assignment logistic regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score,roc_curve\n",
    "import statsmodels.api as smf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>pdays</th>\n",
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       "      <th>poutcome</th>\n",
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       "      <th>0</th>\n",
       "      <td>58</td>\n",
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       "      <td>tertiary</td>\n",
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       "<p>45211 rows × 17 columns</p>\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",
       "...    ...           ...       ...        ...     ...      ...     ...  ...   \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]"
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     },
     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "data=pd.read_csv('bank-full.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "  </tbody>\n",
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       "<p>45211 rows × 16 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 month  duration  campaign  pdays  previous poutcome Target  \n",
       "0        unknown   may       261         1     -1         0  unknown     no  \n",
       "1        unknown   may       151         1     -1         0  unknown     no  \n",
       "2        unknown   may        76         1     -1         0  unknown     no  \n",
       "3        unknown   may        92         1     -1         0  unknown     no  \n",
       "4        unknown   may       198         1     -1         0  unknown     no  \n",
       "...          ...   ...       ...       ...    ...       ...      ...    ...  \n",
       "45206   cellular   nov       977         3     -1         0  unknown    yes  \n",
       "45207   cellular   nov       456         2     -1         0  unknown    yes  \n",
       "45208   cellular   nov      1127         5    184         3  success    yes  \n",
       "45209  telephone   nov       508         4     -1         0  unknown     no  \n",
       "45210   cellular   nov       361         2    188        11    other     no  \n",
       "\n",
       "[45211 rows x 16 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data.drop(['day'],axis=1)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "...    ...           ...       ...        ...     ...      ...     ...  ...   \n",
       "45206   51    technician   married   tertiary      no      825      no   no   \n",
       "45207   71       retired  divorced    primary      no     1729      no   no   \n",
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       "45209   57   blue-collar   married  secondary      no      668      no   no   \n",
       "45210   37  entrepreneur   married  secondary      no     2971      no   no   \n",
       "\n",
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       "...          ...       ...       ...    ...       ...      ...    ...  \n",
       "45206   cellular       977         3     -1         0  unknown    yes  \n",
       "45207   cellular       456         2     -1         0  unknown    yes  \n",
       "45208   cellular      1127         5    184         3  success    yes  \n",
       "45209  telephone       508         4     -1         0  unknown     no  \n",
       "45210   cellular       361         2    188        11    other     no  \n",
       "\n",
       "[45211 rows x 15 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=data1.drop(['month'],axis=1)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>45211 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "       age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0       58    4        1          2        0     2143        1     0        2   \n",
       "1       44    9        2          1        0       29        1     0        2   \n",
       "2       33    2        1          1        0        2        1     1        2   \n",
       "3       47    1        1          3        0     1506        1     0        2   \n",
       "4       33   11        2          3        0        1        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "45206   51    9        1          2        0      825        0     0        0   \n",
       "45207   71    5        0          0        0     1729        0     0        0   \n",
       "45208   72    5        1          1        0     5715        0     0        0   \n",
       "45209   57    1        1          1        0      668        0     0        1   \n",
       "45210   37    2        1          1        0     2971        0     0        0   \n",
       "\n",
       "       duration  campaign  pdays  previous  poutcome  Target  \n",
       "0           261         1     -1         0         3       0  \n",
       "1           151         1     -1         0         3       0  \n",
       "2            76         1     -1         0         3       0  \n",
       "3            92         1     -1         0         3       0  \n",
       "4           198         1     -1         0         3       0  \n",
       "...         ...       ...    ...       ...       ...     ...  \n",
       "45206       977         3     -1         0         3       1  \n",
       "45207       456         2     -1         0         3       1  \n",
       "45208      1127         5    184         3         2       1  \n",
       "45209       508         4     -1         0         3       0  \n",
