LogisticRegressionSUVDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (SUV data analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step 1: collecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\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",
       "      <th>396</th>\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",
       "      <th>397</th>\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",
       "      <th>398</th>\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",
       "      <th>399</th>\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": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import lib\n",
    "%matplotlib inline\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# read csv data\n",
    "suv_data = pd.read_csv('C:\\\\Users\\\\sneha\\\\ML_Prob\\\\suv_data.csv')\n",
    "suv_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data.shape # get data shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step 2: data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223041d8348>"
      ]
     },
     "execution_count": 107,
     "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(x = 'Gender',data = suv_data)  # get countplot based on gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22304213a88>"
      ]
     },
     "execution_count": 108,
     "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(x = 'Gender',hue = 'Purchased',data = suv_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22304287048>"
      ]
     },
     "execution_count": 109,
     "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": [
    "suv_data['Age'].plot.hist() # agewise histogram plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22304300888>"
      ]
     },
     "execution_count": 110,
     "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": [
    "suv_data['EstimatedSalary'].plot.hist() # estimatedsalery wise histogram plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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": [
    "suv_data.info() # get info about data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22304379bc8>"
      ]
     },
     "execution_count": 112,
     "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.boxplot(x= 'Gender',y='Age',data = suv_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x223043d43c8>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,8))\n",
    "sns.boxplot(x = 'Age',y = 'EstimatedSalary',data = suv_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  step 3: data wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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      False   False  False            False      False\n",
       "1      False   False  False            False      False\n",
       "2      False   False  False            False      False\n",
       "3      False   False  False            False      False\n",
       "4      False   False  False            False      False\n",
       "..       ...     ...    ...              ...        ...\n",
       "395    False   False  False            False      False\n",
       "396    False   False  False            False      False\n",
       "397    False   False  False            False      False\n",
       "398    False   False  False            False      False\n",
       "399    False   False  False            False      False\n",
       "\n",
       "[400 rows x 5 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data.isnull()  # check null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data.isnull().sum() # get coumnwise sum of null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22304415448>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(suv_data.isnull())  # draw heatmap to show null value column (no column having null val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "suv_data.dropna(inplace = True) # drop all null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Male\n",
       "0       1\n",
       "1       1\n",
       "2       0\n",
       "3       0\n",
       "4       1\n",
       "..    ...\n",
       "395     0\n",
       "396     1\n",
       "397     0\n",
       "398     1\n",
       "399     0\n",
       "\n",
       "[400 rows x 1 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen = pd.get_dummies(suv_data['Gender'],drop_first = True) # convert string into categorical (for gender column)\n",
    "gen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
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       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased  Male\n",
       "0  15624510    Male   19            19000          0     1\n",
       "1  15810944    Male   35            20000          0     1\n",
       "2  15668575  Female   26            43000          0     0\n",
       "3  15603246  Female   27            57000          0     0\n",
       "4  15804002    Male   19            76000          0     1"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data = pd.concat([suv_data,gen],axis = 1) # concatenate the converted column into actual data\n",
    "suv_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>35</td>\n",
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       "      <td>26</td>\n",
       "      <td>43000</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>27</td>\n",
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       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>46</td>\n",
       "      <td>41000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>51</td>\n",
       "      <td>23000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>50</td>\n",
       "      <td>20000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>36</td>\n",
       "      <td>33000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>49</td>\n",
       "      <td>36000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  EstimatedSalary  Purchased  Male\n",
       "0     19            19000          0     1\n",
       "1     35            20000          0     1\n",
       "2     26            43000          0     0\n",
       "3     27            57000          0     0\n",
       "4     19            76000          0     1\n",
       "..   ...              ...        ...   ...\n",
       "395   46            41000          1     0\n",
       "396   51            23000          1     1\n",
       "397   50            20000          1     0\n",
       "398   36            33000          0     1\n",
       "399   49            36000          1     0\n",
       "\n",
       "[400 rows x 4 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suv_data.drop(['User ID','Gender'],axis = 1,inplace = True) # drop unnecessary column from data\n",
    "suv_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step 4: train & test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = suv_data.drop(['Purchased','Male'],axis = 1)  # get independent variable\n",
    "Y = suv_data['Purchased'] # get dependent variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# perform train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size = 0.25,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the data (independent) as estimated salery is very large in range as compare to age\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.fit_transform(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logmodel = LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "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='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logmodel.fit(x_train,y_train) # model fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logmodel.predict(x_test) # prediction on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### step 5: performance evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[63,  5],\n",
       "       [ 8, 24]], dtype=int64)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       0.89      0.93      0.91        68\\n           1       0.83      0.75      0.79        32\\n\\n    accuracy                           0.87       100\\n   macro avg       0.86      0.84      0.85       100\\nweighted avg       0.87      0.87      0.87       100\\n'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,y_pred)"
   ]
  }
 ],
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