diff --git "a/notebook/MODEL_TRAINING.ipynb" "b/notebook/MODEL_TRAINING.ipynb"
--- "a/notebook/MODEL_TRAINING.ipynb"
+++ "b/notebook/MODEL_TRAINING.ipynb"
@@ -0,0 +1,1098 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Training \n",
+    "#### 1.1 Import Data and Required Packages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<frozen importlib._bootstrap>:241: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216 from C header, got 232 from PyObject\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "## modelling\n",
+    "\n",
+    "from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score\n",
+    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor\n",
+    "from xgboost import XGBRegressor\n",
+    "from catboost import CatBoostRegressor\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "import warnings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import data as pandas dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('data/stud.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>995</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group E</td>\n",
+       "      <td>master's degree</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>88</td>\n",
+       "      <td>99</td>\n",
+       "      <td>95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>male</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>62</td>\n",
+       "      <td>55</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group C</td>\n",
+       "      <td>high school</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>59</td>\n",
+       "      <td>71</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>standard</td>\n",
+       "      <td>completed</td>\n",
+       "      <td>68</td>\n",
+       "      <td>78</td>\n",
+       "      <td>77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>female</td>\n",
+       "      <td>group D</td>\n",
+       "      <td>some college</td>\n",
+       "      <td>free/reduced</td>\n",
+       "      <td>none</td>\n",
+       "      <td>77</td>\n",
+       "      <td>86</td>\n",
+       "      <td>86</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     gender race_ethnicity parental_level_of_education         lunch  \\\n",
+       "995  female        group E             master's degree      standard   \n",
+       "996    male        group C                 high school  free/reduced   \n",
+       "997  female        group C                 high school  free/reduced   \n",
+       "998  female        group D                some college      standard   \n",
+       "999  female        group D                some college  free/reduced   \n",
+       "\n",
+       "    test_preparation_course  math_score  reading_score  writing_score  \n",
+       "995               completed          88             99             95  \n",
+       "996                    none          62             55             55  \n",
+       "997               completed          59             71             65  \n",
+       "998               completed          68             78             77  \n",
+       "999                    none          77             86             86  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of X: (1000, 7)\n",
+      "shape of y: (1000,)\n"
+     ]
+    },
+    {
+     "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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## dependent and independent variables\n",
+    "\n",
+    "X = df.drop('math_score',axis=1)\n",
+    "y = df.math_score\n",
+    "print(f\"shape of X: {X.shape}\")\n",
+    "print(f\"shape of y: {y.shape}\")\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "length of numerical cols: 2\n",
+      "length of categorical cols: 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "## Create columns tranformers\n",
+    "\n",
+    "num_cols = X.select_dtypes(exclude='object').columns\n",
+    "cat_cols = X.select_dtypes(include='object').columns\n",
+    "\n",
+    "print(f\"length of numerical cols: {len(num_cols)}\")\n",
+    "print(f\"length of categorical cols: {len(cat_cols)}\")\n",
+    "\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "\n",
+    "numeric_tranformer = StandardScaler()\n",
+    "oh_transformer = OneHotEncoder()\n",
+    "\n",
+    "preprocessor = ColumnTransformer([\n",
+    "    ('ohe',oh_transformer,cat_cols),\n",
+    "    ('ss',numeric_tranformer,num_cols)\n",
+    "])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of X_train: (700, 7)\n",
+      "shape of X_test: (300, 7)\n",
+      "shape of y_train: (700,)\n",
+      "shape of y_test: (300, 7)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
+    "\n",
+    "print(f\"shape of X_train: {X_train.shape}\")\n",
+    "print(f\"shape of X_test: {X_test.shape}\")\n",
+    "print(f\"shape of y_train: {y_train.shape}\")\n",
+    "print(f\"shape of y_test: {X_test.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of X_train after transform: (700, 19)\n",
+      "shape of X_test after transform: (300, 19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "## fit the pipeline\n",
+    "\n",
+    "X_train = preprocessor.fit_transform(X_train)\n",
+    "X_test = preprocessor.transform(X_test)\n",
+    "\n",
+    "\n",
+    "print(f\"shape of X_train after transform: {X_train.shape}\")\n",
+    "print(f\"shape of X_test after transform: {X_test.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Create an evaluation function\n",
+    "\n",
+    "def evaluate_model(actual, predicted):\n",
+    "    mse = mean_squared_error(actual,predicted)\n",
