Part_5_Compare_Regressors_ML.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNq-XQWWYNXh"
      },
      "source": [
        "# **Bioinformatics Project - Computational Drug Discovery [Part 5] Comparing Regressors**\n",
        "\n",
        "In Part 5, we will be comparing several ML algorithms for build regression models of acetylcholinesterase inhibitors.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnVZXG7QY1D2"
      },
      "source": [
        "## **1. Import libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "`lazypredict` is a Python library designed to simplify the process of training and evaluating multiple machine learning models quickly. It provides a straightforward interface to train and compare various models with minimal code, making it useful for rapid prototyping and model selection."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4i2iuTlZYvQ",
        "outputId": "1172a063-ee94-4abe-a8a8-fa0b0ba1073e"
      },
      "outputs": [],
      "source": [
        "! pip install lazypredict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vf73M_f9XwZ7",
        "outputId": "4256ee85-6e74-4322-fa05-9e79a1779dfd"
      },
      "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",
      "metadata": {
        "id": "lUGwBVH7Y8Rq"
      },
      "source": [
        "## **2. Load the data set**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "WVojHjyWZC9D"
      },
      "outputs": [],
      "source": [
        "# set the %cd /path/to/directory/with/csv/file\n",
        "df = pd.read_csv('bioactivity_data_preprocessed_pIC50_3class_pubchem_fp_BCR-ABL1.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "KbTq4zzKZpVf"
      },
      "outputs": [],
      "source": [
        "X = df.drop('pIC50', axis=1) #Splitting the data into X and Y variables\n",
        "Y = df.pIC50"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Qa6WSGGZ39n"
      },
      "source": [
        "## **3. Data pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtC4SWpBaCVQ",
        "outputId": "1008b65f-5b93-423a-b2bf-0695dc2014ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(328, 95)"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Examine X dimension\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'head' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m Y(\u001b[43mhead\u001b[49m)\n",
            "\u001b[0;31mNameError\u001b[0m: name 'head' is not defined"
          ]
        }
      ],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVHzT2f5Z8sF",
        "outputId": "66fc3cdb-2f97-4694-b915-58c95a49b3d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(328, 95)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "MYck-_XeaHNZ"
      },
      "outputs": [],
      "source": [
        "# Perform data splitting using 80/20 ratio\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkW9nKohamEj"
      },
      "source": [
        "## **4. Compare ML algorithms**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here we look at various machine learning algorithms to see which one performs best on our data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnFbUjA1aUj1",
        "outputId": "84de30a4-1459-4506-fefd-d4495c30fbee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 42/42 [00:06<00:00,  6.95it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000124 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 285\n",
            "[LightGBM] [Info] Number of data points in the train set: 262, number of used features: 95\n",
            "[LightGBM] [Info] Start training from score 7.123840\n",
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            "'tuple' object has no attribute '__name__'\n",
            "Invalid Regressor(s)\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 42/42 [00:06<00:00,  6.64it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000192 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 285\n",
            "[LightGBM] [Info] Number of data points in the train set: 262, number of used features: 95\n",
            "[LightGBM] [Info] Start training from score 7.123840\n",
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          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# Defines and builds the lazyclassifier\n",
        "clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None)\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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cHOFkp-ka5tF",
        "outputId": "7abc08e9-e5b4-44e2-955d-dec0c20b91a1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<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>GaussianProcessRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.53</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.48</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.47</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.41</td>\n",
              "      <td>0.63</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.29</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.28</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.24</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.24</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.22</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.21</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.18</td>\n",
              "      <td>0.48</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.47</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.15</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.13</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.11</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.08</td>\n",
              "      <td>0.41</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.03</td>\n",
              "      <td>0.39</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.37</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.29</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.21</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.56</td>\n",
              "      <td>0.01</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.57</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuantileRegressor</th>\n",
              "      <td>-0.60</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.70</td>\n",
              "      <td>-0.08</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-44.73</td>\n",
              "      <td>-28.09</td>\n",
              "      <td>7.18</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-48513102939948329392569314879790383104.00</td>\n",
              "      <td>-30855076965637634713705135396806983680.00</td>\n",
              "      <td>7394774119242792960.00</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                      Adjusted R-Squared  \\\n",
              "Model                                                                      \n",
              "GaussianProcessRegressor                                            0.58   \n",
              "ExtraTreeRegressor                                                  0.58   \n",
              "ExtraTreesRegressor                                                 0.58   \n",
              "DecisionTreeRegressor                                               0.58   \n",
              "RandomForestRegressor                                               0.53   \n",
              "BaggingRegressor                                                    0.48   \n",
