Assignment No.6 New.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=pd.read_csv(\"bank-full.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>508</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2971</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>other</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 17 columns</p>\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",
       "45206   51    technician   married   tertiary      no      825      no   no   \n",
       "45207   71       retired  divorced    primary      no     1729      no   no   \n",
       "45208   72       retired   married  secondary      no     5715      no   no   \n",
       "45209   57   blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37  entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
       "         contact  day month  duration  campaign  pdays  previous poutcome  \\\n",
       "0        unknown    5   may       261         1     -1         0  unknown   \n",
       "1        unknown    5   may       151         1     -1         0  unknown   \n",
       "2        unknown    5   may        76         1     -1         0  unknown   \n",
       "3        unknown    5   may        92         1     -1         0  unknown   \n",
       "4        unknown    5   may       198         1     -1         0  unknown   \n",
       "...          ...  ...   ...       ...       ...    ...       ...      ...   \n",
       "45206   cellular   17   nov       977         3     -1         0  unknown   \n",
       "45207   cellular   17   nov       456         2     -1         0  unknown   \n",
       "45208   cellular   17   nov      1127         5    184         3  success   \n",
       "45209  telephone   17   nov       508         4     -1         0  unknown   \n",
       "45210   cellular   17   nov       361         2    188        11    other   \n",
       "\n",
       "      Target  \n",
       "0         no  \n",
       "1         no  \n",
       "2         no  \n",
       "3         no  \n",
       "4         no  \n",
       "...      ...  \n",
       "45206    yes  \n",
       "45207    yes  \n",
       "45208    yes  \n",
       "45209     no  \n",
       "45210     no  \n",
       "\n",
       "[45211 rows x 17 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['job']=a['job'].astype('category')\n",
    "a['marital']=a['marital'].astype('category')\n",
    "a['education']=a['education'].astype('category')\n",
    "a['default']=a['default'].astype('category')\n",
    "a['housing']=a['housing'].astype('category')\n",
    "a['loan']=a['loan'].astype('category')\n",
    "a['contact']=a['contact'].astype('category')\n",
    "a['poutcome']=a['poutcome'].astype('category')\n",
    "a['Target']=a['Target'].astype('category')\n",
    "a['month']=a['month'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   age        45211 non-null  int64   \n",
      " 1   job        45211 non-null  category\n",
      " 2   marital    45211 non-null  category\n",
      " 3   education  45211 non-null  category\n",
      " 4   default    45211 non-null  category\n",
      " 5   balance    45211 non-null  int64   \n",
      " 6   housing    45211 non-null  category\n",
      " 7   loan       45211 non-null  category\n",
      " 8   contact    45211 non-null  category\n",
      " 9   day        45211 non-null  int64   \n",
      " 10  month      45211 non-null  category\n",
      " 11  duration   45211 non-null  int64   \n",
      " 12  campaign   45211 non-null  int64   \n",
      " 13  pdays      45211 non-null  int64   \n",
      " 14  previous   45211 non-null  int64   \n",
      " 15  poutcome   45211 non-null  category\n",
      " 16  Target     45211 non-null  category\n",
      "dtypes: category(10), int64(7)\n",
      "memory usage: 2.8 MB\n"
     ]
    }
   ],
   "source": [
    "a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(x):\n",
    "    a[x]=a[x].replace('unknown',0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('job')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('contact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('poutcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blue-collar      9732\n",
       "management       9458\n",
       "technician       7597\n",
       "admin.           5171\n",
       "services         4154\n",
       "retired          2264\n",
       "self-employed    1579\n",
       "entrepreneur     1487\n",
       "unemployed       1303\n",
       "housemaid        1240\n",
       "student           938\n",
       "0                 288\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(y):\n",
    "    a[y]=a[y].replace('blue-collar',1)\n",
    "    a[y]=a[y].replace('management',2)\n",
    "    a[y]=a[y].replace('technician',3)\n",
    "    a[y]=a[y].replace('admin.',4)\n",
    "    a[y]=a[y].replace('services',5)\n",
    "    a[y]=a[y].replace('retired',6)\n",
    "    a[y]=a[y].replace('self-employed',7)\n",
    "    a[y]=a[y].replace('entrepreneur',8)\n",
    "    a[y]=a[y].replace('unemployed',9)\n",
    "    a[y]=a[y].replace('housemaid',10)\n",
    "    a[y]=a[y].replace('student',11)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('job')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     9732\n",
       "2     9458\n",
       "3     7597\n",
       "4     5171\n",
       "5     4154\n",
       "6     2264\n",
       "7     1579\n",
       "8     1487\n",
       "9     1303\n",
       "10    1240\n",
       "11     938\n",
       "0      288\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "married     27214\n",
       "single      12790\n",
       "divorced     5207\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.marital.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(i):\n",
    "    a[i]=a[i].replace('married',1)\n",
    "    a[i]=a[i].replace('single',2)\n",
    "    a[i]=a[i].replace('divorced',3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('marital')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    27214\n",
       "2    12790\n",
       "3     5207\n",
       "Name: marital, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.marital.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary    23202\n",
       "tertiary     13301\n",
       "primary       6851\n",
       "0             1857\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(j):\n",
