Project_Final_Code.ipynb

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      "source": [
        "# Importing necessary libraries \n",
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns \n",
        "import plotly.express as px \n",
        "import statistics \n",
        "import plotly.graph_objects as go  \n",
        "from plotly.subplots import make_subplots \n",
        "import plotly.figure_factory as ff \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from collections import Counter\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "import warnings\n",
        "\n",
        "# Ignoring all warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        "id": "UnTfVRCFfI_q",
        "outputId": "91a3ad31-6553-4f6e-85da-11f052d2640a"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5lnCjSm7MXp"
      },
      "source": [
        "### Importing Datasets\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9172, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# Reading first thyroid dataset\n",
        "data1 = pd.read_csv('/content/drive/MyDrive/IE7275_Data_Mining_Files/Project/Data/Thyroid_Disease_Data/thyroid0387.data', sep = ',', header = None)\n",
        "data1.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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        "id": "sHOW57bBbkj9",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2800, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Reading second thyroid dataset\n",
        "data2 = pd.read_csv('/content/drive/MyDrive/IE7275_Data_Mining_Files/Project/Data/Thyroid_Disease_Data/allhyper.data', sep = ',', header = None)\n",
        "data2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
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        "id": "Q_s_kt7tb47-",
        "outputId": "636f153d-1de8-48fb-c4b1-102faf76a86d"
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          "output_type": "execute_result",
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            "text/plain": [
              "(2800, 30)"
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          "metadata": {},
          "execution_count": 5
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      ],
      "source": [
        "# Reading third thyroid dataset\n",
        "data3 = pd.read_csv('/content/drive/MyDrive/IE7275_Data_Mining_Files/Project/Data/Thyroid_Disease_Data/allhypo.data', sep = ',', header = None)\n",
        "data3.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Dkxu0xLB3jiw"
      },
      "outputs": [],
      "source": [
        "# Making a list of column names to set these for the data read above \n",
        "cols = ['Age', 'Sex', 'On_Thyroxine', 'Query_On_Thyroxine', 'On_Antithyroid_Medication', 'Sick', 'Pregnant', 'Thyroid_Surgery', \n",
        "        'I131_Treatment', 'Query_Hypothyroid', 'Query_Hyperthyroid', 'Lithium', 'Goitre', 'Tumor', 'Hypopituitary', 'Psych', 'TSH_Measured', \n",
        "        'TSH', 'T3_measured', 'T3', 'TT4_Measured', 'TT4', 'T4U_Measured', 'T4U', 'FTI_Measured', 'FTI', 'TBG_Measured', 'TBG', \n",
        "        'Referral_Source', 'Classes']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "I-sTiyF4nYqM"
      },
      "outputs": [],
      "source": [
        "# Setting column names for all datasets\n",
        "data1.columns = cols\n",
        "data2.columns = cols \n",
        "data3.columns = cols "
      ]
    },
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      "execution_count": 8,
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          "data": {
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              "  Age Sex On_Thyroxine Query_On_Thyroxine On_Antithyroid_Medication Sick  \\\n",
              "0  29   F            f                  f                         f    f   \n",
              "1  29   F            f                  f                         f    f   \n",
              "2  41   F            f                  f                         f    f   \n",
              "3  36   F            f                  f                         f    f   \n",
              "4  32   F            f                  f                         f    f   \n",
              "\n",
              "  Pregnant Thyroid_Surgery I131_Treatment Query_Hypothyroid  ... TT4_Measured  \\\n",
              "0        f               f              f                 t  ...            f   \n",
              "1        f               f              f                 f  ...            t   \n",
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              "4        f               f              f                 f  ...            f   \n",
              "\n",
              "   TT4 T4U_Measured T4U FTI_Measured FTI TBG_Measured TBG Referral_Source  \\\n",
              "0    ?            f   ?            f   ?            f   ?           other   \n",
              "1  128            f   ?            f   ?            f   ?           other   \n",
              "2    ?            f   ?            f   ?            t  11           other   \n",
              "3    ?            f   ?            f   ?            t  26           other   \n",
              "4    ?            f   ?            f   ?            t  36           other   \n",
              "\n",
              "        Classes  \n",
              "0  -[840801013]  \n",
              "1  -[840801014]  \n",
              "2  -[840801042]  \n",
              "3  -[840803046]  \n",
              "4  S[840803047]  \n",
              "\n",
              "[5 rows x 30 columns]"
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Concatenating three datasets into one \n",
        "thyroid_data = pd.concat([data1, data2, data3], ignore_index = True)\n",
        "thyroid_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "keeEDVcW1sjh",
        "outputId": "930fddbf-0da6-46bd-c3c9-474b1d36a524"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14772, 30)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "thyroid_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-upprhaZWtX6"
      },
      "outputs": [],
      "source": [
        "thyroid_data.to_csv('thyroid_data.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_yh6PlM_79vv"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "4R75e4mVjc2b"
      },
      "outputs": [],
      "source": [
        "# Separating patient ID from thyroid class in 'Classes' column\n",
        "thyroid_data['Classes'] = thyroid_data['Classes'].str.replace('.', '[')\n",
        "\n",
        "# Creating separate columns for class and patient ID by splitting\n",
        "thyroid_data[['Class', 'Patient_ID']] = thyroid_data['Classes'].str.split('[', expand = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "93nKqniokP1i"
      },
      "outputs": [],
      "source": [
        "# Dropping some columns\n",
        "thyroid_data.drop(['Classes'], axis = 1, inplace = True)\n",
        "thyroid_data.drop(['Referral_Source'], axis = 1, inplace = True)\n",
        "thyroid_data.drop(['Patient_ID'], axis = 1, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jo04gtECosJC",
        "outputId": "c779264b-422b-42d7-8174-7c2196272dfd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['-', 'S', 'F', 'AK', 'R', 'I', 'M', 'N', 'G', 'K', 'A', 'KJ', 'L',\n",
              "       'MK', 'Q', 'J', 'C|I', 'O', 'LJ', 'H|K', 'D', 'GK', 'MI', 'P',\n",
              "       'FK', 'B', 'GI', 'C', 'GKJ', 'OI', 'D|R', 'E', 'negative',\n",
              "       'hyperthyroid', 'T3 toxic', 'goitre', 'compensated hypothyroid',\n",
              "       'primary hypothyroid', 'secondary hypothyroid'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Checking unique values of the class column\n",
        "thyroid_data['Class'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "TvGI3GX4n0Dg"
      },
      "outputs": [],
