AlzheimersDetection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AlzheimersDetection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNsbbYFsYJH0PtmT7VGjk0D",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mansikataria/AlzheimerDetection/blob/master/AlzheimersDetection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvuy4r_4-osB"
      },
      "source": [
        "## Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Tn3uxYP-uz0"
      },
      "source": [
        "Here, I am going to use the Alzheimer's Feature dataset from Kaggle: \n",
        "\n",
        "This is a tabular dataset with following features:\n",
        "\n",
        "\"Group\" is a target for models\n",
        "```\n",
        "Group --> Class\n",
        "Age --> Age\n",
        "EDUC --> Years of Education\n",
        "SES --> Socioeconomic Status / 1-5\n",
        "MMSE --> Mini Mental State Examination\n",
        "CDR --> Clinical Dementia Rating\n",
        "eTIV --> Estimated total intracranial volume\n",
        "nWBV --> Normalize Whole Brain Volume\n",
        "ASF --> Atlas Scaling Factor\n",
        "```\n",
        "\n",
        "You can download the dataset from kaggle: https://www.kaggle.com/brsdincer/alzheimer-features\n",
        "or Google Drive: https://drive.google.com/file/d/1hLq1MoD7RJzi7D7jlz-diD74p6iMK31Y/view?usp=sharing\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BBin8i9AJsn"
      },
      "source": [
        "Let us now implement a regression model to detect Alzheimer's disease based on data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UCV9douADm_"
      },
      "source": [
        "##Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xTO0NZ0YwaAE"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import scale\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn import model_selection\n",
        "import sys\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, roc_auc_score, roc_curve\n",
        "from xgboost import XGBClassifier"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVl7zCm7ArKw"
      },
      "source": [
        "## Import data to pandas DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tRcKa_LBUlf"
      },
      "source": [
        "# Upload this file in google drive at location: '/content/drive/MyDrive/Colab Notebooks/AlzheimerDetection'\n",
        "ALZHEIMER_FEATURES_FILE_NAME = \"alzheimer.csv\""
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9N9RuGMBRF4",
        "outputId": "f5a2967a-5e43-473b-a4e3-d086d943418a"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)\n",
        "\n",
        "BASE_PATH = '/content/drive/MyDrive/Colab Notebooks/AlzheimerDetection'\n",
        "sys.path.insert(0,BASE_PATH)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSUYId6KAuDr"
      },
      "source": [
        "data = pd.read_csv(BASE_PATH + \"/\" + ALZHEIMER_FEATURES_FILE_NAME)\n",
        "data = AlzheimerData.copy() # for VISUALIZATION\n",
        "data[\"Group\"] = pd.Categorical(data[\"Group\"])\n",
        "data[\"M/F\"] = pd.Categorical(data[\"M/F\"])\n",
        "data[\"SES\"] = pd.Categorical(data[\"SES\"])\n",
        "data[\"CDR\"] = pd.Categorical(data[\"CDR\"])\n",
        "data[\"EDUC\"] = pd.Categorical(data[\"EDUC\"])\n",
        "data[\"Age\"] = pd.Categorical(data[\"Age\"])\n",
        "\n",
        "df = data.select_dtypes(include=[\"float64\",\"int64\",\"int32\"])"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5Hncg4PCqtQ"
      },
      "source": [
        "### Data Information and Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "UcRjv_OrDEar",
        "outputId": "01be4847-a21b-49b0-c65f-e4d9ddd519b8"
      },
      "source": [
        "data.head(5)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Group</th>\n",
              "      <th>M/F</th>\n",
              "      <th>Age</th>\n",
              "      <th>EDUC</th>\n",
              "      <th>SES</th>\n",
              "      <th>MMSE</th>\n",
              "      <th>CDR</th>\n",
              "      <th>eTIV</th>\n",
              "      <th>nWBV</th>\n",
              "      <th>ASF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Nondemented</td>\n",
              "      <td>M</td>\n",
              "      <td>87</td>\n",
              "      <td>14</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1987</td>\n",
              "      <td>0.696</td>\n",
              "      <td>0.883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Nondemented</td>\n",
              "      <td>M</td>\n",
              "      <td>88</td>\n",
              "      <td>14</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2004</td>\n",
              "      <td>0.681</td>\n",
              "      <td>0.876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Demented</td>\n",
              "      <td>M</td>\n",
              "      <td>75</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>23.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1678</td>\n",
              "      <td>0.736</td>\n",
              "      <td>1.046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Demented</td>\n",
              "      <td>M</td>\n",
              "      <td>76</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1738</td>\n",
              "      <td>0.713</td>\n",
              "      <td>1.010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Demented</td>\n",
              "      <td>M</td>\n",
              "      <td>80</td>\n",
              "      <td>12</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1698</td>\n",
              "      <td>0.701</td>\n",
              "      <td>1.034</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Group M/F Age EDUC  SES  MMSE  CDR  eTIV   nWBV    ASF\n",
