MLR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3106b333-9531-4bc6-bd3b-d3c8fc0cbc69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1:\n",
      "Mean Squared Error: 2096851.1400177048\n",
      "R-squared: 0.8428476112018002\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\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.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.linear_model import Lasso, Ridge\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(r\"C:\\Users\\admin\\Downloads\\MLR\\MLR\\ToyotaCorolla - MLR.csv\")\n",
    "\n",
    "# Encode categorical features\n",
    "le = LabelEncoder()\n",
    "data['Fuel_Type'] = le.fit_transform(data['Fuel_Type'])\n",
    "data['Automatic'] = le.fit_transform(data['Automatic'])\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X = data.drop('Price', axis=1)\n",
    "y = data['Price']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Model 1: All Features\n",
    "model1 = LinearRegression()\n",
    "model1.fit(X_train, y_train)\n",
    "y_pred1 = model1.predict(X_test)\n",
    "\n",
    "# Model 2: Feature Selection\n",
    "X_train_reduced = X_train[['Age_08_04', 'KM', 'HP', 'cc']]\n",
    "X_test_reduced = X_test[['Age_08_04', 'KM', 'HP', 'cc']]\n",
    "model2 = LinearRegression()\n",
    "model2.fit(X_train_reduced, y_train)\n",
    "y_pred2 = model2.predict(X_test_reduced)\n",
    "\n",
    "# Model 3: Feature Transformation\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly = PolynomialFeatures(degree=2)\n",
    "X_train_poly = poly.fit_transform(X_train)\n",
    "X_test_poly = poly.transform(X_test)\n",
    "model3 = LinearRegression()\n",
    "model3.fit(X_train_poly, y_train)\n",
    "y_pred3 = model3.predict(X_test_poly)\n",
    "\n",
    "# Evaluate models\n",
    "mse1 = mean_squared_error(y_test, y_pred1)\n",
    "r2_1 = r2_score(y_test, y_pred1)\n",
    "print(\"Model 1:\")\n",
    "print(\"Mean Squared Error:\", mse1)\n",
    "print(\"R-squared:\", r2_1)\n",
    "\n",
    "# Apply Lasso and Ridge Regression\n",
    "lasso_reg = Lasso(alpha=0.1)\n",
    "lasso_reg.fit(X_train, y_train)\n",
    "y_pred_lasso = lasso_reg.predict(X_test)\n",
    "\n",
    "ridge_reg = Ridge(alpha=1.0)\n",
    "ridge_reg.fit(X_train, y_train)\n",
    "y_pred_ridge = ridge_reg.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6d51d188-4968-4e6a-a228-50d5c5a23911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Normalization scales data to a fixed range (0,1), while Standardization transforms data to have zero mean and unit variance.\n",
      "\n",
      "Standardization is useful for regression models to ensure features contribute equally and improve numerical stability.\n",
      "\n",
      "Techniques to address multicollinearity:\n",
      "1. Variance Inflation Factor (VIF) to detect high correlation between features.\n",
      "2. Removing correlated features based on a correlation matrix.\n",
      "3. Principal Component Analysis (PCA) to reduce dimensionality.\n",
      "4. Ridge Regression (L2 Regularization) to shrink coefficients.\n",
      "5. Lasso Regression (L1 Regularization) to eliminate some features.\n"
     ]
    }
   ],
   "source": [
    "# Explanation of Normalization & Standardization\n",
    "print(\"\\nNormalization scales data to a fixed range (0,1), while Standardization transforms data to have zero mean and unit variance.\")\n",
    "print(\"\\nStandardization is useful for regression models to ensure features contribute equally and improve numerical stability.\")\n",
    "\n",
    "# Explanation of Multicollinearity Handling\n",
    "print(\"\\nTechniques to address multicollinearity:\")\n",
    "print(\"1. Variance Inflation Factor (VIF) to detect high correlation between features.\")\n",
    "print(\"2. Removing correlated features based on a correlation matrix.\")\n",
    "print(\"3. Principal Component Analysis (PCA) to reduce dimensionality.\")\n",
    "print(\"4. Ridge Regression (L2 Regularization) to shrink coefficients.\")\n",
    "print(\"5. Lasso Regression (L1 Regularization) to eliminate some features.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80512e7a-b738-46ea-b637-b5ca38cbfd21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1436 entries, 0 to 1435\n",
      "Data columns (total 11 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Price      1436 non-null   int64 \n",
      " 1   Age_08_04  1436 non-null   int64 \n",
      " 2   KM         1436 non-null   int64 \n",
      " 3   Fuel_Type  1436 non-null   object\n",
      " 4   HP         1436 non-null   int64 \n",
      " 5   Automatic  1436 non-null   int64 \n",
      " 6   cc         1436 non-null   int64 \n",
      " 7   Doors      1436 non-null   int64 \n",
      " 8   Cylinders  1436 non-null   int64 \n",
      " 9   Gears      1436 non-null   int64 \n",
      " 10  Weight     1436 non-null   int64 \n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 123.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None,\n",
