Artificial_Intelligence/AI_Medicine/Programing_Assignment1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3bba513",
   "metadata": {},
   "source": [
    "<h1><center> Programing Assignment 1</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0001d0e",
   "metadata": {},
   "source": [
    "## 1. NumPy is a package which provides convenient matrix/vector computations: (10%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a27d16",
   "metadata": {},
   "source": [
    "### a. Please generate a 8 × 8 matrix A and find the minimum, mean, maximum values of each row and column using NumPy. (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c1be6c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68f83a97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a 8 x 8 matrix A:\n",
      " [[ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]\n",
      " [56 57 58 59 60 61 62 63]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a 8 x 8 matrix \n",
    "matrix_A = np.arange(64).reshape(8, 8)\n",
    "print(\"This is a 8 x 8 matrix A:\\n\", matrix_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "535a26c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum value of each row for matrix A:\n",
      " [ 0  8 16 24 32 40 48 56]\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum value of each row for matrix A:\\n\", matrix_A.min(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37d66be1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of each row for matrix A:\n",
      " [ 3.5 11.5 19.5 27.5 35.5 43.5 51.5 59.5]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean value of each row for matrix A:\\n\", matrix_A.mean(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c1f0afcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum value of each row for matrix A:\n",
      " [ 7 15 23 31 39 47 55 63]\n"
     ]
    }
   ],
   "source": [
    "print(\"Maximum value of each row for matrix A:\\n\", matrix_A.max(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9c806e7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum value of each column for matrix A:\n",
      " [0 1 2 3 4 5 6 7]\n"
     ]
    }
   ],
   "source": [
    "print(\"Minimum value of each column for matrix A:\\n\", matrix_A.min(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96e14c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean value of each column for matrix A:\n",
      " [28. 29. 30. 31. 32. 33. 34. 35.]\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean value of each column for matrix A:\\n\", matrix_A.mean(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e959741",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum value of each column for matrix A:\n",
      " [56 57 58 59 60 61 62 63]\n"
     ]
    }
   ],
   "source": [
    "print(\"Maximum value of each column for matrix A:\\n\", matrix_A.max(axis = 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3974d534",
   "metadata": {},
   "source": [
    "### b. Please generate another 8 × 8 matrix B and find the transpose and inverse of B. (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13738f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a 8 x 8 matrix B:\n",
      " [[-3 -2 -1  0  1  2  3  4]\n",
      " [ 5  6  7  8  9 10 11 12]\n",
      " [13 14 15 16 17 18 19 20]\n",
      " [21 22 23 24 25 26 27 28]\n",
      " [29 30 31 32 33 34 35 36]\n",
      " [37 38 39 40 41 42 43 44]\n",
      " [45 46 47 48 49 50 51 52]\n",
      " [53 54 55 56 57 58 59 60]]\n"
     ]
    }
   ],
   "source": [
    "# Generate a 8 x 8 matrix B\n",
    "matrix_B = np.arange(start = -3, stop = 61).reshape(8, 8)\n",
    "print(\"This is a 8 x 8 matrix B:\\n\", matrix_B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ad2c6a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Transpose of matrix B:\n",
      " [[-3  5 13 21 29 37 45 53]\n",
      " [-2  6 14 22 30 38 46 54]\n",
      " [-1  7 15 23 31 39 47 55]\n",
      " [ 0  8 16 24 32 40 48 56]\n",
      " [ 1  9 17 25 33 41 49 57]\n",
      " [ 2 10 18 26 34 42 50 58]\n",
      " [ 3 11 19 27 35 43 51 59]\n",
      " [ 4 12 20 28 36 44 52 60]]\n"
     ]
    }
   ],
   "source": [
    "print('\\nTranspose of matrix B:\\n', np.transpose(matrix_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a523af45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Inverse of matrix B:\n",
