PiCOS_Data_Cleaning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Import Library"
      ],
      "metadata": {
        "id": "bF9FqFklVTps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(style='whitegrid')\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "SFP9EVkSVYEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import DATASET"
      ],
      "metadata": {
        "id": "v9ogi74rXHbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_excel(\"/content/PCOS_data_without_infertility.xlsx\",sheet_name=\"Full_new\")\n",
        "print(df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FPdE7ytWrmQ",
        "outputId": "6ad1da00-08d9-4782-8b6b-9edd4ae1bdb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(541, 45)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "7nb_uoTqXEjw",
        "outputId": "61f9d3d4-0321-49b9-99c6-4ffd6dcbe04a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sl. No  Patient File No.  PCOS (Y/N)   Age (yrs)  Weight (Kg)  Height(Cm)   \\\n",
              "0       1                 1           0          28         44.6        152.0   \n",
              "1       2                 2           0          36         65.0        161.5   \n",
              "2       3                 3           1          33         68.8        165.0   \n",
              "3       4                 4           0          37         65.0        148.0   \n",
              "4       5                 5           0          25         52.0        161.0   \n",
              "\n",
              "         BMI  Blood Group  Pulse rate(bpm)   RR (breaths/min)  ...  \\\n",
              "0  19.300000           15                78                22  ...   \n",
              "1  24.921163           15                74                20  ...   \n",
              "2  25.270891           11                72                18  ...   \n",
              "3  29.674945           13                72                20  ...   \n",
              "4  20.060954           11                72                18  ...   \n",
              "\n",
              "   Fast food (Y/N)  Reg.Exercise(Y/N)  BP _Systolic (mmHg)  \\\n",
              "0              1.0                  0                  110   \n",
              "1              0.0                  0                  120   \n",
              "2              1.0                  0                  120   \n",
              "3              0.0                  0                  120   \n",
              "4              0.0                  0                  120   \n",
              "\n",
              "   BP _Diastolic (mmHg)  Follicle No. (L)  Follicle No. (R)  \\\n",
              "0                    80                 3                 3   \n",
              "1                    70                 3                 5   \n",
              "2                    80                13                15   \n",
              "3                    70                 2                 2   \n",
              "4                    80                 3                 4   \n",
              "\n",
              "   Avg. F size (L) (mm) Avg. F size (R) (mm)  Endometrium (mm)  Unnamed: 44  \n",
              "0                  18.0                 18.0               8.5          NaN  \n",
              "1                  15.0                 14.0               3.7          NaN  \n",
              "2                  18.0                 20.0              10.0          NaN  \n",
              "3                  15.0                 14.0               7.5          NaN  \n",
              "4                  16.0                 14.0               7.0          NaN  \n",
              "\n",
              "[5 rows x 45 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ae675ac6-99fe-4390-8e26-8466711d3bd4\">\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>Sl. No</th>\n",
              "      <th>Patient File No.</th>\n",
              "      <th>PCOS (Y/N)</th>\n",
              "      <th>Age (yrs)</th>\n",
              "      <th>Weight (Kg)</th>\n",
              "      <th>Height(Cm)</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Blood Group</th>\n",
              "      <th>Pulse rate(bpm)</th>\n",
              "      <th>RR (breaths/min)</th>\n",
              "      <th>...</th>\n",
              "      <th>Fast food (Y/N)</th>\n",
              "      <th>Reg.Exercise(Y/N)</th>\n",
              "      <th>BP _Systolic (mmHg)</th>\n",
              "      <th>BP _Diastolic (mmHg)</th>\n",
              "      <th>Follicle No. (L)</th>\n",
              "      <th>Follicle No. (R)</th>\n",
              "      <th>Avg. F size (L) (mm)</th>\n",
              "      <th>Avg. F size (R) (mm)</th>\n",
              "      <th>Endometrium (mm)</th>\n",
