From 8ddcefaff1ee798818c4f4351021dbe1c23f1560 Mon Sep 17 00:00:00 2001
From: Arun Kumar Pandey <arunp77@gmail.com>
Date: Thu, 18 Jan 2024 19:20:49 +0100
Subject: [PATCH] lasso regression project

---
 .../Reg-models/Algerian-fire-EDA.ipynb        |    2 +-
 Projects-ML/Reg-models/Model-training.ipynb   | 1983 +++++++++++++++++
 2 files changed, 1984 insertions(+), 1 deletion(-)

diff --git a/Projects-ML/Reg-models/Algerian-fire-EDA.ipynb b/Projects-ML/Reg-models/Algerian-fire-EDA.ipynb
index c151f30..16b22e9 100644
--- a/Projects-ML/Reg-models/Algerian-fire-EDA.ipynb
+++ b/Projects-ML/Reg-models/Algerian-fire-EDA.ipynb
@@ -3422,7 +3422,7 @@
    "source": [
     "# Ridge and lasso regression\n",
     "\n",
-    "For this go to the \"Model training\" notebook 'Model'"
+    "For this go to the \"Model training\" notebook '[**Model-training**](https://github.com/arunp77/Machine-Learning/blob/main/Projects-ML/Reg-models/Model-training.ipynb)'. In this part, we will use the csv file that we created earlier for our convinience. "
    ]
   },
   {
diff --git a/Projects-ML/Reg-models/Model-training.ipynb b/Projects-ML/Reg-models/Model-training.ipynb
index e69de29..48e7d1c 100644
--- a/Projects-ML/Reg-models/Model-training.ipynb
+++ b/Projects-ML/Reg-models/Model-training.ipynb
@@ -0,0 +1,1983 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import matplotlib.pyplot as plt \n",
+    "import numpy as np \n",
+    "import seaborn as sns \n",
+    "%matplotlib inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Algerian_forst_fires_cleaned_dataset.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>day</th>\n",
+       "      <th>month</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Ws</th>\n",
+       "      <th>Rain</th>\n",
+       "      <th>FFMC</th>\n",
+       "      <th>DMC</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>ISI</th>\n",
+       "      <th>BUI</th>\n",
+       "      <th>FWI</th>\n",
+       "      <th>Classes</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>29</td>\n",
+       "      <td>57</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.7</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>29</td>\n",
+       "      <td>61</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>26</td>\n",
+       "      <td>82</td>\n",
+       "      <td>22</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>47.1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>25</td>\n",
+       "      <td>89</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>27</td>\n",
+       "      <td>77</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.8</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   day  month  year  Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  \\\n",
+       "0    1      6  2012           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4   \n",
+       "1    2      6  2012           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9   \n",
+       "2    3      6  2012           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7   \n",
+       "3    4      6  2012           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7   \n",
+       "4    5      6  2012           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9   \n",
+       "\n",
+       "   FWI      Classes  Region  \n",
+       "0  0.5  not fire          0  \n",
+       "1  0.4  not fire          0  \n",
+       "2  0.1  not fire          0  \n",
+       "3  0.0  not fire          0  \n",
+       "4  0.5  not fire          0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['day', 'month', 'year', 'Temperature', 'RH', 'Ws', 'Rain', 'FFMC',\n",
+       "       'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes', 'Region'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# drop day, month and year\n",
+    "df.drop(['day', 'month', 'year'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Temperature</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Ws</th>\n",
+       "      <th>Rain</th>\n",
+       "      <th>FFMC</th>\n",
+       "      <th>DMC</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>ISI</th>\n",
+       "      <th>BUI</th>\n",
+       "      <th>FWI</th>\n",
+       "      <th>Classes</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29</td>\n",
+       "      <td>57</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.7</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>29</td>\n",
+       "      <td>61</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26</td>\n",
+       "      <td>82</td>\n",
+       "      <td>22</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>47.1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25</td>\n",
+       "      <td>89</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>27</td>\n",