      " [[ 4.35130399e+13  5.03855293e-01  3.48104319e+14 -4.35130399e+14\n",
      "  -1.74052160e+14  1.74052160e+14 -1.30539120e+14  1.74052160e+14]\n",
      " [ 1.52785161e+16 -2.24367886e+02  6.25554340e+16 -9.31124662e+16\n",
      "  -6.11140645e+16  6.11140645e+16  1.38371467e+16  1.44136945e+15]\n",
      " [-1.73617029e+16  1.78639300e+02 -4.88216307e+16  7.45378373e+16\n",
      "   6.04396124e+16 -5.14324131e+16 -1.09652860e+16 -6.39641686e+15]\n",
      " [-6.57046902e+15 -2.25179981e+15 -3.45493537e+16  5.44456911e+16\n",
      "   3.07854757e+16 -3.52890753e+16 -7.31019070e+15  7.39721678e+14]\n",
      " [ 6.28763426e+15  2.25179981e+15  5.26507783e+15 -1.33367467e+16\n",
      "  -1.61433378e+16  7.13613854e+15  3.65509535e+15  4.88433873e+15]\n",
      " [ 9.30379912e+00 -8.54761905e+00  4.50359963e+15 -4.50359963e+15\n",
      "  -4.50359963e+15  4.50359963e+15  1.01503513e+02 -3.62512360e+01]\n",
      " [ 2.25179981e+15  2.25179981e+15 -2.02661983e+16  2.02661983e+16\n",
      "   5.27099745e+02 -6.64656833e+02 -9.00719925e+15  4.50359963e+15]\n",
      " [ 7.07086898e+13 -2.25179981e+15  3.09649670e+16 -3.78617838e+16\n",
      "  -9.29003401e+15  1.37936336e+16  9.92097309e+15 -5.34666478e+15]]\n"
     ]
    }
   ],
   "source": [
    "print('\\nInverse of matrix B:\\n', np.linalg.inv(matrix_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a926fa7",
   "metadata": {},
   "source": [
    "### c. Please compute the element-wise multiplication and matrix multiplication of A and B. (4%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1cb7c441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element-wise A * B:\n",
      " [[   0   -2   -2    0    4   10   18   28]\n",
      " [  40   54   70   88  108  130  154  180]\n",
      " [ 208  238  270  304  340  378  418  460]\n",
      " [ 504  550  598  648  700  754  810  868]\n",
      " [ 928  990 1054 1120 1188 1258 1330 1404]\n",
      " [1480 1558 1638 1720 1804 1890 1978 2068]\n",
      " [2160 2254 2350 2448 2548 2650 2754 2860]\n",
      " [2968 3078 3190 3304 3420 3538 3658 3780]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Element-wise A * B:\\n\", np.multiply(matrix_A, matrix_B))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "58dc136b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix multiplication of A and B:\n",
      " [[ 1036  1064  1092  1120  1148  1176  1204  1232]\n",
      " [ 2636  2728  2820  2912  3004  3096  3188  3280]\n",
      " [ 4236  4392  4548  4704  4860  5016  5172  5328]\n",
      " [ 5836  6056  6276  6496  6716  6936  7156  7376]\n",
      " [ 7436  7720  8004  8288  8572  8856  9140  9424]\n",
      " [ 9036  9384  9732 10080 10428 10776 11124 11472]\n",
      " [10636 11048 11460 11872 12284 12696 13108 13520]\n",
      " [12236 12712 13188 13664 14140 14616 15092 15568]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Matrix multiplication of A and B:\\n\", np.matmul(matrix_A, matrix_B))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90c3c77",
   "metadata": {},
   "source": [
    "## 2. Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns. (10%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735cc5c7",
   "metadata": {},
   "source": [
    "### a. Given a table of NBA players’ stats as follows, please generate a Pandas DataFrame based on the table. (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0b25cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e91abffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Pandas DataFrame through the dictionary approach\n",
    "dict_nba = {\n",
    "    \"Player\":[\"James Harden\", \"Kawhi Leonard\", \"Paul George\", \"Stephen Curry\", \\\n",
    "              \"Damian Lillard\", \"Giannis Antetokounmpo\", \"Nikola Jokic\", \"CJ McCollum\", \\\n",
    "              \"Russell Westbrook\", \"DeMar DeRozan\", \"James Harden\"],\n",
    "    \"GP\":[11, 24, 5, 22, 16, 15, 14, 16, 5, 7, 11],\n",
    "    \"MIN\":[38.5, 39.1, 40.8, 38.5, 40.6, 34.3, 39.7, 39.7, 39.4, 35.9, 38.5],\n",
    "    \"PTS\":[31.6, 30.5, 28.6, 28.2, 26.9, 25.5, 25.1, 24.7, 22.8, 22, 31.6],\n",
    "    \"FGM\":[9.9, 10.1, 8.8, 8.6, 8.6, 8.6, 9.4, np.nan, 8, 8.3, 9.9],\n",
    "    \"FGA\":[24, 20.7, 20.2, 19.6, 20.6, 17.4, 18.6, 21.9, 22.2, 17, 24],\n",