              "      <th>Unnamed: 44</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>44.6</td>\n",
              "      <td>152.0</td>\n",
              "      <td>19.300000</td>\n",
              "      <td>15</td>\n",
              "      <td>78</td>\n",
              "      <td>22</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>65.0</td>\n",
              "      <td>161.5</td>\n",
              "      <td>24.921163</td>\n",
              "      <td>15</td>\n",
              "      <td>74</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>68.8</td>\n",
              "      <td>165.0</td>\n",
              "      <td>25.270891</td>\n",
              "      <td>11</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>13</td>\n",
              "      <td>15</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>65.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>29.674945</td>\n",
              "      <td>13</td>\n",
              "      <td>72</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>70</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>7.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>52.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>20.060954</td>\n",
              "      <td>11</td>\n",
              "      <td>72</td>\n",
              "      <td>18</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>16.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 45 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae675ac6-99fe-4390-8e26-8466711d3bd4')\"\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-ae675ac6-99fe-4390-8e26-8466711d3bd4 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-ae675ac6-99fe-4390-8e26-8466711d3bd4');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCYrzEBDaDtO",
        "outputId": "5edf6c0e-92ad-4e23-9964-ec6e5d4e65fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541 entries, 0 to 540\n",
            "Data columns (total 45 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   Sl. No                  541 non-null    int64  \n",
            " 1   Patient File No.        541 non-null    int64  \n",
            " 2   PCOS (Y/N)              541 non-null    int64  \n",
            " 3    Age (yrs)              541 non-null    int64  \n",
            " 4   Weight (Kg)             541 non-null    float64\n",
            " 5   Height(Cm)              541 non-null    float64\n",
            " 6   BMI                     541 non-null    float64\n",
            " 7   Blood Group             541 non-null    int64  \n",
            " 8   Pulse rate(bpm)         541 non-null    int64  \n",
            " 9   RR (breaths/min)        541 non-null    int64  \n",
            " 10  Hb(g/dl)                541 non-null    float64\n",
            " 11  Cycle(R/I)              541 non-null    int64  \n",
            " 12  Cycle length(days)      541 non-null    int64  \n",
            " 13  Marraige Status (Yrs)   540 non-null    float64\n",
            " 14  Pregnant(Y/N)           541 non-null    int64  \n",
            " 15  No. of aborptions       541 non-null    int64  \n",
            " 16    I   beta-HCG(mIU/mL)  541 non-null    float64\n",
            " 17  II    beta-HCG(mIU/mL)  541 non-null    object \n",
            " 18  FSH(mIU/mL)             541 non-null    float64\n",
            " 19  LH(mIU/mL)              541 non-null    float64\n",
            " 20  FSH/LH                  541 non-null    float64\n",
            " 21  Hip(inch)               541 non-null    int64  \n",
            " 22  Waist(inch)             541 non-null    int64  \n",
            " 23  Waist:Hip Ratio         541 non-null    float64\n",
            " 24  TSH (mIU/L)             541 non-null    float64\n",
            " 25  AMH(ng/mL)              541 non-null    object \n",
            " 26  PRL(ng/mL)              541 non-null    float64\n",
            " 27  Vit D3 (ng/mL)          541 non-null    float64\n",
            " 28  PRG(ng/mL)              541 non-null    float64\n",
            " 29  RBS(mg/dl)              541 non-null    float64\n",
            " 30  Weight gain(Y/N)        541 non-null    int64  \n",
            " 31  hair growth(Y/N)        541 non-null    int64  \n",
            " 32  Skin darkening (Y/N)    541 non-null    int64  \n",
            " 33  Hair loss(Y/N)          541 non-null    int64  \n",
            " 34  Pimples(Y/N)            541 non-null    int64  \n",
            " 35  Fast food (Y/N)         540 non-null    float64\n",
            " 36  Reg.Exercise(Y/N)       541 non-null    int64  \n",
            " 37  BP _Systolic (mmHg)     541 non-null    int64  \n",
            " 38  BP _Diastolic (mmHg)    541 non-null    int64  \n",
            " 39  Follicle No. (L)        541 non-null    int64  \n",