              "0  Nondemented   M  87   14  2.0  27.0  0.0  1987  0.696  0.883\n",
              "1  Nondemented   M  88   14  2.0  30.0  0.0  2004  0.681  0.876\n",
              "2     Demented   M  75   12  NaN  23.0  0.5  1678  0.736  1.046\n",
              "3     Demented   M  76   12  NaN  28.0  0.5  1738  0.713  1.010\n",
              "4     Demented   M  80   12  NaN  22.0  0.5  1698  0.701  1.034"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oFqLBnvwCttf",
        "outputId": "ba4a1ee2-c78b-4f3c-9fc7-4c91ab686556"
      },
      "source": [
        "print(data.shape)\n",
        "print(\"-.\"*40)\n",
        "print(data.columns)\n",
        "print(\"-.\"*40)\n",
        "print(data.info())\n",
        "print(\"-.\"*40)\n",
        "print(data.describe().T)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(373, 10)\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Index(['Group', 'M/F', 'Age', 'EDUC', 'SES', 'MMSE', 'CDR', 'eTIV', 'nWBV',\n",
            "       'ASF'],\n",
            "      dtype='object')\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 373 entries, 0 to 372\n",
            "Data columns (total 10 columns):\n",
            " #   Column  Non-Null Count  Dtype   \n",
            "---  ------  --------------  -----   \n",
            " 0   Group   373 non-null    category\n",
            " 1   M/F     373 non-null    category\n",
            " 2   Age     373 non-null    category\n",
            " 3   EDUC    373 non-null    category\n",
            " 4   SES     354 non-null    category\n",
            " 5   MMSE    371 non-null    float64 \n",
            " 6   CDR     373 non-null    category\n",
            " 7   eTIV    373 non-null    int64   \n",
            " 8   nWBV    373 non-null    float64 \n",
            " 9   ASF     373 non-null    float64 \n",
            "dtypes: category(6), float64(3), int64(1)\n",
            "memory usage: 16.5 KB\n",
            "None\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "      count         mean         std  ...       50%       75%       max\n",
            "MMSE  371.0    27.342318    3.683244  ...    29.000    30.000    30.000\n",
            "eTIV  373.0  1488.128686  176.139286  ...  1470.000  1597.000  2004.000\n",
            "nWBV  373.0     0.729568    0.037135  ...     0.729     0.756     0.837\n",
            "ASF   373.0     1.195461    0.138092  ...     1.194     1.293     1.587\n",
            "\n",
            "[4 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5LnfMDgoDSBe",
        "outputId": "2582fe94-0115-49a0-b4b9-ab416371faaa"
      },
      "source": [
        "print(data.groupby([\"Group\",\"SES\"])[\"MMSE\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"SES\"])[\"eTIV\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"SES\"])[\"nWBV\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"SES\"])[\"ASF\"].mean())\n",
        "\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Group        SES\n",
            "Converted    1.0    28.619048\n",
            "             2.0    28.571429\n",
            "             3.0    29.142857\n",
            "             4.0    28.000000\n",
            "             5.0          NaN\n",
            "Demented     1.0    23.884615\n",
            "             2.0    24.480000\n",
            "             3.0    25.545455\n",
            "             4.0    23.657895\n",
            "             5.0    22.800000\n",
            "Nondemented  1.0    29.439024\n",
            "             2.0    29.281690\n",
            "             3.0    28.904762\n",
            "             4.0    29.323529\n",
            "             5.0    28.000000\n",
            "Name: MMSE, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        SES\n",
            "Converted    1.0    1495.666667\n",
            "             2.0    1379.142857\n",
            "             3.0    1413.285714\n",
            "             4.0    1518.500000\n",
            "             5.0            NaN\n",
            "Demented     1.0    1677.153846\n",
            "             2.0    1492.360000\n",
            "             3.0    1411.757576\n",
            "             4.0    1450.973684\n",
            "             5.0    1335.800000\n",
            "Nondemented  1.0    1542.707317\n",
            "             2.0    1499.140845\n",
            "             3.0    1471.666667\n",
            "             4.0    1467.088235\n",
            "             5.0    1382.000000\n",
            "Name: eTIV, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        SES\n",
            "Converted    1.0    0.714238\n",
            "             2.0    0.725143\n",
            "             3.0    0.731571\n",
            "             4.0    0.791000\n",
            "             5.0         NaN\n",
            "Demented     1.0    0.714423\n",
            "             2.0    0.719360\n",
            "             3.0    0.714091\n",
            "             4.0    0.714553\n",
            "             5.0    0.710200\n",
            "Nondemented  1.0    0.719220\n",
            "             2.0    0.741803\n",
            "             3.0    0.765452\n",
            "             4.0    0.734088\n",
            "             5.0    0.753500\n",
            "Name: nWBV, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        SES\n",
            "Converted    1.0    1.186524\n",
            "             2.0    1.272857\n",
            "             3.0    1.245857\n",
            "             4.0    1.155500\n",
            "             5.0         NaN\n",
            "Demented     1.0    1.057308\n",
            "             2.0    1.186680\n",