    "    \"FG%\":[41.3, 49, 43.6, 44.1, 41.8, 49.4, 50.6, 44, 36, 48.7, 41.3],\n",
    "    \"3PM\":[4.4, 2.3, 3, 4.2, np.nan, 1.2, 1.6, 2.9, 2.2, 0, 4.4],\n",
    "    \"3PA\":[12.5, 6, 9.4, 11.1, 9.9, 3.7, 4, 7.3, 6.8, 0.1, 12.5],\n",
    "    \"3P%\":[35, 37.9, 31.9, 37.7, 37.3, 32.7, 39.3, 39.3, 32.4, 0, 35] \n",
    "}\n",
    "nba_data = pd.DataFrame(dict_nba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac8e2a90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>24</td>\n",
       "      <td>39.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Paul George</td>\n",
       "      <td>5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>43.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>22</td>\n",
       "      <td>38.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>16</td>\n",
       "      <td>40.6</td>\n",
       "      <td>26.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>41.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>15</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>14</td>\n",
       "      <td>39.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>16</td>\n",
       "      <td>39.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the Pandas DataFrame without the row index\n",
    "# Import packages\n",
    "from IPython.display import HTML\n",
    "HTML(nba_data.to_html(index = False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ca70928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>24</td>\n",
       "      <td>39.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.1</td>\n",
       "      <td>20.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Paul George</td>\n",
       "      <td>5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.6</td>\n",
       "      <td>8.8</td>\n",
       "      <td>20.2</td>\n",
       "      <td>43.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>22</td>\n",
       "      <td>38.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.6</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>11.1</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>16</td>\n",
       "      <td>40.6</td>\n",
       "      <td>26.9</td>\n",
       "      <td>8.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>41.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.9</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>15</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>14</td>\n",
       "      <td>39.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>9.4</td>\n",
       "      <td>18.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>16</td>\n",
       "      <td>39.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>7.3</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.9</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Pandas DataFrame through importing a csv file\n",
    "# Read CSV file\n",
    "nba_csv = pd.read_csv(\"NBA.csv\", delimiter = \",\", header = 0)\n",
    "HTML(nba_csv.to_html(index = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76dc251",
   "metadata": {},
   "source": [
    "### b. Please check how many data are missing and fill the missing data with the average of other players. (4%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c40dec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of missing values:\n",
      " Player    0\n",
      "GP        0\n",
      "MIN       0\n",
      "PTS       0\n",
      "FGM       1\n",
      "FGA       0\n",
      "FG%       0\n",
      "3PM       1\n",
      "3PA       0\n",
      "3P%       0\n",
      "dtype: int64\n",
      "\n",
      "Proportion of missing values:\n",
      " Player    0.000000\n",
      "GP        0.000000\n",
      "MIN       0.000000\n",
      "PTS       0.000000\n",
      "FGM       0.090909\n",
      "FGA       0.000000\n",
      "FG%       0.000000\n",
      "3PM       0.090909\n",
      "3PA       0.000000\n",
      "3P%       0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check the count of missing data per column\n",
    "print(\"Count of missing values:\\n\", nba_data.isnull().sum())\n",
    "\n",
    "# Check the proportion of missing data per column\n",
    "print(\"\\nProportion of missing values:\\n\", nba_data.isnull().sum()/len(nba_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da008def",
