house_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install --upgrade scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.datasets\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "house_price_dataset = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "X= np.hstack([house_price_dataset.values[::2, :], house_price_dataset.values[1::2, :2]])\n",
    "y = house_price_dataset.values[1::2, 2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24.  21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 15.  18.9 21.7 20.4\n",
      " 18.2 19.9 23.1 17.5 20.2 18.2 13.6 19.6 15.2 14.5 15.6 13.9 16.6 14.8\n",
      " 18.4 21.  12.7 14.5 13.2 13.1 13.5 18.9 20.  21.  24.7 30.8 34.9 26.6\n",
      " 25.3 24.7 21.2 19.3 20.  16.6 14.4 19.4 19.7 20.5 25.  23.4 18.9 35.4\n",
      " 24.7 31.6 23.3 19.6 18.7 16.  22.2 25.  33.  23.5 19.4 22.  17.4 20.9\n",
      " 24.2 21.7 22.8 23.4 24.1 21.4 20.  20.8 21.2 20.3 28.  23.9 24.8 22.9\n",
      " 23.9 26.6 22.5 22.2 23.6 28.7 22.6 22.  22.9 25.  20.6 28.4 21.4 38.7\n",
      " 43.8 33.2 27.5 26.5 18.6 19.3 20.1 19.5 19.5 20.4 19.8 19.4 21.7 22.8\n",
      " 18.8 18.7 18.5 18.3 21.2 19.2 20.4 19.3 22.  20.3 20.5 17.3 18.8 21.4\n",
      " 15.7 16.2 18.  14.3 19.2 19.6 23.  18.4 15.6 18.1 17.4 17.1 13.3 17.8\n",
      " 14.  14.4 13.4 15.6 11.8 13.8 15.6 14.6 17.8 15.4 21.5 19.6 15.3 19.4\n",
      " 17.  15.6 13.1 41.3 24.3 23.3 27.  50.  50.  50.  22.7 25.  50.  23.8\n",
      " 23.8 22.3 17.4 19.1 23.1 23.6 22.6 29.4 23.2 24.6 29.9 37.2 39.8 36.2\n",
      " 37.9 32.5 26.4 29.6 50.  32.  29.8 34.9 37.  30.5 36.4 31.1 29.1 50.\n",
      " 33.3 30.3 34.6 34.9 32.9 24.1 42.3 48.5 50.  22.6 24.4 22.5 24.4 20.\n",
      " 21.7 19.3 22.4 28.1 23.7 25.  23.3 28.7 21.5 23.  26.7 21.7 27.5 30.1\n",
      " 44.8 50.  37.6 31.6 46.7 31.5 24.3 31.7 41.7 48.3 29.  24.  25.1 31.5\n",
      " 23.7 23.3 22.  20.1 22.2 23.7 17.6 18.5 24.3 20.5 24.5 26.2 24.4 24.8\n",
      " 29.6 42.8 21.9 20.9 44.  50.  36.  30.1 33.8 43.1 48.8 31.  36.5 22.8\n",
      " 30.7 50.  43.5 20.7 21.1 25.2 24.4 35.2 32.4 32.  33.2 33.1 29.1 35.1\n",
      " 45.4 35.4 46.  50.  32.2 22.  20.1 23.2 22.3 24.8 28.5 37.3 27.9 23.9\n",
      " 21.7 28.6 27.1 20.3 22.5 29.  24.8 22.  26.4 33.1 36.1 28.4 33.4 28.2\n",
      " 22.8 20.3 16.1 22.1 19.4 21.6 23.8 16.2 17.8 19.8 23.1 21.  23.8 23.1\n",
      " 20.4 18.5 25.  24.6 23.  22.2 19.3 22.6 19.8 17.1 19.4 22.2 20.7 21.1\n",
      " 19.5 18.5 20.6 19.  18.7 32.7 16.5 23.9 31.2 17.5 17.2 23.1 24.5 26.6\n",
      " 22.9 24.1 18.6 30.1 18.2 20.6 17.8 21.7 22.7 22.6 25.  19.9 20.8 16.8\n",
      " 21.9 27.5 21.9 23.1 50.  50.  50.  50.  50.  13.8 13.8 15.  13.9 13.3\n",
      " 13.1 10.2 10.4 10.9 11.3 12.3  8.8  7.2 10.5  7.4 10.2 11.5 15.1 23.2\n",
      "  9.7 13.8 12.7 13.1 12.5  8.5  5.   6.3  5.6  7.2 12.1  8.3  8.5  5.\n",
      " 11.9 27.9 17.2 27.5 15.  17.2 17.9 16.3  7.   7.2  7.5 10.4  8.8  8.4\n",
      " 16.7 14.2 20.8 13.4 11.7  8.3 10.2 10.9 11.   9.5 14.5 14.1 16.1 14.3\n",
      " 11.7 13.4  9.6  8.7  8.4 12.8 10.5 17.1 18.4 15.4 10.8 11.8 14.9 12.6\n",
      " 14.1 13.  13.4 15.2 16.1 17.8 14.9 14.1 12.7 13.5 14.9 20.  16.4 17.7\n",
      " 19.5 20.2 21.4 19.9 19.  19.1 19.1 20.1 19.9 19.6 23.2 29.8 13.8 13.3\n",
      " 16.7 12.  14.6 21.4 23.  23.7 25.  21.8 20.6 21.2 19.1 20.6 15.2  7.\n",
      "  8.1 13.6 20.1 21.8 24.5 23.1 19.7 18.3 21.2 17.5 16.8 22.4 20.6 23.9\n",
      " 22.  11.9]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data=data,columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 13)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  \n",
       "0       15.3  396.90   4.98  \n",
       "1       17.8  396.90   9.14  \n",
       "2       17.8  392.83   4.03  \n",
       "3       18.7  394.63   2.94  \n",
       "4       18.7  396.90   5.33  \n",
       "..       ...     ...    ...  \n",
       "501     21.0  391.99   9.67  \n",
       "502     21.0  396.90   9.08  \n",
       "503     21.0  396.90   5.64  \n",
       "504     21.0  393.45   6.48  \n",
       "505     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"price\"]=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...    ...   \n",
       "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0  273.0   \n",
       "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0  273.0   \n",