            "             3.0    1.249758\n",
            "             4.0    1.220421\n",
            "             5.0    1.332400\n",
            "Nondemented  1.0    1.150878\n",
            "             2.0    1.190380\n",
            "             3.0    1.211500\n",
            "             4.0    1.211088\n",
            "             5.0    1.269500\n",
            "Name: ASF, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5Iz12lGkUeT",
        "outputId": "adf85639-1027-49dd-c6b8-67d22b2707c3"
      },
      "source": [
        "print(data.groupby([\"Group\",\"CDR\"])[\"MMSE\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"CDR\"])[\"eTIV\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"CDR\"])[\"nWBV\"].mean())\n",
        "print(\"-.\"*40)\n",
        "print(data.groupby([\"Group\",\"CDR\"])[\"ASF\"].mean())"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Group        CDR\n",
            "Converted    0.0    29.055556\n",
            "             0.5    28.315789\n",
            "             1.0          NaN\n",
            "             2.0          NaN\n",
            "Demented     0.0          NaN\n",
            "             0.5    25.960784\n",
            "             1.0    21.051282\n",
            "             2.0    20.333333\n",
            "Nondemented  0.0    29.239362\n",
            "             0.5    28.000000\n",
            "             1.0          NaN\n",
            "             2.0          NaN\n",
            "Name: MMSE, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        CDR\n",
            "Converted    0.0    1425.722222\n",
            "             0.5    1491.052632\n",
            "             1.0            NaN\n",
            "             2.0            NaN\n",
            "Demented     0.0            NaN\n",
            "             0.5    1479.382353\n",
            "             1.0    1498.121951\n",
            "             2.0    1538.000000\n",
            "Nondemented  0.0    1492.712766\n",
            "             0.5    1757.500000\n",
            "             1.0            NaN\n",
            "             2.0            NaN\n",
            "Name: eTIV, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        CDR\n",
            "Converted    0.0    0.735111\n",
            "             0.5    0.712947\n",
            "             1.0         NaN\n",
            "             2.0         NaN\n",
            "Demented     0.0         NaN\n",
            "             0.5    0.722235\n",
            "             1.0    0.702244\n",
            "             2.0    0.706667\n",
            "Nondemented  0.0    0.740963\n",
            "             0.5    0.735000\n",
            "             1.0         NaN\n",
            "             2.0         NaN\n",
            "Name: nWBV, dtype: float64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group        CDR\n",
            "Converted    0.0    1.239000\n",
            "             0.5    1.187211\n",
            "             1.0         NaN\n",
            "             2.0         NaN\n",
            "Demented     0.0         NaN\n",
            "             0.5    1.202520\n",
            "             1.0    1.186390\n",
            "             2.0    1.149000\n",
            "Nondemented  0.0    1.193096\n",
            "             0.5    1.000000\n",
            "             1.0         NaN\n",
            "             2.0         NaN\n",
            "Name: ASF, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yi0w7wp4DXeJ",
        "outputId": "d86f6fba-af3f-4263-e327-df1f2ed3a56b"
      },
      "source": [
        "print(data[\"Group\"].value_counts())\n",
        "print(\"-.\"*40)\n",
        "print(data[\"EDUC\"].value_counts())\n",
        "print(\"-.\"*40)\n",
        "print(data[\"M/F\"].value_counts())\n",
        "print(\"-.\"*40)\n",
        "print(df.corr())\n",
        "print(\"-.\"*40)\n",
        "print(data.isnull().sum())"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nondemented    190\n",
            "Demented       146\n",
            "Converted       37\n",
            "Name: Group, dtype: int64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "12    103\n",
            "16     81\n",
            "18     64\n",
            "14     33\n",
            "13     27\n",
            "15     17\n",
            "20     13\n",
            "11     11\n",
            "17      9\n",
            "8       9\n",
            "23      3\n",
            "6       3\n",
            "Name: EDUC, dtype: int64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "F    213\n",
            "M    160\n",
            "Name: M/F, dtype: int64\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "          MMSE      eTIV      nWBV       ASF\n",
            "MMSE  1.000000 -0.032084  0.341912  0.040052\n",
            "eTIV -0.032084  1.000000 -0.210122 -0.988877\n",
            "nWBV  0.341912 -0.210122  1.000000  0.213476\n",
            "ASF   0.040052 -0.988877  0.213476  1.000000\n",
            "-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.\n",
            "Group     0\n",
            "M/F       0\n",
            "Age       0\n",
            "EDUC      0\n",
            "SES      19\n",
            "MMSE      2\n",
            "CDR       0\n",
            "eTIV      0\n",
            "nWBV      0\n",
            "ASF       0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlqrxEDjEpLs"
      },
      "source": [
        "#### Histograms"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "vluIATSQDg_o",
        "outputId": "28060606-ea49-4c8e-f14b-a1e4b869e160"
      },
      "source": [
        "#Histogram of Target values\n",