   "metadata": {},
   "source": [
    "**As we see, for FGM and 3PM, each one has one missing value (~ 9%).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32033b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s1/6460_yvs6ggdv14vc1dsk_r40000gn/T/ipykernel_1702/4293382783.py:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  nba_data_filled = nba_data.fillna(nba_data.mean())\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>24</td>\n",
       "      <td>39.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.10</td>\n",
       "      <td>20.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paul George</td>\n",
       "      <td>5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.6</td>\n",
       "      <td>8.80</td>\n",
       "      <td>20.2</td>\n",
       "      <td>43.6</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.4</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>22</td>\n",
       "      <td>38.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>11.1</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>16</td>\n",
       "      <td>40.6</td>\n",
       "      <td>26.9</td>\n",
       "      <td>8.60</td>\n",
       "      <td>20.6</td>\n",
       "      <td>41.8</td>\n",
       "      <td>2.62</td>\n",
       "      <td>9.9</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>15</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.60</td>\n",
       "      <td>17.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>14</td>\n",
       "      <td>39.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>9.40</td>\n",
       "      <td>18.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>16</td>\n",
       "      <td>39.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>9.02</td>\n",
       "      <td>21.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.30</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Player  GP   MIN   PTS    FGM   FGA   FG%   3PM   3PA   3P%\n",
       "0            James Harden  11  38.5  31.6   9.90  24.0  41.3  4.40  12.5  35.0\n",
       "1           Kawhi Leonard  24  39.1  30.5  10.10  20.7  49.0  2.30   6.0  37.9\n",
       "2             Paul George   5  40.8  28.6   8.80  20.2  43.6  3.00   9.4  31.9\n",
       "3           Stephen Curry  22  38.5  28.2   8.60  19.6  44.1  4.20  11.1  37.7\n",
       "4          Damian Lillard  16  40.6  26.9   8.60  20.6  41.8  2.62   9.9  37.3\n",
       "5   Giannis Antetokounmpo  15  34.3  25.5   8.60  17.4  49.4  1.20   3.7  32.7\n",
       "6            Nikola Jokic  14  39.7  25.1   9.40  18.6  50.6  1.60   4.0  39.3\n",
       "7             CJ McCollum  16  39.7  24.7   9.02  21.9  44.0  2.90   7.3  39.3\n",
       "8       Russell Westbrook   5  39.4  22.8   8.00  22.2  36.0  2.20   6.8  32.4\n",
       "9           DeMar DeRozan   7  35.9  22.0   8.30  17.0  48.7  0.00   0.1   0.0\n",
       "10           James Harden  11  38.5  31.6   9.90  24.0  41.3  4.40  12.5  35.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fill missing data by mean value\n",
    "# First approach\n",
    "nba_data_filled = nba_data.fillna(nba_data.mean())\n",
    "nba_data_filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "097e3afa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>24</td>\n",
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       "      <td>30.5</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paul George</td>\n",
       "      <td>5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>22</td>\n",
       "      <td>38.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>11.1</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>16</td>\n",
       "      <td>40.6</td>\n",
       "      <td>26.9</td>\n",
       "      <td>8.60</td>\n",
       "      <td>20.6</td>\n",
       "      <td>41.8</td>\n",
       "      <td>2.62</td>\n",
       "      <td>9.9</td>\n",
       "      <td>37.3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>15</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.60</td>\n",
       "      <td>17.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>14</td>\n",
       "      <td>39.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>9.40</td>\n",
       "      <td>18.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>16</td>\n",
