Logistic_regression_apartments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q.) A construction company has released a few apartments in the market. Using the previous data about the scales of their apartments, they want to predict the category of people who might be interested in buying this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 : Import all the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>33</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>55000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>54000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age  Salary  Purchased\n",
       "0      M   33   50000          0\n",
       "1      F   28   45000          0\n",
       "2      F   45   55000          1\n",
       "3      M   65   80000          1\n",
       "4      M   36   54000          0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apt_data=pd.read_csv(r\"E:\\Git_repo\\Logistic_House_data.csv\")\n",
    "apt_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 4 columns):\n",
      "Gender       10 non-null object\n",
      "Age          10 non-null int64\n",
      "Salary       10 non-null int64\n",
      "Purchased    10 non-null int64\n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "apt_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 : Analyzing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2984f759a48>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#How many of the people has bought the house\n",
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x=\"Purchased\",data=apt_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Number of people which has not brought the house are more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29851a074c8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "sns.countplot(x=\"Purchased\",hue=\"Gender\",data=apt_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Number of male who had bought the apratment are more then female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 : Data Wrangling (Not required)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   M\n",
       "0  1\n",
       "1  0\n",
       "2  0\n",
       "3  1\n",
       "4  1\n",
       "5  1\n",
       "6  0\n",
       "7  0\n",
       "8  1\n",
       "9  1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dummy variable for male & female\n",
    "dummies_gen=pd.get_dummies(apt_data[\"Gender\"],drop_first=True)\n",
    "dummies_gen"
   ]
  },
  {
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>33</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>28</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>55000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>65</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>36</td>\n",
       "      <td>54000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>F</td>\n",
       "      <td>69</td>\n",
       "      <td>64000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>85000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>M</td>\n",
       "      <td>44</td>\n",
       "      <td>55690</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender  Age  Salary  Purchased  M\n",
       "0      M   33   50000          0  1\n",
       "1      F   28   45000          0  0\n",
       "2      F   45   55000          1  0\n",
       "3      M   65   80000          1  1\n",
       "4      M   36   54000          0  1\n",
       "5      M   19   28000          0  1\n",
       "6      F   69   64000          1  0\n",
       "7      F   27   27000          0  0\n",
       "8      M   39   85000          0  1\n",
       "9      M   44   55690          0  1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apt_data=pd.concat([apt_data,dummies_gen],axis=1)\n",
    "apt_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Salary</th>\n",
       "      <th>Purchased</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>45000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>55000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>80000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>54000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>69</td>\n",
       "      <td>64000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>85000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "      <td>55690</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Salary  Purchased  M\n",
       "0   33   50000          0  1\n",
       "1   28   45000          0  0\n",
       "2   45   55000          1  0\n",
       "3   65   80000          1  1\n",
       "4   36   54000          0  1\n",
       "5   19   28000          0  1\n",
       "6   69   64000          1  0\n",
       "7   27   27000          0  0\n",
       "8   39   85000          0  1\n",
       "9   44   55690          0  1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apt_data=apt_data.drop(\"Gender\",axis=1)\n",
    "apt_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 : Test & Train the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=apt_data.drop(\"Purchased\",axis=1)\n",
    "y=apt_data[\"Purchased\"]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\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=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=model.predict(X_test)\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       1.00      1.00      1.00         1\\n           1       1.00      1.00      1.00         1\\n\\n    accuracy                           1.00         2\\n   macro avg       1.00      1.00      1.00         2\\nweighted avg       1.00      1.00      1.00         2\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "val=classification_report(y_test,pred)\n",
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1\n",
       "0    1\n",
       "Name: Purchased, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0]\n",
      " [0 1]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(metrics.recall_score(y_test,pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 : Check Accurcay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "val2=accuracy_score(y_test,pred)\n",
    "val2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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