RandomizedSearchCV_GridSearchCV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading csv \n",
    "\n",
    "df= pd.read_csv('Real_combine.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>26.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1012.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>284.795833</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>29.1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1011.9</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>219.720833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>28.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1011.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>25.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1011.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>24.8</td>\n",
       "      <td>31.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>223.208333</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>1277</td>\n",
       "      <td>28.5</td>\n",
       "      <td>33.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1012.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>14.8</td>\n",
       "      <td>185.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>1279</td>\n",
       "      <td>24.9</td>\n",
       "      <td>33.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>1011.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>166.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>1281</td>\n",
       "      <td>26.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1011.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>200.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>1283</td>\n",
       "      <td>20.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>349.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>1285</td>\n",
       "      <td>23.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>310.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>643 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0     T    TM    Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0             1  26.7  33.0  20.0  1012.4  60.0  5.1  4.4  13.0  284.795833\n",
       "1             3  29.1  35.0  20.5  1011.9  49.0  5.8  5.2  14.8  219.720833\n",
       "2             5  28.4  36.0  21.0  1011.3  46.0  5.3  5.7  11.1  182.187500\n",
       "3             7  25.9  32.0  20.0  1011.8  56.0  6.1  6.9  11.1  154.037500\n",
       "4             9  24.8  31.1  20.6  1013.6  58.0  4.8  8.3  11.1  223.208333\n",
       "..          ...   ...   ...   ...     ...   ...  ...  ...   ...         ...\n",
       "638        1277  28.5  33.4  20.9  1012.6  59.0  5.3  6.3  14.8  185.500000\n",
       "639        1279  24.9  33.2  14.8  1011.5  48.0  4.2  4.6  13.0  166.875000\n",
       "640        1281  26.4  32.0  20.9  1011.2  70.0  3.9  6.7   9.4  200.333333\n",
       "641        1283  20.8  25.0  14.5  1016.8  78.0  4.7  5.9  11.1  349.291667\n",
       "642        1285  23.3  28.0  14.9  1014.0  71.0  4.5  3.0   9.4  310.250000\n",
       "\n",
       "[643 rows x 10 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
       "      <th>PM 2.5</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>26.7</td>\n",
       "      <td>33.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1012.4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>13.0</td>\n",
       "      <td>284.795833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>1011.9</td>\n",
       "      <td>49.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>219.720833</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28.4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1011.3</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>182.187500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.9</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1011.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>154.037500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.8</td>\n",
       "      <td>31.1</td>\n",
       "      <td>20.6</td>\n",
       "      <td>1013.6</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.3</td>\n",
       "      <td>11.1</td>\n",
       "      <td>223.208333</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>28.5</td>\n",
       "      <td>33.4</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1012.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>6.3</td>\n",
       "      <td>14.8</td>\n",
       "      <td>185.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>24.9</td>\n",
       "      <td>33.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>1011.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.6</td>\n",
       "      <td>13.0</td>\n",
       "      <td>166.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>26.4</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.9</td>\n",
       "      <td>1011.2</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>9.4</td>\n",
       "      <td>200.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>20.8</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1016.8</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.9</td>\n",
       "      <td>11.1</td>\n",
       "      <td>349.291667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>23.3</td>\n",
       "      <td>28.0</td>\n",
       "      <td>14.9</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>310.250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>643 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        T    TM    Tm     SLP     H   VV    V    VM      PM 2.5\n",
       "0    26.7  33.0  20.0  1012.4  60.0  5.1  4.4  13.0  284.795833\n",