       "      <td>39.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>9.02</td>\n",
       "      <td>21.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.30</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Player  GP   MIN   PTS    FGM   FGA   FG%   3PM   3PA   3P%\n",
       "0            James Harden  11  38.5  31.6   9.90  24.0  41.3  4.40  12.5  35.0\n",
       "1           Kawhi Leonard  24  39.1  30.5  10.10  20.7  49.0  2.30   6.0  37.9\n",
       "2             Paul George   5  40.8  28.6   8.80  20.2  43.6  3.00   9.4  31.9\n",
       "3           Stephen Curry  22  38.5  28.2   8.60  19.6  44.1  4.20  11.1  37.7\n",
       "4          Damian Lillard  16  40.6  26.9   8.60  20.6  41.8  2.62   9.9  37.3\n",
       "5   Giannis Antetokounmpo  15  34.3  25.5   8.60  17.4  49.4  1.20   3.7  32.7\n",
       "6            Nikola Jokic  14  39.7  25.1   9.40  18.6  50.6  1.60   4.0  39.3\n",
       "7             CJ McCollum  16  39.7  24.7   9.02  21.9  44.0  2.90   7.3  39.3\n",
       "8       Russell Westbrook   5  39.4  22.8   8.00  22.2  36.0  2.20   6.8  32.4\n",
       "9           DeMar DeRozan   7  35.9  22.0   8.30  17.0  48.7  0.00   0.1   0.0\n",
       "10           James Harden  11  38.5  31.6   9.90  24.0  41.3  4.40  12.5  35.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Second approach\n",
    "# Import package\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "# Impute missing values by mean\n",
    "simple_imp = SimpleImputer(missing_values = np.nan, strategy = \"mean\")\n",
    "simple_imp.fit(nba_data[[\"FGM\", \"3PM\"]])\n",
    "nba_data_filled = nba_data.copy()\n",
    "nba_data_filled[[\"FGM\", \"3PM\"]] = simple_imp.transform(nba_data[[\"FGM\", \"3PM\"]])\n",
    "nba_data_filled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a55954f",
   "metadata": {},
   "source": [
    "**The above two approaches fill the missing values of FGM and 3PM with the average of FGM and 3PM based on other players. The filled results for FGM (9.02) and 3PM (2.62) using two approaches are the same.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4852797",
   "metadata": {},
   "source": [
    "### c. Now, we get the stats of another player as follows, please add his information into our DataFrame. (3%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "af3744ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Player</th>\n",
       "      <th>GP</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PTS</th>\n",
       "      <th>FGM</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3PM</th>\n",
       "      <th>3PA</th>\n",
       "      <th>3P%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Kawhi Leonard</td>\n",
       "      <td>24</td>\n",
       "      <td>39.1</td>\n",
       "      <td>30.5</td>\n",
       "      <td>10.10</td>\n",
       "      <td>20.7</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>6.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Paul George</td>\n",
       "      <td>5</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.6</td>\n",
       "      <td>8.80</td>\n",
       "      <td>20.2</td>\n",
       "      <td>43.6</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9.4</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>22</td>\n",
       "      <td>38.5</td>\n",
       "      <td>28.2</td>\n",
       "      <td>8.60</td>\n",
       "      <td>19.6</td>\n",
       "      <td>44.1</td>\n",
       "      <td>4.20</td>\n",
       "      <td>11.1</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Damian Lillard</td>\n",
       "      <td>16</td>\n",
       "      <td>40.6</td>\n",
       "      <td>26.9</td>\n",
       "      <td>8.60</td>\n",
       "      <td>20.6</td>\n",
       "      <td>41.8</td>\n",
       "      <td>2.62</td>\n",
       "      <td>9.9</td>\n",
       "      <td>37.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Giannis Antetokounmpo</td>\n",
       "      <td>15</td>\n",
       "      <td>34.3</td>\n",
       "      <td>25.5</td>\n",
       "      <td>8.60</td>\n",
       "      <td>17.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.7</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nikola Jokic</td>\n",
       "      <td>14</td>\n",
       "      <td>39.7</td>\n",
       "      <td>25.1</td>\n",
       "      <td>9.40</td>\n",
