Logistic Regression using sklearn 23 August 2020.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import math\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
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       "      <td>76000</td>\n",
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       "      <td>395</td>\n",
       "      <td>15691863</td>\n",
       "      <td>Female</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>15706071</td>\n",
       "      <td>Male</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>15654296</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>15755018</td>\n",
       "      <td>Male</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>15594041</td>\n",
       "      <td>Female</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510    Male   19            19000          0\n",
       "1    15810944    Male   35            20000          0\n",
       "2    15668575  Female   26            43000          0\n",
       "3    15603246  Female   27            57000          0\n",
       "4    15804002    Male   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863  Female   46            41000          1\n",
       "396  15706071    Male   51            23000          1\n",
       "397  15654296  Female   50            20000          1\n",
       "398  15755018    Male   36            33000          0\n",
       "399  15594041  Female   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Social_Network_Ads.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 400 entries, 0 to 399\n",
      "Data columns (total 5 columns):\n",
      "User ID            400 non-null int64\n",
      "Gender             400 non-null object\n",
      "Age                400 non-null int64\n",
      "EstimatedSalary    400 non-null int64\n",
      "Purchased          400 non-null int64\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User ID            0\n",
       "Gender             0\n",
       "Age                0\n",
       "EstimatedSalary    0\n",
       "Purchased          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Male', 'Female'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>15755018</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>15594041</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0    15624510       1   19            19000          0\n",
       "1    15810944       1   35            20000          0\n",
       "2    15668575       0   26            43000          0\n",
       "3    15603246       0   27            57000          0\n",
       "4    15804002       1   19            76000          0\n",
       "..        ...     ...  ...              ...        ...\n",
       "395  15691863       0   46            41000          1\n",
       "396  15706071       1   51            23000          1\n",
       "397  15654296       0   50            20000          1\n",
       "398  15755018       1   36            33000          0\n",
       "399  15594041       0   49            36000          1\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender']=df['Gender'].map({'Male':1,'Female':0})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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       "      <td>399</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Age  EstimatedSalary\n",
       "0         1   19            19000\n",
       "1         1   35            20000\n",
       "2         0   26            43000\n",
       "3         0   27            57000\n",
       "4         1   19            76000\n",
       "..      ...  ...              ...\n",
       "395       0   46            41000\n",
       "396       1   51            23000\n",
       "397       0   50            20000\n",
       "398       1   36            33000\n",
       "399       0   49            36000\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,1:4]\n",
    "y=df.iloc[:,-1].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=x.iloc[:,1:3].values\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "sc.fit_transform(z)\n",
    "z=sc.transform(z)\n",
    "z=pd.DataFrame(z)\n",
    "z=z.rename(columns={0:'Age',1:'EstimatedSalary'})\n",
    "z\n",
    "g=x.iloc[:,0]\n",
    "g=pd.DataFrame(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>-0.158074</td>\n",
       "      <td>-1.078938</td>\n",
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       "      <td>399</td>\n",
       "      <td>0</td>\n",
       "      <td>1.083596</td>\n",
       "      <td>-0.990844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender       Age  EstimatedSalary\n",
       "0         1 -1.781797        -1.490046\n",
       "1         1 -0.253587        -1.460681\n",
       "2         0 -1.113206        -0.785290\n",
       "3         0 -1.017692        -0.374182\n",
       "4         1 -1.781797         0.183751\n",
       "..      ...       ...              ...\n",
       "395       0  0.797057        -0.844019\n",
       "396       1  1.274623        -1.372587\n",
       "397       0  1.179110        -1.460681\n",
       "398       1 -0.158074        -1.078938\n",
       "399       0  1.083596        -0.990844\n",
       "\n",
       "[400 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input1=pd.concat([g,z],axis=1)\n",
    "input1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(320, 3)\n",
      "(80, 3)\n",
      "(320,)\n",
      "(80,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "x_train,x_test,y_train,y_test=train_test_split(input1,y,test_size=.2,random_state=0)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3593, 0.    , 0.    ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_regression\n",
    "p=f_regression(x_train,y_train)[1].round(4)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(random_state=0)\n",
    "lr.fit(x_train,y_train)\n",
    "y_pred=lr.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[181  38]\n",
      " [ 18  83]]\n",
      "0.825\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_pred,y_train)\n",
    "accuracy=accuracy_score(y_pred,y_train)\n",
    "print(cm)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.09869526, 2.0265874 , 1.06229835]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.00827829])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Feature_name=input1.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Feature name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EstimatedSalary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature name\n",
       "0           Gender\n",
       "1              Age\n",
       "2  EstimatedSalary"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_Table=pd.DataFrame(columns=['Feature name'],data=Feature_name)\n",
    "Summary_Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.3593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Age</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EstimatedSalary</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature name  p_value\n",
       "0           Gender   0.3593\n",
       "1              Age   0.0000\n",
       "2  EstimatedSalary   0.0000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_value=p\n",
    "Summary_Table['p_value']=p_value\n",
    "Summary_Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Feature name</th>\n",
       "      <th>p_value</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.3593</td>\n",
       "      <td>0.098695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Age</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.026587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>EstimatedSalary</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.062298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature name  p_value      Coef\n",
       "0           Gender   0.3593  0.098695\n",
       "1              Age   0.0000  2.026587\n",
       "2  EstimatedSalary   0.0000  1.062298"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_Table['Coef']=np.transpose(lr.coef_)\n",
    "Summary_Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Feature name</th>\n",
       "      <th>p_value</th>\n",
       "      <th>Coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.3593</td>\n",
       "      <td>0.098695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Age</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.026587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>EstimatedSalary</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.062298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature name  p_value      Coef\n",
