wine_quality_machine_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from xgboost import XGBRFRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "red_wine = pd.read_csv(\"dataset/winequality-red.csv\", sep= \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.4              0.70         0.00             1.9      0.076   \n",
       "1            7.8              0.88         0.00             2.6      0.098   \n",
       "2            7.8              0.76         0.04             2.3      0.092   \n",
       "3           11.2              0.28         0.56             1.9      0.075   \n",
       "4            7.4              0.70         0.00             1.9      0.076   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      9.4        5  \n",
       "1      9.8        5  \n",
       "2      9.8        5  \n",
       "3      9.8        6  \n",
       "4      9.4        5  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "red_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = red_wine[[\"fixed acidity\", \"volatile acidity\", \"citric acid\", \"residual sugar\",\n",
    "       \"chlorides\", \"free sulfur dioxide\", \"total sulfur dioxide\", \"density\",\n",
    "       \"pH\", \"sulphates\", \"alcohol\"]]\n",
    "y = red_wine[[\"quality\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar = StandardScaler()\n",
    "X = scalar.fit_transform(X)\n",
    "y = scalar.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = []\n",
    "RMSE = []\n",
    "MAE = []\n",
    "MSE = []\n",
    "R_Square = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Linear Regression\", \"Ridge Regression\", \"Lasso Regression\",\n",
    "         \"Decision Tree Regressor\", \"Random Forest Regressor\", \"Gradient Boosting Regressor\",\n",
    "         \"Adaboost Regressor\", \"BaggingRegressor\", \"ExtraTreesRegressor\",\"XGBRegressor\", \"XGBRFRegressor\"]\n",
    "models = [LinearRegression(), Ridge(), Lasso(), DecisionTreeRegressor(),\n",
    "          RandomForestRegressor(), GradientBoostingRegressor(), \n",
    "          AdaBoostRegressor(), BaggingRegressor(), ExtraTreesRegressor(),XGBRegressor(), XGBRFRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(true, predicted):\n",
    "    MAE.append(metrics.mean_absolute_error(true, predicted))\n",
    "    MSE.append(metrics.mean_squared_error(true, predicted))\n",
    "    RMSE.append(np.sqrt(metrics.mean_squared_error(true, predicted)))\n",
    "    R_Square.append(metrics.r2_score(true, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_evaluate(true, predicted):  \n",
    "    mae = metrics.mean_absolute_error(true, predicted)\n",
    "    mse = metrics.mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(metrics.mean_squared_error(true, predicted))\n",
    "    r2_square = metrics.r2_score(true, predicted)\n",
    "    print(\"MAE:\", mae)\n",
    "    print(\"MSE:\", mse)\n",
    "    print(\"RMSE:\", rmse)\n",
    "    print(\"R2 Square\", r2_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_predict(name, model):\n",
    "    variable_of_model = model\n",
    "    variable_of_model.fit(X_train, y_train.ravel())\n",
    "    pred = variable_of_model.predict(X_test)\n",
    "    evaluate(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, model in zip(names, models):\n",
    "    fit_and_predict(name, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataframe = pd.DataFrame({\"Model\": names,\n",
    "                                     \"MAE\": MAE,\n",
    "                                     \"MSE\": MSE,\n",
    "                                     \"RMSE\": RMSE,\n",
    "                                     \"R Squared\": R_Square})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataframe = evaluation_dataframe.sort_values(\"R Squared\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R Squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>0.611594</td>\n",
       "      <td>1.035656</td>\n",
       "      <td>1.017672</td>\n",
       "      <td>-0.032891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>0.848841</td>\n",
       "      <td>1.008285</td>\n",
       "      <td>1.004134</td>\n",
       "      <td>-0.005592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>0.623743</td>\n",
       "      <td>0.598435</td>\n",
       "      <td>0.773586</td>\n",
       "      <td>0.403163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.623709</td>\n",
       "      <td>0.598418</td>\n",
       "      <td>0.773575</td>\n",
       "      <td>0.403180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adaboost Regressor</td>\n",
