model_training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.52</td>\n",
       "      <td>Premium</td>\n",
       "      <td>F</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.33</td>\n",
       "      <td>4.55</td>\n",
       "      <td>13619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.03</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.06</td>\n",
       "      <td>8.12</td>\n",
       "      <td>5.05</td>\n",
       "      <td>13387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.2</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.73</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.32</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.6</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4.38</td>\n",
       "      <td>4.41</td>\n",
       "      <td>2.71</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.70</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.61</td>\n",
       "      <td>4.77</td>\n",
       "      <td>14453</td>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193568</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>61.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.39</td>\n",
       "      <td>2.67</td>\n",
       "      <td>1130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193569</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Premium</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>60.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.77</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193570</th>\n",
       "      <td>0.73</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5.72</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193571</th>\n",
       "      <td>0.34</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>4.49</td>\n",
       "      <td>2.81</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193572</th>\n",
       "      <td>0.71</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>60.8</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.71</td>\n",
       "      <td>3.48</td>\n",
       "      <td>2258</td>\n",
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       "<p>193573 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "        carat        cut color clarity  depth  table     x     y     z  price\n",
       "id                                                                           \n",
       "0        1.52    Premium     F     VS2   62.2   58.0  7.27  7.33  4.55  13619\n",
       "1        2.03  Very Good     J     SI2   62.0   58.0  8.06  8.12  5.05  13387\n",
       "2        0.70      Ideal     G     VS1   61.2   57.0  5.69  5.73  3.50   2772\n",
       "3        0.32      Ideal     G     VS1   61.6   56.0  4.38  4.41  2.71    666\n",
       "4        1.70    Premium     G     VS2   62.6   59.0  7.65  7.61  4.77  14453\n",
       "...       ...        ...   ...     ...    ...    ...   ...   ...   ...    ...\n",
       "193568   0.31      Ideal     D    VVS2   61.1   56.0  4.35  4.39  2.67   1130\n",
       "193569   0.70    Premium     G    VVS2   60.3   58.0  5.75  5.77  3.47   2874\n",
       "193570   0.73  Very Good     F     SI1   63.1   57.0  5.72  5.75  3.62   3036\n",
       "193571   0.34  Very Good     D     SI1   62.9   55.0  4.45  4.49  2.81    681\n",
       "193572   0.71       Good     E     SI2   60.8   64.0  5.73  5.71  3.48   2258\n",
       "\n",
       "[193573 rows x 10 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\SIBADATTA SASMAL\\workspace_setup_example\\data\\gemstone.csv\",index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop(labels=[\"price\"],axis=1)\n",
    "y=df[[\"price\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the custom ranking for each ordinal variable\n",
    "cut_categories = ['Fair', 'Good', 'Very Good','Premium','Ideal']\n",
    "color_categories = ['D', 'E', 'F', 'G', 'H', 'I', 'J']\n",
    "clarity_categories = ['I1','SI2','SI1','VS2','VS1','VVS2','VVS1','IF']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer ## HAndling Missing Values\n",
    "from sklearn.preprocessing import StandardScaler # HAndling Feature Scaling\n",
    "from sklearn.preprocessing import OrdinalEncoder # Ordinal Encoding\n",
    "## pipelines\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pipeline=Pipeline(\n",
    "    \n",
    "    \n",
    "    steps=[\n",
    "        \n",
    "        ('imputer',SimpleImputer()),\n",
    "        ('scaler', StandardScaler())\n",
    "    ]\n",
    "    \n",
    "    \n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_pipeline=Pipeline(\n",
    "    \n",
    "    steps=[\n",
    "        ('imputer',SimpleImputer(strategy='most_frequent')),\n",
    "        ('ordinalencoder',OrdinalEncoder(categories=[cut_categories,color_categories,clarity_categories]))\n",
    "    ]\n",
    "    \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_cols=X.select_dtypes(include='object').columns\n",
    "numerical_cols=X.select_dtypes(exclude='object').columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessor=ColumnTransformer(\n",
    "    [\n",
    "        \n",
    "        ('num_pipeline',num_pipeline,numerical_cols),\n",
    "        ('cat_pipeline',cat_pipeline,categorical_cols)\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.97543926, -0.84960654, -0.12153081, ...,  4.        ,\n",