+       "      <td>77</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.8</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>30</td>\n",
+       "      <td>65</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.4</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>44.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>fire</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>28</td>\n",
+       "      <td>87</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>41.1</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27</td>\n",
+       "      <td>87</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>24</td>\n",
+       "      <td>54</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>79.7</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>24</td>\n",
+       "      <td>64</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>not fire</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>243 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Temperature  RH  Ws  Rain  FFMC   DMC    DC  ISI   BUI  FWI  \\\n",
+       "0             29  57  18   0.0  65.7   3.4   7.6  1.3   3.4  0.5   \n",
+       "1             29  61  13   1.3  64.4   4.1   7.6  1.0   3.9  0.4   \n",
+       "2             26  82  22  13.1  47.1   2.5   7.1  0.3   2.7  0.1   \n",
+       "3             25  89  13   2.5  28.6   1.3   6.9  0.0   1.7  0.0   \n",
+       "4             27  77  16   0.0  64.8   3.0  14.2  1.2   3.9  0.5   \n",
+       "..           ...  ..  ..   ...   ...   ...   ...  ...   ...  ...   \n",
+       "238           30  65  14   0.0  85.4  16.0  44.5  4.5  16.9  6.5   \n",
+       "239           28  87  15   4.4  41.1   6.5   8.0  0.1   6.2  0.0   \n",
+       "240           27  87  29   0.5  45.9   3.5   7.9  0.4   3.4  0.2   \n",
+       "241           24  54  18   0.1  79.7   4.3  15.2  1.7   5.1  0.7   \n",
+       "242           24  64  15   0.2  67.3   3.8  16.5  1.2   4.8  0.5   \n",
+       "\n",
+       "          Classes  Region  \n",
+       "0     not fire          0  \n",
+       "1     not fire          0  \n",
+       "2     not fire          0  \n",
+       "3     not fire          0  \n",
+       "4     not fire          0  \n",
+       "..            ...     ...  \n",
+       "238       fire          1  \n",
+       "239   not fire          1  \n",
+       "240   not fire          1  \n",
+       "241   not fire          1  \n",
+       "242  not fire           1  \n",
+       "\n",
+       "[243 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Classes\n",
+       "fire             131\n",
+       "not fire         101\n",
+       "fire               4\n",
+       "fire               2\n",
+       "not fire           2\n",
+       "not fire           1\n",
+       "not fire           1\n",
+       "not fire           1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Classes'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Classes'] = df['Classes'].str.strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Classes\n",
+       "fire        137\n",
+       "not fire    106\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Classes'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Classes\n",
+       "1    137\n",
+       "0    106\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Replace 'Fire' with 0 and 'Not Fire' with 1 in the 'Classes' column\n",
+    "df['Classes'] = df['Classes'].replace({'fire': 1, 'not fire': 0})\n",
+    "\n",
+    "# Check the updated 'Classes' column\n",
+    "df['Classes'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>Temperature</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Ws</th>\n",
+       "      <th>Rain</th>\n",
+       "      <th>FFMC</th>\n",
+       "      <th>DMC</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>ISI</th>\n",
+       "      <th>BUI</th>\n",
+       "      <th>FWI</th>\n",
+       "      <th>Classes</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>223</th>\n",
+       "      <td>30</td>\n",
+       "      <td>73</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79.2</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>16.6</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>224</th>\n",
+       "      <td>31</td>\n",
+       "      <td>72</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>84.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>29</td>\n",
+       "      <td>49</td>\n",
+       "      <td>19</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88.6</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>33.4</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>28</td>\n",
+       "      <td>81</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>84.6</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>41.5</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>227</th>\n",
