boston housing data.ipynb

{
 
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "      <th>MEDV</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</td>\n",
       "      <td>296</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</td>\n",
       "      <td>242</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</td>\n",
       "      <td>242</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</td>\n",
       "      <td>222</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</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>NaN</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  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90    NaN  36.2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('HousingData.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>486.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>486.000000</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>486.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.611874</td>\n",
       "      <td>11.211934</td>\n",
       "      <td>11.083992</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.518519</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>12.715432</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.720192</td>\n",
       "      <td>23.388876</td>\n",
       "      <td>6.835896</td>\n",
       "      <td>0.255340</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>27.999513</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>7.155871</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>0.081900</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>45.175000</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>7.125000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.253715</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>76.800000</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>11.430000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.560263</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>93.975000</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  486.000000  486.000000  486.000000  486.000000  506.000000  506.000000   \n",
       "mean     3.611874   11.211934   11.083992    0.069959    0.554695    6.284634   \n",
       "std      8.720192   23.388876    6.835896    0.255340    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.253715    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.560263   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  486.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.518519    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     27.999513    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%     45.175000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     76.800000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     93.975000    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        MEDV  \n",
       "count  486.000000  506.000000  \n",
       "mean    12.715432   22.532806  \n",
       "std      7.155871    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      7.125000   17.025000  \n",
       "50%     11.430000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     486 non-null    float64\n",
      " 1   ZN       486 non-null    float64\n",
      " 2   INDUS    486 non-null    float64\n",
      " 3   CHAS     486 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      486 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    486 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TAX        1.000000\n",
       "RAD        0.910228\n",
       "INDUS      0.731055\n",
       "NOX        0.668023\n",
       "CRIM       0.580595\n",
       "LSTAT      0.536110\n",
       "AGE        0.509114\n",
       "PTRATIO    0.460853\n",
       "CHAS      -0.032304\n",
       "RM        -0.292048\n",
       "ZN        -0.312371\n",
       "B         -0.441808\n",
       "MEDV      -0.468536\n",
       "DIS       -0.534432\n",
       "Name: TAX, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()['TAX'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### It means that our data is depending most on the RAD parameter followed by others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing outliers\n",
    "# data = data[data['TAX']<500]\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     486 non-null    float64\n",
      " 1   ZN       486 non-null    float64\n",
      " 2   INDUS    486 non-null    float64\n",
      " 3   CHAS     486 non-null    float64\n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      486 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    486 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(12), int64(2)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "col = ['CRIM','ZN','INDUS','CHAS','AGE','LSTAT']\n",
    "for c in col:\n",
    "    data[c].fillna(data[c].mean(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = data.drop('TAX',axis=1)\n",
    "y = data['TAX']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END ...................................n_estimators=100; total time=   0.3s\n",
      "[CV] END ...................................n_estimators=100; total time=   0.3s\n",
      "[CV] END ...................................n_estimators=100; total time=   0.4s\n",
      "[CV] END ...................................n_estimators=100; total time=   0.4s\n",
      "[CV] END ...................................n_estimators=100; total time=   0.4s\n",
      "[CV] END ...................................n_estimators=200; total time=   0.7s\n",
      "[CV] END ...................................n_estimators=200; total time=   0.7s\n",
