5 CDD_ML_Comparing_Regressors.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Computational Drug Discovery - Comparing Regressors\n",
        "\n",
        "In this Jupyter notebook, I will be building a machine learning model using the ChEMBL bioactivity data.\n",
        "\n",
        "In **Part 5**, I will be comparing several ML algorithms for build regression models of beta-secretase 1 inhibitors.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "6IxhmZhj-GxJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import libraries\n",
        "\n"
      ],
      "metadata": {
        "id": "5AtfHWnJ4jWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install lazypredict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xC_e5fAx2nbb",
        "outputId": "a335ad31-c9eb-4fd6-ae82-d88aef92afec"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.8/dist-packages (from lazypredict) (7.1.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.3.5)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (2.2.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.2.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from lazypredict) (4.64.1)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.8/dist-packages (from lazypredict) (0.90)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lazypredict) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from lightgbm->lazypredict) (1.7.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from lightgbm->lazypredict) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->lazypredict) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lazypredict) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->lazypredict) (1.15.0)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "a5JXiy9k2jaQ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyRegressor "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load the data set"
      ],
      "metadata": {
        "id": "afq1yiTq4qi_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/data/beta_secretase_bioactivity_data_3class_pIC50_pubchem_fp.csv')"
      ],
      "metadata": {
        "id": "ZrXLm7Ue3Cxl"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('pIC50', axis = 1)\n",
        "Y = df.pIC50"
      ],
      "metadata": {
        "id": "YBFIbqEM39X2"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data pre-processing"
      ],
      "metadata": {
        "id": "Q6hOpT854vf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# examine X dimension\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hH819Lps4vDk",
        "outputId": "429bae38-02ea-4adc-8c8a-10cdaa06d46f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7234, 881)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove low variance features\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))\n",
        "X = selection.fit_transform(X)\n",
        "X.shape "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhOPotsG5XkG",
        "outputId": "7a9e7a8b-dc09-4160-e716-7eb68319cbb5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7234, 160)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# data splitting using 80/20 ration\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "7KlH7lxh5ljh"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Compare ML algorithms"
      ],
      "metadata": {
        "id": "b4_cs3jC55h4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# inspired by https://github.com/shankarpandala/lazypredict/issues/317\n",
        "\n",
        "# a way to get training completed faster\n",
        "from sklearn.utils import all_estimators\n",
        "from sklearn.base import RegressorMixin\n",
        "\n",
        "chosen_regressors = [\n",
        "    'DecisionTreeRegressor',\n",
        "    'ExtraTreeRegressor',\n",
        "    'ExtraTreesRegressor',\n",
        "    'GaussianProcessRegressor',\n",
        "    'RandomForestRegressor',\n",
        "    'BaggingRegressor',\n",
        "    'MLPRegressor',\n",
        "    'HistGradientBoostingRegressor',\n",
        "    'LGBMRegressor',\n",
        "    'KNeighborsRegressor',\n",
        "    'SVR',\n",
        "    'NuSVR',\n",
        "    'GradientBoostingRegressor',\n",
        "    'XGBRegressor',\n",
        "    'Ridge',\n",
        "    'LassoCV',\n",
        "    'ElasticNetCV',\n",
        "    'RidgeCV',\n",
        "    'TransformedTargetRegressor',\n",
        "    'LinearRegression',\n",
        "    'SGDRegressor',\n",
        "    'BayesianRidge',\n",
        "    'HuberRegressor',\n",
        "    'LassoLarsCV',\n",
        "    'LassoLarsIC',\n",
        "    'LinearSVR',\n",
        "    'AdaBoostRegressor',\n",
        "    'Lars',\n",
        "    'OrthogonalMatchingPursuitCV',\n",
        "    'OrthogonalMatchingPursuit',\n",
        "    'Lars',\n",
        "    'Lasso',\n",
        "    'ElasticNet',\n",
        "    'DummyRegressor',\n",
        "    'LassoLars',\n",
        "    'PassiveAggressiveRegressor',\n",
        "    'KernelRidge',\n",
        "    'RANSACRegressor'\n",
        "]\n",
        "\n",
        "REGRESSORS = [\n",
        "    est\n",
        "    for est in all_estimators()\n",
        "    if (issubclass(est[1], RegressorMixin) and (est[0] in chosen_regressors))\n",
        "]"
      ],
      "metadata": {
        "id": "soDwrO-iKUIU"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# defines and builds the lazyclassifier (which builds over 20 machine learning models)\n",
        "clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None, regressors = REGRESSORS)\n",
        "models_train,predictions_train = clf.fit(X_train, X_train, Y_train, Y_train)\n",
        "models_test,predictions_test = clf.fit(X_train, X_test, Y_train, Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FEhfxlX55yUs",
        "outputId": "2545df22-62d7-402e-d0c3-eacb4f06e88d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'tuple' object has no attribute '__name__'\n",