+       "      <td>32</td>\n",
+       "      <td>51</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88.7</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>50.2</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>33</td>\n",
+       "      <td>26</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>93.9</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>59.2</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>19.3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>34</td>\n",
+       "      <td>44</td>\n",
+       "      <td>12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.5</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>63.3</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>26.2</td>\n",
+       "      <td>17.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>230</th>\n",
+       "      <td>36</td>\n",
+       "      <td>33</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>90.6</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>77.8</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>28.2</td>\n",
+       "      <td>15.4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>231</th>\n",
+       "      <td>29</td>\n",
+       "      <td>41</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>83.9</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>232</th>\n",
+       "      <td>34</td>\n",
+       "      <td>58</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>79.5</td>\n",
+       "      <td>18.7</td>\n",
+       "      <td>88.0</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>24.4</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>233</th>\n",
+       "      <td>35</td>\n",
+       "      <td>34</td>\n",
+       "      <td>17</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>92.2</td>\n",
+       "      <td>23.6</td>\n",
+       "      <td>97.3</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>21.6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>234</th>\n",
+       "      <td>33</td>\n",
+       "      <td>64</td>\n",
+       "      <td>13</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>88.9</td>\n",
+       "      <td>26.1</td>\n",
+       "      <td>106.3</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>235</th>\n",
+       "      <td>35</td>\n",
+       "      <td>56</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>89.0</td>\n",
+       "      <td>29.4</td>\n",
+       "      <td>115.6</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>236</th>\n",
+       "      <td>26</td>\n",
+       "      <td>49</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>61.3</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>28</td>\n",
+       "      <td>70</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>79.9</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>36.1</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>30</td>\n",
+       "      <td>65</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.4</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>44.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>28</td>\n",
+       "      <td>87</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>41.1</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27</td>\n",
+       "      <td>87</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>24</td>\n",
+       "      <td>54</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>79.7</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>24</td>\n",
+       "      <td>64</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Temperature  RH  Ws  Rain  FFMC   DMC     DC   ISI   BUI   FWI  Classes  \\\n",
+       "223           30  73  14   0.0  79.2   6.5   16.6   2.1   6.6   1.2        0   \n",
+       "224           31  72  14   0.0  84.2   8.3   25.2   3.8   9.1   3.9        1   \n",
+       "225           29  49  19   0.0  88.6  11.5   33.4   9.1  12.4  10.3        1   \n",
+       "226           28  81  15   0.0  84.6  12.6   41.5   4.3  14.3   5.7        1   \n",
+       "227           32  51  13   0.0  88.7  16.0   50.2   6.9  17.8   9.8        1   \n",
+       "228           33  26  13   0.0  93.9  21.2   59.2  14.2  22.4  19.3        1   \n",
+       "229           34  44  12   0.0  92.5  25.2   63.3  11.2  26.2  17.5        1   \n",
+       "230           36  33  13   0.1  90.6  25.8   77.8   9.0  28.2  15.4        1   \n",
+       "231           29  41   8   0.1  83.9  24.9   86.0   2.7  28.9   5.6        1   \n",
+       "232           34  58  13   0.2  79.5  18.7   88.0   2.1  24.4   3.8        0   \n",
+       "233           35  34  17   0.0  92.2  23.6   97.3  13.8  29.4  21.6        1   \n",
+       "234           33  64  13   0.0  88.9  26.1  106.3   7.1  32.4  13.7        1   \n",
+       "235           35  56  14   0.0  89.0  29.4  115.6   7.5  36.0  15.2        1   \n",
