Alzheimers prediction 🧠.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f278fd30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da108132",
   "metadata": {},
   "source": [
    "# Alzheimer's Prediction\n",
    "------------------------------------------------------------------------------------------------------------------------\n",
    "#### ***Final project for partial fulfilment of course Data Science at SCDE of Jawaharlal Nehru Technological University Hyderabad.***\n",
    "________________________________________________________________________________________________________________________\n",
    "\n",
    "\n",
    "Project by: ***yourname***\n",
    "\n",
    "E-Mail:  ***email@domain.com***\n",
    "\n",
    "Project Report: (report link)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "132f8910",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1478059b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ffbb91b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "directory.id               0\n",
       "Subject                    0\n",
       "RID                        0\n",
       "Image.Data.ID              0\n",
       "Modality                   0\n",
       "Visit                      0\n",
       "Acq.Date                   0\n",
       "DX.bl                      0\n",
       "EXAMDATE                   0\n",
       "AGE                        0\n",
       "PTGENDER                   0\n",
       "PTEDUCAT                   0\n",
       "PTETHCAT                   0\n",
       "PTRACCAT                   0\n",
       "APOE4                      0\n",
       "MMSE                       0\n",
       "imputed_genotype           1\n",
       "APOE Genotype              0\n",
       "Dx Codes for Submission    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data\n",
    "data = pd.read_csv('Alzheimer’s Patient data.csv')\n",
    "\n",
    "#Check NA values, 1 NA for imputed_genotype\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9ef3f7",
   "metadata": {},
   "source": [
    "### Remove NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3cd407fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.dropna()\n",
    "data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb047e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>directory.id</th>\n",
       "      <th>Subject</th>\n",
       "      <th>RID</th>\n",
       "      <th>Image.Data.ID</th>\n",
       "      <th>Modality</th>\n",
       "      <th>Visit</th>\n",
       "      <th>Acq.Date</th>\n",
       "      <th>DX.bl</th>\n",
       "      <th>EXAMDATE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype</th>\n",
       "      <th>APOE Genotype</th>\n",
       "      <th>Dx Codes for Submission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4702245ea294ce5d4e9b8a87027dfdf4</td>\n",
       "      <td>011_S_0003</td>\n",
       "      <td>3</td>\n",
       "      <td>32237</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/1/05</td>\n",
       "      <td>AD</td>\n",
       "      <td>9/12/05</td>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2e89e352af743597b2368c412e0f6de2</td>\n",
       "      <td>022_S_0004</td>\n",
       "      <td>4</td>\n",
       "      <td>64631</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/22/05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>11/8/05</td>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "      <td>3,3</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90419199306997753de8042f1fd55e38</td>\n",
       "      <td>011_S_0005</td>\n",
       "      <td>5</td>\n",
       "      <td>32246</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>9/2/05</td>\n",
       "      <td>CN</td>\n",
       "      <td>9/7/05</td>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "      <td>CN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d8d175ffff1e2053e6a18c5df494ccdf</td>\n",
       "      <td>100_S_0006</td>\n",
       "      <td>6</td>\n",
       "      <td>33025</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>11/15/05</td>\n",
       "      <td>LMCI</td>\n",
       "      <td>11/29/05</td>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "      <td>MCI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>986e75b2e604cd44b38feb2188476fb2</td>\n",
       "      <td>011_S_0010</td>\n",
       "      <td>10</td>\n",
       "      <td>32270</td>\n",
       "      <td>MRI</td>\n",
       "      <td>1</td>\n",
       "      <td>11/7/05</td>\n",
       "      <td>AD</td>\n",
       "      <td>11/10/05</td>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "      <td>AD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       directory.id     Subject  RID  Image.Data.ID Modality  \\\n",
       "0  4702245ea294ce5d4e9b8a87027dfdf4  011_S_0003    3          32237      MRI   \n",
       "1  2e89e352af743597b2368c412e0f6de2  022_S_0004    4          64631      MRI   \n",
