Car Price Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "249998d4-7205-48c5-8765-1c8af049d242",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76790c90-d9db-49ac-97c1-99e04d4903cc",
   "metadata": {},
   "source": [
    "# Data Collection and Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a470858-340d-45fe-922f-093144a8f9c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the data from csv file to pandas dataframe\n",
    "car_dataset=pd.read_csv('car data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2bbd4bad-de88-4b22-8ac9-ee0a24b86e77",
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>Petrol</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>Diesel</td>\n",
       "      <td>Dealer</td>\n",
       "      <td>Manual</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000    Petrol   \n",
       "1      sx4  2013           4.75           9.54       43000    Diesel   \n",
       "2     ciaz  2017           7.25           9.85        6900    Petrol   \n",
       "3  wagon r  2011           2.85           4.15        5200    Petrol   \n",
       "4    swift  2014           4.60           6.87       42450    Diesel   \n",
       "\n",
       "  Seller_Type Transmission  Owner  \n",
       "0      Dealer       Manual      0  \n",
       "1      Dealer       Manual      0  \n",
       "2      Dealer       Manual      0  \n",
       "3      Dealer       Manual      0  \n",
       "4      Dealer       Manual      0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspecting the first 5 rows of the dataframe\n",
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db560ddd-0e08-4cc8-a450-e38c69fcf5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(301, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of rows and columns\n",
    "car_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "017254de-3c6e-42a4-8b5b-4fc6d82bb9c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 301 entries, 0 to 300\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Car_Name       301 non-null    object \n",
      " 1   Year           301 non-null    int64  \n",
      " 2   Selling_Price  301 non-null    float64\n",
      " 3   Present_Price  301 non-null    float64\n",
      " 4   Kms_Driven     301 non-null    int64  \n",
      " 5   Fuel_Type      301 non-null    int64  \n",
      " 6   Seller_Type    301 non-null    int64  \n",
      " 7   Transmission   301 non-null    int64  \n",
      " 8   Owner          301 non-null    int64  \n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 21.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# getting some information about the dataset\n",
    "car_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3913af5-eb24-41b7-b3b2-878a90281538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Car_Name         0\n",
       "Year             0\n",
       "Selling_Price    0\n",
       "Present_Price    0\n",
       "Kms_Driven       0\n",
       "Fuel_Type        0\n",
       "Seller_Type      0\n",
       "Transmission     0\n",
       "Owner            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the number of missing values\n",
    "car_dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0b8922ad-95e2-4f4e-95bc-8358e4220764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuel_Type\n",
      "0    239\n",
      "1     60\n",
      "2      2\n",
      "Name: count, dtype: int64\n",
      "Seller_Type\n",
      "0    195\n",
      "1    106\n",
      "Name: count, dtype: int64\n",
      "Transmission\n",
      "0    261\n",
      "1     40\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# checking the distribution of categorical data\n",
    "print(car_dataset.Fuel_Type.value_counts())\n",
    "print(car_dataset.Seller_Type.value_counts())\n",
    "print(car_dataset.Transmission.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "244ad860-eb2c-4156-a9b1-16a78299392c",
   "metadata": {},
   "source": [
    "# Encoding the categorial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "32bb6e2f-2af8-409a-9917-fa3fadc1b000",
   "metadata": {},
   "outputs": [],
   "source": [
    "# encoding \"Fuel_Type\" Column\n",
    "car_dataset.replace({'Fuel_Type':{'Petrol':0,'Diesel':1,'CNG':2}},inplace=True)\n",
    "\n",
    "# encoding \"Seller_Type\" Column\n",
    "car_dataset.replace({'Seller_Type':{'Dealer':0,'Individual':1}},inplace=True)\n",
    "\n",
    "# encoding \"Transmission\" Column\n",
    "car_dataset.replace({'Transmission':{'Manual':0,'Automatic':1}},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7683f273-e3f0-4ce5-a650-c3d136866333",
   "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>Car_Name</th>\n",
       "      <th>Year</th>\n",
       "      <th>Selling_Price</th>\n",
       "      <th>Present_Price</th>\n",
       "      <th>Kms_Driven</th>\n",
       "      <th>Fuel_Type</th>\n",
       "      <th>Seller_Type</th>\n",
       "      <th>Transmission</th>\n",
       "      <th>Owner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ritz</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.35</td>\n",
       "      <td>5.59</td>\n",
       "      <td>27000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sx4</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.75</td>\n",
       "      <td>9.54</td>\n",
       "      <td>43000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ciaz</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.25</td>\n",
       "      <td>9.85</td>\n",
       "      <td>6900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wagon r</td>\n",
       "      <td>2011</td>\n",
       "      <td>2.85</td>\n",
       "      <td>4.15</td>\n",
       "      <td>5200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>swift</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.60</td>\n",
       "      <td>6.87</td>\n",
       "      <td>42450</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Car_Name  Year  Selling_Price  Present_Price  Kms_Driven  Fuel_Type  \\\n",
       "0     ritz  2014           3.35           5.59       27000          0   \n",
       "1      sx4  2013           4.75           9.54       43000          1   \n",
       "2     ciaz  2017           7.25           9.85        6900          0   \n",
       "3  wagon r  2011           2.85           4.15        5200          0   \n",
       "4    swift  2014           4.60           6.87       42450          1   \n",
       "\n",
       "   Seller_Type  Transmission  Owner  \n",
       "0            0             0      0  \n",
       "1            0             0      0  \n",
       "2            0             0      0  \n",
       "3            0             0      0  \n",
       "4            0             0      0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9b2a78-7f90-4a58-9efa-6b8b63bf8741",