       "      <td>0.628907</td>\n",
       "      <td>0.580483</td>\n",
       "      <td>0.761895</td>\n",
       "      <td>0.421067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>0.601593</td>\n",
       "      <td>0.559511</td>\n",
       "      <td>0.748005</td>\n",
       "      <td>0.441983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XGBRFRegressor</td>\n",
       "      <td>0.603533</td>\n",
       "      <td>0.550036</td>\n",
       "      <td>0.741644</td>\n",
       "      <td>0.451433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>0.512384</td>\n",
       "      <td>0.522690</td>\n",
       "      <td>0.722973</td>\n",
       "      <td>0.478706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>0.532241</td>\n",
       "      <td>0.499464</td>\n",
       "      <td>0.706728</td>\n",
       "      <td>0.501869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>0.533016</td>\n",
       "      <td>0.489687</td>\n",
       "      <td>0.699776</td>\n",
       "      <td>0.511621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ExtraTreesRegressor</td>\n",
       "      <td>0.475534</td>\n",
       "      <td>0.443577</td>\n",
       "      <td>0.666016</td>\n",
       "      <td>0.557608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model       MAE       MSE      RMSE  R Squared\n",
       "3       Decision Tree Regressor  0.611594  1.035656  1.017672  -0.032891\n",
       "2              Lasso Regression  0.848841  1.008285  1.004134  -0.005592\n",
       "1              Ridge Regression  0.623743  0.598435  0.773586   0.403163\n",
       "0             Linear Regression  0.623709  0.598418  0.773575   0.403180\n",
       "6            Adaboost Regressor  0.628907  0.580483  0.761895   0.421067\n",
       "5   Gradient Boosting Regressor  0.601593  0.559511  0.748005   0.441983\n",
       "10               XGBRFRegressor  0.603533  0.550036  0.741644   0.451433\n",
       "9                  XGBRegressor  0.512384  0.522690  0.722973   0.478706\n",
       "7              BaggingRegressor  0.532241  0.499464  0.706728   0.501869\n",
       "4       Random Forest Regressor  0.533016  0.489687  0.699776   0.511621\n",
       "8           ExtraTreesRegressor  0.475534  0.443577  0.666016   0.557608"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "white_wine = pd.read_csv(\"dataset/winequality-white.csv\", sep= \";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  \n",
       "0      8.8        6  \n",
       "1      9.5        6  \n",
       "2     10.1        6  \n",
       "3      9.9        6  \n",
       "4      9.9        6  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white_wine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = white_wine[[\"fixed acidity\", \"volatile acidity\", \"citric acid\", \"residual sugar\",\n",
    "       \"chlorides\", \"free sulfur dioxide\", \"total sulfur dioxide\", \"density\",\n",
    "       \"pH\", \"sulphates\", \"alcohol\"]]\n",
    "y = white_wine[[\"quality\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scalar = StandardScaler()\n",
    "X = scalar.fit_transform(X)\n",
    "y = scalar.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state= 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = []\n",
    "RMSE = []\n",
    "MAE = []\n",
    "MSE = []\n",
    "R_Square = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [\"Linear Regression\", \"Ridge Regression\", \"Lasso Regression\",\n",
    "         \"Decision Tree Regressor\", \"Random Forest Regressor\", \"Gradient Boosting Regressor\",\n",
    "         \"Adaboost Regressor\", \"BaggingRegressor\", \"ExtraTreesRegressor\",\"XGBRegressor\", \"XGBRFRegressor\"]\n",
    "models = [LinearRegression(), Ridge(), Lasso(), DecisionTreeRegressor(),\n",
    "          RandomForestRegressor(), GradientBoostingRegressor(), \n",
    "          AdaBoostRegressor(), BaggingRegressor(), ExtraTreesRegressor(),XGBRegressor(), XGBRFRegressor()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(true, predicted):\n",
    "    MAE.append(metrics.mean_absolute_error(true, predicted))\n",
    "    MSE.append(metrics.mean_squared_error(true, predicted))\n",
    "    RMSE.append(np.sqrt(metrics.mean_squared_error(true, predicted)))\n",
    "    R_Square.append(metrics.r2_score(true, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_evaluate(true, predicted):  \n",
    "    mae = metrics.mean_absolute_error(true, predicted)\n",
    "    mse = metrics.mean_squared_error(true, predicted)\n",
    "    rmse = np.sqrt(metrics.mean_squared_error(true, predicted))\n",