       "2  90419199306997753de8042f1fd55e38  011_S_0005    5          32246      MRI   \n",
       "3  d8d175ffff1e2053e6a18c5df494ccdf  100_S_0006    6          33025      MRI   \n",
       "4  986e75b2e604cd44b38feb2188476fb2  011_S_0010   10          32270      MRI   \n",
       "\n",
       "   Visit  Acq.Date DX.bl  EXAMDATE   AGE PTGENDER  PTEDUCAT         PTETHCAT  \\\n",
       "0      1    9/1/05    AD   9/12/05  81.3     Male        18  Not Hisp/Latino   \n",
       "1      1   9/22/05  LMCI   11/8/05  67.5     Male        10      Hisp/Latino   \n",
       "2      1    9/2/05    CN    9/7/05  73.7     Male        16  Not Hisp/Latino   \n",
       "3      1  11/15/05  LMCI  11/29/05  80.4   Female        13  Not Hisp/Latino   \n",
       "4      1   11/7/05    AD  11/10/05  73.9   Female        12  Not Hisp/Latino   \n",
       "\n",
       "  PTRACCAT  APOE4  MMSE imputed_genotype APOE Genotype Dx Codes for Submission  \n",
       "0    White      1    20             True           3,4                      AD  \n",
       "1    White      0    27            False           3,3                     MCI  \n",
       "2    White      0    29             True           3,3                      CN  \n",
       "3    White      0    25             True           3,3                     MCI  \n",
       "4    White      1    24             True           3,4                      AD  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Peek at data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9631b484",
   "metadata": {},
   "source": [
    "## Remove ID's and other features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33d74518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing columns: ['directory.id', 'Subject', 'RID', 'Image.Data.ID', 'Modality', 'Visit', 'Acq.Date', 'DX.bl', 'EXAMDATE', 'Dx Codes for Submission']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTGENDER</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>PTETHCAT</th>\n",
       "      <th>PTRACCAT</th>\n",
       "      <th>APOE4</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>imputed_genotype</th>\n",
       "      <th>APOE Genotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>Male</td>\n",
       "      <td>18</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>Male</td>\n",
       "      <td>10</td>\n",
       "      <td>Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>False</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>Male</td>\n",
       "      <td>16</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>Female</td>\n",
       "      <td>13</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>True</td>\n",
       "      <td>3,3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.9</td>\n",
       "      <td>Female</td>\n",
       "      <td>12</td>\n",
       "      <td>Not Hisp/Latino</td>\n",
       "      <td>White</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>True</td>\n",
       "      <td>3,4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE PTGENDER  PTEDUCAT         PTETHCAT PTRACCAT  APOE4  MMSE  \\\n",
       "0  81.3     Male        18  Not Hisp/Latino    White      1    20   \n",
       "1  67.5     Male        10      Hisp/Latino    White      0    27   \n",
       "2  73.7     Male        16  Not Hisp/Latino    White      0    29   \n",
       "3  80.4   Female        13  Not Hisp/Latino    White      0    25   \n",
       "4  73.9   Female        12  Not Hisp/Latino    White      1    24   \n",
       "\n",
       "  imputed_genotype APOE Genotype  \n",
       "0             True           3,4  \n",
       "1            False           3,3  \n",
       "2             True           3,3  \n",
       "3             True           3,3  \n",
       "4             True           3,4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data\n",
    "Y = data['DX.bl']\n",
    "del data\n",
    "\n",
    "#Remove unnecessary columns (features), remove first 9 columns and 'Dx codes for submission'\n",
    "remove_columns = list(X.columns)[0:9]\n",
    "remove_columns.append('Dx Codes for Submission')\n",
    "print('Removing columns:', remove_columns)\n",
    "\n",
    "X = X.drop(remove_columns, axis=1)\n",
    "\n",
    "features = list(X.columns)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c20d08e",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7422f322",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variable distributions:\n",
      "\n",
      "\n",
      "Distribution of PTRACCAT\n",
      "White    583\n",
      "Black     31\n",
      "Asian     13\n",
      "Name: PTRACCAT, dtype: int64\n",
      "\n",
      "Distribution of imputed_genotype\n",
      "True     466\n",
      "False    161\n",
      "Name: imputed_genotype, dtype: int64\n",
      "\n",
      "Distribution of PTGENDER\n",
      "Male      363\n",
      "Female    264\n",