      "source": [
        "# Filtering out values that do not exist in the target class list\n",
        "class_lst = ['-', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']\n",
        "thyroid_data = thyroid_data[thyroid_data['Class'].isin(class_lst)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "l4CWdrlcpZIp"
      },
      "outputs": [],
      "source": [
        "# Cleaning class column by making all values consistent across all categories of thyroid disorders\n",
        "thyroid_data['Class'].replace('-', 'negative', inplace = True)\n",
        "thyroid_data['Class'].replace('A', 'hyperthyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('B', 'T3 toxic', inplace = True)\n",
        "thyroid_data['Class'].replace('C', 'toxic goitre', inplace = True)\n",
        "thyroid_data['Class'].replace('D', 'secondary toxic', inplace = True)\n",
        "thyroid_data['Class'].replace('E', 'hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('F', 'primary hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('G', 'compensated hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('H', 'secondary hypothyroid', inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCFg3HwXuVVn",
        "outputId": "ded7b550-e565-40c2-fafd-d8b4a42ccc4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['negative', 'primary hypothyroid', 'compensated hypothyroid',\n",
              "       'hyperthyroid', 'secondary toxic', 'T3 toxic', 'toxic goitre',\n",
              "       'hypothyroid'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Unique values in class column after replacement\n",
        "thyroid_data['Class'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhTKryLnqnNB",
        "outputId": "7bb7ccf8-dcdb-445c-f0af-3e1f7a51a2e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7546, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Dimensions of the new dataset\n",
        "thyroid_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmToc-Jbi5X_",
        "outputId": "ccb5191b-2350-4f29-ed29-f35424e91aa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                             0\n",
              "Sex                           250\n",
              "On_Thyroxine                    0\n",
              "Query_On_Thyroxine              0\n",
              "On_Antithyroid_Medication       0\n",
              "Sick                            0\n",
              "Pregnant                        0\n",
              "Thyroid_Surgery                 0\n",
              "I131_Treatment                  0\n",
              "Query_Hypothyroid               0\n",
              "Query_Hyperthyroid              0\n",
              "Lithium                         0\n",
              "Goitre                          0\n",
              "Tumor                           0\n",
              "Hypopituitary                   0\n",
              "Psych                           0\n",
              "TSH_Measured                    0\n",
              "TSH                           722\n",
              "T3_measured                     0\n",
              "T3                           2209\n",
              "TT4_Measured                    0\n",
              "TT4                           354\n",
              "T4U_Measured                    0\n",
              "T4U                           676\n",
              "FTI_Measured                    0\n",
              "FTI                           669\n",
              "TBG_Measured                    0\n",
              "TBG                          7287\n",
              "Class                           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# How many '?' do we have in all columns? \n",
        "thyroid_data.eq('?').sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsgjnquhiUrE",
        "outputId": "55eb3214-9603-45fa-cec3-3020df24a573"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                          False\n",
              "Sex                          False\n",
              "On_Thyroxine                 False\n",
              "Query_On_Thyroxine           False\n",
              "On_Antithyroid_Medication    False\n",
              "Sick                         False\n",
              "Pregnant                     False\n",
              "Thyroid_Surgery              False\n",
              "I131_Treatment               False\n",
              "Query_Hypothyroid            False\n",
              "Query_Hyperthyroid           False\n",
              "Lithium                      False\n",
              "Goitre                       False\n",
              "Tumor                        False\n",
              "Hypopituitary                False\n",
              "Psych                        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",
              "Class                        False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# How many rows with all '?' values?\n",
        "thyroid_data.eq('?').all()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCwmc3_Dg5yy",
        "outputId": "087382f1-0ab4-4c22-b2d6-1862ca710835"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                          0\n",
              "Sex                          0\n",
              "On_Thyroxine                 0\n",
              "Query_On_Thyroxine           0\n",
              "On_Antithyroid_Medication    0\n",
              "Sick                         0\n",
              "Pregnant                     0\n",
              "Thyroid_Surgery              0\n",
              "I131_Treatment               0\n",
              "Query_Hypothyroid            0\n",
              "Query_Hyperthyroid           0\n",
              "Lithium                      0\n",
              "Goitre                       0\n",
              "Tumor                        0\n",
              "Hypopituitary                0\n",
              "Psych                        0\n",
              "TSH_Measured                 0\n",
              "TSH                          0\n",
              "T3_measured                  0\n",
              "T3                           0\n",
              "TT4_Measured                 0\n",
              "TT4                          0\n",
              "T4U_Measured                 0\n",
              "T4U                          0\n",
              "FTI_Measured                 0\n",
              "FTI                          0\n",
              "TBG_Measured                 0\n",
              "TBG                          0\n",
              "Class                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# Number of NaN values in each column\n",
        "thyroid_data.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "TQAu6ntdw4Ha"
      },
      "outputs": [],
      "source": [
        "# Imputing '?' values in sex column by the most commonly occurring value \n",
        "sex_mode = statistics.mode(thyroid_data['Sex'])\n",
        "thyroid_data['Sex'].replace('?', sex_mode, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "lqz2tPBwh6Z8"
      },
      "outputs": [],
      "source": [
        "# Looking for inconsistent values in age column\n",
        "thyroid_data['Age'].unique()\n",
        "\n",
        "# Based on unique values, creating a list of incorrect age values\n",
        "age_lst = [455, 65511, 65512, 65526]\n",
        "thyroid_data = thyroid_data[~thyroid_data['Age'].isin(age_lst)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "g8PiB6VeiEn4"
      },
      "outputs": [],
      "source": [
        "# For missing values in T3, TT4, T4U, TSH, and TBG columns, we can impute 0 based on their measured values. \n",
        "thyroid_data['T3'].replace('?', 0, inplace = True)\n",
        "thyroid_data['TT4'].replace('?', 0, inplace = True)\n",
        "thyroid_data['T4U'].replace('?', 0, inplace = True)\n",
