IPWK8_CORE_DECISION_TREES_&_SVM_ELIZABETH_JOSEPHINE.ipynb

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      "name": "IPWK8_CORE_DECISION_TREES_&_SVM_ELIZABETH_JOSEPHINE",
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      "collapsed_sections": [
        "PzyiSh09V1UE",
        "ONQ30G-iPJn8"
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  "cells": [
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/alfafimel/IPWK8-CORE/blob/main/IPWK8_CORE_DECISION_TREES_%26_SVM_ELIZABETH_JOSEPHINE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzyiSh09V1UE"
      },
      "source": [
        "# **DEFINING THE QUESTION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOAnpTqyXj8J"
      },
      "source": [
        "## **a) Specifying the Question**\n",
        "Nairobi Hospital conducted a clinical camp to test for hypothyroidism. The data collected focused on Thyroid patients. Use the healthcare datasets provided to accomplish the following:  \n",
        "\n",
        "Build a model that determines whether or not the patient's symptoms indicate that the patient has hypothyroid."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UDM4QNnoXsGc"
      },
      "source": [
        "## **b) Defining the metrics for success**\n",
        "\n",
        "Building of a model that determines whether or not the patient's symptoms indicate that the patient has hypothyroid."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8ngyeNGX8Dj"
      },
      "source": [
        "## **c) Understanding the context**\n",
        "\n",
        "Knowing the determinant symptoms of hypothroid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78AjZ_RgYITQ"
      },
      "source": [
        "## **d) Recording the Experimental Design**\n",
        "\n",
        "1.   Define the question, the metric for success, the context, experimental design taken.\n",
        "2. Read and explore the given dataset.\n",
        "3. Define the appropriateness of the available data to answer the given question.\n",
        "4. Find and deal with outliers, anomalies, and missing data within the dataset.\n",
        "5. Perform univariate, bivariate and multivariate analysis recording your observations.\n",
        "6. Performing regression analysis.\n",
        "> - For this section, you should build a model that makes the above prediction. You should not use individual decision trees, rather you should use at least 2 out of the 3 advanced models we have studied: Random forests, Ada boosted trees, and gradient boosted trees.\n",
        "- Try and optimize each of the 2 models, making sure to document how you've set up your hyperparameters.\n",
        "- Identify which of the 2 models you trust most, and use your model to determine which features are most impactful in influencing the prediction\n",
        "7. Provide a recommendation based on your analysis. \n",
        "> C- In this section, you may be required to clean the data a little bit so as to make sense of the features.\n",
        "\n",
        "- Document what transformation you've done on the data.\n",
        "\n",
        "- Apply Polynomial, linear and rbf kernel function to build your SVM model and then evaluate their performance and pick the kernel that performs the best. Remember to tune your parameters to improve the performance of your model. To make your life easier, make sure to visualize the models you've created. Use any two features to build the models for this step.\n",
        "\n",
        "Hint: You may want to use decision trees to give you the most preferable features you can use. but also keep in mind that those features might not be suitable for SVM. It might be a good idea to graph them first.\n",
        "\n",
        "-  After getting your best performing kernel, use this kernel together with your tuned parameters and repeat the prediction but this time using additional features. Compare the model you've just created with the 2-features version. \n",
        "8. Challenge your solution by providing insights on how you can make improvements in model improvement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbCQdWNqYR41"
      },
      "source": [
        "## **e) Relevance of the data**\n",
        "\n",
        "The data used for this project is necessary for building a model that determines whether or not the patient's symptoms indicate that the patient has hypothyroid.\n",
        "\n",
        "[http://bit.ly/hypothyroid_data]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afVKIlhcWTYy"
      },
      "source": [
        "# **DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ihbtr2Xeqxqi"
      },
      "source": [
        "### **Data Checking**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfHhHUEXWWGe"
      },
      "source": [
        "# importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3zdMo9YY0_F"
      },
      "source": [
        "# reading datasets\n",
        "df = pd.read_csv('http://bit.ly/hypothyroid_data')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ee3TnljiZs3b",
        "outputId": "c2edbc63-6e34-4199-c570-940da02d40aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# previewing the dataset\n",
        "df.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3163, 26)"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 3
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    {
      "cell_type": "code",
      "metadata": {
        "id": "pNQNFZaGaxUn",
        "outputId": "189447d1-dc5d-4441-dc58-176fe8e5ae8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
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      },
      "source": [
        "# previewing the top of the dataset\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>status</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on_thyroxine</th>\n",
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              "      <td>hypothyroid</td>\n",
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              "      <td>0.40</td>\n",
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              "      <td>hypothyroid</td>\n",
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              "      <td>7.30</td>\n",
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              "      <td>1.28</td>\n",
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              "      <td>n</td>\n",
              "      <td>?</td>\n",
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            "text/plain": [
              "        status age sex on_thyroxine  ... FTI_measured FTI TBG_measured TBG\n",
              "0  hypothyroid  72   M            f  ...            y  10            n   ?\n",
              "1  hypothyroid  15   F            t  ...            y  17            n   ?\n",
              "2  hypothyroid  24   M            f  ...            y   0            n   ?\n",
              "3  hypothyroid  24   F            f  ...            y   6            n   ?\n",
              "4  hypothyroid  77   M            f  ...            y  44            n   ?\n",
              "\n",
              "[5 rows x 26 columns]"
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "levrJ5VDa09C",
        "outputId": "f1d5f6e3-53a9-4b3e-9139-561800890e30",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "# previewing the tail of the dataset\n",
        "df.tail()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>y</td>\n",
              "      <td>139</td>\n",
              "      <td>y</td>\n",
              "      <td>0.97</td>\n",
              "      <td>y</td>\n",
              "      <td>143</td>\n",
              "      <td>n</td>\n",
              "      <td>?</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        status age sex on_thyroxine  ... FTI_measured  FTI TBG_measured TBG\n",
              "3158  negative  58   F            f  ...            y   95            n   ?\n",
              "3159  negative  29   F            f  ...            y   98            n   ?\n",
              "3160  negative  77   M            f  ...            y  104            n   ?\n",
              "3161  negative  74   F            f  ...            y  137            n   ?\n",
              "3162  negative  56   F            t  ...            y  143            n   ?\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Now4SvbPhzmh",
        "outputId": "b76642ef-2951-4d47-9fac-035ba9348a49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# printing the the number of unique values of the columns\n",
        "print(df.nunique())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "status                         2\n",
            "age                           93\n",
            "sex                            3\n",
            "on_thyroxine                   2\n",
            "query_on_thyroxine             2\n",
            "on_antithyroid_medication      2\n",
            "thyroid_surgery                2\n",
            "query_hypothyroid              2\n",
            "query_hyperthyroid             2\n",
            "pregnant                       2\n",
            "sick                           2\n",
            "tumor                          2\n",
            "lithium                        2\n",
            "goitre                         2\n",
            "TSH_measured                   2\n",
            "TSH                          240\n",
            "T3_measured                    2\n",
            "T3                            70\n",
            "TT4_measured                   2\n",
            "TT4                          269\n",
            "T4U_measured                   2\n",
            "T4U                          159\n",
            "FTI_measured                   2\n",
            "FTI                          281\n",
            "TBG_measured                   2\n",
            "TBG                           53\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KRbNRDuZ2MO",
        "outputId": "a173ab80-53a1-44c0-fc2c-5df19a869776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# previewing the columns of the datasets\n",
        "df.columns"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'sex', 'on_thyroxine', 'query_on_thyroxine',\n",
              "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
              "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre',\n",
              "       'TSH_measured', 'TSH', 'T3_measured', 'T3', 'TT4_measured', 'TT4',\n",
              "       'T4U_measured', 'T4U', 'FTI_measured', 'FTI', 'TBG_measured', 'TBG'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzrucZyWBQa2",
        "outputId": "700ad703-9fbe-4362-f929-e409b6ad9503",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "# replacing values \n",
        "df = df.replace({'?' : np.nan})\n",
        "df.tail()"
      ],
      "execution_count": 8,
      "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>status</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "      <th>TSH_measured</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3_measured</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4_measured</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U_measured</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI_measured</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG_measured</th>\n",
              "      <th>TBG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3158</th>\n",
              "      <td>negative</td>\n",
              "      <td>58</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
              "      <td>5.80</td>\n",
              "      <td>y</td>\n",
              "      <td>1.70</td>\n",
              "      <td>y</td>\n",
              "      <td>86</td>\n",
              "      <td>y</td>\n",
              "      <td>0.91</td>\n",
              "      <td>y</td>\n",
              "      <td>95</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3159</th>\n",
              "      <td>negative</td>\n",
              "      <td>29</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
              "      <td>0.80</td>\n",
              "      <td>y</td>\n",
              "      <td>1.80</td>\n",
              "      <td>y</td>\n",
              "      <td>99</td>\n",
              "      <td>y</td>\n",
              "      <td>1.01</td>\n",
              "      <td>y</td>\n",
              "      <td>98</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3160</th>\n",
              "      <td>negative</td>\n",
              "      <td>77</td>\n",
              "      <td>M</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
              "      <td>1.20</td>\n",
              "      <td>y</td>\n",
              "      <td>0.60</td>\n",
              "      <td>y</td>\n",
              "      <td>71</td>\n",
              "      <td>y</td>\n",
              "      <td>0.68</td>\n",
              "      <td>y</td>\n",
