notebook/2_MODEL_TRAINING.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92e48866",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25791a74",
   "metadata": {},
   "source": [
    "#### 1.1 Import Data and Required Packages\n",
    "##### Importing Pandas, Numpy, Matplotlib, Seaborn and Warings Library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b080dfb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic Import\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "# Modelling\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,AdaBoostRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import LinearRegression, Ridge,Lasso\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45079ad",
   "metadata": {},
   "source": [
    "#### Import the CSV Data as Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e11c6255",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/stud.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20634923",
   "metadata": {},
   "source": [
    "#### Show Top 5 Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e7e412a2",
   "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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>math_score</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  math_score  reading_score  writing_score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd32281",
   "metadata": {},
   "source": [
    "#### Preparing X and Y variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56d72fde",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['math_score'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd613177",
   "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>gender</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>parental_level_of_education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test_preparation_course</th>\n",
       "      <th>reading_score</th>\n",
       "      <th>writing_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race_ethnicity parental_level_of_education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test_preparation_course  reading_score  writing_score  \n",
       "0                    none             72             74  \n",
       "1               completed             90             88  \n",
       "2                    none             95             93  \n",
       "3                    none             57             44  \n",
       "4                    none             78             75  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f237ea14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categories in 'gender' variable:      ['female' 'male']\n",
      "Categories in 'race_ethnicity' variable:   ['group B' 'group C' 'group A' 'group D' 'group E']\n",
      "Categories in'parental level of education' variable: [\"bachelor's degree\" 'some college' \"master's degree\" \"associate's degree\"\n",
      " 'high school' 'some high school']\n",
      "Categories in 'lunch' variable:      ['standard' 'free/reduced']\n",
      "Categories in 'test preparation course' variable:      ['none' 'completed']\n"
     ]
    }
   ],
   "source": [
    "print(\"Categories in 'gender' variable:     \",end=\" \" )\n",
    "print(df['gender'].unique())\n",
    "\n",
    "print(\"Categories in 'race_ethnicity' variable:  \",end=\" \")\n",
    "print(df['race_ethnicity'].unique())\n",
    "\n",
    "print(\"Categories in'parental level of education' variable:\",end=\" \" )\n",
    "print(df['parental_level_of_education'].unique())\n",
    "\n",
    "print(\"Categories in 'lunch' variable:     \",end=\" \" )\n",
    "print(df['lunch'].unique())\n",
    "\n",
    "print(\"Categories in 'test preparation course' variable:     \",end=\" \" )\n",
    "print(df['test_preparation_course'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "924b7f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['math_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ffc69816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      72\n",
       "1      69\n",
       "2      90\n",
       "3      47\n",
       "4      76\n",
       "       ..\n",
       "995    88\n",
       "996    62\n",
       "997    59\n",
       "998    68\n",
       "999    77\n",
       "Name: math_score, Length: 1000, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1e290fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Column Transformer with 3 types of transformers\n",
    "num_features = X.select_dtypes(exclude=\"object\").columns\n",
    "cat_features = X.select_dtypes(include=\"object\").columns\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "numeric_transformer = StandardScaler()\n",
    "oh_transformer = OneHotEncoder()\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    [\n",
    "        (\"OneHotEncoder\", oh_transformer, cat_features),\n",
    "         (\"StandardScaler\", numeric_transformer, num_features),        \n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c68f99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = preprocessor.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72459f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.        ,  0.        , ...,  1.        ,\n",
       "         0.19399858,  0.39149181],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         1.42747598,  1.31326868],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  1.        ,\n",
       "         1.77010859,  1.64247471],\n",
       "       ...,\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.12547206, -0.20107904],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.60515772,  0.58901542],\n",
       "       [ 1.        ,  0.        ,  0.        , ...,  1.        ,\n",