       "1    29.1  35.0  20.5  1011.9  49.0  5.8  5.2  14.8  219.720833\n",
       "2    28.4  36.0  21.0  1011.3  46.0  5.3  5.7  11.1  182.187500\n",
       "3    25.9  32.0  20.0  1011.8  56.0  6.1  6.9  11.1  154.037500\n",
       "4    24.8  31.1  20.6  1013.6  58.0  4.8  8.3  11.1  223.208333\n",
       "..    ...   ...   ...     ...   ...  ...  ...   ...         ...\n",
       "638  28.5  33.4  20.9  1012.6  59.0  5.3  6.3  14.8  185.500000\n",
       "639  24.9  33.2  14.8  1011.5  48.0  4.2  4.6  13.0  166.875000\n",
       "640  26.4  32.0  20.9  1011.2  70.0  3.9  6.7   9.4  200.333333\n",
       "641  20.8  25.0  14.5  1016.8  78.0  4.7  5.9  11.1  349.291667\n",
       "642  23.3  28.0  14.9  1014.0  71.0  4.5  3.0   9.4  310.250000\n",
       "\n",
       "[643 rows x 9 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df.iloc[:,:-1],df.iloc[:,-1],test_size=0.20,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>TM</th>\n",
       "      <th>Tm</th>\n",
       "      <th>SLP</th>\n",
       "      <th>H</th>\n",
       "      <th>VV</th>\n",
       "      <th>V</th>\n",
       "      <th>VM</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>27.7</td>\n",
       "      <td>33.1</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1005.8</td>\n",
       "      <td>68.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22.2</td>\n",
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       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>25.8</td>\n",
       "      <td>32.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1012.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.1</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>27.7</td>\n",
       "      <td>32.8</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1013.1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.4</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>23.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.9</td>\n",
       "      <td>1013.3</td>\n",
       "      <td>41.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>25.2</td>\n",
       "      <td>31.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1015.4</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>11.1</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.7</td>\n",
       "      <td>30.4</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1015.8</td>\n",
       "      <td>46.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.2</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>33.6</td>\n",
       "      <td>40.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1006.9</td>\n",
       "      <td>36.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>24.9</td>\n",
       "      <td>30.4</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1008.9</td>\n",
       "      <td>57.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>26.1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>1001.2</td>\n",
       "      <td>87.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.1</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>23.8</td>\n",
       "      <td>30.2</td>\n",
       "      <td>17.9</td>\n",
       "      <td>1010.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.7</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>514 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        T    TM    Tm     SLP     H   VV     V    VM\n",
       "261  27.7  33.1  23.0  1005.8  68.0  5.5   8.3  22.2\n",
       "470  25.8  32.5  20.0  1012.6  60.0  4.5   6.1  11.1\n",
       "612  27.7  32.8  21.0  1013.1  66.0  4.7   5.4  13.0\n",
       "552  23.2  31.0  12.9  1013.3  41.0  4.0   3.0   9.4\n",
       "12   25.2  31.0  19.0  1015.4  48.0  4.7  11.1  16.5\n",
       "..    ...   ...   ...     ...   ...  ...   ...   ...\n",
       "9    23.7  30.4  17.0  1015.8  46.0  5.1   5.2  14.8\n",
       "359  33.6  40.0  25.0  1006.9  36.0  5.8   6.1  11.1\n",
       "192  24.9  30.4  19.0  1008.9  57.0  4.8   4.6   9.4\n",
       "629  26.1  29.0  22.4  1001.2  87.0  5.0  14.1  22.2\n",
       "559  23.8  30.2  17.9  1010.6  55.0  4.5   3.7   7.6\n",
       "\n",
       "[514 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261     58.125000\n",
       "470    253.291667\n",
       "612    245.666667\n",
       "552    237.625000\n",
       "12     181.012500\n",
       "          ...    \n",
       "9      108.000000\n",
       "359     80.208333\n",
       "192    143.000000\n",
       "629     22.333333\n",
       "559    200.645833\n",
       "Name: PM 2.5, Length: 514, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Without Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model intialising and fitting train data to model and predicting test data using trained model\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "reg_model=RandomForestRegressor(n_estimators=10).fit(X_train,y_train)\n",
    "y_prediction=reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# metric for model performance\n",
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3880.363814494991"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I conside MSE as metric \n",
    "sklearn.metrics.mean_squared_error(y_test,y_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model with RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters range\n",
    "\n",
    "parameters= {'n_estimators': randint(200,2000),\n",
    "               'max_features': ['auto','sqrt','log2'],\n",
    "               'max_depth': randint(10,1000),\n",
    "               'min_samples_split': randint(1,10),\n",
    "               'min_samples_leaf': randint(1,10),\n",
