Diabetes_AutoVaidya.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NkQlyQC78bcu"
      },
      "source": [
        "# Data Background\r\n",
        "**Source:** Originally from National Institute of Diabetes and Digestive and Kidney Diseases. \\\r\n",
        "**Objective:** Diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.\\\r\n",
        "Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_aGwgbG9Nc5"
      },
      "source": [
        "## Independant Variables\r\n",
        "\r\n",
        "1. **Pregnancies:** Number of times pregnant\r\n",
        "2. **Glucose:** Plasma glucose concentration a 2 hours in an oral glucose tolerance test\r\n",
        "3. **Blood Pressure:** Diastolic blood pressure (mm Hg)\r\n",
        "4. **Skin Thickness:** Triceps skin fold thickness (mm)\r\n",
        "5. **Insulin:** 2-Hour serum insulin (mu U/ml)\r\n",
        "6. **BMI**: Body mass index (weight in kg/(height in m)^2)\r\n",
        "7. **Diabetes pedigree:** Diabetes pedigree function\r\n",
        "8. **Age:** Age (years)\r\n",
        "\r\n",
        "## Dependent Variable\r\n",
        "**Outcome(Diabetes case):** Class variable (0 or 1) 268 of 768 are 1, the others are 0 (to indicate positive or negative).\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1drY5xT6tPD8"
      },
      "source": [
        "# Working on the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ukob4k5Tpqb",
        "outputId": "d4ed59bf-9c6f-4c64-b103-3d4cf0f45a29"
      },
      "source": [
        "!pip install pycaret"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pycaret\n",
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            "  Downloading https://files.pythonhosted.org/packages/b3/e7/fcd59e12169de19f0131ff2812077f964c6b960e7c09804d30a7bf2ab461/htmlmin-0.1.12.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/27/d4197ed93c26d9eeedb7c73c0f24462a65c617807c3140e012950c35ccf9/phik-0.11.0.tar.gz (594kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/f7/12/ec3f2e203afa394a149911729357aa48affc59c20e2c1c8297a60f33f133/threadpoolctl-2.1.0-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/12/ae/66029dcaa88ccca77f454dbb29c1178c751ec24fc771ed475a992b49a02d/combo-0.1.2.tar.gz\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/12/9e/6bb67fe85f6a89d71f50c86a0da778a5064f749a485ed9ba498067034227/azure_core-1.10.0-py2.py3-none-any.whl (125kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fa/af/fe27c2cd875bb0621d7fedd8b10dd5bd437e6f40ad2436d115d4e7b8c576/cryptography-3.4.1-cp36-abi3-manylinux2014_x86_64.whl (3.2MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/11/d1800bca0a3bae820b84b7d813ad1eff15a48a64caea9c823fc8c1b119e8/gitdb-4.0.5-py3-none-any.whl (63kB)\n",
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            "Collecting Mako\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5c/db/2d2d88b924aa4674a080aae83b59ea19d593250bfe5ed789947c21736785/Mako-1.1.4.tar.gz (479kB)\n",
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            "\u001b[?25hCollecting python-editor>=0.3\n",
            "  Downloading https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4c/5f/f61b420143ed1c8dc69f9eaec5ff1ac36109d52c80de49d66e0c36c3dfdf/websocket_client-0.57.0-py2.py3-none-any.whl (200kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/18/9dbb772b5ef73a3069c66bb5bf29b9fb4dd57af0d5790c781c3f559bcca6/ImageHash-4.2.0-py2.py3-none-any.whl (295kB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9b/9f/b36f7774ff5ea8e428fdcfc4bb332c39ee5b9362ddd3d40d9516a55221b2/isodate-0.6.0-py2.py3-none-any.whl (45kB)\n",
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            "  Downloading https://files.pythonhosted.org/packages/e5/e3/ede8b8545e7ce7f5fde88d1a89cdfffa281ab3b93815c3c1a464ca3b6855/setuptools_rust-0.11.6-py3-none-any.whl\n",
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            "  Downloading https://files.pythonhosted.org/packages/d5/1e/6130925131f639b2acde0f7f18b73e33ce082ff2d90783c436b52040af5a/smmap-3.0.5-py2.py3-none-any.whl\n",
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            "  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Building wheel for databricks-cli (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for databricks-cli: filename=databricks_cli-0.14.1-cp36-none-any.whl size=100579 sha256=caf8a6730f21a0d7b083edbf33e8478301efa550a9743c86254b6b49d0279d05\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/91/ac/5d417ee5ccbb76c8cca096cf4cfb9ed9d49d889d1d1ca0fc39\n",
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            "  Created wheel for alembic: filename=alembic-1.4.1-py2.py3-none-any.whl size=158155 sha256=6cb11ee9b74c6ed68db5a10162d3289ee8e6f4fcce392d15c2b5df0a8f9559fc\n",
            "  Stored in directory: /root/.cache/pip/wheels/84/07/f7/12f7370ca47a66030c2edeedcc23dec26ea0ac22dcb4c4a0f3\n",
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            "  Stored in directory: /root/.cache/pip/wheels/b4/1f/b8/66bd9bc3a9d6c6987ff6c4dfeb6f1fe97b5a0e5ed5849c0437\n",
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            "  Building wheel for phik (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for phik: filename=phik-0.11.0-cp36-none-any.whl size=599738 sha256=2a2f455fabc4a865088159c1d63061580107e6ebf1ffb79e76dabd61866b5da2\n",
            "  Stored in directory: /root/.cache/pip/wheels/af/54/11/aba77f21075918de02f7964eabfe8c10d5542df9e6ad10b225\n",
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            "  Stored in directory: /root/.cache/pip/wheels/01/d9/bf/d1a371a5f0844cd8a53c04c14daa89974c93f429dda9dceb86\n",
            "  Building wheel for suod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for suod: filename=suod-0.0.6-cp36-none-any.whl size=2154759 sha256=440ce9c1c6282dcec2fe61be6ac23296f4e514c8a47a36e23947bf44f4688937\n",
            "  Stored in directory: /root/.cache/pip/wheels/c5/d7/c1/6c778aee7fccfe3c054ea9bab92c5994ae3a0f6bba7078541e\n",
            "  Building wheel for Mako (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for Mako: filename=Mako-1.1.4-py2.py3-none-any.whl size=75675 sha256=0acd9f01edd1595f3434ff53e79256bad1ea3284105fdaa2c8bbe438ddab2a75\n",
            "  Stored in directory: /root/.cache/pip/wheels/ad/10/d3/aeb26e20d19045e2a68e5d3cbb57432e11b5d9c92c99f98d47\n",
            "Successfully built pyLDAvis pyod databricks-cli alembic prometheus-flask-exporter htmlmin phik combo suod Mako\n",
            "\u001b[31mERROR: pandas-profiling 2.10.1 has requirement requests>=2.24.0, but you'll have requests 2.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: pandas-profiling 2.10.1 has requirement tqdm>=4.48.2, but you'll have tqdm 4.41.1 which is incompatible.\u001b[0m\n",
