Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 6\n",
    "\n",
    "## Shivam Singhal 2K18/CO/340"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**AIM:** Write a program to implement Linear Regression using any appropriate dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Real estate valuation data set.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>No</th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2012.916667</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012.916667</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013.583333</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013.500000</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2012.833333</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  X1 transaction date  X2 house age  \\\n",
       "0   1          2012.916667          32.0   \n",
       "1   2          2012.916667          19.5   \n",
       "2   3          2013.583333          13.3   \n",
       "3   4          2013.500000          13.3   \n",
       "4   5          2012.833333           5.0   \n",
       "\n",
       "   X3 distance to the nearest MRT station  X4 number of convenience stores  \\\n",
       "0                                84.87882                               10   \n",
       "1                               306.59470                                9   \n",
       "2                               561.98450                                5   \n",
       "3                               561.98450                                5   \n",
       "4                               390.56840                                5   \n",
       "\n",
       "   X5 latitude  X6 longitude  Y house price of unit area  \n",
       "0     24.98298     121.54024                        37.9  \n",
       "1     24.98034     121.53951                        42.2  \n",
       "2     24.98746     121.54391                        47.3  \n",
       "3     24.98746     121.54391                        54.8  \n",
       "4     24.97937     121.54245                        43.1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>No</th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "      <td>414.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>207.500000</td>\n",
       "      <td>2013.148953</td>\n",
       "      <td>17.712560</td>\n",
       "      <td>1083.885689</td>\n",
       "      <td>4.094203</td>\n",
       "      <td>24.969030</td>\n",
       "      <td>121.533361</td>\n",
       "      <td>37.980193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>119.655756</td>\n",
       "      <td>0.281995</td>\n",
       "      <td>11.392485</td>\n",
       "      <td>1262.109595</td>\n",
       "      <td>2.945562</td>\n",
       "      <td>0.012410</td>\n",
       "      <td>0.015347</td>\n",
       "      <td>13.606488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2012.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>23.382840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.932070</td>\n",
       "      <td>121.473530</td>\n",
       "      <td>7.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>104.250000</td>\n",
       "      <td>2012.916667</td>\n",
       "      <td>9.025000</td>\n",
       "      <td>289.324800</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.963000</td>\n",
       "      <td>121.528085</td>\n",
       "      <td>27.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>207.500000</td>\n",
       "      <td>2013.166667</td>\n",
       "      <td>16.100000</td>\n",
       "      <td>492.231300</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>24.971100</td>\n",
       "      <td>121.538630</td>\n",
       "      <td>38.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>310.750000</td>\n",
       "      <td>2013.416667</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>1454.279000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>24.977455</td>\n",
       "      <td>121.543305</td>\n",
       "      <td>46.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>414.000000</td>\n",
       "      <td>2013.583333</td>\n",
       "      <td>43.800000</td>\n",
       "      <td>6488.021000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>25.014590</td>\n",
       "      <td>121.566270</td>\n",
       "      <td>117.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               No  X1 transaction date  X2 house age  \\\n",
       "count  414.000000           414.000000    414.000000   \n",
       "mean   207.500000          2013.148953     17.712560   \n",
       "std    119.655756             0.281995     11.392485   \n",
       "min      1.000000          2012.666667      0.000000   \n",