              "      <td>104</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3161</th>\n",
              "      <td>negative</td>\n",
              "      <td>74</td>\n",
              "      <td>F</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
              "      <td>1.30</td>\n",
              "      <td>y</td>\n",
              "      <td>0.10</td>\n",
              "      <td>y</td>\n",
              "      <td>65</td>\n",
              "      <td>y</td>\n",
              "      <td>0.48</td>\n",
              "      <td>y</td>\n",
              "      <td>137</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3162</th>\n",
              "      <td>negative</td>\n",
              "      <td>56</td>\n",
              "      <td>F</td>\n",
              "      <td>t</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>y</td>\n",
              "      <td>0</td>\n",
              "      <td>y</td>\n",
              "      <td>1.80</td>\n",
              "      <td>y</td>\n",
              "      <td>139</td>\n",
              "      <td>y</td>\n",
              "      <td>0.97</td>\n",
              "      <td>y</td>\n",
              "      <td>143</td>\n",
              "      <td>n</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        status age sex on_thyroxine  ... FTI_measured  FTI TBG_measured  TBG\n",
              "3158  negative  58   F            f  ...            y   95            n  NaN\n",
              "3159  negative  29   F            f  ...            y   98            n  NaN\n",
              "3160  negative  77   M            f  ...            y  104            n  NaN\n",
              "3161  negative  74   F            f  ...            y  137            n  NaN\n",
              "3162  negative  56   F            t  ...            y  143            n  NaN\n",
              "\n",
              "[5 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxxpaEu7E4JK"
      },
      "source": [
        "# replacing values and variables\n",
        "df = df.replace({'f' : 0, 't' : 1})\n",
        "df = df.replace({'n' : 0, 'y' : 1})"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hI8GX0ZAM_Zd",
        "outputId": "5c221e42-0968-4c03-c82a-7afced3dedc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# using apply method \n",
        "df[['age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'TBG' ]] =df[['age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'TBG' ]].apply(pd.to_numeric) \n",
        "print(df.dtypes) "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "status                        object\n",
            "age                          float64\n",
            "sex                           object\n",
            "on_thyroxine                   int64\n",
            "query_on_thyroxine             int64\n",
            "on_antithyroid_medication      int64\n",
            "thyroid_surgery                int64\n",
            "query_hypothyroid              int64\n",
            "query_hyperthyroid             int64\n",
            "pregnant                       int64\n",
            "sick                           int64\n",
            "tumor                          int64\n",
            "lithium                        int64\n",
            "goitre                         int64\n",
            "TSH_measured                   int64\n",
            "TSH                          float64\n",
            "T3_measured                    int64\n",
            "T3                           float64\n",
            "TT4_measured                   int64\n",
            "TT4                          float64\n",
            "T4U_measured                   int64\n",
            "T4U                          float64\n",
            "FTI_measured                   int64\n",
            "FTI                          float64\n",
            "TBG_measured                   int64\n",
            "TBG                          float64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PsRplPAIC2R",
        "outputId": "696d25e6-4f57-4afe-cfb4-4bb1b79e0dcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "df.sample(8)"
      ],
      "execution_count": 11,
      "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",
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              "\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>status</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
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              "      <th>goitre</th>\n",
              "      <th>TSH_measured</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3_measured</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4_measured</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U_measured</th>\n",
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              "      <th>FTI_measured</th>\n",
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              "      <th>TBG</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1789</th>\n",
              "      <td>negative</td>\n",
              "      <td>48.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1</td>\n",
              "      <td>65.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>negative</td>\n",
              "      <td>18.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2.7</td>\n",
              "      <td>1</td>\n",
              "      <td>131.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "      <td>149.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2358</th>\n",
              "      <td>negative</td>\n",
              "      <td>34.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1</td>\n",
              "      <td>144.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.58</td>\n",
              "      <td>1</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2245</th>\n",
              "      <td>negative</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.20</td>\n",
              "      <td>1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>97.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1</td>\n",
              "      <td>95.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1116</th>\n",
              "      <td>negative</td>\n",
              "      <td>71.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.60</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>87.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "      <td>97.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2108</th>\n",
              "      <td>negative</td>\n",
              "      <td>64.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.16</td>\n",
              "      <td>1</td>\n",
              "      <td>107.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3089</th>\n",
              "      <td>negative</td>\n",
              "      <td>68.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>87.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.85</td>\n",
              "      <td>1</td>\n",
              "      <td>102.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>863</th>\n",
              "      <td>negative</td>\n",
              "      <td>65.0</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.20</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>123.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1</td>\n",
              "      <td>134.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        status   age  sex  on_thyroxine  ...  FTI_measured    FTI  TBG_measured  TBG\n",
              "1789  negative  48.0    M             0  ...             1   65.0             0  NaN\n",
              "423   negative  18.0    M             1  ...             1  149.0             0  NaN\n",
              "2358  negative  34.0    F             0  ...             1   91.0             0  NaN\n",
              "2245  negative   NaN  NaN             0  ...             1   95.0             0  NaN\n",
              "1116  negative  71.0    F             0  ...             1   97.0             0  NaN\n",
              "2108  negative  64.0    F             0  ...             1  107.0             0  NaN\n",
              "3089  negative  68.0    F             0  ...             1  102.0             0  NaN\n",
              "863   negative  65.0    M             0  ...             1  134.0             0  NaN\n",
              "\n",
              "[8 rows x 26 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONQ30G-iPJn8"
      },
      "source": [
        "### **Note:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrY_ygXAL3Gs"
      },
      "source": [
        "## df.dropna()\n",
        "\n",
        "##droping null values does away with a good section of the dataset leaving a dataset that is dysfunctional and insignificant.\n",
        "# therefoore the null values in this dataset were not dropped"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vM4lO36PFgCx",
        "outputId": "f5c3b02b-b3bb-43eb-f12c-db538f5fe9cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# previewing the various datatypes for every column\n",
        "df.dtypes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                        object\n",
              "age                          float64\n",
              "sex                           object\n",
              "on_thyroxine                   int64\n",
              "query_on_thyroxine             int64\n",
              "on_antithyroid_medication      int64\n",
              "thyroid_surgery                int64\n",
              "query_hypothyroid              int64\n",
              "query_hyperthyroid             int64\n",
              "pregnant                       int64\n",
              "sick                           int64\n",
              "tumor                          int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "TSH_measured                   int64\n",
              "TSH                          float64\n",
              "T3_measured                    int64\n",
              "T3                           float64\n",
              "TT4_measured                   int64\n",
              "TT4                          float64\n",
              "T4U_measured                   int64\n",
              "T4U                          float64\n",
              "FTI_measured                   int64\n",
              "FTI                          float64\n",
              "TBG_measured                   int64\n",
              "TBG                          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgnXOwbybVk0"
      },
      "source": [
        "### **Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ngc4Q-6WC4R7",
        "outputId": "96460275-e6c6-48d3-e4ce-4d6368a684ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# dropping unnecessary columns\n",
        "df1 = df.drop(['TSH_measured', 'TSH', 'T3_measured', 'T3', 'TT4_measured', 'TT4',\n",
        "       'T4U_measured', 'T4U', 'FTI_measured', 'FTI', 'TBG_measured', 'TBG'], axis = 1)\n",
        "df1.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'sex', 'on_thyroxine', 'query_on_thyroxine',\n",
              "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
              "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1t5fMIBaC2G",
        "outputId": "fc8ee0e7-658c-456c-a634-87bff9dde5d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# checking count of null values\n",
        "df1.isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                         0\n",
              "age                          446\n",
              "sex                           73\n",
              "on_thyroxine                   0\n",
              "query_on_thyroxine             0\n",
              "on_antithyroid_medication      0\n",
              "thyroid_surgery                0\n",
              "query_hypothyroid              0\n",
              "query_hyperthyroid             0\n",
              "pregnant                       0\n",
              "sick                           0\n",
              "tumor                          0\n",
              "lithium                        0\n",
              "goitre                         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c5EqMELkIzg0",
        "outputId": "04fd8157-c324-4462-94f5-a367bcdd2166",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# checking the datatypes\n",
        "df1.dtypes"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                        object\n",
              "age                          float64\n",
              "sex                           object\n",
              "on_thyroxine                   int64\n",
              "query_on_thyroxine             int64\n",
              "on_antithyroid_medication      int64\n",
              "thyroid_surgery                int64\n",
              "query_hypothyroid              int64\n",
              "query_hyperthyroid             int64\n",