        "thyroid_data['FTI'].replace('?', 0, inplace = True)\n",
        "thyroid_data['TBG'].replace('?', 0, inplace = True)\n",
        "thyroid_data['TSH'].replace('?', 0, inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mp5H4kDl2v9C",
        "outputId": "ebce6d42-5046-449f-9019-47cf39b0c9f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                          object\n",
              "Sex                          object\n",
              "On_Thyroxine                 object\n",
              "Query_On_Thyroxine           object\n",
              "On_Antithyroid_Medication    object\n",
              "Sick                         object\n",
              "Pregnant                     object\n",
              "Thyroid_Surgery              object\n",
              "I131_Treatment               object\n",
              "Query_Hypothyroid            object\n",
              "Query_Hyperthyroid           object\n",
              "Lithium                      object\n",
              "Goitre                       object\n",
              "Tumor                        object\n",
              "Hypopituitary                object\n",
              "Psych                        object\n",
              "TSH_Measured                 object\n",
              "TSH                          object\n",
              "T3_measured                  object\n",
              "T3                           object\n",
              "TT4_Measured                 object\n",
              "TT4                          object\n",
              "T4U_Measured                 object\n",
              "T4U                          object\n",
              "FTI_Measured                 object\n",
              "FTI                          object\n",
              "TBG_Measured                 object\n",
              "TBG                          object\n",
              "Class                        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# Checking datatypes of all columns \n",
        "thyroid_data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "PfqkEjrveAqv"
      },
      "outputs": [],
      "source": [
        "# Converting numerical columns from dtype object to int \n",
        "thyroid_data['Age'] = thyroid_data['Age'].astype('int')\n",
        "thyroid_data['TSH'] = thyroid_data['TSH'].astype('float')\n",
        "thyroid_data['T3'] = thyroid_data['T3'].astype('float')\n",
        "thyroid_data['TT4'] = thyroid_data['TT4'].astype('float')\n",
        "thyroid_data['T4U'] = thyroid_data['T4U'].astype('float')\n",
        "thyroid_data['FTI'] = thyroid_data['FTI'].astype('float')\n",
        "thyroid_data['TBG'] = thyroid_data['TBG'].astype('float')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phmgzD7U4k1l",
        "outputId": "f3e7a854-6d5d-4079-91e4-91bfbbf19091"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                            int64\n",
              "Sex                           object\n",
              "On_Thyroxine                  object\n",
              "Query_On_Thyroxine            object\n",
              "On_Antithyroid_Medication     object\n",
              "Sick                          object\n",
              "Pregnant                      object\n",
              "Thyroid_Surgery               object\n",
              "I131_Treatment                object\n",
              "Query_Hypothyroid             object\n",
              "Query_Hyperthyroid            object\n",
              "Lithium                       object\n",
              "Goitre                        object\n",
              "Tumor                         object\n",
              "Hypopituitary                 object\n",
              "Psych                         object\n",
              "TSH_Measured                  object\n",
              "TSH                          float64\n",
              "T3_measured                   object\n",
              "T3                           float64\n",
              "TT4_Measured                  object\n",
              "TT4                          float64\n",
              "T4U_Measured                  object\n",
              "T4U                          float64\n",
              "FTI_Measured                  object\n",
              "FTI                          float64\n",
              "TBG_Measured                  object\n",
              "TBG                          float64\n",
              "Class                         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# Checking datatypes after conversion\n",
        "thyroid_data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHUK45aARNqZ",
        "outputId": "b6a68023-a5bb-4443-92dc-50eb8c38a2e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['negative', 'hypothyroid', 'hyperthyroid'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "# Merging all hyperthyroid subclasses into hyperthyroid class. Similarly, for hypothyroid subclasses\n",
        "thyroid_data['Class'].replace('T3 toxic', 'hyperthyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('toxic goitre', 'hyperthyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('secondary toxic', 'hyperthyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('primary hypothyroid', 'hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('secondary hypothyroid', 'hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].replace('compensated hypothyroid', 'hypothyroid', inplace = True)\n",
        "thyroid_data['Class'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsIfworV3C8Q",
        "outputId": "336a05be-853f-404e-b013-b30470581937"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Class\n",
              "hyperthyroid     182\n",
              "hypothyroid      593\n",
              "negative        6767\n",
              "Name: Class, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Grouping by to get the count of each class in the target variable\n",
        "thyroid_data.groupby(['Class'])['Class'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "sNv5C_kdcfNM"
      },
      "outputs": [],
      "source": [
        "# Performing label encoder to encode categorical target values to numerical target values\n",
        "le = LabelEncoder()\n",
        "thyroid_data['Class'] = le.fit_transform(thyroid_data['Class'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "M1FRuO6M4pzw"
      },
      "outputs": [],
      "source": [
        "# Label Encoding converted the target variable to 0 (hyperthyroid), 1 (hypothyroid), and 2 (negative) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "Z8ztIUvc8a8v"
      },
      "outputs": [],
      "source": [
        "# Getting numerical and categorical columns\n",
        "num_cols = ['Age', 'TSH', 'T3', 'TT4', 'T4U', 'FTI', 'TBG']\n",
        "cat_cols = ['Sex', 'On_Thyroxine', 'Query_On_Thyroxine', 'On_Antithyroid_Medication', 'Sick', 'Pregnant', 'Thyroid_Surgery',\n",
        "            'I131_Treatment', 'Query_Hypothyroid', 'Query_Hyperthyroid', 'Lithium', 'Goitre', 'Tumor', 'Hypopituitary', 'Psych', \n",
        "            'TSH_Measured', 'T3_measured', 'TT4_Measured', 'T4U_Measured', 'FTI_Measured', 'TBG_Measured']\n",
        "\n",
        "# Creating a column tranformer to implement one hot encoding and normalization\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers = [\n",
        "        ('num', StandardScaler(), num_cols), \n",
        "        ('cat', OneHotEncoder(), cat_cols)\n",
        "    ])\n",
        "\n",
        "# Fitting the column tranformer on original data \n",
        "processed_data = preprocessor.fit_transform(thyroid_data)\n",
        "\n",
        "# Creating a new dataframe with the processed data\n",
        "processed_df = pd.DataFrame(\n",
        "    processed_data,\n",
