22.245884</td>\n",
       "      <td>58.879697</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>3766.0</td>\n",
       "      <td>0.406798</td>\n",
       "      <td>1.609466</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean         std     min    25%    50%    75%     max\n",
       "age       3766.0   39.480351    9.068517    20.0   32.0   38.0   46.0    63.0\n",
       "balance   3766.0 -317.677642  368.994983 -8019.0 -422.0 -230.0  -93.0    -1.0\n",
       "day       3766.0   16.439724    8.578670     1.0    9.0   16.0   24.0    31.0\n",
       "duration  3766.0  252.096389  250.280003     4.0  105.0  176.0  305.0  3253.0\n",
       "campaign  3766.0    2.926713    3.474046     1.0    1.0    2.0    3.0    43.0\n",
       "pdays     3766.0   22.245884   58.879697     0.0    0.0    0.0    0.0   182.0\n",
       "previous  3766.0    0.406798    1.609466     0.0    0.0    0.0    0.0    25.0"
      ]
     },
     "execution_count": 1962,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[BankRaw['balance']<0].describe().transpose()\n",
    "#There are 3766 customer records with -ve balance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1963,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc5fdc5d0>"
      ]
     },
     "execution_count": 1963,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets analyze 'balance' and 'loan' and housing together as I am suspecting/assuming customers with negetive balance may have\n",
    "#either housing loan/personal loan\n",
    "sns.barplot(BankRaw['loan'], BankRaw['balance']<0) \n",
    "#Inference: From below graph, it gives sense that more number of customers with balance <0 has opted for loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1964,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc609cb10>"
      ]
     },
     "execution_count": 1964,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's perform similar analysis with housing loan as well\n",
    "sns.barplot(BankRaw['housing'], BankRaw['balance']<0)\n",
    "# From below graph, it gives sense that more number of customers with balance <0 has opted for housing loan as well\n",
    "# Overall we can conclude that customers with -ve balance are the ones who opted for either personal loan or housing loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1965,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     45211.000000\n",
       "mean       1362.272058\n",
       "std        3044.765829\n",
       "min       -8019.000000\n",
       "25%          72.000000\n",
       "50%         448.000000\n",
       "75%        1428.000000\n",
       "max      102127.000000\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 1965,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's replace the -ve values with 25% for balance \n",
    "BankRaw.balance.describe().transpose()\n",
    "#So 25% value is 72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1966,
   "metadata": {},
   "outputs": [],
   "source": [
    "BankRaw[\"balance\"]=np.where(BankRaw[\"balance\"]<0, 72,BankRaw['balance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1967,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     45211.000000\n",
       "mean       1394.731548\n",
       "std        3026.845286\n",
       "min           0.000000\n",
       "25%          72.000000\n",
       "50%         448.000000\n",
       "75%        1428.000000\n",
       "max      102127.000000\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 1967,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.balance.describe().transpose() # the -ve value is replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1968,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>42.808436</td>\n",
       "      <td>10.279132</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>9826.109666</td>\n",
       "      <td>7263.815214</td>\n",
       "      <td>5003.0</td>\n",
       "      <td>5961.0</td>\n",
       "      <td>7578.0</td>\n",
       "      <td>10758.0</td>\n",
       "      <td>102127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>16.099473</td>\n",
       "      <td>7.896902</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>276.048858</td>\n",
       "      <td>282.555242</td>\n",
       "      <td>0.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>2.666432</td>\n",
       "      <td>3.030578</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>31.634446</td>\n",
       "      <td>63.857502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>2845.0</td>\n",
       "      <td>0.686116</td>\n",
       "      <td>2.166480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count         mean          std     min     25%     50%      75%  \\\n",
       "age       2845.0    42.808436    10.279132    19.0    34.0    41.0     51.0   \n",
       "balance   2845.0  9826.109666  7263.815214  5003.0  5961.0  7578.0  10758.0   \n",
       "day       2845.0    16.099473     7.896902     1.0     9.0    18.0     21.0   \n",
       "duration  2845.0   276.048858   282.555242     0.0   107.0   189.0    340.0   \n",
       "campaign  2845.0     2.666432     3.030578     1.0     1.0     2.0      3.0   \n",
       "pdays     2845.0    31.634446    63.857502     0.0     0.0     0.0      0.0   \n",
       "previous  2845.0     0.686116     2.166480     0.0     0.0     0.0      0.0   \n",
       "\n",
       "               max  \n",
       "age           70.0  \n",
       "balance   102127.0  \n",
       "day           31.0  \n",
       "duration    4918.0  \n",
       "campaign      63.0  \n",
       "pdays        182.0  \n",
       "previous      35.0  "
      ]
     },
     "execution_count": 1968,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# From boxplot, we could sense that balance also has outliers (i.e balance >5000)hence needs replacement\n",
    "BankRaw[BankRaw['balance']>5000].describe().transpose()\n",
    "#There are 2845 records with balance >5000, lets replace this with 75% value i.e., 1428"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1969,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.000000\n",
       "mean       866.262326\n",
       "std       1044.015957\n",
       "min          0.000000\n",
       "25%         72.000000\n",
       "50%        448.000000\n",
       "75%       1428.000000\n",
       "max       5000.000000\n",
       "Name: balance, dtype: float64"
      ]
     },
     "execution_count": 1969,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[\"balance\"]=np.where(BankRaw[\"balance\"]>5000, 1428,BankRaw['balance'])\n",
    "BankRaw.balance.describe().transpose() # the outliers are replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1970,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc60eb210>"
      ]
     },
     "execution_count": 1970,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the presence of outliers\n",
    "sns.boxplot(BankRaw['duration'])\n",