       "         1.15336989,  1.18158627]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ed5c4e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((800, 19), (200, 19))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate dataset into train and test\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=0.2,random_state=42)\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd80317",
   "metadata": {},
   "source": [
    "#### Create an Evaluate Function to give all metrics after model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c247bd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(true, predicted):\n",
    "    mae = mean_absolute_error(true, predicted)\n",
    "    mse = mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(mean_squared_error(true, predicted))\n",
    "    r2_square = r2_score(true, predicted)\n",
    "    return mae, rmse, r2_square\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79ccb8e7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 5.3293\n",
      "- Mean Absolute Error: 4.2715\n",
      "- R2 Score: 0.8740\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 5.4252\n",
      "- Mean Absolute Error: 4.2222\n",
      "- R2 Score: 0.8790\n",
      "===================================\n",
      "\n",
      "\n",
      "Lasso\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 6.5938\n",
      "- Mean Absolute Error: 5.2063\n",
      "- R2 Score: 0.8071\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 6.5197\n",
      "- Mean Absolute Error: 5.1579\n",
      "- R2 Score: 0.8253\n",
      "===================================\n",
      "\n",
      "\n",
      "Ridge\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 5.3233\n",
      "- Mean Absolute Error: 4.2650\n",
      "- R2 Score: 0.8743\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 5.3904\n",
      "- Mean Absolute Error: 4.2111\n",
      "- R2 Score: 0.8806\n",
      "===================================\n",
      "\n",
      "\n",
      "K-Neighbors Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 5.7079\n",
      "- Mean Absolute Error: 4.5168\n",
      "- R2 Score: 0.8555\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 7.2530\n",
      "- Mean Absolute Error: 5.6210\n",
      "- R2 Score: 0.7838\n",
      "===================================\n",
      "\n",
      "\n",
      "Decision Tree\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 0.2795\n",
      "- Mean Absolute Error: 0.0187\n",
      "- R2 Score: 0.9997\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 7.8326\n",
      "- Mean Absolute Error: 6.1700\n",
      "- R2 Score: 0.7479\n",
      "===================================\n",
      "\n",
      "\n",
      "Random Forest Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 2.3357\n",
      "- Mean Absolute Error: 1.8685\n",
      "- R2 Score: 0.9758\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 5.9661\n",
      "- Mean Absolute Error: 4.6439\n",
      "- R2 Score: 0.8537\n",
      "===================================\n",
      "\n",
      "\n",
      "XGBRegressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 1.0073\n",
      "- Mean Absolute Error: 0.6875\n",
      "- R2 Score: 0.9955\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 6.4733\n",
      "- Mean Absolute Error: 5.0577\n",
      "- R2 Score: 0.8278\n",
      "===================================\n",
      "\n",
      "\n",
      "CatBoosting Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 3.0427\n",
      "- Mean Absolute Error: 2.4054\n",
      "- R2 Score: 0.9589\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 6.0086\n",
      "- Mean Absolute Error: 4.6125\n",
      "- R2 Score: 0.8516\n",
      "===================================\n",
      "\n",
      "\n",
      "AdaBoost Regressor\n",
      "Model performance for Training set\n",
      "- Root Mean Squared Error: 5.8370\n",
      "- Mean Absolute Error: 4.7690\n",
      "- R2 Score: 0.8489\n",
      "----------------------------------\n",
      "Model performance for Test set\n",
      "- Root Mean Squared Error: 6.0895\n",
      "- Mean Absolute Error: 4.7429\n",
      "- R2 Score: 0.8476\n",
      "===================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = {\n",
    "    \"Linear Regression\": LinearRegression(),\n",
    "    \"Lasso\": Lasso(),\n",
    "    \"Ridge\": Ridge(),\n",
    "    \"K-Neighbors Regressor\": KNeighborsRegressor(),\n",
    "    \"Decision Tree\": DecisionTreeRegressor(),\n",
    "    \"Random Forest Regressor\": RandomForestRegressor(),\n",
    "    \"XGBRegressor\": XGBRegressor(), \n",
    "    \"CatBoosting Regressor\": CatBoostRegressor(verbose=False),\n",
    "    \"AdaBoost Regressor\": AdaBoostRegressor()\n",
    "}\n",
    "model_list = []\n",
    "r2_list =[]\n",
    "\n",
    "for i in range(len(list(models))):\n",
    "    model = list(models.values())[i]\n",
    "    model.fit(X_train, y_train) # Train model\n",
    "\n",
    "    # Make predictions\n",
    "    y_train_pred = model.predict(X_train)\n",
    "    y_test_pred = model.predict(X_test)\n",
    "    \n",
    "    # Evaluate Train and Test dataset\n",
    "    model_train_mae , model_train_rmse, model_train_r2 = evaluate_model(y_train, y_train_pred)\n",
    "\n",
    "    model_test_mae , model_test_rmse, model_test_r2 = evaluate_model(y_test, y_test_pred)\n",
    "\n",
    "    \n",
    "    print(list(models.keys())[i])\n",
    "    model_list.append(list(models.keys())[i])\n",
    "    \n",
    "    print('Model performance for Training set')\n",
    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_train_rmse))\n",
    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_train_mae))\n",