            "Installing collected packages: threadpoolctl, scikit-learn, scikit-plot, azure-core, isodate, msrest, semantic-version, setuptools-rust, cryptography, azure-storage-blob, databricks-cli, smmap, gitdb, gitpython, Mako, python-editor, alembic, querystring-parser, gunicorn, websocket-client, docker, prometheus-flask-exporter, mlflow, funcy, pyLDAvis, lightgbm, htmlmin, confuse, phik, tangled-up-in-unicode, imagehash, visions, pandas-profiling, imbalanced-learn, xgboost, catboost, yellowbrick, combo, suod, pyod, kmodes, pycaret\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: lightgbm 2.2.3\n",
            "    Uninstalling lightgbm-2.2.3:\n",
            "      Successfully uninstalled lightgbm-2.2.3\n",
            "  Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "  Found existing installation: imbalanced-learn 0.4.3\n",
            "    Uninstalling imbalanced-learn-0.4.3:\n",
            "      Successfully uninstalled imbalanced-learn-0.4.3\n",
            "  Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "  Found existing installation: yellowbrick 0.9.1\n",
            "    Uninstalling yellowbrick-0.9.1:\n",
            "      Successfully uninstalled yellowbrick-0.9.1\n",
            "Successfully installed Mako-1.1.4 alembic-1.4.1 azure-core-1.10.0 azure-storage-blob-12.7.1 catboost-0.24.4 combo-0.1.2 confuse-1.4.0 cryptography-3.4.1 databricks-cli-0.14.1 docker-4.4.1 funcy-1.15 gitdb-4.0.5 gitpython-3.1.12 gunicorn-20.0.4 htmlmin-0.1.12 imagehash-4.2.0 imbalanced-learn-0.7.0 isodate-0.6.0 kmodes-0.10.2 lightgbm-3.1.1 mlflow-1.13.1 msrest-0.6.21 pandas-profiling-2.10.1 phik-0.11.0 prometheus-flask-exporter-0.18.1 pyLDAvis-3.1.0 pycaret-2.2.3 pyod-0.8.6 python-editor-1.0.4 querystring-parser-1.2.4 scikit-learn-0.23.2 scikit-plot-0.3.7 semantic-version-2.8.5 setuptools-rust-0.11.6 smmap-3.0.5 suod-0.0.6 tangled-up-in-unicode-0.0.6 threadpoolctl-2.1.0 visions-0.6.0 websocket-client-0.57.0 xgboost-1.3.3 yellowbrick-1.2.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-P7MyvgUMOye"
      },
      "source": [
        "#Data Manipulation\r\n",
        "import pandas as pd # data processing, CSV file I/O\r\n",
        "import numpy as np  # linear algebra\r\n",
        "#Data Visualization \r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "#importing all methods of pycarat classification class\r\n",
        "from pycaret.classification import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRxVYqceyeUK"
      },
      "source": [
        "#Load DataSet\r\n",
        "df = pd.read_csv('/content/diabetes.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Ob0XGvj9y9pZ",
        "outputId": "5625699e-ce52-4f4d-99b6-bb09df5a16ae"
      },
      "source": [
        "df.head() #first  5 observations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>33.6</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "4ovDDXp1zjtN",
        "outputId": "e9357b7c-0c3f-4642-ee8e-dc923c9e114b"
      },
      "source": [
        "df.tail() #last 5 observations"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>763</th>\n",
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              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "763           10      101             76             48      180  32.9   \n",
              "764            2      122             70             27        0  36.8   \n",
              "765            5      121             72             23      112  26.2   \n",
              "766            1      126             60              0        0  30.1   \n",
              "767            1       93             70             31        0  30.4   \n",
              "\n",
              "     DiabetesPedigreeFunction  Age  Outcome  \n",
              "763                     0.171   63        0  \n",
              "764                     0.340   27        0  \n",
              "765                     0.245   30        0  \n",
              "766                     0.349   47        1  \n",
              "767                     0.315   23        0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-JPiUaU0YhG"
      },
      "source": [
        "# **Descriptive Statistics**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHfE3s-30eEZ",
        "outputId": "cd61d6de-76e5-47fb-d1d5-4da4db6dd1a4"
      },
      "source": [
        "df.shape  #datapoints, characteristic features"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eullUnNv1DSn",
        "outputId": "1e610584-4308-435d-dcd8-31d9b4765797"
      },
      "source": [
        "df.dtypes #data types"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pregnancies                   int64\n",
              "Glucose                       int64\n",
              "BloodPressure                 int64\n",
              "SkinThickness                 int64\n",
              "Insulin                       int64\n",
              "BMI                         float64\n",
              "DiabetesPedigreeFunction    float64\n",
              "Age                           int64\n",
              "Outcome                       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKVyn9Tz1U0V",
        "outputId": "064d2a83-c347-4f84-83ff-faccb79a1e0f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B35NRh9R1hZC",
        "outputId": "624683c6-2d37-40a4-fc54-3e28829c33b7"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GV9kCDRe1rmQ"
      },
      "source": [
        "*   Data has float and int values\r\n",
        "*  No null/missing values are present\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "dM4a06zZ18ei",
        "outputId": "661e9983-7755-440e-fa5f-d43927f1d430"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cufKXMYt2j9J"
      },
      "source": [
        "Since the min value of BMI, Glucose, Skin Thickness and Blood Pressure cannot be 0. Therefore. we replace it with the average values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_3TPXhTEmJg"
      },
      "source": [
        "#dataframe.loc[] : pandas library\r\n",
        "df.loc[df.BMI == 0, 'BMI'] = df.BMI.mean()\r\n",
        "df.loc[df.Glucose == 0, 'Glucose'] = df.Glucose.mean()\r\n",
        "df.loc[df.SkinThickness == 0, 'SkinThickness'] = df.SkinThickness.mean()\r\n",
        "df.loc[df.BloodPressure == 0, 'BloodPressure'] = df.BloodPressure.mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "umf0upn9QnRo",
        "outputId": "efbba477-2a0b-4a78-c623-0263bc9bca5e"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>121.681605</td>\n",
              "      <td>72.254807</td>\n",
              "      <td>26.606479</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>32.450805</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>30.436016</td>\n",
              "      <td>12.115932</td>\n",