       "1           Gender   0.3593  0.098695\n",
       "2              Age   0.0000  2.026587\n",
       "3  EstimatedSalary   0.0000  1.062298"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_Table.index=Summary_Table.index+1\n",
    "Summary_Table"
   ]
  },
  {
   "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></th>\n",
       "      <th>Feature name</th>\n",
       "      <th>p_value</th>\n",
       "      <th>Coef</th>\n",
       "      <th>odds_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Age</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.026587</td>\n",
       "      <td>7.588147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>EstimatedSalary</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.062298</td>\n",
       "      <td>2.893013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Gender</td>\n",
       "      <td>0.3593</td>\n",
       "      <td>0.098695</td>\n",
       "      <td>1.103730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Feature name  p_value      Coef  odds_ratio\n",
       "2              Age   0.0000  2.026587    7.588147\n",
       "3  EstimatedSalary   0.0000  1.062298    2.893013\n",
       "1           Gender   0.3593  0.098695    1.103730"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summary_Table['odds_ratio']=np.exp(Summary_Table.Coef)\n",
    "Summary_Table.sort_values('odds_ratio',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>395</td>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>396</td>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>397</td>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary\n",
       "0     19            19000\n",
       "1     35            20000\n",
       "2     26            43000\n",
       "3     27            57000\n",
       "4     19            76000\n",
       "..   ...              ...\n",
       "395   46            41000\n",
       "396   51            23000\n",
       "397   50            20000\n",
       "398   36            33000\n",
       "399   49            36000\n",
       "\n",
       "[400 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=df.iloc[:,2:4]\n",
    "y=df.iloc[:,-1].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "sc.fit_transform(x)\n",
    "x=sc.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(320, 2)\n",
      "(80, 2)\n",
      "(320,)\n",
      "(80,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=.2,random_state=0)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0.])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_regression\n",
    "p=f_regression(x_train,y_train)[1].round(4)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PC\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=0, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr=LogisticRegression(random_state=0)\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(x)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
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      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Predicted Purchased</th>\n",
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       "      <td>398</td>\n",
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       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <td>399</td>\n",
       "      <td>15594041</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased  Predicted Purchased\n",
       "0    15624510       1   19            19000          0                    0\n",
       "1    15810944       1   35            20000          0                    0\n",
       "2    15668575       0   26            43000          0                    0\n",
       "3    15603246       0   27            57000          0                    0\n",
       "4    15804002       1   19            76000          0                    0\n",
       "..        ...     ...  ...              ...        ...                  ...\n",
       "395  15691863       0   46            41000          1                    0\n",
       "396  15706071       1   51            23000          1                    1\n",
       "397  15654296       0   50            20000          1                    0\n",
       "398  15755018       1   36            33000          0                    0\n",
       "399  15594041       0   49            36000          1                    1\n",
       "\n",
       "[400 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predicted Purchased']=y_pred\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=lr.predict(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[180  39]\n",
      " [ 19  82]]\n",
      "0.81875\n"
     ]
    }
   ],
   "source": [
    "cm1=confusion_matrix(y_pred1,y_train)\n",
    "accuracy1=accuracy_score(y_pred1,y_train)\n",
    "print(cm1)\n",
    "print(accuracy1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "       [0.40936124],\n",
       "       [0.9037126 ],\n",
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       "       [0.43969842],\n",
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       "       [0.35217506],\n",
       "       [0.87173277],\n",
       "       [0.7087694 ],\n",
       "       [0.55729545],\n",
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       "       [0.68652905],\n",
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       "       [0.55582163],\n",
       "       [0.81926116],\n",
       "       [0.42594363],\n",
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       "       [0.48287694],\n",
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       "       [0.56799113],\n",
       "       [0.34093311],\n",
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       "       [0.46890181],\n",
       "       [0.08168435],\n",
       "       [0.54496276]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_prob=lr.predict_proba(x)\n",
    "pred_prob=pred_prob[:,1]\n",
    "pred_prob=pred_prob.reshape(-1,1)\n",
    "pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Predicted Purchased</th>\n",
       "      <th>Predict_prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>27</td>\n",
       "      <td>57000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>50</td>\n",
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       "      <td>0.468902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>398</td>\n",
       "      <td>15755018</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.081684</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.544963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      User ID  Gender  Age  EstimatedSalary  Purchased  Predicted Purchased  \\\n",
       "0    15624510       1   19            19000          0                    0   \n",
       "1    15810944       1   35            20000          0                    0   \n",
       "2    15668575       0   26            43000          0                    0   \n",
       "3    15603246       0   27            57000          0                    0   \n",
       "4    15804002       1   19            76000          0                    0   \n",
       "..        ...     ...  ...              ...        ...                  ...   \n",
       "395  15691863       0   46            41000          1                    0   \n",
       "396  15706071       1   51            23000          1                    1   \n",
       "397  15654296       0   50            20000          1                    0   \n",
       "398  15755018       1   36            33000          0                    0   \n",
       "399  15594041       0   49            36000          1                    1   \n",
       "\n",
       "     Predict_prob  \n",
       "0        0.002165  \n",
       "1        0.046668  \n",
       "2        0.017347  \n",
       "3        0.032031  \n",
       "4        0.012616  \n",
       "..            ...  \n",
       "395      0.439647  \n",
       "396      0.540310  \n",
       "397      0.468902  \n",
       "398      0.081684  \n",
       "399      0.544963  \n",
       "\n",
       "[400 rows x 7 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Predict_prob']=pred_prob\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.scatterplot(y='EstimatedSalary',x='Age',data=df,hue='Predicted Purchased',palette='gist_rainbow')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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