            "Invalid Regressor(s)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35/35 [01:06<00:00,  1.89s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'tuple' object has no attribute '__name__'\n",
            "Invalid Regressor(s)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 35/35 [00:52<00:00,  1.49s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# performance table of the training set (80% subset)\n",
        "predictions_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "M6YJps3z6YOV",
        "outputId": "b33922bd-1255-452a-d741-c85b02bb4d45"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Adjusted R-Squared  \\\n",
              "Model                                                       \n",
              "ExtraTreesRegressor                                  0.84   \n",
              "DecisionTreeRegressor                                0.84   \n",
              "ExtraTreeRegressor                                   0.84   \n",
              "GaussianProcessRegressor                             0.84   \n",
              "RandomForestRegressor                                0.80   \n",
              "BaggingRegressor                                     0.78   \n",
              "MLPRegressor                                         0.73   \n",
              "HistGradientBoostingRegressor                        0.58   \n",
              "KNeighborsRegressor                                  0.57   \n",
              "SVR                                                  0.52   \n",
              "NuSVR                                                0.52   \n",
              "GradientBoostingRegressor                            0.40   \n",
              "Ridge                                                0.32   \n",
              "RidgeCV                                              0.32   \n",
              "LassoCV                                              0.31   \n",
              "ElasticNetCV                                         0.31   \n",
              "LinearRegression                                     0.31   \n",
              "TransformedTargetRegressor                           0.31   \n",
              "HuberRegressor                                       0.31   \n",
              "BayesianRidge                                        0.30   \n",
              "SGDRegressor                                         0.30   \n",
              "LinearSVR                                            0.29   \n",
              "OrthogonalMatchingPursuitCV                          0.23   \n",
              "OrthogonalMatchingPursuit                            0.23   \n",
              "LassoLarsCV                                          0.23   \n",
              "LassoLarsIC                                          0.23   \n",
              "AdaBoostRegressor                                    0.19   \n",
              "ElasticNet                                          -0.03   \n",
              "DummyRegressor                                      -0.03   \n",
              "Lasso                                               -0.03   \n",
              "LassoLars                                           -0.03   \n",
              "PassiveAggressiveRegressor                          -0.43   \n",
              "Lars                                                -3.67   \n",
              "KernelRidge                                        -22.22   \n",
              "RANSACRegressor               -63887845574846239997952.00   \n",
              "\n",
              "                                                R-Squared            RMSE  \\\n",
              "Model                                                                       \n",
              "ExtraTreesRegressor                                  0.85            0.54   \n",
              "DecisionTreeRegressor                                0.85            0.54   \n",
              "ExtraTreeRegressor                                   0.85            0.54   \n",
              "GaussianProcessRegressor                             0.85            0.54   \n",
              "RandomForestRegressor                                0.80            0.62   \n",
              "BaggingRegressor                                     0.78            0.65   \n",
              "MLPRegressor                                         0.73            0.72   \n",
              "HistGradientBoostingRegressor                        0.59            0.88   \n",
              "KNeighborsRegressor                                  0.58            0.90   \n",
              "SVR                                                  0.54            0.94   \n",
              "NuSVR                                                0.53            0.95   \n",
              "GradientBoostingRegressor                            0.41            1.06   \n",
              "Ridge                                                0.34            1.13   \n",
              "RidgeCV                                              0.34            1.13   \n",
              "LassoCV                                              0.33            1.13   \n",
              "ElasticNetCV                                         0.33            1.13   \n",
              "LinearRegression                                     0.33            1.14   \n",
              "TransformedTargetRegressor                           0.33            1.14   \n",
              "HuberRegressor                                       0.33            1.14   \n",
              "BayesianRidge                                        0.32            1.14   \n",
              "SGDRegressor                                         0.32            1.15   \n",
              "LinearSVR                                            0.31            1.15   \n",
              "OrthogonalMatchingPursuitCV                          0.25            1.20   \n",
              "OrthogonalMatchingPursuit                            0.25            1.20   \n",