+       "236           26  49   6   2.0  61.3  11.9   28.1   0.6  11.9   0.4        0   \n",
+       "237           28  70  15   0.0  79.9  13.8   36.1   2.4  14.1   3.0        0   \n",
+       "238           30  65  14   0.0  85.4  16.0   44.5   4.5  16.9   6.5        1   \n",
+       "239           28  87  15   4.4  41.1   6.5    8.0   0.1   6.2   0.0        0   \n",
+       "240           27  87  29   0.5  45.9   3.5    7.9   0.4   3.4   0.2        0   \n",
+       "241           24  54  18   0.1  79.7   4.3   15.2   1.7   5.1   0.7        0   \n",
+       "242           24  64  15   0.2  67.3   3.8   16.5   1.2   4.8   0.5        0   \n",
+       "\n",
+       "     Region  \n",
+       "223       1  \n",
+       "224       1  \n",
+       "225       1  \n",
+       "226       1  \n",
+       "227       1  \n",
+       "228       1  \n",
+       "229       1  \n",
+       "230       1  \n",
+       "231       1  \n",
+       "232       1  \n",
+       "233       1  \n",
+       "234       1  \n",
+       "235       1  \n",
+       "236       1  \n",
+       "237       1  \n",
+       "238       1  \n",
+       "239       1  \n",
+       "240       1  \n",
+       "241       1  \n",
+       "242       1  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Independent and dependent features\n",
+    "X = df.drop('FWI', axis =1)\n",
+    "y = df['FWI']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "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>Temperature</th>\n",
+       "      <th>RH</th>\n",
+       "      <th>Ws</th>\n",
+       "      <th>Rain</th>\n",
+       "      <th>FFMC</th>\n",
+       "      <th>DMC</th>\n",
+       "      <th>DC</th>\n",
+       "      <th>ISI</th>\n",
+       "      <th>BUI</th>\n",
+       "      <th>Classes</th>\n",
+       "      <th>Region</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>29</td>\n",
+       "      <td>57</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>65.7</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>29</td>\n",
+       "      <td>61</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>7.6</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26</td>\n",
+       "      <td>82</td>\n",
+       "      <td>22</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>47.1</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25</td>\n",
+       "      <td>89</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>28.6</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>27</td>\n",
+       "      <td>77</td>\n",
+       "      <td>16</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>64.8</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>30</td>\n",
+       "      <td>65</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.4</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>44.5</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>16.9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>28</td>\n",
+       "      <td>87</td>\n",
+       "      <td>15</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>41.1</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>27</td>\n",
+       "      <td>87</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>24</td>\n",
+       "      <td>54</td>\n",
+       "      <td>18</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>79.7</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>24</td>\n",
+       "      <td>64</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>67.3</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>243 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Temperature  RH  Ws  Rain  FFMC   DMC    DC  ISI   BUI  Classes  Region\n",
+       "0             29  57  18   0.0  65.7   3.4   7.6  1.3   3.4        0       0\n",
+       "1             29  61  13   1.3  64.4   4.1   7.6  1.0   3.9        0       0\n",
+       "2             26  82  22  13.1  47.1   2.5   7.1  0.3   2.7        0       0\n",
+       "3             25  89  13   2.5  28.6   1.3   6.9  0.0   1.7        0       0\n",
+       "4             27  77  16   0.0  64.8   3.0  14.2  1.2   3.9        0       0\n",
+       "..           ...  ..  ..   ...   ...   ...   ...  ...   ...      ...     ...\n",
+       "238           30  65  14   0.0  85.4  16.0  44.5  4.5  16.9        1       1\n",
+       "239           28  87  15   4.4  41.1   6.5   8.0  0.1   6.2        0       1\n",
+       "240           27  87  29   0.5  45.9   3.5   7.9  0.4   3.4        0       1\n",
+       "241           24  54  18   0.1  79.7   4.3  15.2  1.7   5.1        0       1\n",
+       "242           24  64  15   0.2  67.3   3.8  16.5  1.2   4.8        0       1\n",
+       "\n",
+       "[243 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## train test split\n",
+    "from sklearn.model_selection import train_test_split \n",