              "pregnant                       int64\n",
              "sick                           int64\n",
              "tumor                          int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_r7ib8yjP7dv",
        "outputId": "ded04022-28e2-4715-916e-fd8ef94fac2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "# filling the null values with zeros\n",
        "df1.fillna(0)"
      ],
      "execution_count": 17,
      "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>status</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>72.0</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>15.0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>24.0</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>24.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>77.0</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3158</th>\n",
              "      <td>negative</td>\n",
              "      <td>58.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3159</th>\n",
              "      <td>negative</td>\n",
              "      <td>29.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3160</th>\n",
              "      <td>negative</td>\n",
              "      <td>77.0</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3161</th>\n",
              "      <td>negative</td>\n",
              "      <td>74.0</td>\n",
              "      <td>F</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3162</th>\n",
              "      <td>negative</td>\n",
              "      <td>56.0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3163 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           status   age sex  on_thyroxine  ...  sick  tumor  lithium  goitre\n",
              "0     hypothyroid  72.0   M             0  ...     0      0        0       0\n",
              "1     hypothyroid  15.0   F             1  ...     0      0        0       0\n",
              "2     hypothyroid  24.0   M             0  ...     0      0        0       0\n",
              "3     hypothyroid  24.0   F             0  ...     0      0        0       0\n",
              "4     hypothyroid  77.0   M             0  ...     0      0        0       0\n",
              "...           ...   ...  ..           ...  ...   ...    ...      ...     ...\n",
              "3158     negative  58.0   F             0  ...     0      0        0       0\n",
              "3159     negative  29.0   F             0  ...     0      0        0       0\n",
              "3160     negative  77.0   M             0  ...     0      0        0       0\n",
              "3161     negative  74.0   F             0  ...     0      0        0       0\n",
              "3162     negative  56.0   F             1  ...     0      0        0       0\n",
              "\n",
              "[3163 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayk88SdeZgiq",
        "outputId": "46a96e61-6ec4-4df1-dc11-d1b9ff575ead",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# checking for duplicates\n",
        "df1.duplicated().any()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVLillrDZnGl"
      },
      "source": [
        "# dropping duplicates\n",
        "df1.drop_duplicates(inplace=True)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX729vCBaSlI",
        "outputId": "363102de-7c56-4a66-aad7-b3c9273ee81d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# checking count of duplicate entries\n",
        "df1.duplicated().sum()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZiUoOC1Da-Pm",
        "outputId": "00ca9fe3-7114-4e57-c110-c1727df11a90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# previewing the dataset\n",
        "df1.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(914, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFjjfmJxrf1u",
        "outputId": "e48e802c-c003-4ee8-b6cb-e8041cd46735",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# previewing the columns\n",
        "df1.columns"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'sex', 'on_thyroxine', 'query_on_thyroxine',\n",
              "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
              "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OUhVqsXDQt4h",
        "outputId": "c410ae2e-590e-4924-8edb-f35a6d7e28d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "# describing the numerical features\n",
        "df1.describe()"
      ],
      "execution_count": 23,
      "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>age</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>873.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>48.325315</td>\n",
              "      <td>0.213348</td>\n",
              "      <td>0.049234</td>\n",
              "      <td>0.037199</td>\n",
              "      <td>0.094092</td>\n",
              "      <td>0.154267</td>\n",
              "      <td>0.160832</td>\n",
              "      <td>0.044858</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.002188</td>\n",
              "      <td>0.084245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20.374465</td>\n",
              "      <td>0.409896</td>\n",
              "      <td>0.216475</td>\n",
              "      <td>0.189353</td>\n",
              "      <td>0.292116</td>\n",
              "      <td>0.361402</td>\n",
              "      <td>0.367577</td>\n",
              "      <td>0.207105</td>\n",
              "      <td>0.262562</td>\n",
              "      <td>0.178270</td>\n",
              "      <td>0.046752</td>\n",
              "      <td>0.277907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>65.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age  on_thyroxine  ...     lithium      goitre\n",
              "count  873.000000    914.000000  ...  914.000000  914.000000\n",
              "mean    48.325315      0.213348  ...    0.002188    0.084245\n",
              "std     20.374465      0.409896  ...    0.046752    0.277907\n",
              "min      1.000000      0.000000  ...    0.000000    0.000000\n",
              "25%     31.000000      0.000000  ...    0.000000    0.000000\n",
              "50%     49.000000      0.000000  ...    0.000000    0.000000\n",
              "75%     65.000000      0.000000  ...    0.000000    0.000000\n",
              "max     98.000000      1.000000  ...    1.000000    1.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsqE00SxfUQ2",
        "outputId": "c8b0f765-d00b-4d15-958f-8ca8f677db8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# checking for anomalies\n",
        "\n",
        "q11 = df1['on_thyroxine'].quantile(.25)\n",
        "q31 = df1['on_thyroxine'].quantile(.75)\n",
        "\n",
        "iqr11 = q31 - q11\n",
        "iqr11\n",
        "##\n",
        "q11, q31 = np.percentile(df1['on_thyroxine'], [25, 75]) \n",
        "\n",
        "iqr = q31 - q11\n",
        "\n",
        "l_bound = q11 - (1.5*iqr)\n",
        "u_bound = q31 + (1.5 * iqr)\n",
        "\n",
        "print(iqr11, iqr)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0 0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrTAc8jjRSxL",
        "outputId": "cdf18a13-80ae-492d-e295-2cf5eacb5c89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# previewing the columns\n",
        "df1.columns"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'sex', 'on_thyroxine', 'query_on_thyroxine',\n",
              "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
              "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AgNzZkFSK7_",
        "outputId": "47d0eeb5-c137-4250-f301-da4ac461ecd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# previewing the datatypes\n",
        "df1.dtypes"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                        object\n",
              "age                          float64\n",
              "sex                           object\n",
              "on_thyroxine                   int64\n",
              "query_on_thyroxine             int64\n",
              "on_antithyroid_medication      int64\n",
              "thyroid_surgery                int64\n",
              "query_hypothyroid              int64\n",
              "query_hyperthyroid             int64\n",
              "pregnant                       int64\n",
              "sick                           int64\n",
              "tumor                          int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Pa5h1nkbicy",
        "outputId": "fdb7f3b9-db71-4329-a000-afbf347e589d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# checking for outliers\n",
        "# saving the column names onto a dictionary\n",
        "columns_dict = {'goitre': 1, 'age': 2, 'on_thyroxine': 3, 'query_on_thyroxine': 4, 'on_antithyroid_medication': 5, 'thyroid_surgery': 6,\n",
        "       'query_hypothyroid': 7, 'query_hyperthyroid': 8, 'pregnant': 9, 'sick': 10, 'tumor': 11, 'lithium': 12}\n",
        "\n",
        "plt.figure(figsize=(20,30))\n",
        "\n",
        "# make a boxplot for each numerical column\n",
        "for variable,i in columns_dict.items():\n",
        "  plt.subplot(5,4,i)\n",
        "  plt.boxplot(df1[variable])\n",
        "  plt.title(variable)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py:1239: RuntimeWarning: invalid value encountered in less_equal\n",
            "  wiskhi = x[x <= hival]\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py:1246: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  wisklo = x[x >= loval]\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py:1254: RuntimeWarning: invalid value encountered in less\n",
            "  x[x < stats['whislo']],\n",
            "/usr/local/lib/python3.6/dist-packages/matplotlib/cbook/__init__.py:1255: RuntimeWarning: invalid value encountered in greater\n",
            "  x[x > stats['whishi']],\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x2160 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCfJQQixbsYY",
        "outputId": "c9af53a1-ed50-4d2e-c85b-36800f661b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# dealing with the outliers using quantiles\n",
        "Q1 = df1.quantile(0.25)\n",
        "Q3 = df1.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Removing outliers based on the IQR range \n",
        "dfx = df1[~((df1 < (Q1 - 1.5 * IQR)) | (df1 > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
        "\n",
        "# Printing the shape of our new dataset\n",
        "print(dfx.shape)\n",
        "\n",
        "# Printing the shape of our old dataset\n",
        "print(df1.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(226, 14)\n",
            "(914, 14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8vhK3S3Ex22"
      },
      "source": [
        "\n",
        "\n",
        "> Since removing the outliers has a graeat effect on the data, for the exploratory analysis, i will be using the initial dataset, the df1\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOpN6rL2WOYG"
      },
      "source": [
        "# **EXPLORATORY DATA ANALYSIS**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGR6EmNRWZcP"
      },
      "source": [
        "## **a) Univariate Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9tgPl2UEWYs_",
        "outputId": "4f5db8f1-f5cb-49b3-8694-e3d66facd302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Ploting the univariate summaries and recording our observations\n",
        "# plot histograms for all numeric columns\n",
        "# we use the dictionary we had earlier on saved\n",
        "plt.figure(figsize = (20,30))\n",
        "for variable,i in columns_dict.items():\n",
        "  \n",
        "  plt.subplot(5,4,i)\n",
        "  sns.distplot(df1[variable])\n",
        "  plt.title(variable)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# this shows the relationship between the variables"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x2160 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0lmPzC2XcG1q",
        "outputId": "5f49e319-9972-4397-9af1-eb301e7d724a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "# describing the statistical features of the data\n",
        "df1.describe()"
      ],
      "execution_count": 30,
      "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>age</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>873.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "      <td>914.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>48.325315</td>\n",
              "      <td>0.213348</td>\n",
              "      <td>0.049234</td>\n",
              "      <td>0.037199</td>\n",
              "      <td>0.094092</td>\n",
              "      <td>0.154267</td>\n",
              "      <td>0.160832</td>\n",
              "      <td>0.044858</td>\n",
              "      <td>0.074398</td>\n",
              "      <td>0.032823</td>\n",
              "      <td>0.002188</td>\n",