        "    columns = num_cols + list(preprocessor.named_transformers_['cat'].get_feature_names_out(cat_cols))\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "uZMjbBlxiE-R",
        "outputId": "1e90872e-595a-44ab-fa2d-cf49b6f240cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Age       TSH        T3       TT4       T4U       FTI       TBG  \\\n",
              "0 -1.231760 -0.185266 -1.248935 -2.581264 -2.780032 -2.143621 -0.182088   \n",
              "1 -1.231760 -0.132967  0.411433  0.714243 -2.780032 -2.143621 -0.182088   \n",
              "2 -0.587260 -0.197335 -1.248935 -2.581264 -2.780032 -2.143621  2.358791   \n",
              "3 -0.855801 -0.197335 -1.248935 -2.581264 -2.780032 -2.143621  5.823627   \n",
              "4  0.433199 -0.197335 -1.248935 -2.581264 -2.780032 -2.143621  5.823627   \n",
              "\n",
              "   Sex_F  Sex_M  On_Thyroxine_f  ...  T3_measured_f  T3_measured_t  \\\n",
              "0    1.0    0.0             1.0  ...            1.0            0.0   \n",
              "1    1.0    0.0             1.0  ...            0.0            1.0   \n",
              "2    1.0    0.0             1.0  ...            1.0            0.0   \n",
              "3    1.0    0.0             1.0  ...            1.0            0.0   \n",
              "4    1.0    0.0             1.0  ...            1.0            0.0   \n",
              "\n",
              "   TT4_Measured_f  TT4_Measured_t  T4U_Measured_f  T4U_Measured_t  \\\n",
              "0             1.0             0.0             1.0             0.0   \n",
              "1             0.0             1.0             1.0             0.0   \n",
              "2             1.0             0.0             1.0             0.0   \n",
              "3             1.0             0.0             1.0             0.0   \n",
              "4             1.0             0.0             1.0             0.0   \n",
              "\n",
              "   FTI_Measured_f  FTI_Measured_t  TBG_Measured_f  TBG_Measured_t  \n",
              "0             1.0             0.0             1.0             0.0  \n",
              "1             1.0             0.0             1.0             0.0  \n",
              "2             1.0             0.0             0.0             1.0  \n",
              "3             1.0             0.0             0.0             1.0  \n",
              "4             1.0             0.0             0.0             1.0  \n",
              "\n",
              "[5 rows x 48 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dbf5754-c9be-4f6f-b484-52ca46319196\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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",
              "      <th>Sex_F</th>\n",
              "      <th>Sex_M</th>\n",
              "      <th>On_Thyroxine_f</th>\n",
              "      <th>...</th>\n",
              "      <th>T3_measured_f</th>\n",
              "      <th>T3_measured_t</th>\n",
              "      <th>TT4_Measured_f</th>\n",
              "      <th>TT4_Measured_t</th>\n",
              "      <th>T4U_Measured_f</th>\n",
              "      <th>T4U_Measured_t</th>\n",
              "      <th>FTI_Measured_f</th>\n",
              "      <th>FTI_Measured_t</th>\n",
              "      <th>TBG_Measured_f</th>\n",
              "      <th>TBG_Measured_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.231760</td>\n",
              "      <td>-0.185266</td>\n",
              "      <td>-1.248935</td>\n",
              "      <td>-2.581264</td>\n",
              "      <td>-2.780032</td>\n",
              "      <td>-2.143621</td>\n",
              "      <td>-0.182088</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.231760</td>\n",
              "      <td>-0.132967</td>\n",
              "      <td>0.411433</td>\n",
              "      <td>0.714243</td>\n",
              "      <td>-2.780032</td>\n",
              "      <td>-2.143621</td>\n",
              "      <td>-0.182088</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.587260</td>\n",
              "      <td>-0.197335</td>\n",
              "      <td>-1.248935</td>\n",
              "      <td>-2.581264</td>\n",
              "      <td>-2.780032</td>\n",
              "      <td>-2.143621</td>\n",
              "      <td>2.358791</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.855801</td>\n",
              "      <td>-0.197335</td>\n",
              "      <td>-1.248935</td>\n",
              "      <td>-2.581264</td>\n",
              "      <td>-2.780032</td>\n",
              "      <td>-2.143621</td>\n",
              "      <td>5.823627</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.433199</td>\n",
              "      <td>-0.197335</td>\n",
              "      <td>-1.248935</td>\n",
              "      <td>-2.581264</td>\n",
              "      <td>-2.780032</td>\n",
              "      <td>-2.143621</td>\n",
              "      <td>5.823627</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 48 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dbf5754-c9be-4f6f-b484-52ca46319196')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3dbf5754-c9be-4f6f-b484-52ca46319196 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3dbf5754-c9be-4f6f-b484-52ca46319196');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "processed_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5-geLP3FAiu",
        "outputId": "6399f3ca-1ece-4514-944e-dac1fb703b7c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7542, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "processed_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wIeQUgZcaiI4"
      },
      "source": [
        "### Data Exploration & Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "upCPYT6ZhacY",
        "outputId": "d10f2a29-8649-42b0-aae2-d4e94c45f1e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age   Sex On_Thyroxine Query_On_Thyroxine  \\\n",
              "count   7542.000000  7542         7542               7542   \n",
              "unique          NaN     2            2                  2   \n",
              "top             NaN     F            f                  f   \n",
              "freq            NaN  5148         6695               7419   \n",
              "mean      51.934235   NaN          NaN                NaN   \n",
              "std       18.620319   NaN          NaN                NaN   \n",
              "min        1.000000   NaN          NaN                NaN   \n",
              "25%       37.000000   NaN          NaN                NaN   \n",
              "50%       55.000000   NaN          NaN                NaN   \n",
              "75%       67.000000   NaN          NaN                NaN   \n",
              "max       97.000000   NaN          NaN                NaN   \n",
              "\n",
              "       On_Antithyroid_Medication  Sick Pregnant Thyroid_Surgery  \\\n",
              "count                       7542  7542     7542            7542   \n",
              "unique                         2     2        2               2   \n",
              "top                            f     f        f               f   \n",
              "freq                        7450  7262     7509            7436   \n",
              "mean                         NaN   NaN      NaN             NaN   \n",
              "std                          NaN   NaN      NaN             NaN   \n",
              "min                          NaN   NaN      NaN             NaN   \n",
              "25%                          NaN   NaN      NaN             NaN   \n",
              "50%                          NaN   NaN      NaN             NaN   \n",
              "75%                          NaN   NaN      NaN             NaN   \n",
              "max                          NaN   NaN      NaN             NaN   \n",
              "\n",
