Liver Health Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/100-Super-Notebooks/blob/main/Liver%20Health%20Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "94e442bb-e3e6-4afb-9b37-5bc478db5f4f",
        "_uuid": "51898d40216d04490c572d5ac7598091b73d3615",
        "id": "Cue-Il4K4Sn2"
      },
      "cell_type": "markdown",
      "source": [
        "# Analysis and Prediction-Indian Liver Patients."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "77c7f1f3-9467-49d9-a113-b3b345b08110",
        "_uuid": "c8034ad4f2e2bb5d1c932c32232753c82a6b0e33",
        "id": "05q-a2dD4Sn6"
      },
      "cell_type": "markdown",
      "source": [
        "The dataset was downloaded from the UCI ML Repository.\n",
        "\n",
        "Here are the steps I'm going to perform:  \n",
        "1) Data Analysis: This is in general looking at the data to figure out whats going on. Inspect the data: Check whether there is any missing data, irrelevant data and do a cleanup.  \n",
        "2) Data Visualization:   \n",
        "3) Feature selection.  \n",
        "4) Search for any trends, relations & correlations.  \n",
        "5) Draw an inference and predict whether the patient can be identified to be having liver disease or not  "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "ead33c46-262f-428c-a300-5372b4f15a7a",
        "_uuid": "2cfb47f6cfc64578a9bff30d0e5ec6ab95a8e06b",
        "collapsed": true,
        "trusted": true,
        "id": "DxKR_gL34Sn7"
      },
      "cell_type": "code",
      "source": [
        "#Import all required libraries for reading data, analysing and visualizing data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "d58aeb11-2795-4ba6-8dd1-f48160dec59c",
        "_uuid": "3b99a454b4a233394a3fa3c8a5190b198cbc84ba",
        "id": "VeVyXB3E4Sn8"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Analysis"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "518b7cef-cafc-433e-94e5-ed91f9b70ba6",
        "_uuid": "104d9fe12aae79b3eff8c1ad92e432a77a2b92bc",
        "collapsed": true,
        "trusted": true,
        "id": "bAVeaoM44Sn9"
      },
      "cell_type": "code",
      "source": [
        "#Read the training & test data\n",
        "liver_df = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets/master/indian_liver_patient.csv')"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "04286350-1e1c-46ac-9099-d29585d30287",
        "_uuid": "897e4e62d506ee354a42d13005f604da99f12e59",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "m2iMgArF4Sn9",
        "outputId": "d1d5a6d4-1d2e-43a7-86de-112396c45f19"
      },
      "cell_type": "code",
      "source": [
        "liver_df.head()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "0   65  Female              0.7               0.1                   187   \n",
              "1   62    Male             10.9               5.5                   699   \n",
              "2   62    Male              7.3               4.1                   490   \n",
              "3   58    Male              1.0               0.4                   182   \n",
              "4   72    Male              3.9               2.0                   195   \n",
              "\n",
              "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "0                        16                          18             6.8   \n",
              "1                        64                         100             7.5   \n",
              "2                        60                          68             7.0   \n",
              "3                        14                          20             6.8   \n",
              "4                        27                          59             7.3   \n",
              "\n",
              "   Albumin  Albumin_and_Globulin_Ratio  Outcome  \n",
              "0      3.3                        0.90        1  \n",
              "1      3.2                        0.74        1  \n",
              "2      3.3                        0.89        1  \n",
              "3      3.4                        1.00        1  \n",
              "4      2.4                        0.40        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ae0b41d-715d-4a2a-b5ab-ca745e1bd72f\">\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>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>182</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>72</td>\n",
              "      <td>Male</td>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>195</td>\n",
              "      <td>27</td>\n",
              "      <td>59</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ae0b41d-715d-4a2a-b5ab-ca745e1bd72f')\"\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-3ae0b41d-715d-4a2a-b5ab-ca745e1bd72f 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-3ae0b41d-715d-4a2a-b5ab-ca745e1bd72f');\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": 31
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "9a45eba7-cb42-4ac8-bd56-e75fee941808",
        "_uuid": "3e60087458159fe381e86febe8f9bca1bd097ff4",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuqeK9V14Sn-",
        "outputId": "d987835b-021f-4db6-e742-495ead450820"
      },
      "cell_type": "code",
      "source": [
        "liver_df.info()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 583 entries, 0 to 582\n",
            "Data columns (total 11 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   Age                         583 non-null    int64  \n",
            " 1   Gender                      583 non-null    object \n",
            " 2   Total_Bilirubin             583 non-null    float64\n",
            " 3   Direct_Bilirubin            583 non-null    float64\n",
            " 4   Alkaline_Phosphotase        583 non-null    int64  \n",
            " 5   Alamine_Aminotransferase    583 non-null    int64  \n",
            " 6   Aspartate_Aminotransferase  583 non-null    int64  \n",
            " 7   Total_Protiens              583 non-null    float64\n",
            " 8   Albumin                     583 non-null    float64\n",
            " 9   Albumin_and_Globulin_Ratio  579 non-null    float64\n",
            " 10  Outcome                     583 non-null    int64  \n",
            "dtypes: float64(5), int64(5), object(1)\n",
            "memory usage: 50.2+ KB\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "aa1480e6-27f0-4073-83c9-9901d677b615",
        "_uuid": "3bc3e850db0d915e15a149433a4a3600ecc59783",
        "id": "FXMoEbNf4Sn_"
      },
      "cell_type": "markdown",
      "source": [
        "Here is the observation from the dataset:   \n",
        "1) Only gender is non-numeric veriable. All others are numeric.   \n",
        "2) There are 10 features and 1 output - dataset. Value 1 indicates that the patient has liver disease and 0 indicates the patient does not have liver disease.  "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7336699b-7ef0-4ee9-9e3d-1f7741ef3692",
        "_uuid": "f98b0529723f1b862688a5738c7afce79365e352",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "dePJTq_T4SoA",
        "outputId": "8e60500b-1a93-4796-db5e-db650f65eb63"
      },
      "cell_type": "code",
      "source": [
        "#NUMERICAL STATISTICS OF THE DATASET\n",
        "liver_df.describe(include='all')\n",
        "#We can see that there are missing values for Albumin_and_Globulin_Ratio as only 579 entries have valid values indicating 4 missing values.\n",
        "#Gender has only 2 values - Male/Female"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Age Gender  Total_Bilirubin  Direct_Bilirubin  \\\n",
              "count   583.000000    583       583.000000        583.000000   \n",
              "unique         NaN      2              NaN               NaN   \n",
              "top            NaN   Male              NaN               NaN   \n",
              "freq           NaN    441              NaN               NaN   \n",
              "mean     44.746141    NaN         3.298799          1.486106   \n",
              "std      16.189833    NaN         6.209522          2.808498   \n",
              "min       4.000000    NaN         0.400000          0.100000   \n",
              "25%      33.000000    NaN         0.800000          0.200000   \n",
              "50%      45.000000    NaN         1.000000          0.300000   \n",
              "75%      58.000000    NaN         2.600000          1.300000   \n",
              "max      90.000000    NaN        75.000000         19.700000   \n",
              "\n",
              "        Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
              "count             583.000000                583.000000   \n",
              "unique                   NaN                       NaN   \n",
              "top                      NaN                       NaN   \n",
              "freq                     NaN                       NaN   \n",
              "mean              290.576329                 80.713551   \n",
              "std               242.937989                182.620356   \n",
              "min                63.000000                 10.000000   \n",
              "25%               175.500000                 23.000000   \n",
              "50%               208.000000                 35.000000   \n",
              "75%               298.000000                 60.500000   \n",
              "max              2110.000000               2000.000000   \n",
              "\n",
              "        Aspartate_Aminotransferase  Total_Protiens     Albumin  \\\n",
              "count                   583.000000      583.000000  583.000000   \n",
              "unique                         NaN             NaN         NaN   \n",
              "top                            NaN             NaN         NaN   \n",
              "freq                           NaN             NaN         NaN   \n",
              "mean                    109.910806        6.483190    3.141852   \n",
              "std                     288.918529        1.085451    0.795519   \n",
              "min                      10.000000        2.700000    0.900000   \n",
              "25%                      25.000000        5.800000    2.600000   \n",
              "50%                      42.000000        6.600000    3.100000   \n",
              "75%                      87.000000        7.200000    3.800000   \n",
              "max                    4929.000000        9.600000    5.500000   \n",
              "\n",
              "        Albumin_and_Globulin_Ratio     Outcome  \n",
              "count                   579.000000  583.000000  \n",
              "unique                         NaN         NaN  \n",
              "top                            NaN         NaN  \n",
              "freq                           NaN         NaN  \n",
              "mean                      0.947064    1.286449  \n",
              "std                       0.319592    0.452490  \n",
              "min                       0.300000    1.000000  \n",