        "data[\"Group\"].hist(figsize=(5,5))\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "hdPrbjYPDkhD",
        "outputId": "e61752d5-664c-4fd4-d3a7-155a04b23d7a"
      },
      "source": [
        "#Histogram of Sex Feature\n",
        "data[\"M/F\"].hist(figsize=(5,5))\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "pqyiaRB2DyjT",
        "outputId": "b381af93-9fee-4782-ce47-bd479d83fe3d"
      },
      "source": [
        "#Histogram of Socioeconomic Status\n",
        "data[\"SES\"].hist(figsize=(5,5))\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "UlPi_jWiEBCm",
        "outputId": "cc599e47-3df2-4c3e-d4a4-bebb82b73cf3"
      },
      "source": [
        "#Histogram of Age\n",
        "data[\"Age\"].hist(figsize=(5,5))\n",
        "plt.show()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "c85pBNmkEFBl",
        "outputId": "11d764fe-a6ea-4c7f-e1ab-495a8c7d6644"
      },
      "source": [
        "#Histogram of Clinical Dementia Rating\n",
        "data[\"CDR\"].hist(figsize=(5,5))\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7q-YjHYEieW"
      },
      "source": [
        "#### Some Scatter plot & Line plot visualizations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "tJ6xrsn-EejG",
        "outputId": "322dedf8-009d-4188-8d9c-1df17921ac8f"
      },
      "source": [
        "sns.scatterplot(x=\"SES\",y=\"MMSE\",hue=\"Group\",data=data)\n",
        "plt.show()\n",
        "sns.lineplot(x=\"SES\", y=\"MMSE\",hue=\"Group\", data=data)\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ER5q0lV7wNN17yzkkm0JiYPafmrbN+H/GwZiduOxwztpTyaicQ1BVhg4bgscAOZHUqtIcyNxr9POHDoaI4aCU2VE5rcL8VA4XHCKvLJ/+Qf2ICY/D26e72WE5rTOZR6nOOw4lmdAjCt+wgXQLkQOxFlWVQeY+o9snqA9EjICzeH+55Th+IYRwZ50+jl8p1Q94B+gFaOB1rfUypdRi4DdAXcW032mt/233AE5ug6x9UF0OYUNhwBTwcsnRq3Zx+lQaezJKKKyoYXCYPyP698Lb19/ssJxXTgqcTjZ+KYXGQN9EkD7rlhWcgPQdUHIagvtDn7F2H5vuUsqLIGOnsd969IXe8eBvv7LyjsyE1cAjWuvtSqlAIEkp9bXluRe01ksdtuWTPxnDE3Mto3g8veGad+C8Sx22ya4s49RJ7luTwrY0Y7irh4LXr6vmogT79Sm6lPzj8OWjcGR9Q9ulS2D8HebF5MxKsmDDn2DnPxvapi2CKY+At1xVb6W6En78K2x4pqFt4r0w/XfgE9Dy6zrAYSd3tdYZWuvtlvvFwH6gczr1TiU3JH2AmirY9Dzkn+yUzXc1ezNK65M+QK2Gxf85SW72aROjcmIZO5smfYBvn7Y5jFgAp/c0TfpgfB5P7zQnHmeXewS+f7Zp29aXIfeQ3TbRKaN6lFLRwGjgR0vTvUqpXUqpfyilbP5+UUrdoZTappTalp3dwTr6pTaWL0yDyuKOrcdNFFZYl4HNKCynrErKDNtUXmSjrRAqSzo/lq6gvMC6raYKKuTzaFNFse3S37bed2fJ4YlfKdUd+Ah4UGtdBLwGxAAJQAbwF1uv01q/rrVO1Fonhod3cJYeW1O6xV4BoVJt0pbBYX54NBvAc8XIECJ6Sp+1TWFDwKtZF0X/iRAyyJx4nF3oYGg+c1RojDFsUVjrOcA4D9KYfyiE2K/st0MTv1LKGyPpr9RarwHQWmdqrWu01rXAG4D9C5z0SYS5L0Bgb2NM+pgFMPIqObnbghH9e/PGdcPo19MHLw/F1XGh3Hd+X7r52ac/0eVEjYNr3jYGDSgPGDILZj0DMo2gbZFxcM0K4wSlUjBgMlz+KoREmx2ZcwrsDde9B9HnG/urbyLc8C/rL4Nz4LDhnEopBbwN5GmtH2zUHqm1zrDcfwj4hdZ6fmvrOuvhnKf3GD8pw4ac1RhYd5ObfZqyyhoiQoIl6bdHYZrRxRMUBX5SyrpNRRlQlgfde0GAzE3cpvJiY3/59YSzLGVtRlnmycDNwG6lVLKl7XfA9UqpBIwhnqnAnY4K4EzIeVRW1xLsc/b1rN1JaI8eUF0GkvTbxyfImD5QSlu0T1CkcRPt4xto3BzAYYlfa70JsHXpp/3H7DdTXVPLD0fzeHH9QbJLKvnV5IHMGdWb8EBfR2+6a9IaTv4A3z1nVAAccwvEz4ceModBi479Fza/CNkHYdglMPpm2+eWhHBCLtnpvTu9kFv+8SO1ll6sxZ/upbZWc9sUmRPVpsy98M7lUF1hPP72/6CsAGY+BR6e5sbmjNJ3GGVyKyyjLH563ej2mfdX6fIRXYJLFmnbcbKgPunXeXPTMXJLKswJyNll7m1I+nV+fh2KpGyuTVn7G5J+nUPrIMd+46yFcCSXTPzdfayPUoP9venm6ZJ/7rnz9rNu8wmyLg0rDN42ugy9fK2HeArhpFwyE47p35Ow7k2T1iMzhxLo521SRE4uMh56NhuDPvP/ILCXOfE4u95xxq2xSfcbFRSF6AJctjrn4cxifjqWR/6ZSn4xMJT4fj3o5iX91S3KTYHjW4zunf4TjLHDdqoL4pJO74ETP0D+UWNf9ZsAPfqYHZUQTZgxnNNUQ3oFMqSXDLNrt9AY4ybap/dI4yZEF+SyiZ/yIuNkW9UZ49JwG9MxikZKc+H0bqgoNPaXJLXWVZVBzmE4k2tcUSlfmq0rL4SMXVB82hgm3HcseMk5pBbV1hjF2opOGRe8hQ0xqgzbiWsm/uLT8NX/g90fGo+D+sINH0oya0lBGmxcAttXGI/9Q+Dqt2DQdBODcmIVJfDT34xhr1obF3DN/ycMnGp2ZM6pvBC2vwPrnzQSmpcv/PJFiL/e7Mick9aw/zNY8xuoqQQPL/jlSxB3HXjaJ2W75Mld0rc1JH2AonQjsTUfsigMp5Iakj7AmTz4+kkokrLMNmXtg2/+YHxAwaimuPZuY1pBYS1jZ0PSB2NypHWLIC3J3LicVV4KfPJbI+kD1FbD5w9A7mG7bcI1E3/OEeu2E1ugrLDzY+kKim0k+IxkKMvt/Fi6guIM67bCNKO7TFgrTLcuM1xeKF+ULSnNhsrSpm01VbY/p2fJNRN/xHnWbTEXGcWOhLUe/azb+k2AAJkazyZb+yt0MHSX/WVTz/7W/dP+IcYk4sJa90jwbXYFuJev0WVtJ66Z+PsmwoR7jJKmABGxMPkB8JJx/Db1HQtTHm4ozxA8AC56UsoMtyRiOMx5oeGCre4RMO9VqTjZkt6j4dKl4G2Zw9mvJ8x9EfokmBuXswqJhqvebEj+3brDlW8YBxd24rLj+KkqN86KV5dBz4HyoWxL5Rk4vcsYDRUSA2EySqVVdaMuyvKNXwAyaqx1NTXGuaSSLOPIte9osyNyfvmpRvdOQIQxCYuyVfOydS2N43fdxC+EEG7O7S7gEh10fCukfAP5x2HwDGMqwZ7RZkclXMXp3ZCywRg00H+SMfQ1XKZCbVFhOhzbCKn/Nc63DZpunCuxE0n8Ak7+DP9a0DDKYveHcNFimPKQmVEJV5F/HD5/CNJ+Nh7v+ciYCvXS58Ff5nW2UlECX/8e9qw2HievhJgLjX5/f/sMUHHNk7uiY7L2Wg+t27wMMveZE49wLVl7G5J+nT0fQba8v2zKPdKQ9OukfGPXst+S+IVxgUhz1RXWY6+FOBs1Nt5fYIxNF9Za+tzZ+pyeJUn8AsJHGEPGGkv8tZQZFvYRMQJCmpX9jj4fwm1cbyOMuk8DJjdti4g16vXYifTxC4ieBNe9B9vfNvpjR8yDwReBp5SxFnYQNhiufB2S3zfKqQy6AGKvlPkeWuIXDJcth10fGjO7xcyA+BvseoGgDOcUDaoqoKrUuKpSCHurrYXKYuurUkXLqsqMq3bPYgw/uOtwztN7jL7q0BjjW1S0rjTLeKN5dgOf7m0v7+5O74XyAuMCLjsOtXNZmZb9FRBuu6yKsGZrWlQ7cM3EX5JlDIHauNQ4gh16CUx/HCLj2n6tO6oqh/2fwrpHjStRYy6Ei5+VcdYtqTgDBz+Hr/7XGA3VZzTMegaiJ7f9Wne1/zP4z++g4ITRt3/xsxBzgdlRuS3XPLl74kdYvxgqS4zSuQf/DUlvtTy6wN2d3mXU/i7LNx6nfAPfPm18IQhr6T/D2rsahsCe2mEktfzj5sblrI5vhY/vNJI+QPYB+PReY2IWYQrXTPynbbyhDnwBhSc7P5auwFad7wOfGb+chLW8o9ZD7jKSGxKbaKog1brMcGGafB5N5JqJ31bBrNAhclKpJf42qnCGxEg/f0v8bRT88w+V91dLbL2/vHzBV867mcU1E3/UOIhsVPK1WwBM/R8ZrdKSyHgYdknDY09vmLNU9ldLeo0whiPWUR5w0R/kHFJLQofAL+5q2nbBE9BnrDnxCBcezplzxOjyqSw16qdHWY1oEo2VZEPmHmNmpLAhED4cPFzzuMAuclOMUSpnso1fR30SwVd+IbUo+7BRcqDkNAT3My4aDI4yOyqXJ2WZhRDCzXT6OH6lVD/gHaAXoIHXtdbLlFIhwCogGkgFrtVa5zsqDtFOGbuNI9iKIuO6h/4ToZu/2VE5r5IsY1TKmWwIGQy9RzpszLVLyD8JGTuMiUV69oc+Y2SqShM5chx/NfCI1nq7UioQSFJKfQ3cCnyjtX5WKfUY8BiwyIFxiLZk7IYvGpXNVR7GVG+jrjY3LmdVmgv/Xgj71ja0XfE3iJ9vXkzOrCQbNj4HO95taJv+O5j8IHj7mBeXG3NYJ67WOkNrvd1yvxjYD/QFLgfetiz2NjDPUTGIdsrc07Rsrq6Fb/4AOTaGeQqjzHDjpA+wbhEUyPBEmzJ3N036AP9darQLU3TK2TulVDQwGvgR6KW1zrA8dRqjK8jWa+5QSm1TSm3Lzs7ujDDdV3mhdVtRGlSd6fxYuoKyAuu28gLjgkFhrcxGT25Npe33negUDk/8SqnuwEfAg1rrosbPaePMss2zy1rr17XWiVrrxPBwG+