    "#Inference: Presence of outliers confirmed which needs to be treated ideally. However as per the information provided\n",
    "#in the assignment this variable needs to be ignored during preparing model as it may impact the accuracy of prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1971,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc5ae7a10>"
      ]
     },
     "execution_count": 1971,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the presence of outliers\n",
    "sns.boxplot(BankRaw['campaign'])\n",
    "#From boxplot, we can see the presence of outliers which needs to be replaced with median value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1972,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.000000\n",
       "mean         2.763841\n",
       "std          3.098021\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          3.000000\n",
       "max         63.000000\n",
       "Name: campaign, dtype: float64"
      ]
     },
     "execution_count": 1972,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.campaign.describe().transpose()\n",
    "#Presence of outliers is evident and we can assume campaign >6 as outliers and replace it with 75% value, i.e., 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1973,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.000000\n",
       "mean         2.188914\n",
       "std          1.289155\n",
       "min          1.000000\n",
       "25%          1.000000\n",
       "50%          2.000000\n",
       "75%          3.000000\n",
       "max          6.000000\n",
       "Name: campaign, dtype: float64"
      ]
     },
     "execution_count": 1973,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[\"campaign\"]=np.where(BankRaw[\"campaign\"]>6, 3,BankRaw['campaign'])\n",
    "BankRaw.campaign.describe().transpose() # the outliers are replaced successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1974,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bc6132a90>"
      ]
     },
     "execution_count": 1974,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAMvUlEQVR4nO3dfWxddR3H8c9nvYMxISLdNATR8hRgcYrQGFQEEpm2TQwaJWDUNQZDoojyB38gA7cJIWIiCS4+YSB0aoT4hISs1aGiQALayUOB8TBkBITwUCOIbLp2X/+4515uy23Xjd775dy9X0lzT885Pef8dnrfvffc9dYRIQBA+y3IPgAA2FsRYABIQoABIAkBBoAkBBgAklR2Z+UlS5ZET09Piw4FADrTpk2bXoiIpdPn71aAe3p6NDo6On9HBQB7AdtPNJvPJQgASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkbQnwunXrtG7dunbsCgBKoy0BHhkZ0cjISDt2BQClwSUIAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASBJpR07eeWVV9qxGwAolbYEOCLasRsAKBUuQQBAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkq7dzZqaee2pb9DAwMaMOGDerq6tLk5GT91rYuueQS3XjjjVq9erW6u7s1Pj6utWvXanBwUKtWrZJtXXrppbrmmmvq093d3bPur7aN2janz5P0muUAyqHZ/Xu+dOQj4A0bNkiSJicnp9xGhC6//HKNjY1p/fr1kqShoSGNjY1p9erV2r59u7Zt26Y1a9Zo8+bNevDBB+vrzaa2jcZ1G+c1Ww6gHFp5/+3IAM9mYmJCEaGRkRFt2bJFIyMjigi9/PLL9XUap4eHhzU+Pj7j9sbHx+vbGBkZ0fj4+JR5w8PDGh4enrIcQDk0u3/Pp70uwDWTk5O67LLLtHPnzlnX27Fjx6w/+YaGhurbmJycrD/irc3bsWOHJiYmpiwHUA7N7t/zaZcBtn2O7VHbo88///y87jzTxMSEtm7dWo/jTCJCGzdunHH5LbfcUt/GxMSENm7cOGVeRCgipiwHUA7N7t/zaZcBjoirI6I3InqXLl06rzvPVKlU1NPTo0pl9tchbWvFihUzLj/ttNPq26hUKlqxYsWUebZle8pyAOXQ7P49n/baSxBdXV26+OKLtWDB7P8ECxcu1MqVK2dcPjg4WN9GV1eXVq5cOWXewoUL6yewthxAOTS7f8+nvS7AlUpFttXX16cjjzxSfX19sq3999+/vk7jdH9//6z/9aS7u7u+jb6+PnV3d0+Z19/fr/7+/inLAZRDs/v3fOrIAA8MDEiq/sRqvLWtiy66SMuXL6//JBscHNTy5cu1du1aLVq0SPvtt5/WrFmjY489VsuWLZvTT7zaNhrXbZzXbDmAcmjl/de1F4jmore3N0ZHR3d7J7VfwLj11lt3+2sBoOxsb4qI3unzO/IRMACUAQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCSVduzEdjt2AwCl0pYAL168uB27AYBS4RIEACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJCHAAJCEAANAEgIMAEkIMAAkIcAAkIQAA0ASAgwASQgwACQhwACQhAADQBICDABJCDAAJKm0Yyd9fX3t2A0AlEpbAnzeeee1YzcAUCpcggCAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACSEGAASEKAASAJAQaAJAQYAJIQYABIQoABIAkBBoAkBBgAkhBgAEhCgAEgiSNi7ivbz0t6Yg/3tUTSC3v4tW90nTw2qbPHx9jKq0zje2dELJ0+c7cC/HrYHo2I3rbsrM06eWxSZ4+PsZVXJ4yPSxAAkIQAA0CSdgb46jbuq906eWxSZ4+PsZVX6cfXtmvAAICpuAQBAEkIMAAkaXmAbffZftj2FtsXtnp/7WB7q+0x2/fYHi3mHWR7o+1Hi9u3ZB/nXNi+1vZztu9vmNd0LK76TnEu77N9fN6Rz80M41tj+x/F+bvH9kDDsq8V43vY9kdzjnpubB9q+4+2N9t+wPZXi/mlP3+zjK0jzl1dRLTsQ1KXpMckHS5pH0n3SlrWyn2240PSVklLps37lqQLi+kLJV2RfZxzHMvJko6XdP+uxiJpQNKwJEs6UdJd2ce/h+NbI+mCJusuK75H95V0WPG925U9hlnGdrCk44vpAyQ9Uoyh9OdvlrF1xLmrfbT6EfD7JG2JiL9HxP8kXS/p9BbvM8vpkoaK6SFJH088ljmLiD9L+ue02TON5XRJ66PqTkkH2j64PUe6Z2YY30xOl3R9RPw3Ih6XtEXV7+E3pIh4JiL+Vkz/W9JmSYeoA87fLGObSanOXU2rA3yIpCcbPn9Ks/8jlkVI+p3tTbbPKea9LSKekarfPJLemnZ0r99MY+mk8/nl4mn4tQ2Xi0o7Pts9kt4r6S512PmbNjapg85dqwPsJvM64f+9fTAijpfUL+lc2ydnH1CbdMr5/L6kIyQdJ+kZSd8u5pdyfLb3l/RLSedHxEuzrdpk3ht6fE3G1lHnrtUBfkrSoQ2fv13S0y3eZ8tFxNPF7XOSfq3qU51na0/nitvn8o7wdZtpLB1xPiPi2YiYjIidkn6kV5+qlm58theqGqifRsSvitkdcf6aja2Tzp3U+gD/VdJRtg+zvY+ksyTd1OJ9tpTtN9k+oDYt6SOS7ld1XIPFaoOSfpNzhPNiprHcJGll8Wr6iZJerD3VLZNp1z0/oer5k6rjO8v2vrYPk3SUpL+0+/jmyrYlXSNpc0Rc2bCo9OdvprF1yrmra8OrmQOqvoL5mKRV2a86zsN4Dlf11dZ7JT1QG5Okbkm/l/RocXtQ9rHOcTw/U/Wp3A5VH0WcPdNYVH2a993iXI5J6s0+/j0c34+L479P1TvuwQ3rryrG97Ck/uzj38XYTlL1afZ9ku4pPgY64fzNMraOOHe1D34VGQCS8JtwAJCEAANAEgIMAEkIMAAkIcAAkIQAo6PY/obt07KPA5gL/hsa3rBsd0XEZPZxAK3CI2CksN1j+yHbQ8Ubq/zC9uLivZa/bvt2SWfYPsL2SPHGR7fZPsb2m4v1FhTbWmz7SdsLbV9n+1PF/A/bvtvV926+1va+xfyttpcU0722by2mT2l4n9m7a7/xCLQKAUamoyVdHRHvlvSSpC8V87dHxEkRcb2qf3jxvIg4QdIFkr4XES+q+puIpxTrf0zSbyNiR23DthdJuk7SmRGxXFJF0hd3cTwXSDo3Io6T9CFJ2+ZhjMCMCDAyPRkRdxTTP1H1108l6Qap/k5YH5D0c9v3SPqhqm/UXVvnzGL6rNrXNDha0uMR8Ujx+ZCqb84+mzskXWn7K5IOjIiJ3R8SMHcEGJmmvwBR+/w/xe0CSf+KiOMaPo4tlt0kqd/2QZJOkPSHadtq9vaENRN69Xt/UX3nEd+U9AVJ+0m60/YxuzUaYDcRYGR6h+33F9OflnR748Kovv/r47bPkOp/0+w9xbKXVX23q6sk3dzkxbqHJPXYPrL4/HOS/lRMb1U12pL0ydoX2D4iIsYi4gpJo5IIMFqKACPTZkmDtu+TdJCqb7Y93WcknW279u5zjX/S6gZJn9VrLz8oIrZL+ryqly/GJO2U9INi8VpJV9m+TVJjuM+3fX+xr22q/v00oGX4b2hIUfyZmZsj4l3JhwKk4REwACThETAAJOERMAAkIcAAkIQAA0ASAgwASQgwACT5Px+wcvdSpjVTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking the presence of outliers\n",
    "sns.boxplot(BankRaw['previous'])\n",
    "#Inference: Too many outliers, needs to be treated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1975,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>40.127528</td>\n",
       "      <td>10.075831</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>992.242340</td>\n",
       "      <td>1077.094304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>14.289209</td>\n",
       "      <td>7.919176</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>260.118687</td>\n",
       "      <td>235.208105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>193.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>2219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>1.924549</td>\n",
       "      <td>1.190543</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>155.725687</td>\n",
       "      <td>42.995481</td>\n",
       "      <td>1.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>8257.0</td>\n",
       "      <td>3.177546</td>\n",