              "25%                       0.700000    1.000000  \n",
              "50%                       0.930000    1.000000  \n",
              "75%                       1.100000    2.000000  \n",
              "max                       2.800000    2.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdac0978-059f-4598-86c3-e7caa05c1e65\">\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>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>583.000000</td>\n",
              "      <td>583</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>579.000000</td>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Male</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>441</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.746141</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.298799</td>\n",
              "      <td>1.486106</td>\n",
              "      <td>290.576329</td>\n",
              "      <td>80.713551</td>\n",
              "      <td>109.910806</td>\n",
              "      <td>6.483190</td>\n",
              "      <td>3.141852</td>\n",
              "      <td>0.947064</td>\n",
              "      <td>1.286449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.189833</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.209522</td>\n",
              "      <td>2.808498</td>\n",
              "      <td>242.937989</td>\n",
              "      <td>182.620356</td>\n",
              "      <td>288.918529</td>\n",
              "      <td>1.085451</td>\n",
              "      <td>0.795519</td>\n",
              "      <td>0.319592</td>\n",
              "      <td>0.452490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>175.500000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>298.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.700000</td>\n",
              "      <td>2110.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4929.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdac0978-059f-4598-86c3-e7caa05c1e65')\"\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-cdac0978-059f-4598-86c3-e7caa05c1e65 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-cdac0978-059f-4598-86c3-e7caa05c1e65');\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": 33
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e379fc6e-e467-47bd-978c-c8eae32f3193",
        "_uuid": "a371d92f66a0641259d6053f2aaf33e3cfb64994",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6qigapr4SoA",
        "outputId": "a449a874-7136-4277-8b3e-741361c34cde"
      },
      "cell_type": "code",
      "source": [
        "#Which features are available in the dataset?\n",
        "liver_df.columns"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin',\n",
              "       'Alkaline_Phosphotase', 'Alamine_Aminotransferase',\n",
              "       'Aspartate_Aminotransferase', 'Total_Protiens', 'Albumin',\n",
              "       'Albumin_and_Globulin_Ratio', 'Outcome'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b6d201e0-a049-462a-a4c3-e66fdd3c1ff1",
        "_uuid": "0ab1acc33cd91286825dba1d5d77fcbf171348d5",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2en13Wqn4SoB",
        "outputId": "49680777-13b1-4b13-cf31-8e9021776ddf"
      },
      "cell_type": "code",
      "source": [
        "#Check for any null values\n",
        "liver_df.isnull().sum()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                           0\n",
              "Gender                        0\n",
              "Total_Bilirubin               0\n",
              "Direct_Bilirubin              0\n",
              "Alkaline_Phosphotase          0\n",
              "Alamine_Aminotransferase      0\n",
              "Aspartate_Aminotransferase    0\n",
              "Total_Protiens                0\n",
              "Albumin                       0\n",
              "Albumin_and_Globulin_Ratio    4\n",
              "Outcome                       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "1d6972b0-3916-4ec4-9c4e-050b4be86f0d",
        "_uuid": "5d2737231e0a088725b3403778a48e9c0ba2c629",
        "id": "Rz9wDI_m4SoB"
      },
      "cell_type": "markdown",
      "source": [
        "The only data that is null is the Albumin_and_Globulin_Ratio - Only 4 rows are null. Lets see whether this is an important feature    "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "888444b8-b4a9-45b6-b2c2-11b826ff9da7",
        "_uuid": "2d518d8a9f73f3ef58ebef6cd7c918a15178a656",
        "id": "kdXySNYQ4SoB"
      },
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "544cd4dc-5cb5-45ac-ba26-c6c2be5d3db1",
        "_uuid": "0f20a9162e6c41a4f602f2bbcdfd21c012e035da",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "qOrYCFj44SoC",
        "outputId": "c0a37238-73a2-4376-8b3b-d4794ecdf84c"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(data=liver_df, x = 'Outcome', label='Count')\n",
        "\n",
        "LD, NLD = liver_df['Outcome'].value_counts()\n",
        "print('Number of patients diagnosed with liver disease: ',LD)\n",
        "print('Number of patients not diagnosed with liver disease: ',NLD)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of patients diagnosed with liver disease:  416\n",
            "Number of patients not diagnosed with liver disease:  167\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "3e8bf602-3b81-4a53-b190-8ba363295904",
        "_uuid": "d0e41e3d9a88577572fe5fea081de4de19f6ad6a",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "3JHnfJR74SoC",
        "outputId": "3685a7d3-f697-4545-f869-c1471eb8ff6d"
      },
      "cell_type": "code",
      "source": [
        "sns.countplot(data=liver_df, x = 'Gender', label='Count')\n",
        "\n",
        "M, F = liver_df['Gender'].value_counts()\n",
        "print('Number of patients that are male: ',M)\n",
        "print('Number of patients that are female: ',F)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of patients that are male:  441\n",
            "Number of patients that are female:  142\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "dff35f12-ee87-43d2-a4f3-67ad7a12de76",
        "_uuid": "8b1e4c43e56c9eb7f571709bc896f3a9806dc1cd",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "nJdvHpLn4SoD",
        "outputId": "1360ce0e-a5e3-44e6-c323-1dbecc3e13c1"
      },
      "cell_type": "code",
      "source": [
        "sns.factorplot(x=\"Age\", y=\"Gender\", hue=\"Outcome\", data=liver_df);"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/categorical.py:3717: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 413.25x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d1f71ba8-caa8-47e2-b675-e234abfab05c",
        "_uuid": "059e213bb14b66c18d67a022679d485e00003e25",
        "id": "7oD3_sI34SoD"
      },
      "cell_type": "markdown",
      "source": [
        "Age seems to be a factor for liver disease for both male and female genders"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8201c43b-3d29-4076-a385-d46f4d016670",
        "_uuid": "e39a78707778302672d3e058a2b386b53630c062",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "bCZ6gFR34SoD",
        "outputId": "92795c28-4962-4699-9d75-fe2a3210dfb9"
      },
      "cell_type": "code",
      "source": [
        "liver_df[['Gender', 'Outcome','Age']].groupby(['Outcome','Gender'], as_index=False).count().sort_values(by='Outcome', ascending=False)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Outcome  Gender  Age\n",
              "2        2  Female   50\n",
              "3        2    Male  117\n",
              "0        1  Female   92\n",
              "1        1    Male  324"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-136b38e8-2fc3-4a85-94ce-307d921a6d20\">\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>Outcome</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>324</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-136b38e8-2fc3-4a85-94ce-307d921a6d20')\"\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-136b38e8-2fc3-4a85-94ce-307d921a6d20 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-136b38e8-2fc3-4a85-94ce-307d921a6d20');\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": 39
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b0a4a24b-f24f-4e5e-9511-ef3a99965192",
        "_uuid": "258fc14a5fcf4a33f7560dba890ddcf6b7eab0e7",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "lkAfYBrm4SoD",
        "outputId": "86db0700-d870-423b-b4ed-a2aa38cedb41"
      },
      "cell_type": "code",
      "source": [
        "liver_df[['Gender', 'Outcome','Age']].groupby(['Outcome','Gender'], as_index=False).mean().sort_values(by='Outcome', ascending=False)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Outcome  Gender        Age\n",
              "2        2  Female  42.740000\n",
              "3        2    Male  40.598291\n",
              "0        1  Female  43.347826\n",
              "1        1    Male  46.950617"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eaf84699-9e31-40a7-b687-4d70232d1f0a\">\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>Outcome</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Female</td>\n",
              "      <td>42.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>40.598291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Female</td>\n",
              "      <td>43.347826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>46.950617</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eaf84699-9e31-40a7-b687-4d70232d1f0a')\"\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-eaf84699-9e31-40a7-b687-4d70232d1f0a 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-eaf84699-9e31-40a7-b687-4d70232d1f0a');\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": 40
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "d5bd9ed4-6d96-4893-bbf4-795ab25d2c3a",
        "_uuid": "6c3f4fefb376a5f538b90e1cf47ee6bc6abb5e70",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "nuXYhtW14SoE",
        "outputId": "eb9a0691-8bef-43b8-9533-d19218664679"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Outcome\", row=\"Gender\", margin_titles=True)\n",
        "g.map(plt.hist, \"Age\", color=\"red\")\n",
        "plt.subplots_adjust(top=0.9)\n",
        "g.fig.suptitle('Disease by Gender and Age');"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "67d473ed-ea9c-41f2-a3f0-204ef4f0ec97",
        "_uuid": "c49b4d8c56d507f819b413686e38560c26310d9f",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2izdLdIk4SoE",
        "outputId": "c2f5dfbd-c200-4c7f-ef79-05ff4ac58749"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Direct_Bilirubin\", \"Total_Bilirubin\", edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "bed3776c-37d8-46a5-a0e2-c9a3aca3b873",
        "_uuid": "79b749d653ba9554007543a8d72f9f74ed5e3332",
        "id": "-ewtMMb34SoE"
      },
      "cell_type": "markdown",
      "source": [
        "There seems to be direct relationship between Total_Bilirubin and Direct_Bilirubin. We have the possibility of removing one of this feature."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "698b86f0-9f90-44e4-a455-4e544665e0de",