              "      <td>0.084245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>20.374465</td>\n",
              "      <td>0.409896</td>\n",
              "      <td>0.216475</td>\n",
              "      <td>0.189353</td>\n",
              "      <td>0.292116</td>\n",
              "      <td>0.361402</td>\n",
              "      <td>0.367577</td>\n",
              "      <td>0.207105</td>\n",
              "      <td>0.262562</td>\n",
              "      <td>0.178270</td>\n",
              "      <td>0.046752</td>\n",
              "      <td>0.277907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>49.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>65.000000</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>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age  on_thyroxine  ...     lithium      goitre\n",
              "count  873.000000    914.000000  ...  914.000000  914.000000\n",
              "mean    48.325315      0.213348  ...    0.002188    0.084245\n",
              "std     20.374465      0.409896  ...    0.046752    0.277907\n",
              "min      1.000000      0.000000  ...    0.000000    0.000000\n",
              "25%     31.000000      0.000000  ...    0.000000    0.000000\n",
              "50%     49.000000      0.000000  ...    0.000000    0.000000\n",
              "75%     65.000000      0.000000  ...    0.000000    0.000000\n",
              "max     98.000000      1.000000  ...    1.000000    1.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zba3GzgHWjFt"
      },
      "source": [
        "## **b) Bivariate Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHMH1sW8cX2C",
        "outputId": "d32a712b-a189-4ff2-dcfc-8b6466d9db72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Ploting the bivariate summaries and recording our observations\n",
        "# scatterplot\n",
        "sns.pairplot(df1)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x2160 with 156 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFGvlrxKWmf4",
        "outputId": "35060a60-27c8-4fff-eab8-0897a1a1b921",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        }
      },
      "source": [
        "### Categoric Feature Analysis\n",
        "# Boxplot gives a good idea about how the age is distributed across the categories.\n",
        "# Distribution charts show how well each category is populated.\n",
        "c = 0\n",
        "len_c = 3 # (len(df1.columns)-2)\n",
        "fig, axes = plt.subplots(len_c, 2, figsize=(10, 13))     # fig height = 70 -> in figsize(width,height)\n",
        "for i, ax in enumerate(fig.axes):\n",
        "    if (c < len_c) & (i % 2 == 0):\n",
        "        ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)\n",
        "        sns.countplot(x=df1.columns[c], alpha=0.7, data=df1, ax=ax)\n",
        "\n",
        "    if (c < len_c) & (i % 2 != 0):\n",
        "        ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)\n",
        "        sns.boxplot(data = df1, x=df1.columns[c], y='age', ax=ax)\n",
        "        c = c + 1\n",
        "fig.tight_layout()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x936 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZNIdh1cf9S-",
        "outputId": "6973be6f-6fef-4474-df16-06bce157c8a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "# plotting the correlation using a heatmap\n",
        "sns.heatmap(df1.corr(),annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9spYPXdccxS",
        "outputId": "d7dc619e-0c99-478b-d4a7-72954dc63d5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        }
      },
      "source": [
        "# tabulating the correlation coefficients\n",
        "corr = df1.corr()\n",
        "corr"
      ],
      "execution_count": 34,
      "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>age</th>\n",
              "      <th>on_thyroxine</th>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <th>pregnant</th>\n",
              "      <th>sick</th>\n",
              "      <th>tumor</th>\n",
              "      <th>lithium</th>\n",
              "      <th>goitre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012807</td>\n",
              "      <td>-0.002483</td>\n",
              "      <td>-0.090608</td>\n",
              "      <td>0.012864</td>\n",
              "      <td>0.052702</td>\n",
              "      <td>-0.050616</td>\n",
              "      <td>-0.199151</td>\n",
              "      <td>0.108849</td>\n",
              "      <td>-0.053991</td>\n",
              "      <td>-0.029005</td>\n",
              "      <td>-0.022302</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on_thyroxine</th>\n",
              "      <td>-0.012807</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.056790</td>\n",
              "      <td>-0.060029</td>\n",
              "      <td>0.015112</td>\n",
              "      <td>0.088118</td>\n",
              "      <td>-0.162563</td>\n",
              "      <td>-0.022544</td>\n",
              "      <td>-0.127292</td>\n",
              "      <td>-0.065959</td>\n",
              "      <td>-0.024388</td>\n",
              "      <td>-0.071419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>query_on_thyroxine</th>\n",
              "      <td>-0.002483</td>\n",
              "      <td>-0.056790</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.044730</td>\n",
              "      <td>-0.021376</td>\n",
              "      <td>-0.069189</td>\n",
              "      <td>-0.099622</td>\n",
              "      <td>-0.049315</td>\n",
              "      <td>-0.064516</td>\n",
              "      <td>0.043225</td>\n",
              "      <td>-0.010656</td>\n",
              "      <td>-0.032608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>on_antithyroid_medication</th>\n",
              "      <td>-0.090608</td>\n",
              "      <td>-0.060029</td>\n",
              "      <td>-0.044730</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.043546</td>\n",
              "      <td>-0.083949</td>\n",
              "      <td>0.102787</td>\n",
              "      <td>-0.014668</td>\n",
              "      <td>-0.055727</td>\n",
              "      <td>-0.036210</td>\n",
              "      <td>-0.009205</td>\n",
              "      <td>-0.059618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thyroid_surgery</th>\n",
              "      <td>0.012864</td>\n",
              "      <td>0.015112</td>\n",
              "      <td>-0.021376</td>\n",
              "      <td>-0.043546</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.054644</td>\n",
              "      <td>-0.039084</td>\n",
              "      <td>-0.051738</td>\n",
              "      <td>-0.091370</td>\n",
              "      <td>-0.059370</td>\n",
              "      <td>-0.015092</td>\n",
              "      <td>-0.084258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>query_hypothyroid</th>\n",
              "      <td>0.052702</td>\n",
              "      <td>0.088118</td>\n",
              "      <td>-0.069189</td>\n",
              "      <td>-0.083949</td>\n",
              "      <td>-0.054644</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.088034</td>\n",
              "      <td>-0.048656</td>\n",
              "      <td>-0.121085</td>\n",
              "      <td>-0.078678</td>\n",
              "      <td>0.044823</td>\n",
              "      <td>-0.085918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>query_hyperthyroid</th>\n",
              "      <td>-0.050616</td>\n",
              "      <td>-0.162563</td>\n",
              "      <td>-0.099622</td>\n",
              "      <td>0.102787</td>\n",
              "      <td>-0.039084</td>\n",
              "      <td>-0.088034</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.034615</td>\n",
              "      <td>-0.124117</td>\n",
              "      <td>-0.080648</td>\n",
              "      <td>-0.020501</td>\n",
              "      <td>0.006605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pregnant</th>\n",
              "      <td>-0.199151</td>\n",
              "      <td>-0.022544</td>\n",
              "      <td>-0.049315</td>\n",
              "      <td>-0.014668</td>\n",
              "      <td>-0.051738</td>\n",
              "      <td>-0.048656</td>\n",
              "      <td>0.034615</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.061440</td>\n",
              "      <td>-0.010257</td>\n",
              "      <td>-0.010149</td>\n",
              "      <td>0.067480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sick</th>\n",
              "      <td>0.108849</td>\n",
              "      <td>-0.127292</td>\n",
              "      <td>-0.064516</td>\n",
              "      <td>-0.055727</td>\n",
              "      <td>-0.091370</td>\n",
              "      <td>-0.121085</td>\n",
              "      <td>-0.124117</td>\n",
              "      <td>-0.061440</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.052228</td>\n",
              "      <td>-0.013277</td>\n",
              "      <td>-0.085991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tumor</th>\n",
              "      <td>-0.053991</td>\n",
              "      <td>-0.065959</td>\n",
              "      <td>0.043225</td>\n",
              "      <td>-0.036210</td>\n",
              "      <td>-0.059370</td>\n",
              "      <td>-0.078678</td>\n",
              "      <td>-0.080648</td>\n",
              "      <td>-0.010257</td>\n",
              "      <td>-0.052228</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.008627</td>\n",
              "      <td>-0.055875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lithium</th>\n",
              "      <td>-0.029005</td>\n",
              "      <td>-0.024388</td>\n",
              "      <td>-0.010656</td>\n",
              "      <td>-0.009205</td>\n",
              "      <td>-0.015092</td>\n",
              "      <td>0.044823</td>\n",
              "      <td>-0.020501</td>\n",
              "      <td>-0.010149</td>\n",
              "      <td>-0.013277</td>\n",
              "      <td>-0.008627</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.014204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>goitre</th>\n",
              "      <td>-0.022302</td>\n",
              "      <td>-0.071419</td>\n",
              "      <td>-0.032608</td>\n",
              "      <td>-0.059618</td>\n",
              "      <td>-0.084258</td>\n",
              "      <td>-0.085918</td>\n",
              "      <td>0.006605</td>\n",
              "      <td>0.067480</td>\n",
              "      <td>-0.085991</td>\n",
              "      <td>-0.055875</td>\n",
              "      <td>-0.014204</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                age  on_thyroxine  ...   lithium    goitre\n",
              "age                        1.000000     -0.012807  ... -0.029005 -0.022302\n",
              "on_thyroxine              -0.012807      1.000000  ... -0.024388 -0.071419\n",
              "query_on_thyroxine        -0.002483     -0.056790  ... -0.010656 -0.032608\n",
              "on_antithyroid_medication -0.090608     -0.060029  ... -0.009205 -0.059618\n",
              "thyroid_surgery            0.012864      0.015112  ... -0.015092 -0.084258\n",
              "query_hypothyroid          0.052702      0.088118  ...  0.044823 -0.085918\n",
              "query_hyperthyroid        -0.050616     -0.162563  ... -0.020501  0.006605\n",
              "pregnant                  -0.199151     -0.022544  ... -0.010149  0.067480\n",
              "sick                       0.108849     -0.127292  ... -0.013277 -0.085991\n",
              "tumor                     -0.053991     -0.065959  ... -0.008627 -0.055875\n",
              "lithium                   -0.029005     -0.024388  ...  1.000000 -0.014204\n",
              "goitre                    -0.022302     -0.071419  ... -0.014204  1.000000\n",
              "\n",
              "[12 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-pqIHEeWnAP"
      },
      "source": [
        "## **c) Multivariate Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2aRRJQBklHos"
      },
      "source": [
        "### **i) Factor Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGmkk6woWSbV",
        "outputId": "f38c810a-2284-4bc5-cd54-671865caa9d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# Performing adequacy tests\n",
        "# Installing factor analyzer \n",
        "!pip install factor_analyzer==0.2.3\n",
        "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
        "chi_square_value,p_value=calculate_bartlett_sphericity(df1)\n",
        "chi_square_value, p_value\n",
        "# In Bartlett ’s test, the p-value is 4.1645423146182944e-27. The test was statistically insignificant, \n",
        "# indicating that the observed correlation matrix is an identity matrix."