       "25%    104.250000          2012.916667      9.025000   \n",
       "50%    207.500000          2013.166667     16.100000   \n",
       "75%    310.750000          2013.416667     28.150000   \n",
       "max    414.000000          2013.583333     43.800000   \n",
       "\n",
       "       X3 distance to the nearest MRT station  \\\n",
       "count                              414.000000   \n",
       "mean                              1083.885689   \n",
       "std                               1262.109595   \n",
       "min                                 23.382840   \n",
       "25%                                289.324800   \n",
       "50%                                492.231300   \n",
       "75%                               1454.279000   \n",
       "max                               6488.021000   \n",
       "\n",
       "       X4 number of convenience stores  X5 latitude  X6 longitude  \\\n",
       "count                       414.000000   414.000000    414.000000   \n",
       "mean                          4.094203    24.969030    121.533361   \n",
       "std                           2.945562     0.012410      0.015347   \n",
       "min                           0.000000    24.932070    121.473530   \n",
       "25%                           1.000000    24.963000    121.528085   \n",
       "50%                           4.000000    24.971100    121.538630   \n",
       "75%                           6.000000    24.977455    121.543305   \n",
       "max                          10.000000    25.014590    121.566270   \n",
       "\n",
       "       Y house price of unit area  \n",
       "count                  414.000000  \n",
       "mean                    37.980193  \n",
       "std                     13.606488  \n",
       "min                      7.600000  \n",
       "25%                     27.700000  \n",
       "50%                     38.450000  \n",
       "75%                     46.600000  \n",
       "max                    117.500000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describe the whole dataframe\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>No</th>\n",
       "      <th>X1 transaction date</th>\n",
       "      <th>X2 house age</th>\n",
       "      <th>X3 distance to the nearest MRT station</th>\n",
       "      <th>X4 number of convenience stores</th>\n",
       "      <th>X5 latitude</th>\n",
       "      <th>X6 longitude</th>\n",
       "      <th>Y house price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\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>freq</th>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "           No X1 transaction date X2 house age  \\\n",
       "count     414                 414          414   \n",
       "unique      1                   1            1   \n",
       "top     False               False        False   \n",
       "freq      414                 414          414   \n",
       "\n",
       "       X3 distance to the nearest MRT station X4 number of convenience stores  \\\n",
       "count                                     414                             414   \n",
       "unique                                      1                               1   \n",
       "top                                     False                           False   \n",
       "freq                                      414                             414   \n",
       "\n",
       "       X5 latitude X6 longitude Y house price of unit area  \n",
       "count          414          414                        414  \n",
       "unique           1            1                          1  \n",
       "top          False        False                      False  \n",
       "freq           414          414                        414  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null Value Check \n",
    "df.isnull().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new = np.array(df)[: , 2:7]\n",
    "Y_new = np.array(df)[: , 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(331, 5)\n",
      "(83, 5)\n",
      "(331,)\n",
      "(83,)\n"
     ]
    }
   ],
   "source": [
    "# Train Test Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_new , Y_new , shuffle= True , test_size = 0.2, random_state=5)\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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Normalizing\n",
    "#Normalise this dataset\n",
    "#Each feature must have 0 mean, unit variance\n",
    "\n",
    "u_train = np.mean(X_train,axis=0) \n",
    "u_test = np.mean(X_test,axis=0)\n",
    "std_train = np.std(X_train,axis=0)\n",
    "std_test = np.std(X_test,axis=0)\n",
