houseprice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b148bb3b",
   "metadata": {},
   "source": [
    "# House Price\n",
    "Training a model that can predict house price of diffrent houses in Tehran in USD"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cebe1d",
   "metadata": {},
   "source": [
    "### Importing the required libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d96be789",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model\n",
    "from joblib import dump\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d44882fa",
   "metadata": {},
   "source": [
    "### Reading the data and trying to understand it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2bb11642",
   "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>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahran</td>\n",
       "      <td>1.850000e+09</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Pardis</td>\n",
       "      <td>5.500000e+08</td>\n",
       "      <td>18333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Qods</td>\n",
       "      <td>9.025000e+08</td>\n",
       "      <td>30083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Shahrake Gharb</td>\n",
       "      <td>7.000000e+09</td>\n",
       "      <td>233333.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Area  Room  Parking  Warehouse  Elevator         Address         Price  \\\n",
       "0   63     1     True       True      True         Shahran  1.850000e+09   \n",
       "1   60     1     True       True      True         Shahran  1.850000e+09   \n",
       "2   79     2     True       True      True          Pardis  5.500000e+08   \n",
       "3   95     2     True       True      True   Shahrake Qods  9.025000e+08   \n",
       "4  123     2     True       True      True  Shahrake Gharb  7.000000e+09   \n",
       "\n",
       "   Price(USD)  \n",
       "0    61666.67  \n",
       "1    61666.67  \n",
       "2    18333.33  \n",
       "3    30083.33  \n",
       "4   233333.33  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"houseprice.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd93dd8",
   "metadata": {},
   "source": [
    "## Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5543f707",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing empty Address fields and converting to encoded labels\n",
    "df['Address'].replace('', np.nan, inplace=True)\n",
    "df.dropna(subset=['Address'], inplace=True)\n",
    "le = preprocessing.LabelEncoder()\n",
    "df['Address'] = le.fit_transform(df['Address'])\n",
    "\n",
    "# Converting boolean variables to binary\n",
    "df['Warehouse'].replace(False, 0, inplace=True)\n",
    "df['Warehouse'].replace(True, 1, inplace=True)\n",
    "df['Elevator'].replace(False, 0, inplace=True)\n",
    "df['Elevator'].replace(True, 1, inplace=True)\n",
    "df['Parking'].replace(True, 1, inplace=True)\n",
    "df['Parking'].replace(False, 0, inplace=True)\n",
    "\n",
    "# Removing redundant Area values \n",
    "df.Area = pd.to_numeric(df.Area, errors='coerce')\n",
    "df.dropna(subset=['Area'], inplace=True)\n",
    "df = df.drop(df.index[df['Area'] > 400])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23acbecf",
   "metadata": {},
   "source": [
    "Selecting some features that we will use to train our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad4f8d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Room</th>\n",
       "      <th>Warehouse</th>\n",
       "      <th>Parking</th>\n",
       "      <th>Elevator</th>\n",
       "      <th>Address</th>\n",
       "      <th>Price(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>61666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>79.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "      <td>18333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>152</td>\n",
       "      <td>30083.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>123.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>233333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>106</td>\n",
       "      <td>68333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>87.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "      <td>20000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>71666.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>16433.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Area  Room  Warehouse  Parking  Elevator  Address  Price(USD)\n",
       "0   63.0     1          1        1         1      156    61666.67\n",
       "1   60.0     1          1        1         1      156    61666.67\n",
       "2   79.0     2          1        1         1      117    18333.33\n",
       "3   95.0     2          1        1         1      152    30083.33\n",
       "4  123.0     2          1        1         1      150   233333.33\n",
       "5   70.0     2          1        1         0      106    68333.33\n",
       "6   87.0     2          1        1         1      117    20000.00\n",
       "7   59.0     1          1        1         1      156    71666.67\n",
       "8   54.0     2          1        1         0       13    16433.33"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf = df[['Area', 'Room', 'Warehouse', 'Parking', 'Elevator', 'Address', 'Price(USD)']]\n",
    "\n",
    "cdf.head(9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "424d5003",
   "metadata": {},
   "source": [
    "### Plotting the datas to see the suitable relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20164140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price(USD)')"
      ]
     },
     "execution_count": 5,
     "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": [
    "plt.scatter(cdf.Area, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Area\")\n",
    "plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "050ef424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price(USD)')"
      ]
     },
     "execution_count": 6,
     "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": [
    "plt.scatter(cdf.Room, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Room\")\n",
    "plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b86d817d",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1056073404.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_111243/1056073404.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    plt.xlabel(\"Warehouse\")plt.ylabel(\"Price(USD)\")\u001b[0m\n\u001b[0m                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "plt.scatter(cdf.Warehouse, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Warehouse\")plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28d0d722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price(USD)')"
      ]
     },
     "execution_count": 8,
     "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": [
    "plt.scatter(cdf.Elevator, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Elevator\")\n",
    "plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6bfdbf77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price(USD)')"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(cdf.Parking, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Parking\")\n",
    "plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cfe8d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price(USD)')"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.scatter(cdf.Address, cdf['Price(USD)'],  color='blue')\n",
    "plt.xlabel(\"Address\")\n",
    "plt.ylabel(\"Price(USD)\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb631bf5",
   "metadata": {},
   "source": [
    "## Splitting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ca6f9f10",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.asanyarray(cdf[['Area', 'Room', 'Parking', 'Warehouse', 'Elevator', 'Address']])\n",
    "y = np.asanyarray(cdf[['Price(USD)']])\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=4924)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bf22f8",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fa9d333e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients:  [[  4200.58166528 -46777.25441722   2730.57570532  28433.36561938\n",
      "    6084.62352428     47.68271142]]\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# The coefficients\n",
    "print ('Coefficients: ', regr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b812dbde",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88c48dfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance score: 0.77\n"
     ]
    }
   ],
   "source": [
    "y_hat = regr.predict(x_test)\n",
    "print('Variance score: %.2f' % regr.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b38c285",
   "metadata": {},
   "source": [
    "## Saving the model for future use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06ec3a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['houseprice-model.joblib']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(regr, 'houseprice-model.joblib') "
   ]
  }
 ],
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