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting factor_analyzer==0.2.3\n",
            "  Downloading https://files.pythonhosted.org/packages/79/1b/84808bbeee0f3a8753c3d8034baf0aa0013cf08957eff750f366ce83f04a/factor_analyzer-0.2.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.1.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.18.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from factor_analyzer==0.2.3) (1.4.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas->factor_analyzer==0.2.3) (2.8.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.7.3->pandas->factor_analyzer==0.2.3) (1.15.0)\n",
            "Installing collected packages: factor-analyzer\n",
            "Successfully installed factor-analyzer-0.2.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(316.3276219649843, 4.1645423146182944e-27)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kGJZ6RSPNGvW"
      },
      "source": [
        "### **minor adjustments:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9PfGklShbSwy",
        "outputId": "6ffb7e4c-ee0e-4104-9c9f-9189958920d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# filling null values with zeros\n",
        "df2 = df1.fillna(0)\n",
        "df2.isnull().any()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                       False\n",
              "age                          False\n",
              "sex                          False\n",
              "on_thyroxine                 False\n",
              "query_on_thyroxine           False\n",
              "on_antithyroid_medication    False\n",
              "thyroid_surgery              False\n",
              "query_hypothyroid            False\n",
              "query_hyperthyroid           False\n",
              "pregnant                     False\n",
              "sick                         False\n",
              "tumor                        False\n",
              "lithium                      False\n",
              "goitre                       False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zz-i66epLnzm",
        "outputId": "64666b3d-3096-4be3-cd8f-b630fc6e5ef3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# previewing the datatypes\n",
        "df2.dtypes"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                        object\n",
              "age                          float64\n",
              "sex                           object\n",
              "on_thyroxine                   int64\n",
              "query_on_thyroxine             int64\n",
              "on_antithyroid_medication      int64\n",
              "thyroid_surgery                int64\n",
              "query_hypothyroid              int64\n",
              "query_hyperthyroid             int64\n",
              "pregnant                       int64\n",
              "sick                           int64\n",
              "tumor                          int64\n",
              "lithium                        int64\n",
              "goitre                         int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bKGXMGG9Nek5",
        "outputId": "49260308-537e-4eae-ebd7-99e4c54a4d15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "# checking for null values\n",
        "df2.isnull().sum()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                       0\n",
              "age                          0\n",
              "sex                          0\n",
              "on_thyroxine                 0\n",
              "query_on_thyroxine           0\n",
              "on_antithyroid_medication    0\n",
              "thyroid_surgery              0\n",
              "query_hypothyroid            0\n",
              "query_hyperthyroid           0\n",
              "pregnant                     0\n",
              "sick                         0\n",
              "tumor                        0\n",
              "lithium                      0\n",
              "goitre                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2a1berMbBmo"
      },
      "source": [
        "# dropping unnecessary columns\n",
        "df2 = df2.drop(['status'], axis=1)\n",
        "df3 = df2.drop(['sex'], axis=1)"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAM7bi8zb33n"
      },
      "source": [
        "# converting variables from float to int \n",
        "df3['age'] = df3['age'].astype(int)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dS2P5DuycuI2",
        "outputId": "8117053f-3820-4f6e-9995-b5fbd16ae3ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# printing the shape and datatypes\n",
        "print(df3.shape)\n",
        "print(df3.dtypes)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(914, 12)\n",
            "age                          int64\n",
            "on_thyroxine                 int64\n",
            "query_on_thyroxine           int64\n",
            "on_antithyroid_medication    int64\n",
            "thyroid_surgery              int64\n",
            "query_hypothyroid            int64\n",
            "query_hyperthyroid           int64\n",
            "pregnant                     int64\n",
            "sick                         int64\n",
            "tumor                        int64\n",
            "lithium                      int64\n",
            "goitre                       int64\n",
            "dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zA8YdV_lOJP"
      },
      "source": [
        "### **ii) Principal Component Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dE-PtYVblUsu"
      },
      "source": [
        "# preprocessing\n",
        "X = df3.iloc[:, 0:11].values\n",
        "y = df3.iloc[:, 11].values"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Lk9yS7-lqal"
      },
      "source": [
        "# splitting the dataset into training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8_WEFKGl1DK"
      },
      "source": [
        "# normalization\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4Jbz_yrl3P7"
      },
      "source": [
        "# applyting PCA\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA()\n",
        "X_train = pca.fit_transform(X_train)\n",
        "X_test = pca.transform(X_test)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qrRRGZVWl-jD",
        "outputId": "f065eebb-83c4-48e3-8ed8-e8ca8f2370a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# explained variance ratio\n",
        "explained_variance = pca.explained_variance_ratio_\n",
        "explained_variance"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.12684918, 0.11402903, 0.10723858, 0.09806844, 0.09432151,\n",
              "       0.0902463 , 0.08838177, 0.08532877, 0.07441717, 0.06958401,\n",
              "       0.05153524])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMqXEJE-mIP8"
      },
      "source": [
        "# using 1 principal component\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=1)\n",
        "X_train = pca.fit_transform(X_train)\n",
        "X_test = pca.transform(X_test)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQLMaDXbmgQf"
      },
      "source": [
        "# training and making predictions\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ev42o4cmP7q",
        "outputId": "4689d0b1-f184-491f-f053-eadb1a30729f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# performance evaluation\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "print('Accuracy' , accuracy_score(y_test, y_pred))\n",
        "\n",
        "## the accuracy of this evaluation is less than 1"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[170   0]\n",
            " [ 13   0]]\n",
            "Accuracy 0.9289617486338798\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c6a4qqvjlWyY"
      },
      "source": [
        "### **iii) Linear Discriminant Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ODsBuM7FP8cm"
      },
      "source": [
        "\n",
        "\n",
        "> ***with the two reduction tecnhiques shown, I tried to use the LDA method too, though for this case, it was not essential hence the incomplete analysis.***\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tMca-9ylaou"
      },
      "source": [
        "# preprocessing\n",
        "X = df3.iloc[:, 0:11].values\n",
        "y = df3.iloc[:, 11].values"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6ZgGfkmpG4g"
      },
      "source": [
        "# divides data into training and test sets\n",
        "#\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUVPnzffpIIt"
      },
      "source": [
        "# Feature scaling\n",
        "# We now need to perform feature scaling. We execute the following code to do so:\n",
        "# \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1CBpB3hpP7Y"
      },
      "source": [
        "# Peforming LDA\n",
        "\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
        "lda = LDA(n_components=1)\n",
        "X_train = lda.fit_transform(X_train, y_train)\n",
        "X_test = lda.transform(X_test)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1ih1AOfpSIU"
      },
      "source": [
        "# Training and Making Predictions\n",
        "#from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "#classifier = RandomForestClassifier(max_depth=2, random_state=0)\n",
        "#classifier.fit(X_train, y_train)\n",
        "#y_pred = classifier.predict(X_test)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeDSBnVYpYmQ"
      },
      "source": [
        "# Evaluating the Performance\n",
        "#from sklearn.metrics import confusion_matrix\n",
        "#from sklearn.metrics import accuracy_score\n",
        "\n",
        "#cm = confusion_matrix(y_test, y_pred)\n",
        "#print(cm)\n",
        "#print('Accuracy' + str(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGDWSTWwgOZz"
      },
      "source": [
        "# **IMPLEMENTING THE SOLUTION**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ABZCKPoREYa"
      },
      "source": [
        "Nairobi Hospital conducted a clinical camp to test for hypothyroidism. The data collected focused on Thyroid patients. Use the healthcare datasets provided to accomplish the following:  \n",
        "\n",
        "\n",
        "\n",
        "> ***Build a model that determines whether or not the patient's symptoms indicate that the patient has hypothyroid.***\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QQLJl6IV8NK"
      },
      "source": [
        "## **PART 1: DECISION TREES**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cok6CzrAR3e-"
      },
      "source": [
        "# Build a model that determines whether or not the patient's symptoms indicate that the patient has hypothyroid."