              "LassoLarsCV                                          0.25            1.20   \n",
              "LassoLarsIC                                          0.25            1.20   \n",
              "AdaBoostRegressor                                    0.21            1.23   \n",
              "ElasticNet                                           0.00            1.39   \n",
              "DummyRegressor                                       0.00            1.39   \n",
              "Lasso                                                0.00            1.39   \n",
              "LassoLars                                            0.00            1.39   \n",
              "PassiveAggressiveRegressor                          -0.39            1.64   \n",
              "Lars                                                -3.54            2.95   \n",
              "KernelRidge                                        -21.58            6.58   \n",
              "RANSACRegressor               -62121157829948998680576.00 345414472171.26   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "ExtraTreesRegressor                  8.88  \n",
              "DecisionTreeRegressor                0.18  \n",
              "ExtraTreeRegressor                   0.15  \n",
              "GaussianProcessRegressor            13.35  \n",
              "RandomForestRegressor                6.68  \n",
              "BaggingRegressor                     0.93  \n",
              "MLPRegressor                         6.21  \n",
              "HistGradientBoostingRegressor        0.99  \n",
              "KNeighborsRegressor                  0.88  \n",
              "SVR                                  5.80  \n",
              "NuSVR                                5.12  \n",
              "GradientBoostingRegressor            2.48  \n",
              "Ridge                                0.05  \n",
              "RidgeCV                              0.14  \n",
              "LassoCV                              4.11  \n",
              "ElasticNetCV                         2.52  \n",
              "LinearRegression                     0.09  \n",
              "TransformedTargetRegressor           0.08  \n",
              "HuberRegressor                       0.27  \n",
              "BayesianRidge                        0.16  \n",
              "SGDRegressor                         0.13  \n",
              "LinearSVR                            1.09  \n",
              "OrthogonalMatchingPursuitCV          0.13  \n",
              "OrthogonalMatchingPursuit            0.05  \n",
              "LassoLarsCV                          0.23  \n",
              "LassoLarsIC                          0.13  \n",
              "AdaBoostRegressor                    1.00  \n",
              "ElasticNet                           0.05  \n",
              "DummyRegressor                       0.03  \n",
              "Lasso                                0.10  \n",
              "LassoLars                            0.06  \n",
              "PassiveAggressiveRegressor           0.12  \n",
              "Lars                                 0.20  \n",
              "KernelRidge                          2.63  \n",
              "RANSACRegressor                      1.02  "
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.84</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.54</td>\n",
              "      <td>8.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.84</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.84</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>0.84</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.54</td>\n",
              "      <td>13.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.62</td>\n",
              "      <td>6.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.72</td>\n",
              "      <td>6.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.94</td>\n",
              "      <td>5.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.95</td>\n",
              "      <td>5.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.13</td>\n",
              "      <td>4.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.13</td>\n",
              "      <td>2.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.33</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.15</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.29</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.15</td>\n",
              "      <td>1.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.23</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.43</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>1.64</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-3.67</td>\n",
              "      <td>-3.54</td>\n",
              "      <td>2.95</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-22.22</td>\n",
              "      <td>-21.58</td>\n",
              "      <td>6.58</td>\n",
              "      <td>2.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-63887845574846239997952.00</td>\n",
              "      <td>-62121157829948998680576.00</td>\n",
              "      <td>345414472171.26</td>\n",
              "      <td>1.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fee1f8f-cfad-40fe-a7f7-4374d0c70dab')\"\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4fee1f8f-cfad-40fe-a7f7-4374d0c70dab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# performance table fo the test set (20% subset)\n",
        "predictions_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3AlcD2go6h4-",
        "outputId": "a3d0f761-2d9c-431a-d9a2-a0ae25e7485b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       Adjusted R-Squared  \\\n",
              "Model                                                       \n",
              "HistGradientBoostingRegressor                        0.38   \n",
              "NuSVR                                                0.37   \n",
              "SVR                                                  0.37   \n",