              "      <td>9.631241</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>6.875374</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.200000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.750000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.500000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  121.681605      72.254807      26.606479   79.799479   \n",
              "std       3.369578   30.436016      12.115932       9.631241  115.244002   \n",
              "min       0.000000   44.000000      24.000000       7.000000    0.000000   \n",
              "25%       1.000000   99.750000      64.000000      20.536458    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    32.450805                  0.471876   33.240885    0.348958  \n",
              "std      6.875374                  0.331329   11.760232    0.476951  \n",
              "min     18.200000                  0.078000   21.000000    0.000000  \n",
              "25%     27.500000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9A20n6YOKs_"
      },
      "source": [
        "## Plots"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "113VEVHeOn4w"
      },
      "source": [
        "**The figsize** attribute allows us to specify the width and height of a figure in unit inches. \r\n",
        "\r\n",
        "**Pandas df.groupby():** It splits the data into groups based on the different categories.\\\r\n",
        "First set of plots represent the outcome 0 and the next represents outcome as 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "go4SXQiLVMBx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VLUVA6K4OQAT",
        "outputId": "baec8c7e-958d-42c9-f157-aebceae1906e"
      },
      "source": [
        "df.groupby('Outcome').hist(figsize=(10, 10),color='blue')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Outcome\n",
              "0    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
              "1    [[AxesSubplot(0.125,0.670278;0.215278x0.209722...\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYAex0YAQSYT"
      },
      "source": [
        "* **corr()** is used to find the pairwise correlation of all columns in the dataframe. \r\n",
        "* Any na values are automatically excluded. \r\n",
        "* For any non-numeric data type columns in the dataframe it is ignored."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        },
        "id": "1SVvV6T0PnTT",
        "outputId": "c92148cf-3479-46f0-e7e4-1b7be7c7c858"
      },
      "source": [
        "corr = df.corr()\r\n",
        "plt.figure(figsize=(9,9))\r\n",
        "plt.title('Correlation',color='black',size=30)\r\n",
        "plt.xticks(size=20,color='black')\r\n",
        "plt.yticks(size=20,color='black')\r\n",
        "sns.heatmap(corr,annot=True,cmap='Blues_r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7fad4a7780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_iVHO3FjV3fq"
      },
      "source": [
        "# Data Preparation\r\n",
        "1. **Setup():** used for preparing the dataset \\\r\n",
        "2. **Target:** is set as \"Outcome\" since the dataset provided is of diabetes and the parameter to check it is named \"Outcome\".\r\n",
        "3. **Train size:** Train size is set as 80%. The data is split into 80% train and 20% test,\r\n",
        "4. **Normalize = True :** During processing, it normalizes the dataset. \r\n",
        "5.**session_id:** To make the modelling process reproducible you can set the session_id.\r\n",
        "6. **numeric_feature** or **Categorical** can also be set up here.\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "b98c0ee1cc9b429bb2e896ecae847e73",
            "002c97a4b1f74c669eee3eb0f19e77b0",
            "3306508a704a49df80857381c6ee923c",
            "4b7e804115d7483e81bd6cea7de58d54",
            "1b20a1ec7c5f4e24913083b0f89286b8",
            "ac73b832680141599bf21c22a37f8287"
          ]
        },
        "id": "2wyqq1dwWLGk",
        "outputId": "e33c7a4e-0596-4c5d-aafd-e39f7d812ee1"
      },
      "source": [
        "dia_clf = setup(\r\n",
        "    data = df,\r\n",
        "    target = 'Outcome',\r\n",
        "    train_size = 0.8,\r\n",
        ")\r\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>2241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Target</td>\n",
              "      <td>Outcome</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Target Type</td>\n",
              "      <td>Binary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Label Encoded</td>\n",
              "      <td>0: 0, 1: 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(768, 9)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Transformed Train Set</td>\n",
              "      <td>(614, 24)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Transformed Test Set</td>\n",
              "      <td>(154, 24)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Shuffle Train-Test</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Stratify Train-Test</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Fold Generator</td>\n",
              "      <td>StratifiedKFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Fold Number</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>clf-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>USI</td>\n",
              "      <td>a480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>constant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Fix Imbalance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Fix Imbalance Method</td>\n",
              "      <td>SMOTE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description             Value\n",
              "0                               session_id              2241\n",
              "1                                   Target           Outcome\n",
              "2                              Target Type            Binary\n",
              "3                            Label Encoded        0: 0, 1: 1\n",
              "4                            Original Data          (768, 9)\n",
              "5                           Missing Values             False\n",
              "6                         Numeric Features                 7\n",
              "7                     Categorical Features                 1\n",
              "8                         Ordinal Features             False\n",
              "9                High Cardinality Features             False\n",
              "10                 High Cardinality Method              None\n",
              "11                   Transformed Train Set         (614, 24)\n",
              "12                    Transformed Test Set         (154, 24)\n",
              "13                      Shuffle Train-Test              True\n",
              "14                     Stratify Train-Test             False\n",
              "15                          Fold Generator   StratifiedKFold\n",
              "16                             Fold Number                10\n",