        "_uuid": "46347f3bba50466b2398940b3c975188474723d6",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "vw0XTjJg4SoF",
        "outputId": "cf71e0e8-6c8d-4420-bcc9-27f77e74869c"
      },
      "cell_type": "code",
      "source": [
        "sns.jointplot(\"Total_Bilirubin\", \"Direct_Bilirubin\", data=liver_df, kind=\"reg\")"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f855876eb10>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "0e313f91-b5e0-437c-8496-583dc84b86df",
        "_uuid": "2f0eef12382e1bdfb8f25881a8e91e6017e13e94",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "PsRiGU6W4SoF",
        "outputId": "37bb7ffd-41e9-4477-fcef-acc560d7447a"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Aspartate_Aminotransferase\", \"Alamine_Aminotransferase\",  edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "ff7fe013-f0a1-4489-ba53-576807d6feed",
        "_uuid": "e501b5d2dd39ff930376992e9d97c3b991a38d2a",
        "id": "mBxXhnD94SoF"
      },
      "cell_type": "markdown",
      "source": [
        "There is linear relationship between Aspartate_Aminotransferase and Alamine_Aminotransferase and the gender. We have the possibility of removing one of this feature."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7d71e81c-f7e3-48f6-aba7-fce8c071df3b",
        "_uuid": "065ebcad6c036771378419b1d4a4e92767eaeaa8",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "wMtPSZnC4SoF",
        "outputId": "4846c655-3a2e-4422-b2b0-681157416fea"
      },
      "cell_type": "code",
      "source": [
        "sns.jointplot(\"Aspartate_Aminotransferase\", \"Alamine_Aminotransferase\", data=liver_df, kind=\"reg\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f8558863350>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "dc59414a-bbea-4dfd-b160-9c4b9bc441b5",
        "_uuid": "913a7c6227545ad9f1802df2d74ab2dac51574c0",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AdvWeofH4SoF",
        "outputId": "d73dd23f-b9d2-424c-bac8-371e3fab6d8b"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Alkaline_Phosphotase\", \"Alamine_Aminotransferase\",  edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAGWCAYAAAAtwhcdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde5wcVZ338c93ZjJJZhKExMBGbgET2AcEAgQQ8dnl4nLbFRAWl7hAvCy4KxhYRAFxNbq6yqqsRF1uLmsQTQRBjD4gIKAoIiQhMQFUCCEIGElIuCQzydz69/xR1ZOeSc9MzUx3pmf6+3696tXdp6uqf51Uz6k6dc7vKCIwMzOrNDVDHYCZmVkxrqDMzKwiuYIyM7OK5ArKzMwqkisoMzOrSK6gzMysIrmCGkKSdpH0PUmrJC2R9Iik95Ro30dL+kkp9pXx8+ZIeknSsnT5Uhk/6/2SvlGu/Vt5jMDjPSRNLSi7OC2b0ce2P+9rHUu4ghoikgTcCTwUEXtHxKHAWcBuQxRPXQl2818RMT1dLi/B/myEGKHH+wqS75B3JvBkCfZrKVdQQ+dYoDUirssXRMTzEfF1AEm1kr4saZGk5ZI+nJYfnZ6B/UDS7yV9N/3xI+nEtOxx4PT8fiU1SrpJ0mOSlko6NS1/v6SFkh4A7i/1F+zjO/xC0o/Ss+kvSfrHNL4Vkt6arvduSY+mMf9M0i5FPmOSpNvTz1gk6ahSfw8riZF4vN8J5Pf9VuB14JWCOK6VtFjSk5I+W2wHko5PryQfl3SbpHEliGvEKMVZhA3M/sDjvbz/IeD1iDhM0mjgYUn3pu8dnG7/J+Bh4ChJi4EbSf4QrAS+X7CvK4EHIuKDknYEHpP0s/S9Q4ADI2JD9wAk/RIYXyS2SyPiZ0XK/1XS2enzy4A9e/kOBwH/B9gArAK+FRGHS7oI+ChwMfAr4O0REZL+CfgE8LFun3kNyZXbryTtAdyT7tcqy0g83t8AXpD0NpKK6vvABwrjiIgNkmqB+yUdGBHLCz7vzcCngHdFRJOky4BLgM/1+K9UZVxBVQhJ3wTeSXKWeRhwPHCgpL9PV3kTMA1oBR6LiBfT7ZYBU4BNwHMR8Uxafgtwfrrt8cApki5NX48B9kif31fsxwoQEf+3n1/jvyLiKwXf6Qe9fIdFEbEmXe9ZIP/HaAVwTPp8N+D7kiYD9cBzRT7zXcB+6Uk1wA6SxkXEpn7GbtvRCDneARaQNPOdABxH1wrqvZLOJ/k7OxnYD1he8P7b07KH0+O3HnhkADGMWK6ghs6TwBn5FxFxQXpGtTgtEvDRiLincCNJRwMtBUUd9P3/KOCMiPhDt30dATT1uFH/zyiLfW6W75AreJ1j6/f5OnB1RCxMt5lT5DNqSK6ytmSIx4bOSD3efwJ8GVgcEW/kT5Qk7QVcChwWEa9K+jZJRdk9zvsiYmYf36dq+R7U0HkAGCPpXwrKGgqe3wP8i6RRAJL2kdTYy/5+D0zJ378BCg/6e4CPFrTdH5wlwIj4vwWdHgqXLJXTQL5Dd28CXkqfz+phnXtJmgRJP2N6P/Zv28+IPN4jopmkOfsL3d7agaQyfD29d3pSkc1/Q9JcOTWNs1HSPllirRauoIZIJGnkTwP+WtJzkh4D5pEc7ADfAp4CHpf0BHA9vZw5plcQ5wP/L71pvLbg7X8HRgHLJT2Zvt4e+vUdipgD3CZpCQU3n7uZDcxIb6w/BfzzIOK1MhnJx3tELIiIx7uV/RZYSlKRfo/k3ln37dYB7wfmS1pO0rz3l+WMdbiRp9swM7NK5CsoMzOrSK6gzMysIrmCMjOziuQKyszMKpIrKDMzq0gjtoI68cQTA/DipVKXAfFx7aXCl5IasRXUK6/0NGzGbPjycW3VZMRWUGZmNry5gjIzs4pU1gpK0u6SHpT0VDonykVp+QRJ90l6Jn3cKS2XpLmSVqapaw4p2NesdP1nJM0qZ9xmI1UuF2xqaScX6WOu5LcNzEqm3FdQ7cDHImI/ktTyF0jaD7gcuD8ippFMHJafffUkkhT700jybF0LSYUGfAY4Ajgc+Ey+UjOzbHK5YH1TK+fNW8w+V97NefMWs76p1ZWUVayyVlARsSafRDEiNgK/A3YlmdxrXrraPJIkkqTlN0fiN8CO6VxAJ5DO4xIRrwL3ASeWM3azkaa5rYPZ85fyyKr1tOeCR1atZ/b8pTS3dQx1aGZFbbd7UJKmkMyM+SiwS36yOuDPQH4q712BFwo2ezEt66m8+2ecr2SK5cXr1q0rafxmQ6VUx3VDfS2LVnedq2/R6g001NcONkSzstguFZSkccDtwMUR8Ubhe2ka/pK0MUTEDRExIyJmTJo0qRS7NBtypTqum1s7OGzKhC5lh02ZQHOrr6CsMpW9gkonILsd+G5E3JEWv5w23ZE+5udyeQnYvWDz3dKynsrNLKOGUbXMnXkwR+49kboaceTeE5k782AaRvkKyipTWad8T2e0/B/gdxFxdcFbC0lmSP1S+vijgvILJS0g6RDxekSskXQP8B8FHSOOB64oZ+xmI01NjZjYWM+Ns2bQUF9Lc2sHDaNqqanRUIdmVlRZKyjgKOAcYIWkZWnZJ0kqplslfQh4Hnhv+t5dwMnASqAZ+ABARGyQ9O/AonS9z0VE18Z0M+tTTY0YNzr52ecfzSpVWY/QiPgV0NPp2XFF1g/ggh72dRNwU+miMzOzSuZMEmZmVpFcQZmZWUVyBWVmZhXJFZSZmVUkV1BmZlaRMldQksZK2recwZiZmeVlqqAkvRtYBvw0fT1d0sJyBmZmZtUt6xXUHJJpLl4DiIhlwF5lisnMzCxzBdUWEa93K/MkMmZmVjZZM0k8Kel9QK2kacBs4NflC8vMzKpd1iuojwL7Ay3AfOAN4OJyBWVmZpbpCioimoErgSsl1QKNEbGlrJGZmVlVy9qL73uSdpDUCKwAnpL08fKGZmZm1SxrE99+6Uy4pwF3k/TgO6dsUZmZWdXLWkGNSmfGPQ1YGBFtuBefmZmVUdYK6npgNdAIPCRpT5KOEmZmZmWRqYKKiLkRsWtEnByJ54FjyhybmZVRLhdsamknF+ljzo0iVlkyz6gr6W9JupqPKSj+XMkjMrOyy+WC9U2tzJ6/lEWrN3DYlAnMnXkwExvrqanpaRJss+0ray++64B/IBkPJeBMYM8yxmVmZdTc1sHs+Ut5ZNV62nPBI6vWM3v+UprbOoY6NLNOWa+g3hERB0paHhGflfRVkt58ZjYMjR1Vw5xT9mfqzuNYuXYT33xwJXetWENDfe1Qh2bWKWsFlR+U2yzpLcB6YHJ5QjKzcso3781Z+GRn895VZxzI1EmNNLd2MG505pZ/s7LK2ovvx5J2BL4MPE7So+975QrKzMqnua2Di+Yv69K8d9nty3n/UXvRMMpXUFY5+jxVklQD3B8RrwG3S/oJMKZIdnMzGwYa6mtZtHpDl7JFqzcwbkwdNXIHCascfV5BRUQO+GbB6xZXTmbDV3NrB4dNmdCl7LApE2hudQcJqyxZm/jul3SG5NMrs+GuYVQtc2cezJF7T6SuRhy590TmzjzYzXtWcbLeDf0wcAnQIWkzSVfziIgdyhaZmZVFTY2Y2FjPjbNm0FBfS3NrBw2jaj3+ySpO1uk2xpc7EDPbfmpq1Nlbz732rFJlHagrSWdL+rf09e6SDi9vaGZmVs2y3oP6b+BI4H3p600UdJwwMzMrtawV1BERcQHpgN2IeBWo72sjSTdJWivpiYKyOZJekrQsXU4ueO8KSSsl/UHSCQXlJ6ZlKyVdnvnbmZnZsJW1gmpLp3oPAEmTgFyG7b4NnFik/L8iYnq63JXucz/gLJKEtCcC/y2pNv3cbwInAfsBM9N1zcxsBMtaQc0FfgjsLOkLwK+A/+hro4h4CNjQ13qpU4EF6Tir54CVwOHpsjIiVkVEK7AgXdfMzEawXisoSXsBRMR3gU8AXwTWAKdFxG2D+NwLJS1PmwB3Sst2BV4oWOfFtKyn8mLxni9psaTF69atG0R4ZpXDx7VVq76uoH4AIOn+iPh9RHwzIr4REb8bxGdeC7wVmE5S2X11EPvqIiJuiIgZETFj0qRJpdqt2ZDycW3Vqq8BEDWSPgnsI+mS7m9GxNX9/cCIeDn/XNKNwE/Sly8BuxesultaRi/lZmY2QvV1BXUW0EFSkY0vsvSbpMJpOt4D5Hv4LQTOkjQ6bVqcBjwGLAKmSdpLUn0a08KBfLaZmQ0fvV5BRcQfgKvSiQr7PUGhpPnA0cCbJb0IfAY4WtJ0kh6Bq0nSKBERT0q6FXgKaAcuiIiOdD8XAvcAtcBNEfFkf2MxM7PhJWuOk30kPQxsBL4FHAxcHhH39rZRRMwsUvw/vaz/BeALRcrvAu7KGKuZmY0AWbuZfzAi3gCOByYA5wBfKltUZmY24kg6TtJH0+XYvtbPWkHl0xyfDNycNrE59bGZmfVJ0q6SHgM+DUxJl09LelRS0WFDkL2Jb4mke4G9gCskjSdbJgkzM7NrgbkRcUthoaRzSTIFnVZso6xXUB8CLgcOi4hmkjx8Hxh4rGZWSXK5YFNLO7lIH3Mx1CHZyPKX3SsngIi4mSSFXVFZ54PKSXoZ2E+SJ48xG0FyuWB9Uyuz5y9l0eoNHDZlAnNnHszExnpPYmilIkm1+Z7ZhYX0crso63xQVwEPA58CPp4ulw48VjOrFM1tHcyev5RHVq2nPRc8smo9s+cvpbmto++NzbK5C7hWUucs7OmtosJkDdvIejV0GrBvRLQMKkQzqzgN9bUsWt01p/Oi1RtoqK8doohsBLqUZAjRs5JeIBkHuydwE/DJnjbKWkGtAkYBrqDMRpjm1g4OmzKBR1at7yw7bMoEmls7PB28lUREtAGfkPRpYGpavDIitvS2XdajrxlYJul+CiqpiJg9kGDNrHI0jKpl7syDt7kH1TDKV1BWOpKOIulenq93DgXm9bZN1gpqIc5/ZzYi1dSIiY313DhrBg31tTS3dtAwqtYdJKxkJN1CUjktY+sQJVGKCioiet2JmQ1vNTWiYVRaOVVRJZXLBc1t1fWdh8ghwP4R0a/xC5kqKEnTSCYr3A8Yky+PiL3782FmVpmqsat5NX7nIfQ7kqmSXuhrxUJZB+r+L8lI4HbgGOBmYJtBV2Y2PFVjV/Nq/M5DaCfgCUn3Sfpxfulro6z3oMZGxP2SFBHPA3MkLSHJq2Rmw1w1djWvxu88hOYMZKOsV1AtkmqAZyRdKOk9wLiBfKCZVZ58V/NC+a7mI1U1fuehEhEPdV+Ak/raLmsFdRHQAMwm6Rp4NjBrwNGaWUXJdzU/cu+J1NWII/eeOOK7mlfjdx4qkr4oab2kjenyBvDx9PkVPW3XZxOfpFrgHyLiUmATThJrNuJUY1fzavzOQ+hUYOfCXHySHo+IQ3rbqM8KKiI6JL2zBAGaWQWrqVFn5ohqySBRjd95iCzpnigWeLKvjbL+jyyVtBC4DWjKF0bEHdnjMzOzahQR50jaD8jPovtARJzT13ZZ70GNAdanO393uvzdQAI1M7Pqkk5MeAewS7rckZb1KusV1Lci4uFuH3hUv6M0M7Nq9HHgHRGxAUDS1cDPScbU9ijrFdTXM5aZmZl1156vnAAi4lW25uTrUa9XUJKOBN4BTJJ0ScFbOwDui2lmZlkslbRTWjEhaUdgeV8b9dXEV08yILcOGF9Q/gbw9wMM1MzMqkhEfLDb69fIMJa21woqIn4B/ELStyPieUnj0vJNgwnWzMyqh6Q9gLkkLXICHgE+mqbO61HWe1DjJS0l6bf+pKQlkt42mIDNzKxq/C/wA2Ay8BfArSTTvfcqawV1A3BJROwZEXsCH0vLzMzM+jIxIm6JiI50uQWY2NdGWSuoxoh4MP8iIn4ONA4sTjMzqzLrJL1fUl26fABY19dGWSuoVZL+TdKUdPkUsGpQ4ZqZWbX4AEmChz+lyylkyOuatYL6IDCJZCTwHenzD/a6BSDpJklrJT1RUDYhnbTqmfRxp7RckuZKWilpuaRDCraZla7/jCRnUTczG0Yi4sWIOCMidk6X90TEi31tlymTRNp3ffYA4vo28A26jha+HLg/Ir4k6fL09WUkc4NMS5cjSGbwPULSBOAzwAwggCWSFub705uZWWWTdBNJ770uIqLXq6hMFZSkfYBLgSmF20TEsT1tk77/kKQp3YpPBY5On88jSXdxWVp+c0QE8BtJO0qanK57X0GKjPuAE4H5WWI3M7Mh95OC56NJmvte62ujrLn4bgOuA74FDHa6yV0iYk36/M8kiQMBdgVeKFjvxbSsp/JtSDofOB9gjz32GGSYZpXBx7UNd0Vmvpgv6ZG+tstaQbVHxLX9D6t3ERGSooT7u4G0+/uMGTNKtl+zoTRUx3UuFzS3dXgyPxs0SXsWvKwBDiDpy9CrrBXUjyV9BPgh0JIvLEz+1w8vS5ocEWvSJry1aflLwO4F6+2Wlr3E1ibBfPnPB/C5ZpZRLhesb2pl9vylLFq9gcOmTGDuzIOZ2FjvSsoG4sck96CCpInvL0h68vUqay++WSTp0n8NLEmXxQMKExayNQfTLOBHBeXnpr353g68njYF3gMcL2mntMff8WmZmZVJc1sHs+cv5ZFV62nPBY+sWs/s+UtpbhtsC79Vo4g4MCIOSB/3BY4ESjMfVETsNZCgJM0nufp5s6QXSXrjfQm4VdKHgOeB96ar3wWcDKwEmkn7yEfEBkn/DixK1/vcAK/czCyjhvpaFq3u+jNbtHoDDfWexMAGLyKeSmfL6FVf020cGxEPSDq9hw/pdcr3iJjZw1vHFVk3gAt62M9NZMjbZGal0dzawWFTJvDIqvWdZYdNmUBzawfjRme9M2CW6NbNvAbYH/hNX9v1daT9NfAASZfA7oJk0K6ZjTANo2qZO/Pgbe5BNYzyFZQNSGE383bgqxExuPmgIuIz6WOfKSnMbHjr6MjR3NZB4+g6mlrb2WnsKG6cNcO9+GzQIuIOSW8G3k5ycfNSlu2yDtTdkeSG1hS6DtQdSHYJM6swHR051je1ctGCZZ1XTNecNT3ptSe5Wc8GRdLRJJmFHibp6PaUpM9HxH29bZe1F99dJJXTCrb24lsywFjNrMI0t3Vw0YJlXXrtXbRgmXvtWan8J3BcRPwjSeKFE4HP9rVR1tOiMRFxySCCM7MK1ji6rmivvUZfOVlpjI6IZ9PniojNkur72ijrFdR3JJ0naXKajXxCmsTVzEaAppZ2DpvS9Sd92JQJNLW0D1FENsKEpIb0+ShJnwCe7W0DyF5BtQJfJplHfrADdc2swjSMquWas6Zz5N4TqasRR+49kWvOmu5ee1Yqn2JrDtVHSLJJ9Nn5Luv1+8eAqRHxysBiM+uZc74NvdraGiY21nPDuYcmvfha2mkYVUttbdZzWLOeRcRPCp6fl3W7rBVUPruDWUk559v209eJQG1tDePTCmn8mFFDFaZZp6wVVBOwTNKDdE0W627mNiiFOd+AzpxvN86a4a7NJeQTARuOsv4FuDNdCnk6Cxs053zbPnwiYMNRpgbmiJhXuJCkP9qlr+3M+pLP+VYon/PNSscnAlYJJF1a+NiXzHdAJU2S9BFJvySZj8kVlA1aPudbYe8x53wrPZ8IWIX4x/TxfVlW7iub+Xjg9HRn+5Akh90rInYbTIRmeTU1YmJjvXO+lZmTv9pw1Ffj81rgMZI+7L9Kp2h/T/nDsmpSU7M115vvh5SHTwRsOOqrie8KkgFV/w1cIemt5Q/JzMohfyKQT/7qyskqXa8VVER8LSLeDpyaFt0JvEXSZZL2KXt0ZmZWtbL24lsVEf8REQcAM4AdSDKcm5mZZfWL9PGhLCv3O49JRDwREVdGxNR8maRH+rsfMzOrLhFxceFjX0qVaGtMifZjZmYGlK6CclYJs2Eglws2tbSTi/Qx55+uVS736TWrEs7HZ8NNqa6gfHSbVbjCfHz5ad1nz1/qad2tYvUn1dGekt6VPh+bZpnIO6fkkZlZSTkfnw0VSUV77Uk6RtLdPW2XqYlP0nnA+cAE4K3AbsB1wHGQ9Ozrb8Bmtn3l8/HlM5rD1nx8zuBhZbavpKPZtr9CA/D2njbKelReABwOPAoQEc9I2nkAQZrZEHE+PhtC40hmZi/WK+eXPW2UtYJqiYhWKbnVJKmuhw8yswrlfHw2hNZHxLv7u1HWe1C/kPRJYKykvwFuA37c3w8zs6HlfHw2RD40kI2yVlCXA+uAFcCHSdIcfWogH2hmZtUlIu4byHaZmvgiIgfcmC4lIWk1sBHoANojYoakCcD3gSnAauC9EfGqkrbFa4CTgWbg/RHxeKli6a9cLmhu63AziZlZGWW6gpJ0lKT7JD0taZWk5yStKsHnHxMR0yNiRvr6cuD+iJgG3J++BjgJmJYu5wPXluCzByQ/2PG8eYvZ58q7OW/eYtY3tXpEvplZiWVt4vsf4GrgncBhJBnNDytDPKcC89Ln84DTCspvjsRvgB0lTS7D5/fJgx1tJHMqJKskWXvxvR4RPQ6mGqAA7pUUwPURcQOwS0SsSd//M7BL+nxX4IWCbV9My9YUlCHpfJIrLPbYY48Sh5vwYEfb3rbHcQ1OhWSVJ+sV1IOSvizpSEmH5JdBfvY7I+IQkua7CyT9VeGbERH0syt7RNwQETMiYsakSZMGGV5x+cGOhfKDHc3KYXsc1+DWAas8Wa+gjkgfZxSUBXDsQD84Il5KH9dK+iHJQOCXJU2OiDVpE97adPWXgN0LNt8tLdvuPNjRhrueOvm4dcAqTdZefMeU8kMlNQI1EbExfX488DlgITAL+FL6+KN0k4XAhZIWkFSWrxc0BW5XHuw4dNx7cvB6a8ZrbnMqJKssvR51ks6OiFskXVLs/Yi4eoCfuwvwwzQzRR3wvYj4qaRFwK2SPgQ8D7w3Xf8uki7mK0m6mX9ggJ9bEvnBjoB/uNuJ74+URmEzHtDZjHfjrBmMravhmpnTuWj+sh5bB3ySYNtTX39dG9PH8b2u1U8RsQo4qEj5etIEtN3KgyQfoFWp3v6w+iQhu96a8dZvamXBo39kzin7M3XncTS1tHepgHySYNtbr7/siLg+ffzs9gnHrDjfHymNppb2os14W1o7aGpp54Jjp7Fy7Sb+9fvLWLexhevPOZRxaeXT1NrOhMZ65pyyP998cCULf/snnyRYWWWdbmMv4KMkGR46t4mIU8oTVmVzM8f256kiSqOhvparzjiQy25fvvUq6KzpNLW2c8UdKzrLrjrjQK6+7w80jq5jS3sHTS0dXa6crjrjQADuWrHGJwlWNll/2XeSDNb9MZArXziVz80cQ8O9J0tjc1uOO5e+2NmMt3LtJtpzwSW3/rZL8+llty/ni6cfwMq1m9h1x7HbNK9edvty5pyyP+s2tvgkwcom61G1JSLmljWSYcL3QoaGe0+WRsOoWma9YwqvNrcBMLquhl3eNKZo8+keExv4xv3PcOFx04q+P3XncT5JsLLKOlD3GkmfKfFA3WHJ90KGjqeKKI3WjhxX3LGCfT91N1fcsYJNW9qLDj5/+fUtzDxiT5pbehqc3u6WAyurrKf8BwDnkAzMzTfxDWqg7nDleyE2nCUtAMu6tAB8++Hntulefs3M6ewwuo5RtTVsbsvx3fOO4I/rm/naz57m5TdamDvzYBrrfZJg5ZX1L+qZwN4R0VrOYIYD3wux4axYC8DcB1ZywbFTuf6cQ2kcXcfKtZtY8OgfOfcdU9jY0s7sLuOiptM4uo4xdW5etfLLWkE9AezI1tRDVcv3Qmw4660F4MPfWdKl/JTpu3LFHSu63W9dxo2zZvh4t+0i6z2oHYHfS7pH0sL8Us7AKpnvhdhwlW8BOHLvidTViCP3npi0ABS5stp9QsOg7rd66g4brKxXUJ8paxRmtl301AJQLA/fCxuaB3y/1cMxrBQyXUFFxC+KLeUOzsxKr1gLQMOoWq6ZOb3LlVVjfS1ffe9B215tZbjf6qk7rBSyZpJ4O/B14P8A9UAt0BQRO5QxNjPbTmpqxMSG+i4dJeY/9kdmvWMKN547g4bR/bvf6uEYVgpZ70F9A5gJPAOMBf4J+Ga5gqp0HR05Nm5pIxfBxi1tdHRUdXINGyFqa2sYN7qOlvYOdt1xLBceN43aGlGT9a9EAU/saaWQ+dCLiJVAbUR0RMT/AieWL6zK1dGRY31TK+ffvIR9rryb829ewvqmVldSNiwU67hQWNbc2kFbe47zbl7MPlfezXk3L2FDUyuXfH8Z581bzPqm1kydHXrsjOHhGNYPWSuoZkn1wDJJ/ynpX/ux7YjS3NbBRQuWdWlbv2jBMretW8Xr6MixqaWdsaNqeeblTdz0y1Vs3NLG+qYWzpuXr5AWs7GlnUnjR3ce3x+/bTn/cvTUbe4jda/s8vvPRZJMeULDKG6cNYOnv3ASN86a4Q4S1m9ZK5lzSO47XQg0kUy/fka5gqpkjaPriratNzqLhFWwXC5Y39zKtx9+jpVrNzF153G8/6i9kNSZWaKwQrrgmKmd2+bz7uWfN9TXdvbS66zY0qurm365auvr5lbG1tV4OIYNWNZefM9HxOaIeCMiPhsRl6RNflUnP59OocOmTKCppb3f+/I4Edtemts6WPDoHznt4N2Ys/BJ9v3U3Xz4O0sYN6b4CVe+QoLk+P7z65u55+K/4g+fP4mmlna2tG/bS++iBcs44W2Tt76ev4ym1g4f1zZgvVZQklZIWt7D8tvtFeT2UlhhbNzSRntBZ4h8BdIwqpZrzuraHfeas6b3u229pzNQ/5htsIqd+DTU13LC2yZz2e3Lu1Qqf1zfXPSE64UNzZ3H99ffNx1QZ8V2/s1LaGppZ5cdRnfZrnvFlm9ZyNr87RM2666vdqm/K1Imkia+K0ofztDpPrBw9rFTOevwPbhowbJtBhpObKznhnOT7rj5abFra/t3S257TNvhiRWrT/EBstNprK9j6s7jtrla+trPnmbuzOnb5turr+PpL5xEc0sHQfDR7y3ZJuXRF08/gDuX/alzX4dNmcDKtZu6vP7z65t509j6zg4YPR2DHthrxfT6VzVt2ns+Ip4HJpDcg/o58DngrvKHt/10H1h4wtsmb9MZIn+DuLa2hvFjRqkPBEIAACAASURBVFEjMX7MqH5XTlCecSJdzkC3tLNxS5uv0KpM8QGyy3h9c9s202qcctBbuOzEv2TiuNHccO6h/OHzJ3LDuYcyobGe9lzwjfufYfrn7qWhvngz4B4TG7ZpSbjniTXU1YhL3jWN6885lMk7juWVTS199gLsaWBvU2vXzhe+sqouvZ6qS9qHZPzTTOAV4PuAIuKY7RDbdtW9wih2tlnKgYalnraj2Bnol888sEtvLE+sOPL1dOKz8w5j+OXTazun1dhlh9F84sS/5JJbf7t1io2zprPgsT8y94GVnVdSZxy6G396bXPxY7WlvUtLwti6Wt5/1F585JiprG9q5cPfWdJliviv3PuHHo/Bnk/Y6li/qaVbXL6yqhZ9nfr/nmTOp7+LiHdGxNeBEdmfuvvAwpVrN5V1oGFf40T62x5f7Ay0WG8sj+Qf2XoaILty7SYm79jAgkf/yJxT9ufzpx3Ax9Jp3nvq5DB7/jLaOoIxdTV85czuKY+m05GLLuMBN2xupXF0LZvbOri4W+vDZbcnx2JPx2BvcW8bl1MmVYu+TqVPB84CHpT0U2AByT2oEaf7PE/NrW2dZ4dvbG7jkWdfYcaUiSUbaNjbtB0DaY/v6Qy0e2+skTaxou+zdTW2rmbbyQfPms6E9Nj527krufpnz/Dsf5zc5/GyaPUGdp/QwNnfepSvnTW98/fQ3NpBjeBD317c5b7URfOXMeeU/XtsfZi687iix2AuFxBsMyli/qqrWFwDOdEaicfKSPxOhXr9SxURdwJ3SmoETgUuBnaWdC3ww4i4dzvEuF0UVhhj6mrYkGaLKPyR7zR2VEn/8/NJO4EuP9iBdKDoqckw3xtrJE6s6BvrXeWvujs6gu+edwTNLR1I8NDTa/no/GU8fPmx/OySv2b3CQ2dwyW6Hy/dOzmsXLuJXXYYTWN9HWPra2hqae8c89dTJZRvfSh2LHY/Bnvq1DF2VC1jRtVywTFTmTqpsUtcX585vTOOrJ2URuKxMhK/U3dZx0E1RcT3IuLdwG7AUuCyskY2BPIVxuYeskVsbu97BH0pbuL21oGi+77zsTTU13L9OYdyybumdWmG2XmH0SN2JL8zZne1pb2DjS3tXHLrbzuzQrza3MqBu+3IyQdMJhfBFXesYN9P3Z1M815kuES+k0P++Hnk2Ve49IR9+eUza9m0pYOG+jqeeXkT6za29Ngk980HV3LVGQdu0yS48/jRNI7ueoLUU6eOVza18pf/9lPmLHySsw7fg1XrNlJXI679x4M5dM8J/Uo1lssFTa3tTGisZ84p+3PyAZOHxbHSVzN/NRz//W7riYhXgRvSZcTJ5aLXbBH5ymhLew4CxtbXsL6ptUuTymDPYnq6Gvrj+mYaR9d17rvYGdQ1M6dzwbFT2dyW63K5P250XZfKbKiaA0rZJFGunpDDtckkl4OP37a887iZNH407R3BHhMb+PfT3sa8h5/rfO/qnz0D0Nlst6mlndeaWjj3HVO48LhpvLG5jdF1NZx68K60tueYMWUC/3zL1haFq//hIK49+xD+5ZbHu7Qy3LViDQt/+yemTmrkunMOZVy673kPP8eqV5q4+F37sMfEBjZtaWfsqJoe/w93n9DQ5eTwhnMP5ekvJIOEz7+5a5f3/Pvj06uowv/DLW0dNHWbtv6qMw4E4K4Vayr2nmyWq6NqyBhflfn0etPc2sEbm9uKnh1u2tIOggDaOpKEmivXNnFRQaqYSeNHJ1klBJu2ZL+aKjxbqhHM7TY3z1VnHMjV9z2dnCGlHTWKnUFdNH8Zm9ty26SWqYSBwblcMgD6lY0tRMArG1uSgdADjKHUGbMr4d9oMBpGb/2DdcpBb+HS4/flijtWsM+Vd/PP31nCaQfvxikHvYVTDnoL91z8V3wk7UATAU1b2tlhbD3/csvj7HPl3fzLLY/zanMbo2tryOVim3RIl3z/t2xu7eArZx7Isk8fz3fPO4KOXPDugybz80uP5sLjpjGqRjS3tjNudB3vOWQ3rjz5/3TGc97Nyb/ta02tPV6J5eVPDmukPlONdf8/XPtGyzax5ztsVHJ29SxXR9szY/xQDaKuugqqp2zOzS3tbNrSxtj6GhrSG83dmz8eXrmOlWubeK25rfNMtfCGcPc/CufdvJhXmlpozzcB9vCf2j2J57ceWkV9bQ03njuDP3z+JOacsj9fufcPLPztn5IzpNG1ndkBsp5BVUJzQL4JKt/MdMUdK9iYps0ZiFJnzK6Ef6PBaE7vK51y0Fv43Kn7s+tOY7s0aT35p9f4/Glv42tnTecv3jSG1ze3cf7NS9j3U3dzya2/ZVO3JLEfu/W35AJ2edOYHruud+TozHx+2+IX6MjBFXes4JLvL2NDc2vn/j/xg+W0dOS67P+iBcuoq63hy2d2bQ786nsP4psPbs2kVvhHt69UY1vakyumW/7pCP7f7P/b47T1U3cex3VnHwLB1mlzcr3/TrenLL/t7ZUxfihP3EZOd64Mil02X3f2IQA0tXbwg8UvMOsdU2hpz7HgsT929khqamlnTK044W2TgeSMM3/wFN4QvuCYqZ2pZGBrz6brzj6Ueb9+jplH7LlN018+iedF3Zog5v16NR94516c/a1Hizb1vXn86M7XWcZSVUJzQPcmqHxX+BvPnTGg/fXWE3IgKuHfaKByuUAS1519CBtb2rs0vV11xoGcfvBb2O8tb+oyNqn7OLmP37acOafsz8LfJtkh8idDL7++pehxtnFLW5fjvXBw+z0X/1XR/+vu+x83uo7XN7d1dupoSq+41m1s6ezcc81Z0xlbl5xL51ONFWZ4uWbmdBrqa9m0pZ2OXI4r7lhR8Ps+tGjsLW0dtHbkmN3t3+nOpS8W/Z1ub1nGSZb6+O8xlu2Q9aYnw+oKStKJkv4gaaWky/u7fbEz5Feb23i1uY2P3fpbTnjbZAK4aMEyrv7ZM5zwtYd46yfv4sPfWcKWjuA7j6zmjc1tvLBha/6ywhvCPXWvHTemjhPeNrno2XhzW0eXJsJ8E8QJb5tMQ31t0aa+r/3saRrqa/t1BlUJE8gVNkHl5f8IDlSx6csHqhL+jQaqua2DMekfp3zFUHg8HbLnhG06/vSWtRyS7/7G5jbqarTNcXjNWdMZ3y3RbOHx31tX88L9b9zSzid+sLyzxaGlLceYuhrmnLJ/Z+vBgsf+yOb2pBNEbW1NZ6qxp79wEtefcygLHv0j+37qp0WnCpn36207g8ydeTC5oGjTX0+/0+0t62+7lMd/j7EM4YnbsLmCklRLMovv3wAvAoskLYyIp7Luo9g/9O4TGoCtPx6pePfZxtF1nDp9V8aNrqOtPceXzzyQj9+2nLtWrGHqpEauP+dQNvdw1pOf3qDYf2pv45c2t3XQOLqOL55+ALtPaGDl2k185d4/sG5jS+eZVNYzqO7jvIai23lzSw9nhS0djBsz9IdiJfwbDVRDfdI8PG2Xnk+SslQYhcMSrjrjQMaNruOcWx7jxnMP5YunH9DZweHbDz/H+4/aq8v/Z2FrQm9dzTuvjGZOZ96vn+tyZn7Z7cu59uxDOOFrD3VuV1cjLjxuWufr2toaxtfWsKmlnQ9/Z0mvV2lzH1jJBcdO3Wbaenr4nff0O93ettfVURalznrTH8PpCupwYGVErIqIVpJBw6f2ZwfFzpBf2NDceUW0cu2mHjtIrFy7iR3GjuLZdU3Mf+yPCLjx3GQytjNn7E6NxA+WvMDXztr2iueeJ9Z0/mC7n433dNaeH98xpq6WxtF1nP2tR/nbub9k3caWLn80s55BFR7wQ9XtvNgV4dy0eaYSVMK/0UA1t3ZwzxNrOk8CCuWPp76yln/lzIMYVSue/kJy5XLn0hd5dl1TepVbx4TGer7zyGrGjanjlOm7Ul/b9crqnifWdF6tXPvzldvcW/rKmQcxtr6Wp79wEl88/QAmNtYz94Gus/YsWr2BHcaM2ibOYlexWQenb27NMW5M199Ib5krKuWqeXtcHWUxlLMjK2LobwhmIenvgRMj4p/S1+cAR0TEhQXrnA+cD7DHHnsc+vzzz3fZR+Z7UB25be4J3bn0Rc59xxQ+/aMn+cSJ+/Lx25Zv0712zo+fYs679+M9h+zGuNF1rFy7iXueWMNpB+/WY9t2T13FJzbUdw4+HM5dn7sbSd9lkDJ/6b6Oa9h6HDXW17KhubXL8XnVGQeyat1G9nvLm4rcu6lj7KjazvFL6za2MOeU/Zmz8MnOTA7rNrZww7mHsmT1Bq5/6Dm+ePoB1NWKCQ31oKTjwsRxo2lu6aClvZ3W9mCXN41h45Y2RNLzLv9bOOuIPZjYWM+fXtuSJJVN0y3lHbn3RK4/59Au98p6GraxqaWd8+Yt3mb7L55+AO+6+he9blvsd1dJ96AqTT9+tyX9RxtRFVShGTNmxOLFi7cpL/YPncsFrR05chGMra+lvT1HS0d0/WEdvgd3P7GGnz7xMl9/33Q2t+Z4y45jaWppJyL454KbrdedfQg1Ndo60r2+dptxSX3F5B/HiDeg/+CejmtIjqO29g42t+fY3NqRVhLJGKS5D6zk6zOn885pkzqPy3Ubt1BXU8tlt3c92Zo4rp4XNmzuTDl0zczpPPWn17n+F88xd+Z0cgFj6pKM/gDrm1qZ/+jznPC2yZ2diupqxJh0HFIul9x/zDcN5pO+Xnf2IUlHhW5jCCc0jGJze67P30OPU4uMrmPMqL5/S4W/uyy/U8ukaiuoI4E5EXFC+voKgIj4YrH1e/shF9PenqM1HY2ePzbb0kG7TS3tjKoR9aOSA7lWyY+vsz0bXMFYf5W8gsprb8+xuT05HlvacuQiqSCa0qEMmwuP1fSxcXQdzS1Jjr0aQXtaqTS3tFNTI8aM6vmPeNYTrGLrweB+Oz65qzgl/ccf+jvT2S0CpknaC3iJJInt+0q187q6Gurqut6SG5M+ji9oEy98XniDsFhOPbOhUFdXw/h8t+zRW4/p/LGbz7gwfkzXx2IdVcYVOfbHje76O+kpp2R3Pa03mN9O1s+24WnY/I9GRLukC4F7gFrgpoh4cojDMjOzMhk2FRRARNzFCJvJ18zMihtO3czNzKyKDJtOEv0laR2Q74/7ZpIp6ytNJcblmLIZbEyvRMSJ/d1oGBzXlRgTVGZcIzGmAR3XPRmxFVQhSYsjYmAJ38qoEuNyTNlUQkyVEEN3lRgTVGZcjqlvbuIzM7OK5ArKzMwqUrVUUJU6+28lxuWYsqmEmCohhu4qMSaozLgcUx+q4h6UmZkNP9VyBWVmZsOMKygzM6tIrqDMzKwiuYIyM7OK5ArKzMwqkisoMzOrSK6gzMysIrmCMjOzijRiK6gTTzwxAC9eKnUZEB/XXip8KakRW0G98kqlZbE3Gzwf11ZNRmwFZWZmw5srKDMzq0hlraAk7S7pQUlPSXpS0kVp+QRJ90l6Jn3cKS2XpLmSVkpaLumQgn3NStd/RtKscsZtZmZDr9xXUO3AxyJiP+DtwAWS9gMuB+6PiGnA/elrgJOAaelyPnAtJBUa8BngCOBw4DP5Ss3MzEamslZQEbEmIh5Pn28EfgfsCpwKzEtXmweclj4/Fbg5Er8BdpQ0GTgBuC8iNkTEq8B9QMnmvTczs8qz3e5BSZoCHAw8CuwSEWvSt/4M7JI+3xV4oWCzF9Oynsq7f8b5khZLWrxu3bqSxm82VHxcW7XaLhWUpHHA7cDFEfFG4XuRzJhYkv7zEXFDRMyIiBmTJk0qxS7NhpyPa6tWZa+gJI0iqZy+GxF3pMUvp013pI9r0/KXgN0LNt8tLeup3MzMRqhy9+IT8D/A7yLi6oK3FgL5nnizgB8VlJ+b9uZ7O/B62hR4D3C8pJ3SzhHHp2VmZjZC1ZV5/0cB5wArJC1Lyz4JfAm4VdKHgOeB96bv3QWcDKwEmoEPAETEBkn/DixK1/tcRGwoc+xmZjaEylpBRcSvAPXw9nFF1g/ggh72dRNwU+miMzOzSuZMEmZmVpFcQZmZWUVyBWVmZhXJFZSZmVUkV1BmZlaRMldQksZK2recwZiZmeVlqqAkvRtYBvw0fT1d0sJyBmZmZtUt6xXUHJJpLl4DiIhlwF5lisnMzCxzBdUWEa93KytJglczM7NismaSeFLS+4BaSdOA2cCvyxeWmZlVu6xXUB8F9gdagPnAG8DF5QrKzMws0xVURDQDVwJXSqoFGiNiS1kjMzOzqpa1F9/3JO0gqRFYATwl6ePlDc3MzKpZ1ia+/dKZcE8D7ibpwXdO2aIyM7Oql7WCGpXOjHsasDAi2nAvPjMzK6OsFdT1wGqgEXhI0p4kHSXMzMzKImsnibnA3IKi5yUdU56QzMzM+jGjrqS/JelqPqag+HMlj8jMzIzsvfiuA/6BZDyUgDOBPcsYl5mZVbms96DeERHnAq9GxGeBI4F9yheWmZlVu6wVVH5QbrOktwBtwOTyhGRmZpb9HtSPJe0IfBl4nKSL+Y1li8rMzKpenxWUpBrg/oh4Dbhd0k+AMUWym5uZmZVMn018EZEDvlnwusWVk5mZlVvWe1D3SzpDksoajZmZWSprBfVh4DagVdIbkjZKciYJMzMrm6yZJMaXOxAzM7NCWQfqStLZkv4tfb27pMPLG5qZmVWzrE18/00yOPd96etNFHScMDMzK7WsFdQREXEB6YDdiHgVqO9rI0k3SVor6YmCsjmSXpK0LF1OLnjvCkkrJf1B0gkF5SemZSslXZ7525mZ2bCVtYJqS6d6DwBJk4Bchu2+DZxYpPy/ImJ6utyV7nM/4CyShLQnAv8tqTb93G8CJwH7ATPTdc3MbATLWkHNBX4I7CzpC8CvgP/oa6OIeAjYkPEzTgUWpOOsngNWAoeny8qIWBURrcCCdF0zMxvBeq2gJO0FEBHfBT4BfBFYA5wWEbcN4nMvlLQ8bQLcKS3bFXihYJ0X07KeyovFe76kxZIWr1u3bhDhmVUOH9dWrfq6gvoBgKT7I+L3EfHNiPhGRPxuEJ95LfBWYDpJZffVQeyri4i4ISJmRMSMSZMmlWq3ZkPKx7VVq77GQdVI+iSwj6RLur8ZEVf39wMj4uX8c0k3Aj9JX74E7F6w6m5pGb2Um5nZCNXXFdRZQAdJRTa+yNJvkgqn6XgPkO/htxA4S9LotGlxGvAYsAiYJmkvSfVpTAsH8tlmZjZ89HoFFRF/AK6StDwi7u7vziXNB44G3izpReAzwNGSppP0CFxNkkaJiHhS0q3AU0A7cEFEdKT7uRC4B6gFboqIJ/sbi5mZDS9Z54PaR9LDwEbgW8DBwOURcW9vG0XEzCLF/9PL+l8AvlCk/C7groyxmpnZCJC1m/kHI+IN4HhgAnAO8KWyRWVmZiOOpOMkfTRdju1r/awVVH6ajZOBm9MmNk+9YWZmfZK0q6THgE8DU9Ll05IelVR02BBkb+JbIuleYC/gCknjyZZJwszM7FpgbkTcUlgo6VySTEGnFdso6xXUh4DLgcMiopkkD98HBh6rmZlVkb/sXjkBRMTNJCnsiso6H1RO0svAfpKyXnWZmZlBMmtTbb5ndmEhvdwuylTZSLoK+AeSLuD5DwjgoYHFamZmVeQu4FpJl6Yd7khvFf0XW5M1bCPr1dBpwL4R0TLoMM3MrNpcSjKE6FlJL5Bc4OwJ3AR8sqeNslZQq4BRgCsoMzPrl4hoAz4h6dPA1LR4ZURs6W27rBVUM7BM0v0UVFIRMXsgwZqZWXWRdBRJ9/J8vXMoMK+3bbJWUAtx/jszMxsASbeQVE7L2DpESZSigoqIXndiZmbWi0OA/SMi+rNR1l5800gmK9wPGJMvj4i9+/NhZmZWlX5HMlXSC32tWChrE9//kmQi/y/gGJJBulkH+ZqZWXXbCXgiTXfU2TEiIt7d20ZZK6ixEXG/JEXE88AcSUtI8iqZmZn1Zs5ANspaQbVIqgGeSedmegkYN5APNDOz6hIR2yR1kPRF+kj2kLWZ7iKgAZhN0jXwbGBWP2M0M7MqJOmLktZL2pgubwAfT59f0dN2fV5BSaoF/iEiLgU24SSxZmbWP6cCOxfm4pP0eEQc0ttGfV5BpTt85+DjMzOzKrWke6JY4Mm+Nsp6D2qppIXAbUBTvjAi7sgen5mZVaOIOEfSfkB+Ft0HIuKcvrbLeg9qDLA+3fm70+XvBhKomZlVl3RiwjuAXdLljrSsV1mvoL4VEQ93+8Cj+h2lmZlVo48D74iIDQCSrgZ+Dtzc20ZZr6C+nrHMzMysu/Z85QQQEa+yNSdfj3q9gpJ0JPAOYJKkSwre2gGoHWCgZmZWXZZK2imtmJC0I7C8r436auKrJxmQWweMLyh/A/j7AQZqZmZVJCI+2O31a2QYS9trBRURvwB+IenbEfG8pHFp+abBBGtmZtVD0h7AXJIWOQGPAB9NU+f1KOs9qPGSlpL0W39S0hJJbxtMwGZmVjX+F/gBMBn4C+BWkunee5W1groBuCQi9oyIPYGPpWVmZmZ9mRgRt0RER7rcAkzsa6OsFVRjRDyYfxERPwcaBxanmZlVmXWS3i+pLl0+AKzra6OsFdQqSf8maUq6fApYNahwzcysWnyAJMHDn9LlFDLkdc1aQX0QmEQyEviO9PkHe90CkHSTpLWSnigomyDpPknPpI87peWSNFfSSknLJR1SsM2sdP1nJDmLupnZMBIRL0bEGRGxc7q8JyJe7Gu7TJkk0r7rswcQ17eBb9B1tPDlwP0R8SVJl6evLwNOAqalyxHAtcARkiaQzOY7AwhgiaSF+f70ZmZW2STdRNJ7r4uI6PUqKlMFJWkf4FJgSuE2EXFsT9uk7z8kaUq34lOBo9Pn80jSXVyWlt8cEQH8RtKOkian695XkCLjPuBEYH6W2M3MbMj9pOD5aJLmvtf62ihrLr7bgOuAbwHdU6b31y4RsSZ9/meSxIEAuwIvFKz3YlrWU/k2JJ0PnA+wxx57DDJMs8rg49qGuyIzX8yX9Ehf22WtoNoj4tr+h9W7iAhJUcL93UDa/X3GjBkl26/ZUPJxbcOdpD0LXtYAB5D0ZehV1grqx5I+AvwQaMkXFib/64eXJU2OiDVpE97atPwlYPeC9XZLy15ia5NgvvznA/hcMzMbGj8muQcVJE18f0HSk69XWXvxzSJJl/5rYEm6LB5QmLCQrTmYZgE/Kig/N+3N93bg9bQp8B7geEk7pT3+jk/LzMxsGIiIAyPigPRxX+BIoDTzQUXEXgMJStJ8kqufN0t6kaQ33peAWyV9CHgeeG+6+l3AycBKoJm0j3xEbJD078CidL3PDfDKzczMKkBEPJXOltGrvqbbODYiHpB0eg8f0uuU7xExs4e3jiuybgAX9LCfm8iQt8nMzCpPt27mNcD+wG/62q6vK6i/Bh4g6RLYXZAM2jUzM+tNYTfzduCrETG4+aAi4jPpY58pKczMzIqJiDskvRl4O8nFzUtZtss6UHdHkhtaU+g6UHcg2SXMzKyKSDqaJLPQwyQd3Z6S9PmIuK+37bJ2M7+LpL1wBRnmkTczMyvwn8BxEfGspMdJsgHdD5SkghoTEZcMMkAzM6tOoyPi2fS5ImKzpPq+Nso6Duo7ks6TNDnNRj4hTeJqZmbWl5DUkD4fJekTwLO9bQDZr6BagS8DV5Lc4CJ93Lu/UZqZWdX5FEkO1WeAR0iySfTZ+S5rBfUxYGpEvDLg8MzMrCpFxE8Knp+XdbusTXz57A5mZmbbRdYrqCZgmaQH6Zos1t3MzcysLLJWUHemSyGn/Tczs7LJmix2XuFrSbsDZ5UlIjMzG5EkXRoRX8k/9rV+1ntQSJok6SOSfkkyH9MufWxiZmZW6B/Tx/dlWbmvbObjgdPTne1Dkhx2r4jYbTARmpmZ9aWvJr61wGMkfdh/lU7R/p7yh2VmZtWurya+K0gGVP03cIWkt5Y/JDMzsz4qqIj4WkS8HTg1LboTeIukyyTtU/bozMysamXqJBERqyLiPyLiAGAGsANJhnMzM7OsfpE+PpRl5cy9+PIi4omIuDIipubLJD3S3/2YmVl1iYiLCx/70u8KqgdjSrQfMzMzoHQVlLNKmJlZSZWqgjIzMyupUlVQKtF+zMxshJH0j5JGFSmfKumgnrbrT6qjPSW9K30+Ns0ykXdOv6I1M7NqchPw0271BiS3h67taaNMFZSk84AfANenRbtRkN08Ip7oV6hmZlZNngR+BPxc0uR8YUQ8C3SvtDplvYK6ADgKeCPd6TPAzgMO1czMqokiYi7weeBBSYcDpJVVR08bZZ0PqiUiWqXkVpOkOtxzz8zMsgmAiPihpBeBb0tqI7nQ6XFMVNYK6heSPgmMlfQ3wEeAHw8yYDMzqw7vzz+JiEXA/mlu13UR8UZPG2Vt4rscWAesAD5MkuboUwMO1czMqkZELC9S9mxvlRNkn1E3B9yYLiUhaTWwkaT9sT0iZkiaAHwfmAKsBt4bEa8qaVu8BjgZaAbeHxGPlyoWMzOrPFl78R0l6T5JT0taJek5SatK8PnHRMT0iJiRvr4cuD8ipgH3p68BTgKmpcv59NIt0czMRoas96D+B/hXYAm99LgogVOBo9Pn80imlr8sLb85IgL4jaQdJU2OiDVljMXMzIZQ1ntQr0fE3RGxNiLW55dBfnYA90paIun8tGyXgkrnz8Au6fNdgRcKtn0xLetC0vmSFktavG7dukGGZ1YZfFxbtcp6BfWgpC8DdwAt+cJB3gd6Z0S8JGln4D5Jvy98M51evl9d2SPiBuAGgBkzZrgbvI0IPq6tWmWtoI5IH2cUlAVw7EA/OCJeSh/XSvohcDjwcr7pLh3AtTZd/SVg94LNd0vLzMxshMrai++YUn6opEagJiI2ps+PBz4HLARmAV9KH3+UbrIQuFDSApLK8nXffzIzG9l6raAknR0Rt0i6pNj7EXH1AD93F+CHaWaKOuB7EfFTSYuAWyV9CHgeeG+6/l0kXcxXknQz/8AA6/B5zAAAEj1JREFUP9fMzIaJvq6gGtPHHpP5DURErAK2SbGedrw4rkh5kOQDNDOzKtFrBRUR16ePn90+4ZiZmSUy3YOStBfwUZIMD53bRMQp5QnLzMyqXdZefHeSDNb9MZArXzhmZmaJrBXUlnQuDzMzs+0iawV1jaTPAPdSuoG6ZmZmPcpaQR0AnEMyMDffxDeogbpmZma9yVpBnQnsHRGt5QzGzMwsL2uy2CeAHcsZiJmZWaGsV1A7Ar9PMz0U3oNyN3MzMyuLrBXUZ8oahZmZWTdZk8X+otyBmJmZFco65fvbJS2StElSq6QOSW+UOzgzM6teWTtJfAOYCTwDjAX+CfhmuYIyMzPLWkERESuB2ojoiIj/BU4sX1hmZlbtsnaSaJZUDyyT9J/AGvpRuZmZmfVX1krmHKAWuBBoIpl+/YxyBWVmI1suF2xqaScX6WMuhjokq0BZe/E9nz7dDHhuKDMbsFwuWN/Uyuz5S1m0egOHTZnA3JkHM7GxnpoaDXV4VkH6mvJ9BUnOvWIiIraZFbda5XJBc1sHDfW1NLd20DCq1j82syKa2zqYPX8pj6xaD8Ajq9Yze/5Sbpw1g3Gjs951sGrQ19Hwd0XKRNLEd0XpwxmefEZo1WigJ2UN9bUsWr2hS9mi1RtoqK8tV6j/v71zD46ruu/456vVy5IMtmzwGGxijINb86hsDI2nQHmkGMgDE6CxSiYm7ZCEJNhOBgoU2rgpSTGkGXDoMBMnDq/U7lADoSWueQQT8yh+YOFHwLawjYG6fhJjSUjyrn79456VV+uVtJJW0kr+fWbu3LNn7577073fq/M75/zuOc4ApcMxKDN7L7kBlURjUCuAHwC/6X3zBgapHmG8xVo9wobDif42zXF6haRTduMjazj9zmXc+Mga9tc3ZzWW1NCc4NxxlW3yzh1XSUOzPy9OWzqsoCSdLun7kt4BfgrsBGRmF5vZg31i4QDAPUJnsJBt8EJPnLKyohgLqiczbfwICgvEtPEjWFA9mbIif16ctnTWxfcOsBL4fHgPCknf7XWrBhhJjzDZpw5HPELvU3cGCl3pqu6JU1ZQIEaUF7Nw1lQfs3U6pLMw8y8RvfP0kqSFki4lGoMalHQ39NU9QmcgkKrvhuY4dY1ttd6VVlFPu+kKCkRFSSEFCnuvnDz0PgMduvdm9jTwtKRy4CpgLnCipIeAp8zsuT6wsU/oSaCDe4ROPtPSYpEmS2LsO9TEy1v2cOkfj+LWJ9a30XpleVHWraKkU5b+vLhT1j080CozWb2oa2b1ZvZvZvYFYAywDritVy3rY3oa6OAeoZOPtAYzPBoFM7y96yBfmjKGW59Yf7TWu9AqSnXKtvzwChbOmnrM/zPtCR5olZkuD5CY2UfAz8I2aMh1oEMi0ULD4QTlJYXUN8UpK4oRi/nsUE7f0nA4weI33uPH153NiPJimhNGeUlhRq2XlxSyoLqK2YtrWr34B6qrGFKYWbdJpwzwsdYe4oFWmXFVBRqbE7x6+yWUFxdSVhLjQH0zZUUxGpqirpGGpjhlxdm1jBKJFvbXNzNnScqDPrOKEeXFXkk5fUJyTGlIUQFfO/9UYgVqrZw+/uQwsy+ZwE9e2Np6/LnjKqlvilNZXszPvnpO63EFgqZEC2Wu217FA60y46ojqlAACgSLXtnG1t11DBtSRMKMsuIYW3fXseiV7eyvb6IhyxDcJat2Mu+LZ7D57iuY98UzWLJqZ2tz3QdDnd4kHm+hvjlOWXGMpsMtJILevvHYWibetYybHn+T6j89hTf+7lJmVJ3EtPEjopZSUYzaPfX88pXt7PpDI6+/uw8QpUUxDjUeJpFoyajdzvTc5vvGOA3Nrv10PNAqMzIbOAKRdDnwANHEtT83s3vaO3bq1Km2Zs2adstKfQu+rjFOUzzB4jd2MmPyGJ5e9wEzJo/htqVHBpHnX3M2T6/7gOumjuWCe1/qcBAz0dLChx81HvX7k4eXIuSDoQ50Mxq2M10nW+9LVu3k6iljOHn4EBqbE+w51MTYyjJq99Tx+rv7uGjiiZwyooy6xjhFMbHwd9tY8NvaVq1u+t8/MOVTlcxd0ra7ryRWwDcffzNFu1UUxwp45LUdTD9zNBNOrKC+KU55cdSlnWnw/77rzubHyzez++Mm134Kg2S6tJwaPGBaUJJiRIskXgFMAqolTepOWelvwT/86nZGVJQw/czR3LZ0fes+dcAymT/q+NKsQnAz/b6hOeGDoU6vkmy9z5g8hr/9j/V8799rqG+Oc8eTG5h41zKWb9zFlWeN5o4nN3D6ncv4xmNrOdQUp3ZvfRutTjttJHOX1LTR6ZzFNXzUcDhNuzU0xluYMXkM857ZxMS7ojL3NzS3G7p+6xPruemiCa79NDzQ6mgGTAUFnAfUmtk2M2sGlhCFvneZ9Idm+pmj2bm/gQknVrB6x4HWfSrJ/IamRJu8TIOYHQ1C+2Co05uUlxS2cbBuumgCsxfXtNH6nAwVz7cvntBaxuodBzhuSOaQ87GVZUfllRcXHuWQzVlc09oaaO9ZSqZd+057DKQK6mTg/ZTPH4S8LpP+0Ew4sYL7X9hCXWOcc8dVUrunLmO4bV1TnIOfNLfJyxSC21G4rs9D5vQm9U3xNg5WurPVkfOVJBkwkUmn7x9oOCqvrKR9p6s9vdfuqWtNu/ad9hhIFVSnSPq6pDWS1uzdu7fd49Ifmto9dez+uImn1n3A/TOrWL5xF/OvObvNgOUDM6uICZau/aDTQcyOBjx9MNTpKtnqGqCoQG0ql3Rnqz3n6/0DDUe0Xl3FkMIY98+savsMVFcxvKwoTbtVrY5depnJcZR0vd933dk8tKLWte90yoAJkpA0DZhnZtPD5zsAzOyfMx3f0WBy+sDt7EsmMPO8U5izpIZRx5Uw97OnM7ZyCHVNCYaWFtLQlECC1Tv2M25EBaeMKOs07LyjAc9BMhjq9IxeCZJoaTEaDyeoa44zZ3Gk51umT2ydNSJV66nBDxXFhZQWx6hvivPK1r3cvLiG2ZdMYNafncrQ0iPv8kk6Srtmxv6GZuakvD+VGvzQRu9NCQoKoLTItT9IyenNHEgVVCGwBbgU+BBYDfyVmW3KdHxXovgamhOUxgpojCcoKymkoSlOgURpeKAKC+Bwi/lLt04u6ZUKCkIlFU/Q0gJlJTEamxMk7Ih+hxTG+CSeaPO5MdHS2pJJTovUWpkUdl6JuNPlBHJ60wfMG2BmFpf0HWA5UZj5ovYqp2zI9BZ8RXhjvqK0qPW4itLou9LweWjKd46TjxQUiLLiI492WcqLnkn9Dg1aT36uSJktIqn55D7bc/qsEk6uGVBKMrPf4AslOo7jHBN4P5XjOI6Tl3gF5TiO4+QlAyZIoqtI2gu8Fz6OBPb1ozntkY92uU3Z0VOb9pnZ5V390QDQdT7aBPlp12C0qVu6bo9BW0GlImmNmU3tbzvSyUe73KbsyAeb8sGGdPLRJshPu9ymzvEuPsdxHCcv8QrKcRzHyUuOlQoqX1f/zUe73KbsyAeb8sGGdPLRJshPu9ymTjgmxqAcx3Gcgcex0oJyHMdxBhiDvoKSdLmkzZJqJd3ex+feIWmDpBpJa0JepaTnJW0N++EhX5IWBDvXS5qSIxsWSdojaWNKXpdtkDQrHL9V0qxesGmepA/DtaqRdGXKd3cEmzZLmp6Sn7N7K2mspJck/V7SJklzQn6/XqsO7HVdu66zsWlA6foozGzQbkRz9r0LjAeKgbeASX14/h3AyLS8e4HbQ/p2YH5IXwksI5ps8TPAGzmy4UJgCrCxuzYAlcC2sB8e0sNzbNM84JYMx04K960EODXcz1iu7y0wGpgS0kOJJiae1N/XynXtuj5WdJ1pG+wtqJytwptDrgIeCelHgBkp+Y9axP8AwySN7unJzOx3wIG07K7aMB143swOmNlHwPNAt1/Ga8em9rgKWGJmTWa2Hagluq85vbdmtsvM3gzpQ8DbRAti9uu1agfXtes6W5sGkq6PYrBXUDlbhbebGPCcpLWSvh7yRpnZrpD+P2BUSPelrV21oa9s+07oVliU7HLoD5skjQMmA2+Qn9fKdZ2ZfLxX4LruNoO9gupvzjezKcAVwLclXZj6pUVt534No8wHGwIPAacBVcAu4F/6wwhJFcBSYK6ZfZz6XR5dq/7GdZ09ruseMNgrqA+BsSmfx4S8PsHMPgz7PcBTRM333ckujrDf0w+2dtWGXrfNzHabWcLMWoCFRNeqT22SVET0EP/KzJ4M2Xl3rfroHO3ius4e13XPGOwV1Grg05JOlVQMzASe6YsTSyqXNDSZBi4DNobzJyNgZgG/DulngK+GKJrPAAdTmuC5pqs2LAcukzQ8dFFcFvJyRtq4xNVE1ypp00xJJZJOBT4NrCLH91aSgF8Ab5vZT1K+yrtrheu6PfLuXrmue0hvR2H090YUlbKFKDLmzj4873iiCJy3gE3JcwMjgBeBrcALQGXIF/Cvwc4NwNQc2bGYqGvhMFG/8d90xwbgr4kGcmuBr/WCTY+Fc64nekhGpxx/Z7BpM3BFb9xb4Hyibo71QE3Yruzva+W6dl0fS7pO33wmCcdxHCcvGexdfI7jOM4AxSsox3EcJy/xCspxHMfJS7yCchzHcfISr6Acx3GcvMQrKMdxHCcv8Qqql5A0Q5JJ+qPweZzCNPySbpD0YBfKmifplpD+gaTP5tDOiyQdVLQUwNuSvt8dG7tx3ta/Kcvjh0n6Vm/Z42SH67rT87quc4hXUL1HNfBK2OcMM/sHM3shl2UCK82sCpgKfEU5WrMnxwwD/EHuf1zXucV13QFeQfUCYWLG84neJJ/ZybGfk/S6pJGSbpS0WtJbkpZKKstw/MOSrg3pHZL+UdKbihaQS3q15YpmTl4laZ2krKbrN7N6YC0wIWSdJOm/FS1Qdm+KDdXhfBslzQ95sWDbxvDdd0P+CkkPBE92o6TzUk45KXy/TdLslPK/F47dKGluyL4HOC2Uc5+kCkkvpvztV6X87c+Ga7hR0pdD/jmSXlY0A/dy5WDJh2MN17Xrus/pqylSjqUNuB74RUi/BpwDjCMsZAbcADxINDfXSsLCX8CIlDLuBm4O6XmERc+Ah4FrQ3pHyjHfAn4e0j8CvhLSw4imTSlvx9aLgP9Knj+UeUawcRtwPFAKvEc0WeRJwE7gBKAQ+C3RWjLnEK0Xkyx3WNivABaG9IUp12BeuDYlwEhgP1AUytkAlAMVRNPpTE69fuH3hcBxIT2SaPoVAdckzxe+Oz6U+xpwQsj7MrCov3Uy0DbXteu6rzdvQfUO1UQLjRH2mbpDLgFuAz5n0QJgAGdKWilpA9E/gzOyOFdyduK1RGKHaCLH2yXVED1IpcApHZRxgaR1wHPAPWa2KeS/aGYHzawR+D3wKeBcYIWZ7TWzOPArogd0GzBe0k8lXQ6kTum/GFoXdDtO0rCQ/6xFC7btI5pNeRSRh/6UmdWbWV34+y7IYLOAH0laTzSX2Mnh9xuAv5A0X9IFZnYQmAicCTwfrsldRLMxO13Dde267lMK+9uAwYakSqKH9CxJRrSEsxFNwJhKclnn04E1Ie9hYIaZvSXpBiIvsDOawj7Bkfsp4Boz25yl2SvN7PMdlJ1e/lGY2UeS/oRo5c1vAn9JNLkkHL3WTPJz1uVn4Hoib/ccMzssaQdQamZbFI01XAncLelFoiUhNpnZtC6U76TgunZd9wfegso91wKPmdmnzGycmY0FttN2LRWIuhauAR6VlPQohwK7FK3fcn0PbFgO3CxJAJIm96CsdFYBfx7GFmJEXvTLkkYCBWa2lMiTSx2QTvaXn080ff/BDspfCcyQVKZoOYdkd9EhouuT5HhgT3iILybygpF0EtBgZo8D9wU7NgMnSJoWjilKueZOdriuXdd9jregck81MD8tbylwR/qBZvaOpOuBJyR9Afh7ouWY94b90PTfZMk/AfcD6yUVEP0jyeRJdhkz2yXpduAlIo/2WTP7dfAyfxnOB23/3sbQ1VLEEe+zvfLflPQw0T8MiMYf1gFIelVRSPMyomv8n6HbaA3wTjj+LOA+SS1Eyx7cZGbNigbgF0g6nkj39xONAzjZ4bqOcF33Ib7chtOrSFpBNBC+prNjHWeg4LruG7yLz3Ecx8lLvAV1jCBpOkd30Ww3s6v7wx7HyQWu68GNV1CO4zhOXuJdfI7jOE5e4hWU4ziOk5d4BeU4juPkJV5BOY7jOHmJV1CO4zhOXvL/qBRbDElh7xAAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "da7eef26-8d4a-4c05-9c80-570b11499bc9",
        "_uuid": "21739cb6eaf4ac688108a1806ac71cb940071493",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "DWEHcF3E4SoF",
        "outputId": "76a82723-04ff-4008-8b77-af6d9b65d7fe"
      },
      "cell_type": "code",
      "source": [
        "sns.jointplot(\"Alkaline_Phosphotase\", \"Alamine_Aminotransferase\", data=liver_df, kind=\"reg\")"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f855818e650>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "8269092e-f173-446f-aaaa-257b75d15633",
        "_uuid": "2b63345e2bb199ede233e177754ee5347d0350db",
        "id": "CoJ7XEke4SoG"
      },
      "cell_type": "markdown",
      "source": [
        "No linear correlation between Alkaline_Phosphotase and Alamine_Aminotransferase"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "9d767027-3dda-43aa-8cc1-f908d631d992",
        "_uuid": "716befce65e13a87415987a9885eb36f977be73e",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "KwkxitJG4SoG",
        "outputId": "31b5fc48-d306-4391-9221-fe9da8f285a1"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Total_Protiens\", \"Albumin\",  edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "cd3415c5-e60a-45dc-a9ce-6e33561715ea",
        "_uuid": "2d09fb0a3ec7978315959d87667cd068b2e10e02",
        "id": "1MLIjEsm4SoG"
      },
      "cell_type": "markdown",
      "source": [
        "There is linear relationship between Total_Protiens and Albumin and the gender. We have the possibility of removing one of this feature."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b18f4c6f-7742-4edd-a0d3-be517b953d35",
        "_uuid": "58a74e764ac2bb213ef0701846ca66123a89b946",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Oha3e-vZ4SoG",
        "outputId": "e061d37b-04e3-4733-eb41-f22ee2e29301"
      },
      "cell_type": "code",
      "source": [
        "sns.jointplot(\"Total_Protiens\", \"Albumin\", data=liver_df, kind=\"reg\")"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f8559259e90>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "c2f9376c-218b-458c-9ef9-0246f7f30c6f",
        "_uuid": "d64847416d1a611e041aed536b5391565a1e247b",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "W9h3gUBt4SoG",
        "outputId": "661e278b-ebdf-4885-93fb-2f9598eab3dd"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Albumin\", \"Albumin_and_Globulin_Ratio\",  edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b82e75f5-f87c-4788-bd96-3608b9066cb0",
        "_uuid": "e1acbd8a2f18bad8f794a2a08b300a6c1aae935a",
        "id": "G4YkxAvi4SoG"
      },
      "cell_type": "markdown",
      "source": [
        "There is linear relationship between Albumin_and_Globulin_Ratio and Albumin. We have the possibility of removing one of this feature."