              "RandomForestRegressor                                0.34   \n",
              "KNeighborsRegressor                                  0.32   \n",
              "BaggingRegressor                                     0.31   \n",
              "GradientBoostingRegressor                            0.30   \n",
              "MLPRegressor                                         0.27   \n",
              "RidgeCV                                              0.23   \n",
              "ElasticNetCV                                         0.22   \n",
              "LassoCV                                              0.22   \n",
              "Ridge                                                0.22   \n",
              "BayesianRidge                                        0.22   \n",
              "HuberRegressor                                       0.21   \n",
              "SGDRegressor                                         0.20   \n",
              "LinearRegression                                     0.19   \n",
              "TransformedTargetRegressor                           0.19   \n",
              "LinearSVR                                            0.17   \n",
              "OrthogonalMatchingPursuitCV                          0.15   \n",
              "OrthogonalMatchingPursuit                            0.15   \n",
              "LassoLarsCV                                          0.14   \n",
              "LassoLarsIC                                          0.14   \n",
              "ExtraTreesRegressor                                  0.13   \n",
              "ExtraTreeRegressor                                   0.11   \n",
              "AdaBoostRegressor                                    0.10   \n",
              "DecisionTreeRegressor                                0.09   \n",
              "LassoLars                                           -0.13   \n",
              "ElasticNet                                          -0.13   \n",
              "DummyRegressor                                      -0.13   \n",
              "Lasso                                               -0.13   \n",
              "PassiveAggressiveRegressor                          -0.57   \n",
              "Lars                                                -6.22   \n",
              "GaussianProcessRegressor                           -11.08   \n",
              "KernelRidge                                        -23.23   \n",
              "RANSACRegressor               -67696456524419477536768.00   \n",
              "\n",
              "                                                R-Squared            RMSE  \\\n",
              "Model                                                                       \n",
              "HistGradientBoostingRegressor                        0.45            1.05   \n",
              "NuSVR                                                0.44            1.06   \n",
              "SVR                                                  0.44            1.06   \n",
              "RandomForestRegressor                                0.42            1.08   \n",
              "KNeighborsRegressor                                  0.40            1.10   \n",
              "BaggingRegressor                                     0.39            1.10   \n",
              "GradientBoostingRegressor                            0.38            1.11   \n",
              "MLPRegressor                                         0.35            1.14   \n",
              "RidgeCV                                              0.31            1.17   \n",
              "ElasticNetCV                                         0.31            1.17   \n",
              "LassoCV                                              0.31            1.17   \n",
              "Ridge                                                0.31            1.18   \n",
              "BayesianRidge                                        0.31            1.18   \n",
              "HuberRegressor                                       0.30            1.18   \n",
              "SGDRegressor                                         0.29            1.19   \n",
              "LinearRegression                                     0.28            1.20   \n",
              "TransformedTargetRegressor                           0.28            1.20   \n",
              "LinearSVR                                            0.26            1.21   \n",
              "OrthogonalMatchingPursuitCV                          0.24            1.23   \n",
              "OrthogonalMatchingPursuit                            0.24            1.23   \n",
              "LassoLarsCV                                          0.24            1.23   \n",
              "LassoLarsIC                                          0.24            1.23   \n",
              "ExtraTreesRegressor                                  0.23            1.24   \n",
              "ExtraTreeRegressor                                   0.21            1.26   \n",
              "AdaBoostRegressor                                    0.20            1.27   \n",
              "DecisionTreeRegressor                                0.19            1.27   \n",
              "LassoLars                                           -0.00            1.41   \n",
              "ElasticNet                                          -0.00            1.41   \n",
              "DummyRegressor                                      -0.00            1.41   \n",
              "Lasso                                               -0.00            1.41   \n",
              "PassiveAggressiveRegressor                          -0.40            1.67   \n",
              "Lars                                                -5.42            3.58   \n",
              "GaussianProcessRegressor                            -9.74            4.63   \n",
              "KernelRidge                                        -20.55            6.56   \n",
