P28. Supervised ML - Logistic Regression - Appointing Attorney or not.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supervised ML - Logistic Regression - Appointing Attorney or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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>CASENUM</th>\n",
       "      <th>ATTORNEY</th>\n",
       "      <th>CLMSEX</th>\n",
       "      <th>CLMINSUR</th>\n",
       "      <th>SEATBELT</th>\n",
       "      <th>CLMAGE</th>\n",
       "      <th>LOSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>34.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.038</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>34100</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>34110</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>34113</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>34145</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>34153</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1340 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CASENUM  ATTORNEY  CLMSEX  CLMINSUR  SEATBELT  CLMAGE    LOSS\n",
       "0           5         0     0.0       1.0       0.0    50.0  34.940\n",
       "1           3         1     1.0       0.0       0.0    18.0   0.891\n",
       "2          66         1     0.0       1.0       0.0     5.0   0.330\n",
       "3          70         0     0.0       1.0       1.0    31.0   0.037\n",
       "4          96         1     0.0       1.0       0.0    30.0   0.038\n",
       "...       ...       ...     ...       ...       ...     ...     ...\n",
       "1335    34100         1     0.0       1.0       0.0     NaN   0.576\n",
       "1336    34110         0     1.0       1.0       0.0    46.0   3.705\n",
       "1337    34113         1     1.0       1.0       0.0    39.0   0.099\n",
       "1338    34145         0     1.0       0.0       0.0     8.0   3.177\n",
       "1339    34153         1     1.0       1.0       0.0    30.0   0.688\n",
       "\n",
       "[1340 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataset\n",
    "claimants=pd.read_csv('Database/claimants.csv')\n",
    "claimants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the case number columns as it is not required\n",
    "claimants.drop([\"CASENUM\"],inplace=True,axis=1)"
   ]
  },
  {
   "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>ATTORNEY</th>\n",
       "      <th>CLMSEX</th>\n",
       "      <th>CLMINSUR</th>\n",
       "      <th>SEATBELT</th>\n",
       "      <th>CLMAGE</th>\n",
       "      <th>LOSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>34.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.038</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1338</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1340 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ATTORNEY  CLMSEX  CLMINSUR  SEATBELT  CLMAGE    LOSS\n",
       "0            0     0.0       1.0       0.0    50.0  34.940\n",
       "1            1     1.0       0.0       0.0    18.0   0.891\n",
       "2            1     0.0       1.0       0.0     5.0   0.330\n",
       "3            0     0.0       1.0       1.0    31.0   0.037\n",
       "4            1     0.0       1.0       0.0    30.0   0.038\n",
       "...        ...     ...       ...       ...     ...     ...\n",
       "1335         1     0.0       1.0       0.0     NaN   0.576\n",
       "1336         0     1.0       1.0       0.0    46.0   3.705\n",
       "1337         1     1.0       1.0       0.0    39.0   0.099\n",
       "1338         0     1.0       0.0       0.0     8.0   3.177\n",
       "1339         1     1.0       1.0       0.0    30.0   0.688\n",
       "\n",
       "[1340 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claimants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1340 entries, 0 to 1339\n",
      "Data columns (total 6 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   ATTORNEY  1340 non-null   int64  \n",
      " 1   CLMSEX    1328 non-null   float64\n",
      " 2   CLMINSUR  1299 non-null   float64\n",
      " 3   SEATBELT  1292 non-null   float64\n",
      " 4   CLMAGE    1151 non-null   float64\n",
      " 5   LOSS      1340 non-null   float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 62.9 KB\n"
     ]
    }
   ],
   "source": [
    "claimants.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ATTORNEY      0\n",
       "CLMSEX       12\n",
       "CLMINSUR     41\n",
       "SEATBELT     48\n",
       "CLMAGE      189\n",
       "LOSS          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claimants.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ATTORNEY</th>\n",
       "      <th>CLMSEX</th>\n",
       "      <th>CLMINSUR</th>\n",
       "      <th>SEATBELT</th>\n",
       "      <th>CLMAGE</th>\n",
       "      <th>LOSS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>34.940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.038</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>3.705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1096 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ATTORNEY  CLMSEX  CLMINSUR  SEATBELT  CLMAGE    LOSS\n",
       "0            0     0.0       1.0       0.0    50.0  34.940\n",
       "1            1     1.0       0.0       0.0    18.0   0.891\n",
       "2            1     0.0       1.0       0.0     5.0   0.330\n",
       "3            0     0.0       1.0       1.0    31.0   0.037\n",
       "4            1     0.0       1.0       0.0    30.0   0.038\n",
       "...        ...     ...       ...       ...     ...     ...\n",
       "1091         1     1.0       1.0       0.0    16.0   0.060\n",
       "1092         0     1.0       1.0       0.0    46.0   3.705\n",
       "1093         1     1.0       1.0       0.0    39.0   0.099\n",
       "1094         0     1.0       0.0       0.0     8.0   3.177\n",
       "1095         1     1.0       1.0       0.0    30.0   0.688\n",
       "\n",
       "[1096 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing NA values in data set\n",
    "claimants=claimants.dropna().reset_index(drop=True)\n",
    "claimants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividing our data into input and output variables \n",
    "x=claimants.iloc[:,1:]\n",
    "y=claimants.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic regression model\n",
    "classifier=LogisticRegression()\n",
    "classifier.fit(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict for x dataset\n",
    "y_pred=classifier.predict(x)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>actual_y</th>\n",
       "      <th>y_pred_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1091</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1092</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1093</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1095</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1096 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      actual_y  y_pred_prob\n",
       "0            0            0\n",
       "1            1            1\n",
       "2            1            1\n",
       "3            0            0\n",
       "4            1            1\n",
       "...        ...          ...\n",
       "1091         1            1\n",
       "1092         0            0\n",
       "1093         1            1\n",
       "1094         0            0\n",
       "1095         1            1\n",
       "\n",
       "[1096 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_df=pd.DataFrame({'actual_y':y,'y_pred_prob':y_pred})\n",
    "y_pred_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Model Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[381, 197],\n",
       "       [123, 395]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix for the model accuracy\n",
    "confusion_matrix = confusion_matrix(y,y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.708029197080292"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The model accuracy is calculated by (a+d)/(a+b+c+d)\n",
    "(381+395)/(381+197+123+395)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The Model accuracy is 70.80%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.04621690e-06, 5.07148875e-01, 5.75166843e-01, ...,\n",
       "       7.39028665e-01, 2.85940593e-01, 6.80431213e-01])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.predict_proba(x)[:,1]   # [;,1] means 1 dimentional array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 accuracy: 0.7108589063606365\n"
     ]
    }
   ],
   "source": [
    "# ROC Curve plotting and finding AUC value\n",
    "fpr,tpr,thresholds=roc_curve(y,classifier.predict_proba(x)[:,1])\n",
    "plt.plot(fpr,tpr,color='red')\n",
    "auc=roc_auc_score(y,y_pred)\n",
    "\n",
    "plt.plot(fpr,tpr,color='red',label='logit model(area  = %0.2f)'%auc)\n",
    "plt.plot([0,1],[0,1],'k--')  # k-- is for 45 deg diagonal line\n",
    "plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "print('auc accuracy:',auc)"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}