       "    Price  Age_08_04     KM Fuel_Type  HP  Automatic    cc  Doors  Cylinders  \\\n",
       " 0  13500         23  46986    Diesel  90          0  2000      3          4   \n",
       " 1  13750         23  72937    Diesel  90          0  2000      3          4   \n",
       " 2  13950         24  41711    Diesel  90          0  2000      3          4   \n",
       " 3  14950         26  48000    Diesel  90          0  2000      3          4   \n",
       " 4  13750         30  38500    Diesel  90          0  2000      3          4   \n",
       " \n",
       "    Gears  Weight  \n",
       " 0      5    1165  \n",
       " 1      5    1165  \n",
       " 2      5    1165  \n",
       " 3      5    1165  \n",
       " 4      5    1170  )"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "file_path = r\"C:\\Users\\admin\\Downloads\\MLR\\MLR\\ToyotaCorolla - MLR.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Display basic information and first few rows\n",
    "df.info(), df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee2470c6-c6a3-4101-ae20-d04c4e30c7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Price</th>\n",
       "      <th>Age_08_04</th>\n",
       "      <th>KM</th>\n",
       "      <th>HP</th>\n",
       "      <th>Automatic</th>\n",
       "      <th>cc</th>\n",
       "      <th>Doors</th>\n",
       "      <th>Cylinders</th>\n",
       "      <th>Gears</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.00000</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>1436.000000</td>\n",
       "      <td>1436.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10730.824513</td>\n",
       "      <td>55.947075</td>\n",
       "      <td>68533.259749</td>\n",
       "      <td>101.502089</td>\n",
       "      <td>0.055710</td>\n",
       "      <td>1576.85585</td>\n",
       "      <td>4.033426</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.026462</td>\n",
       "      <td>1072.45961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3626.964585</td>\n",
       "      <td>18.599988</td>\n",
       "      <td>37506.448872</td>\n",
       "      <td>14.981080</td>\n",
       "      <td>0.229441</td>\n",
       "      <td>424.38677</td>\n",
       "      <td>0.952677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.188510</td>\n",
       "      <td>52.64112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4350.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1300.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8450.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>43000.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1400.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1040.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9900.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>63389.500000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1070.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11950.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>87020.750000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1600.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1085.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>32500.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>243000.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16000.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1615.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price    Age_08_04             KM           HP    Automatic  \\\n",
       "count   1436.000000  1436.000000    1436.000000  1436.000000  1436.000000   \n",
       "mean   10730.824513    55.947075   68533.259749   101.502089     0.055710   \n",
       "std     3626.964585    18.599988   37506.448872    14.981080     0.229441   \n",
       "min     4350.000000     1.000000       1.000000    69.000000     0.000000   \n",
       "25%     8450.000000    44.000000   43000.000000    90.000000     0.000000   \n",
       "50%     9900.000000    61.000000   63389.500000   110.000000     0.000000   \n",
       "75%    11950.000000    70.000000   87020.750000   110.000000     0.000000   \n",
       "max    32500.000000    80.000000  243000.000000   192.000000     1.000000   \n",
       "\n",
       "                cc        Doors  Cylinders        Gears      Weight  \n",
       "count   1436.00000  1436.000000     1436.0  1436.000000  1436.00000  \n",
       "mean    1576.85585     4.033426        4.0     5.026462  1072.45961  \n",
       "std      424.38677     0.952677        0.0     0.188510    52.64112  \n",
       "min     1300.00000     2.000000        4.0     3.000000  1000.00000  \n",
       "25%     1400.00000     3.000000        4.0     5.000000  1040.00000  \n",
       "50%     1600.00000     4.000000        4.0     5.000000  1070.00000  \n",
       "75%     1600.00000     5.000000        4.0     5.000000  1085.00000  \n",
       "max    16000.00000     5.000000        4.0     6.000000  1615.00000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Summary statistics\n",
    "summary_stats = df.describe()\n",
    "\n",