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7d80b0d8-1fa0-47ef-9667-9e1621752d9b",
        "_uuid": "84126004350eca1fad4180dd154d301bdc2c1c9e",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "pzxT1JLq4SoH",
        "outputId": "f087261e-344a-40f6-91fd-49c2399cfdac"
      },
      "cell_type": "code",
      "source": [
        "sns.jointplot(\"Albumin_and_Globulin_Ratio\", \"Albumin\", data=liver_df, kind=\"reg\")"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f8557a7cb10>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "820ae8a1-03b8-4cc5-8135-84d631f49fad",
        "_uuid": "2f564e383e82994d7710f13b5cf94023486af391",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "lhgOiOaC4SoH",
        "outputId": "ddfb2200-93b9-4436-a2a8-2176a6fb7b4e"
      },
      "cell_type": "code",
      "source": [
        "g = sns.FacetGrid(liver_df, col=\"Gender\", row=\"Outcome\", margin_titles=True)\n",
        "g.map(plt.scatter,\"Albumin_and_Globulin_Ratio\", \"Total_Protiens\",  edgecolor=\"w\")\n",
        "plt.subplots_adjust(top=0.9)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "79d7d365-9eab-453e-adc1-97b02461c8a1",
        "_uuid": "730a2d0a33cdb3db4ec8e783dbc9e03470af1f4f",
        "id": "09xZqYuX4SoH"
      },
      "cell_type": "markdown",
      "source": [
        "# Observation:"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "824ec774-42ef-4fad-849f-82893dd03849",
        "_uuid": "5b9a21eded807cc989db8eab46435ee77bc74e50",
        "id": "Qv_MdNWq4SoH"
      },
      "cell_type": "markdown",
      "source": [
        "From the above jointplots and scatterplots, we find direct relationship between the following features:  \n",
        "Direct_Bilirubin & Total_Bilirubin  \n",
        "Aspartate_Aminotransferase & Alamine_Aminotransferase  \n",
        "Total_Protiens & Albumin  \n",
        "Albumin_and_Globulin_Ratio & Albumin  \n",
        "\n",
        "Hence, we can very well find that we can omit one of the features. I'm going to keep the follwing features:  \n",
        "Total_Bilirubin  \n",
        "Alamine_Aminotransferase  \n",
        "Total_Protiens  \n",
        "Albumin_and_Globulin_Ratio  \n",
        "Albumin  "
      ]
    },
    {
      "metadata": {
        "_cell_guid": "af447fa3-486a-4c9f-a0bb-143d240a8b41",
        "_uuid": "6b2c57a7dc86c5a48f4749fb0ff55f76bf1ce2c1",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "XAbwmkas4SoH",
        "outputId": "64f0bd72-a422-4128-facd-a47dc91e0657"
      },
      "cell_type": "code",
      "source": [
        "liver_df.head(3)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "0   65  Female              0.7               0.1                   187   \n",
              "1   62    Male             10.9               5.5                   699   \n",
              "2   62    Male              7.3               4.1                   490   \n",
              "\n",
              "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "0                        16                          18             6.8   \n",
              "1                        64                         100             7.5   \n",
              "2                        60                          68             7.0   \n",
              "\n",
              "   Albumin  Albumin_and_Globulin_Ratio  Outcome  \n",
              "0      3.3                        0.90        1  \n",
              "1      3.2                        0.74        1  \n",
              "2      3.3                        0.89        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16a4f1a4-fca9-403c-95bd-bda5c3b59631\">\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>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16a4f1a4-fca9-403c-95bd-bda5c3b59631')\"\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-16a4f1a4-fca9-403c-95bd-bda5c3b59631 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-16a4f1a4-fca9-403c-95bd-bda5c3b59631');\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": 53
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e1bd4a7c-ee90-480d-89d4-981ab14ad59f",
        "_uuid": "de6437c17e7114936ceaafcf2c292cd8660391f0",
        "id": "oW6imwxc4SoI"
      },
      "cell_type": "markdown",
      "source": [
        "Convert categorical variable \"Gender\" to indicator variables"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "21a8680c-ee95-4943-97a4-699a6936683a",
        "_uuid": "e3eda36cb69d136d9f05502abae8760acf45349d",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "C9nQN2gP4SoI",
        "outputId": "0312fd05-2f1f-4f88-a622-fa3add44b92e"
      },
      "cell_type": "code",
      "source": [
        "pd.get_dummies(liver_df['Gender'], prefix = 'Gender').head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender_Female  Gender_Male\n",
              "0              1            0\n",
              "1              0            1\n",
              "2              0            1\n",
              "3              0            1\n",
              "4              0            1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f65aae4-dc25-4d83-8af3-68879fd76095\">\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>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f65aae4-dc25-4d83-8af3-68879fd76095')\"\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-0f65aae4-dc25-4d83-8af3-68879fd76095 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-0f65aae4-dc25-4d83-8af3-68879fd76095');\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": 54
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "20bc6e16-444b-4319-8ad0-bc3fee31baac",
        "_uuid": "c0072980444041499d7be75dd70863794e2d6a6a",
        "collapsed": true,
        "trusted": true,
        "id": "KtgQtAI_4SoI"
      },
      "cell_type": "code",
      "source": [
        "liver_df = pd.concat([liver_df,pd.get_dummies(liver_df['Gender'], prefix = 'Gender')], axis=1)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "bd025bf6-cad5-4d92-a4bc-d8924b46abe5",
        "_uuid": "6755149ea5a286853b00e64cedd297a6a1564d72",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "xCuaF_6P4SoI",
        "outputId": "f2f3dd83-2664-4e23-a4c2-7960d85aa9b3"
      },
      "cell_type": "code",
      "source": [
        "liver_df.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "0   65  Female              0.7               0.1                   187   \n",
              "1   62    Male             10.9               5.5                   699   \n",
              "2   62    Male              7.3               4.1                   490   \n",
              "3   58    Male              1.0               0.4                   182   \n",
              "4   72    Male              3.9               2.0                   195   \n",
              "\n",
              "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "0                        16                          18             6.8   \n",
              "1                        64                         100             7.5   \n",
              "2                        60                          68             7.0   \n",
              "3                        14                          20             6.8   \n",
              "4                        27                          59             7.3   \n",
              "\n",
              "   Albumin  Albumin_and_Globulin_Ratio  Outcome  Gender_Female  Gender_Male  \n",
              "0      3.3                        0.90        1              1            0  \n",
              "1      3.2                        0.74        1              0            1  \n",
              "2      3.3                        0.89        1              0            1  \n",
              "3      3.4                        1.00        1              0            1  \n",
              "4      2.4                        0.40        1              0            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c74911ff-a301-4709-a249-85924aa80995\">\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>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>Male</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>58</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>182</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>72</td>\n",
              "      <td>Male</td>\n",
              "      <td>3.9</td>\n",
              "      <td>2.0</td>\n",
              "      <td>195</td>\n",
              "      <td>27</td>\n",
              "      <td>59</td>\n",
              "      <td>7.3</td>\n",
              "      <td>2.4</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c74911ff-a301-4709-a249-85924aa80995')\"\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-c74911ff-a301-4709-a249-85924aa80995 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-c74911ff-a301-4709-a249-85924aa80995');\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": 56
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "c6011e12-8e58-4e38-aa82-33bd336723ce",
        "_uuid": "6b834a47790a238c390fc7b552587c6fc6bcb24d",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "A4agpyIX4SoI",
        "outputId": "41853e6f-4c83-4a03-df4c-cbc1aad76ada"
      },
      "cell_type": "code",
      "source": [
        "liver_df.describe()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "count  583.000000       583.000000        583.000000            583.000000   \n",
              "mean    44.746141         3.298799          1.486106            290.576329   \n",
              "std     16.189833         6.209522          2.808498            242.937989   \n",
              "min      4.000000         0.400000          0.100000             63.000000   \n",
              "25%     33.000000         0.800000          0.200000            175.500000   \n",
              "50%     45.000000         1.000000          0.300000            208.000000   \n",
              "75%     58.000000         2.600000          1.300000            298.000000   \n",
              "max     90.000000        75.000000         19.700000           2110.000000   \n",
              "\n",
              "       Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "count                583.000000                  583.000000      583.000000   \n",
              "mean                  80.713551                  109.910806        6.483190   \n",
              "std                  182.620356                  288.918529        1.085451   \n",
              "min                   10.000000                   10.000000        2.700000   \n",
              "25%                   23.000000                   25.000000        5.800000   \n",
              "50%                   35.000000                   42.000000        6.600000   \n",
              "75%                   60.500000                   87.000000        7.200000   \n",
              "max                 2000.000000                 4929.000000        9.600000   \n",
              "\n",
              "          Albumin  Albumin_and_Globulin_Ratio     Outcome  Gender_Female  \\\n",
              "count  583.000000                  579.000000  583.000000     583.000000   \n",
              "mean     3.141852                    0.947064    1.286449       0.243568   \n",
              "std      0.795519                    0.319592    0.452490       0.429603   \n",
              "min      0.900000                    0.300000    1.000000       0.000000   \n",
              "25%      2.600000                    0.700000    1.000000       0.000000   \n",
              "50%      3.100000                    0.930000    1.000000       0.000000   \n",
              "75%      3.800000                    1.100000    2.000000       0.000000   \n",
              "max      5.500000                    2.800000    2.000000       1.000000   \n",
              "\n",
              "       Gender_Male  \n",
              "count   583.000000  \n",
              "mean      0.756432  \n",
              "std       0.429603  \n",
              "min       0.000000  \n",
              "25%       1.000000  \n",
              "50%       1.000000  \n",
              "75%       1.000000  \n",
              "max       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a97d2fde-cc08-4372-b767-c3d24dc4e91a\">\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>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>579.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "      <td>583.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.746141</td>\n",
              "      <td>3.298799</td>\n",
              "      <td>1.486106</td>\n",
              "      <td>290.576329</td>\n",
              "      <td>80.713551</td>\n",
              "      <td>109.910806</td>\n",
              "      <td>6.483190</td>\n",
              "      <td>3.141852</td>\n",
              "      <td>0.947064</td>\n",
              "      <td>1.286449</td>\n",
              "      <td>0.243568</td>\n",
              "      <td>0.756432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.189833</td>\n",
              "      <td>6.209522</td>\n",
              "      <td>2.808498</td>\n",
              "      <td>242.937989</td>\n",
              "      <td>182.620356</td>\n",
              "      <td>288.918529</td>\n",
              "      <td>1.085451</td>\n",
              "      <td>0.795519</td>\n",
              "      <td>0.319592</td>\n",
              "      <td>0.452490</td>\n",
              "      <td>0.429603</td>\n",
              "      <td>0.429603</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.700000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>175.500000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>2.600000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>298.000000</td>\n",
              "      <td>60.500000</td>\n",
              "      <td>87.000000</td>\n",
              "      <td>7.200000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>1.100000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>19.700000</td>\n",
              "      <td>2110.000000</td>\n",
              "      <td>2000.000000</td>\n",
              "      <td>4929.000000</td>\n",
              "      <td>9.600000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a97d2fde-cc08-4372-b767-c3d24dc4e91a')\"\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-a97d2fde-cc08-4372-b767-c3d24dc4e91a 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-a97d2fde-cc08-4372-b767-c3d24dc4e91a');\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": 57
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "f8b7af4e-9b7c-4943-bc0c-d3de640b4544",
        "_uuid": "047946d891f4734a97a0fcc5c60192b4b4b7d74e",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 239
        },
        "id": "gKxMn2OG4SoI",
        "outputId": "6b2c382b-9cd6-4f57-e56d-9158718c41f6"
      },
      "cell_type": "code",
      "source": [
        "liver_df[liver_df['Albumin_and_Globulin_Ratio'].isnull()]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Gender  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "209   45  Female              0.9               0.3                   189   \n",
              "241   51    Male              0.8               0.2                   230   \n",
              "253   35  Female              0.6               0.2                   180   \n",
              "312   27    Male              1.3               0.6                   106   \n",
              "\n",
              "     Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "209                        23                          33             6.6   \n",
              "241                        24                          46             6.5   \n",
              "253                        12                          15             5.2   \n",
              "312                        25                          54             8.5   \n",
              "\n",
              "     Albumin  Albumin_and_Globulin_Ratio  Outcome  Gender_Female  Gender_Male  \n",
              "209      3.9                         NaN        1              1            0  \n",
              "241      3.1                         NaN        1              0            1  \n",
              "253      2.7                         NaN        2              1            0  \n",
              "312      4.8                         NaN        2              0            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc0bbc38-4256-4b26-8566-6d53d8b569d1\">\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>Gender</th>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Outcome</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>45</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.3</td>\n",
              "      <td>189</td>\n",
              "      <td>23</td>\n",
              "      <td>33</td>\n",
              "      <td>6.6</td>\n",
              "      <td>3.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>241</th>\n",
              "      <td>51</td>\n",
              "      <td>Male</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>230</td>\n",
              "      <td>24</td>\n",
              "      <td>46</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>35</td>\n",
              "      <td>Female</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.2</td>\n",
              "      <td>180</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>312</th>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>106</td>\n",
              "      <td>25</td>\n",
              "      <td>54</td>\n",
              "      <td>8.5</td>\n",
              "      <td>4.8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc0bbc38-4256-4b26-8566-6d53d8b569d1')\"\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-dc0bbc38-4256-4b26-8566-6d53d8b569d1 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-dc0bbc38-4256-4b26-8566-6d53d8b569d1');\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": 58
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "a22f10fd-a319-401f-9a4a-1c02a8357975",
        "_uuid": "a00f9b9d4f6b3c086b4bf8b328955a6b49642057",
        "collapsed": true,
        "trusted": true,
        "id": "y2-7fIrM4SoI"
      },
      "cell_type": "code",
      "source": [