       "45210       361         2    188        11         1       0  \n",
       "\n",
       "[45211 rows x 15 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label=preprocessing.LabelEncoder()\n",
    "data2['job']=label.fit_transform(data.job)\n",
    "data2['marital']=label.fit_transform(data.marital)\n",
    "data2['education']=label.fit_transform(data.education)\n",
    "data2['default']=label.fit_transform(data.default)\n",
    "data2['housing']=label.fit_transform(data.housing)\n",
    "data2['loan']=label.fit_transform(data.loan)\n",
    "data2['contact']=label.fit_transform(data.contact)\n",
    "data2['poutcome']=label.fit_transform(data.poutcome)\n",
    "data2['Target']=label.fit_transform(data.Target)\n",
    "data2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "Target       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>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>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>4.339762</td>\n",
       "      <td>1.167725</td>\n",
       "      <td>1.224813</td>\n",
       "      <td>0.018027</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>0.555838</td>\n",
       "      <td>0.160226</td>\n",
       "      <td>0.640242</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "      <td>2.559974</td>\n",
       "      <td>0.116985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3.272657</td>\n",
       "      <td>0.608230</td>\n",
       "      <td>0.747997</td>\n",
       "      <td>0.133049</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>0.496878</td>\n",
       "      <td>0.366820</td>\n",
       "      <td>0.897951</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "      <td>0.989059</td>\n",
       "      <td>0.321406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age           job       marital     education       default  \\\n",
       "count  45211.000000  45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210      4.339762      1.167725      1.224813      0.018027   \n",
       "std       10.618762      3.272657      0.608230      0.747997      0.133049   \n",
       "min       18.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%       33.000000      1.000000      1.000000      1.000000      0.000000   \n",
       "50%       39.000000      4.000000      1.000000      1.000000      0.000000   \n",
       "75%       48.000000      7.000000      2.000000      2.000000      0.000000   \n",
       "max       95.000000     11.000000      2.000000      3.000000      1.000000   \n",
       "\n",
       "             balance       housing          loan       contact      duration  \\\n",
       "count   45211.000000  45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean     1362.272058      0.555838      0.160226      0.640242    258.163080   \n",
       "std      3044.765829      0.496878      0.366820      0.897951    257.527812   \n",
       "min     -8019.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        72.000000      0.000000      0.000000      0.000000    103.000000   \n",
       "50%       448.000000      1.000000      0.000000      0.000000    180.000000   \n",
       "75%      1428.000000      1.000000      0.000000      2.000000    319.000000   \n",
       "max    102127.000000      1.000000      1.000000      2.000000   4918.000000   \n",
       "\n",
       "           campaign         pdays      previous      poutcome        Target  \n",
       "count  45211.000000  45211.000000  45211.000000  45211.000000  45211.000000  \n",
       "mean       2.763841     40.197828      0.580323      2.559974      0.116985  \n",
       "std        3.098021    100.128746      2.303441      0.989059      0.321406  \n",
       "min        1.000000     -1.000000      0.000000      0.000000      0.000000  \n",
       "25%        1.000000     -1.000000      0.000000      3.000000      0.000000  \n",
       "50%        2.000000     -1.000000      0.000000      3.000000      0.000000  \n",
       "75%        3.000000     -1.000000      0.000000      3.000000      0.000000  \n",
       "max       63.000000    871.000000    275.000000      3.000000      1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Target', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "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(data.Target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Target'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "pd.crosstab(data2.Target,data2.job).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>marital</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4585</td>\n",
       "      <td>24459</td>\n",
       "      <td>10878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>622</td>\n",
       "      <td>2755</td>\n",
       "      <td>1912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "marital     0      1      2\n",
       "Target                     \n",
       "0        4585  24459  10878\n",
       "1         622   2755   1912"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data2.Target,data2.marital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>education</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6260</td>\n",
       "      <td>20752</td>\n",
       "      <td>11305</td>\n",
       "      <td>1605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>591</td>\n",
       "      <td>2450</td>\n",
       "      <td>1996</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "education     0      1      2     3\n",