    "# Plot histograms for numerical variables\n",
    "df.hist(figsize=(12, 8), bins=20, edgecolor=\"black\")\n",
    "plt.suptitle(\"Feature Distributions\", fontsize=14)\n",
    "plt.show()\n",
    "\n",
    "# Boxplot for outlier detection\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=df.drop(columns=[\"Fuel_Type\"]))  \n",
    "plt.xticks(rotation=45)\n",
    "plt.title(\"Boxplot of Numeric Variables\")\n",
    "plt.show()\n",
    "\n",
    "summary_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f68f8c63-80aa-4148-b9ec-cc335327849e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Encoding categorical variable 'Fuel_Type' using one-hot encoding\n",
    "df = pd.get_dummies(df, columns=['Fuel_Type'], drop_first=True)\n",
    "\n",
    "# Compute correlation matrix\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=0.5)\n",
    "plt.title(\"Correlation Matrix of Features\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac58c6a8-c4ce-425b-9dbd-981742583f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price               110\n",
       "Age_08_04             7\n",
       "KM                   49\n",
       "HP                   11\n",
       "Automatic            80\n",
       "cc                  123\n",
       "Doors                 0\n",
       "Cylinders             0\n",
       "Gears                46\n",
       "Weight               66\n",
       "Fuel_Type_Diesel    155\n",
       "Fuel_Type_Petrol    172\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying outliers using IQR method\n",
    "Q1 = df.quantile(0.25)\n",
    "Q3 = df.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Defining lower and upper bounds for outlier detection\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Finding outliers\n",
    "outliers = ((df < lower_bound) | (df > upper_bound)).sum()\n",
    "\n",
    "# Display number of outliers per column\n",
    "outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31974a2e-2113-4447-a081-373f065971e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price               110\n",
       "Age_08_04             7\n",
       "KM                   49\n",
       "HP                   11\n",
       "Automatic            80\n",
       "cc                  123\n",
       "Doors                 0\n",
       "Cylinders             0\n",
       "Gears                46\n",
       "Weight               66\n",
       "Fuel_Type_Diesel    155\n",
       "Fuel_Type_Petrol    172\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capping outliers at 1st and 99th percentile\n",
    "for col in [\"Price\", \"KM\", \"cc\", \"Weight\"]:\n",
    "    lower_limit = df[col].quantile(0.01)\n",
    "    upper_limit = df[col].quantile(0.99)\n",
    "    df[col] = df[col].clip(lower=lower_limit, upper=upper_limit)\n",
    "\n",
    "# Verify if outliers are reduced\n",
    "new_outliers = ((df < lower_bound) | (df > upper_bound)).sum()\n",
    "new_outliers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e2e0229-23d3-4fa3-b3b9-8c3d7eeedfcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1148, 11), (288, 11), (1148,), (288,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Define features (X) and target (y)\n",
    "X = df.drop(columns=[\"Price\"])  # Features\n",
    "y = df[\"Price\"]  # Target variable\n",
    "\n",
    "# Split data into 80% training and 20% testing\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Check the shape of the split datasets\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66d6cdda-32b6-4d53-b9f2-4a4df0f74cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(888.9216819025836, 1372022.7755441973, 1171.3337592437936, 0.8820579704665981)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "# Initialize and train a basic multiple linear regression model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred = lr_model.predict(X_test)\n",
    "\n",
    "# Evaluate model performance\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "rmse = mse ** 0.5\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "# Display results\n",
    "mae, mse, rmse, r2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "262de8ac-b51d-40b7-a822-d2b048379815",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889.9784614991897, 1375113.2637497475, 1172.6522347864893, 0.8817923054516226)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "# Initialize and train Ridge regression model\n",
    "ridge_model = Ridge(alpha=1.0)  # Regularization strength set to 1.0\n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_ridge = ridge_model.predict(X_test)\n",
    "\n",
    "# Evaluate Ridge model performance\n",
    "mae_ridge = mean_absolute_error(y_test, y_pred_ridge)\n",
    "mse_ridge = mean_squared_error(y_test, y_pred_ridge)\n",
    "rmse_ridge = mse_ridge ** 0.5\n",
    "r2_ridge = r2_score(y_test, y_pred_ridge)\n",
    "\n",
    "# Display results\n",