       "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0  273.0   \n",
       "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0  273.0   \n",
       "505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0  273.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  price  \n",
       "0       15.3  396.90   4.98   24.0  \n",
       "1       17.8  396.90   9.14   21.6  \n",
       "2       17.8  392.83   4.03   34.7  \n",
       "3       18.7  394.63   2.94   33.4  \n",
       "4       18.7  396.90   5.33   36.2  \n",
       "..       ...     ...    ...    ...  \n",
       "501     21.0  391.99   9.67   22.4  \n",
       "502     21.0  396.90   9.08   20.6  \n",
       "503     21.0  396.90   5.64   23.9  \n",
       "504     21.0  393.45   6.48   22.0  \n",
       "505     21.0  396.90   7.88   11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
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       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  price  \n",
       "0     15.3  396.90   4.98   24.0  \n",
       "1     17.8  396.90   9.14   21.6  \n",
       "2     17.8  392.83   4.03   34.7  \n",
       "3     18.7  394.63   2.94   33.4  \n",
       "4     18.7  396.90   5.33   36.2  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677083</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT       price  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation=df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#burada tablodaki değerlerin birbirleri ile ilişkisine bakıyoruz\n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(correlation,cbar=True,square=True,fmt=\".1f\",annot=True,annot_kws={\"size\":8},cmap=\"Blues\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop([\"price\"],axis=1)\n",
    "y=df[\"price\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "splitting the data into Training data and Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13) (404, 13) (102, 13)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.3\n"
     ]
    }
   ],
   "source": [
    "print(y_train[211])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy for prediction on training data\n",
    "training_data_predict=model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23.147501  20.99463   20.090284  34.69053   13.903663  13.510157\n",
      " 21.998634  15.1940975 10.899711  22.709627  13.832816   5.592794\n",
      " 29.810236  49.99096   34.89215   20.607384  23.351097  19.23555\n",
      " 32.695698  19.641418  26.991022   8.401829  46.00729   21.708961\n",
      " 27.062933  19.321356  19.288303  24.809872  22.61626   31.70493\n",
      " 18.542515   8.697379  17.395294  23.700663  13.304856  10.492197\n",
      " 12.688369  25.016556  19.67495   14.902088  24.193798  25.007143\n",
      " 14.900281  16.995798  15.6009035 12.699232  24.51537   14.999952\n",
      " 50.00104   17.525454  21.184624  31.998049  15.613355  22.89754\n",
      " 19.325378  18.717896  23.301125  37.222923  30.09486   33.102703\n",
      " 21.00072   49.999332  13.405827   5.0280113 16.492886   8.405072\n",
      " 28.64328   19.499939  20.586452  45.402164  39.79833   33.407326\n",
      " 19.83506   33.406372  25.271482  50.001534  12.521657  17.457413\n",
      " 18.61758   22.602625  50.002117  23.801117  23.317268  23.087355\n",
      " 41.700035  16.119293  31.620516  36.069206   7.0022025 20.3827\n",
      " 19.996452  11.986318  25.023014  49.970123  37.881588  23.123034\n",
      " 41.292133  17.596548  16.305374  30.034231  22.860699  19.810343\n",
      " 17.098848  18.898268  18.96717   22.606049  23.141363  33.183487\n",
      " 15.010934  11.693824  18.78828   20.80524   17.99983   19.68991\n",
      " 50.00332   17.207317  16.404053  17.520426  14.593481  33.110855\n",
      " 14.508482  43.821655  34.939106  20.381636  14.655634   8.094332\n",
      " 11.7662115 11.846876  18.69599    6.314154  23.983706  13.084503\n",
      " 19.603905  49.989143  22.300608  18.930315  31.197134  20.69645\n",
      " 32.21111   36.15102   14.240763  15.698188  49.99381   20.423601\n",
      " 16.184978  13.409128  50.01321   31.602146  12.271495  19.219482\n",
      " 29.794909  31.536846  22.798779  10.189648  24.08648   23.710463\n",
      " 21.991894  13.802495  28.420696  33.181534  13.105958  18.988266\n",
      " 26.576572  36.967175  30.794083  22.77071   10.201246  22.213818\n",
