Model_Comparisions.ipynb

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      "name": "Model Comparisions.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMkKK4+deo8nQGnf/bLt6hX",
      "include_colab_link": true
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rishit605/drug-classification/blob/main/Model_Comparisions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Libraries**\n",
        "\n",
        "We first install the **Lazypredict** library, which is a library that helps build a lot of basic models without much code and helps understand which models works better without any parameter tuning.\n",
        "\n",
        "Using this LIbrary we can import, use and compare multiple Machine Learning Models without adjusting or fine tuning any paramters."
      ],
      "metadata": {
        "id": "giZrFHiW0a5y"
      }
    },
    {
      "cell_type": "code",
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      "metadata": {
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          "height": 1000
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        "outputId": "4dab423b-dd91-436f-d54a-adc5ec9aa14a"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Building wheels for collected packages: PyYAML\n",
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            "  Attempting uninstall: numpy\n",
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            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.0.2\n",
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            "    Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
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            "      Successfully uninstalled xgboost-0.90\n",
            "  Attempting uninstall: tqdm\n",
            "    Found existing installation: tqdm 4.64.0\n",
            "    Uninstalling tqdm-4.64.0:\n",
            "      Successfully uninstalled tqdm-4.64.0\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
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            "  Attempting uninstall: pytest\n",
            "    Found existing installation: pytest 3.6.4\n",
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            "      Successfully uninstalled pytest-3.6.4\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
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            "      Successfully uninstalled pandas-1.3.5\n",
            "  Attempting uninstall: lightgbm\n",
            "    Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "pymc3 3.11.5 requires scipy<1.8.0,>=1.7.3, but you have scipy 1.5.4 which is incompatible.\n",
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            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed PyYAML-5.3.1 joblib-1.0.0 lazypredict-0.2.9 lightgbm-2.3.1 numpy-1.19.1 pandas-1.0.5 pluggy-0.13.1 pytest-5.4.3 scikit-learn-0.23.1 scipy-1.5.4 tqdm-4.56.0 xgboost-1.1.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "! pip install lazypredict"
      ]
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "W5bN3lU00MUa"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Loading the Dataset**"
      ],
      "metadata": {
        "id": "4wto_3w_1hRp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/dataset/4bioactivity_class_pIC50__4data_pubchem_fp.csv')"
      ],
      "metadata": {
        "id": "d0MkqrvQ1gIi"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('pIC50', axis =1)\n",
        "y= df.pIC50"
      ],
      "metadata": {
        "id": "Vm9kl4NZ19x6"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing** "
      ],
      "metadata": {
        "id": "1YxCas6d2LzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XSCwP55t1-sI",
        "outputId": "dfec2061-60fb-49a9-ff29-3495564b576d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(133, 881)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing Low Varience\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "selec = VarianceThreshold(threshold=(0.8 * (1 -  0.8)))\n",
        "X = selec.fit_transform(X)\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DvEAY1xC2T_R",
        "outputId": "07fde55e-9683-43d2-8ed8-39bab6e01b0d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(133, 200)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Spliting the Train-Test Data\n",
        "X_train, X_test, y_train, y_test  = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "zlbPQdAs2zbx"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Comparing Ml Algorithims**"
      ],
      "metadata": {
        "id": "0e6866K83WPR"
      }
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm6LDN_M3UIB",
        "outputId": "7967d3df-f88f-4807-8b76-2930cb77df4e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 42/42 [00:14<00:00,  2.99it/s]\n",
            "100%|██████████| 42/42 [00:00<00:00, 209465.84it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(predictions_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-uWU-ji63fkA",
        "outputId": "d31ed95e-132e-4916-8e4d-656ad93748dc"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                              Adjusted R-Squared  \\\n",
              "Model                                                                              \n",
              "Lars                          53314238496847601890465871028875931195568271616...   \n",
              "KernelRidge                                                                 7.39   \n",
              "LarsCV                                                                      2.31   \n",
              "LassoLars                                                                   2.11   \n",
              "DummyRegressor                                                              2.11   \n",
              "Lasso                                                                       1.95   \n",
              "ElasticNet                                                                  1.68   \n",
              "LassoLarsIC                                                                 1.67   \n",
              "PassiveAggressiveRegressor                                                  1.61   \n",
              "SVR                                                                         1.58   \n",
              "LassoLarsCV                                                                 1.52   \n",
              "OrthogonalMatchingPursuitCV                                                 1.51   \n",
              "NuSVR                                                                       1.49   \n",
              "LinearSVR                                                                   1.47   \n",
              "LassoCV                                                                     1.46   \n",
              "ElasticNetCV                                                                1.46   \n",
              "HuberRegressor                                                              1.46   \n",
              "KNeighborsRegressor                                                         1.45   \n",
              "BayesianRidge                                                               1.45   \n",
              "GammaRegressor                                                              1.42   \n",
              "TweedieRegressor                                                            1.41   \n",
              "GeneralizedLinearRegressor                                                  1.41   \n",
              "SGDRegressor                                                                1.41   \n",