              "GradientBoostingRegressor                                           0.47   \n",
              "MLPRegressor                                                        0.41   \n",
              "HistGradientBoostingRegressor                                       0.40   \n",
              "LGBMRegressor                                                       0.36   \n",
              "SVR                                                                 0.31   \n",
              "TransformedTargetRegressor                                          0.30   \n",
              "LinearRegression                                                    0.30   \n",
              "Ridge                                                               0.29   \n",
              "NuSVR                                                               0.28   \n",
              "HuberRegressor                                                      0.24   \n",
              "RidgeCV                                                             0.24   \n",
              "SGDRegressor                                                        0.22   \n",
              "KNeighborsRegressor                                                 0.21   \n",
              "LinearSVR                                                           0.18   \n",
              "PoissonRegressor                                                    0.17   \n",
              "AdaBoostRegressor                                                   0.15   \n",
              "BayesianRidge                                                       0.13   \n",
              "ElasticNetCV                                                        0.11   \n",
              "OrthogonalMatchingPursuit                                           0.08   \n",
              "LassoLarsCV                                                         0.06   \n",
              "LassoLarsIC                                                         0.06   \n",
              "TweedieRegressor                                                    0.04   \n",
              "GammaRegressor                                                      0.04   \n",
              "LassoCV                                                             0.03   \n",
              "OrthogonalMatchingPursuitCV                                         0.01   \n",
              "LarsCV                                                             -0.11   \n",
              "RANSACRegressor                                                    -0.31   \n",
              "ElasticNet                                                         -0.56   \n",
              "DummyRegressor                                                     -0.57   \n",
              "Lasso                                                              -0.57   \n",
              "LassoLars                                                          -0.57   \n",
              "QuantileRegressor                                                  -0.60   \n",
              "PassiveAggressiveRegressor                                         -0.70   \n",
              "KernelRidge                                                       -44.73   \n",
              "Lars                          -48513102939948329392569314879790383104.00   \n",
              "\n",
              "                                                               R-Squared  \\\n",
              "Model                                                                      \n",
              "GaussianProcessRegressor                                            0.73   \n",
              "ExtraTreeRegressor                                                  0.73   \n",
              "ExtraTreesRegressor                                                 0.73   \n",
              "DecisionTreeRegressor                                               0.73   \n",
              "RandomForestRegressor                                               0.70   \n",
              "BaggingRegressor                                                    0.67   \n",
              "GradientBoostingRegressor                                           0.67   \n",
              "MLPRegressor                                                        0.63   \n",
              "HistGradientBoostingRegressor                                       0.62   \n",
              "LGBMRegressor                                                       0.60   \n",
              "SVR                                                                 0.56   \n",
              "TransformedTargetRegressor                                          0.56   \n",
              "LinearRegression                                                    0.56   \n",
              "Ridge                                                               0.55   \n",
              "NuSVR                                                               0.55   \n",
              "HuberRegressor                                                      0.52   \n",
              "RidgeCV                                                             0.51   \n",
              "SGDRegressor                                                        0.50   \n",
              "KNeighborsRegressor                                                 0.50   \n",
              "LinearSVR                                                           0.48   \n",
              "PoissonRegressor                                                    0.47   \n",
              "AdaBoostRegressor                                                   0.46   \n",
              "BayesianRidge                                                       0.44   \n",
              "ElasticNetCV                                                        0.43   \n",
              "OrthogonalMatchingPursuit                                           0.41   \n",
              "LassoLarsCV                                                         0.40   \n",
              "LassoLarsIC                                                         0.40   \n",
              "TweedieRegressor                                                    0.39   \n",
              "GammaRegressor                                                      0.39   \n",
              "LassoCV                                                             0.39   \n",
              "OrthogonalMatchingPursuitCV                                         0.37   \n",
              "LarsCV                                                              0.29   \n",
              "RANSACRegressor                                                     0.17   \n",
              "ElasticNet                                                          0.01   \n",
              "DummyRegressor                                                      0.00   \n",
              "Lasso                                                               0.00   \n",
              "LassoLars                                                           0.00   \n",
              "QuantileRegressor                                                  -0.02   \n",
              "PassiveAggressiveRegressor                                         -0.08   \n",
              "KernelRidge                                                       -28.09   \n",
              "Lars                          -30855076965637634713705135396806983680.00   \n",
              "\n",
              "                                                RMSE  Time Taken  \n",