OAhf2ExlgXgTpvLgQPMCceZxc62Khn1NiAKdBDRqnYZOsampBB1qWaRadxaOJXSnljJP2VWus1luZMpVSk5flIQC4PNVu/8TDvrxDU1+jfH34ZTLpfCtu1JGI43Pgv4wsAjFpQc58Hn0Bz43JWkXFw9VsN8zv0+wVc/iqEDDQ3LjfmsOGcSimF0Yefp7V+sFH7EiC30cndEK31o62tS4ZzdpLsg0b3Ts+BkvTbozQXKoqN0SkyAqptRafgTC507yUjejqJGWWZJwM3A7uVUsmWtt8BzwIfKqV+DRwHrnVYBGcKoKYcAns7bBMuJXgAVJdL0m+vgFDjJtonqI9xE6ZzWOLXWm8CWpo94EJHbRcwJhRJ+caYMLwsFxJuNLovQmMcutku7eSP8P1SyDsCY241hnLaqnkkhOjyXLMe//HN8OFNDRUU1y827k/9H1PDclqn98DblxlH+wDrf298YV74JHjI9ItCuBrXLMaSnmRdNjdpBeRL2VybMvc2JP06P/4VijJsLy+E6NJcM/F3C7Bu8w2yHoInDN6+1m0+geDpmj8IhXB3rpn4+42HgGY106c8DEFyktemyHjoGd207aI/yElxIVyUax7SRSXC/PchdROU5UH0+dB/ktlROa+e0XDjR3B8kzEz0oDJxpwGQgiX1GriV0oFNb/attFz/bXWzttp3m+8cRPtEzbYuAkhXF5bXT3f1d1RSn3T7LlmVapEl1Z5Bk4lw7GNUmxMCLPV1kL2ITj6HWTth5pqu66+ra6exuPwm8/D19IYfdHVnMmHTc/DlpeMxwFhcMOH0FemxhOi02kNBz6HNb8xRtt5esMvl8Ooa+w24KKtI37dwn1bj0VXdXpnQ9IHKM0xygyX2+zlE0I4Ut5RWHtXwxDrmir47D7ItV+Z9La+PiKUUg9jHN3X3cfyWEpmuorCdOu2tJ+Ncrq+QZ0fjxDurCTLmCu8sZoqY/ayiOF22URbif8NINDGfYC/2yUCYb7gftZtAyaDX/PePSGEwwX2Bp8gYxrUOl4+dq1z1Gri11o/ZbctCefVO8GYCm/jc8YVzz36wcV/Al8pMyxEpwsZCFf9HT663Uj+3v4w77WGMuB20GpZZqXUb4DvtNaHLWWW3wSuwqiquUBrvcNukbRCyjJ3gupKow+xotgY1y8XbwlhrryjRvdO9wgIsTFZUjucbVnmB4AVlvvXA/HAIIxpFF8Czu9wJMI5eXWDXrFmRyGEqOPAWcraSvzVWusqy/25wDta61xgvVLqzw6JyF5O74bDX0NJJgy7BKLGy2QZwn5yUyBlgzFRfcwMGDDJukyIaJCeBKmbjf0VNQ76T4TeI82Oym21lfhrLdMj5mPU0H+m0XN+DovqXGXugxVzjQmwwag0ee27MOIyc+MSrqEoHT64EbL3G4+T3oJpj8G0R6WMtS3ZR+DLx405HwB2rYJR18KsP0KgDA40Q1vj+H8PbANSgU+11nsBlFLTgKOODe0cpP3ckPTrbPgjlBXYXl6Ijsjc15D062x+AfJTTQnH6eXsb0j6dfb8y2gXpmhrVM/nSqkBQKDWOr/RU9uA6xwa2bloXlsejLlkm9foF+Js1FTZbtPy/rLJ1v7S2na76BRtFWm7stF9W4ussXdAdhE1zrjMufEba8qDMj+qsI+I4eAfakwcXif+emPOYmEtfBj0HAj5xxraBkyGsGHmxeTm2hrOWQskW27QtD6P1lrf5sDY6nV4OGdtLZz8Aba8DMWnYNwdMHS2JH5hPxm74Kc34NR2iLsWYq+A4P5mR+W8jm+B5PfhVBIMnG7sr35S+tvRWhrO2VbinwfMBwYDnwDva62POCzKFpz1OP7qKtDV4O2856FFF1ZbY3Qr2prxTVirroTyfOjey+xI3N8rGUUAABwXSURBVEZLib/Vk7ta67Va6/nANCAF+ItSapPl5K7z8/KWpC8cpzTbqHNUUWx2JF1D0SkoSIMCG7WhRKdq79SL5UAhUAR0B2xM0iqEm6itgUP/gTcugFfGwT+vMyasFy07+CW8Ow/+PgPevwaObTI7IrfWauJXSs1QSr0OJAEXAMu01gla6/90SnRCOKOs/fDBDcYRLMDxzfDp/VBWaG5czurUDvjo1w0ndzP3GmWHc1PMjcuNtXUB13pgF7AJ8AFuUUrdUvek1vp+B8YmhHPKOwq1zWZESt9mDCTw62FOTM4s9yhUljRtKzxpfBGExpgTk5trK/Hfhky4IkRT/jbKVfuHgI9UM7XJ1mg6Lx/w69n5sQig7Qu4VnRSHEJ0HRGxkHAjJK80HisFc56HHlHmxuWsesfD+Dvhp781tF3whNEuTNHWBVyftva81lqK3wj3498TZj0NcfONkT2hMRAxwuyonJd/T5jyMMRcAMUZxoVufcfabf5Y0XFt7fmJwEngfeBHZIJ1IQz+ITBoqtlRdB1BvSHoErOjEBZtJf7ewEyMWvw3AF9gXMTV5tg1pdQ/MEo5Z2mtR1raFgO/AbIti/1Oa/3vswtdCBOVZEPmbuPf0BjoNQq8fcyOynmdyTf2V1EG9Bxg7C8fufDNLG318dcAXwJfKqV8ML4AvlNKPaW1frmNda8AXgbeadb+gtZ66VnGK4T5SnPhy8dgz+qGtivfMEo3CGsVJfDfJbD1lYa2i/9klFKR7h5TtHkBl1LKx1Ks7T3gHoyZtz5u63Va641A3jlHKISzydrbNOkDrHsUCk6aE4+zyznUNOkDfP0k5Mk4frO0dXL3HWAk8G/gKa31Hjts817LtQDbgEealXtuvO07gDsA+veX4lfCidia16Es33qsujCU2fiI11RKqQsTtXXEfxMwBGPu3S1KqSLLrVgpVXQW23sNiAESgAzgLy0tqLV+XWudqLVODA+XWXqEEwmNMcp+N9ZvIgT1NSceZxcyEHyCmrb1HAg95IDOLG0VafPQWgdabkGNboFa66DWXtvC+jK11jVa61rgDWD82QYuhGnCh8P1H0LPaOPx4JnwyxfAt8MfCfcQMghu/BDCLUNe+02Aa9+BwAhz43JjnXpmRSkVqbXOsDy8ArBH15EQncvDAwbPgF+vN7orAntJaea29J8Iv/rC6CYLCJMvSZM5LPErpd4HpgNhSqk04ElgulIqAaMMRCpwp6O2L4TDdQ83bqJ9/ENsl7sQnc5hiV9rfb2N5jcdtT0hhBDt0956/EIIIVyEJH4hhHAzkviFEMLNSOIXQgg3I4lfCCHcjCR+IYRwM5L4hRDCzUjiF0IINyOJXwgh3IwkfiGEcDOS+IUQws1I4hdCCDcjiV8IIdyMJH4hhHAzkviFEMLNSOIXQgg3I4lfCCHcjCR+IYRwM5L4hRDCzUjiF0IIN+OwydaFEF1fVVUVaWlplJeXmx2KaIWvry9RUVF4e3u3a3lJ/EKIFqWlpREYGEh0dDRKKbPDETZorcnNzSUtLY2BAwe26zXS1SOEaFF5eTmhoaGS9J2YUorQ0NAO/SqTxC+EaJUkfefX0f8jSfxCCOFmJPELIbqszMxMbrjhBgYNGsTYsWOZOHEiH3/8sdlhOT1J/EKILklrzbx585g6dSpHjx4lKSmJDz74gLS0tCbLVVdXmxSh85LEL4Tokr799lu6devGXXfdVd82YMAA7rvvPlasWMFll13GjBkzuPDCC8nLy2PevHnExcUxYcIEdu3aBcDixYtZunRp/etHjhxJamoqqampnHfeedx4440MHz6cq6++mjNnznT63+gokviFEF3S3r17GTNmTIvPb9++ndWrV/P999/z5JNPMnr0aHbt2sUf//hHbrnlljbXf/DgQX7729+yf/9+goKCePXVV+0ZvqkclviVUv9QSmUppfY0agtRSn2tlDps+beno7YvhHAv99xzD/Hx8YwbNw6AmTNnEhISAsCmTZu4+eabAZgxYwa5ubkUFRW1ur5+/foxefJkAG666SY2bdrkwOg7lyOP+FcAFzdrewz4Rms9BPjG8lgIITosNjaW7du31z9+5ZVX+Oabb8jOzgYgICCgzXV4eXlRW1tb/7jxWPjmQyRdaVirwxK/1nojkNes+XLgbcv9t4F5jtq+EMK1zZgxg/Lycl577bX6tpb64c8//3xWrlwJwHfffUdYWBhBQUFER0fXf3ls376dY8eO1b/mxIkTbN26FYB//vOfTJkyxVF/Sqfr7D7+XlrrDMv900CvlhZUSt2hlNqmlNpW9w0uhBB1lFKsXbuW77//noEDBzJ+/HgWLFjAc889Z7Xs4sWLSUpKIi4ujscee4y33zaOP6+66iry8vKIjY3l5ZdfZujQofWvGTZsGK+88grDhw8nPz+fu+++u9P+NkdTWmvHrVypaOBzrfVIy+MCrXVwo+fztdZt9vMnJibqbdu2OSxOIYRt+/fvZ/jw4WaH0elSU1OZO3cue/bsaXthJ2Hr/0oplaS1Tmy+bGcf8WcqpSItAUUCWZ28fSGEcHudnfg/BRZY7i8APunk7QshRJuio6O71NF+RzlyOOf7wFZgmFIqTSn1a+BZYKZS6jBwkeWxEEKITuSwevxa6+tbeOpCR21TCCFE2+TKXSGEcDOS+IUQws1I4hdCOLXTp08zf/58YmJiGDt2LJdeeimHDh3q9DhWrFjBqVOnOvSa1NRURo4c6aCIzp7MuSuEsJu1O9JZ8p+DnCooo0+wHwtnD2Pe6L5nvT6tNVdccQULFizggw8+AGDnzp1kZmY2udjK0WpqalixYgUjR46kT58+nbZdR5EjfiGEXazdkc7ja3aTXlCGBtILynh8zW7W7kg/63Vu2LABb2/vJqWX4+PjmTJlCgsXLmTkyJGMGjWKVatWAUY5hunTp3P11VfXl1XWWvPll19yzTXX1K/ju+++Y+7cuQB89dVXTJw4kTFjxnDNNddQUlICGEM6Fy1axJgxY3j//ffZtm0bN954IwkJCZSVlZGUlMS0adMYO3Yss2fPJiPDKEqQlJREfHw88fHxvPLKK2f9tzuSJH4hhF0s+c9ByqpqmrSVVdWw5D8Hz3qde/bsYezYsVbta9asITk5mZ07d7J+/XoWLlxYn3h37NjBiy++yL59+zh69CibN2/moosu4scff6S0tBSAVatWMX/+fHJycnj66adZv34927dvJzExkeeff75+O6GhoWzfvp2bbrqJxMREVq5cSXJyMl5eXtx3332sXr2apKQkbrvtNp544gkAfvWrX7F8+XJ27tx51n+3o0lXjxDCLk4VlHWo/Vxs2rSJ66+/Hk9PT3r16sW0adP4+eefCQoKYvz48URFRQGQkJBAamoqU6ZM4eKLL+azzz7j6quv5osvvuDPf/4z33//Pfv27asvv1xZWcnEiRPrt3PdddfZ3P7BgwfZs2cPM2fOBIyuoMjISAoKCigoKGDq1KkA3Hzzzaxbt87uf/+5ksQvhLCLPsF+pNtI8n2C/c56nbGxsaxevbpDr/Hx8am/7+npWT/14vz583n55ZcJCQkhMTGRwMBAtNbMnDmT999/3+a6WirtrLUmNja2vnpnnYKCgg7Fahbp6hFC2MXC2cPw8/Zs0ubn7cnC2cPOep0zZsygoqKC119/vb5t165dBAcHs2rVKmpqasjOzmbjxo2MHz++1XVNmzaN7du388YbbzB//nwAJkyYwObNmzly5AgApaWlLY4YCgwMpLi4GDAqd2ZnZ9cn/qqqKvbu3UtwcDDBwcH1k7bUlYJ2NpL4hRB2MW90X/505Sj6BvuhgL7BfvzpylHnNKpHKcXHH3/M+vXriYmJITY2lscff5wbbriBuLg44uPjmTFjBn/+85/p3bt3q+vy9PRk7ty5rFu3rv7Ebnh4OCtWrOD6668nLi6OiRMncuDAAZuvv/XWW7nrrrtISEigpqaG1atXs2jRIuLj40lISGDLli0AvPXWW9xzzz0kJCTgyOrH58KhZZntRcoyC2EOdy3L3BU5c1lmIYQQJpPEL4QQbkYSvxBCuBnXHc6ZuQ+OboDSHIiZAVGJ4H32w8qEEMJVuGbizzoAb8+BM3nG403Pw/x/wnlzzI1LCCGcgGt29Zz8sSHp1/n2GSgrNCceIYRwIq6Z+KvOWLdVFkNtVefHIoQ4J56eniQkJBAbG0t8fDx/+ctfqK2tNSWWgoICXn311Q6/bvHixSxdutQBEZ0d10z8UePBo1kv1qT7ISDMnHiEcBe7PoQXRsLiYOPfXR+e8yr9/PxITk5m7969fP3116xbt46nnnrKDsF23Nkmfmfjmom/TwLc8olxUjdiBPxyGYyYZ3ZUQri2XR/CZ/dD4UlAG/9+dr9dkn+diIgIXn/9dV5++WW01tTU1LBw4ULGjRtHXFwcf/vb3wCj7PK0adO4/PLLGTRoEI899hgrV65k/PjxjBo1ipSUFACys7O56qqrGDduHOPGjWPz5s2AcYR+2223MX36dAYNGsRLL70EwGOPPUZKSgoJCQksXLgQgCVLltRv/8knn6yP9ZlnnmHo0KFMmTKFgwfPvkKpI7jmyV0PT4ieAn0Tje4dn0CzIxLC9X3zB6hqVqStqsxoj7vWbpsZNGgQNTU1ZGVl8cknn9CjRw9+/vlnKioqmDx5MrNmzQKMCVv2799PSEgIgwYN4vbbb+enn35i2bJlLF++nBdffJEHHniAhx56iClTpnDixAlmz57N/v37AThw4AAbNmyguLiYYcOGcffdd/Pss8+yZ88ekpOTAaOW/+HDh/npp5/QWnPZZZexceNGAgIC+OCDD0hOTqa6upoxY8bYLC9tFtdM/HW8fQFfs6MQwj0UpnWs3Q6++uordu3aVV/Bs7CwkMOHD9OtWzfGjRtHZGQkADExMfVfCKNGjWLDhg0ArF+/nn379tWvr6ioqH4iljlz5uDj44OPjw8RERFkZmba3P5XX33F6NGjASgpKeHw4cMUFxdzxRVX4O/vD8Bll13moD1wdlw78QshOk+PKEs3j412Ozp69Cienp5ERESgtWb58uXMnj27yTLfffddk/LMHh4e9Y89PDzqSzXX1tbyww8/4OtrfYDYUnnnxrTWPP7449x5551N2l988cWz/wM7gWv28QshOt+Fv7e+SNLbz2i3k+zsbO666y7uvfdelFLMnj2b1157jaoqY8TeoUOH6mfZao9Zs2axfPny+sd1XTgtaVyaGWD27Nn84x//qP+VkJ6eTlZWFlOnTmXt2rWUlZVRXFzMZ5991pE/0+HkiF8IYR91/fjf/MHo3ukRZST9c+zfLysrIyEhgaqqKry8vLj55pt5+OGHAbj99ttJTU1lzJgxaK0JDw9n7dq17V73Sy+9xD333ENcXBzV1dVMnTqVv/71ry0uHxoayuTJkxk5ciSXXHIJS5YsYf/+/fWzdnXv3p333nuPMWPGcN111xEfH09ERATjxo07p31gb1KWWQjRIinL3HVIWWYhhBAtksQvhBBuRhK/EEK4GVNO7iqlUoFioAaottUHJYQQwjHMHNVzgdY6x8TtCyGEW5KuHiGEcDNmJX4NfKWUSlJK3WFrAaXUHUqpbUqpbdnZ2Z0cnhDCWSileOSRR+ofL126lMWLF9tl3bfeemt9uYfOtnbt2iblItqre/fu57xtsxL/FK31GOAS4B6l1NTmC2itX9daJ2qtE8PDwzs/QiFEh31x9AtmrZ5F3NtxzFo9iy+OfnHO6/Tx8WHNmjXk5LhWz/DZJn57MCXxa63TLf9mAR8D482IQwhhP18c/YLFWxaTUZqBRpNRmsHiLYvPOfl7eXlxxx138MILL1g9l5qayowZM4iLi+PCCy/kxIkTgHEkf//99zNp0iQGDRpUf1Svtebee+9l2LBhXHTRRWRlZdWvKykpiWnTpjF27Fhmz55NRkYGANOnT+ehhx4iMTGR4cOH8/PPP3PllVcyZMgQ/vd//7f+9e+99x7jx48nISGBO++8k5qaGsA4Qn/iiSeIj49nwoQJZGZmsmXLFj799FMWLlxIQkICKSkppKSkcPHFFzN27FjOP/98Dhw4AMCxY8eYOHEio0aNarK9c9HpiV8pFaCUCqy7D8wC9nR2HEII+1q2fRnlNeVN2sprylm2fdk5r/uee+5h5cqVFBY2nT71vvvuY8GCBezatYsbb7yR+++/v/65jIwMNm3axOeff85jjz0GwMcff8zBgwfZt28f77zzDlu2bAGgqqqK++67j9WrV5OUlMRtt93GE088Ub+ubt26sW3bNu666y4uv/xyXnnlFfbs2cOKFSvIzc1l//79rFq1is2bN5OcnIynpycrV64EoLS0lAkTJrBz506mTp3KG2+8waRJk7jssstYsmQJycnJxMTEcMcdd7B8+XKSkpJYunQpv/3tbwF44IEHuPvuu9m9e3d9tdFzZcaonl7Ax0qpuu3/U2v9pUO2VF0Fugq8/R2yeiFEg9OlpzvU3hFBQUHccsstvPTSS/j5NRSC27p1K2vWrAHg5ptv5tFHH61/bt68eXh4eDBixIj6ksobN27k+uuvx9PTkz59+jBjxgwADh48yJ49e5g5cyYANTU1TZJsXVnlUaNGERsbW//coEGDOHnyJJs2bSIpKam+Jk9ZWRkRERGA8aUxd+5cAMaOHcvXX39t9feVlJSwZcsWrrnmmvq2iooKADZv3sxHH31U/zcuWrTo7HZiI52e+LXWR4F4h26kthZOboXNy6H4FIy/A4ZeLFMvCuFAvQN6k1GaYbPdHh588EHGjBnDr371q3Yt37iscls1ybTWxMbGsnXr1lbX1bi8c93j6upqtNYsWLCAP/3pT1av9fb2xnKg22J559raWoKDg1usDlr3entxzeGcGcnwzuVwaB1k7IRP7oF9n5odlRAu7YExD+Dr2bSuva+nLw+MecAu6w8JCeHaa6/lzTffrG+bNGkSH3zwAQArV67k/PPPb3UdU6dOZdWqVdTU1JCRkVE/IcuwYcPIzs6uT/xVVVXs3bu33bFdeOGFrF69uv6cQV5eHsePH2/1NY1LPAcFBTFw4ED+9a9/AcYX0c6dOwGYPHlyk7/RHlwz8af9DDVVTds2vwiluebEI4QbmDNoDosnLSYyIBKFIjIgksWTFjNn0By7beORRx5pMrpn+fLlvPXWW8TFxfHuu++ybFnr5xOuuOIKhgwZwogRI7jlllvqyyl369aN1atXs2jRIuLj40lISKjv/2+PESNG8PTTTzNr1izi4uKYOXNm/cnhlsyfP58lS5YwevRoUlJSWLlyJW+++Sbx8fHExsbyySefALBs2TJeeeUVRo0aRXp6ertjao1rlmVOetuY5Lmx8OHw6/+Abw/7BieEC5OyzF2HlGXuNx58g5u2zXhCkr4QQuCqM3BFDIdbv4AjX0NJNgydDVHONQOOEEKYxTUTP0DvkcZNCHFOtNZ2H1Ui7KujXfau2dUjhLALX19fcnNzO5xYROfRWpObm4uvr2/bC1u47hG/EOKcRUVFkZaWhhRKdG6+vr5ERUW1e3lJ/EKIFnl7ezNw4ECzwxB2Jl09QgjhZiTxCyGEm5HEL4QQbqZLXLmrlMoGWi980bIwwBlncJC4Okbi6hiJq2OcNS44t9gGaK2tZrLqEon/XCilttm6ZNlsElfHSFwdI3F1jLPGBY6JTbp6hBDCzUjiF0IIN+MOif91swNogcTVMRJXx0hcHeOscYEDYnP5Pn4hhBBNucMRvxBCiEYk8QshhJtxicSvlPqHUipLKbWnheeVUuolpdQRpdQupdQYJ4lrulKqUCmVbLn9vpPi6qeU2qCU2qeU2quUspoU1Yx91s64On2fKaV8lVI/KaV2WuJ6ysYyPkqpVZb99aNSKtpJ4rpVKZXdaH/d7ui4Gm3bUym1Qyn1uY3nOn1/tTMuU/aXUipVKbXbsk2r6Qbt/nnUWnf5GzAVGAPsaeH5S4F1gAImAD86SVzTgc9N2F+RwBjL/UDgEDDC7H3Wzrg6fZ9Z9kF3y31v4EdgQrNlfgv81XJ/PrDKSeK6FXi5s99jlm0/DPzT1v+XGfurnXGZsr+AVCCsleft+nl0iSN+rfVGIK+VRS4H3tGGH4BgpVSkE8RlCq11htZ6u+V+MbAf6NtssU7fZ+2Mq9NZ9kGJ5aG35dZ8VMTlwNuW+6uBC5WDZy9pZ1ymUEpFAXOAv7ewSKfvr3bG5azs+nl0icTfDn2Bk40ep+EECcViouWn+jqlVGxnb9zyE3s0xtFiY6bus1biAhP2maV7IBnIAr7WWre4v7TW1UAhEOoEcQFcZekeWK2U6ufomCxeBB4Falt43pT91Y64wJz9pYGvlFJJSqk7bDxv18+juyR+Z7Udo5ZGPLAcWNuZG1dKdQc+Ah7UWhd15rZb00ZcpuwzrXWN1joBiALGK6WcYl7PdsT1GRCttY4DvqbhKNthlFJzgSytdZKjt9UR7Yyr0/eXxRSt9RjgEuAepdRUR27MXRJ/OtD4mzvK0mYqrXVR3U91rfW/AW+lVFhnbFsp5Y2RXFdqrdfYWMSUfdZWXGbuM8s2C4ANwMXNnqrfX0opL6AHkGt2XFrrXK11heXh34GxnRDOZOAypVQq8AEwQyn1XrNlzNhfbcZl0v5Ca51u+TcL+BgY32wRu34e3SXxfwrcYjkzPgEo1FpnmB2UUqp3Xb+mUmo8xv+Hw5OFZZtvAvu11s+3sFin77P2xGXGPlNKhSulgi33/YCZwIFmi30KLLDcvxr4VlvOypkZV7N+4Mswzps4lNb6ca11lNY6GuPE7bda65uaLdbp+6s9cZmxv5RSAUqpwLr7wCyg+UhAu34eXWLqRaXU+xijPcKUUmnAkxgnutBa/xX4N8ZZ8SPAGeBXThLX1cDdSqlqoAyY7+g3v8Vk4GZgt6V/GOB3QP9GsZmxz9oTlxn7LBJ4WynlifFF86HW+nOl1B+AbVrrTzG+sN5VSh3BOKE/38ExtTeu+5VSlwHVlrhu7YS4bHKC/dWeuMzYX72Ajy3HM17AP7XWXyql7gLHfB6lZIMQQrgZd+nqEUIIYSGJXwgh3IwkfiGEcDOS+IUQws1I4hdCCDfjEsM5hXAkpdQTwA1ADcal/ncCz2EMpyyzLHZEa321UmoY8DcgGPAB/qu1tnUJvhCmkcQvRCuUUhOBuRhVQyssVwl3szx9o9a6eQndl4AXtNafWF4/qvOiFaJ9JPEL0bpIIKfuMn6tdQ5AK4UkIzEKaGFZfrejAxSio6SPX4jWfQX0U0odUkq9qpSa1ui5laphwo4llrYXgG8tlUMfqiupIIQzkSt3hWiDpSTC+cAFGP37j2Fcyv8/Nrp6UEr1wSiWdjkwDIhvVPhLCNNJ4heiA5RSV2MUFwukhcTfbPk9wAJnK1Es3Jt09QjRCqXUMKXUkEZNCcDxVpa/2FJaGqVUb4zJRUwvAS5EY3JyV4jWdQeWW/rqqzGqI96BMV3gSqVU3XDOHK31RRgldZcppcot7Qu11qc7O2ghWiNdPUII4Wakq0cIIdyMJH4hhHAzkviFEMLNSOIXQgg3I4lfCCHcjCR+IYRwM5L4hRDCzfx/c2kXXKKgFk4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "8PnzVspdEsLX",