       "      <td>4.560820</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean          std   min    25%    50%     75%     max\n",
       "age       8257.0   40.127528    10.075831  18.0   33.0   38.0    47.0    70.0\n",
       "balance   8257.0  992.242340  1077.094304   0.0  168.0  602.0  1428.0  5000.0\n",
       "day       8257.0   14.289209     7.919176   1.0    7.0   14.0    20.0    31.0\n",
       "duration  8257.0  260.118687   235.208105   1.0  113.0  193.0   324.0  2219.0\n",
       "campaign  8257.0    1.924549     1.190543   1.0    1.0    2.0     2.0     6.0\n",
       "pdays     8257.0  155.725687    42.995481   1.0  133.0  182.0   182.0   182.0\n",
       "previous  8257.0    3.177546     4.560820   1.0    1.0    2.0     4.0   275.0"
      ]
     },
     "execution_count": 1975,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.previous.describe().transpose()\n",
    "BankRaw[BankRaw['previous']>0].describe().transpose()\n",
    "#There are 8257 records with previous value >0, which is very small representation\n",
    "# Let's replace it with median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1976,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 1976,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.previous.median()#Replacing  outliers with median value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1977,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    45211.0\n",
       "mean         0.0\n",
       "std          0.0\n",
       "min          0.0\n",
       "25%          0.0\n",
       "50%          0.0\n",
       "75%          0.0\n",
       "max          0.0\n",
       "Name: previous, dtype: float64"
      ]
     },
     "execution_count": 1977,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw[\"previous\"]=np.where(BankRaw[\"previous\"]>0, 0,BankRaw['previous'])\n",
    "BankRaw.previous.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1978,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>age</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>40.528876</td>\n",
       "      <td>9.925626</td>\n",
       "      <td>18.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>balance</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>866.262326</td>\n",
       "      <td>1044.015957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>448.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>day</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>duration</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>319.0</td>\n",
       "      <td>4918.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>campaign</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>2.188914</td>\n",
       "      <td>1.289155</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pdays</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>28.440579</td>\n",
       "      <td>62.910455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>previous</td>\n",
       "      <td>45211.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count        mean          std   min    25%    50%     75%     max\n",
       "age       45211.0   40.528876     9.925626  18.0   33.0   39.0    48.0    70.0\n",
       "balance   45211.0  866.262326  1044.015957   0.0   72.0  448.0  1428.0  5000.0\n",
       "day       45211.0   15.806419     8.322476   1.0    8.0   16.0    21.0    31.0\n",
       "duration  45211.0  258.163080   257.527812   0.0  103.0  180.0   319.0  4918.0\n",
       "campaign  45211.0    2.188914     1.289155   1.0    1.0    2.0     3.0     6.0\n",
       "pdays     45211.0   28.440579    62.910455   0.0    0.0    0.0     0.0   182.0\n",
       "previous  45211.0    0.000000     0.000000   0.0    0.0    0.0     0.0     0.0"
      ]
     },
     "execution_count": 1978,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BankRaw.describe().transpose()#Verifying if all -ve values and outliers are replaced in data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1979,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(False, False, <matplotlib.axes._subplots.AxesSubplot at 0x1bc60dc550>)"
      ]
     },
     "execution_count": 1979,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confirming if any na/nan/null values are not added to data set after replacement\n",
    "BankRaw.isna().sum()\n",
    "BankRaw.isnull().values.any(),BankRaw.isna().values.any()\n",
    "BankRaw.isnull().values.any(),BankRaw.isna().values.any(),sns.heatmap(BankRaw.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1980,
   "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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1506</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no   \n",
       "1   44    technician   single  secondary      no       29     yes   no   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes   \n",
       "3   47   blue-collar  married    unknown      no     1506     yes   no   \n",
       "4   33       unknown   single    unknown      no        1      no   no   \n",
       "\n",
       "   contact  day month  duration  campaign  pdays  previous poutcome Target  \n",
       "0  unknown    5   may       261         1      0         0  unknown     no  \n",
       "1  unknown    5   may       151         1      0         0  unknown     no  \n",
       "2  unknown    5   may        76         1      0         0  unknown     no  \n",
       "3  unknown    5   may        92         1      0         0  unknown     no  \n",
       "4  unknown    5   may       198         1      0         0  unknown     no  "
      ]
     },
     "execution_count": 1980,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Confirming all columns are present after data treatment\n",
    "BankRaw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1981,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check for appropriate data types and make changes if required\n",
    "#We have categorical values which needs to be represnted in numeric format\n",
    "#We will use labelling for variables like education, month,default,housing,loan and target as these variables has significance w.r.t sequence/ranking involved\n",
    "#We will use one-hot encoding for variables like job,marital,contact,poutcome as these variables \n",
    "#does not have any significance w.r.t sequence/ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1982,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Labelling the categorical variables\n",
    "cleanup_nums = { \"education\":     {\"primary\": 1, \"secondary\": 2 ,\"tertiary\": 3 ,\"unknown\": -1},\n",
    "                 \"housing\":       {\"no\": 0,\"yes\": 1},\n",
    "                 \"loan\":          {\"no\": 0, \"yes\": 1},\n",
    "                 \"default\":       {\"no\": 0, \"yes\": 1},\n",
    "                 \"Target\":        {\"no\": 0, \"yes\": 1},\n",
    "                 \"month\":         {\"jan\": 1, \"feb\": 2, \"mar\": 3, \"apr\": 4, \"may\": 5, \"jun\": 6,\n",
