House Price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>price</th>\n",
       "      <th>place</th>\n",
       "      <th>built</th>\n",
       "      <th>sqft</th>\n",
       "      <th>sale</th>\n",
       "      <th>yearsOld</th>\n",
       "      <th>floor</th>\n",
       "      <th>totalFloor</th>\n",
       "      <th>bhk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1341</td>\n",
       "      <td>6300000</td>\n",
       "      <td>BTM Layout</td>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>1450</td>\n",
       "      <td>Resale</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2126</td>\n",
       "      <td>11500000</td>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>2190</td>\n",
       "      <td>Resale</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>816</td>\n",
       "      <td>3800000</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>1019</td>\n",
       "      <td>Resale</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2708</td>\n",
       "      <td>10500000</td>\n",
       "      <td>Ambalipura</td>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>1857</td>\n",
       "      <td>Resale</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1041</td>\n",
       "      <td>11500000</td>\n",
       "      <td>Yelahanka</td>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>2190</td>\n",
       "      <td>Resale</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     price       place                 built  sqft    sale  \\\n",
       "0        1341   6300000  BTM Layout  Super built-up  Area  1450  Resale   \n",
       "1        2126  11500000   Yelahanka  Super built-up  Area  2190  Resale   \n",
       "2         816   3800000  Whitefield  Super built-up  Area  1019  Resale   \n",
       "3        2708  10500000  Ambalipura  Super built-up  Area  1857  Resale   \n",
       "4        1041  11500000   Yelahanka  Super built-up  Area  2190  Resale   \n",
       "\n",
       "   yearsOld  floor  totalFloor  bhk  \n",
       "0         5      1           4    1  \n",
       "1         5      3           5    3  \n",
       "2         1      2           5    2  \n",
       "3        15      4           5    4  \n",
       "4         5      3           5    3  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp = pd.read_csv('hp_data.csv')\n",
    "hp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature Enginerring\n",
    "y = hp.price\n",
    "X = hp.loc[:,['sqft', 'floor', 'totalFloor', 'yearsOld']]\n",
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training-Testing\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X,y, test_size=0.2, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear regression model object\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the linear regression line with the data\n",
    "model.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictig test data\n",
    "ypredict = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8626741090701253"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the linear regression R2-Score\n",
    "r2_score(ytest,ypredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1940707077938395"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the skewness of the data\n",
    "stats.skew(hp.totalFloor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.988906140538009"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test the Kurtosis\n",
    "stats.kurtosis(hp.totalFloor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13803f51cf8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the data to check for outlier\n",
    "X.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2118264 , -0.66873633, -0.53157095, -0.68425918],\n",
       "       [ 1.56614485, -0.25047737, -0.30741453, -0.68425918],\n",
       "       [-1.24737451, -0.45960685, -0.30741453, -1.73615645],\n",
       "       ...,\n",
       "       [-0.50014606, -0.66873633, -0.53157095,  0.63061242],\n",
       "       [-0.81249236, -0.66873633, -0.53157095,  0.63061242],\n",
       "       [-1.24737451, -0.45960685, -0.30741453, -1.73615645]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scaling down the sqft\n",
    "from sklearn.preprocessing import scale\n",
    "Xscaled = scale(X)\n",
    "Xscaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(Xscaled,y, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictig the test data\n",
    "ypredict = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8691627997984066"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the linear regression R2-Score\n",
    "r2_score(ytest,ypredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Label to Location data\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "enc = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Abbaiah Reddy Layout', 'Ambalipura', 'BTM Layout',\n",
       "       'Devarabeesana Halli', 'Electronics City Phase 1', 'Frazer Town',\n",
       "       'Gunjur', 'KR Puram', 'Malleshwaram', 'Rajaji Nagar',\n",
       "       'Sarakki Nagar', 'Subramanyapura', 'Whitefield', 'Yelachenahalli',\n",
       "       'Yelahanka'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp.place = enc.fit_transform(hp.place)\n",
    "enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Built-up  Area', 'Super built-up  Area'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hp.built = enc.fit_transform(hp.built)\n",
    "enc.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding features\n",
    "X = hp.loc[:,['sqft', 'floor', 'totalFloor', 'yearsOld', 'place', 'built']]\n",
    "y = hp.price\n",
    "#Scale the X\n",
    "XscaledWithPlace = scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(XscaledWithPlace,y, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictig the test data\n",
    "ypredict = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88133147504618"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the linear regression R2-Score\n",
    "r2_score(ytest,ypredict)"
   ]
  },
  {
   "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>sqft</th>\n",
       "      <th>floor</th>\n",
       "      <th>totalFloor</th>\n",
       "      <th>yearsOld</th>\n",
       "      <th>place</th>\n",
       "      <th>built</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1450</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sqft  floor  totalFloor  yearsOld  place  built\n",
       "0  1450      1           4         5      2      1"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final Result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "88.133 percent"
   ]
  }
 ],
 "metadata": {
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