    "    print(\"- R2 Score: {:.4f}\".format(model_train_r2))\n",
    "\n",
    "    print('----------------------------------')\n",
    "    \n",
    "    print('Model performance for Test set')\n",
    "    print(\"- Root Mean Squared Error: {:.4f}\".format(model_test_rmse))\n",
    "    print(\"- Mean Absolute Error: {:.4f}\".format(model_test_mae))\n",
    "    print(\"- R2 Score: {:.4f}\".format(model_test_r2))\n",
    "    r2_list.append(model_test_r2)\n",
    "    \n",
    "    print('='*35)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06480b5a",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e0159e5f",
   "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>Model Name</th>\n",
       "      <th>R2_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ridge</td>\n",
       "      <td>0.880593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.879046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>0.853727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CatBoosting Regressor</td>\n",
       "      <td>0.851632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AdaBoost Regressor</td>\n",
       "      <td>0.847613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>0.827797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.825320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>K-Neighbors Regressor</td>\n",
       "      <td>0.783813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.747882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Model Name  R2_Score\n",
       "2                    Ridge  0.880593\n",
       "0        Linear Regression  0.879046\n",
       "5  Random Forest Regressor  0.853727\n",
       "7    CatBoosting Regressor  0.851632\n",
       "8       AdaBoost Regressor  0.847613\n",
       "6             XGBRegressor  0.827797\n",
       "1                    Lasso  0.825320\n",
       "3    K-Neighbors Regressor  0.783813\n",
       "4            Decision Tree  0.747882"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(list(zip(model_list, r2_list)), columns=['Model Name', 'R2_Score']).sort_values(by=[\"R2_Score\"],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "357a7c1c",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a6ad559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Accuracy of the model is 87.90\n"
     ]
    }
   ],
   "source": [
    "lin_model = LinearRegression(fit_intercept=True)\n",
    "lin_model = lin_model.fit(X_train, y_train)\n",
    "y_pred = lin_model.predict(X_test)\n",
    "score = r2_score(y_test, y_pred)*100\n",
    "print(\" Accuracy of the model is %.2f\" %score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d31453e",
   "metadata": {},
   "source": [
    "## Plot y_pred and y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "eb557b0a",
   "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,y_pred);\n",
    "plt.xlabel('Actual');\n",
    "plt.ylabel('Predicted');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1e707ec3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=y_test,y=y_pred,ci=None,color ='red');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c2fe28",
   "metadata": {},
   "source": [
    "#### Difference between Actual and Predicted Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c9a8b48",
   "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>Actual Value</th>\n",
       "      <th>Predicted Value</th>\n",
       "      <th>Difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>91</td>\n",
       "      <td>76.4375</td>\n",
       "      <td>14.5625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>737</th>\n",
       "      <td>53</td>\n",
       "      <td>59.3125</td>\n",
       "      <td>-6.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>80</td>\n",
       "      <td>76.6250</td>\n",
       "      <td>3.3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>74</td>\n",
       "      <td>76.5625</td>\n",
       "      <td>-2.5625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>84</td>\n",
       "      <td>87.7500</td>\n",
       "      <td>-3.7500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>52</td>\n",
       "      <td>43.6875</td>\n",
       "      <td>8.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>62</td>\n",
       "      <td>62.3125</td>\n",
       "      <td>-0.3125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>74</td>\n",
       "      <td>67.7500</td>\n",
       "      <td>6.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>65</td>\n",
       "      <td>67.0000</td>\n",
       "      <td>-2.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>61</td>\n",
       "      <td>62.6875</td>\n",
       "      <td>-1.6875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Actual Value  Predicted Value  Difference\n",
       "521            91          76.4375     14.5625\n",
       "737            53          59.3125     -6.3125\n",
       "740            80          76.6250      3.3750\n",
       "660            74          76.5625     -2.5625\n",
       "411            84          87.7500     -3.7500\n",
       "..            ...              ...         ...\n",
       "408            52          43.6875      8.3125\n",
       "332            62          62.3125     -0.3125\n",
       "208            74          67.7500      6.2500\n",
       "613            65          67.0000     -2.0000\n",
       "78             61          62.6875     -1.6875\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df=pd.DataFrame({'Actual Value':y_test,'Predicted Value':y_pred,'Difference':y_test-y_pred})\n",
    "pred_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3acf1fbc",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}