        "outputId": "1ba8c502-6b03-400f-b3b3-9b52ba4bcb30"
      },
      "source": [
        "sns.scatterplot(x=\"SES\",y=\"eTIV\",hue=\"Group\",data=data)\n",
        "plt.show()\n",
        "sns.lineplot(x=\"SES\", y=\"eTIV\",hue=\"Group\", data=data)\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "AJWeAEjZEyQH",
        "outputId": "9e5fdc12-dfd8-4310-b4d0-ee299cba79de"
      },
      "source": [
        "sns.scatterplot(x=\"SES\",y=\"ASF\",hue=\"Group\",data=data)\n",
        "plt.show()\n",
        "sns.lineplot(x=\"SES\", y=\"ASF\",hue=\"Group\", data=data)\n",
        "plt.show()"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hnXR2aOFMRT"
      },
      "source": [
        "#### Visualizing the correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdYSNXbTFPzY"
      },
      "source": [
        "Features = [\"MMSE\",\"eTIV\",\"nWBV\",\"ASF\"]"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7keSzMuFi-h"
      },
      "source": [
        "corrPearson = data[Features].corr(method=\"pearson\")"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "8tmB6gGQFmTf",
        "outputId": "c119e428-2d76-4a91-de7d-3fbe8781f47b"
      },
      "source": [
        "fig = plt.figure(figsize=(10,8))\n",
        "sns.heatmap(corrPearson,annot=True, vmin=-1, vmax=+1)\n",
        "\n",
        "plt.title(\"Pearson Correlation\")\n",
        "plt.show()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Gqywaz-FxP5"
      },
      "source": [
        "### Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GgRDVZ05GEMn"
      },
      "source": [
        "Group:\n",
        "```\n",
        "Converted = 0\n",
        "Demented = 1\n",
        "Nondemented = 2\n",
        "```\n",
        "M/F:\n",
        "```\n",
        "F = 0\n",
        "M = 1\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duKDaBETFwiE",
        "outputId": "88540c22-37a8-4080-be4b-3a1ea1e6236c"
      },
      "source": [
        "Columns = [\"Group\",\"M/F\"]\n",
        "encode = LabelEncoder()\n",
        "for i in Columns:\n",
        "    print(data[i].value_counts())\n",
        "    print(\"----\")\n",
        "    data[i] = encode.fit_transform(data[i])\n",
        "    print(data[i].value_counts())\n",
        "    print(\"----\"*30)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nondemented    190\n",
            "Demented       146\n",
            "Converted       37\n",
            "Name: Group, dtype: int64\n",
            "----\n",
            "2    190\n",
            "1    146\n",
            "0     37\n",
            "Name: Group, dtype: int64\n",
            "------------------------------------------------------------------------------------------------------------------------\n",
            "F    213\n",
            "M    160\n",
            "Name: M/F, dtype: int64\n",
            "----\n",
            "0    213\n",
            "1    160\n",
            "Name: M/F, dtype: int64\n",
            "------------------------------------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRPtsvobHVNx",
        "outputId": "e87fb348-c808-4a37-f9fd-57a5f276e3e2"
      },
      "source": [
        "data[\"SES\"] = AlzheimerData[\"SES\"]\n",
        "data[\"CDR\"] = AlzheimerData[\"CDR\"]\n",
        "data[\"EDUC\"] = AlzheimerData[\"EDUC\"]\n",
        "data[\"Age\"] = AlzheimerData[\"Age\"]\n",
        "\n",
        "print(data.info())"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 373 entries, 0 to 372\n",
            "Data columns (total 10 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Group   373 non-null    int64  \n",
            " 1   M/F     373 non-null    int64  \n",
            " 2   Age     373 non-null    int64  \n",
            " 3   EDUC    373 non-null    int64  \n",
            " 4   SES     354 non-null    float64\n",
            " 5   MMSE    371 non-null    float64\n",
            " 6   CDR     373 non-null    float64\n",
            " 7   eTIV    373 non-null    int64  \n",
            " 8   nWBV    373 non-null    float64\n",
            " 9   ASF     373 non-null    float64\n",
            "dtypes: float64(5), int64(5)\n",
            "memory usage: 29.3 KB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LqLlF1ztHmJK"
      },
      "source": [
        "#### Fill in missing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMafjDcfHoUK",
        "outputId": "57dfc62b-1477-460b-ad33-5a222e7022b9"
      },
      "source": [
        "data[\"SES\"].fillna(data[\"SES\"].mean(), inplace=True)\n",
        "data[\"MMSE\"].fillna(data[\"MMSE\"].mean(), inplace=True)\n",
        "print(data.isnull().sum())"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Group    0\n",
            "M/F      0\n",
            "Age      0\n",
            "EDUC     0\n",
            "SES      0\n",
            "MMSE     0\n",
            "CDR      0\n",
            "eTIV     0\n",
            "nWBV     0\n",
            "ASF      0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGzhpdy0HvV0"
      },
      "source": [
        "#### Divide data into train and test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9DkvThoHyKC"
      },
      "source": [
        "x = data.drop(\"Group\",axis=1)\n",
        "y = data[\"Group\"]\n",
        "\n",
        "xTrain, xTest, yTrain, yTest = train_test_split(x,y,test_size=0.20,random_state=42)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmxbrcs8IE6S"
      },
      "source": [
        "### Training and Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvVSpabtIINf"
      },
      "source": [
        "####Regression Classifier Tree (CART)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbZjyhgKIHQl",