              "AdaBoostRegressor                                                           1.41   \n",
              "HistGradientBoostingRegressor                                               1.39   \n",
              "LGBMRegressor                                                               1.39   \n",
              "TransformedTargetRegressor                                                  1.39   \n",
              "LinearRegression                                                            1.39   \n",
              "OrthogonalMatchingPursuit                                                   1.38   \n",
              "PoissonRegressor                                                            1.38   \n",
              "BaggingRegressor                                                            1.36   \n",
              "RidgeCV                                                                     1.35   \n",
              "RandomForestRegressor                                                       1.34   \n",
              "MLPRegressor                                                                1.32   \n",
              "GradientBoostingRegressor                                                   1.32   \n",
              "Ridge                                                                       1.32   \n",
              "XGBRegressor                                                                1.31   \n",
              "GaussianProcessRegressor                                                    1.31   \n",
              "ExtraTreesRegressor                                                         1.31   \n",
              "ExtraTreeRegressor                                                          1.31   \n",
              "DecisionTreeRegressor                                                       1.31   \n",
              "\n",
              "                                                                       R-Squared  \\\n",
              "Model                                                                              \n",
              "Lars                          -4823669197333830065222347473711797698138361219...   \n",
              "KernelRidge                                                                -4.78   \n",
              "LarsCV                                                                     -0.18   \n",
              "LassoLars                                                                   0.00   \n",
              "DummyRegressor                                                              0.00   \n",
              "Lasso                                                                       0.14   \n",
              "ElasticNet                                                                  0.38   \n",
              "LassoLarsIC                                                                 0.39   \n",
              "PassiveAggressiveRegressor                                                  0.45   \n",
              "SVR                                                                         0.48   \n",
              "LassoLarsCV                                                                 0.53   \n",
              "OrthogonalMatchingPursuitCV                                                 0.53   \n",
              "NuSVR                                                                       0.56   \n",
              "LinearSVR                                                                   0.57   \n",
              "LassoCV                                                                     0.58   \n",
              "ElasticNetCV                                                                0.58   \n",
              "HuberRegressor                                                              0.59   \n",
              "KNeighborsRegressor                                                         0.59   \n",
              "BayesianRidge                                                               0.60   \n",
              "GammaRegressor                                                              0.62   \n",
              "TweedieRegressor                                                            0.63   \n",
              "GeneralizedLinearRegressor                                                  0.63   \n",
              "SGDRegressor                                                                0.63   \n",
              "AdaBoostRegressor                                                           0.63   \n",
              "HistGradientBoostingRegressor                                               0.65   \n",
              "LGBMRegressor                                                               0.65   \n",
              "TransformedTargetRegressor                                                  0.65   \n",
              "LinearRegression                                                            0.65   \n",
              "OrthogonalMatchingPursuit                                                   0.65   \n",
              "PoissonRegressor                                                            0.66   \n",
              "BaggingRegressor                                                            0.67   \n",
              "RidgeCV                                                                     0.68   \n",
              "RandomForestRegressor                                                       0.69   \n",
              "MLPRegressor                                                                0.71   \n",
              "GradientBoostingRegressor                                                   0.71   \n",
              "Ridge                                                                       0.71   \n",
              "XGBRegressor                                                                0.72   \n",
              "GaussianProcessRegressor                                                    0.72   \n",
              "ExtraTreesRegressor                                                         0.72   \n",
              "ExtraTreeRegressor                                                          0.72   \n",
              "DecisionTreeRegressor                                                       0.72   \n",
              "\n",
              "                                                                            RMSE  \\\n",
              "Model                                                                              \n",
              "Lars                          12198741107911777775894405666324698267750422702...   \n",
              "KernelRidge                                                                 4.22   \n",
              "LarsCV                                                                      1.91   \n",
              "LassoLars                                                                   1.76   \n",
              "DummyRegressor                                                              1.76   \n",
              "Lasso                                                                       1.63   \n",
              "ElasticNet                                                                  1.38   \n",
              "LassoLarsIC                                                                 1.37   \n",
              "PassiveAggressiveRegressor                                                  1.31   \n",
              "SVR                                                                         1.27   \n",
              "LassoLarsCV                                                                 1.20   \n",
              "OrthogonalMatchingPursuitCV                                                 1.20   \n",