+    "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.25, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Feature selection based on correlation\n",
+    "# Calculate the correlation matrix\n",
+    "correlation_matrix = X_train.corr()\n",
+    "\n",
+    "# Create a mask to hide the upper triangle of the correlation matrix\n",
+    "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool), k=1)\n",
+    "\n",
+    "# Plotting the heatmap with the upper triangle masked\n",
+    "plt.figure(figsize=(12, 10))\n",
+    "sns.heatmap(correlation_matrix, annot=True, cmap='Greens', fmt=\".2f\", linewidths=.5, mask=mask)\n",
+    "plt.title('Trimmed Correlation Matrix Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SO we should remove any very highly correlated correlations and negative may be of our interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Checking the multicolinearlity we set threshold \n",
+    "\n",
+    "def correlation(dataset, threshold):\n",
+    "    col_corr =set()\n",
+    "    corr_matrix =dataset.corr()\n",
+    "    for i in range(len(corr_matrix.columns)):\n",
+    "        for j in range(i):\n",
+    "            if abs(corr_matrix.iloc[i,j]) > threshold:\n",
+    "                colname = corr_matrix.columns[i]\n",
+    "                col_corr.add((colname)) # col_corr.add((colname, corr_matrix.columns[j])) can be use to see which pair shows the correlation above threshold\n",
+    "    return col_corr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'BUI', 'DC'}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_features = correlation(X_train, 0.85) # here thresholds are normally set by domain experts. But in our present case, we are just choosing 85%.\n",
+    "corr_features"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "so we can drop these columns.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((182, 9), (61, 9))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop features when correlation is more than 0.85\n",
+    "X_train.drop(corr_features, axis=1, inplace=True)\n",
+    "X_test.drop(corr_features, axis=1, inplace=True)\n",
+    "X_train.shape, X_test.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Feature scaling or standardization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled =scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Box plot to understand effect of standard scaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_3972\\3943478109.py:2: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
+      "  plt.subplot(1,2,1)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(15,5))\n",
+    "plt.subplot(1,2,1)\n",
+    "sns.boxplot(data = X_train)\n",
+    "plt.title(\"X_train before scaling\")\n",
+    "plt.subplot(1,2,2)\n",
+    "sns.boxplot(data = X_train_scaled)\n",
+    "plt.title(\"X_train After scaling\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 0.5468236465249976\n",
+      "Mean squared error = 0.6742766873791581\n",
+      "R-squared value = 0.9847657384266951\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "linreg = LinearRegression()\n",
+    "linreg.fit(X_train_scaled, y_train)\n",
+    "y_pred =linreg.predict(X_test_scaled)\n",
+    "\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "mse  = mean_squared_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f'Mean absolute error = {mae}')\n",
+    "print(f'Mean squared error = {mse}')\n",
+    "print(f'R-squared value = {score}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x25df1808e90>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lasso regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 1.1331759949144085\n",
+      "Mean squared error = 2.2483458918974746\n",
+      "R-squared value = 0.9492020263112388\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Lasso\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "lasso = Lasso()\n",
+    "lasso.fit(X_train_scaled, y_train)\n",
+    "y_pred =lasso.predict(X_test_scaled)\n",
+    "\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "mse  = mean_squared_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f'Mean absolute error = {mae}')\n",
+    "print(f'Mean squared error = {mse}')\n",
+    "print(f'R-squared value = {score}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Lasso cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#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>LassoCV(cv=5)</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\">LassoCV</label><div class=\"sk-toggleable__content\"><pre>LassoCV(cv=5)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LassoCV(cv=5)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LassoCV\n",
+    "lassocv = LassoCV(cv=5)\n",