      " 24.483162  36.178806  23.09194   20.097307  19.470194  10.786644\n",
      " 22.671095  19.502405  20.109184   9.611871  42.799637  48.794792\n",
      " 13.097208  20.28583   24.793974  14.110478  21.701134  22.217012\n",
      " 33.003544  21.11041   25.00658   19.122992  32.398567  13.605098\n",
      " 15.1145315 23.088867  27.474783  19.364998  26.487135  27.499458\n",
      " 28.697094  21.21718   18.703201  26.775208  14.010719  21.692347\n",
      " 18.372562  43.11582   29.081839  20.289959  23.680176  18.308306\n",
      " 17.204844  18.320065  24.393475  26.396057  19.094141  13.3019905\n",
      " 22.15311   22.185797   8.516214  18.894428  21.792608  19.331121\n",
      " 18.197924   7.5006843 22.406403  20.004215  14.412416  22.503702\n",
      " 28.53306   21.591028  13.810223  20.497831  21.898977  23.104464\n",
      " 49.99585   16.242056  30.294561  50.001595  17.771557  19.053703\n",
      " 10.399217  20.378187  16.49973   17.183376  16.70228   19.495337\n",
      " 30.507633  28.98067   19.528809  23.148346  24.391027   9.521643\n",
      " 23.886024  49.995125  21.167099  22.597813  19.965279  13.4072275\n",
      " 19.948694  17.087479  12.738807  23.00453   15.222122  20.604322\n",
      " 26.207253  18.09243   24.090246  14.105     21.689667  20.08065\n",
      " 25.010437  27.874954  22.92366   18.509727  22.190847  24.004797\n",
      " 14.788686  19.89675   24.39812   17.796036  24.556297  31.970308\n",
      " 17.774675  23.356768  16.134794  13.009915  10.98219   24.28906\n",
      " 15.56895   35.209793  19.605724  42.301712   8.797891  24.400295\n",
      " 14.086652  15.408639  17.301126  22.127419  23.09363   44.79579\n",
      " 17.776684  31.50014   22.835577  16.888603  23.925127  12.097476\n",
      " 38.685944  21.388391  15.98878   23.912495  11.909485  24.960499\n",
      "  7.2018585 24.696215  18.201897  22.489008  23.03332   24.260433\n",
      " 17.101519  17.805563  13.493165  27.105328  13.311978  21.913465\n",
      " 20.00738   15.405392  16.595737  22.301016  24.708412  21.422579\n",
      " 22.878702  29.606575  21.877811  19.900253  29.605219  23.407152\n",
      " 13.781474  24.454706  11.897682   7.2203646 20.521074   9.725295\n",
      " 48.30087   25.19501   11.688618  17.404732  14.480284  28.618876\n",
      " 19.397131  22.468653   7.0117908 20.602013  22.970919  19.719397\n",
      " 23.693787  25.048244  27.977154  13.393578  14.513882  20.309145\n",
      " 19.306028  24.095829  14.894031  26.382381  33.298378  23.61644\n",
      " 24.591206  18.514652  20.900269  10.406055  23.303423  13.092017\n",
      " 24.675085  22.582184  20.502762  16.820635  10.220605  33.81239\n",
      " 18.608067  49.999187  23.775583  23.909609  21.192276  18.805798\n",
      "  8.502987  21.50807   23.204473  21.012218  16.611097  28.100965\n",
      " 21.193024  28.419638  14.294126  49.99958   30.988504  24.991066\n",
      " 21.433628  18.975573  28.991457  15.206939  22.817244  21.765755\n",
      " 19.915497  23.7961   ]\n"
     ]
    }
   ],
   "source": [
    "print(training_data_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared error: 0.9999948236320982\n",
      "mean absolute error: 0.0145848437110976\n"
     ]
    }
   ],
   "source": [
    "#R squared error\n",
    "score_1=metrics.r2_score(y_train,training_data_predict)\n",
    "\n",
    "#Mean Absolute Error\n",
    "score_2=metrics.mean_absolute_error(y_train,training_data_predict)\n",
    "\n",
    "print(\"R squared error:\",score_1)\n",
    "print(\"mean absolute error:\",score_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_train,training_data_predict)\n",
    "plt.xlabel(\"Actual prices\")\n",
    "plt.ylabel(\"predicted prices\")\n",
    "plt.title(\"actual price vs prediced price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_predict=model.predict(X_test)\n",
    "\n",
    "score_1=metrics.r2_score(y_test,test_data_predict)\n",
    "score_2=metrics.mean_absolute_error(y_test,test_data_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared error: 0.8711660369151691\n",
      "mean absolute error 2.2834744154238233\n"
     ]
    }
   ],
   "source": [
    "print(\"R squared error:\",score_1)\n",
    "print(\"mean absolute error\",score_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}