       "Target                             \n",
       "0          6260  20752  11305  1605\n",
       "1           591   2450   1996   252"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(data2.Target,data2.education)"
   ]
  },
  {
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   "execution_count": 17,
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     "execution_count": 17,
     "metadata": {},
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    "x"
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   "metadata": {},
   "outputs": [
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   "execution_count": 19,
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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     "text": [
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: 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"
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       "LogisticRegression()"
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     "execution_count": 19,
     "metadata": {},
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   "source": [
    "model=LogisticRegression()\n",
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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   "execution_count": 26,
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   "outputs": [
    {
     "data": {
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       "array([[0.95049147, 0.04950853],\n",
       "       [0.9486273 , 0.0513727 ],\n",
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     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>668</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>508</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45210</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>2</td>\n",
       "      <td>188</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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    4        1          2        0     2143        1     0        2   \n",
       "1       44    9        2          1        0       29        1     0        2   \n",
       "2       33    2        1          1        0        2        1     1        2   \n",
       "3       47    1        1          3        0     1506        1     0        2   \n",
       "4       33   11        2          3        0        1        0     0        2   \n",
       "...    ...  ...      ...        ...      ...      ...      ...   ...      ...   \n",
       "45206   51    9        1          2        0      825        0     0        0   \n",
       "45207   71    5        0          0        0     1729        0     0        0   \n",
       "45208   72    5        1          1        0     5715        0     0        0   \n",
       "45209   57    1        1          1        0      668        0     0        1   \n",
       "45210   37    2        1          1        0     2971        0     0        0   \n",
       "\n",
       "       duration  campaign  pdays  previous  poutcome  Target  y_pred  \n",
       "0           261         1     -1         0         3       0       0  \n",
       "1           151         1     -1         0         3       0       0  \n",
       "2            76         1     -1         0         3       0       0  \n",
       "3            92         1     -1         0         3       0       0  \n",
       "4           198         1     -1         0         3       0       0  \n",
       "...         ...       ...    ...       ...       ...     ...     ...  \n",
       "45206       977         3     -1         0         3       1       1  \n",
       "45207       456         2     -1         0         3       1       0  \n",
       "45208      1127         5    184         3         2       1       0  \n",
       "45209       508         4     -1         0         3       0       0  \n",
       "45210       361         2    188        11         1       0       1  \n",
       "\n",
       "[45211 rows x 16 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['y_pred']=y_pred\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39120,   802],\n",
       "       [ 4262,  1027]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_mat=confusion_matrix(y,y_pred)\n",
    "con_mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>col_0</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39120</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4262</td>\n",
       "      <td>1027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0       0     1\n",
       "Target             \n",
       "0       39120   802\n",
       "1        4262  1027"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(y,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8879918603879586"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=sum(y==y_pred)/data2.shape[-2]\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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      0.19      0.29      5289\n",
      "\n",
      "    accuracy                           0.89     45211\n",
      "   macro avg       0.73      0.59      0.61     45211\n",
      "weighted avg       0.86      0.89      0.86     45211\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5870437095198181"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_score=roc_auc_score(y,y_pred)\n",
    "roc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y,model.predict_proba(x)[:,1]) \n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area=%0.2f)'% roc_score)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2=smf.Logit(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.277175\n",