              "Model                                                             \n",
              "GaussianProcessRegressor                        0.69        0.03  \n",
              "ExtraTreeRegressor                              0.69        0.01  \n",
              "ExtraTreesRegressor                             0.69        0.23  \n",
              "DecisionTreeRegressor                           0.69        0.02  \n",
              "RandomForestRegressor                           0.73        0.30  \n",
              "BaggingRegressor                                0.76        0.04  \n",
              "GradientBoostingRegressor                       0.77        0.17  \n",
              "MLPRegressor                                    0.81        0.37  \n",
              "HistGradientBoostingRegressor                   0.82        0.11  \n",
              "LGBMRegressor                                   0.85        0.03  \n",
              "SVR                                             0.88        0.03  \n",
              "TransformedTargetRegressor                      0.89        0.02  \n",
              "LinearRegression                                0.89        0.05  \n",
              "Ridge                                           0.89        0.01  \n",
              "NuSVR                                           0.90        0.03  \n",
              "HuberRegressor                                  0.92        0.04  \n",
              "RidgeCV                                         0.93        0.02  \n",
              "SGDRegressor                                    0.94        0.02  \n",
              "KNeighborsRegressor                             0.95        0.03  \n",
              "LinearSVR                                       0.96        0.05  \n",
              "PoissonRegressor                                0.97        0.32  \n",
              "AdaBoostRegressor                               0.98        0.04  \n",
              "BayesianRidge                                   0.99        0.02  \n",
              "ElasticNetCV                                    1.00        1.33  \n",
              "OrthogonalMatchingPursuit                       1.02        0.01  \n",
              "LassoLarsCV                                     1.03        0.03  \n",
              "LassoLarsIC                                     1.03        0.02  \n",
              "TweedieRegressor                                1.04        0.21  \n",
              "GammaRegressor                                  1.04        0.04  \n",
              "LassoCV                                         1.04        0.70  \n",
              "OrthogonalMatchingPursuitCV                     1.05        0.02  \n",
              "LarsCV                                          1.12        0.13  \n",
              "RANSACRegressor                                 1.21        0.33  \n",
              "ElasticNet                                      1.33        0.01  \n",
              "DummyRegressor                                  1.33        0.01  \n",
              "Lasso                                           1.33        0.01  \n",
              "LassoLars                                       1.33        0.01  \n",
              "QuantileRegressor                               1.34        0.43  \n",
              "PassiveAggressiveRegressor                      1.39        0.02  \n",
              "KernelRidge                                     7.18        0.19  \n",
              "Lars                          7394774119242792960.00        0.05  "
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Performance table of the training set (80% subset)\n",
        "predictions_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KP6qT55ebcfd",
        "outputId": "b25cc626-135a-4c86-8a4b-90f44a63dabe"
      },
      "outputs": [
        {
          "data": {
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              "  <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",
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              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Lars</th>\n",
              "      <td>12157740250082386507858690995435333891769659997...</td>\n",
              "      <td>-5611264730807255761012207330478744320412099295...</td>\n",
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              "      <th>KernelRidge</th>\n",
              "      <td>77.19</td>\n",
              "      <td>-34.17</td>\n",
              "      <td>7.23</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>34.56</td>\n",
              "      <td>-14.49</td>\n",
              "      <td>4.80</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>3.83</td>\n",
              "      <td>-0.31</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>3.73</td>\n",
              "      <td>-0.26</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuantileRegressor</th>\n",
              "      <td>3.37</td>\n",
              "      <td>-0.10</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>3.23</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>3.23</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>3.23</td>\n",
              "      <td>-0.03</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>3.21</td>\n",
              "      <td>-0.02</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>3.11</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>3.11</td>\n",
              "      <td>0.03</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>3.09</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.20</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>2.89</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>2.85</td>\n",
              "      <td>0.14</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>2.76</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.10</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>2.66</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>2.60</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>2.57</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>2.57</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>2.55</td>\n",
              "      <td>0.28</td>\n",
              "      <td>1.03</td>\n",
              "      <td>0.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>2.53</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>2.50</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.01</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>2.47</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>2.42</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>2.42</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>2.41</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>2.40</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>2.39</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>2.36</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>2.35</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>2.34</td>\n",
              "      <td>0.38</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>2.32</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>2.31</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.95</td>\n",