      "Name: PTGENDER, dtype: int64\n",
      "\n",
      "Distribution of APOE Genotype\n",
      "3,3    275\n",
      "3,4    221\n",
      "4,4     71\n",
      "2,3     45\n",
      "2,4     13\n",
      "2,2      2\n",
      "Name: APOE Genotype, dtype: int64\n",
      "\n",
      "Distribution of APOE4\n",
      "0    322\n",
      "1    234\n",
      "2     71\n",
      "Name: APOE4, dtype: int64\n",
      "\n",
      "Distribution of PTETHCAT\n",
      "Not Hisp/Latino    616\n",
      "Hisp/Latino          8\n",
      "Unknown              3\n",
      "Name: PTETHCAT, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "numerical_vars = ['AGE', 'MMSE', 'PTEDUCAT']\n",
    "cat_vars = list(set(features) - set(numerical_vars))\n",
    "\n",
    "print('Categorical variable distributions:\\n')\n",
    "\n",
    "for var in cat_vars:\n",
    "    print('\\nDistribution of', var)\n",
    "    \n",
    "    print(X[var].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e43efb",
   "metadata": {},
   "source": [
    "### Categorical Var Distribution Notes\n",
    "- Ethnicity and Race are separate vars\n",
    "- Distribution of Hispanic, Black, White, Asian not quite same as overall American ethnic/racial distribution\n",
    "- APOE Genotype with 3 in the first spot seems to be overwhelming majority 3,3 and 3,4\n",
    "- More males in study than females\n",
    "### Numerical Variable Distributions\n",
    "- All 3 distributions seem a bit right-skewed\n",
    "- Age: mean = 75, min=55, max=89 - seems to be no obvious bad data\n",
    "- MMSE: mean = 26.9, min=20, max=30 - seems to be no obvious bad data, NOTE: MMSE \n",
    "- PTEDUCAT: mean = 15.6, min=6, max=20 - seems to be no obvious bad data. 13-14 yrs is probably high school/GED and 17- 18 years is likely Bachelor's degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c44d2830",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Var Distributions:\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      75.161882\n",
      "std        6.689979\n",
      "min       55.100000\n",
      "25%       71.200000\n",
      "50%       75.400000\n",
      "75%       80.000000\n",
      "max       89.600000\n",
      "Name: AGE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      26.921850\n",
      "std        2.563934\n",
      "min       20.000000\n",
      "25%       25.000000\n",
      "50%       27.000000\n",
      "75%       29.000000\n",
      "max       30.000000\n",
      "Name: MMSE, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    627.000000\n",
      "mean      15.612440\n",
      "std        2.978845\n",
      "min        6.000000\n",
      "25%       13.000000\n",
      "50%       16.000000\n",
      "75%       18.000000\n",
      "max       20.000000\n",
      "Name: PTEDUCAT, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print('Numerical Var Distributions:\\n')\n",
    "\n",
    "for var in numerical_vars:\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.histplot(data=X, x=var, bins=10, kde=True)\n",
    "    plt.title(var + ' Distribution')\n",
    "    plt.show()\n",
    "    \n",
    "    # descriptive stats\n",
    "    print(X[var].describe())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f0155b8",
   "metadata": {},
   "source": [
    "### Analyze target (y) distribution\n",
    "- High number (304) of limited impairment (LMCI). Maybe old age? Or the patients being evaluated are not random elderly patients but patients already known to have cognitive issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56183266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "counts = Y.value_counts()\n",
    "plt.bar(counts.index, counts.values)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66c71f3a",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08263c6b",
   "metadata": {},
   "source": [
    "Convert Categorical Variables to 1-Hot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a798cfe",
   "metadata": {},
   "source": [
    "- Next time use sklearn.preprocessing.OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aeb02d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AGE</th>\n",
       "      <th>PTEDUCAT</th>\n",
       "      <th>MMSE</th>\n",
       "      <th>PTRACCAT_0</th>\n",
       "      <th>PTRACCAT_1</th>\n",
       "      <th>PTRACCAT_2</th>\n",
       "      <th>imputed_genotype_0</th>\n",
       "      <th>imputed_genotype_1</th>\n",
       "      <th>PTGENDER_0</th>\n",
       "      <th>PTGENDER_1</th>\n",
       "      <th>...</th>\n",
       "      <th>APOE Genotype_2</th>\n",
       "      <th>APOE Genotype_3</th>\n",
       "      <th>APOE Genotype_4</th>\n",
       "      <th>APOE Genotype_5</th>\n",
       "      <th>APOE4_0</th>\n",
       "      <th>APOE4_1</th>\n",
       "      <th>APOE4_2</th>\n",
       "      <th>PTETHCAT_0</th>\n",
       "      <th>PTETHCAT_1</th>\n",