   "metadata": {},
   "source": [
    "# Splitting the Data and Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e171dffd-3034-4bb6-b3fe-8eaf2c19e737",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = car_dataset.drop(['Car_Name','Selling_Price'],axis=1)\n",
    "Y = car_dataset['Selling_Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f2ac226-96ea-4715-a42e-ae04d40a3372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Year  Present_Price  Kms_Driven  Fuel_Type  Seller_Type  Transmission  \\\n",
      "0    2014           5.59       27000          0            0             0   \n",
      "1    2013           9.54       43000          1            0             0   \n",
      "2    2017           9.85        6900          0            0             0   \n",
      "3    2011           4.15        5200          0            0             0   \n",
      "4    2014           6.87       42450          1            0             0   \n",
      "..    ...            ...         ...        ...          ...           ...   \n",
      "296  2016          11.60       33988          1            0             0   \n",
      "297  2015           5.90       60000          0            0             0   \n",
      "298  2009          11.00       87934          0            0             0   \n",
      "299  2017          12.50        9000          1            0             0   \n",
      "300  2016           5.90        5464          0            0             0   \n",
      "\n",
      "     Owner  \n",
      "0        0  \n",
      "1        0  \n",
      "2        0  \n",
      "3        0  \n",
      "4        0  \n",
      "..     ...  \n",
      "296      0  \n",
      "297      0  \n",
      "298      0  \n",
      "299      0  \n",
      "300      0  \n",
      "\n",
      "[301 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "16b9eb2e-c934-4831-a00b-ed5e40482b6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       3.35\n",
      "1       4.75\n",
      "2       7.25\n",
      "3       2.85\n",
      "4       4.60\n",
      "       ...  \n",
      "296     9.50\n",
      "297     4.00\n",
      "298     3.35\n",
      "299    11.50\n",
      "300     5.30\n",
      "Name: Selling_Price, Length: 301, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5b093a-eccc-49f0-bc2a-b3937bc80c3d",
   "metadata": {},
   "source": [
    "**Splitting Training and Test data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7139739f-1c47-4992-8b1b-9c77e989de52",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.1, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbb662c-20de-4be4-8e4f-e67faf7f4b7d",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5ca6f7-3291-4059-8eba-a1f70126114e",
   "metadata": {},
   "source": [
    "**1.Linear Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "56b7ab1b-a0e3-413b-9dde-d092e70ef4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the linear regression model\n",
    "lin_reg_model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1680cd1c-a873-4101-94fe-2e2372d5edaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21e1097-b610-4373-b061-3cf905794516",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0602b05c-35cc-47a2-8917-cc73772fbae9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction = lin_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0bf904b-f9c1-4962-85b9-53381f1c6412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8799451660493708\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74be8aa8-911a-4463-aea1-f21aae1736f0",
   "metadata": {},
   "source": [
    "**Visualize the actual prices and Predicted prices**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aa08f2fe-737e-499e-be7d-ec1012f38472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c4ae9453-3fc6-4cda-9c17-e2a5616d6752",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "test_data_prediction = lin_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "36f4350f-bb0b-496f-9a9a-75c7a47b7ae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8365766715026374\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2e65d83-ac79-427f-aac4-11c3d08da1cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f780cf-42b3-4a44-90e2-858216260bb9",
   "metadata": {},
   "source": [
    "# 2.Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bc4aac78-d26f-4d99-a1b2-2349ce3ea736",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the linear regression model\n",
    "lass_reg_model = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1bb5c293-a4b5-4f86-b422-d154406d39d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Lasso<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.Lasso.html\">?<span>Documentation for Lasso</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Lasso()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lass_reg_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84044a2-2f81-48f0-9f97-c2923e85337f",
   "metadata": {},
   "source": [
    "**Model Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69681250-1ca0-4884-aadb-0152d23d2ad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "training_data_prediction = lass_reg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6198d1b2-d2b2-47c3-885f-d692a031f887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8427856123435794\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_train, training_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "690e801b-3468-4091-9e86-3218ec795f97",
   "metadata": {},
   "source": [
    "**Visualize the actual prices and Predicted prices**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "64b1042f-ec0e-4c66-8a0f-170a13df86e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_train, training_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fdbecca6-d180-4e8b-900d-676473ae065a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction on Training data\n",
    "test_data_prediction = lass_reg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f20e5be8-3f40-480a-b0d7-a029673cf694",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R squared Error :  0.8709167941173195\n"
     ]
    }
   ],
   "source": [
    "# R squared Error\n",
    "error_score = metrics.r2_score(Y_test, test_data_prediction)\n",
    "print(\"R squared Error : \", error_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5fa85288-b392-4b39-8183-3fab87e62e20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Y_test, test_data_prediction)\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"Predicted Price\")\n",
    "plt.title(\" Actual Prices vs Predicted Prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9be4f2c-e773-45d0-bdc2-a9dabf7871b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}