              "17                                CPU Jobs                -1\n",
              "18                                 Use GPU             False\n",
              "19                          Log Experiment             False\n",
              "20                         Experiment Name  clf-default-name\n",
              "21                                     USI              a480\n",
              "22                         Imputation Type            simple\n",
              "23          Iterative Imputation Iteration              None\n",
              "24                         Numeric Imputer              mean\n",
              "25      Iterative Imputation Numeric Model              None\n",
              "26                     Categorical Imputer          constant\n",
              "27  Iterative Imputation Categorical Model              None\n",
              "28           Unknown Categoricals Handling    least_frequent\n",
              "29                               Normalize             False\n",
              "30                        Normalize Method              None\n",
              "31                          Transformation             False\n",
              "32                   Transformation Method              None\n",
              "33                                     PCA             False\n",
              "34                              PCA Method              None\n",
              "35                          PCA Components              None\n",
              "36                     Ignore Low Variance             False\n",
              "37                     Combine Rare Levels             False\n",
              "38                    Rare Level Threshold              None\n",
              "39                         Numeric Binning             False\n",
              "40                         Remove Outliers             False\n",
              "41                      Outliers Threshold              None\n",
              "42                Remove Multicollinearity             False\n",
              "43             Multicollinearity Threshold              None\n",
              "44                              Clustering             False\n",
              "45                    Clustering Iteration              None\n",
              "46                     Polynomial Features             False\n",
              "47                       Polynomial Degree              None\n",
              "48                    Trignometry Features             False\n",
              "49                    Polynomial Threshold              None\n",
              "50                          Group Features             False\n",
              "51                       Feature Selection             False\n",
              "52            Features Selection Threshold              None\n",
              "53                     Feature Interaction             False\n",
              "54                           Feature Ratio             False\n",
              "55                   Interaction Threshold              None\n",
              "56                           Fix Imbalance             False\n",
              "57                    Fix Imbalance Method             SMOTE"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ckrg4KpJUqsJ"
      },
      "source": [
        "## Compare Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508,
          "referenced_widgets": [
            "70646d0348a6440383ff1b7f505e96ce",
            "119426f368764ac4aeabb9331cadcd27",
            "9ad6921f7ac64bdbacbcae6834f293a9"
          ]
        },
        "id": "UJmvtXsbRYFO",
        "outputId": "71aa4cf5-7eb3-4956-b35c-9063171365a3"
      },
      "source": [
        "compare_models(sort = 'AUC')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>catboost</th>\n",
              "      <td>CatBoost Classifier</td>\n",
              "      <td>0.7618</td>\n",
              "      <td>0.8404</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.7020</td>\n",
              "      <td>0.6467</td>\n",
              "      <td>0.4696</td>\n",
              "      <td>0.4764</td>\n",
              "      <td>1.291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.7718</td>\n",
              "      <td>0.8345</td>\n",
              "      <td>0.5963</td>\n",
              "      <td>0.7192</td>\n",
              "      <td>0.6483</td>\n",
              "      <td>0.4825</td>\n",
              "      <td>0.4896</td>\n",
              "      <td>0.209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.7471</td>\n",
              "      <td>0.8339</td>\n",
              "      <td>0.6004</td>\n",
              "      <td>0.6646</td>\n",
              "      <td>0.6247</td>\n",
              "      <td>0.4368</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.7734</td>\n",
              "      <td>0.8294</td>\n",
              "      <td>0.5827</td>\n",
              "      <td>0.7299</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.4812</td>\n",
              "      <td>0.4914</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.7668</td>\n",
              "      <td>0.8225</td>\n",
              "      <td>0.5920</td>\n",
              "      <td>0.7222</td>\n",
              "      <td>0.6432</td>\n",
              "      <td>0.4741</td>\n",
              "      <td>0.4843</td>\n",
              "      <td>0.457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.7521</td>\n",
              "      <td>0.8128</td>\n",
              "      <td>0.6052</td>\n",
              "      <td>0.6725</td>\n",
              "      <td>0.6324</td>\n",
              "      <td>0.4474</td>\n",
              "      <td>0.4523</td>\n",
              "      <td>0.090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.7439</td>\n",
              "      <td>0.8092</td>\n",
              "      <td>0.6141</td>\n",
              "      <td>0.6481</td>\n",
              "      <td>0.6253</td>\n",
              "      <td>0.4333</td>\n",
              "      <td>0.4364</td>\n",
              "      <td>0.048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.7587</td>\n",
              "      <td>0.8075</td>\n",
              "      <td>0.5781</td>\n",
              "      <td>0.7138</td>\n",
              "      <td>0.6316</td>\n",
              "      <td>0.4560</td>\n",
              "      <td>0.4670</td>\n",
              "      <td>0.452</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>Extreme Gradient Boosting</td>\n",
              "      <td>0.7342</td>\n",
              "      <td>0.7891</td>\n",
              "      <td>0.6152</td>\n",
              "      <td>0.6345</td>\n",
              "      <td>0.6178</td>\n",
              "      <td>0.4171</td>\n",
              "      <td>0.4218</td>\n",