    "                                  \"jul\": 7, \"aug\": 8, \"sep\": 9, \"oct\": 10, \"nov\": 11, \"dec\": 12}}\n",
    "               \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1983,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2143</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>unknown</td>\n",
       "      <td>single</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>231</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>139</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>447</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>divorced</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>380</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>593</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job   marital  education  default  balance  housing  loan  \\\n",
       "0   58    management   married          3        0     2143        1     0   \n",
       "1   44    technician    single          2        0       29        1     0   \n",
       "2   33  entrepreneur   married          2        0        2        1     1   \n",
       "3   47   blue-collar   married         -1        0     1506        1     0   \n",
       "4   33       unknown    single         -1        0        1        0     0   \n",
       "5   35    management   married          3        0      231        1     0   \n",
       "6   28    management    single          3        0      447        1     1   \n",
       "7   42  entrepreneur  divorced          3        1        2        1     0   \n",
       "8   58       retired   married          1        0      121        1     0   \n",
       "9   43    technician    single          2        0      593        1     0   \n",
       "\n",
       "   contact  day  month  duration  campaign  pdays  previous poutcome  Target  \n",
       "0  unknown    5      5       261         1      0         0  unknown       0  \n",
       "1  unknown    5      5       151         1      0         0  unknown       0  \n",
       "2  unknown    5      5        76         1      0         0  unknown       0  \n",
       "3  unknown    5      5        92         1      0         0  unknown       0  \n",
       "4  unknown    5      5       198         1      0         0  unknown       0  \n",
       "5  unknown    5      5       139         1      0         0  unknown       0  \n",
       "6  unknown    5      5       217         1      0         0  unknown       0  \n",
       "7  unknown    5      5       380         1      0         0  unknown       0  \n",
       "8  unknown    5      5        50         1      0         0  unknown       0  \n",
       "9  unknown    5      5        55         1      0         0  unknown       0  "
      ]
     },
     "execution_count": 1983,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replacing in the data set\n",
    "BankRaw.replace(cleanup_nums, inplace=True)\n",
    "BankRaw.head(10)\n",
    "#Inference: Labelling and replacement is reflecting successfully"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1984,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
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       "      <th>...</th>\n",
       "      <th>marital_divorced</th>\n",
       "      <th>marital_married</th>\n",
       "      <th>marital_single</th>\n",
       "      <th>contact_cellular</th>\n",
       "      <th>contact_telephone</th>\n",
       "      <th>contact_unknown</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_other</th>\n",
       "      <th>poutcome_success</th>\n",
       "      <th>poutcome_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1506</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>92</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  education  default  balance  housing  loan  day  month  duration  \\\n",
       "0   58          3        0     2143        1     0    5      5       261   \n",
       "1   44          2        0       29        1     0    5      5       151   \n",
       "2   33          2        0        2        1     1    5      5        76   \n",
       "3   47         -1        0     1506        1     0    5      5        92   \n",
       "4   33         -1        0        1        0     0    5      5       198   \n",
       "\n",
       "   campaign  ...  marital_divorced  marital_married  marital_single  \\\n",
       "0         1  ...                 0                1               0   \n",
       "1         1  ...                 0                0               1   \n",
       "2         1  ...                 0                1               0   \n",
       "3         1  ...                 0                1               0   \n",
       "4         1  ...                 0                0               1   \n",
       "\n",
       "   contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
       "0                 0                  0                1                 0   \n",
       "1                 0                  0                1                 0   \n",
       "2                 0                  0                1                 0   \n",
       "3                 0                  0                1                 0   \n",
       "4                 0                  0                1                 0   \n",
       "\n",
       "   poutcome_other  poutcome_success  poutcome_unknown  \n",
       "0               0                 0                 1  \n",
       "1               0                 0                 1  \n",
       "2               0                 0                 1  \n",
       "3               0                 0                 1  \n",
       "4               0                 0                 1  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 1984,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Proceeding with one hot encoding\n",
    "#from sklearn.feature_extraction.text import CountVectorizer \n",
    "oneHotCols= [\"job\",\"marital\",\"contact\",\"poutcome\"]\n",
    "BankRaw=pd.get_dummies(BankRaw, columns=oneHotCols)\n",
    "BankRaw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1985,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45211 entries, 0 to 45210\n",
      "Data columns (total 35 columns):\n",
      "age                  45211 non-null int64\n",
      "education            45211 non-null int64\n",
      "default              45211 non-null int64\n",
      "balance              45211 non-null int64\n",
      "housing              45211 non-null int64\n",
      "loan                 45211 non-null int64\n",
      "day                  45211 non-null int64\n",
      "month                45211 non-null int64\n",
      "duration             45211 non-null int64\n",
      "campaign             45211 non-null int64\n",
      "pdays                45211 non-null int64\n",