    "    a[j]=a[j].replace('secondary',1)\n",
    "    a[j]=a[j].replace('tertiary',2)\n",
    "    a[j]=a[j].replace('primary',3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    23202\n",
       "2    13301\n",
       "3     6851\n",
       "0     1857\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.education.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     44396\n",
       "yes      815\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(k):\n",
    "    a[k]=a[k].replace('no',1)\n",
    "    a[k]=a[k].replace('yes',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('default')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    44396\n",
       "2      815\n",
       "Name: default, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.default.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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",
      " #   Column     Non-Null Count  Dtype   \n",
      "---  ------     --------------  -----   \n",
      " 0   age        45211 non-null  int64   \n",
      " 1   job        45211 non-null  category\n",
      " 2   marital    45211 non-null  category\n",
      " 3   education  45211 non-null  category\n",
      " 4   default    45211 non-null  category\n",
      " 5   balance    45211 non-null  int64   \n",
      " 6   housing    45211 non-null  category\n",
      " 7   loan       45211 non-null  category\n",
      " 8   contact    45211 non-null  category\n",
      " 9   day        45211 non-null  int64   \n",
      " 10  month      45211 non-null  category\n",
      " 11  duration   45211 non-null  int64   \n",
      " 12  campaign   45211 non-null  int64   \n",
      " 13  pdays      45211 non-null  int64   \n",
      " 14  previous   45211 non-null  int64   \n",
      " 15  poutcome   45211 non-null  category\n",
      " 16  Target     45211 non-null  category\n",
      "dtypes: category(10), int64(7)\n",
      "memory usage: 2.8 MB\n"
     ]
    }
   ],
   "source": [
    "a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    25130\n",
       "no     20081\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.housing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(l):\n",
    "    a[l]=a[l].replace('no',1)\n",
    "    a[l]=a[l].replace('yes',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('housing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    25130\n",
       "1    20081\n",
       "Name: housing, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.housing.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     37967\n",
       "yes     7244\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.loan.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(m):\n",
    "    a[m]=a[m].replace('no',1)\n",
    "    a[m]=a[m].replace('yes',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('loan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    37967\n",
       "2     7244\n",
       "Name: loan, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.loan.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cellular     29285\n",
       "0            13020\n",
       "telephone     2906\n",
       "Name: contact, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.contact.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(n):\n",
    "    a[n]=a[n].replace('cellular',1)\n",
    "    a[n]=a[n].replace('telephone',2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('contact')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    29285\n",
       "0    13020\n",
       "2     2906\n",
       "Name: contact, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.contact.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "may    13766\n",
       "jul     6895\n",
       "aug     6247\n",
       "jun     5341\n",
       "nov     3970\n",
       "apr     2932\n",
       "feb     2649\n",
       "jan     1403\n",
       "oct      738\n",
       "sep      579\n",
       "mar      477\n",
       "dec      214\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(o):\n",
    "    a[o]=a[o].replace('jan',1)\n",
    "    a[o]=a[o].replace('feb',2)\n",
    "    a[o]=a[o].replace('mar',3)\n",
    "    a[o]=a[o].replace('apr',4)\n",
    "    a[o]=a[o].replace('may',5)\n",
    "    a[o]=a[o].replace('jun',6)\n",
    "    a[o]=a[o].replace('jul',7)\n",
    "    a[o]=a[o].replace('aug',8)\n",
    "    a[o]=a[o].replace('sep',9)\n",
    "    a[o]=a[o].replace('oct',10)\n",
    "    a[o]=a[o].replace('nov',11)\n",
    "    a[o]=a[o].replace('dec',12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     13766\n",
       "7      6895\n",
       "8      6247\n",
       "6      5341\n",
       "11     3970\n",
       "4      2932\n",
       "2      2649\n",
       "1      1403\n",
       "10      738\n",
       "9       579\n",
       "3       477\n",
       "12      214\n",
       "Name: month, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.month.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          36959\n",
       "failure     4901\n",
       "other       1840\n",
       "success     1511\n",
       "Name: poutcome, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.poutcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(p):\n",
    "    a[p]=a[p].replace('failure',1)\n",
    "    a[p]=a[p].replace('success',2)\n",
    "    a[p]=a[p].replace('other',3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('poutcome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    36959\n",
       "1     4901\n",
       "3     1840\n",
       "2     1511\n",
       "Name: poutcome, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.poutcome.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     39922\n",
       "yes     5289\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.Target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(q):\n",
    "    a[q]=a[q].replace('no',0)\n",
    "    a[q]=a[q].replace('yes',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn('Target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    39922\n",
       "1     5289\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.Target.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "a['job']=pd.to_numeric(a['job'],errors='coerce')\n",
    "a['marital']=pd.to_numeric(a['marital'],errors='coerce')\n",