              "      <td>3.852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.7196</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.5515</td>\n",
              "      <td>0.6220</td>\n",
              "      <td>0.5801</td>\n",
              "      <td>0.3727</td>\n",
              "      <td>0.3768</td>\n",
              "      <td>0.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.6856</td>\n",
              "      <td>0.7501</td>\n",
              "      <td>0.2569</td>\n",
              "      <td>0.6522</td>\n",
              "      <td>0.3630</td>\n",
              "      <td>0.2055</td>\n",
              "      <td>0.2467</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.7017</td>\n",
              "      <td>0.6790</td>\n",
              "      <td>0.6006</td>\n",
              "      <td>0.5807</td>\n",
              "      <td>0.5883</td>\n",
              "      <td>0.3554</td>\n",
              "      <td>0.3569</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.5815</td>\n",
              "      <td>0.6016</td>\n",
              "      <td>0.5162</td>\n",
              "      <td>0.4863</td>\n",
              "      <td>0.4222</td>\n",
              "      <td>0.1334</td>\n",
              "      <td>0.1637</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.6044</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4647</td>\n",
              "      <td>0.4778</td>\n",
              "      <td>0.3788</td>\n",
              "      <td>0.1446</td>\n",
              "      <td>0.1903</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7767</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.5874</td>\n",
              "      <td>0.7378</td>\n",
              "      <td>0.6468</td>\n",
              "      <td>0.4886</td>\n",
              "      <td>0.5001</td>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "catboost              CatBoost Classifier    0.7618  0.8404  0.6097  0.7020   \n",
              "lr                    Logistic Regression    0.7718  0.8345  0.5963  0.7192   \n",
              "gbc          Gradient Boosting Classifier    0.7471  0.8339  0.6004  0.6646   \n",
              "lda          Linear Discriminant Analysis    0.7734  0.8294  0.5827  0.7299   \n",
              "rf               Random Forest Classifier    0.7668  0.8225  0.5920  0.7222   \n",
              "ada                  Ada Boost Classifier    0.7521  0.8128  0.6052  0.6725   \n",
              "lightgbm  Light Gradient Boosting Machine    0.7439  0.8092  0.6141  0.6481   \n",
              "et                 Extra Trees Classifier    0.7587  0.8075  0.5781  0.7138   \n",
              "xgboost         Extreme Gradient Boosting    0.7342  0.7891  0.6152  0.6345   \n",
              "knn                K Neighbors Classifier    0.7196  0.7649  0.5515  0.6220   \n",
              "nb                            Naive Bayes    0.6856  0.7501  0.2569  0.6522   \n",
              "dt               Decision Tree Classifier    0.7017  0.6790  0.6006  0.5807   \n",
              "qda       Quadratic Discriminant Analysis    0.5815  0.6016  0.5162  0.4863   \n",
              "svm                   SVM - Linear Kernel    0.6044  0.0000  0.4647  0.4778   \n",
              "ridge                    Ridge Classifier    0.7767  0.0000  0.5874  0.7378   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "catboost  0.6467  0.4696  0.4764     1.291  \n",
              "lr        0.6483  0.4825  0.4896     0.209  \n",
              "gbc       0.6247  0.4368  0.4420     0.112  \n",
              "lda       0.6422  0.4812  0.4914     0.016  \n",
              "rf        0.6432  0.4741  0.4843     0.457  \n",
              "ada       0.6324  0.4474  0.4523     0.090  \n",
              "lightgbm  0.6253  0.4333  0.4364     0.048  \n",
              "et        0.6316  0.4560  0.4670     0.452  \n",
              "xgboost   0.6178  0.4171  0.4218     3.852  \n",
              "knn       0.5801  0.3727  0.3768     0.115  \n",
              "nb        0.3630  0.2055  0.2467     0.015  \n",
              "dt        0.5883  0.3554  0.3569     0.016  \n",
              "qda       0.4222  0.1334  0.1637     0.015  \n",
              "svm       0.3788  0.1446  0.1903     0.014  \n",
              "ridge     0.6468  0.4886  0.5001     0.013  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f7fae2c4a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542,
          "referenced_widgets": [
            "58d3604c11964e6b89fe15e8f4d0d5aa",
            "1ad048f1c2034e29927d3bd4e5bd6671",
            "fc38e24b2c814a1b933dce53a267da87"
          ]
        },
        "id": "5Bb3Q0G8TIgh",
        "outputId": "e959e871-7aea-4691-f78b-15afc05bc8d9"
      },
      "source": [
        "compare_models(sort='Accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "      <th>TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ridge</th>\n",
              "      <td>Ridge Classifier</td>\n",
              "      <td>0.7767</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.5874</td>\n",
              "      <td>0.7378</td>\n",
              "      <td>0.6468</td>\n",
              "      <td>0.4886</td>\n",
              "      <td>0.5001</td>\n",
              "      <td>0.013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lda</th>\n",
              "      <td>Linear Discriminant Analysis</td>\n",
              "      <td>0.7734</td>\n",
              "      <td>0.8294</td>\n",
              "      <td>0.5827</td>\n",
              "      <td>0.7299</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.4812</td>\n",
              "      <td>0.4914</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lr</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.7718</td>\n",
              "      <td>0.8345</td>\n",
              "      <td>0.5963</td>\n",
              "      <td>0.7192</td>\n",
              "      <td>0.6483</td>\n",
              "      <td>0.4825</td>\n",
              "      <td>0.4896</td>\n",
              "      <td>0.407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rf</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>0.7668</td>\n",
              "      <td>0.8225</td>\n",
              "      <td>0.5920</td>\n",
              "      <td>0.7222</td>\n",
              "      <td>0.6432</td>\n",
              "      <td>0.4741</td>\n",
              "      <td>0.4843</td>\n",
              "      <td>0.459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>catboost</th>\n",
              "      <td>CatBoost Classifier</td>\n",
              "      <td>0.7618</td>\n",
              "      <td>0.8404</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.7020</td>\n",
              "      <td>0.6467</td>\n",
              "      <td>0.4696</td>\n",
              "      <td>0.4764</td>\n",
              "      <td>1.321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>et</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>0.7587</td>\n",