              "      <td>1.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>2.30</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>2.30</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>2.29</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>2.28</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>2.27</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>2.26</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>2.25</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                              Adjusted R-Squared  \\\n",
              "Model                                                                              \n",
              "Lars                          12157740250082386507858690995435333891769659997...   \n",
              "KernelRidge                                                                77.19   \n",
              "GaussianProcessRegressor                                                   34.56   \n",
              "PassiveAggressiveRegressor                                                  3.83   \n",
              "RANSACRegressor                                                             3.73   \n",
              "QuantileRegressor                                                           3.37   \n",
              "LassoLars                                                                   3.23   \n",
              "Lasso                                                                       3.23   \n",
              "DummyRegressor                                                              3.23   \n",
              "ElasticNet                                                                  3.21   \n",
              "ExtraTreesRegressor                                                         3.11   \n",
              "DecisionTreeRegressor                                                       3.11   \n",
              "ExtraTreeRegressor                                                          3.09   \n",
              "LinearSVR                                                                   2.89   \n",
              "MLPRegressor                                                                2.85   \n",
              "HuberRegressor                                                              2.76   \n",
              "LarsCV                                                                      2.66   \n",
              "GradientBoostingRegressor                                                   2.60   \n",
              "LinearRegression                                                            2.57   \n",
              "TransformedTargetRegressor                                                  2.57   \n",
              "RandomForestRegressor                                                       2.55   \n",
              "KNeighborsRegressor                                                         2.53   \n",
              "OrthogonalMatchingPursuit                                                   2.50   \n",
              "OrthogonalMatchingPursuitCV                                                 2.47   \n",
              "LassoCV                                                                     2.42   \n",
              "Ridge                                                                       2.42   \n",
              "BaggingRegressor                                                            2.41   \n",
              "LassoLarsIC                                                                 2.40   \n",
              "LassoLarsCV                                                                 2.39   \n",
              "SVR                                                                         2.36   \n",
              "LGBMRegressor                                                               2.35   \n",
              "GammaRegressor                                                              2.34   \n",
              "SGDRegressor                                                                2.32   \n",
              "ElasticNetCV                                                                2.31   \n",
              "TweedieRegressor                                                            2.30   \n",
              "NuSVR                                                                       2.30   \n",
              "RidgeCV                                                                     2.29   \n",
              "HistGradientBoostingRegressor                                               2.28   \n",
              "PoissonRegressor                                                            2.27   \n",
              "AdaBoostRegressor                                                           2.26   \n",
              "BayesianRidge                                                               2.25   \n",
              "\n",
              "                                                                       R-Squared  \\\n",
              "Model                                                                              \n",
              "Lars                          -5611264730807255761012207330478744320412099295...   \n",
              "KernelRidge                                                               -34.17   \n",
              "GaussianProcessRegressor                                                  -14.49   \n",
              "PassiveAggressiveRegressor                                                 -0.31   \n",
              "RANSACRegressor                                                            -0.26   \n",
              "QuantileRegressor                                                          -0.10   \n",
              "LassoLars                                                                  -0.03   \n",
              "Lasso                                                                      -0.03   \n",
              "DummyRegressor                                                             -0.03   \n",
              "ElasticNet                                                                 -0.02   \n",
              "ExtraTreesRegressor                                                         0.03   \n",
              "DecisionTreeRegressor                                                       0.03   \n",
              "ExtraTreeRegressor                                                          0.04   \n",
              "LinearSVR                                                                   0.13   \n",
              "MLPRegressor                                                                0.14   \n",
              "HuberRegressor                                                              0.19   \n",
              "LarsCV                                                                      0.23   \n",
              "GradientBoostingRegressor                                                   0.26   \n",
              "LinearRegression                                                            0.27   \n",
              "TransformedTargetRegressor                                                  0.27   \n",
              "RandomForestRegressor                                                       0.28   \n",
              "KNeighborsRegressor                                                         0.30   \n",
              "OrthogonalMatchingPursuit                                                   0.31   \n",
              "OrthogonalMatchingPursuitCV                                                 0.32   \n",
              "LassoCV                                                                     0.34   \n",
              "Ridge                                                                       0.35   \n",