        "liver_df[\"Albumin_and_Globulin_Ratio\"] = liver_df.Albumin_and_Globulin_Ratio.fillna(liver_df['Albumin_and_Globulin_Ratio'].mean())"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "ab697d55-f4e6-4e43-87a8-e853f893349c",
        "_uuid": "73b9eacd147f702a0cb8bdd00bb0e6d52d8b8cb1",
        "collapsed": true,
        "trusted": true,
        "id": "tV6RqWqi4SoI"
      },
      "cell_type": "code",
      "source": [
        "#liver_df[liver_df['Albumin_and_Globulin_Ratio'] == 0.9470639032815201]"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "f2cc0e7b-0e62-4cd4-99a7-e213b983b9e4",
        "_uuid": "cdab8936d73dfe0533c10d723739df09b73c23ad",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 207
        },
        "id": "DoLnjtKf4SoJ",
        "outputId": "2b4d8e1f-b39e-457e-b3d1-09905cadece4"
      },
      "cell_type": "code",
      "source": [
        "# The input variables/features are all the inputs except Dataset. The prediction or label is 'Dataset' that determines whether the patient has liver disease or not. \n",
        "X = liver_df.drop(['Gender','Outcome'], axis=1)\n",
        "X.head(3)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  Total_Bilirubin  Direct_Bilirubin  Alkaline_Phosphotase  \\\n",
              "0   65              0.7               0.1                   187   \n",
              "1   62             10.9               5.5                   699   \n",
              "2   62              7.3               4.1                   490   \n",
              "\n",
              "   Alamine_Aminotransferase  Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "0                        16                          18             6.8   \n",
              "1                        64                         100             7.5   \n",
              "2                        60                          68             7.0   \n",
              "\n",
              "   Albumin  Albumin_and_Globulin_Ratio  Gender_Female  Gender_Male  \n",
              "0      3.3                        0.90              1            0  \n",
              "1      3.2                        0.74              0            1  \n",
              "2      3.3                        0.89              0            1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b33ad29-3589-435e-b2f0-3d54fc8afb69\">\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>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>187</td>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>62</td>\n",
              "      <td>10.9</td>\n",
              "      <td>5.5</td>\n",
              "      <td>699</td>\n",
              "      <td>64</td>\n",
              "      <td>100</td>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62</td>\n",
              "      <td>7.3</td>\n",
              "      <td>4.1</td>\n",
              "      <td>490</td>\n",
              "      <td>60</td>\n",
              "      <td>68</td>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b33ad29-3589-435e-b2f0-3d54fc8afb69')\"\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-7b33ad29-3589-435e-b2f0-3d54fc8afb69 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-7b33ad29-3589-435e-b2f0-3d54fc8afb69');\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": 61
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "134edf24-b704-483f-91f6-a000aa7525a5",
        "_uuid": "13e14244f1f54357aba978576a2d1a9515dece6e",
        "collapsed": true,
        "trusted": true,
        "id": "Ampallmh4SoJ"
      },
      "cell_type": "code",
      "source": [
        "y = liver_df['Outcome'] # 1 for liver disease; 2 for no liver disease"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "551ce97e-ab7e-4ffc-a9fd-f10ee1d9332a",
        "_uuid": "3ca13b008594f58b54943bd1c02f2984a10032e1",
        "trusted": true,
        "collapsed": true,
        "id": "354wJLE_4SoJ"
      },
      "cell_type": "code",
      "source": [
        "# Correlation\n",
        "liver_corr = X.corr()"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "8678807f-c33a-4efd-86de-23dc5b0acf80",
        "_uuid": "ab8a278a98ba9b59e8763198652c88233e6fa6d6",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "CMmmARpW4SoJ",
        "outputId": "7924d1db-04d1-4010-a9d3-b14840e7cdbe"
      },
      "cell_type": "code",
      "source": [
        "liver_corr"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Age  Total_Bilirubin  Direct_Bilirubin  \\\n",
              "Age                         1.000000         0.011763          0.007529   \n",
              "Total_Bilirubin             0.011763         1.000000          0.874618   \n",
              "Direct_Bilirubin            0.007529         0.874618          1.000000   \n",
              "Alkaline_Phosphotase        0.080425         0.206669          0.234939   \n",
              "Alamine_Aminotransferase   -0.086883         0.214065          0.233894   \n",
              "Aspartate_Aminotransferase -0.019910         0.237831          0.257544   \n",
              "Total_Protiens             -0.187461        -0.008099         -0.000139   \n",
              "Albumin                    -0.265924        -0.222250         -0.228531   \n",
              "Albumin_and_Globulin_Ratio -0.216089        -0.206159         -0.200004   \n",
              "Gender_Female              -0.056560        -0.089291         -0.100436   \n",
              "Gender_Male                 0.056560         0.089291          0.100436   \n",
              "\n",
              "                            Alkaline_Phosphotase  Alamine_Aminotransferase  \\\n",
              "Age                                     0.080425                 -0.086883   \n",
              "Total_Bilirubin                         0.206669                  0.214065   \n",
              "Direct_Bilirubin                        0.234939                  0.233894   \n",
              "Alkaline_Phosphotase                    1.000000                  0.125680   \n",
              "Alamine_Aminotransferase                0.125680                  1.000000   \n",
              "Aspartate_Aminotransferase              0.167196                  0.791966   \n",
              "Total_Protiens                         -0.028514                 -0.042518   \n",
              "Albumin                                -0.165453                 -0.029742   \n",
              "Albumin_and_Globulin_Ratio             -0.233960                 -0.002374   \n",
              "Gender_Female                           0.027496                 -0.082332   \n",
              "Gender_Male                            -0.027496                  0.082332   \n",
              "\n",
              "                            Aspartate_Aminotransferase  Total_Protiens  \\\n",
              "Age                                          -0.019910       -0.187461   \n",
              "Total_Bilirubin                               0.237831       -0.008099   \n",
              "Direct_Bilirubin                              0.257544       -0.000139   \n",
              "Alkaline_Phosphotase                          0.167196       -0.028514   \n",
              "Alamine_Aminotransferase                      0.791966       -0.042518   \n",
              "Aspartate_Aminotransferase                    1.000000       -0.025645   \n",
              "Total_Protiens                               -0.025645        1.000000   \n",
              "Albumin                                      -0.085290        0.784053   \n",
              "Albumin_and_Globulin_Ratio                   -0.070024        0.233904   \n",
              "Gender_Female                                -0.080336        0.089121   \n",
              "Gender_Male                                   0.080336       -0.089121   \n",
              "\n",
              "                             Albumin  Albumin_and_Globulin_Ratio  \\\n",
              "Age                        -0.265924                   -0.216089   \n",
              "Total_Bilirubin            -0.222250                   -0.206159   \n",
              "Direct_Bilirubin           -0.228531                   -0.200004   \n",
              "Alkaline_Phosphotase       -0.165453                   -0.233960   \n",
              "Alamine_Aminotransferase   -0.029742                   -0.002374   \n",
              "Aspartate_Aminotransferase -0.085290                   -0.070024   \n",
              "Total_Protiens              0.784053                    0.233904   \n",
              "Albumin                     1.000000                    0.686322   \n",
              "Albumin_and_Globulin_Ratio  0.686322                    1.000000   \n",
              "Gender_Female               0.093799                    0.003404   \n",
              "Gender_Male                -0.093799                   -0.003404   \n",
              "\n",
              "                            Gender_Female  Gender_Male  \n",
              "Age                             -0.056560     0.056560  \n",
              "Total_Bilirubin                 -0.089291     0.089291  \n",
              "Direct_Bilirubin                -0.100436     0.100436  \n",
              "Alkaline_Phosphotase             0.027496    -0.027496  \n",
              "Alamine_Aminotransferase        -0.082332     0.082332  \n",
              "Aspartate_Aminotransferase      -0.080336     0.080336  \n",
              "Total_Protiens                   0.089121    -0.089121  \n",
              "Albumin                          0.093799    -0.093799  \n",
              "Albumin_and_Globulin_Ratio       0.003404    -0.003404  \n",
              "Gender_Female                    1.000000    -1.000000  \n",
              "Gender_Male                     -1.000000     1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a43ac699-420b-4f0a-826b-f11618dca420\">\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>Total_Bilirubin</th>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <th>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.011763</td>\n",
              "      <td>0.007529</td>\n",
              "      <td>0.080425</td>\n",
              "      <td>-0.086883</td>\n",
              "      <td>-0.019910</td>\n",
              "      <td>-0.187461</td>\n",
              "      <td>-0.265924</td>\n",
              "      <td>-0.216089</td>\n",
              "      <td>-0.056560</td>\n",
              "      <td>0.056560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Bilirubin</th>\n",
              "      <td>0.011763</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.874618</td>\n",
              "      <td>0.206669</td>\n",
              "      <td>0.214065</td>\n",
              "      <td>0.237831</td>\n",
              "      <td>-0.008099</td>\n",
              "      <td>-0.222250</td>\n",
              "      <td>-0.206159</td>\n",
              "      <td>-0.089291</td>\n",
              "      <td>0.089291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Direct_Bilirubin</th>\n",
              "      <td>0.007529</td>\n",
              "      <td>0.874618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.234939</td>\n",
              "      <td>0.233894</td>\n",
              "      <td>0.257544</td>\n",
              "      <td>-0.000139</td>\n",
              "      <td>-0.228531</td>\n",
              "      <td>-0.200004</td>\n",
              "      <td>-0.100436</td>\n",
              "      <td>0.100436</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alkaline_Phosphotase</th>\n",
              "      <td>0.080425</td>\n",
              "      <td>0.206669</td>\n",
              "      <td>0.234939</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.125680</td>\n",
              "      <td>0.167196</td>\n",
              "      <td>-0.028514</td>\n",
              "      <td>-0.165453</td>\n",
              "      <td>-0.233960</td>\n",
              "      <td>0.027496</td>\n",
              "      <td>-0.027496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alamine_Aminotransferase</th>\n",
              "      <td>-0.086883</td>\n",
              "      <td>0.214065</td>\n",
              "      <td>0.233894</td>\n",
              "      <td>0.125680</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.791966</td>\n",
              "      <td>-0.042518</td>\n",
              "      <td>-0.029742</td>\n",
              "      <td>-0.002374</td>\n",
              "      <td>-0.082332</td>\n",
              "      <td>0.082332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aspartate_Aminotransferase</th>\n",
              "      <td>-0.019910</td>\n",
              "      <td>0.237831</td>\n",
              "      <td>0.257544</td>\n",
              "      <td>0.167196</td>\n",
              "      <td>0.791966</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.025645</td>\n",
              "      <td>-0.085290</td>\n",
              "      <td>-0.070024</td>\n",
              "      <td>-0.080336</td>\n",
              "      <td>0.080336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total_Protiens</th>\n",
              "      <td>-0.187461</td>\n",
              "      <td>-0.008099</td>\n",
              "      <td>-0.000139</td>\n",
              "      <td>-0.028514</td>\n",
              "      <td>-0.042518</td>\n",
              "      <td>-0.025645</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.784053</td>\n",
              "      <td>0.233904</td>\n",
              "      <td>0.089121</td>\n",
              "      <td>-0.089121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albumin</th>\n",
              "      <td>-0.265924</td>\n",
              "      <td>-0.222250</td>\n",
              "      <td>-0.228531</td>\n",
              "      <td>-0.165453</td>\n",
              "      <td>-0.029742</td>\n",
              "      <td>-0.085290</td>\n",
              "      <td>0.784053</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.686322</td>\n",
              "      <td>0.093799</td>\n",
              "      <td>-0.093799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albumin_and_Globulin_Ratio</th>\n",
              "      <td>-0.216089</td>\n",
              "      <td>-0.206159</td>\n",
              "      <td>-0.200004</td>\n",
              "      <td>-0.233960</td>\n",
              "      <td>-0.002374</td>\n",
              "      <td>-0.070024</td>\n",
              "      <td>0.233904</td>\n",
              "      <td>0.686322</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003404</td>\n",
              "      <td>-0.003404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Female</th>\n",
              "      <td>-0.056560</td>\n",
              "      <td>-0.089291</td>\n",
              "      <td>-0.100436</td>\n",
              "      <td>0.027496</td>\n",
              "      <td>-0.082332</td>\n",
              "      <td>-0.080336</td>\n",
              "      <td>0.089121</td>\n",
              "      <td>0.093799</td>\n",
              "      <td>0.003404</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gender_Male</th>\n",
              "      <td>0.056560</td>\n",
              "      <td>0.089291</td>\n",
              "      <td>0.100436</td>\n",
              "      <td>-0.027496</td>\n",
              "      <td>0.082332</td>\n",
              "      <td>0.080336</td>\n",
              "      <td>-0.089121</td>\n",
              "      <td>-0.093799</td>\n",
              "      <td>-0.003404</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a43ac699-420b-4f0a-826b-f11618dca420')\"\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-a43ac699-420b-4f0a-826b-f11618dca420 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-a43ac699-420b-4f0a-826b-f11618dca420');\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": 64
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7f17a7ba-96e2-4d91-899c-2378dc63f8fa",
        "_uuid": "44e9f67a5c8d33edaa35d750616a5ef8ae292412",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N8bFhRCM4SoJ",
        "outputId": "474ed882-b379-4507-9d22-8822977fe311"
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30, 30))\n",
        "sns.heatmap(liver_corr, cbar = True,  square = True, annot=True, fmt= '.2f',annot_kws={'size': 15},\n",
        "           cmap= 'coolwarm')\n",
        "plt.title('Correlation between features');"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x2160 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "e5ddc363-f1eb-4225-979b-64d86c2aa578",
        "_uuid": "7879565f25f9f5c349c8e39c506f57243d597636",
        "collapsed": true,
        "trusted": true,
        "id": "s2hbEJpg4SoJ"
      },
      "cell_type": "code",
      "source": [
        "#The above correlation also indicates the following correlation\n",
        "# Total_Protiens & Albumin\n",
        "# Alamine_Aminotransferase & Aspartate_Aminotransferase\n",
        "# Direct_Bilirubin & Total_Bilirubin\n",
        "# There is some correlation between Albumin_and_Globulin_Ratio and Albumin. But its not as high as Total_Protiens & Albumin"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "d452f2ac-60ed-4330-98a6-70db275af530",
        "_uuid": "458d0033ebbc367888af6082573eef6b7e27e2c9",
        "id": "ln8aCsPC4SoK"
      },
      "cell_type": "markdown",
      "source": [
        "# Machine Learning"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "10d22778-b70f-4190-a277-82b4d020f281",
        "_uuid": "6bc5635a45d3950a0ea94cf088c8571ec8029cbb",
        "collapsed": true,
        "trusted": true,
        "id": "320rs2K74SoK"
      },
      "cell_type": "code",
      "source": [
        "# Importing modules\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neural_network import MLPClassifier"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "ade84f4a-7907-4ff1-a20d-7493ac06c16b",
        "_uuid": "bc317a65e95970b06de98f20e046e1845cb72f75",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loVnxead4SoK",
        "outputId": "b3ebdba2-4f75-40bb-d828-052940f3da2e"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=101)\n",
        "print (X_train.shape)\n",
        "print (y_train.shape)\n",
        "print (X_test.shape)\n",