              "NuSVR                                                                       1.17   \n",
              "LinearSVR                                                                   1.15   \n",
              "LassoCV                                                                     1.14   \n",
              "ElasticNetCV                                                                1.13   \n",
              "HuberRegressor                                                              1.13   \n",
              "KNeighborsRegressor                                                         1.12   \n",
              "BayesianRidge                                                               1.12   \n",
              "GammaRegressor                                                              1.08   \n",
              "TweedieRegressor                                                            1.07   \n",
              "GeneralizedLinearRegressor                                                  1.07   \n",
              "SGDRegressor                                                                1.07   \n",
              "AdaBoostRegressor                                                           1.07   \n",
              "HistGradientBoostingRegressor                                               1.04   \n",
              "LGBMRegressor                                                               1.04   \n",
              "TransformedTargetRegressor                                                  1.04   \n",
              "LinearRegression                                                            1.04   \n",
              "OrthogonalMatchingPursuit                                                   1.04   \n",
              "PoissonRegressor                                                            1.03   \n",
              "BaggingRegressor                                                            1.01   \n",
              "RidgeCV                                                                     0.99   \n",
              "RandomForestRegressor                                                       0.97   \n",
              "MLPRegressor                                                                0.95   \n",
              "GradientBoostingRegressor                                                   0.94   \n",
              "Ridge                                                                       0.94   \n",
              "XGBRegressor                                                                0.93   \n",
              "GaussianProcessRegressor                                                    0.93   \n",
              "ExtraTreesRegressor                                                         0.93   \n",
              "ExtraTreeRegressor                                                          0.93   \n",
              "DecisionTreeRegressor                                                       0.93   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "Lars                                 0.28  \n",
              "KernelRidge                          0.05  \n",
              "LarsCV                               1.15  \n",
              "LassoLars                            0.05  \n",
              "DummyRegressor                       0.02  \n",
              "Lasso                                0.05  \n",
              "ElasticNet                           0.03  \n",
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              "SVR                                  0.03  \n",
              "LassoLarsCV                          0.10  \n",
              "OrthogonalMatchingPursuitCV          0.04  \n",
              "NuSVR                                0.04  \n",
              "LinearSVR                            0.09  \n",
              "LassoCV                              4.63  \n",
              "ElasticNetCV                         4.90  \n",
              "HuberRegressor                       0.12  \n",
              "KNeighborsRegressor                  0.05  \n",
              "BayesianRidge                        0.03  \n",
              "GammaRegressor                       0.03  \n",
              "TweedieRegressor                     0.06  \n",
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              "SGDRegressor                         0.03  \n",
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              "RidgeCV                              0.03  \n",
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              "MLPRegressor                         0.39  \n",
              "GradientBoostingRegressor            0.16  \n",
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              "XGBRegressor                         0.16  \n",
              "GaussianProcessRegressor             0.03  \n",
              "ExtraTreesRegressor                  0.30  \n",
              "ExtraTreeRegressor                   0.03  \n",
              "DecisionTreeRegressor                0.03  "
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              "      <th>Lars</th>\n",
              "      <td>53314238496847601890465871028875931195568271616...</td>\n",
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              "      <th>ElasticNet</th>\n",
              "      <td>1.68</td>\n",
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              "      <th>SVR</th>\n",
              "      <td>1.58</td>\n",
              "      <td>0.48</td>\n",
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              "      <th>LassoLarsCV</th>\n",
              "      <td>1.52</td>\n",
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              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>1.51</td>\n",
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              "      <td>1.46</td>\n",
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              "      <td>1.46</td>\n",
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              "      <th>HuberRegressor</th>\n",
              "      <td>1.46</td>\n",
              "      <td>0.59</td>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5188171b-5d7f-47a2-bc96-738ce64d97fc')\"\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-5188171b-5d7f-47a2-bc96-738ce64d97fc 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-5188171b-5d7f-47a2-bc96-738ce64d97fc');\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": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data visualization of model performance**"
      ],
      "metadata": {
        "id": "49jjMbtO5NzQ"
      }
    },
    {
      "cell_type": "code",
      "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)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "aey2jvDi4Fm_",
        "outputId": "e4a32438-be1b-42f1-c68d-a8d6564f2e9a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "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)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "gT7qsYOO5Ru3",
        "outputId": "92a6b2c1-a856-49a6-f643-c57e8d2cb286"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "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",
        "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)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "PxnzJDaf5Vdn",
        "outputId": "974bbde2-dc15-4d0e-928e-6bf03bb4f101"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "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": [
        ""
      ],
      "metadata": {
        "id": "PWzaLOPk5Wf_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}