       "         5.        ,  5.        ],\n",
       "       [ 0.2351953 ,  1.83363716, -0.12153081, ...,  1.        ,\n",
       "         1.        ,  2.        ],\n",
       "       [ 0.49461699,  0.81585507,  0.39980029, ...,  3.        ,\n",
       "         3.        ,  4.        ],\n",
       "       ...,\n",
       "       [ 0.45138004,  1.55606023, -0.6428619 , ...,  1.        ,\n",
       "         3.        ,  2.        ],\n",
       "       [ 0.66756478, -1.77486298,  1.44246248, ...,  4.        ,\n",
       "         3.        ,  4.        ],\n",
       "       [ 0.25681377,  0.81585507, -0.12153081, ...,  4.        ,\n",
       "         3.        ,  2.        ]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Train test split\n",
    "\n",
    "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=0.30,random_state=30)\n",
    "preprocessor.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['num_pipeline__carat', 'num_pipeline__depth',\n",
       "       'num_pipeline__table', 'num_pipeline__x', 'num_pipeline__y',\n",
       "       'num_pipeline__z', 'cat_pipeline__cut', 'cat_pipeline__color',\n",
       "       'cat_pipeline__clarity'], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.get_feature_names_out()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=pd.DataFrame(preprocessor.fit_transform(X_train),columns=preprocessor.get_feature_names_out())\n",
    "X_test=pd.DataFrame(preprocessor.transform(X_test),columns=preprocessor.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_pipeline__carat</th>\n",
       "      <th>num_pipeline__depth</th>\n",
       "      <th>num_pipeline__table</th>\n",
       "      <th>num_pipeline__x</th>\n",
       "      <th>num_pipeline__y</th>\n",
       "      <th>num_pipeline__z</th>\n",
       "      <th>cat_pipeline__cut</th>\n",
       "      <th>cat_pipeline__color</th>\n",
       "      <th>cat_pipeline__clarity</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.849607</td>\n",
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       "      <td>-1.042757</td>\n",
       "      <td>-1.080970</td>\n",
       "      <td>-1.123150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.235195</td>\n",
       "      <td>1.833637</td>\n",
       "      <td>-0.121531</td>\n",
       "      <td>0.318447</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.494617</td>\n",
       "      <td>0.815855</td>\n",
       "      <td>0.399800</td>\n",
       "      <td>0.570855</td>\n",
       "      <td>0.606458</td>\n",
       "      <td>0.673737</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.018676</td>\n",
       "      <td>0.260701</td>\n",
       "      <td>0.921131</td>\n",
       "      <td>-1.214034</td>\n",
       "      <td>-1.244270</td>\n",
       "      <td>-1.195605</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.953821</td>\n",
       "      <td>-0.664555</td>\n",
       "      <td>-0.642862</td>\n",
       "      <td>-1.069801</td>\n",
       "      <td>-1.044681</td>\n",
       "      <td>-1.094168</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135496</th>\n",
       "      <td>-1.040295</td>\n",
       "      <td>-0.016876</td>\n",
       "      <td>-0.642862</td>\n",
       "      <td>-1.268122</td>\n",
       "      <td>-1.244270</td>\n",
       "      <td>-1.239078</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135497</th>\n",
       "      <td>0.991842</td>\n",
       "      <td>0.168176</td>\n",
       "      <td>-0.642862</td>\n",
       "      <td>1.048629</td>\n",
       "      <td>1.114501</td>\n",
       "      <td>1.079486</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135498</th>\n",
       "      <td>0.451380</td>\n",
       "      <td>1.556060</td>\n",
       "      <td>-0.642862</td>\n",
       "      <td>0.516768</td>\n",
       "      <td>0.588314</td>\n",
       "      <td>0.702719</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135499</th>\n",
       "      <td>0.667565</td>\n",
       "      <td>-1.774863</td>\n",
       "      <td>1.442462</td>\n",
       "      <td>0.868337</td>\n",
       "      <td>0.951202</td>\n",
       "      <td>0.688228</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135500</th>\n",
       "      <td>0.256814</td>\n",
       "      <td>0.815855</td>\n",
       "      <td>-0.121531</td>\n",
       "      <td>0.381549</td>\n",
       "      <td>0.415942</td>\n",
       "      <td>0.470863</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135501 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        num_pipeline__carat  num_pipeline__depth  num_pipeline__table  \\\n",
       "0                 -0.975439            -0.849607            -0.121531   \n",
       "1                  0.235195             1.833637            -0.121531   \n",
       "2                  0.494617             0.815855             0.399800   \n",
       "3                 -1.018676             0.260701             0.921131   \n",
       "4                 -0.953821            -0.664555            -0.642862   \n",
       "...                     ...                  ...                  ...   \n",
       "135496            -1.040295            -0.016876            -0.642862   \n",
       "135497             0.991842             0.168176            -0.642862   \n",