              "       I131_Treatment Query_Hypothyroid  ...           T3 TT4_Measured  \\\n",
              "count            7542              7542  ...  7542.000000         7542   \n",
              "unique              2                 2  ...          NaN            2   \n",
              "top                 f                 f  ...          NaN            t   \n",
              "freq             7402              7042  ...          NaN         7188   \n",
              "mean              NaN               NaN  ...     1.429187          NaN   \n",
              "std               NaN               NaN  ...     1.144401          NaN   \n",
              "min               NaN               NaN  ...     0.000000          NaN   \n",
              "25%               NaN               NaN  ...     0.000000          NaN   \n",
              "50%               NaN               NaN  ...     1.700000          NaN   \n",
              "75%               NaN               NaN  ...     2.200000          NaN   \n",
              "max               NaN               NaN  ...    18.000000          NaN   \n",
              "\n",
              "                TT4 T4U_Measured          T4U FTI_Measured          FTI  \\\n",
              "count   7542.000000         7542  7542.000000         7542  7542.000000   \n",
              "unique          NaN            2          NaN            2          NaN   \n",
              "top             NaN            t          NaN            t          NaN   \n",
              "freq            NaN         6866          NaN         6873          NaN   \n",
              "mean     100.258242          NaN     0.880593          NaN   100.759996   \n",
              "std       38.843337          NaN     0.316777          NaN    47.007687   \n",
              "min        0.000000          NaN     0.000000          NaN     0.000000   \n",
              "25%       84.250000          NaN     0.830000          NaN    88.000000   \n",
              "50%      101.000000          NaN     0.950000          NaN   105.000000   \n",
              "75%      120.000000          NaN     1.050000          NaN   123.000000   \n",
              "max      430.000000          NaN     2.120000          NaN   839.000000   \n",
              "\n",
              "        TBG_Measured          TBG        Class  \n",
              "count           7542  7542.000000  7542.000000  \n",
              "unique             2          NaN          NaN  \n",
              "top                f          NaN          NaN  \n",
              "freq            7283          NaN          NaN  \n",
              "mean             NaN     0.788299     1.873111  \n",
              "std              NaN     4.329497     0.398839  \n",
              "min              NaN     0.000000     0.000000  \n",
              "25%              NaN     0.000000     2.000000  \n",
              "50%              NaN     0.000000     2.000000  \n",
              "75%              NaN     0.000000     2.000000  \n",
              "max              NaN    45.000000     2.000000  \n",
              "\n",
              "[11 rows x 29 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></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>Sick</th>\n",
              "      <th>Pregnant</th>\n",
              "      <th>Thyroid_Surgery</th>\n",
              "      <th>I131_Treatment</th>\n",
              "      <th>Query_Hypothyroid</th>\n",
              "      <th>...</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",
              "      <th>Class</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7542.000000</td>\n",
              "      <td>7542</td>\n",
              "      <td>7542</td>\n",
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              "      <td>7542</td>\n",
              "      <td>7542</td>\n",
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              "      <td>7542</td>\n",
              "      <td>7542</td>\n",
              "      <td>7542</td>\n",
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              "      <td>7542</td>\n",
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              "      <td>7542.000000</td>\n",
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              "    <tr>\n",
              "      <th>unique</th>\n",
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              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>f</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
              "      <td>NaN</td>\n",
              "      <td>t</td>\n",
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              "      <td>f</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>freq</th>\n",
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              "      <td>6695</td>\n",
              "      <td>7419</td>\n",
              "      <td>7450</td>\n",
              "      <td>7262</td>\n",
              "      <td>7509</td>\n",
              "      <td>7436</td>\n",
              "      <td>7402</td>\n",
              "      <td>7042</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7188</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6866</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6873</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7283</td>\n",
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              "      <th>mean</th>\n",
              "      <td>51.934235</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1.429187</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100.258242</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.880593</td>\n",
              "      <td>NaN</td>\n",
              "      <td>100.759996</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.788299</td>\n",
              "      <td>1.873111</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>1.144401</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.843337</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.316777</td>\n",
              "      <td>NaN</td>\n",
              "      <td>47.007687</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.830000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>1.700000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>67.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>2.200000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.050000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>97.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>430.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.120000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>839.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11 rows × 29 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-246c415a-a6aa-46f8-a998-094c5beead81')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-246c415a-a6aa-46f8-a998-094c5beead81 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-246c415a-a6aa-46f8-a998-094c5beead81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Understanding statistics of the values in columns \n",
        "\n",
        "thyroid_data.describe(include = 'all')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "sDl9CO0K29Zd",
        "outputId": "ac3524c9-b4e1-4a4f-c2e1-fe5d9ce6eb43"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Correlation Between Numerical Variables')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Plotting heatmap to understand the relation between numerical variables\n",
        "plt.figure(figsize = (10, 6))\n",
        "fig1 = sns.heatmap(thyroid_data.corr(),annot = True, cmap = 'magma')\n",
        "fig1.set_title('Correlation Between Numerical Variables', fontdict = {'fontsize': 12}, pad = 12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXMqG7biSb-G",
        "outputId": "55873a67-977b-46bf-f1d3-35eafa16fedd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class = 2, n = 6767\n",