              "      <td>0.8075</td>\n",
              "      <td>0.5781</td>\n",
              "      <td>0.7138</td>\n",
              "      <td>0.6316</td>\n",
              "      <td>0.4560</td>\n",
              "      <td>0.4670</td>\n",
              "      <td>0.453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ada</th>\n",
              "      <td>Ada Boost Classifier</td>\n",
              "      <td>0.7521</td>\n",
              "      <td>0.8128</td>\n",
              "      <td>0.6052</td>\n",
              "      <td>0.6725</td>\n",
              "      <td>0.6324</td>\n",
              "      <td>0.4474</td>\n",
              "      <td>0.4523</td>\n",
              "      <td>0.091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gbc</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.7471</td>\n",
              "      <td>0.8339</td>\n",
              "      <td>0.6004</td>\n",
              "      <td>0.6646</td>\n",
              "      <td>0.6247</td>\n",
              "      <td>0.4368</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lightgbm</th>\n",
              "      <td>Light Gradient Boosting Machine</td>\n",
              "      <td>0.7439</td>\n",
              "      <td>0.8092</td>\n",
              "      <td>0.6141</td>\n",
              "      <td>0.6481</td>\n",
              "      <td>0.6253</td>\n",
              "      <td>0.4333</td>\n",
              "      <td>0.4364</td>\n",
              "      <td>0.052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>xgboost</th>\n",
              "      <td>Extreme Gradient Boosting</td>\n",
              "      <td>0.7342</td>\n",
              "      <td>0.7891</td>\n",
              "      <td>0.6152</td>\n",
              "      <td>0.6345</td>\n",
              "      <td>0.6178</td>\n",
              "      <td>0.4171</td>\n",
              "      <td>0.4218</td>\n",
              "      <td>2.816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K Neighbors Classifier</td>\n",
              "      <td>0.7196</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.5515</td>\n",
              "      <td>0.6220</td>\n",
              "      <td>0.5801</td>\n",
              "      <td>0.3727</td>\n",
              "      <td>0.3768</td>\n",
              "      <td>0.115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dt</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.7017</td>\n",
              "      <td>0.6790</td>\n",
              "      <td>0.6006</td>\n",
              "      <td>0.5807</td>\n",
              "      <td>0.5883</td>\n",
              "      <td>0.3554</td>\n",
              "      <td>0.3569</td>\n",
              "      <td>0.016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>nb</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.6856</td>\n",
              "      <td>0.7501</td>\n",
              "      <td>0.2569</td>\n",
              "      <td>0.6522</td>\n",
              "      <td>0.3630</td>\n",
              "      <td>0.2055</td>\n",
              "      <td>0.2467</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>SVM - Linear Kernel</td>\n",
              "      <td>0.6044</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.4647</td>\n",
              "      <td>0.4778</td>\n",
              "      <td>0.3788</td>\n",
              "      <td>0.1446</td>\n",
              "      <td>0.1903</td>\n",
              "      <td>0.014</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>qda</th>\n",
              "      <td>Quadratic Discriminant Analysis</td>\n",
              "      <td>0.5815</td>\n",
              "      <td>0.6016</td>\n",
              "      <td>0.5162</td>\n",
              "      <td>0.4863</td>\n",
              "      <td>0.4222</td>\n",
              "      <td>0.1334</td>\n",
              "      <td>0.1637</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    Model  Accuracy     AUC  Recall   Prec.  \\\n",
              "ridge                    Ridge Classifier    0.7767  0.0000  0.5874  0.7378   \n",
              "lda          Linear Discriminant Analysis    0.7734  0.8294  0.5827  0.7299   \n",
              "lr                    Logistic Regression    0.7718  0.8345  0.5963  0.7192   \n",
              "rf               Random Forest Classifier    0.7668  0.8225  0.5920  0.7222   \n",
              "catboost              CatBoost Classifier    0.7618  0.8404  0.6097  0.7020   \n",
              "et                 Extra Trees Classifier    0.7587  0.8075  0.5781  0.7138   \n",
              "ada                  Ada Boost Classifier    0.7521  0.8128  0.6052  0.6725   \n",
              "gbc          Gradient Boosting Classifier    0.7471  0.8339  0.6004  0.6646   \n",
              "lightgbm  Light Gradient Boosting Machine    0.7439  0.8092  0.6141  0.6481   \n",
              "xgboost         Extreme Gradient Boosting    0.7342  0.7891  0.6152  0.6345   \n",
              "knn                K Neighbors Classifier    0.7196  0.7649  0.5515  0.6220   \n",
              "dt               Decision Tree Classifier    0.7017  0.6790  0.6006  0.5807   \n",
              "nb                            Naive Bayes    0.6856  0.7501  0.2569  0.6522   \n",
              "svm                   SVM - Linear Kernel    0.6044  0.0000  0.4647  0.4778   \n",
              "qda       Quadratic Discriminant Analysis    0.5815  0.6016  0.5162  0.4863   \n",
              "\n",
              "              F1   Kappa     MCC  TT (Sec)  \n",
              "ridge     0.6468  0.4886  0.5001     0.013  \n",
              "lda       0.6422  0.4812  0.4914     0.015  \n",
              "lr        0.6483  0.4825  0.4896     0.407  \n",
              "rf        0.6432  0.4741  0.4843     0.459  \n",
              "catboost  0.6467  0.4696  0.4764     1.321  \n",
              "et        0.6316  0.4560  0.4670     0.453  \n",
              "ada       0.6324  0.4474  0.4523     0.091  \n",
              "gbc       0.6247  0.4368  0.4420     0.111  \n",
              "lightgbm  0.6253  0.4333  0.4364     0.052  \n",
              "xgboost   0.6178  0.4171  0.4218     2.816  \n",
              "knn       0.5801  0.3727  0.3768     0.115  \n",
              "dt        0.5883  0.3554  0.3569     0.016  \n",
              "nb        0.3630  0.2055  0.2467     0.015  \n",
              "svm       0.3788  0.1446  0.1903     0.014  \n",
              "qda       0.4222  0.1334  0.1637     0.015  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
              "                max_iter=None, normalize=False, random_state=2241,\n",
              "                solver='auto', tol=0.001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "6522168a394147449311879b7ef526a3",
            "6e6af22cac5641e1a2157c78bc3bbcf9",