              "BaggingRegressor                                                            0.35   \n",
              "LassoLarsIC                                                                 0.35   \n",
              "LassoLarsCV                                                                 0.36   \n",
              "SVR                                                                         0.37   \n",
              "LGBMRegressor                                                               0.38   \n",
              "GammaRegressor                                                              0.38   \n",
              "SGDRegressor                                                                0.39   \n",
              "ElasticNetCV                                                                0.40   \n",
              "TweedieRegressor                                                            0.40   \n",
              "NuSVR                                                                       0.40   \n",
              "RidgeCV                                                                     0.40   \n",
              "HistGradientBoostingRegressor                                               0.41   \n",
              "PoissonRegressor                                                            0.42   \n",
              "AdaBoostRegressor                                                           0.42   \n",
              "BayesianRidge                                                               0.42   \n",
              "\n",
              "                                                             RMSE  Time Taken  \n",
              "Model                                                                          \n",
              "Lars                          91311559522403438598016091553792.00        0.08  \n",
              "KernelRidge                                                  7.23        0.15  \n",
              "GaussianProcessRegressor                                     4.80        0.02  \n",
              "PassiveAggressiveRegressor                                   1.39        0.01  \n",
              "RANSACRegressor                                              1.37        0.31  \n",
              "QuantileRegressor                                            1.28        0.45  \n",
              "LassoLars                                                    1.24        0.01  \n",
              "Lasso                                                        1.24        0.01  \n",
              "DummyRegressor                                               1.24        0.01  \n",
              "ElasticNet                                                   1.23        0.01  \n",
              "ExtraTreesRegressor                                          1.20        0.24  \n",
              "DecisionTreeRegressor                                        1.20        0.01  \n",
              "ExtraTreeRegressor                                           1.20        0.01  \n",
              "LinearSVR                                                    1.14        0.04  \n",
              "MLPRegressor                                                 1.13        0.41  \n",
              "HuberRegressor                                               1.10        0.04  \n",
              "LarsCV                                                       1.07        0.67  \n",
              "GradientBoostingRegressor                                    1.05        0.17  \n",
              "LinearRegression                                             1.04        0.02  \n",
              "TransformedTargetRegressor                                   1.04        0.02  \n",
              "RandomForestRegressor                                        1.03        0.32  \n",
              "KNeighborsRegressor                                          1.02        0.04  \n",
              "OrthogonalMatchingPursuit                                    1.01        0.01  \n",
              "OrthogonalMatchingPursuitCV                                  1.00        0.02  \n",
              "LassoCV                                                      0.99        0.73  \n",
              "Ridge                                                        0.99        0.01  \n",
              "BaggingRegressor                                             0.98        0.03  \n",
              "LassoLarsIC                                                  0.98        0.02  \n",
              "LassoLarsCV                                                  0.98        0.03  \n",
              "SVR                                                          0.97        0.02  \n",
              "LGBMRegressor                                                0.96        0.03  \n",
              "GammaRegressor                                               0.96        0.05  \n",
              "SGDRegressor                                                 0.95        0.02  \n",
              "ElasticNetCV                                                 0.95        1.26  \n",
              "TweedieRegressor                                             0.95        0.23  \n",
              "NuSVR                                                        0.94        0.02  \n",
              "RidgeCV                                                      0.94        0.02  \n",
              "HistGradientBoostingRegressor                                0.94        0.10  \n",
              "PoissonRegressor                                             0.93        0.34  \n",
              "AdaBoostRegressor                                            0.93        0.03  \n",
              "BayesianRidge                                                0.93        0.01  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Performance table of the test set (20% subset)\n",
        "predictions_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM-lcHz6byNz"
      },
      "source": [
        "## **5. Data visualization of model performance**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "MLsi5uSvbpC7",
        "outputId": "2cca0f31-9f81-4d37-8ccd-e88d35268272"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Bar plot of R-squared values\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#train[\"R-Squared\"] = [0 if i < 0 else i for i in train.iloc[:,0] ]\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, hue=\"Model\")\n",
        "ax.set(xlim=(0, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "OLkiiTTwb-Cb",
        "outputId": "6efb0857-c5fe-4bbc-dead-1bfd98a6f5d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "execution_count": 66,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Bar plot of RMSE values\n",
        "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=\"RMSE\", data=predictions_train, hue=\"Model\")\n",
        "ax.set(xlim=(0, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "pCdcxpzScqXX",
        "outputId": "62dbbf7b-d569-4280-ce60-0c191bbd8e2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Bar plot of calculation time\n",
        "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=\"Time Taken\", data=predictions_train, hue=\"Model\")\n",
        "ax.set(xlim=(0, 10))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "bioinfo",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.15"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}