            "Class = 1, n = 593\n",
            "Class = 0, n = 182\n"
          ]
        }
      ],
      "source": [
        "# Understanding the distribution of classes\n",
        "counter = Counter(thyroid_data['Class'])\n",
        "\n",
        "for k, v in counter.items():\n",
        "  print('Class = {}, n = {}'.format(k, v))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "isP-h0ASSpEb",
        "outputId": "478d179d-8ab0-4213-becf-7dbb63f9c659"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Displaying distribution of classes\n",
        "fig = px.bar(x = counter.keys(), y = counter.values())\n",
        "\n",
        "fig.update_layout(\n",
        "    title = 'Class Distribution',\n",
        "    xaxis_title = 'Classes',\n",
        "    yaxis_title = 'Count', \n",
        "    height = 500, \n",
        "    width = 600\n",
        ")\n",
        "\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vCNu4M0j9cMi",
        "outputId": "e9533148-20a9-45cf-b97d-5781ca88675f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Using box plots to show the distribution of values for all numerical variables \n",
        "fig3 = make_subplots(rows = 2, cols = 4)\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['Age'], name = 'Age'),\n",
        "    row = 1, \n",
        "    col = 1\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['TSH'], name = 'TSH'),\n",
        "    row = 1, \n",
        "    col = 2\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['T3'], name = 'T3'),\n",
        "    row = 1, \n",
        "    col = 3\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['TT4'], name = 'TT4'),\n",
        "    row = 1, \n",
        "    col = 4\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['T4U'], name = 'T4U'),\n",
        "    row = 2, \n",
        "    col = 1\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['FTI'], name = 'FTI'),\n",
        "    row = 2, \n",
        "    col = 2\n",
        ")\n",
        "fig3.add_trace(\n",
        "    go.Box(y = thyroid_data['TBG'], name = 'TBG'),\n",
        "    row = 2, \n",
        "    col = 3, \n",
        ")\n",
        "fig3.update_layout(height = 800, \n",
        "                  width = 1000, \n",
        "                  title_text = 'Distribution of Numerical Variables', \n",
        "                  title_x = 0.5)\n",
        "fig3.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yFYmXRXmqqhy",
        "outputId": "90937da8-df35-44b8-d97b-5c1c0b501c3d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age Sex On_Thyroxine Query_On_Thyroxine On_Antithyroid_Medication Sick  \\\n",
              "0   29   F            f                  f                         f    f   \n",
              "1   29   F            f                  f                         f    f   \n",
              "2   41   F            f                  f                         f    f   \n",
              "3   36   F            f                  f                         f    f   \n",
              "5   60   F            f                  f                         f    f   \n",
              "\n",
              "  Pregnant Thyroid_Surgery I131_Treatment Query_Hypothyroid  ...   T3  \\\n",
              "0        f               f              f                 t  ...  0.0   \n",
              "1        f               f              f                 f  ...  1.9   \n",
              "2        f               f              f                 f  ...  0.0   \n",
              "3        f               f              f                 f  ...  0.0   \n",
              "5        f               f              f                 f  ...  0.0   \n",
              "\n",
              "  TT4_Measured    TT4 T4U_Measured  T4U FTI_Measured  FTI  TBG_Measured   TBG  \\\n",
              "0            f    0.0            f  0.0            f  0.0             f   0.0   \n",
              "1            t  128.0            f  0.0            f  0.0             f   0.0   \n",
              "2            f    0.0            f  0.0            f  0.0             t  11.0   \n",
              "3            f    0.0            f  0.0            f  0.0             t  26.0   \n",
              "5            f    0.0            f  0.0            f  0.0             t  26.0   \n",
              "\n",
              "   Class  \n",
              "0      2  \n",
              "1      2  \n",
              "2      2  \n",
              "3      2  \n",
              "5      2  \n",
              "\n",
              "[5 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b43a366-725c-47bd-982d-c6525fe0238b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Sex</th>\n",
              "      <th>On_Thyroxine</th>\n",
              "      <th>Query_On_Thyroxine</th>\n",
              "      <th>On_Antithyroid_Medication</th>\n",
              "      <th>Sick</th>\n",
              "      <th>Pregnant</th>\n",
              "      <th>Thyroid_Surgery</th>\n",
              "      <th>I131_Treatment</th>\n",
              "      <th>Query_Hypothyroid</th>\n",
              "      <th>...</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",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\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>t</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>...</td>\n",
              "      <td>1.9</td>\n",
              "      <td>t</td>\n",
              "      <td>128.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</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>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>t</td>\n",
              "      <td>11.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>36</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>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>t</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>60</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>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>f</td>\n",
              "      <td>0.0</td>\n",
              "      <td>t</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
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          "metadata": {},
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      "source": [
        "thyroid_data.head()"
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          "metadata": {}
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      ],
      "source": [
        "# What percentage of males/females are affected by thyroid disorders in this dataset? Do these values align with the number of males/females affected by thyroid problems worldwide?\n",
        "df1 = thyroid_data[thyroid_data['Class'].isin([0, 1])]\n",
        "df1 = df1.groupby(['Class', 'Sex'])['Sex'].count().reset_index(name = 'Count')\n",
        "\n",
        "# Mapping label encoded values to actual class values for pie chart labeling\n",
        "mapping = {0: 'Hyperthyroid', 1: 'Hypothyroid'}\n",
        "df1['Class'] = df1['Class'].replace(mapping)\n",
        "\n",
        "fig = px.pie(df1, values = 'Count', names = 'Sex', hole = 0.3)\n",
        "fig.update_layout(title = '% of Males Vs. Females Affected by Thyroid Disorders')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-daCunohG9Bv"
      },
      "source": [
        "### Model Exploration & Selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mecrrIgl-USj"
      },
      "source": [
        "#### Feature Selection & Data Splitting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vn70WyWJ7hLf",
        "outputId": "3c7efc5d-9d2e-40e2-df55-34cfdfe35724"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TSH: 0.5763323005012321\n",
            "FTI: 0.10985044524127083\n",
            "TT4: 0.10901560600161643\n",
            "T3: 0.06753269308898792\n",