      "previous             45211 non-null int64\n",
      "Target               45211 non-null int64\n",
      "job_admin.           45211 non-null uint8\n",
      "job_blue-collar      45211 non-null uint8\n",
      "job_entrepreneur     45211 non-null uint8\n",
      "job_housemaid        45211 non-null uint8\n",
      "job_management       45211 non-null uint8\n",
      "job_retired          45211 non-null uint8\n",
      "job_self-employed    45211 non-null uint8\n",
      "job_services         45211 non-null uint8\n",
      "job_student          45211 non-null uint8\n",
      "job_technician       45211 non-null uint8\n",
      "job_unemployed       45211 non-null uint8\n",
      "job_unknown          45211 non-null uint8\n",
      "marital_divorced     45211 non-null uint8\n",
      "marital_married      45211 non-null uint8\n",
      "marital_single       45211 non-null uint8\n",
      "contact_cellular     45211 non-null uint8\n",
      "contact_telephone    45211 non-null uint8\n",
      "contact_unknown      45211 non-null uint8\n",
      "poutcome_failure     45211 non-null uint8\n",
      "poutcome_other       45211 non-null uint8\n",
      "poutcome_success     45211 non-null uint8\n",
      "poutcome_unknown     45211 non-null uint8\n",
      "dtypes: int64(13), uint8(22)\n",
      "memory usage: 5.4 MB\n"
     ]
    }
   ],
   "source": [
    "BankRaw.info()\n",
    "#Inference: All categorical values are replaced with numeric values and same is confirmed below\n",
    "#We are all set to proceed with modelling as all required data treatments are completed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1986,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping duration column as it is highly impacting the accuracy of model (mentioned in the instructions)\n",
    "Bank=BankRaw.drop('duration',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1987,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(       age  education  default  balance  housing  loan  day  month  campaign  \\\n",
       " 24951   58          1        0     1021        0     0   18     11         1   \n",
       " 22129   55         -1        0      196        0     0   21      8         2   \n",
       " 986     50          2        0      159        1     0    7      5         2   \n",
       " 14999   43          1        0     1880        1     0   17      7         6   \n",
       " 4027    53          3        0      647        1     0   16      5         6   \n",
       " ...    ...        ...      ...      ...      ...   ...  ...    ...       ...   \n",
       " 30403   35          3        0      995        0     0    5      2         1   \n",
       " 21243   35          3        0      750        1     0   18      8         3   \n",
       " 42613   35          3        0      323        0     0   11      1         2   \n",
       " 43567   70          2        0      616        0     0   27      4         2   \n",
       " 2732    46          2        0       57        1     0   14      5         4   \n",
       " \n",
       "        pdays  ...  marital_divorced  marital_married  marital_single  \\\n",
       " 24951      0  ...                 0                1               0   \n",
       " 22129      0  ...                 0                1               0   \n",
       " 986        0  ...                 0                1               0   \n",
       " 14999      0  ...                 0                1               0   \n",
       " 4027       0  ...                 1                0               0   \n",
       " ...      ...  ...               ...              ...             ...   \n",
       " 30403      0  ...                 0                0               1   \n",
       " 21243      0  ...                 0                1               0   \n",
       " 42613      0  ...                 0                1               0   \n",
       " 43567    182  ...                 0                1               0   \n",
       " 2732       0  ...                 0                0               1   \n",
       " \n",
       "        contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
       " 24951                 0                  0                1                 0   \n",
       " 22129                 1                  0                0                 0   \n",
       " 986                   0                  0                1                 0   \n",
       " 14999                 1                  0                0                 0   \n",
       " 4027                  0                  0                1                 0   \n",
       " ...                 ...                ...              ...               ...   \n",
       " 30403                 1                  0                0                 0   \n",
       " 21243                 1                  0                0                 0   \n",
       " 42613                 1                  0                0                 0   \n",
       " 43567                 1                  0                0                 1   \n",
       " 2732                  0                  0                1                 0   \n",
       " \n",
       "        poutcome_other  poutcome_success  poutcome_unknown  \n",
       " 24951               0                 0                 1  \n",
       " 22129               0                 0                 1  \n",
       " 986                 0                 0                 1  \n",
       " 14999               0                 0                 1  \n",
       " 4027                0                 0                 1  \n",
       " ...               ...               ...               ...  \n",
       " 30403               0                 0                 1  \n",
       " 21243               0                 0                 1  \n",
       " 42613               0                 0                 1  \n",
       " 43567               0                 0                 0  \n",
       " 2732                0                 0                 1  \n",
       " \n",
       " [31647 rows x 33 columns],\n",
       "        age  education  default  balance  housing  loan  day  month  campaign  \\\n",
       " 14001   37          3        0     1403        1     0   10      7         3   \n",
       " 32046   36          2        0       72        1     0   14      4         1   \n",
       " 13318   47          1        0       72        0     1    8      7         5   \n",
       " 42991   57          1        0      501        1     1   11      2         3   \n",
       " 14237   33          2        1       72        1     1   14      7         2   \n",
       " ...    ...        ...      ...      ...      ...   ...  ...    ...       ...   \n",
       " 2666    31          3        0        0        1     1   13      5         2   \n",