    "a['education']=pd.to_numeric(a['education'],errors='coerce')\n",
    "a['default']=pd.to_numeric(a['default'],errors='coerce')\n",
    "a['housing']=pd.to_numeric(a['housing'],errors='coerce')\n",
    "a['loan']=pd.to_numeric(a['loan'],errors='coerce')\n",
    "a['month']=pd.to_numeric(a['month'],errors='coerce')\n",
    "a['poutcome']=pd.to_numeric(a['poutcome'],errors='coerce')\n",
    "a['Target']=pd.to_numeric(a['Target'],errors='coerce')\n",
    "a['contact']=pd.to_numeric(a['contact'],errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   age        45211 non-null  int64\n",
      " 1   job        45211 non-null  int64\n",
      " 2   marital    45211 non-null  int64\n",
      " 3   education  45211 non-null  int64\n",
      " 4   default    45211 non-null  int64\n",
      " 5   balance    45211 non-null  int64\n",
      " 6   housing    45211 non-null  int64\n",
      " 7   loan       45211 non-null  int64\n",
      " 8   contact    45211 non-null  int64\n",
      " 9   day        45211 non-null  int64\n",
      " 10  month      45211 non-null  int64\n",
      " 11  duration   45211 non-null  int64\n",
      " 12  campaign   45211 non-null  int64\n",
      " 13  pdays      45211 non-null  int64\n",
      " 14  previous   45211 non-null  int64\n",
      " 15  poutcome   45211 non-null  int64\n",
      " 16  Target     45211 non-null  int64\n",
      "dtypes: int64(17)\n",
      "memory usage: 5.9 MB\n"
     ]
    }
   ],
   "source": [
    "a.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2143</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2</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>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1506</td>\n",
       "      <td>2</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>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>198</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>825</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>977</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1729</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>456</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5715</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>1127</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>668</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>508</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2971</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       58    2        1          2        1     2143        2     1        0   \n",
       "1       44    3        2          1        1       29        2     1        0   \n",
       "2       33    8        1          1        1        2        2     2        0   \n",
       "3       47    1        1          0        1     1506        2     1        0   \n",
       "4       33    0        2          0        1        1        1     1        0   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "45206   51    3        1          2        1      825        1     1        1   \n",
       "45207   71    6        3          3        1     1729        1     1        1   \n",
       "45208   72    6        1          1        1     5715        1     1        1   \n",
       "45209   57    1        1          1        1      668        1     1        2   \n",
       "45210   37    8        1          1        1     2971        1     1        1   \n",
       "\n",
       "       day  month  duration  campaign  pdays  previous  poutcome  \n",
       "0        5      5       261         1     -1         0         0  \n",
       "1        5      5       151         1     -1         0         0  \n",
       "2        5      5        76         1     -1         0         0  \n",
       "3        5      5        92         1     -1         0         0  \n",
       "4        5      5       198         1     -1         0         0  \n",
       "...    ...    ...       ...       ...    ...       ...       ...  \n",
       "45206   17     11       977         3     -1         0         0  \n",
       "45207   17     11       456         2     -1         0         0  \n",
       "45208   17     11      1127         5    184         3         2  \n",
       "45209   17     11       508         4     -1         0         0  \n",
       "45210   17     11       361         2    188        11         3  \n",
       "\n",
       "[45211 rows x 16 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=a.drop('Target', axis= 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45211 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Target\n",
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           0\n",
       "...       ...\n",
       "45206       1\n",
       "45207       1\n",
       "45208       1\n",
       "45209       0\n",
       "45210       0\n",
       "\n",
       "[45211 rows x 1 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=a[['Target']]\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ROHAN\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n",
      "C:\\Users\\ROHAN\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "pre=model.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf=confusion_matrix(Y,pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39235,   687],\n",
       "       [ 4426,   863]], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8851707902802793"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_train ,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94     39922\n",
      "           1       0.56      0.16      0.25      5289\n",
      "\n",
      "    accuracy                           0.89     45211\n",
      "   macro avg       0.73      0.57      0.60     45211\n",
      "weighted avg       0.86      0.89      0.86     45211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(Y,pre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auc: 0.57298\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "pre=model.predict(X)\n",
    "fpr, tpr, thresholds = roc_curve(Y, model.predict_proba(X)[:,1])\n",
    "auc=roc_auc_score(Y,pre)\n",
    "\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.3f)')\n",
    "plt.plot([0, 1], [0, 1],'k--')\n",
    "\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "\n",
    "plt.show()\n",
    "print('auc:',np.round(auc,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}