            "T4U: 0.03485316169250359\n",
            "Age: 0.022296489263361353\n"
          ]
        }
      ],
      "source": [
        "# Performing feature selection to get the most optimal features for our model\n",
        "rf = RandomForestClassifier()\n",
        "hyper_rf = rf.fit(processed_df, thyroid_data['Class'])\n",
        "imp = hyper_rf.feature_importances_\n",
        "sorted_features = sorted(zip(imp, processed_df), reverse=True)\n",
        "\n",
        "features = []\n",
        "for importance, feature in sorted_features[:6]:\n",
        "  features.append(feature)\n",
        "  print(\"{}: {}\".format(feature, importance))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A_MqEd2BA4wT"
      },
      "source": [
        "With the help of random forest classifier for getting the importance of features in our dataset, we were able to conclude that TSH, TT4, FTI, T3, T4U, Age, On_Thyroxine_f, Query_Hyperthyroid, T4U_Measured_t, TSH_Measured_t are the ten most important features. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "RWbk4Qab9uh8"
      },
      "outputs": [],
      "source": [
        "# Based on feature selection results, keeping top 10 most important features\n",
        "X = processed_df.loc[:, processed_df.columns.isin(features)]\n",
        "y = thyroid_data['Class']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "oc0HsHmpiX2s"
      },
      "outputs": [],
      "source": [
        "# Splitting data into training and testing sets \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 1) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7y-gvIY_XG8k"
      },
      "source": [
        "#### K-Nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAzW70SZ2eYZ",
        "outputId": "fd6a8eb7-2f0e-4565-b522-41c3a78a5a93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9448568398727466\n",
            "Precision:  0.9458184095343478\n",
            "Recall:  0.9448568398727466\n",
            "F1 Score:  0.9395889470499865\n"
          ]
        }
      ],
      "source": [
        "# Implementing KNN with grid search cv  \n",
        "param_grid = {'n_neighbors': range(1, 11)}\n",
        "\n",
        "knn = KNeighborsClassifier()\n",
        "knn_gridsearch = GridSearchCV(knn, param_grid, cv = 5)\n",
        "knn_gridsearch.fit(X_train, y_train)\n",
        "y_pred1 = knn_gridsearch.predict(X_test)\n",
        "\n",
        "# Evaluating model performance \n",
        "knn_acc = accuracy_score(y_test, y_pred1)\n",
        "knn_pre = precision_score(y_test, y_pred1, average = 'weighted')\n",
        "knn_rec = recall_score(y_test, y_pred1, average = 'weighted')\n",
        "knn_f1 = f1_score(y_test, y_pred1, average = 'weighted')\n",
        "\n",
        "print('Accuracy: ', knn_acc)\n",
        "print('Precision: ', knn_pre)\n",
        "print('Recall: ', knn_rec)\n",
        "print('F1 Score: ', knn_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUZNoekvXQ_C"
      },
      "source": [
        "#### Support Vector Machine\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aLxPnvOU3Dwz",
        "outputId": "950fedd4-3f2b-4903-d640-63562ba19e41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9734888653234358\n",
            "Precision:  0.9739263893700045\n",
            "Recall:  0.9734888653234358\n",
            "F1 Score:  0.9734978902347312\n"
          ]
        }
      ],
      "source": [
        "# Define the hyperparameter grid for tuning\n",
        "param_grid = {'C': [0.1, 1, 10],  # Values for the regularization parameter C\n",
        "              'kernel': ['linear', 'rbf'],  # Kernels to try: linear and radial basis function (RBF)\n",
        "              'gamma': ['scale', 'auto'] + [0.1, 1, 10]}  # Values for the gamma parameter\n",
        "\n",
        "# Create an instance of SVM classifier\n",
        "svm = SVC()\n",
        "\n",
        "# Create GridSearchCV with SVM classifier and the hyperparameter grid\n",
        "grid_search = GridSearchCV(svm, param_grid, cv=5)  # Use 5-fold cross-validation\n",
        "\n",
        "# Fit GridSearchCV to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters from GridSearchCV\n",
        "best_params = grid_search.best_params_\n",
        "best_C = best_params['C']\n",
        "best_kernel = best_params['kernel']\n",
        "best_gamma = best_params['gamma']\n",
        "\n",
        "# Create a new instance of SVM classifier with the best hyperparameters\n",
        "best_svm = SVC(C=best_C, kernel=best_kernel, gamma=best_gamma)\n",
        "\n",
        "# Fit the best SVM classifier to the training data\n",
        "best_svm.fit(X_train, y_train)\n",
        "\n",
        "# Make predictions on the test data\n",
        "y_pred2 = best_svm.predict(X_test)\n",
        "\n",
        "# Computing the accuracy of the classifier\n",
        "svm_acc = accuracy_score(y_test, y_pred2)\n",
        "svm_pre = precision_score(y_test, y_pred2, average = 'weighted')\n",
        "svm_rec = recall_score(y_test, y_pred2, average = 'weighted')\n",
        "svm_f1 = f1_score(y_test, y_pred2, average = 'weighted')\n",
        "\n",
        "print('Accuracy: ', svm_acc)\n",
        "print('Precision: ', svm_pre)\n",
        "print('Recall: ', svm_rec)\n",
        "print('F1 Score: ', svm_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4-hkDdpDJ81"
      },
      "source": [
        "#### Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7nY7DKBieMCu",
        "outputId": "85e42cba-ee30-421b-bc12-1f851e5fc23c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters: {'criterion': 'entropy', 'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
            "Accuracy: 0.9814422057264051\n",
            "Precision: 0.982893934283357\n",
            "Recall: 0.9814422057264051\n",
            "F1 Score: 0.9820378064517467\n"
          ]
        }
      ],
      "source": [
        "# Instantiating Decision Trees object\n",
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "# Defining hyperparameter grid for grid search\n",
        "param_grid = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [None, 10, 20, 30],\n",
        "    'min_samples_split': [2, 5, 10],\n",
        "    'min_samples_leaf': [1, 2, 4]\n",
        "}\n",
        "\n",
        "# Performing grid search with cross-validation\n",
        "grid_search = GridSearchCV(dt, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Extracting best parameters and best model from grid search\n",
        "best_params = grid_search.best_params_\n",
        "best_dt = grid_search.best_estimator_\n",
        "\n",
        "# Predicting new values for target class using best model\n",
        "y_pred3 = best_dt.predict(X_test)\n",
        "\n",
        "# Computing the accuracy of the classifier\n",
        "dt_acc = accuracy_score(y_test, y_pred3)\n",
        "dt_pre = precision_score(y_test, y_pred3, average='weighted')\n",
        "dt_rec = recall_score(y_test, y_pred3, average='weighted')\n",
        "dt_f1 = f1_score(y_test, y_pred3, average='weighted')\n",
        "\n",
        "print('Best Parameters:', best_params)\n",
        "print('Accuracy:', dt_acc)\n",
        "print('Precision:', dt_pre)\n",
        "print('Recall:', dt_rec)\n",
        "print('F1 Score:', dt_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbCjn5QoDNVl"
      },
      "source": [
        "#### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dd7-cMorJqe",
        "outputId": "2531923c-8160-4689-c5be-8b00c2aaa161"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters:  {'C': 100, 'penalty': 'l2'}\n",
            "Accuracy:  0.9655355249204666\n",
            "Precision:  0.9639575466946768\n",
            "Recall:  0.9655355249204666\n",
            "F1 Score:  0.9638462360020006\n"
          ]
        }
      ],
      "source": [