       "      <td>18.6</td>\n",
       "      <td>50.6</td>\n",
       "      <td>1.60</td>\n",
       "      <td>4.0</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CJ McCollum</td>\n",
       "      <td>16</td>\n",
       "      <td>39.7</td>\n",
       "      <td>24.7</td>\n",
       "      <td>9.02</td>\n",
       "      <td>21.9</td>\n",
       "      <td>44.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>7.3</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Russell Westbrook</td>\n",
       "      <td>5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>22.8</td>\n",
       "      <td>8.00</td>\n",
       "      <td>22.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>32.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>DeMar DeRozan</td>\n",
       "      <td>7</td>\n",
       "      <td>35.9</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.30</td>\n",
       "      <td>17.0</td>\n",
       "      <td>48.7</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>James Harden</td>\n",
       "      <td>11</td>\n",
       "      <td>38.5</td>\n",
       "      <td>31.6</td>\n",
       "      <td>9.90</td>\n",
       "      <td>24.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.40</td>\n",
       "      <td>12.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Lou Williams</td>\n",
       "      <td>6</td>\n",
       "      <td>29.3</td>\n",
       "      <td>21.7</td>\n",
       "      <td>7.50</td>\n",
       "      <td>17.3</td>\n",
       "      <td>43.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new row to the existing Pandas DataFrame\n",
    "nba_data_new = nba_data_filled.append({\n",
    "    \"Player\":\"Lou Williams\",\n",
    "    \"GP\":6,\n",
    "    \"MIN\":29.3,\n",
    "    \"PTS\":21.7,\n",
    "    \"FGM\":7.5, \n",
    "    \"FGA\":17.3,\n",
    "    \"FG%\":43.3,\n",
    "    \"3PM\":1,\n",
    "    \"3PA\":3,\n",
    "    \"3P%\":33.3},\n",
    "    ignore_index = True)\n",
    "\n",
    "# Print the Pandas DataFrame without the row index\n",
    "HTML(nba_data_new.to_html(index = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a6741c",
   "metadata": {},
   "source": [
    "## 3. Parkinson Dataset with replicated acoustic features Data Set (http://archive.ics.uci.edu/ml/datasets/Parkinson+Dataset+with+replicated+acoustic+features+ ) contains acoustic features extracted from 3 voice recording replications of the sustained /a/ phonation for each one of the 80 subjects (Some of them with Parkinson's Disease, i.e., status=1). Please find the data as Parkinson.csv file. (Hint: columns ‘ID’ and ‘Recording’ can not be considered as the features.) (40%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61cd9d34",
   "metadata": {},
   "source": [
    "### a. As we discussed in class, given a dataset to analyze, before designing supervised learning model or unsupervised model, we need to understand the structure and statistics of the data, i.e., distribution of class labels, distribution of each feature, etc. Please implement such data analysis using Python. (10%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "86589323",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the package for visualization\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15d027cb",
   "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>ID</th>\n",
       "      <th>Recording</th>\n",
       "      <th>Status</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>...</th>\n",
       "      <th>Delta3</th>\n",
       "      <th>Delta4</th>\n",
       "      <th>Delta5</th>\n",
       "      <th>Delta6</th>\n",
       "      <th>Delta7</th>\n",
       "      <th>Delta8</th>\n",
       "      <th>Delta9</th>\n",
       "      <th>Delta10</th>\n",
       "      <th>Delta11</th>\n",
       "      <th>Delta12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CONT-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25546</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.030256</td>\n",
       "      <td>0.26313</td>\n",
       "      <td>...</td>\n",
       "      <td>1.407701</td>\n",
       "      <td>1.417218</td>\n",
       "      <td>1.380352</td>\n",
       "      <td>1.420670</td>\n",
       "      <td>1.451240</td>\n",
       "      <td>1.440295</td>\n",
       "      <td>1.403678</td>\n",
