House Prediction Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset/data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "street           0\n",
       "city             0\n",
       "statezip         0\n",
       "country          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>street</th>\n",
       "      <th>city</th>\n",
       "      <th>statezip</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1340</td>\n",
       "      <td>7912</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1340</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2005</td>\n",
       "      <td>18810 Densmore Ave N</td>\n",
       "      <td>Shoreline</td>\n",
       "      <td>WA 98133</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>2384000.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3650</td>\n",
       "      <td>9050</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3370</td>\n",
       "      <td>280</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>709 W Blaine St</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA 98119</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>342000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1930</td>\n",
       "      <td>11947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1930</td>\n",
       "      <td>0</td>\n",
       "      <td>1966</td>\n",
       "      <td>0</td>\n",
       "      <td>26206-26214 143rd Ave SE</td>\n",
       "      <td>Kent</td>\n",
       "      <td>WA 98042</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>420000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>8030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>857 170th Pl NE</td>\n",
       "      <td>Bellevue</td>\n",
       "      <td>WA 98008</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-02 00:00:00</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1940</td>\n",
       "      <td>10500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1140</td>\n",
       "      <td>800</td>\n",
       "      <td>1976</td>\n",
       "      <td>1992</td>\n",
       "      <td>9105 170th Ave NE</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>WA 98052</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date      price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
       "0  2014-05-02 00:00:00   313000.0       3.0       1.50         1340      7912   \n",
       "1  2014-05-02 00:00:00  2384000.0       5.0       2.50         3650      9050   \n",
       "2  2014-05-02 00:00:00   342000.0       3.0       2.00         1930     11947   \n",
       "3  2014-05-02 00:00:00   420000.0       3.0       2.25         2000      8030   \n",
       "4  2014-05-02 00:00:00   550000.0       4.0       2.50         1940     10500   \n",
       "\n",
       "   floors  waterfront  view  condition  sqft_above  sqft_basement  yr_built  \\\n",
       "0     1.5           0     0          3        1340              0      1955   \n",
       "1     2.0           0     4          5        3370            280      1921   \n",
       "2     1.0           0     0          4        1930              0      1966   \n",
       "3     1.0           0     0          4        1000           1000      1963   \n",
       "4     1.0           0     0          4        1140            800      1976   \n",
       "\n",
       "   yr_renovated                    street       city  statezip country  \n",
       "0          2005      18810 Densmore Ave N  Shoreline  WA 98133     USA  \n",
       "1             0           709 W Blaine St    Seattle  WA 98119     USA  \n",
       "2             0  26206-26214 143rd Ave SE       Kent  WA 98042     USA  \n",
       "3             0           857 170th Pl NE   Bellevue  WA 98008     USA  \n",
       "4          1992         9105 170th Ave NE    Redmond  WA 98052     USA  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Declaring X and y columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors', 'waterfront', 'view', 'condition']]\n",
    "y = data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1340</td>\n",
       "      <td>7912</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>3650</td>\n",
       "      <td>9050</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1930</td>\n",
       "      <td>11947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2000</td>\n",
       "      <td>8030</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1940</td>\n",
       "      <td>10500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   bedrooms  bathrooms  sqft_living  sqft_lot  floors  waterfront  view  \\\n",
       "0       3.0       1.50         1340      7912     1.5           0     0   \n",
       "1       5.0       2.50         3650      9050     2.0           0     4   \n",
       "2       3.0       2.00         1930     11947     1.0           0     0   \n",
       "3       3.0       2.25         2000      8030     1.0           0     0   \n",
       "4       4.0       2.50         1940     10500     1.0           0     0   \n",
       "\n",
       "   condition  \n",
       "0          3  \n",
       "1          5  \n",
       "2          4  \n",
       "3          4  \n",
       "4          4  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model = LinearRegression()\n",
    "reg_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating mse and r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 986869414953.9735\n",
      "R-squared: 0.032335189956331445\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model/house_prediction_model.pkl']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(reg_model, 'model/house_prediction_model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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