+    "    mae = mean_squared_error(actual,predicted)\n",
+    "    rmse = np.sqrt(mean_squared_error(actual,predicted))\n",
+    "    r2 =  r2_score(actual,predicted)\n",
+    "    return mae, mse, rmse, r2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LinearRegression\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 27.834805385044643\n",
+      "-- Mean Squared Error: 27.834805385044643\n",
+      "-- Root Mean Squared Error: 5.275870106915507\n",
+      "-- R2 Score 0.8848768448103892\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 31.053682454427083\n",
+      "-- Mean Squared Error: 31.053682454427083\n",
+      "-- Root Mean Squared Error: 5.572583104308727\n",
+      "-- R2 Score 0.845791335372628\n",
+      "\n",
+      "\n",
+      "Ridge\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 27.739417770649194\n",
+      "-- Mean Squared Error: 27.739417770649194\n",
+      "-- Root Mean Squared Error: 5.266822359891132\n",
+      "-- R2 Score 0.88527136250086\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 30.76468904919587\n",
+      "-- Mean Squared Error: 30.76468904919587\n",
+      "-- Root Mean Squared Error: 5.546592562032646\n",
+      "-- R2 Score 0.8472264401197775\n",
+      "\n",
+      "\n",
+      "Lasso\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 43.58337232191542\n",
+      "-- Mean Squared Error: 43.58337232191542\n",
+      "-- Root Mean Squared Error: 6.6017703930018214\n",
+      "-- R2 Score 0.8197416771522217\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 43.51995000348161\n",
+      "-- Mean Squared Error: 43.51995000348161\n",
+      "-- Root Mean Squared Error: 6.596965211631907\n",
+      "-- R2 Score 0.7838854253586233\n",
+      "\n",
+      "\n",
+      "KNeighborsRegressor\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 34.57731428571429\n",
+      "-- Mean Squared Error: 34.57731428571429\n",
+      "-- Root Mean Squared Error: 5.8802478081892335\n",
+      "-- R2 Score 0.8569902155416903\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 48.27386666666667\n",
+      "-- Mean Squared Error: 48.27386666666667\n",
+      "-- Root Mean Squared Error: 6.947939742590365\n",
+      "-- R2 Score 0.7602780756842187\n",
+      "\n",
+      "\n",
+      "DecisionTreeRegressor\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 0.002857142857142857\n",
+      "-- Mean Squared Error: 0.002857142857142857\n",
+      "-- Root Mean Squared Error: 0.05345224838248488\n",
+      "-- R2 Score 0.999988183021365\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 54.67333333333333\n",
+      "-- Mean Squared Error: 54.67333333333333\n",
+      "-- Root Mean Squared Error: 7.394141825346153\n",
+      "-- R2 Score 0.7284991325446313\n",
+      "\n",
+      "\n",
+      "AdaBoostRegressor\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 35.601696238383866\n",
+      "-- Mean Squared Error: 35.601696238383866\n",
+      "-- Root Mean Squared Error: 5.96671569947688\n",
+      "-- R2 Score 0.8527534306646536\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 41.20842698518752\n",
+      "-- Mean Squared Error: 41.20842698518752\n",
+      "-- Root Mean Squared Error: 6.419379018658076\n",
+      "-- R2 Score 0.7953641566952268\n",
+      "\n",
+      "\n",
+      "RandomForestRegressor\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 5.512448115267249\n",
+      "-- Mean Squared Error: 5.512448115267249\n",
+      "-- Root Mean Squared Error: 2.3478603270355007\n",
+      "-- R2 Score 0.9772008314385572\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 36.26516815006047\n",
+      "-- Mean Squared Error: 36.26516815006047\n",
+      "-- Root Mean Squared Error: 6.02205680395498\n",
+      "-- R2 Score 0.8199117556793764\n",
+      "\n",
+      "\n",
+      "CatBoostRegressor\n",
+      "Model performance on train data\n",
+      "\n",
+      "-- Mean Absolute Error: 7.617468052163866\n",
+      "-- Mean Squared Error: 7.617468052163866\n",
+      "-- Root Mean Squared Error: 2.759976096302985\n",
+      "-- R2 Score 0.9684945899714342\n",
+      "==============================================================\n",
+      "\n",
+      "Model Perfromance on test data\n",
+      "\n",
+      "-- Mean Absolute Error: 33.10743895683533\n",
+      "-- Mean Squared Error: 33.10743895683533\n",
+      "-- Root Mean Squared Error: 5.753906408418139\n",
+      "-- R2 Score 0.8355926399950043\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"LinearRegression\":LinearRegression(),\n",
+    "    \"Ridge\":Ridge(),\n",
+    "    \"Lasso\":Lasso(),\n",
+    "    \"KNeighborsRegressor\":KNeighborsRegressor(),\n",
+    "    \"DecisionTreeRegressor\":DecisionTreeRegressor(),\n",
+    "    \"AdaBoostRegressor\":AdaBoostRegressor(),\n",
+    "    \"RandomForestRegressor\":RandomForestRegressor(),\n",
+    "    \"CatBoostRegressor\":CatBoostRegressor(verbose=False),\n",
+    "}\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)\n",
+    "\n",
+    "    ## make predictions\n",
+    "\n",
+    "    y_train_pred = model.predict(X_train)\n",
+    "    y_test_pred = model.predict(X_test)\n",
+    "\n",
+    "    ## evaluate models\n",
+    "\n",
+    "    train_mae, train_mse, train_rmse, train_r2 = evaluate_model(y_train, y_train_pred)\n",
+    "\n",
+    "    test_mae, test_mse, test_rmse, 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 on train data\\n\")\n",
+    "\n",
+    "    print(f\"-- Mean Absolute Error: {train_mae}\")\n",
+    "    print(f\"-- Mean Squared Error: {train_mse}\")\n",