+    "lassocv.fit(X_train_scaled, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 8.17490595,  7.68312478, -0.25676525,  4.72643402,  6.78715772,\n",
+       "        1.77624325,  2.23148094,  7.64057821,  1.99176323,  3.39941035,\n",
+       "        0.62808928,  9.95945488,  9.36168319, 16.98503659, 18.28488762,\n",
+       "        1.61644108,  1.62751276, -0.6415713 ,  7.28510526,  3.10926518,\n",
+       "        1.95541903,  0.18069335,  6.47563129,  0.14318503, 20.99597009,\n",
+       "        5.11755206,  5.86208849,  9.75914403, -0.77037467,  9.91838577,\n",
+       "        6.72277075, -0.31776007, 10.31109643, 14.4365551 ,  1.71022677,\n",
+       "        0.83439752,  2.03414915,  5.97488529, -0.6263644 , -0.56200288,\n",
+       "        6.47253729,  2.07971408,  8.46741557, -0.8464481 , 15.40443856,\n",
+       "        8.32941189,  8.48782486,  1.44030355, 13.02752812,  1.20911545,\n",
+       "       29.08623849,  5.49737681, 17.15937199, 19.28890096, 13.71102991,\n",
+       "       16.05355549,  0.99056448,  9.0873725 ,  3.84455993, 14.43991192,\n",
+       "        5.23034139])"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = lassocv.predict(X_test_scaled)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([7.05853002, 6.58280872, 6.13914944, 5.72539132, 5.33951911,\n",
+       "       4.97965339, 4.64404142, 4.33104857, 4.03915039, 3.76692517,\n",
+       "       3.51304702, 3.27627941, 3.05546914, 2.84954075, 2.65749124,\n",
+       "       2.47838523, 2.31135036, 2.15557308, 2.01029467, 1.87480753,\n",
+       "       1.74845178, 1.63061198, 1.52071419, 1.41822315, 1.32263965,\n",
+       "       1.23349817, 1.15036452, 1.0728338 , 1.00052839, 0.93309613,\n",
+       "       0.87020857, 0.81155943, 0.75686304, 0.705853  , 0.65828087,\n",
+       "       0.61391494, 0.57253913, 0.53395191, 0.49796534, 0.46440414,\n",
+       "       0.43310486, 0.40391504, 0.37669252, 0.3513047 , 0.32762794,\n",
+       "       0.30554691, 0.28495408, 0.26574912, 0.24783852, 0.23113504,\n",
+       "       0.21555731, 0.20102947, 0.18748075, 0.17484518, 0.1630612 ,\n",
+       "       0.15207142, 0.14182231, 0.13226397, 0.12334982, 0.11503645,\n",
+       "       0.10728338, 0.10005284, 0.09330961, 0.08702086, 0.08115594,\n",
+       "       0.0756863 , 0.0705853 , 0.06582809, 0.06139149, 0.05725391,\n",
+       "       0.05339519, 0.04979653, 0.04644041, 0.04331049, 0.0403915 ,\n",
+       "       0.03766925, 0.03513047, 0.03276279, 0.03055469, 0.02849541,\n",
+       "       0.02657491, 0.02478385, 0.0231135 , 0.02155573, 0.02010295,\n",
+       "       0.01874808, 0.01748452, 0.01630612, 0.01520714, 0.01418223,\n",
+       "       0.0132264 , 0.01233498, 0.01150365, 0.01072834, 0.01000528,\n",
+       "       0.00933096, 0.00870209, 0.00811559, 0.00756863, 0.00705853])"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lassocv.alphas_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting alphas against mean squared errors\n",
+    "plt.plot(lassocv.alphas_, lassocv.mse_path_.mean(axis=-1), marker='o')\n",
+    "plt.xscale('log')  # Using a logarithmic scale for better visualization\n",
+    "plt.xlabel('Alpha')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.title('LassoCV Alpha Selection')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot MSE path\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(lassocv.alphas_, lassocv.mse_path_, ':')\n",
+    "plt.plot(lassocv.alphas_, lassocv.mse_path_.mean(axis=-1), 'k', label='Average MSE')\n",
+    "plt.axvline(lassocv.alpha_, linestyle='--', color='r', label='Selected Alpha')\n",
+    "plt.legend()\n",
+    "plt.xscale('log')\n",
+    "plt.xlabel('Alpha (Regularization Strength)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.title('LassoCV Mean Squared Error Path')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ridge regression model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 0.5642305340105693\n",
+      "Mean squared error = 0.6949198918152067\n",
+      "R-squared value = 0.9842993364555513\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "ridge = Ridge()\n",
+    "ridge.fit(X_train_scaled, y_train)\n",
+    "y_pred =ridge.predict(X_test_scaled)\n",
+    "\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "mse  = mean_squared_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f'Mean absolute error = {mae}')\n",
+    "print(f'Mean squared error = {mse}')\n",
+    "print(f'R-squared value = {score}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ridge cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error=0.5642305340105693\n",
+      "R-squared value = 0.9842993364555513\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import RidgeCV \n",
+    "ridgecv = RidgeCV(cv=5)\n",
+    "ridgecv.fit(X_train_scaled, y_train)\n",
+    "y_pred = ridgecv.predict(X_test_scaled)\n",