       "      <th>PTETHCAT_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>81.3</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67.5</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73.7</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>80.4</td>\n",
       "      <td>13</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    AGE  PTEDUCAT  MMSE  PTRACCAT_0  PTRACCAT_1  PTRACCAT_2  \\\n",
       "0  81.3        18    20         0.0         0.0         1.0   \n",
       "1  67.5        10    27         0.0         0.0         1.0   \n",
       "2  73.7        16    29         0.0         0.0         1.0   \n",
       "3  80.4        13    25         0.0         0.0         1.0   \n",
       "\n",
       "   imputed_genotype_0  imputed_genotype_1  PTGENDER_0  PTGENDER_1  ...  \\\n",
       "0                 0.0                 1.0         0.0         1.0  ...   \n",
       "1                 1.0                 0.0         0.0         1.0  ...   \n",
       "2                 0.0                 1.0         0.0         1.0  ...   \n",
       "3                 0.0                 1.0         1.0         0.0  ...   \n",
       "\n",
       "   APOE Genotype_2  APOE Genotype_3  APOE Genotype_4  APOE Genotype_5  \\\n",
       "0              0.0              0.0              1.0              0.0   \n",
       "1              0.0              1.0              0.0              0.0   \n",
       "2              0.0              1.0              0.0              0.0   \n",
       "3              0.0              1.0              0.0              0.0   \n",
       "\n",
       "   APOE4_0  APOE4_1  APOE4_2  PTETHCAT_0  PTETHCAT_1  PTETHCAT_2  \n",
       "0      0.0      1.0      0.0         0.0         1.0         0.0  \n",
       "1      1.0      0.0      0.0         1.0         0.0         0.0  \n",
       "2      1.0      0.0      0.0         0.0         1.0         0.0  \n",
       "3      1.0      0.0      0.0         0.0         1.0         0.0  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# instantiate the OneHotEncoder with sparse=False to return a dense array\n",
    "onehot_encoder = OneHotEncoder(sparse=False)\n",
    "\n",
    "# apply OneHotEncoder to each categorical variable\n",
    "for var in cat_vars:\n",
    "    # fit and transform the OneHotEncoder on the variable\n",
    "    onehot_encoded = onehot_encoder.fit_transform(X[[var]])\n",
    "    \n",
    "    # create column names for the new one-hot encoded columns\n",
    "    col_names = [var + '_' + str(i) for i in range(onehot_encoded.shape[1])]\n",
    "    \n",
    "    # add the new one-hot encoded columns to the original dataframe\n",
    "    X[col_names] = onehot_encoded\n",
    "    \n",
    "    # drop the original categorical column\n",
    "    X = X.drop(var, axis=1)\n",
    "\n",
    "X.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed366ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(X):\n",
    "    \"\"\"\n",
    "    Normalize numerical variables for faster and better convergence\n",
    "    \n",
    "    Args:\n",
    "        X (pd.DataFrame): data\n",
    "        \n",
    "    Returns:\n",
    "        X (pd.DataFrame): same data but with normalized numerical features\n",
    "    \"\"\"\n",
    "    numerical_vars = X.select_dtypes(include='number').columns.tolist()\n",
    "\n",
    "    #Normalize numerical variables to speed up convergence\n",
    "    for var in numerical_vars:\n",
    "        mean = X[var].mean()\n",
    "        sd = X[var].std()\n",
    "        print('\\nNormalizing', var, 'with mean=', format(mean, '.2f'), 'and sd=', format(sd, '.2f'))\n",
    "\n",
    "        X[var] = (X[var] - mean) / sd\n",
    "\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23301f6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (438, 22) \ty_train: (438,)\n",
      "X_test: (189, 22) \ty_test: (189,)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Combine X and Y into one numpy array\n",
    "data = np.column_stack((X, Y))\n",
    "\n",
    "# Shuffle the data\n",
    "np.random.shuffle(data)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "split_idx = int(0.7 * len(data))\n",
    "train_data = data[:split_idx]\n",
    "test_data = data[split_idx:]\n",
    "\n",
    "# Separate X and Y from the training and testing sets\n",
    "X_train, y_train = train_data[:, :-1], train_data[:, -1]\n",
    "X_test, y_test = test_data[:, :-1], test_data[:, -1]\n",
    "\n",
    "print('X_train:', X_train.shape, '\\ty_train:', y_train.shape)\n",
    "print('X_test:', X_test.shape, '\\ty_test:', y_test.shape)\n",
    "num_test = X_test.shape[0]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a22c8c15",
   "metadata": {},
   "source": [
    "## Predicting model using different Algorithms "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40dd5a6f",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "891cf9e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7407407407407407\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# create the classifier\n",