            " 40  Follicle No. (R)        541 non-null    int64  \n",
            " 41  Avg. F size (L) (mm)    541 non-null    float64\n",
            " 42  Avg. F size (R) (mm)    541 non-null    float64\n",
            " 43  Endometrium (mm)        541 non-null    float64\n",
            " 44  Unnamed: 44             2 non-null      object \n",
            "dtypes: float64(19), int64(23), object(3)\n",
            "memory usage: 190.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"AMH(ng/mL)\"] = pd.to_numeric(df[\"AMH(ng/mL)\"], errors=\"coerce\") \n",
        "df[\"II    beta-HCG(mIU/mL)\"] = pd.to_numeric(df[\"II    beta-HCG(mIU/mL)\"], errors=\"coerce\")"
      ],
      "metadata": {
        "id": "WQDqnwIYaKYZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns=lambda x: x.strip())\n",
        "df = df.drop(['Unnamed: 44'], axis = 1) \n",
        "df = df.drop(['Sl. No'], axis = 1) \n",
        "df = df.drop(['Patient File No.'], axis = 1) \n"
      ],
      "metadata": {
        "id": "DhwX_HE2b1qB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df= df.drop(['Pulse rate(bpm)','RR (breaths/min)', 'Hb(g/dl)', 'I   beta-HCG(mIU/mL)', 'II    beta-HCG(mIU/mL)'], axis=1)"
      ],
      "metadata": {
        "id": "P0TUmJuSWXKF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['FSH(mIU/mL)', 'LH(mIU/mL)','FSH/LH', 'TSH (mIU/L)', 'AMH(ng/mL)'], axis=1)"
      ],
      "metadata": {
        "id": "cRhmbSWtWeo5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['PRL(ng/mL)', 'Vit D3 (ng/mL)',  'PRG(ng/mL)', 'RBS(mg/dl)', 'BP _Systolic (mmHg)', 'BP _Diastolic (mmHg)'], axis=1)"
      ],
      "metadata": {
        "id": "Y9D-_KZ7Wz38"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Follicle No. (L)', 'Follicle No. (R)'], axis=1) "
      ],
      "metadata": {
        "id": "sXvxm308W9JC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Avg. F size (L) (mm)', 'Avg. F size (R) (mm)', 'Endometrium (mm)'], axis=1)"
      ],
      "metadata": {
        "id": "__xnkOcdXK_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "7-WmRPQNcGbx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "3114e9b8-b66c-4dba-f27b-966be664a2b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PCOS (Y/N)  Age (yrs)  Weight (Kg)  Height(Cm)        BMI  Blood Group  \\\n",
              "0           0         28         44.6       152.0  19.300000           15   \n",
              "1           0         36         65.0       161.5  24.921163           15   \n",
              "2           1         33         68.8       165.0  25.270891           11   \n",
              "3           0         37         65.0       148.0  29.674945           13   \n",
              "4           0         25         52.0       161.0  20.060954           11   \n",
              "\n",
              "   Cycle(R/I)  Cycle length(days)  Marraige Status (Yrs)  Pregnant(Y/N)  ...  \\\n",
              "0           2                   5                    7.0              0  ...   \n",
              "1           2                   5                   11.0              1  ...   \n",
              "2           2                   5                   10.0              1  ...   \n",
              "3           2                   5                    4.0              0  ...   \n",
              "4           2                   5                    1.0              1  ...   \n",
              "\n",
              "   Hip(inch)  Waist(inch)  Waist:Hip Ratio  Weight gain(Y/N)  \\\n",
              "0         36           30         0.833333                 0   \n",
              "1         38           32         0.842105                 0   \n",
              "2         40           36         0.900000                 0   \n",
              "3         42           36         0.857143                 0   \n",
              "4         37           30         0.810811                 0   \n",
              "\n",
              "   hair growth(Y/N)  Skin darkening (Y/N)  Hair loss(Y/N)  Pimples(Y/N)  \\\n",
              "0                 0                     0               0             0   \n",
              "1                 0                     0               0             0   \n",
              "2                 0                     0               1             1   \n",
              "3                 0                     0               0             0   \n",
              "4                 0                     0               1             0   \n",
              "\n",
              "   Fast food (Y/N)  Reg.Exercise(Y/N)  \n",
              "0              1.0                  0  \n",
              "1              0.0                  0  \n",
              "2              1.0                  0  \n",