      "         Iterations 8\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Logit Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Target</td>      <th>  No. Observations:  </th>  <td> 45211</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                 <td>Logit</td>      <th>  Df Residuals:      </th>  <td> 45197</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    13</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 17 Feb 2021</td> <th>  Pseudo R-squ.:     </th>  <td>0.2319</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>16:11:29</td>     <th>  Log-Likelihood:    </th> <td> -12531.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>converged:</th>             <td>True</td>       <th>  LL-Null:           </th> <td> -16315.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th>  <td> 0.000</td> \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.0254</td> <td>    0.001</td> <td>  -20.459</td> <td> 0.000</td> <td>   -0.028</td> <td>   -0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>job</th>       <td>   -0.0177</td> <td>    0.005</td> <td>   -3.447</td> <td> 0.001</td> <td>   -0.028</td> <td>   -0.008</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>marital</th>   <td>   -0.2102</td> <td>    0.026</td> <td>   -8.184</td> <td> 0.000</td> <td>   -0.261</td> <td>   -0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>education</th> <td>    0.0181</td> <td>    0.022</td> <td>    0.818</td> <td> 0.413</td> <td>   -0.025</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>default</th>   <td>   -0.4219</td> <td>    0.159</td> <td>   -2.657</td> <td> 0.008</td> <td>   -0.733</td> <td>   -0.111</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>balance</th>   <td> 2.009e-05</td> <td> 4.52e-06</td> <td>    4.447</td> <td> 0.000</td> <td> 1.12e-05</td> <td>  2.9e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>housing</th>   <td>   -1.2798</td> <td>    0.036</td> <td>  -35.673</td> <td> 0.000</td> <td>   -1.350</td> <td>   -1.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>loan</th>      <td>   -0.8335</td> <td>    0.056</td> <td>  -14.813</td> <td> 0.000</td> <td>   -0.944</td> <td>   -0.723</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>contact</th>   <td>   -0.5823</td> <td>    0.026</td> <td>  -22.565</td> <td> 0.000</td> <td>   -0.633</td> <td>   -0.532</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>duration</th>  <td>    0.0038</td> <td> 5.99e-05</td> <td>   63.136</td> <td> 0.000</td> <td>    0.004</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>campaign</th>  <td>   -0.1807</td> <td>    0.010</td> <td>  -17.264</td> <td> 0.000</td> <td>   -0.201</td> <td>   -0.160</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>pdays</th>     <td> 8.798e-05</td> <td>    0.000</td> <td>    0.390</td> <td> 0.696</td> <td>   -0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>previous</th>  <td>    0.0537</td> <td>    0.008</td> <td>    6.771</td> <td> 0.000</td> <td>    0.038</td> <td>    0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>poutcome</th>  <td>   -0.2523</td> <td>    0.022</td> <td>  -11.679</td> <td> 0.000</td> <td>   -0.295</td> <td>   -0.210</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Logit Regression Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                 Target   No. Observations:                45211\n",
       "Model:                          Logit   Df Residuals:                    45197\n",
       "Method:                           MLE   Df Model:                           13\n",
       "Date:                Wed, 17 Feb 2021   Pseudo R-squ.:                  0.2319\n",
       "Time:                        16:11:29   Log-Likelihood:                -12531.\n",
       "converged:                       True   LL-Null:                       -16315.\n",
       "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "age           -0.0254      0.001    -20.459      0.000      -0.028      -0.023\n",
       "job           -0.0177      0.005     -3.447      0.001      -0.028      -0.008\n",
       "marital       -0.2102      0.026     -8.184      0.000      -0.261      -0.160\n",
       "education      0.0181      0.022      0.818      0.413      -0.025       0.061\n",
       "default       -0.4219      0.159     -2.657      0.008      -0.733      -0.111\n",
       "balance     2.009e-05   4.52e-06      4.447      0.000    1.12e-05     2.9e-05\n",
       "housing       -1.2798      0.036    -35.673      0.000      -1.350      -1.209\n",
       "loan          -0.8335      0.056    -14.813      0.000      -0.944      -0.723\n",
       "contact       -0.5823      0.026    -22.565      0.000      -0.633      -0.532\n",
       "duration       0.0038   5.99e-05     63.136      0.000       0.004       0.004\n",
       "campaign      -0.1807      0.010    -17.264      0.000      -0.201      -0.160\n",
       "pdays       8.798e-05      0.000      0.390      0.696      -0.000       0.001\n",
       "previous       0.0537      0.008      6.771      0.000       0.038       0.069\n",
       "poutcome      -0.2523      0.022    -11.679      0.000      -0.295      -0.210\n",
       "==============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.fit().summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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