            "9e726862497f4389aa63177d7b3f0666"
          ]
        },
        "id": "D8WgVIQGWLoI",
        "outputId": "a3a88cca-dcfe-4280-9ec0-b9a04068056f"
      },
      "source": [
        "lr = create_model('lr')\r\n",
        "# plotting a model\r\n",
        "plot_model(lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QpCMvJyXalec"
      },
      "source": [
        "**A confusion matrix** is a table that is often used to describe the performance of a classification model (or \"classifier\") on a set of test data for which the true values are known. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "gzbaOq9XZsK0",
        "outputId": "0108d68f-af4c-4e67-8f6c-03a8ddfdd5df"
      },
      "source": [
        "plot_model(lr, plot='confusion_matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jDDoiwIaH_S"
      },
      "source": [
        "**PyCaret provides tune_model function:** It tunes hyperparameters within a pre-defined search space.\\\r\n",
        "Two important points for tune_model function:\r\n",
        "1. Tune_model takes a model name as input just like create_model function. It does not require you to first train a model and then tune it.\r\n",
        "2. By default, tune_model tries to optimize the accuracy metric but it can be changed using optimize parameter."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
          "referenced_widgets": [
            "9c2ce5eb34ae4837a166a1d56c1e527e",
            "a8c56eab7c61450889fde2866134d23c",
            "c029603ca4b748409678cda01fa1a175"
          ]
        },
        "id": "BbbQq2RLWbvA",
        "outputId": "1fd62728-cd19-4248-f85f-8a9b9ab4721b"
      },
      "source": [
        "tuned_model=tune_model(lr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8226</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6818</td>\n",
              "      <td>0.7895</td>\n",
              "      <td>0.7317</td>\n",
              "      <td>0.6002</td>\n",
              "      <td>0.6038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7581</td>\n",
              "      <td>0.8216</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.6512</td>\n",
              "      <td>0.4661</td>\n",
              "      <td>0.4664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8065</td>\n",
              "      <td>0.9000</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>0.5592</td>\n",
              "      <td>0.5654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.8065</td>\n",
              "      <td>0.8841</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.7778</td>\n",
              "      <td>0.7000</td>\n",
              "      <td>0.5592</td>\n",
              "      <td>0.5654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.7213</td>\n",
              "      <td>0.7917</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.5833</td>\n",
              "      <td>0.6222</td>\n",
              "      <td>0.4030</td>\n",
              "      <td>0.4052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.7705</td>\n",
              "      <td>0.8595</td>\n",
              "      <td>0.4762</td>\n",
              "      <td>0.7692</td>\n",
              "      <td>0.5882</td>\n",
              "      <td>0.4411</td>\n",
              "      <td>0.4655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.7377</td>\n",
              "      <td>0.7611</td>\n",
              "      <td>0.5455</td>\n",
              "      <td>0.6667</td>\n",
              "      <td>0.6000</td>\n",
              "      <td>0.4078</td>\n",
              "      <td>0.4123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.7377</td>\n",
              "      <td>0.7844</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.5556</td>\n",
              "      <td>0.3823</td>\n",
              "      <td>0.4019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.7377</td>\n",
              "      <td>0.8030</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.5556</td>\n",
              "      <td>0.3823</td>\n",
              "      <td>0.4019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.8033</td>\n",
              "      <td>0.8601</td>\n",
              "      <td>0.6818</td>\n",
              "      <td>0.7500</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.5663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.7702</td>\n",
              "      <td>0.8309</td>\n",
              "      <td>0.5870</td>\n",
              "      <td>0.7209</td>\n",
              "      <td>0.6419</td>\n",
              "      <td>0.4766</td>\n",
              "      <td>0.4854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0349</td>\n",
              "      <td>0.0434</td>\n",
              "      <td>0.0899</td>\n",
              "      <td>0.0626</td>\n",
              "      <td>0.0633</td>\n",
              "      <td>0.0813</td>\n",
              "      <td>0.0773</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.8226  0.8432  0.6818  0.7895  0.7317  0.6002  0.6038\n",
              "1       0.7581  0.8216  0.6364  0.6667  0.6512  0.4661  0.4664\n",
              "2       0.8065  0.9000  0.6364  0.7778  0.7000  0.5592  0.5654\n",
              "3       0.8065  0.8841  0.6364  0.7778  0.7000  0.5592  0.5654\n",
              "4       0.7213  0.7917  0.6667  0.5833  0.6222  0.4030  0.4052\n",
              "5       0.7705  0.8595  0.4762  0.7692  0.5882  0.4411  0.4655\n",
              "6       0.7377  0.7611  0.5455  0.6667  0.6000  0.4078  0.4123\n",
              "7       0.7377  0.7844  0.4545  0.7143  0.5556  0.3823  0.4019\n",
              "8       0.7377  0.8030  0.4545  0.7143  0.5556  0.3823  0.4019\n",
              "9       0.8033  0.8601  0.6818  0.7500  0.7143  0.5648  0.5663\n",
              "Mean    0.7702  0.8309  0.5870  0.7209  0.6419  0.4766  0.4854\n",
              "SD      0.0349  0.0434  0.0899  0.0626  0.0633  0.0813  0.0773"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USv0Mzwzb1P8"
      },
      "source": [
        "**Ensemble Model**: It ensembles the trained base estimator using the method defined in parameter (method = 'name_of_parameter') \\\r\n",
        "By default, it is Bagging. \\\r\n",
        "Output shows the score grid that shows containing Accuracy, AUC, Recall, Precision, F1 and Kappa by fold (default = 10 Fold) \\\r\n",
        "\r\n",
        "**Boosting:** In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402,
          "referenced_widgets": [
            "a0043c143625444495d7d2e40e8e1922",
            "8968688bd5dd4dcc9ccf07c244990246",
            "9033cf948e524e4a9c92f9cf74f32ccb"
          ]
        },
        "id": "ldaejBUbbqs_",
        "outputId": "4162515f-c51f-4e4e-9839-e02a89d5f588"
      },
      "source": [
        "em = ensemble_model(tuned_model,method='Boosting')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Prec.</th>\n",