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrBp7FxuSq_s",
        "outputId": "8aaffeb1-58d1-4d5e-f543-5c84cc01ec38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "# previewing the columns in the dataset\n",
        "df.columns"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'sex', 'on_thyroxine', 'query_on_thyroxine',\n",
              "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
              "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre',\n",
              "       'TSH_measured', 'TSH', 'T3_measured', 'T3', 'TT4_measured', 'TT4',\n",
              "       'T4U_measured', 'T4U', 'FTI_measured', 'FTI', 'TBG_measured', 'TBG'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBAvoGjFbKNy"
      },
      "source": [
        "### **Using Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7QBf5VTcQE9",
        "outputId": "06683512-df7a-4d68-f659-dc039e99620d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "# filling null values with zeros\n",
        "df4 = df.fillna(0)\n",
        "df4.isnull().any()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status                       False\n",
              "age                          False\n",
              "sex                          False\n",
              "on_thyroxine                 False\n",
              "query_on_thyroxine           False\n",
              "on_antithyroid_medication    False\n",
              "thyroid_surgery              False\n",
              "query_hypothyroid            False\n",
              "query_hyperthyroid           False\n",
              "pregnant                     False\n",
              "sick                         False\n",
              "tumor                        False\n",
              "lithium                      False\n",
              "goitre                       False\n",
              "TSH_measured                 False\n",
              "TSH                          False\n",
              "T3_measured                  False\n",
              "T3                           False\n",
              "TT4_measured                 False\n",
              "TT4                          False\n",
              "T4U_measured                 False\n",
              "T4U                          False\n",
              "FTI_measured                 False\n",
              "FTI                          False\n",
              "TBG_measured                 False\n",
              "TBG                          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0J9xoaOeSFs"
      },
      "source": [
        "# converting variables from float to int\n",
        "m = df4.select_dtypes(np.number)\n",
        "df4[m.columns]= m.round().astype('Int64')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4A7FCZhgyyP",
        "outputId": "9aa1ccba-13f4-438b-c90f-a312caea8b23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "# dropping unnecessary columns\n",
        "df5 = df4.drop(['sex','on_thyroxine', 'query_on_thyroxine',\n",
        "       'on_antithyroid_medication', 'thyroid_surgery', 'query_hypothyroid',\n",
        "       'query_hyperthyroid', 'pregnant', 'sick', 'tumor', 'lithium', 'goitre', \n",
        "       'TSH_measured', 'T3_measured', 'TT4_measured', 'T4U_measured', 'FTI_measured', 'TBG_measured'], axis=1)\n",
        "df5"
      ],
      "execution_count": 60,
      "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>status</th>\n",
              "      <th>age</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>72</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>15</td>\n",
              "      <td>145</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>24</td>\n",
              "      <td>430</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>77</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3158</th>\n",
              "      <td>negative</td>\n",
              "      <td>58</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>86</td>\n",
              "      <td>1</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3159</th>\n",
              "      <td>negative</td>\n",
              "      <td>29</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3160</th>\n",
              "      <td>negative</td>\n",
              "      <td>77</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>71</td>\n",
              "      <td>1</td>\n",
              "      <td>104</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3161</th>\n",
              "      <td>negative</td>\n",
              "      <td>74</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3162</th>\n",
              "      <td>negative</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>143</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3163 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           status  age  TSH  T3  TT4  T4U  FTI  TBG\n",
              "0     hypothyroid   72   30   1   15    1   10    0\n",
              "1     hypothyroid   15  145   2   19    1   17    0\n",
              "2     hypothyroid   24    0   0    4    1    0    0\n",
              "3     hypothyroid   24  430   0    6    1    6    0\n",
              "4     hypothyroid   77    7   1   57    1   44    0\n",
              "...           ...  ...  ...  ..  ...  ...  ...  ...\n",
              "3158     negative   58    6   2   86    1   95    0\n",
              "3159     negative   29    1   2   99    1   98    0\n",
              "3160     negative   77    1   1   71    1  104    0\n",
              "3161     negative   74    1   0   65    0  137    0\n",
              "3162     negative   56    0   2  139    1  143    0\n",
              "\n",
              "[3163 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOa7kMVphU2I",
        "outputId": "9bfdfa0f-09e4-40e1-ceaf-29d98184d9f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# previewing the dataset\n",
        "df5.columns"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['status', 'age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'TBG'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_EMiLPHbIeA",
        "outputId": "9b0da1f9-9da6-4ba7-e0b0-03c5f0fd16c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# importing the required libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# the prediction model to be built will be used to determine whether or not \n",
        "# the patient's symptoms indicate that the patient has hypothyroid.\n",
        "# for that we will use the status column as our Y\n",
        "y = df5['status'].values\n",
        "\n",
        "# test_features = [['age', 'TSH_measured', 'TSH', 'T3_measured', 'T3', 'TT4_measured', 'TT4',\n",
        "       # 'T4U_measured', 'T4U', 'FTI_measured', 'FTI', 'TBG_measured', 'TBG']\n",
        "test_features = df5.columns.to_list()\n",
        "test_features.remove('status')\n",
        "\n",
        "# We should be able to use nearly all the other features, but for the sake of simplifying our visualization later, we only pick a few.\n",
        "X = df5[test_features].values\n",
        "\n",
        "# Train using 70% of the data.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(2214, 7)\n",
            "(2214,)\n",
            "(949, 7)\n",
            "(949,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WyCVDvy7f4oZ",
        "outputId": "07ad6fd3-01d4-4172-db14-b70061122a98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "# Importing Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "forest=RandomForestClassifier(n_estimators=100, random_state=42, min_samples_split = 20, max_depth=5)\n",
        "forest.fit(X_train,y_train)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=5, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=20,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOnIBXQql5mU",
        "outputId": "41910ee9-515e-437c-a982-54b399943e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "# previewing the statistical features of the data\n",
        "df5.describe()"
      ],
      "execution_count": 64,
      "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>age</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "      <td>3163.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.941195</td>\n",
              "      <td>5.036358</td>\n",
              "      <td>1.515650</td>\n",
              "      <td>100.281378</td>\n",
              "      <td>0.944040</td>\n",
              "      <td>106.386342</td>\n",
              "      <td>2.570977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>25.235169</td>\n",
              "      <td>22.163221</td>\n",
              "      <td>1.218658</td>\n",
              "      <td>52.588161</td>\n",
              "      <td>0.353484</td>\n",
              "      <td>65.607437</td>\n",
              "      <td>10.204035</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>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>124.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>98.000000</td>\n",
              "      <td>530.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>881.000000</td>\n",
              "      <td>122.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          TSH  ...          FTI          TBG\n",
              "count  3163.000000  3163.000000  ...  3163.000000  3163.000000\n",
              "mean     43.941195     5.036358  ...   106.386342     2.570977\n",
              "std      25.235169    22.163221  ...    65.607437    10.204035\n",
              "min       0.000000     0.000000  ...     0.000000     0.000000\n",
              "25%      27.000000     0.000000  ...    85.000000     0.000000\n",
              "50%      48.000000     0.000000  ...   105.000000     0.000000\n",
              "75%      64.000000     2.000000  ...   126.000000     0.000000\n",
              "max      98.000000   530.000000  ...   881.000000   122.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J70XjU2xfhpQ"
      },
      "source": [
        "# Predicting based on the model trained\n",
        "y_pred = forest.predict(X_test)\n",
        "\n",
        "#comparison_frame = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
        "#comparison_frame.describe()\n",
        "#\n",
        "# for this case, the ones with 'hypothroid' means that the symptoms indicate \n",
        "# that a person has hypothrois while the negatives idicate that the person doesnt have the disease"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "42VIUX0CjVa7"
      },
      "source": [
        "### **Using Gradient boosting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6geuP-RjYtn"
      },
      "source": [
        "# importing libraries\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNOgutLym3d0",
        "outputId": "83a1bcef-0554-4bf3-b7ae-e9ad8a74c00d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "# train with Gradient Boosting algorithm\n",
        "# computing the accuracy scores on train and validation sets when training with different learning rates\n",
        "\n",
        "learning_rates = [0.05, 0.1, 0.25, 0.5, 0.75, 1]\n",
        "for learning_rate in learning_rates:\n",
        "    gb = GradientBoostingClassifier(n_estimators=20, learning_rate = learning_rate, max_features=2, max_depth = 2, random_state = 0)\n",
        "    gb.fit(X_train, y_train)\n",
        "    print(\"Learning rate: \", learning_rate)\n",
        "    print(\"Accuracy score (training): {0:.3f}\".format(gb.score(X_train, y_train)))\n",
        "    print(\"Accuracy score (validation): {0:.3f}\".format(gb.score(X_test, y_test)))\n",
        "    print()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate:  0.05\n",
            "Accuracy score (training): 0.980\n",
            "Accuracy score (validation): 0.978\n",
            "\n",
            "Learning rate:  0.1\n",
            "Accuracy score (training): 0.989\n",
            "Accuracy score (validation): 0.981\n",
            "\n",
            "Learning rate:  0.25\n",
            "Accuracy score (training): 0.993\n",
            "Accuracy score (validation): 0.987\n",
            "\n",
            "Learning rate:  0.5\n",
            "Accuracy score (training): 0.894\n",
            "Accuracy score (validation): 0.889\n",
            "\n",
            "Learning rate:  0.75\n",
            "Accuracy score (training): 0.994\n",
            "Accuracy score (validation): 0.988\n",
            "\n",
            "Learning rate:  1\n",
            "Accuracy score (training): 0.887\n",
            "Accuracy score (validation): 0.891\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5k1METZnK2G",
        "outputId": "2d42faa6-a7e1-4a54-b5f3-a349f6029de9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# Output confusion matrix and classification report of Gradient Boosting algorithm on validation set\n",