              "RANSACRegressor               -60205838928356470423552.00 346537359412.01   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "HistGradientBoostingRegressor        0.95  \n",
              "NuSVR                                2.15  \n",
              "SVR                                  2.85  \n",
              "RandomForestRegressor                6.14  \n",
              "KNeighborsRegressor                  0.27  \n",
              "BaggingRegressor                     0.65  \n",
              "GradientBoostingRegressor            2.50  \n",
              "MLPRegressor                         6.26  \n",
              "RidgeCV                              0.13  \n",
              "ElasticNetCV                         2.41  \n",
              "LassoCV                              2.96  \n",
              "Ridge                                0.04  \n",
              "BayesianRidge                        0.14  \n",
              "HuberRegressor                       0.23  \n",
              "SGDRegressor                         0.11  \n",
              "LinearRegression                     0.18  \n",
              "TransformedTargetRegressor           0.08  \n",
              "LinearSVR                            0.96  \n",
              "OrthogonalMatchingPursuitCV          0.11  \n",
              "OrthogonalMatchingPursuit            0.04  \n",
              "LassoLarsCV                          0.49  \n",
              "LassoLarsIC                          0.20  \n",
              "ExtraTreesRegressor                  8.86  \n",
              "ExtraTreeRegressor                   0.18  \n",
              "AdaBoostRegressor                    0.79  \n",
              "DecisionTreeRegressor                0.17  \n",
              "LassoLars                            0.12  \n",
              "ElasticNet                           0.03  \n",
              "DummyRegressor                       0.03  \n",
              "Lasso                                0.04  \n",
              "PassiveAggressiveRegressor           0.10  \n",
              "Lars                                 0.11  \n",
              "GaussianProcessRegressor             8.33  \n",
              "KernelRidge                          2.27  \n",
              "RANSACRegressor                      1.16  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.38</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.37</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.08</td>\n",
              "      <td>6.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.11</td>\n",
              "      <td>2.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.27</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1.14</td>\n",
              "      <td>6.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.23</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.17</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.17</td>\n",
              "      <td>2.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.17</td>\n",
              "      <td>2.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.20</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.14</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.14</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.24</td>\n",
              "      <td>8.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.57</td>\n",
              "      <td>-0.40</td>\n",
              "      <td>1.67</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-6.22</td>\n",
              "      <td>-5.42</td>\n",
              "      <td>3.58</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-11.08</td>\n",
              "      <td>-9.74</td>\n",
              "      <td>4.63</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-23.23</td>\n",
              "      <td>-20.55</td>\n",
              "      <td>6.56</td>\n",
              "      <td>2.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-67696456524419477536768.00</td>\n",
              "      <td>-60205838928356470423552.00</td>\n",
              "      <td>346537359412.01</td>\n",
              "      <td>1.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ac06085-9f55-4d54-bfce-f3443ad91ce2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ac06085-9f55-4d54-bfce-f3443ad91ce2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ac06085-9f55-4d54-bfce-f3443ad91ce2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data visualization of model performance"
      ],
      "metadata": {
        "id": "SN2lE9036myQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# bar plot of R-squared values"
      ],
      "metadata": {
        "id": "HAiJrXMR6miH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5,10))\n",
        "sns.set_theme(style= \"whitegrid\")\n",
        "ax = sns.barplot(y = predictions_train.index, x = \"R-Squared\", data = predictions_train)\n",
        "ax.set(xlim = (0, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "Xd_ZrHPV6lqo",
        "outputId": "1765ca51-2cb7-4fe7-8fc4-22c2f69a6420"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bar plot of RMSE values\n",
        "plt.figure(figsize = (5, 10))\n",
        "sns.set_theme(style = \"whitegrid\")\n",
        "ax = sns.barplot(y = predictions_train.index, x = \"RMSE\", data = predictions_train)\n",
        "ax.set(xlim = (0, 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "c-H2w4bkQOxl",
        "outputId": "85d61d46-04c3-4e6b-af04-2a77465bce22"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# bar plot of calculation time\n",
        "plt.figure(figsize = (5, 10))\n",
        "sns.set_theme(style = \"whitegrid\")\n",
        "ax = sns.barplot(y = predictions_train.index, x = \"Time Taken\", data = predictions_train)\n",
        "ax.set(xlim = (0, 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "uwX62_cTQw4m",
        "outputId": "e8d655e1-8170-42b3-ed02-380a9f62e11c"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}