    "mae_ridge, mse_ridge, rmse_ridge, r2_ridge\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e5cd45b-e7a9-4998-9a38-3ecc999f84c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889.0650635996576, 1372488.144992335, 1171.5323917810958, 0.8820179663076478)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "# Initialize and train Lasso regression model\n",
    "lasso_model = Lasso(alpha=0.1)  \n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "# Predict on the test set\n",
    "y_pred_lasso = lasso_model.predict(X_test)\n",
    "\n",
    "# Evaluate Lasso model performance\n",
    "mae_lasso = mean_absolute_error(y_test, y_pred_lasso)\n",
    "mse_lasso = mean_squared_error(y_test, y_pred_lasso)\n",
    "rmse_lasso = mse_lasso ** 0.5\n",
    "r2_lasso = r2_score(y_test, y_pred_lasso)\n",
    "\n",
    "# Display results\n",
    "mae_lasso, mse_lasso, rmse_lasso, r2_lasso\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8736c78c-2cb0-4ae6-95b5-41c7fa6b72ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fuel_Type_Petrol    1396.778718\n",
       "Fuel_Type_Diesel    1172.107192\n",
       "Gears                479.265507\n",
       "Automatic             97.128472\n",
       "HP                    29.396364\n",
       "Weight                28.601547\n",
       "KM                    -0.014715\n",
       "cc                    -2.586055\n",
       "Doors                -75.372573\n",
       "Age_08_04           -117.103811\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting feature importance from Lasso model\n",
    "lasso_coefficients = pd.Series(lasso_model.coef_, index=X.columns)\n",
    "\n",
    "# Displaying non-zero coefficients (features selected by Lasso)\n",
    "lasso_coefficients[lasso_coefficients != 0].sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b08e8055-3182-4c6c-bb18-73f1016e1632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized:\n",
      "    Age   KM\n",
      "0  0.0  0.0\n",
      "1  0.5  0.5\n",
      "2  1.0  1.0\n",
      "\n",
      "Standardized:\n",
      "         Age        KM\n",
      "0 -1.224745 -1.224745\n",
      "1  0.000000  0.000000\n",
      "2  1.224745  1.224745\n",
      "VIF Values:\n",
      "   Feature            VIF\n",
      "0      x1  329473.666662\n",
      "1      x2  329473.666662\n",
      "Reduced Model Coefficients: [0.01]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Q1. What is Normalization and Standardization? Why are they important?\n",
    "\n",
    "Normalization (Min-Max Scaling) scales features to a 0 to 1 range.\n",
    "Formula: (X - min) / (max - min)\n",
    "Example: KM = [20000, 50000, 80000] becomes [0.0, 0.5, 1.0]\n",
    "\n",
    "Standardization (Z-score Scaling) transforms data to have mean = 0 and std = 1.\n",
    "Formula: (X - mean) / std\n",
    "Example: Age = [20, 30, 40] becomes [-1.22, 0.0, 1.22]\n",
    "\n",
    "Use when:\n",
    "Features have different units or scales.\n",
    "Models like Linear Regression, KNN, SVM perform better with scaled data.\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "\n",
    "data = pd.DataFrame({'Age': [20, 30, 40], 'KM': [20000, 50000, 80000]})\n",
    "\n",
    "# Normalization\n",
    "norm = MinMaxScaler()\n",
    "data_norm = pd.DataFrame(norm.fit_transform(data), columns=data.columns)\n",
    "\n",
    "# Standardization\n",
    "std = StandardScaler()\n",
    "data_std = pd.DataFrame(std.fit_transform(data), columns=data.columns)\n",
    "\n",
    "print(\"Normalized:\\n\", data_norm)\n",
    "print(\"\\nStandardized:\\n\", data_std)\n",
    "\n",
    "\"\"\"\n",
    "Q2. How to detect and handle multicollinearity in multiple linear regression?\n",
    "\n",
    "Multicollinearity occurs when two or more features are highly correlated.\n",
    "It makes coefficient estimates unstable and hard to interpret.\n",
    "\n",
    "Detect using:\n",
    "VIF (Variance Inflation Factor) where VIF > 5 indicates high multicollinearity.\n",
    "Formula: VIF = 1 / (1 - R squared)\n",
    "\n",
    "Example: x1 and x2 both increase linearly. VIF for each may be very high.\n",
    "\n",
    "Solutions:\n",
    "Remove one of the correlated features.\n",
    "Use Ridge or Lasso regression to reduce coefficient size.\n",
    "Apply PCA to create uncorrelated features.\n",
    "\n",
    "Example:\n",
    "\"\"\"\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'x1': [100, 200, 300],\n",
    "    'x2': [101, 201, 301],\n",
    "    'y': [1, 2, 3]\n",
    "})\n",
    "\n",
    "X = df[['x1', 'x2']]\n",
    "y = df['y']\n",
    "\n",
    "# Calculate VIF\n",
    "vif = pd.DataFrame()\n",
    "vif['Feature'] = X.columns\n",
    "vif['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(\"VIF Values:\\n\", vif)\n",
    "\n",
    "# Drop one correlated feature (x2)\n",
    "X_reduced = X.drop(columns='x2')\n",
    "model = LinearRegression().fit(X_reduced, y)\n",
    "print(\"Reduced Model Coefficients:\", model.coef_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18bf5adb-430f-4680-af09-e9156d4a69e5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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