        "\n",
        "gb = GradientBoostingClassifier(n_estimators=20,learning_rate = 0.5,random_state = 7)\n",
        "gb.fit(X_train, y_train)\n",
        "predictions = gb.predict(X_test)\n",
        "\n",
        "print(\"Confusion Matrix for Gradient boosting:\")\n",
        "print(confusion_matrix(y_test, predictions))\n",
        "print()\n",
        "print(\"Classification Report for Gradient Boosting\")\n",
        "print(classification_report(y_test, predictions))"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix for Gradient boosting:\n",
            "[[ 31   8]\n",
            " [  9 901]]\n",
            "\n",
            "Classification Report for Gradient Boosting\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " hypothyroid       0.78      0.79      0.78        39\n",
            "    negative       0.99      0.99      0.99       910\n",
            "\n",
            "    accuracy                           0.98       949\n",
            "   macro avg       0.88      0.89      0.89       949\n",
            "weighted avg       0.98      0.98      0.98       949\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjZH5AcJqEd0"
      },
      "source": [
        "Part 1: Decision trees:\n",
        "- For this section, you should build a model that makes the above prediction. You should not use individual decision trees, rather you should use at least 2 out of the 3 advanced models we have studied: Random forests, Ada boosted trees, and gradient boosted trees.\n",
        "- Try and optimize each of the 2 models, making sure to document how you've set up your hyperparameters.\n",
        "- Identify which of the 2 models you trust most, and use your model to determine which features are most impactful in influencing the prediction\n",
        "- Note that with decision trees, you don't need to do a lot of data cleaning. This will be very different with SVM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8GXLgJzWEBc"
      },
      "source": [
        "## **PART 2: SUPPORT VECTOR MACHINES (SVM)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS0GH4NBWKOQ"
      },
      "source": [
        "# importing libraries\n",
        "import seaborn as sns; sns.set(font_scale=1.2)\n",
        "# Import Suport Vector Classifier module from svm library. We'll use SVC to model our data\n",
        "from sklearn.svm import SVC,LinearSVC\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Import scikit-learn metrics module for accuracy calculation\n",
        "from sklearn.metrics import  accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4vZo0rwrp1s",
        "outputId": "fe8060c3-370a-49f6-daae-b71260ad02dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# previewing the dataset \n",
        "df5.dtypes"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status    object\n",
              "age        Int64\n",
              "TSH        Int64\n",
              "T3         Int64\n",
              "TT4        Int64\n",
              "T4U        Int64\n",
              "FTI        Int64\n",
              "TBG        Int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n6MIK0WNr0F-",
        "outputId": "594849b4-c05b-4c82-d184-d8a04124abc4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# dropping unnecessary columns\n",
        "df6 = df5.drop(['age'], axis=1)\n",
        "df6.head()"
      ],
      "execution_count": 71,
      "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>status</th>\n",
              "      <th>TSH</th>\n",
              "      <th>T3</th>\n",
              "      <th>TT4</th>\n",
              "      <th>T4U</th>\n",
              "      <th>FTI</th>\n",
              "      <th>TBG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>145</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>430</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hypothyroid</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        status  TSH  T3  TT4  T4U  FTI  TBG\n",
              "0  hypothyroid   30   1   15    1   10    0\n",
              "1  hypothyroid  145   2   19    1   17    0\n",
              "2  hypothyroid    0   0    4    1    0    0\n",
              "3  hypothyroid  430   0    6    1    6    0\n",
              "4  hypothyroid    7   1   57    1   44    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sCGUBQ08ubPo"
      },
      "source": [
        "# declaring feature vector and target variables\n",
        "X = df6.drop(['status'], axis=1)\n",
        "y = df6['status']"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyKebEF_up5v"
      },
      "source": [
        "# splitting data into separate training and test sets\n",
        "from sklearn.model_selection import train_test_split\n",
        "# training 80% of the dats\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWgt9O6Gu9Jg",
        "outputId": "ac5aa552-0d94-43fc-bb83-45744ae3088d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# checking the shape of X_train and X_test\n",
        "X_train.shape, X_test.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((2530, 6), (633, 6))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11_GbDdBvEan",
        "outputId": "0d8c56c1-f1fe-4879-a734-bc666f5c0bcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "# feature scaling\n",
        "#cols = X_train.column\n",
        "\n",
        "#\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# X_train = pd.DataFrame(X_train, columns=[cols])\n",
        "# X_test = pd.DataFrame(X_test, columns=[cols])\n",
        "# X_train.describe()\n",
        "X_train"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.13410658,  0.38487128,  1.40568704,  0.1648068 ,  0.65096694,\n",
              "        -0.2541657 ],\n",
              "       [-0.22452354, -0.42589003,  0.09084372,  0.1648068 , -0.04566504,\n",
              "        -0.2541657 ],\n",
              "       [-0.22452354,  1.19563259,  0.22232805,  0.1648068 ,  0.4497177 ,\n",
              "        -0.2541657 ],\n",
              "       ...,\n",
              "       [-0.04368962, -0.42589003,  0.59799757,  0.1648068 ,  0.38779486,\n",
              "        -0.2541657 ],\n",
              "       [-0.17931506, -1.23665135, -0.86711355, -2.67166402,  1.23923394,\n",
              "        -0.2541657 ],\n",
              "       [-0.13410658,  0.38487128, -0.15334147,  0.1648068 ,  0.17106491,\n",
              "        -0.2541657 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8WmoDohwzVL",
        "outputId": "9b0b8d83-1d6e-43dc-b3ee-4878b1ca2e9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM with the default hyperparameters\n",
        "# import SVC classifier\n",
        "from sklearn.svm import SVC\n",
        "# import metrics to compute accuracy\n",
        "from sklearn.metrics import accuracy_score\n",
        "#\n",
        "# instantiate classifier with default hyperparameters\n",
        "svc=SVC() \n",
        "# fitting classifier to training set\n",
        "#\n",
        "svc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=svc.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with default hyperparameters: {0:0.4f}'. format(accuracy_score(y_test, y_pred)))\n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with default hyperparameters: 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ojsv2mAzjx-"
      },
      "source": [
        "## **`Running SVM classifier with rbf Kernel`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tupnko0vyQnk",
        "outputId": "bb920786-1dfb-45a0-e95e-0d813646d56d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# the dataset has several outliers\n",
        "# as such, the need to minimize them requires a higher value of C\n",
        "# running SVM classifier with rbf kernel and C=100.0 we have:\n",
        "#\n",
        "svc=SVC(C=100.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "svc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=svc.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with rbf kernel and C=100.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with rbf kernel and C=100.0 : 0.9842\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2bXvT2CyfcL",
        "outputId": "281f1fee-a979-4faf-817a-311dd0f51614",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with rbf kernel and C=1000.0 we have:\n",
        "#\n",
        "svc=SVC(C=1000.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "svc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=svc.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with rbf kernel and C=1000.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with rbf kernel and C=1000.0 : 0.9842\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eh5ZYNB2zYSV"
      },
      "source": [
        "\n",
        "\n",
        "## **`Running SVM classifier with Linear Kernel`**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSWlly_vy5Rp",
        "outputId": "d0a4d6a0-8403-40b6-ffb6-e2acf9eb47f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with linear kernel and C=1.0 we have:\n",
        "#\n",
        "linear_svc=SVC(kernel='linear', C=1.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "linear_svc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred_test=linear_svc.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with linear kernel and C=1.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred_test)))"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with linear kernel and C=1.0 : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HBeQ_pg0gFI",
        "outputId": "e5dd1e21-e55d-47aa-b3c9-0868d5a4e6ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with linear kernel and C=100.0 we have:\n",
        "#\n",
        "linear_svc100=SVC(kernel='linear', C=100.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "linear_svc100.fit(X_train, y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=linear_svc100.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with linear kernel and C=100.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with linear kernel and C=100.0 : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-78JkxjsHWT",
        "outputId": "7b31eabb-b140-4d0d-f00d-f48548d1d53d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with linear kernel and C=1000.0 we have:\n",
        "#\n",
        "linear_svc1000=SVC(kernel='linear', C=1000.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "linear_svc1000.fit(X_train, y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=linear_svc1000.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with linear kernel and C=1000.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with linear kernel and C=1000.0 : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NndbFHW-1cwI",
        "outputId": "b83eb304-f36f-4103-9763-6ee09fee0990",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# comparing the accuracy of both the train and test sets\n",
        "y_pred_train = linear_svc.predict(X_train)\n",
        "y_pred_train"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['negative', 'negative', 'negative', ..., 'negative', 'negative',\n",
              "       'negative'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wjguGOeM1kj4",
        "outputId": "770b5d39-ffb0-47fc-8a42-0952993aa93b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# printing the accuracy of the training set\n",
        "print('Training-set accuracy score: {0:0.4f}'. format(accuracy_score(y_train, y_pred_train)))"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training-set accuracy score: 0.9830\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1w0ZHF414It",
        "outputId": "9fe7becc-5ac3-4765-834c-f953e3b659c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# checking for under and overfitting\n",
        "# printing the scores on training and test set\n",
        "\n",
        "print('Training set score: {:.4f}'.format(linear_svc.score(X_train, y_train)))\n",
        "\n",
        "print('Test set score: {:.4f}'.format(linear_svc.score(X_test, y_test)))"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training set score: 0.9830\n",
            "Test set score: 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9M3O12W2G0A",
        "outputId": "88d148d3-e954-4913-ae9c-89ae73f170ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "# comparing the model and null accuracies\n",
        "# checking the class distribution in test set\n",
        "\n",
        "y_test.value_counts()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "negative       607\n",
              "hypothyroid     26\n",
              "Name: status, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bj6p964p2W2Z",