+    "    print(f\"-- Root Mean Squared Error: {train_rmse}\")\n",
+    "    print(f\"-- R2 Score {train_r2}\")\n",
+    "\n",
+    "\n",
+    "    print(\"==============================================================\\n\")\n",
+    "\n",
+    "    print(f\"Model Perfromance on test data\\n\")\n",
+    "    print(f\"-- Mean Absolute Error: {test_mae}\")\n",
+    "    print(f\"-- Mean Squared Error: {test_mse}\")\n",
+    "    print(f\"-- Root Mean Squared Error: {test_rmse}\")\n",
+    "    print(f\"-- R2 Score {test_r2}\\n\\n\")\n",
+    "\n",
+    "    r2_list.append(test_r2)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RESULTS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>1</th>\n",
+       "      <td>Ridge</td>\n",
+       "      <td>0.847226</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LinearRegression</td>\n",
+       "      <td>0.845791</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>CatBoostRegressor</td>\n",
+       "      <td>0.835593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>RandomForestRegressor</td>\n",
+       "      <td>0.819912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AdaBoostRegressor</td>\n",
+       "      <td>0.795364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Lasso</td>\n",
+       "      <td>0.783885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KNeighborsRegressor</td>\n",
+       "      <td>0.760278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>DecisionTreeRegressor</td>\n",
+       "      <td>0.728499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Model Name  R2_Score\n",
+       "1                  Ridge  0.847226\n",
+       "0       LinearRegression  0.845791\n",
+       "7      CatBoostRegressor  0.835593\n",
+       "6  RandomForestRegressor  0.819912\n",
+       "5      AdaBoostRegressor  0.795364\n",
+       "2                  Lasso  0.783885\n",
+       "3    KNeighborsRegressor  0.760278\n",
+       "4  DecisionTreeRegressor  0.728499"
+      ]
+     },
+     "execution_count": 36,
+     "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",
+   "metadata": {},
+   "source": [
+    "## Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "R2 score (fit of the model): 84.58\n"
+     ]
+    }
+   ],
+   "source": [
+    "lin_model = LinearRegression(fit_intercept=True)\n",
+    "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(f\"R2 score (fit of the model): {'%.2f'%score}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## plot y_pred and y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(Text(0.5, 0, 'Actual'), Text(0, 0.5, 'predicted'))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(y_test,y_pred)\n",
+    "plt.xlabel(\"Actual\"),plt.ylabel(\"predicted\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='math_score'>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x=y_test,y=y_pred,color='g')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "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>Differenec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>69</td>\n",
+       "      <td>78.187500</td>\n",
+       "      <td>-9.187500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>90</td>\n",
+       "      <td>84.343750</td>\n",
+       "      <td>5.656250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>71</td>\n",
+       "      <td>72.765625</td>\n",
+       "      <td>-1.765625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>64</td>\n",
+       "      <td>66.015625</td>\n",
+       "      <td>-2.015625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>58</td>\n",
+       "      <td>58.406250</td>\n",
+       "      <td>-0.406250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>992</th>\n",
+       "      <td>55</td>\n",
+       "      <td>66.125000</td>\n",
+       "      <td>-11.125000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>993</th>\n",
+       "      <td>62</td>\n",
+       "      <td>62.968750</td>\n",
+       "      <td>-0.968750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>62</td>\n",
+       "      <td>58.796875</td>\n",
+       "      <td>3.203125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>68</td>\n",
+       "      <td>66.828125</td>\n",
+       "      <td>1.171875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>77</td>\n",
+       "      <td>76.343750</td>\n",
+       "      <td>0.656250</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>300 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Actual Value  Predicted Value  Differenec\n",
+       "1              69        78.187500   -9.187500\n",
+       "2              90        84.343750    5.656250\n",
+       "5              71        72.765625   -1.765625\n",
+       "8              64        66.015625   -2.015625\n",
+       "10             58        58.406250   -0.406250\n",
+       "..            ...              ...         ...\n",
+       "992            55        66.125000  -11.125000\n",
+       "993            62        62.968750   -0.968750\n",
+       "996            62        58.796875    3.203125\n",
+       "998            68        66.828125    1.171875\n",
+       "999            77        76.343750    0.656250\n",
+       "\n",
+       "[300 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame({'Actual Value':y_test,\n",
+    "              \"Predicted Value\":y_pred,\n",
+    "              \"Differenec\":y_test-y_pred}).sort_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.10.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}