+    "plt.scatter(y_test,y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f\"Mean absolute error={mae}\")\n",
+    "print(f\"R-squared value = {score}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridgecv.alpha_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1, 1.0, 10.0)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridgecv.alphas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "so out of (0.1, 1.0, 10.0) alphas, it choose 1.0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'alpha_per_target': False,\n",
+       " 'alphas': (0.1, 1.0, 10.0),\n",
+       " 'cv': 5,\n",
+       " 'fit_intercept': True,\n",
+       " 'gcv_mode': None,\n",
+       " 'scoring': None,\n",
+       " 'store_cv_values': False}"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ridgecv.get_params()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Elasticnet regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 1.8822353634896\n",
+      "Mean squared error = 5.517251101025224\n",
+      "R-squared value = 0.8753460589519703\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNet\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "elastic = ElasticNet()\n",
+    "elastic.fit(X_train_scaled, y_train)\n",
+    "y_pred =elastic.predict(X_test_scaled)\n",
+    "\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "mse  = mean_squared_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f'Mean absolute error = {mae}')\n",
+    "print(f'Mean squared error = {mse}')\n",
+    "print(f'R-squared value = {score}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ELasricnet cross validation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean absolute error = 0.6575946731430904\n",
+      "Mean squared error = 0.8222830416276272\n",
+      "R-squared value = 0.9814217587854941\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import ElasticNetCV\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
+    "elasticcv = ElasticNetCV()\n",
+    "elasticcv.fit(X_train_scaled, y_train)\n",
+    "y_pred =elasticcv.predict(X_test_scaled)\n",
+    "\n",
+    "plt.scatter(y_test,y_pred)\n",
+    "\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "mse  = mean_squared_error(y_test, y_pred)\n",
+    "score = r2_score(y_test, y_pred)\n",
+    "print(f'Mean absolute error = {mae}')\n",
+    "print(f'Mean squared error = {mse}')\n",
+    "print(f'R-squared value = {score}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14.11706004, 13.16561744, 12.27829889, 11.45078264, 10.67903821,\n",
+       "        9.95930678,  9.28808283,  8.66209714,  8.07830078,  7.53385034,\n",
+       "        7.02609405,  6.55255882,  6.11093829,  5.6990815 ,  5.31498248,\n",
+       "        4.95677045,  4.62270071,  4.31114616,  4.02058933,  3.74961507,\n",
+       "        3.49690356,  3.26122397,  3.04142839,  2.83644629,  2.64527931,\n",
+       "        2.46699633,  2.30072904,  2.1456676 ,  2.00105679,  1.86619226,\n",
+       "        1.74041714,  1.62311885,  1.51372607,  1.411706  ,  1.31656174,\n",
+       "        1.22782989,  1.14507826,  1.06790382,  0.99593068,  0.92880828,\n",
+       "        0.86620971,  0.80783008,  0.75338503,  0.7026094 ,  0.65525588,\n",
+       "        0.61109383,  0.56990815,  0.53149825,  0.49567705,  0.46227007,\n",
+       "        0.43111462,  0.40205893,  0.37496151,  0.34969036,  0.3261224 ,\n",
+       "        0.30414284,  0.28364463,  0.26452793,  0.24669963,  0.2300729 ,\n",
+       "        0.21456676,  0.20010568,  0.18661923,  0.17404171,  0.16231189,\n",
+       "        0.15137261,  0.1411706 ,  0.13165617,  0.12278299,  0.11450783,\n",
+       "        0.10679038,  0.09959307,  0.09288083,  0.08662097,  0.08078301,\n",
+       "        0.0753385 ,  0.07026094,  0.06552559,  0.06110938,  0.05699082,\n",
+       "        0.05314982,  0.0495677 ,  0.04622701,  0.04311146,  0.04020589,\n",
+       "        0.03749615,  0.03496904,  0.03261224,  0.03041428,  0.02836446,\n",
+       "        0.02645279,  0.02466996,  0.02300729,  0.02145668,  0.02001057,\n",
+       "        0.01866192,  0.01740417,  0.01623119,  0.01513726,  0.01411706])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elasticcv.alphas_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot MSE path\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(lassocv.alphas_, lassocv.mse_path_, ':')\n",
+    "plt.plot(elasticcv.alphas_, elasticcv.mse_path_.mean(axis=-1), 'k', label='Average MSE')\n",
+    "plt.axvline(lassocv.alpha_, linestyle='--', color='r', label='Selected Alpha')\n",
+    "plt.legend()\n",
+    "plt.xscale('log')\n",
+    "plt.xlabel('Alpha (Regularization Strength)')\n",
+    "plt.ylabel('Mean Squared Error (MSE)')\n",
+    "plt.title('Elasticnet Cross validation Mean Squared Error Path')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}