    "log_clf = LogisticRegression(solver='lbfgs', penalty='l2', max_iter=1000000, multi_class='multinomial')\n",
    "\n",
    "# fit the classifier on the training data\n",
    "log_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = log_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539efe59",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "47c3fb8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5978835978835979\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# create the classifier\n",
    "dt_clf = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "# fit the classifier on the training data\n",
    "dt_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = dt_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6982d540",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "96bcb5c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6931216931216931\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# create the classifier\n",
    "rf_clf = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# fit the classifier on the training data\n",
    "rf_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = rf_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b695372a",
   "metadata": {},
   "source": [
    "### Support Vector Machines (SVMs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "43d7cbcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6984126984126984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# create the classifier\n",
    "svm_clf = SVC(kernel='linear', C=1)\n",
    "\n",
    "# fit the classifier on the training data\n",
    "svm_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = svm_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e5be47",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b36816d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6243386243386243\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Koushik Reddy\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# create the classifier\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# fit the classifier on the training data\n",
    "knn_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = knn_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ff7b557",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2cea1b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.3333333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# create the classifier\n",
    "nb_clf = GaussianNB()\n",
    "\n",
    "# fit the classifier on the training data\n",
    "nb_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = nb_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9af0081",
   "metadata": {},
   "source": [
    "### Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "751944e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# create the classifier\n",
    "gb_clf = GradientBoostingClassifier(random_state=42)\n",
    "\n",
    "# fit the classifier on the training data\n",
    "gb_clf.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the test data\n",
    "y_pred = gb_clf.predict(X_test)\n",
    "\n",
    "# evaluate the classifier's accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print('Accuracy:', accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e17d1afc",
   "metadata": {},
   "source": [
    "## Accuracy of all the models we used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "863166c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LogisticRegression: 0.5873015873015873\n",
      "Accuracy of DecisionTreeClassifier: 0.5978835978835979\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Koushik Reddy\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: 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",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RandomForestClassifier: 0.6666666666666666\n",
      "Accuracy of SVC: 0.4656084656084656\n",
      "Accuracy of KNeighborsClassifier: 0.6455026455026455\n",
      "Accuracy of GaussianNB: 0.3333333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Koushik Reddy\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GradientBoostingClassifier: 0.7142857142857143\n"
     ]
    }
   ],
   "source": [
    "classifiers = [LogisticRegression(), DecisionTreeClassifier(), RandomForestClassifier(), SVC(), KNeighborsClassifier(), GaussianNB(), GradientBoostingClassifier()]\n",
    "\n",
    "\n",
    "for clf in classifiers:\n",
    "    clf.fit(X_train, y_train)\n",
    "    y_pred = clf.predict(X_test)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    print(f\"Accuracy of {clf.__class__.__name__}: {acc}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ecc846",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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