        "outputId": "3657ee81-678f-4221-fbcd-cb75efd49145"
      },
      "source": [
        "print(\"==============Results from Decision Tree Classifier before tuning==============\")\n",
        "cartc = DecisionTreeClassifier().fit(xTrain,yTrain)\n",
        "predict = cartc.predict(xTest)\n",
        "\n",
        "print(\"Accuracy Score: \", accuracy_score(yTest,predict))\n",
        "R2CV = cross_val_score(cartc,xTest,yTest,cv=10).mean()\n",
        "print(\"Cross Validaition Score: \", R2CV)\n",
        "\n",
        "error = mean_squared_error(yTest,predict)\n",
        "print(\"Mean Squared Error: \", np.sqrt(error))\n",
        "\n",
        "print(\"==============Results from Decision Tree Classifier after tuning==============\")\n",
        "cartctuned = DecisionTreeClassifier(max_depth=1,min_samples_split=2).fit(xTrain,yTrain)\n",
        "predicttuned = cartctuned.predict(xTest)\n",
        "print(\"Accuracy Score: \", accuracy_score(yTest,predicttuned))\n",
        "R2CVtuned = cross_val_score(cartctuned,xTest,yTest,cv=10).mean()\n",
        "print(\"Cross Validaition Score: \",R2CVtuned)\n",
        "\n",
        "errortuned = mean_squared_error(yTest,predicttuned)\n",
        "print(\"Mean Squared Error: \",np.sqrt(errortuned))"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Results from Decision Tree Classifier before tuning==============\n",
            "Accuracy Score:  0.8533333333333334\n",
            "Cross Validaition Score:  0.8017857142857142\n",
            "Mean Squared Error:  0.6531972647421809\n",
            "==============Results from Decision Tree Classifier after tuning==============\n",
            "Accuracy Score:  0.84\n",
            "Cross Validaition Score:  0.8392857142857142\n",
            "Mean Squared Error:  0.6928203230275509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l7rtytOSLd3a"
      },
      "source": [
        "####XGBoost Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jn36tkMdLgN4",
        "outputId": "5d62b1c1-df89-4b8f-c05a-85fc99005dca"
      },
      "source": [
        "xgbc = XGBClassifier(verbose=False).fit(xTrain,yTrain)\n",
        "predict = xgbc.predict(xTest)\n",
        "print(\"==============Results from XGB Classifier before tuning==============\")\n",
        "print(\"Accuracy Score: \", accuracy_score(yTest,predict))\n",
        "R2CV = cross_val_score(xgbc,xTest,yTest,cv=10).mean()\n",
        "print(\"Cross Validation Score: \", R2CV)\n",
        "# 0.84\n",
        "error = mean_squared_error(yTest,predict)\n",
        "print(\"Root Mean Squared Error: \", np.sqrt(error))\n",
        "\n",
        "\n",
        "xgbctuned = XGBClassifier(learning_rate=0.01, max_depth=6, min_samples_split=2,\n",
        "                              n_estimators=100, subsample=0.8).fit(xTrain, yTrain)\n",
        "print(\"==============Results from XGB Classifier after tuning==============\")\n",
        "predicttuned = xgbctuned.predict(xTest)\n",
        "print(\"Accuracy Score: \", accuracy_score(yTest,predicttuned))\n",
        "R2CVtuned = cross_val_score(xgbctuned,xTest,yTest,cv=10).mean()\n",
        "print(\"Cross Validation Score: \", R2CVtuned)\n",
        "# 0.82\n",
        "errortuned = mean_squared_error(yTest,predicttuned)\n",
        "print(\"Root Mean Squared Error: \", np.sqrt(errortuned))\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Results from XGB Classifier before tuning==============\n",
            "Accuracy Score:  0.8533333333333334\n",
            "Cross Validation Score:  0.8285714285714285\n",
            "Root Mean Squared Error:  0.6531972647421809\n",
            "==============Results from XGB Classifier after tuning==============\n",
            "Accuracy Score:  0.84\n",
            "Cross Validation Score:  0.8285714285714285\n",
            "Root Mean Squared Error:  0.6928203230275509\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3welGvujUoR9"
      },
      "source": [
        "#### Comparing the two classifiers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "1k5p1eoeUqx2",
        "outputId": "0df09826-2f12-4aef-ba18-8bbddddda6ee"
      },
      "source": [
        "models = [cartctuned,xgbc]\n",
        "r = pd.DataFrame(columns=[\"MODELS\",\"ACC\"])\n",
        "\n",
        "for model in models:\n",
        "    name = model.__class__.__name__\n",
        "    predict = model.predict(xTest)\n",
        "    accuracy = accuracy_score(yTest, predict)\n",
        "    print(\"-\" * 28)\n",
        "    print(name + \": \")\n",
        "    print(f\"Accuracy: {accuracy}\")\n",
        "    result = pd.DataFrame([[name,accuracy*100]],columns=[\"MODELS\",\"ACC\"])\n",
        "    r = r.append(result)\n",
        "    \n",
        "sns.barplot(x=\"ACC\",y=\"MODELS\",data=r,color=\"b\")\n",
        "plt.xlabel(\"ACC\")\n",
        "plt.title(\"MODEL ACCURACY COMPARISON\")\n",
        "plt.show()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------\n",
            "DecisionTreeClassifier: \n",
            "Accuracy: 0.84\n",
            "----------------------------\n",
            "XGBClassifier: \n",
            "Accuracy: 0.8533333333333334\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0HAref_lA4q",
        "outputId": "c50e52ba-dd10-4590-96bb-3dfc32a84988"
      },
      "source": [
        "%%shell\n",
        "jupyter nbconvert --to html /content/AlzheimersDetection.ipynb"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[NbConvertApp] Converting notebook /content/AlzheimersDetection.ipynb to html\n",
            "[NbConvertApp] Writing 587164 bytes to /content/AlzheimersDetection.html\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ""
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    }
  ]
}