      "[CV] END ...................................n_estimators=200; total time=   0.7s\n",
      "[CV] END ...................................n_estimators=200; total time=   0.7s\n",
      "[CV] END ...................................n_estimators=200; total time=   0.6s\n",
      "[CV] END ...................................n_estimators=300; total time=   1.0s\n",
      "[CV] END ...................................n_estimators=300; total time=   1.0s\n",
      "[CV] END ...................................n_estimators=300; total time=   1.0s\n",
      "[CV] END ...................................n_estimators=300; total time=   1.0s\n",
      "[CV] END ...................................n_estimators=300; total time=   0.9s\n",
      "[CV] END ...................................n_estimators=400; total time=   1.4s\n",
      "[CV] END ...................................n_estimators=400; total time=   1.3s\n",
      "[CV] END ...................................n_estimators=400; total time=   1.2s\n",
      "[CV] END ...................................n_estimators=400; total time=   1.3s\n",
      "[CV] END ...................................n_estimators=400; total time=   1.3s\n",
      "[CV] END ...................................n_estimators=500; total time=   1.6s\n",
      "[CV] END ...................................n_estimators=500; total time=   1.5s\n",
      "[CV] END ...................................n_estimators=500; total time=   1.6s\n",
      "[CV] END ...................................n_estimators=500; total time=   1.5s\n",
      "[CV] END ...................................n_estimators=500; total time=   1.6s\n",
      "[CV] END ...................................n_estimators=600; total time=   2.0s\n",
      "[CV] END ...................................n_estimators=600; total time=   1.9s\n",
      "[CV] END ...................................n_estimators=600; total time=   2.0s\n",
      "[CV] END ...................................n_estimators=600; total time=   1.9s\n",
      "[CV] END ...................................n_estimators=600; total time=   1.9s\n",
      "[CV] END ...................................n_estimators=700; total time=   2.2s\n",
      "[CV] END ...................................n_estimators=700; total time=   2.1s\n",
      "[CV] END ...................................n_estimators=700; total time=   2.2s\n",
      "[CV] END ...................................n_estimators=700; total time=   2.2s\n",
      "[CV] END ...................................n_estimators=700; total time=   2.1s\n",
      "[CV] END ...................................n_estimators=800; total time=   2.5s\n",
      "[CV] END ...................................n_estimators=800; total time=   2.5s\n",
      "[CV] END ...................................n_estimators=800; total time=   2.5s\n",
      "[CV] END ...................................n_estimators=800; total time=   2.4s\n",
      "[CV] END ...................................n_estimators=800; total time=   2.4s\n",
      "[CV] END ...................................n_estimators=900; total time=   2.8s\n",
      "[CV] END ...................................n_estimators=900; total time=   2.8s\n",
      "[CV] END ...................................n_estimators=900; total time=   2.8s\n",
      "[CV] END ...................................n_estimators=900; total time=   2.7s\n",
      "[CV] END ...................................n_estimators=900; total time=   2.7s\n",
      "[CV] END ..................................n_estimators=1000; total time=   3.2s\n",
      "[CV] END ..................................n_estimators=1000; total time=   3.1s\n",
      "[CV] END ..................................n_estimators=1000; total time=   3.1s\n",
      "[CV] END ..................................n_estimators=1000; total time=   3.1s\n",
      "[CV] END ..................................n_estimators=1000; total time=   3.2s\n",
      "Random Forest accuracy is -->  98.0164405516794\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rfc = RandomForestRegressor()\n",
    "params = {'n_estimators':[100,200,300,400,500,600,700,800,900,1000]}\n",
    "\n",
    "grid_model = GridSearchCV(rfc, params,verbose=2)\n",
    "grid_model.fit(X_train,y_train)\n",
    "\n",
    "pred = grid_model.predict(X_test)\n",
    "\n",
    "print('Random Forest accuracy is --> ',r2_score(y_test,pred)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Y_Test</th>\n",
       "      <th>PRED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>296</td>\n",
       "      <td>372.190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>254</td>\n",
       "      <td>256.726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>711</td>\n",
       "      <td>681.808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>305</td>\n",
       "      <td>305.098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>666</td>\n",
       "      <td>666.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Y_Test     PRED\n",
       "173     296  372.190\n",
       "274     254  256.726\n",
       "491     711  681.808\n",
       "72      305  305.098\n",
       "452     666  666.000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = pd.DataFrame()\n",
    "res['Y_Test'] = y_test\n",
    "res['PRED'] = pred\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sajjad/jupyter/environment/lib/python3.10/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'predicted values')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(y_test,pred)\n",
    "plt.xlabel('real values')\n",
    "plt.ylabel('predicted values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}