        "print (y_test.shape)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(408, 11)\n",
            "(408,)\n",
            "(175, 11)\n",
            "(175,)\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "6a33cbbf-4c26-4509-aca6-7d8160a68fa7",
        "_uuid": "79462fc8649ec0e0916e874fc6d8766ef6f3b31a",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "JtgLRS7K4SoK",
        "outputId": "620fc236-e9a6-43ab-99ab-a1f5fe35541c"
      },
      "cell_type": "code",
      "source": [
        "#2) Logistic Regression\n",
        "# Create logistic regression object\n",
        "logreg = LogisticRegression()\n",
        "# Train the model using the training sets and check score\n",
        "logreg.fit(X_train, y_train)\n",
        "#Predict Output\n",
        "log_predicted= logreg.predict(X_test)\n",
        "\n",
        "logreg_score = round(logreg.score(X_train, y_train) * 100, 2)\n",
        "logreg_score_test = round(logreg.score(X_test, y_test) * 100, 2)\n",
        "#Equation coefficient and Intercept\n",
        "print('Logistic Regression Training Score: \\n', logreg_score)\n",
        "print('Logistic Regression Test Score: \\n', logreg_score_test)\n",
        "print('Coefficient: \\n', logreg.coef_)\n",
        "print('Intercept: \\n', logreg.intercept_)\n",
        "print('Accuracy: \\n', accuracy_score(y_test,log_predicted))\n",
        "print('Confusion Matrix: \\n', confusion_matrix(y_test,log_predicted))\n",
        "print('Classification Report: \\n', classification_report(y_test,log_predicted))\n",
        "\n",
        "sns.heatmap(confusion_matrix(y_test,log_predicted),annot=True,fmt=\"d\")\n"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Training Score: \n",
            " 72.06\n",
            "Logistic Regression Test Score: \n",
            " 68.0\n",
            "Coefficient: \n",
            " [[-0.00994993 -0.09851216 -0.30688722 -0.00082939 -0.01078829 -0.00275598\n",
            "  -0.23899671  0.40208933  0.59475502  0.25335289  0.09115993]]\n",
            "Intercept: \n",
            " [0.36100671]\n",
            "Accuracy: \n",
            " 0.68\n",
            "Confusion Matrix: \n",
            " [[107  17]\n",
            " [ 39  12]]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.73      0.86      0.79       124\n",
            "           2       0.41      0.24      0.30        51\n",
            "\n",
            "    accuracy                           0.68       175\n",
            "   macro avg       0.57      0.55      0.55       175\n",
            "weighted avg       0.64      0.68      0.65       175\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8554c776d0>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "89140e45-c762-4514-9474-821c9d85447f",
        "_uuid": "12a4e1da350653eb0e1c0307cd1d6d17015df422",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "oGu1Xu9v4SoK",
        "outputId": "92ce7025-973a-4b97-cb7e-b8e2484154a9"
      },
      "cell_type": "code",
      "source": [
        "coeff_df = pd.DataFrame(X.columns)\n",
        "coeff_df.columns = ['Feature']\n",
        "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
        "pd.Series(logreg.coef_[0])\n",
        "\n",
        "coeff_df.sort_values(by='Correlation', ascending=False)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Feature  Correlation\n",
              "8   Albumin_and_Globulin_Ratio     0.594755\n",
              "7                      Albumin     0.402089\n",
              "9                Gender_Female     0.253353\n",
              "10                 Gender_Male     0.091160\n",
              "3         Alkaline_Phosphotase    -0.000829\n",
              "5   Aspartate_Aminotransferase    -0.002756\n",
              "0                          Age    -0.009950\n",
              "4     Alamine_Aminotransferase    -0.010788\n",
              "1              Total_Bilirubin    -0.098512\n",
              "6               Total_Protiens    -0.238997\n",
              "2             Direct_Bilirubin    -0.306887"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e179e057-0ce1-47a8-98be-7ac58b7e228c\">\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>Feature</th>\n",
              "      <th>Correlation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Albumin_and_Globulin_Ratio</td>\n",
              "      <td>0.594755</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Albumin</td>\n",
              "      <td>0.402089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Gender_Female</td>\n",
              "      <td>0.253353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Gender_Male</td>\n",
              "      <td>0.091160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alkaline_Phosphotase</td>\n",
              "      <td>-0.000829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Aspartate_Aminotransferase</td>\n",
              "      <td>-0.002756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Age</td>\n",
              "      <td>-0.009950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alamine_Aminotransferase</td>\n",
              "      <td>-0.010788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Total_Bilirubin</td>\n",
              "      <td>-0.098512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Total_Protiens</td>\n",
              "      <td>-0.238997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Direct_Bilirubin</td>\n",
              "      <td>-0.306887</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e179e057-0ce1-47a8-98be-7ac58b7e228c')\"\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-e179e057-0ce1-47a8-98be-7ac58b7e228c 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-e179e057-0ce1-47a8-98be-7ac58b7e228c');\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": 70
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "133bdeb1-d6d9-4eed-8c95-88a4fa237d84",
        "_uuid": "da565cb49c73098981463b6dd5e941cddd2cab9b",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "7rtvVbWG4SoL",
        "outputId": "e83a0a67-d7ae-4fd2-85be-6afbf99c5fce"
      },
      "cell_type": "code",
      "source": [
        "# Gaussian Naive Bayes\n",
        "\n",
        "gaussian = GaussianNB()\n",
        "gaussian.fit(X_train, y_train)\n",
        "#Predict Output\n",
        "gauss_predicted = gaussian.predict(X_test)\n",
        "\n",
        "gauss_score = round(gaussian.score(X_train, y_train) * 100, 2)\n",
        "gauss_score_test = round(gaussian.score(X_test, y_test) * 100, 2)\n",
        "print('Gaussian Score: \\n', gauss_score)\n",
        "print('Gaussian Test Score: \\n', gauss_score_test)\n",
        "print('Accuracy: \\n', accuracy_score(y_test, gauss_predicted))\n",
        "print(confusion_matrix(y_test,gauss_predicted))\n",
        "print(classification_report(y_test,gauss_predicted))\n",
        "\n",
        "sns.heatmap(confusion_matrix(y_test,gauss_predicted),annot=True,fmt=\"d\")"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Gaussian Score: \n",
            " 56.13\n",
            "Gaussian Test Score: \n",
            " 53.14\n",
            "Accuracy: \n",
            " 0.5314285714285715\n",
            "[[44 80]\n",
            " [ 2 49]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.96      0.35      0.52       124\n",
            "           2       0.38      0.96      0.54        51\n",
            "\n",
            "    accuracy                           0.53       175\n",
            "   macro avg       0.67      0.66      0.53       175\n",
            "weighted avg       0.79      0.53      0.53       175\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8554cb9490>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "3ebf85d5-2867-49a8-821d-4566f7d066a0",
        "_uuid": "1613eb76e677ff43f9253c41775f0064a6162b1d",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dm8KJkjj4SoL",
        "outputId": "06468f39-c325-4aec-8757-926bb3ea52de"
      },
      "cell_type": "code",
      "source": [
        "# Random Forest\n",
        "\n",
        "random_forest = RandomForestClassifier(n_estimators=100)\n",
        "random_forest.fit(X_train, y_train)\n",
        "#Predict Output\n",
        "rf_predicted = random_forest.predict(X_test)\n",
        "\n",
        "random_forest_score = round(random_forest.score(X_train, y_train) * 100, 2)\n",
        "random_forest_score_test = round(random_forest.score(X_test, y_test) * 100, 2)\n",
        "print('Random Forest Score: \\n', random_forest_score)\n",
        "print('Random Forest Test Score: \\n', random_forest_score_test)\n",
        "print('Accuracy: \\n', accuracy_score(y_test,rf_predicted))\n",
        "print(confusion_matrix(y_test,rf_predicted))\n",
        "print(classification_report(y_test,rf_predicted))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Score: \n",
            " 100.0\n",
            "Random Forest Test Score: \n",
            " 68.0\n",
            "Accuracy: \n",
            " 0.68\n",
            "[[99 25]\n",
            " [31 20]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.76      0.80      0.78       124\n",
            "           2       0.44      0.39      0.42        51\n",
            "\n",
            "    accuracy                           0.68       175\n",
            "   macro avg       0.60      0.60      0.60       175\n",
            "weighted avg       0.67      0.68      0.67       175\n",
            "\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "43ad5917-e40f-4e2a-bae5-182d2baadb6c",
        "_uuid": "2d8812b1ccb4a56cfde468f223a6539974b54394",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "G9emFXQu4SoL",
        "outputId": "cabd34f4-bff4-44ca-d954-dfc0755d2724"
      },
      "cell_type": "code",
      "source": [
        "###Model evaluation\n",
        "#We can now rank our evaluation of all the models to choose the best one for our problem. \n",
        "models = pd.DataFrame({\n",
        "    'Model': [ 'Logistic Regression', 'Gaussian Naive Bayes','Random Forest'],\n",
        "    'Score': [ logreg_score, gauss_score, random_forest_score],\n",
        "    'Test Score': [ logreg_score_test, gauss_score_test, random_forest_score_test]})\n",
        "models.sort_values(by='Test Score', ascending=False)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Model   Score  Test Score\n",
              "0   Logistic Regression   72.06       68.00\n",
              "2         Random Forest  100.00       68.00\n",
              "1  Gaussian Naive Bayes   56.13       53.14"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d658b54-c7f7-4175-a173-21046971a257\">\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>Model</th>\n",
              "      <th>Score</th>\n",
              "      <th>Test Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>72.06</td>\n",
              "      <td>68.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>100.00</td>\n",
              "      <td>68.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gaussian Naive Bayes</td>\n",
              "      <td>56.13</td>\n",
              "      <td>53.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d658b54-c7f7-4175-a173-21046971a257')\"\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-2d658b54-c7f7-4175-a173-21046971a257 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-2d658b54-c7f7-4175-a173-21046971a257');\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": 73
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "65f3a976213dfb21de9375c8bd75e75b59463aec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34oRohhV4SoL",
        "outputId": "c639dec2-7d3e-45bf-f580-f951d08d2b1a"
      },
      "cell_type": "code",
      "source": [
        "linear = linear_model.LinearRegression()\n",
        "# Train the model using the training sets and check score\n",
        "linear.fit(X_train, y_train)\n",
        "#Predict Output\n",
        "lin_predicted = linear.predict(X_test)\n",
        "\n",
        "linear_score = round(linear.score(X_train, y_train) * 100, 2)\n",
        "linear_score_test = round(linear.score(X_test, y_test) * 100, 2)\n",
        "#Equation coefficient and Intercept\n",
        "print('Linear Regression Score: \\n', linear_score)\n",
        "print('Linear Regression Test Score: \\n', linear_score_test)\n",
        "print('Coefficient: \\n', linear.coef_)\n",
        "print('Intercept: \\n', linear.intercept_)\n",
        "\n",
        "from sklearn.feature_selection import RFE\n",
        "rfe =RFE(linear, n_features_to_select=3)\n",
        "rfe.fit(X,y)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression Score: \n",
            " 13.12\n",
            "Linear Regression Test Score: \n",
            " 8.07\n",
            "Coefficient: \n",
            " [-2.53338525e-03 -4.02775624e-04 -2.29658815e-02 -2.17508414e-04\n",
            " -3.93047877e-04  6.12504741e-05 -5.55814496e-02  8.31023933e-02\n",
            "  6.22099710e-02  4.61371689e-02 -4.61371689e-02]\n",
            "Intercept: \n",
            " 1.5942528884830836\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RFE(estimator=LinearRegression(), n_features_to_select=3)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "baeb67c97907ccb86bfba6cfa3478b5b57e8aa36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQz2JAYH4SoL",
        "outputId": "476beb8b-385c-456e-df6b-35f6a98ddac1"
      },
      "cell_type": "code",
      "source": [
        "for i in range(len(rfe.ranking_)):\n",
        "    if rfe.ranking_[i] == 1:\n",
        "        print(X.columns.values[i])"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total_Protiens\n",
            "Albumin\n",
            "Gender_Male\n"
          ]
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "02e32ee7-5d28-40c4-b3fd-6a945d9a4830",
        "_uuid": "bc5d9679e8ac9ef04048e24eb735e189cfd1aa50",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "8UoF6b-Q4SoL",
        "outputId": "9f3ddd12-448d-4e60-fb82-8727e106b94d"
      },
      "cell_type": "code",
      "source": [
        "#I'm considering seven important features based on recursive feature elimination\n",
        "#finX = liver_df[['Age','Direct_Bilirubin','Total_Protiens','Albumin', 'Gender_Female', 'Gender_Male']]\n",
        "finX = liver_df[['Total_Protiens','Albumin', 'Gender_Male']]\n",
        "finX.head(4)"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Total_Protiens  Albumin  Gender_Male\n",
              "0             6.8      3.3            0\n",
              "1             7.5      3.2            1\n",
              "2             7.0      3.3            1\n",
              "3             6.8      3.4            1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bcff4fb7-e09f-4543-b3c5-ca89c3bb425e\">\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>Total_Protiens</th>\n",
              "      <th>Albumin</th>\n",
              "      <th>Gender_Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.5</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.0</td>\n",
              "      <td>3.3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.8</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcff4fb7-e09f-4543-b3c5-ca89c3bb425e')\"\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-bcff4fb7-e09f-4543-b3c5-ca89c3bb425e 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-bcff4fb7-e09f-4543-b3c5-ca89c3bb425e');\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": 76
        }
      ]
    },
    {
      "metadata": {
        "_cell_guid": "7f8cb246-bf58-4f0a-914b-f4d72e39638c",
        "_uuid": "868e712ae862044058d4390fb26a6d91db86edc2",
        "collapsed": true,
        "trusted": true,
        "id": "ZlAJli_24SoM"
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(finX, y, test_size=0.30, random_state=101)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8f6d358db23a877239ad211bb57b2f952d5c4149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 686
        },
        "id": "egRltdHl4SoM",
        "outputId": "af1f6bf5-ab6a-4a93-9cfb-acad5da82827"
      },
      "cell_type": "code",
      "source": [
        "#Logistic Regression\n",
        "logreg = LogisticRegression()\n",
        "# Train the model using the training sets and check score\n",
        "logreg.fit(X_train, y_train)\n",
        "#Predict Output\n",
        "log_predicted= logreg.predict(X_test)\n",
        "\n",
        "logreg_score = round(logreg.score(X_train, y_train) * 100, 2)\n",
        "logreg_score_test = round(logreg.score(X_test, y_test) * 100, 2)\n",
        "#Equation coefficient and Intercept\n",
        "print('Logistic Regression Training Score: \\n', logreg_score)\n",
        "print('Logistic Regression Test Score: \\n', logreg_score_test)\n",
        "print('Coefficient: \\n', logreg.coef_)\n",
        "print('Intercept: \\n', logreg.intercept_)\n",
        "print('Accuracy: \\n', accuracy_score(y_test,log_predicted))\n",
        "print('Confusion Matrix: \\n', confusion_matrix(y_test,log_predicted))\n",
        "print('Classification Report: \\n', classification_report(y_test,log_predicted))\n",
        "\n",
        "sns.heatmap(confusion_matrix(y_test,log_predicted),annot=True,fmt=\"d\")\n"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Training Score: \n",
            " 71.08\n",
            "Logistic Regression Test Score: \n",
            " 71.43\n",
            "Coefficient: \n",
            " [[-0.58254377  1.08940697 -0.54185122]]\n",
            "Intercept: \n",
            " [-0.20423275]\n",
            "Accuracy: \n",
            " 0.7142857142857143\n",
            "Confusion Matrix: \n",
            " [[120   4]\n",
            " [ 46   5]]\n",
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           1       0.72      0.97      0.83       124\n",
            "           2       0.56      0.10      0.17        51\n",
            "\n",
            "    accuracy                           0.71       175\n",
            "   macro avg       0.64      0.53      0.50       175\n",
            "weighted avg       0.67      0.71      0.63       175\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8554aea510>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.4",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "name": "liver 01.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}