    "\n",
    "X_train = (X_train-u_train)/std_train\n",
    "X_test = (X_test-u_test)/std_test\n",
    "\n",
    "#plot feature 2 vs y\n",
    "plt.style.use('seaborn')\n",
    "plt.scatter(X_train[:,0],Y_train)\n",
    "\n",
    "m, b = np.polyfit(X_train[:,0], Y_train, 1)\n",
    "plt.plot(X_train[:,0], m*X_train[:,0] + b , 'r' , label = 'Expected Linear Line')\n",
    "\n",
    "plt.legend(loc = 'upper right',fontsize = 16)\n",
    "plt.xlabel(\"House Age\",fontsize = 20)\n",
    "plt.ylabel(\"House price of unit area\",fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(331, 6) (83, 6)\n"
     ]
    }
   ],
   "source": [
    "## To make suitable for Linear reg equation y = theta*x , since coefficien of theta[0] must be 1\n",
    "ones = np.ones((X_train.shape[0],1))\n",
    "X_train = np.hstack((ones,X_train))\n",
    "ones = np.ones((X_test.shape[0],1))\n",
    "X_test = np.hstack((ones,X_test))\n",
    "print(X_train.shape ,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X - Matrix ( m x n)\n",
    "# x - Vector (Single Example with n features)\n",
    "\n",
    "#Calculation of y = theta*x.\n",
    "def hypothesis(X,theta):\n",
    "    return np.dot(X,theta)\n",
    "\n",
    "# Calculation of Error\n",
    "def error(X,y,theta):\n",
    "    e = 0.0\n",
    "    y_ = hypothesis(X,theta)\n",
    "    e = np.sum((y-y_)**2)\n",
    "    return e/X.shape[0]\n",
    "\n",
    "# Formula for calculating R2 Score\n",
    "def r2_score(y,y_):\n",
    "    num = np.sum((y-y_)**2)\n",
    "    denom = np.sum((y- y.mean())**2)\n",
    "    score = (1- num/denom)\n",
    "    return score*100\n",
    "\n",
    "# This function will calculate R2 Score of Test and Train Set\n",
    "def calc_score(X ,y, theta):\n",
    "    predicted = []\n",
    "    for i in range(X.shape[0]):\n",
    "        pred = hypothesis(X[i],theta)\n",
    "        predicted.append(pred)\n",
    "    predicted = np.array(predicted)\n",
    "    score = r2_score(y , predicted)\n",
    "    return score   \n",
    "    \n",
    "def gradient(X,y,theta):\n",
    "    y_ = hypothesis(X,theta)\n",
    "    grad = np.dot(X.T,(y_ - y))\n",
    "    m = X.shape[0]\n",
    "    return grad/X.shape[0]\n",
    "\n",
    "def gradient_descent(X,y,learning_rate = 0.1,max_iters=300):\n",
    "    n = X.shape[1]\n",
    "    theta = np.zeros((n,))\n",
    "    error_list = []\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    \n",
    "    for i in range(max_iters):\n",
    "        e = error(X,y,theta)\n",
    "        error_list.append(e)\n",
    "        \n",
    "        #Gradient descent\n",
    "        grad = gradient(X,y,theta)\n",
    "        theta = theta - learning_rate*grad\n",
    "        \n",
    "        score_train = calc_score(X_train , Y_train , theta)\n",
    "        score_test = calc_score(X_test , Y_test , theta)\n",
    "        train_score.append(score_train)\n",
    "        test_score.append(score_test)\n",
    "        \n",
    "    return theta,error_list,train_score,test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta,error_list,train_score,test_score = gradient_descent(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "plt.plot(error_list , 'b')\n",
    "\n",
    "plt.xlabel(\"Epoch\",fontsize = 20)\n",
    "plt.ylabel(\"Error\",fontsize = 20)\n",
    "plt.title(\"Error vs Number of Epochs\",fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "plt.plot(train_score , 'b' , label = 'Train')\n",
    "plt.plot(test_score , 'r' , label = 'Test')\n",
    "\n",
    "plt.legend(loc = 'lower right',fontsize = 16)\n",
    "plt.xlabel(\"Epoch\",fontsize = 20)\n",
    "plt.ylabel(\"R2 Score\",fontsize = 20)\n",
    "plt.title(\"R2 Score vs Number of Epochs\",fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score of Train 56.86818296791959\n",
      "R2 Score of Test 56.87873096385656\n"
     ]
    }
   ],
   "source": [
    "#R2 Score of Train and Test \n",
    "print('R2 Score of Train', train_score[-1])\n",
    "print('R2 Score of Test', test_score[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn')\n",
    "plt.scatter(X_test[:,1],Y_test , label = 'Actual Price')\n",
    "\n",
    "predicted = []\n",
    "for i in range(X_test.shape[0]):\n",
    "    pred = hypothesis(X_test[i],theta)\n",
    "    predicted.append(pred)\n",
    "predicted = np.array(predicted)\n",
    "\n",
    "plt.scatter(X_test[:,1] , predicted , label = 'Predicted Price')\n",
    "\n",
    "plt.legend(loc = 'upper left',fontsize = 16)\n",
    "\n",
    "plt.xlabel(\"House Age\",fontsize = 20)\n",
    "plt.ylabel(\"House price of unit area\",fontsize = 20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}