        "# Instantiate logistic regression object\n",
        "lr = LogisticRegression()\n",
        "\n",
        "# Define hyperparameter grid for grid search\n",
        "param_grid = {'penalty': ['l1', 'l2'], \n",
        "              'C': [0.01, 0.1, 1, 10, 100]}\n",
        "\n",
        "# Perform grid search cross-validation\n",
        "grid_search = GridSearchCV(lr, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get best hyperparameters and fit model with best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "best_lr = LogisticRegression(penalty=best_params['penalty'], C=best_params['C'])\n",
        "best_lr.fit(X_train, y_train)\n",
        "\n",
        "# Predict on test set\n",
        "y_pred4 = best_lr.predict(X_test)\n",
        "\n",
        "# Compute accuracy, precision, recall, and F1 score\n",
        "lr_acc = accuracy_score(y_test, y_pred4)\n",
        "lr_pre = precision_score(y_test, y_pred4, average='weighted')\n",
        "lr_rec = recall_score(y_test, y_pred4, average='weighted')\n",
        "lr_f1 = f1_score(y_test, y_pred4, average='weighted')\n",
        "\n",
        "print('Best Hyperparameters: ', best_params)\n",
        "print('Accuracy: ', lr_acc)\n",
        "print('Precision: ', lr_pre)\n",
        "print('Recall: ', lr_rec)\n",
        "print('F1 Score: ', lr_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HCFITK7rT0Z_"
      },
      "source": [
        "#### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ips9uD2RT3IY",
        "outputId": "17893821-cea4-4997-de86-7449ed676c9d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.957051961823966\n",
            "Precision:  0.956060607919178\n",
            "Recall:  0.957051961823966\n",
            "F1 Score:  0.9551409817034863\n"
          ]
        }
      ],
      "source": [
        "# Instantiating naive bayes object\n",
        "nb = GaussianNB()\n",
        "\n",
        "# Training model with data for hyperthyroid\n",
        "nb.fit(X_train, y_train)\n",
        "\n",
        "# Predicting new values for hyperthyroid \n",
        "y_pred5 = nb.predict(X_test)\n",
        "\n",
        "# Evaluating the classifier\n",
        "nb_acc = accuracy_score(y_test, y_pred5)\n",
        "nb_pre = precision_score(y_test, y_pred5, average = 'weighted')\n",
        "nb_rec = recall_score(y_test, y_pred5, average = 'weighted')\n",
        "nb_f1 = f1_score(y_test, y_pred5, average = 'weighted')\n",
        "\n",
        "print('Accuracy: ', nb_acc)\n",
        "print('Precision: ', nb_pre)\n",
        "print('Recall: ', nb_rec)\n",
        "print('F1 Score: ', nb_f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--H7yjlHlDLM"
      },
      "source": [
        "#### Multi-layer Perceptron"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40TCbll0lhMK",
        "outputId": "9bdc4c0f-92b7-4d91-f478-19a5c63beed8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters:  {'activation': 'relu', 'alpha': 0.0001, 'batch_size': 32, 'hidden_layer_sizes': (32, 16), 'max_iter': 200, 'random_state': 42}\n",
            "Accuracy:  0.9798515376458112\n",
            "Precision:  0.9820133318465822\n",
            "Recall:  0.9798515376458112\n",
            "F1-score:  0.980645684591132\n"
          ]
        }
      ],
      "source": [
        "# Define MLP Classifier\n",
        "mlp = MLPClassifier()\n",
        "\n",
        "# Define hyperparameters and their possible values for tuning\n",
        "param_grid = {\n",
        "    'hidden_layer_sizes': [(64,), (32, 16,), (16, 8,)],\n",
        "    'activation': ['relu', 'tanh'],\n",
        "    'alpha': [0.0001, 0.001, 0.01],\n",
        "    'batch_size': [32, 64],\n",
        "    'max_iter': [100, 200],\n",
        "    'random_state': [42]\n",
        "}\n",
        "\n",
        "# Perform GridSearchCV for hyperparameter tuning\n",
        "grid_search = GridSearchCV(mlp, param_grid, cv=5)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_params = grid_search.best_params_\n",
        "\n",
        "# Train the MLP Model with the best hyperparameters\n",
        "best_mlp = MLPClassifier(**best_params)\n",
        "best_mlp.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate the MLP Model\n",
        "y_pred6 = best_mlp.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, y_pred6)\n",
        "precision = precision_score(y_test, y_pred6, average='weighted')\n",
        "recall = recall_score(y_test, y_pred6, average='weighted')\n",
        "f1 = f1_score(y_test, y_pred6, average='weighted')\n",
        "\n",
        "print('Best Hyperparameters: ', best_params)\n",
        "print('Accuracy: ', accuracy)\n",
        "print('Precision: ', precision)\n",
        "print('Recall: ', recall)\n",
        "print('F1-score: ', f1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cYZug8Ijkyb"
      },
      "source": [
        "#### Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "LDqKdo1LjbcM",
        "outputId": "7a320bfc-8fd2-4ff9-e4df-c755d696ba0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Algorithm  Accuracy  Precision    Recall  F1 Score\n",
              "0     K-Nearest Neighbors  0.944857   0.945818  0.944857  0.939589\n",
              "1  Support Vector Machine  0.973489   0.973926  0.973489  0.973498\n",
              "2          Decision Trees  0.981442   0.982894  0.981442  0.982038\n",
              "3     Logistic Regression  0.965536   0.963958  0.965536  0.963846\n",
              "4             Naive Bayes  0.957052   0.956061  0.957052  0.955141\n",
              "5  Multi-layer Perceptron  0.979852   0.982013  0.979852  0.980646"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1 Score</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbors</td>\n",
              "      <td>0.944857</td>\n",
              "      <td>0.945818</td>\n",
              "      <td>0.944857</td>\n",
              "      <td>0.939589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Support Vector Machine</td>\n",
              "      <td>0.973489</td>\n",
              "      <td>0.973926</td>\n",
              "      <td>0.973489</td>\n",
              "      <td>0.973498</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Trees</td>\n",
              "      <td>0.981442</td>\n",
              "      <td>0.982894</td>\n",
              "      <td>0.981442</td>\n",
              "      <td>0.982038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.965536</td>\n",
              "      <td>0.963958</td>\n",
              "      <td>0.965536</td>\n",
              "      <td>0.963846</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.957052</td>\n",
              "      <td>0.956061</td>\n",
              "      <td>0.957052</td>\n",
              "      <td>0.955141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Multi-layer Perceptron</td>\n",
              "      <td>0.979852</td>\n",
              "      <td>0.982013</td>\n",
              "      <td>0.979852</td>\n",
              "      <td>0.980646</td>\n",
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          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        " # Summarizing model performance\n",
        "algo = ['K-Nearest Neighbors', 'Support Vector Machine', 'Decision Trees', 'Logistic Regression', 'Naive Bayes', 'Multi-layer Perceptron']\n",
        "acc = [knn_acc, svm_acc, dt_acc, lr_acc, nb_acc, accuracy]\n",
        "pre = [knn_pre, svm_pre, dt_pre, lr_pre, nb_pre, precision]\n",
        "rec = [knn_rec, svm_rec, dt_rec, lr_rec, nb_rec, recall]\n",
        "f1_score = [knn_f1, svm_f1, dt_f1, lr_f1, nb_f1, f1]\n",
        "\n",
        "val_dict = {'Algorithm': algo, 'Accuracy': acc, 'Precision': pre, 'Recall': rec, 'F1 Score': f1_score}\n",
        "val_df = pd.DataFrame(val_dict)\n",
        "val_df"
      ]
    }
  ],
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