              "      <th>F1</th>\n",
              "      <th>Kappa</th>\n",
              "      <th>MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.8065</td>\n",
              "      <td>0.8500</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.8125</td>\n",
              "      <td>0.6842</td>\n",
              "      <td>0.5496</td>\n",
              "      <td>0.5641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7903</td>\n",
              "      <td>0.8386</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.7368</td>\n",
              "      <td>0.6829</td>\n",
              "      <td>0.5275</td>\n",
              "      <td>0.5307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.8226</td>\n",
              "      <td>0.9034</td>\n",
              "      <td>0.6818</td>\n",
              "      <td>0.7895</td>\n",
              "      <td>0.7317</td>\n",
              "      <td>0.6002</td>\n",
              "      <td>0.6038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.7581</td>\n",
              "      <td>0.8693</td>\n",
              "      <td>0.5455</td>\n",
              "      <td>0.7059</td>\n",
              "      <td>0.6154</td>\n",
              "      <td>0.4431</td>\n",
              "      <td>0.4510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.7213</td>\n",
              "      <td>0.7631</td>\n",
              "      <td>0.6190</td>\n",
              "      <td>0.5909</td>\n",
              "      <td>0.6047</td>\n",
              "      <td>0.3896</td>\n",
              "      <td>0.3899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.7869</td>\n",
              "      <td>0.8833</td>\n",
              "      <td>0.5238</td>\n",
              "      <td>0.7857</td>\n",
              "      <td>0.6286</td>\n",
              "      <td>0.4874</td>\n",
              "      <td>0.5071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.7869</td>\n",
              "      <td>0.7529</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.7368</td>\n",
              "      <td>0.6829</td>\n",
              "      <td>0.5237</td>\n",
              "      <td>0.5269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.7213</td>\n",
              "      <td>0.7716</td>\n",
              "      <td>0.5000</td>\n",
              "      <td>0.6471</td>\n",
              "      <td>0.5641</td>\n",
              "      <td>0.3642</td>\n",
              "      <td>0.3707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.7377</td>\n",
              "      <td>0.8217</td>\n",
              "      <td>0.4545</td>\n",
              "      <td>0.7143</td>\n",
              "      <td>0.5556</td>\n",
              "      <td>0.3823</td>\n",
              "      <td>0.4019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.7869</td>\n",
              "      <td>0.8869</td>\n",
              "      <td>0.6364</td>\n",
              "      <td>0.7368</td>\n",
              "      <td>0.6829</td>\n",
              "      <td>0.5237</td>\n",
              "      <td>0.5269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mean</th>\n",
              "      <td>0.7718</td>\n",
              "      <td>0.8341</td>\n",
              "      <td>0.5825</td>\n",
              "      <td>0.7256</td>\n",
              "      <td>0.6433</td>\n",
              "      <td>0.4791</td>\n",
              "      <td>0.4873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SD</th>\n",
              "      <td>0.0335</td>\n",
              "      <td>0.0522</td>\n",
              "      <td>0.0693</td>\n",
              "      <td>0.0636</td>\n",
              "      <td>0.0554</td>\n",
              "      <td>0.0761</td>\n",
              "      <td>0.0752</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Accuracy     AUC  Recall   Prec.      F1   Kappa     MCC\n",
              "0       0.8065  0.8500  0.5909  0.8125  0.6842  0.5496  0.5641\n",
              "1       0.7903  0.8386  0.6364  0.7368  0.6829  0.5275  0.5307\n",
              "2       0.8226  0.9034  0.6818  0.7895  0.7317  0.6002  0.6038\n",
              "3       0.7581  0.8693  0.5455  0.7059  0.6154  0.4431  0.4510\n",
              "4       0.7213  0.7631  0.6190  0.5909  0.6047  0.3896  0.3899\n",
              "5       0.7869  0.8833  0.5238  0.7857  0.6286  0.4874  0.5071\n",
              "6       0.7869  0.7529  0.6364  0.7368  0.6829  0.5237  0.5269\n",
              "7       0.7213  0.7716  0.5000  0.6471  0.5641  0.3642  0.3707\n",
              "8       0.7377  0.8217  0.4545  0.7143  0.5556  0.3823  0.4019\n",
              "9       0.7869  0.8869  0.6364  0.7368  0.6829  0.5237  0.5269\n",
              "Mean    0.7718  0.8341  0.5825  0.7256  0.6433  0.4791  0.4873\n",
              "SD      0.0335  0.0522  0.0693  0.0636  0.0554  0.0761  0.0752"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376,
          "referenced_widgets": [
            "5e0b4891161a49ba8e8e6b2fcb0ee3f3",
            "8fc72eb16ed24e28bf91c06af3d06187",
            "ce3d1a142dd6426a85ad1896a95efab6"
          ]
        },
        "id": "V0BGOGB6cS9T",
        "outputId": "4ed97b33-57ed-4093-e49b-28aa5ce5be9d"
      },
      "source": [
        "plot_model(em,plot='learning')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339,
          "referenced_widgets": [
            "004a0a1592654332aded05616e69c13a",
            "1c3772ef3ea3419c8e9baa03f5cd6989",
            "b8ecd92ebbe64972902baabc2bdc0b73",
            "812c401a83a0480b96912a5519d421b8",
            "ee74ebd2a77340e5b0ff29b62d36aa78",
            "94c75407182544f486154ed09ed06d2a",
            "ff0bdf3a33424c8e9ebf090fc12edb46",
            "77d8d808f0ba4c6d8fdd69a4bc129b0a",
            "c69f0ad974e14749be6fe7e296fc66f5",
            "d416f2091f794b5190f1209e9def6764",
            "6932c53e82084612b2b703175e57a98e",
            "5b3b211ed80a4f73935d2a9dd7000a73",
            "302504c98f4a436d89cdbea1e5572489",
            "94300039c0334ad7ad7d4b8aa6cfcbe9",
            "c6a50bde216940a8a66ee2bbbd870777",
            "cd4ef05baecd4f77b0d926b3828d0423",
            "52a893a5f0ef4558b5e6fffde137b41f",
            "e5d5cab8b82442059697f536655cfff3",
            "cde37ebfc3064ec0a1fce7678a955bce",
            "6494eb1d15574b60bb6a7a04233c7ddf",
            "e31d4b8ebab6403f9dabc059bc05c60a",
            "c1c00f39e09a4db698ebe55a2a364e09",
            "740be580ff4746628ae2b1b78790cd94",
            "fa57e22584ad4f82b94331d3228c9742",
            "2cb10e5f001346c6983bec5d511bceb0"
          ]
        },
        "id": "Hh9RAmkTc-wb",
        "outputId": "2b0d9d4c-963b-41c3-ca90-e0c6984a5593"
      },
      "source": [
        "evaluate_model(em)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "004a0a1592654332aded05616e69c13a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}