       " 35730   48          2        0     1428        1     0    8      5         1   \n",
       " 11495   39          3        0        4        1     1   19      6         1   \n",
       " 20975   42          3        0     1428        0     1   14      8         2   \n",
       " 31975   38          2        0     1663        1     0   13      4         3   \n",
       " \n",
       "        pdays  ...  marital_divorced  marital_married  marital_single  \\\n",
       " 14001      0  ...                 0                1               0   \n",
       " 32046      0  ...                 0                0               1   \n",
       " 13318      0  ...                 0                1               0   \n",
       " 42991    182  ...                 0                1               0   \n",
       " 14237      0  ...                 0                1               0   \n",
       " ...      ...  ...               ...              ...             ...   \n",
       " 2666       0  ...                 0                1               0   \n",
       " 35730      0  ...                 0                1               0   \n",
       " 11495      0  ...                 0                1               0   \n",
       " 20975      0  ...                 0                1               0   \n",
       " 31975    182  ...                 0                1               0   \n",
       " \n",
       "        contact_cellular  contact_telephone  contact_unknown  poutcome_failure  \\\n",
       " 14001                 1                  0                0                 0   \n",
       " 32046                 1                  0                0                 0   \n",
       " 13318                 0                  1                0                 0   \n",
       " 42991                 1                  0                0                 1   \n",
       " 14237                 1                  0                0                 0   \n",
       " ...                 ...                ...              ...               ...   \n",
       " 2666                  0                  0                1                 0   \n",
       " 35730                 0                  1                0                 0   \n",
       " 11495                 0                  0                1                 0   \n",
       " 20975                 1                  0                0                 0   \n",
       " 31975                 1                  0                0                 1   \n",
       " \n",
       "        poutcome_other  poutcome_success  poutcome_unknown  \n",
       " 14001               0                 0                 1  \n",
       " 32046               0                 0                 1  \n",
       " 13318               0                 0                 1  \n",
       " 42991               0                 0                 0  \n",
       " 14237               0                 0                 1  \n",
       " ...               ...               ...               ...  \n",
       " 2666                0                 0                 1  \n",
       " 35730               0                 0                 1  \n",
       " 11495               0                 0                 1  \n",
       " 20975               0                 0                 1  \n",
       " 31975               0                 0                 0  \n",
       " \n",
       " [13564 rows x 33 columns],\n",
       " 24951    0\n",
       " 22129    0\n",
       " 986      0\n",
       " 14999    0\n",
       " 4027     0\n",
       "         ..\n",
       " 30403    0\n",
       " 21243    0\n",
       " 42613    1\n",
       " 43567    0\n",
       " 2732     0\n",
       " Name: Target, Length: 31647, dtype: int64,\n",
       " 14001    0\n",
       " 32046    1\n",
       " 13318    0\n",
       " 42991    0\n",
       " 14237    0\n",
       "         ..\n",
       " 2666     0\n",
       " 35730    0\n",
       " 11495    0\n",
       " 20975    0\n",
       " 31975    0\n",
       " Name: Target, Length: 13564, dtype: int64)"
      ]
     },
     "execution_count": 1987,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the training set and test set in ration of 70:30\n",
    "#Importing required libaries\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "#Drop the target 'Personal Loan' from data\n",
    "x=Bank.drop('Target',axis=1)\n",
    "y=Bank['Target']\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.70,test_size=0.30,random_state=0)\n",
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1988,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do data scaling/Normalization if required\n",
    "#This is needed we have variables in different scale ex: balance, pdays etc\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1989,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In below steps I will be applying multiple models by fitting train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1990,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the decision tree model using “entropy” method of finding the split columns and fit it to training data.\n",
    "# invoking the decision tree classifier function. Using 'entropy' method of finding the split columns.  \n",
    "#Taking the depth of the tree as 5  randomly\n",
    "model_entropy=DecisionTreeClassifier(criterion='entropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1991,
   "metadata": {},
   "outputs": [],
   "source": [
    "entropy=model_entropy.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1992,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 1992,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the accuracy of the model & print the confusion matrix.\n",
    "model_entropy.score(x_train, y_train)# performance on train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1993,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8373636095547037"
      ]
     },
     "execution_count": 1993,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_entropy.score(x_test, y_test) # performance on test data indicates the overfitting by ~17%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1994,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_pred = entropy.predict(x_test)\n",
    "acc_DT = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1995,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10824,  1145],\n",
       "       [ 1061,   534]])"
      ]
     },
     "execution_count": 1995,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_entropy= confusion_matrix(y_test, y_pred)\n",
    "cm_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1996,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.90      0.91     11969\n",
      "           1       0.32      0.33      0.33      1595\n",
      "\n",
      "    accuracy                           0.84     13564\n",