    "              'criterion':['mse','mae']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model initialization\n",
    "\n",
    "tuned_model=RandomForestRegressor()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# applying randomizedsearchcv on paramers using model\n",
    "randomsearch=RandomizedSearchCV(estimator=tuned_model,param_distributions=parameters,n_iter=100,cv=3,verbose=2,\n",
    "                               random_state=100,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   21.4s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  2.4min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:  5.6min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=100,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'criterion': ['mse', 'mae'],\n",
       "                                        'max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022311323518>,\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002231131F390>,\n",
       "                                        'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002231131FCF8>,\n",
       "                                        'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000022311323198>},\n",
       "                   random_state=100, verbose=2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomsearch.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'mse',\n",
       " 'max_depth': 123,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 9,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 893}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best parameters given by randomizedsearchcv\n",
    "randomsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model with best parameters given by randomziedsearchcv\n",
    "best_model=randomsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting y with best model\n",
    "y_prediction=best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3546.128199022413"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performance of hyperparameter tuned model\n",
    "sklearn.metrics.mean_squared_error(y_test,y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model with GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am using parameters which are given by the randomizedsearchcv\n",
    "\n",
    "I take randomizedsearchcv paramters and slightly add some tolearences to it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'mse',\n",
       " 'max_depth': 123,\n",
       " 'max_features': 'sqrt',\n",
       " 'min_samples_leaf': 9,\n",
       " 'min_samples_split': 8,\n",
       " 'n_estimators': 893}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameters range \n",
    "\n",
    "parameters = {\n",
    "    'criterion': [randomsearch.best_params_['criterion']],\n",
    "    'max_depth': [randomsearch.best_params_['max_depth']],\n",
    "    'max_features': [randomsearch.best_params_['max_features']],\n",
    "    'min_samples_leaf': [randomsearch.best_params_['min_samples_leaf'], \n",
    "                         randomsearch.best_params_['min_samples_leaf']+5, \n",
    "                         randomsearch.best_params_['min_samples_leaf'] + 5],\n",
    "    'min_samples_split': [randomsearch.best_params_['min_samples_split'] - 5,\n",
    "                          randomsearch.best_params_['min_samples_split'] - 3,\n",
    "                          randomsearch.best_params_['min_samples_split'], \n",
    "                          randomsearch.best_params_['min_samples_split'] +3,\n",
    "                         randomsearch.best_params_['min_samples_split'] + 5],\n",
    "    'n_estimators': [randomsearch.best_params_['n_estimators'] - 200, randomsearch.best_params_['n_estimators'] - 100, \n",
    "                     randomsearch.best_params_['n_estimators'], \n",
    "                     randomsearch.best_params_['n_estimators'] + 100, randomsearch.best_params_['n_estimators'] + 200]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model intilization\n",
    "tuning_model=RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Applying Gridsearchcv on model\n",
    "gridsearch=GridSearchCV(estimator=tuning_model,param_grid=parameters,cv=10,n_jobs=-1,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 75 candidates, totalling 750 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:   11.2s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:  1.3min\n",
      "[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  3.3min\n",
      "[Parallel(n_jobs=-1)]: Done 632 tasks      | elapsed:  5.9min\n",
      "[Parallel(n_jobs=-1)]: Done 750 out of 750 | elapsed:  7.1min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=RandomForestRegressor(), n_jobs=-1,\n",
       "             param_grid={'criterion': ['mse'], 'max_depth': [123],\n",
       "                         'max_features': ['sqrt'],\n",
       "                         'min_samples_leaf': [9, 14, 14],\n",
       "                         'min_samples_split': [3, 5, 8, 11, 13],\n",
       "                         'n_estimators': [693, 793, 893, 993, 1093]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gridsearch.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model with best parameteres given by Gridsearchcv\n",
    "best_model_2=gridsearch.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_prediction=best_model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3650.556336520121"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# performance of hyperparameter tuned model\n",
    "sklearn.metrics.mean_squared_error(y_test,y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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