    "    r2_square = metrics.r2_score(true, predicted)\n",
    "    print(\"MAE:\", mae)\n",
    "    print(\"MSE:\", mse)\n",
    "    print(\"RMSE:\", rmse)\n",
    "    print(\"R2 Square\", r2_square)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_predict(name, model):\n",
    "    variable_of_model = model\n",
    "    variable_of_model.fit(X_train, y_train.ravel())\n",
    "    pred = variable_of_model.predict(X_test)\n",
    "    evaluate(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name, model in zip(names, models):\n",
    "    fit_and_predict(name, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataframe = pd.DataFrame({\"Model\": names,\n",
    "                                     \"MAE\": MAE,\n",
    "                                     \"MSE\": MSE,\n",
    "                                     \"RMSE\": RMSE,\n",
    "                                     \"R Squared\": R_Square})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluation_dataframe = evaluation_dataframe.sort_values(\"R Squared\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R Squared</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lasso Regression</td>\n",
       "      <td>0.762216</td>\n",
       "      <td>0.988967</td>\n",
       "      <td>0.994468</td>\n",
       "      <td>-0.001382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Decision Tree Regressor</td>\n",
       "      <td>0.546185</td>\n",
       "      <td>0.871815</td>\n",
       "      <td>0.933710</td>\n",
       "      <td>0.117241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ridge Regression</td>\n",
       "      <td>0.662095</td>\n",
       "      <td>0.725718</td>\n",
       "      <td>0.851891</td>\n",
       "      <td>0.265172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>0.662038</td>\n",
       "      <td>0.725616</td>\n",
       "      <td>0.851831</td>\n",
       "      <td>0.265275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adaboost Regressor</td>\n",
       "      <td>0.669610</td>\n",
       "      <td>0.702998</td>\n",
       "      <td>0.838450</td>\n",
       "      <td>0.288177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>XGBRFRegressor</td>\n",
       "      <td>0.616557</td>\n",
       "      <td>0.612491</td>\n",
       "      <td>0.782618</td>\n",
       "      <td>0.379820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gradient Boosting Regressor</td>\n",
       "      <td>0.605481</td>\n",
       "      <td>0.589388</td>\n",
       "      <td>0.767716</td>\n",
       "      <td>0.403213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>XGBRegressor</td>\n",
       "      <td>0.502619</td>\n",
       "      <td>0.493866</td>\n",
       "      <td>0.702756</td>\n",
       "      <td>0.499935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BaggingRegressor</td>\n",
       "      <td>0.500439</td>\n",
       "      <td>0.490025</td>\n",
       "      <td>0.700018</td>\n",
       "      <td>0.503823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Random Forest Regressor</td>\n",
       "      <td>0.475861</td>\n",
       "      <td>0.449725</td>\n",
       "      <td>0.670615</td>\n",
       "      <td>0.544630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ExtraTreesRegressor</td>\n",
       "      <td>0.415516</td>\n",
       "      <td>0.416181</td>\n",
       "      <td>0.645121</td>\n",
       "      <td>0.578594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Model       MAE       MSE      RMSE  R Squared\n",
       "2              Lasso Regression  0.762216  0.988967  0.994468  -0.001382\n",
       "3       Decision Tree Regressor  0.546185  0.871815  0.933710   0.117241\n",
       "1              Ridge Regression  0.662095  0.725718  0.851891   0.265172\n",
       "0             Linear Regression  0.662038  0.725616  0.851831   0.265275\n",
       "6            Adaboost Regressor  0.669610  0.702998  0.838450   0.288177\n",
       "10               XGBRFRegressor  0.616557  0.612491  0.782618   0.379820\n",
       "5   Gradient Boosting Regressor  0.605481  0.589388  0.767716   0.403213\n",
       "9                  XGBRegressor  0.502619  0.493866  0.702756   0.499935\n",
       "7              BaggingRegressor  0.500439  0.490025  0.700018   0.503823\n",
       "4       Random Forest Regressor  0.475861  0.449725  0.670615   0.544630\n",
       "8           ExtraTreesRegressor  0.415516  0.416181  0.645121   0.578594"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluation_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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