      "   macro avg       0.61      0.62      0.62     13564\n",
      "weighted avg       0.84      0.84      0.84     13564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Classification Report to evaluate model performance\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 90% of customers who may reject the Term Deposit if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the Term Deposit offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 33% as potential customers who may opt for Term Deposit if offered. \n",
    "    # Precision and recall value for potential Term Deposit accepters is not that great.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1997,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=100, splitter='best')"
      ]
     },
     "execution_count": 1997,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#There is a slight overfitting in the model due to which the test accuracy drops by ~17%\n",
    "#Regularize/prune the decision tree by limiting the max. depth of trees and print the accuracy\n",
    "clf_pruned = DecisionTreeClassifier(criterion = \"entropy\", random_state = 100,\n",
    "                               max_depth=3, min_samples_leaf=5)\n",
    "clf_pruned.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1998,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_pruned = clf_pruned.predict(x_test)\n",
    "preds_pruned_train = clf_pruned.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1999,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8902978472427012\n",
      "0.8944923689449237\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test,preds_pruned))\n",
    "print(accuracy_score(y_train,preds_pruned_train))\n",
    "#Inference: We can observe that at max depth=3, model performance is good and both train and test data \n",
    "#shows consistent accuracy of ~89%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2000,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11796,   173],\n",
       "       [ 1315,   280]])"
      ]
     },
     "execution_count": 2000,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Lets print confusion matrix and check\n",
    "#Printing confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_entropy_pruned= confusion_matrix(y_test, y_pred_pruned)\n",
    "cm_entropy_pruned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2001,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94     11969\n",
      "           1       0.62      0.18      0.27      1595\n",
      "\n",
      "    accuracy                           0.89     13564\n",
      "   macro avg       0.76      0.58      0.61     13564\n",
      "weighted avg       0.87      0.89      0.86     13564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Classification Report to evaluate model performance\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred_pruned))\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 99% of customers who may reject the Term Deposit if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the Term Deposit offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 18% as potential customers who may opt for Term Deposit if offered. \n",
    "    # Precision and recall value for potential Term Deposit accepters is not that great.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2020,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_DT = accuracy_score(y_test, y_pred_pruned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2003,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.890298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  accuracy\n",
       "0  Decision Tree  0.890298"
      ]
     },
     "execution_count": 2003,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Store the accuracy results for each model in a dataframe for final comparison\n",
    "resultsDf = pd.DataFrame({'Method':['Decision Tree'], 'accuracy': acc_DT})\n",
    "resultsDf = resultsDf[['Method', 'accuracy']]\n",
    "resultsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2004,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply the Random forest model and print the accuracy of Random forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfcl = RandomForestClassifier(n_estimators = 50)\n",
    "rfcl = rfcl.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2005,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9989256485606851, 0.8905927455028015)"
      ]
     },
     "execution_count": 2005,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfcl.score(x_train, y_train),rfcl.score(x_test, y_test)\n",
    "#The model is overfitting by ~10%. Lets print confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2006,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11756,   213],\n",
       "       [ 1271,   324]])"
      ]
     },
     "execution_count": 2006,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing confusion matrix\n",
    "y_pred = rfcl.predict(x_test)\n",
    "acc_RF = accuracy_score(y_test, y_pred)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_RFCL = confusion_matrix(y_test, y_pred)\n",
    "cm_RFCL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2007,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     11969\n",
      "           1       0.60      0.20      0.30      1595\n",
      "\n",
      "    accuracy                           0.89     13564\n",
      "   macro avg       0.75      0.59      0.62     13564\n",
      "weighted avg       0.87      0.89      0.87     13564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Classification Report to evaluate model performance\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 98% of customers who may reject the Term Deposit if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the Term Deposit offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 20%% as potential customers who may opt for Term Deposit if offered. \n",
    "    # Precision and recall value for potential Term Deposit accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2008,
   "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>Method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.890298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.890593</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  accuracy\n",
       "0  Decision Tree  0.890298\n",
       "0  Random Forest  0.890593"
      ]
     },
     "execution_count": 2008,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Storing results in table for further comparision\n",
    "tempResultsDf = pd.DataFrame({'Method':['Random Forest'], 'accuracy': [acc_RF]})\n",
    "resultsDf = pd.concat([resultsDf, tempResultsDf])\n",
    "resultsDf = resultsDf[['Method', 'accuracy']]\n",
    "resultsDf\n",
    "resultsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2009,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply Bagging Classifier Algorithm and print the accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2010,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier\n",