              "3              0.0                  0  \n",
              "4              0.0                  0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cd8d775a-4be5-4c3a-a6c6-3e1326328004\">\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>PCOS (Y/N)</th>\n",
              "      <th>Age (yrs)</th>\n",
              "      <th>Weight (Kg)</th>\n",
              "      <th>Height(Cm)</th>\n",
              "      <th>BMI</th>\n",
              "      <th>Blood Group</th>\n",
              "      <th>Cycle(R/I)</th>\n",
              "      <th>Cycle length(days)</th>\n",
              "      <th>Marraige Status (Yrs)</th>\n",
              "      <th>Pregnant(Y/N)</th>\n",
              "      <th>...</th>\n",
              "      <th>Hip(inch)</th>\n",
              "      <th>Waist(inch)</th>\n",
              "      <th>Waist:Hip Ratio</th>\n",
              "      <th>Weight gain(Y/N)</th>\n",
              "      <th>hair growth(Y/N)</th>\n",
              "      <th>Skin darkening (Y/N)</th>\n",
              "      <th>Hair loss(Y/N)</th>\n",
              "      <th>Pimples(Y/N)</th>\n",
              "      <th>Fast food (Y/N)</th>\n",
              "      <th>Reg.Exercise(Y/N)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>44.6</td>\n",
              "      <td>152.0</td>\n",
              "      <td>19.300000</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>36</td>\n",
              "      <td>30</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>65.0</td>\n",
              "      <td>161.5</td>\n",
              "      <td>24.921163</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>32</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>68.8</td>\n",
              "      <td>165.0</td>\n",
              "      <td>25.270891</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>40</td>\n",
              "      <td>36</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>65.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>29.674945</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>42</td>\n",
              "      <td>36</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>52.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>20.060954</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>37</td>\n",
              "      <td>30</td>\n",
              "      <td>0.810811</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cd8d775a-4be5-4c3a-a6c6-3e1326328004')\"\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-cd8d775a-4be5-4c3a-a6c6-3e1326328004 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-cd8d775a-4be5-4c3a-a6c6-3e1326328004');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lst_missing_columns = df.columns[df.isna().any()].tolist()\n",
        "lst_missing_columns"
      ],
      "metadata": {
        "id": "udl553jkcUUt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "207e79ee-8289-4ce6-bb8e-504e8a63d0f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Marraige Status (Yrs)', 'Fast food (Y/N)']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in lst_missing_columns:\n",
        "    df[x] = df[x].fillna(df[x].median())"
      ],
      "metadata": {
        "id": "JtZj0t5fcc5O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns[df.isna().any()].tolist()"
      ],
      "metadata": {
        "id": "Q9yzGMK3chp3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc3fff05-227e-4870-db95-c1612649ce74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Blood Group'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GLLAXao0ceps",
        "outputId": "a338fb57-f5c5-417c-99c9-97779257a28b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15, 11, 13, 17, 16, 12, 14, 18])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_data = df"
      ],
      "metadata": {
        "id": "VEDl264zYIMa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "excel_file_path = '/content/PiCOS_cleaned_data.xlsx'\n",
        "\n",
        "# Create an ExcelWriter object\n",
        "excel_writer = pd.ExcelWriter(excel_file_path)\n",
        "\n",
        "# Export the DataFrame to Excel\n",
        "clean_data.to_excel(excel_writer, sheet_name='Sheet1', index=False)\n",
        "\n",
        "# Save the Excel file\n",
        "excel_writer.save()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42lWEuLCZbee",
        "outputId": "6b72343d-fe4c-46f9-e69e-69b291add815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-15e0aa016502>:10: FutureWarning: save is not part of the public API, usage can give unexpected results and will be removed in a future version\n",
            "  excel_writer.save()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v0tLtXXcZPOa"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}