       "135498             0.451380             1.556060            -0.642862   \n",
       "135499             0.667565            -1.774863             1.442462   \n",
       "135500             0.256814             0.815855            -0.121531   \n",
       "\n",
       "        num_pipeline__x  num_pipeline__y  num_pipeline__z  cat_pipeline__cut  \\\n",
       "0             -1.042757        -1.080970        -1.123150                4.0   \n",
       "1              0.318447         0.279859         0.485354                1.0   \n",
       "2              0.570855         0.606458         0.673737                3.0   \n",
       "3             -1.214034        -1.244270        -1.195605                3.0   \n",
       "4             -1.069801        -1.044681        -1.094168                4.0   \n",
       "...                 ...              ...              ...                ...   \n",
       "135496        -1.268122        -1.244270        -1.239078                4.0   \n",
       "135497         1.048629         1.114501         1.079486                4.0   \n",
       "135498         0.516768         0.588314         0.702719                1.0   \n",
       "135499         0.868337         0.951202         0.688228                4.0   \n",
       "135500         0.381549         0.415942         0.470863                4.0   \n",
       "\n",
       "        cat_pipeline__color  cat_pipeline__clarity  \n",
       "0                       5.0                    5.0  \n",
       "1                       1.0                    2.0  \n",
       "2                       3.0                    4.0  \n",
       "3                       3.0                    3.0  \n",
       "4                       6.0                    5.0  \n",
       "...                     ...                    ...  \n",
       "135496                  1.0                    2.0  \n",
       "135497                  3.0                    1.0  \n",
       "135498                  3.0                    2.0  \n",
       "135499                  3.0                    4.0  \n",
       "135500                  3.0                    2.0  \n",
       "\n",
       "[135501 rows x 9 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'linear regression\\nridge regression\\nlasso regression\\nelastic net'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"linear regression\n",
    "ridge regression\n",
    "lasso regression\n",
    "elastic net\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Model Training\n",
    "\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge,ElasticNet\n",
    "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Train multiple models\n",
    "\n",
    "models={\n",
    "    'LinearRegression':LinearRegression(),\n",
    "    'Lasso':Lasso(),\n",
    "    'Ridge':Ridge(),\n",
    "    'Elasticnet':ElasticNet()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_model_list=[]\n",
    "model_list=[]\n",
    "r2_list=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LinearRegression', 'Lasso', 'Ridge', 'Elasticnet']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(models)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n",
      "Lasso()\n",
      "Ridge()\n",
      "ElasticNet()\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(list(models))):\n",
    "    model=list(models.values())[i]\n",
    "    print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression\n",
      "Model Training Performance\n",
      "RMSE: 1013.9047094344004\n",
      "MAE: 674.0255115796833\n",
      "R2 score 93.68908248567512\n",
      "===================================\n",
      "\n",
      "\n",
      "Lasso\n",
      "Model Training Performance\n",
      "RMSE: 1013.8723151049943\n",
      "MAE: 675.2036880701621\n",
      "R2 score 93.68948574778251\n",
      "===================================\n",
      "\n",
      "\n",
      "Ridge\n",
      "Model Training Performance\n",
      "RMSE: 1013.905899776106\n",
      "MAE: 674.0565132295936\n",
      "R2 score 93.68906766741328\n",
      "===================================\n",
      "\n",
      "\n",
      "Elasticnet\n",
      "Model Training Performance\n",
      "RMSE: 1513.9140350229761\n",
      "MAE: 1050.7468664314322\n",
      "R2 score 85.92978759337907\n",
      "===================================\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "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",
    "    y_pred=model.predict(X_test)\n",
    "\n",
    "    #this is a validation(test) score\n",
    "    mae, rmse, r2_square=evaluate_model(y_test,y_pred)\n",
    "\n",
    "    print(list(models.keys())[i])\n",
    "    model_list.append(list(models.keys())[i])\n",
    "\n",
    "    print('Model Training Performance')\n",
    "    print(\"RMSE:\",rmse)\n",
    "    print(\"MAE:\",mae)\n",
    "    print(\"R2 score\",r2_square*100)\n",
    "\n",
    "    r2_list.append(r2_square)\n",
    "    \n",
    "    print('='*35)\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9368908248567511,\n",
       " 0.9368948574778251,\n",
       " 0.9368906766741328,\n",
       " 0.8592978759337908]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_list\n"
   ]
  }
 ],
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