    "bgcl = BaggingClassifier(n_estimators=50, max_samples= .7, bootstrap=True, oob_score=True, random_state=22)\n",
    "bgcl = bgcl.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2011,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9824311941100262, 0.8905927455028015)"
      ]
     },
     "execution_count": 2011,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the model is overfitting\n",
    "bgcl.score(x_train, y_train),rfcl.score(x_test, y_test)\n",
    "#The model is overfitting by ~9%. Lets print confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2012,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11675,   294],\n",
       "       [ 1200,   395]])"
      ]
     },
     "execution_count": 2012,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing confusion matrix\n",
    "y_pred =bgcl.predict(x_test)\n",
    "acc_BG = accuracy_score(y_test, y_pred)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_bgcl = confusion_matrix(y_test, y_pred)\n",
    "cm_bgcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2013,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.98      0.94     11969\n",
      "           1       0.57      0.25      0.35      1595\n",
      "\n",
      "    accuracy                           0.89     13564\n",
      "   macro avg       0.74      0.61      0.64     13564\n",
      "weighted avg       0.87      0.89      0.87     13564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Print Classification Report to evaluate the model performance\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 98% of customers who may reject the Term Deposit if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the Term Deposit offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 25% as potential customers who may opt for Term Deposit if offered. \n",
    "    # Precision and recall value for potential Term Deposit accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2014,
   "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>Method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.890298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.890593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.889855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  accuracy\n",
       "0  Decision Tree  0.890298\n",
       "0  Random Forest  0.890593\n",
       "0        Bagging  0.889855"
      ]
     },
     "execution_count": 2014,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempResultsDf = pd.DataFrame({'Method':['Bagging'], 'accuracy': [acc_BG]})\n",
    "resultsDf = pd.concat([resultsDf, tempResultsDf])\n",
    "resultsDf = resultsDf[['Method', 'accuracy']]\n",
    "resultsDf\n",
    "resultsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2015,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply GradientBoost Classifier Algorithm for the same data and print the accuracy\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbcl = GradientBoostingClassifier(n_estimators = 50, learning_rate = 0.1, random_state=22)\n",
    "gbcl = gbcl.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2016,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8963566846778526, 0.8921409613683279)"
      ]
     },
     "execution_count": 2016,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking if the model is overfitting\n",
    "gbcl.score(x_train, y_train),gbcl.score(x_test, y_test)\n",
    "#From below score we can conclude that model is not overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2017,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11816,   153],\n",
       "       [ 1310,   285]])"
      ]
     },
     "execution_count": 2017,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing confusion matrix\n",
    "y_pred =gbcl.predict(x_test)\n",
    "acc_GB = accuracy_score(y_test, y_pred)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm_gbcl = confusion_matrix(y_test, y_pred)\n",
    "cm_gbcl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2018,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.99      0.94     11969\n",
      "           1       0.65      0.18      0.28      1595\n",
      "\n",
      "    accuracy                           0.89     13564\n",
      "   macro avg       0.78      0.58      0.61     13564\n",
      "weighted avg       0.87      0.89      0.86     13564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))\n",
    "#Inference: Based on below report We could conclude below things:\n",
    "#1. Model is identifying correctly 99% of customers who may reject the Term Deposit if offerred to them. \n",
    "    #Hence model is good in idenifying the customers who may tend to reject the Term Deposit offered as precision and recall is high for value 'O'\n",
    "#2. Model is identifying correctly 18%% as potential customers who may opt for Term Deposit if offered. \n",
    "    # Precision and recall value for potential Term Deposit accepters is not that great."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2019,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.890298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.890593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.889855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Gradient Boost</td>\n",
       "      <td>0.892141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Method  accuracy\n",
       "0   Decision Tree  0.890298\n",
       "0   Random Forest  0.890593\n",
       "0         Bagging  0.889855\n",
       "0  Gradient Boost  0.892141"
      ]
     },
     "execution_count": 2019,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempResultsDf = pd.DataFrame({'Method':['Gradient Boost'], 'accuracy': [acc_GB]})\n",
    "resultsDf = pd.concat([resultsDf, tempResultsDf])\n",
    "resultsDf = resultsDf[['Method', 'accuracy']]\n",
    "resultsDf\n",
    "resultsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1911,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Overall Conclusion:\n",
    "#From the above analysis, I feel that Gradient Booster seems to be comparatively good model for below reasons:\n",
    "# 1. It has 0% overfitting/underfitting as ~0% bias/variance  is observed across train data and test data and accuracy is also same (ie., 89%)\n",
    "# 2. Overall model accuracy is 89.21% which is compartively more than any other model:Decision Tree, Random Forest, Bagger present above\n",
    "# 3. Eventhough recall in identifying customers who may opt for Term Deposit is slightly less (18%), precision  is more 65% for it\n",
    "# compared to any other model\n",
    "#4. This model is consistent and accurate in identifying the customers who may not opt for Term Deposit (precision 90%, recall 99%)\n",
    "# compared to any other model\n"
   ]
  }
 ],
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