        "outputId": "72c141ef-db98-4ab6-9c29-2b2fb7de2a48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# checking the null accuracy score\n",
        "\n",
        "null_accuracy = (3306/(3306+274))\n",
        "##\n",
        "print('Null accuracy score: {0:0.4f}'. format(null_accuracy))"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Null accuracy score: 0.9235\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpVLcU7n0O1G"
      },
      "source": [
        "## **`Running SVM classifier with Polynomial Kernel`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yS2gDd380-aC",
        "outputId": "5a739ab2-622a-4027-8efc-48eea208f6ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with  with polynomial kernel and C=1.0\n",
        "poly_svc=SVC(kernel='poly', C=1.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "poly_svc.fit(X_train,y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=poly_svc.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with polynomial kernel and C=1.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with polynomial kernel and C=1.0 : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEPIq0j8298F",
        "outputId": "0aa24806-8fc0-4e96-8458-bb5b615b9dcb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# running SVM classifier with polynomial kernel and C=100.0\n",
        "poly_svc100=SVC(kernel='poly', C=100.0) \n",
        "\n",
        "# fitting classifier to training set\n",
        "poly_svc100.fit(X_train, y_train)\n",
        "\n",
        "# making predictions on test set\n",
        "y_pred=poly_svc100.predict(X_test)\n",
        "\n",
        "# computing and printing the accuracy score\n",
        "print('Model accuracy score with polynomial kernel and C=100.0 : {0:0.4f}'. format(accuracy_score(y_test, y_pred)))"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy score with polynomial kernel and C=100.0 : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhppaeOg2lKr",
        "outputId": "15328a02-f3dd-406c-8319-1edf1710d438",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "# Printing the Confusion Matrix and slicing it into four pieces\n",
        "##\n",
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred_test)\n",
        "###\n",
        "print('Confusion matrix\\n\\n', cm)\n",
        "print('\\nTrue Positives(TP) = ', cm[0,0])\n",
        "print('\\nTrue Negatives(TN) = ', cm[1,1])\n",
        "print('\\nFalse Positives(FP) = ', cm[0,1])\n",
        "print('\\nFalse Negatives(FN) = ', cm[1,0])"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix\n",
            "\n",
            " [[ 17   9]\n",
            " [  6 601]]\n",
            "\n",
            "True Positives(TP) =  17\n",
            "\n",
            "True Negatives(TN) =  601\n",
            "\n",
            "False Positives(FP) =  9\n",
            "\n",
            "False Negatives(FN) =  6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dJBlGaoc3_8b"
      },
      "source": [
        "\n",
        "True Positives(TP) =  17\n",
        "\n",
        "True Negatives(TN) =  601\n",
        "\n",
        "False Positives(FP) =  9\n",
        "\n",
        "False Negatives(FN) =  6\n",
        "The confusion matrix shows 17 + 601 = 618 correct predictions and 9 + 6 = 15 incorrect predictions.\n",
        "\n",
        "In this case, we have\n",
        "\n",
        "True Positives (Actual Positive:1 and Predict Positive:1) - 17\n",
        "True Negatives (Actual Negative:0 and Predict Negative:0) - 601\n",
        "False Positives (Actual Negative:0 but Predict Positive:1) - 9 (Type I error)\n",
        "False Negatives (Actual Positive:1 but Predict Negative:0) - 6 (Type II error)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIVRbT0338Nw",
        "outputId": "212d0e00-1bf9-4620-92c7-c3a24bd67627",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "# visualizing the confusion matrix with seaborn heatmap\n",
        "cm_matrix = pd.DataFrame(data=cm, columns=['Actual Positive:1', 'Actual Negative:0'], \n",
        "                                 index=['Predict Positive:1', 'Predict Negative:0'])\n",
        "\n",
        "sns.heatmap(cm_matrix, annot=True, fmt='d', cmap='YlGnBu')"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f58c37e3b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfturZal2lk1",
        "outputId": "8cf39caa-4911-48bb-ca84-4ed2478b978d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "# classification metrices\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred_test))"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " hypothyroid       0.74      0.65      0.69        26\n",
            "    negative       0.99      0.99      0.99       607\n",
            "\n",
            "    accuracy                           0.98       633\n",
            "   macro avg       0.86      0.82      0.84       633\n",
            "weighted avg       0.98      0.98      0.98       633\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7ogtwwq2k9K"
      },
      "source": [
        "# classification accuracies\n",
        "TP = cm[0,0]\n",
        "TN = cm[1,1]\n",
        "FP = cm[0,1]\n",
        "FN = cm[1,0]"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sUCnIQUy42_8",
        "outputId": "2724a70c-f421-4c7a-e0d6-3cd659aed46d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# printing the classification accuracy\n",
        "\n",
        "classification_accuracy = (TP + TN) / float(TP + TN + FP + FN)\n",
        "print('Classification accuracy : {0:0.4f}'.format(classification_accuracy))"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification accuracy : 0.9763\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0-wpFXJ2ka4",
        "outputId": "0d05d11f-4002-4e61-abd3-f4745dfdb4ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# printing the classification error\n",
        "\n",
        "classification_error = (FP + FN) / float(TP + TN + FP + FN)\n",
        "print('Classification error : {0:0.4f}'.format(classification_error))"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classification error : 0.0237\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Hyszo-e5FE5",
        "outputId": "ee47e16f-fe7c-437f-d05b-226ab2805274",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# printing the precision score\n",
        "\n",
        "precision = TP / float(TP + FP)\n",
        "print('Precision : {0:0.4f}'.format(precision))"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision : 0.6538\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYAhcJf35MXC",
        "outputId": "d82a3ad6-6287-4092-95f9-0b4322eb2d41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# printing recall\n",
        "\n",
        "recall = TP / float(TP + FN)\n",
        "print('Recall or Sensitivity : {0:0.4f}'.format(recall))"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recall or Sensitivity : 0.7391\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSp7c32v5nta",
        "outputId": "54fa2059-f2c6-4744-b5fa-bee201c2cea3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# specificity\n",
        "\n",
        "specificity = TN / (TN + FP)\n",
        "print('Specificity : {0:0.4f}'.format(specificity))"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Specificity : 0.9852\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNwh-M9E6AN9",
        "outputId": "a6b6561c-0449-4528-814e-98d1aa2e3e07",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# true positive rate\n",
        "\n",
        "true_positive_rate = TP / float(TP + FN)\n",
        "print('True Positive Rate : {0:0.4f}'.format(true_positive_rate))"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True Positive Rate : 0.7391\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UoHD22OU6Ql5",
        "outputId": "a9238645-b8fb-49a4-8c9b-7df5c58a52c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# false positive rate\n",
        "false_positive_rate = FP / float(FP + TN)\n",
        "print('False Positive Rate : {0:0.4f}'.format(false_positive_rate))"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "False Positive Rate : 0.0148\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jB-YC3iS6kXO"
      },
      "source": [
        "## **`Hyperparameter Optimization using GridSearch CV`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZZWz73j7605r",
        "outputId": "ac3e1556-d05e-46bd-a405-d7da0c460498",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "# import GridSearchCV\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# import SVC classifier\n",
        "from sklearn.svm import SVC\n",
        "# runing SVM classifier with default hyperparameters with kernel=rbf, C=1.0 and gamma=auto\n",
        "svc=SVC() \n",
        "# declare parameters for hyperparameter tuning\n",
        "parameters = [ {'C':[1, 10, 100, 1000], 'kernel':['linear']},\n",
        "               {'C':[1, 10, 100, 1000], 'kernel':['rbf'], 'gamma':[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]},\n",
        "               {'C':[1, 10, 100, 1000], 'kernel':['poly'], 'degree': [2,3,4] ,'gamma':[0.01,0.02,0.03,0.04,0.05]} \n",
        "              ]\n",
        "#\n",
        "grid_search = GridSearchCV(estimator = svc,  \n",
        "                           param_grid = parameters,\n",
        "                           scoring = 'accuracy',\n",
        "                           cv = 5,\n",
        "                           verbose=0)\n",
        "#\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, error_score=nan,\n",
              "             estimator=SVC(C=1.0, break_ties=False, cache_size=200,\n",
              "                           class_weight=None, coef0=0.0,\n",
              "                           decision_function_shape='ovr', degree=3,\n",
              "                           gamma='scale', kernel='rbf', max_iter=-1,\n",
              "                           probability=False, random_state=None, shrinking=True,\n",
              "                           tol=0.001, verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid=[{'C': [1, 10, 100, 1000], 'kernel': ['linear']},\n",
              "                         {'C': [1, 10, 100, 1000],\n",
              "                          'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,\n",
              "                                    0.9],\n",
              "                          'kernel': ['rbf']},\n",
              "                         {'C': [1, 10, 100, 1000], 'degree': [2, 3, 4],\n",
              "                          'gamma': [0.01, 0.02, 0.03, 0.04, 0.05],\n",
              "                          'kernel': ['poly']}],\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RG8dfYOO7Epk",
        "outputId": "92070366-9ff5-4d15-a7ff-0b3ccae2006e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "# examining the best model\n",
        "# best score achieved during the GridSearchCV\n",
        "print('GridSearch CV best score : {:.4f}\\n\\n'.format(grid_search.best_score_))\n",
        "# print parameters that give the best results\n",
        "print('Parameters that give the best results :','\\n\\n', (grid_search.best_params_))\n",
        "# print estimator that was chosen by the GridSearch\n",
        "print('\\n\\nEstimator that was chosen by the search :','\\n\\n', (grid_search.best_estimator_))"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GridSearch CV best score : 0.9862\n",
            "\n",
            "\n",
            "Parameters that give the best results : \n",
            "\n",
            " {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}\n",
            "\n",
            "\n",
            "Estimator that was chosen by the search : \n",
            "\n",
            " SVC(C=100, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
            "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
            "    tol=0.001, verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ZCHdzjTQ6Bq"
      },
      "source": [
        "from the analysis and predictions above, the parameters that did give the best results were Parameters that give the best results : \n",
        "\n",
        " {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-_silxj7nSh",
        "outputId": "43d50adb-66a3-4b8f-ff4e-a184fa716746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# calculating GridSearch CV score on test set\n",
        "\n",
        "print('GridSearch CV score on test set: {0:0.4f}'.format(grid_search.score(X_test, y_test)))"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GridSearch CV score on test set: 0.9842\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}