       "      <td>1.405495</td>\n",
       "      <td>1.416705</td>\n",
       "      <td>1.354610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CONT-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.36964</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.001932</td>\n",
       "      <td>0.002245</td>\n",
       "      <td>0.023146</td>\n",
       "      <td>0.20217</td>\n",
       "      <td>...</td>\n",
       "      <td>1.331232</td>\n",
       "      <td>1.227338</td>\n",
       "      <td>1.213377</td>\n",
       "      <td>1.352739</td>\n",
       "      <td>1.354242</td>\n",
       "      <td>1.365692</td>\n",
       "      <td>1.322870</td>\n",
       "      <td>1.314549</td>\n",
       "      <td>1.318999</td>\n",
       "      <td>1.323508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CONT-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.23514</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.001353</td>\n",
       "      <td>0.001546</td>\n",
       "      <td>0.019338</td>\n",
       "      <td>0.16710</td>\n",
       "      <td>...</td>\n",
       "      <td>1.412304</td>\n",
       "      <td>1.324674</td>\n",
       "      <td>1.276088</td>\n",
       "      <td>1.429634</td>\n",
       "      <td>1.455996</td>\n",
       "      <td>1.368882</td>\n",
       "      <td>1.438053</td>\n",
       "      <td>1.388910</td>\n",
       "      <td>1.305469</td>\n",
       "      <td>1.305402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CONT-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.29320</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>0.001444</td>\n",
       "      <td>0.024716</td>\n",
       "      <td>0.20892</td>\n",
       "      <td>...</td>\n",
       "      <td>1.501200</td>\n",
       "      <td>1.534170</td>\n",
       "      <td>1.323993</td>\n",
       "      <td>1.496442</td>\n",
       "      <td>1.472926</td>\n",
       "      <td>1.643177</td>\n",
       "      <td>1.551286</td>\n",
       "      <td>1.638346</td>\n",
       "      <td>1.604008</td>\n",
       "      <td>1.621456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CONT-02</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.23075</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>0.001404</td>\n",
       "      <td>0.013119</td>\n",
       "      <td>0.11607</td>\n",
       "      <td>...</td>\n",
       "      <td>1.508468</td>\n",
       "      <td>1.334511</td>\n",
       "      <td>1.610694</td>\n",
       "      <td>1.685021</td>\n",
       "      <td>1.417614</td>\n",
       "      <td>1.574895</td>\n",
       "      <td>1.640088</td>\n",
       "      <td>1.533666</td>\n",
       "      <td>1.297536</td>\n",
       "      <td>1.382023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>PARK-39</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57585</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.003701</td>\n",
       "      <td>0.005149</td>\n",
       "      <td>0.016868</td>\n",
       "      <td>0.14928</td>\n",
       "      <td>...</td>\n",
       "      <td>1.116409</td>\n",
       "      <td>1.104511</td>\n",
       "      <td>1.099866</td>\n",
       "      <td>1.080320</td>\n",
       "      <td>1.154057</td>\n",
       "      <td>1.117423</td>\n",
       "      <td>1.167076</td>\n",
       "      <td>1.132436</td>\n",
       "      <td>1.107824</td>\n",
       "      <td>1.109144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>PARK-39</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.23322</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.001270</td>\n",
       "      <td>0.001497</td>\n",
       "      <td>0.017923</td>\n",
       "      <td>0.16720</td>\n",
       "      <td>...</td>\n",
       "      <td>1.107477</td>\n",
       "      <td>1.083859</td>\n",
       "      <td>1.101819</td>\n",
       "      <td>1.114161</td>\n",
       "      <td>1.090095</td>\n",
       "      <td>1.140705</td>\n",
       "      <td>1.126667</td>\n",
       "      <td>1.158444</td>\n",
       "      <td>1.096073</td>\n",
       "      <td>1.141835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>PARK-40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.26862</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.001354</td>\n",
       "      <td>0.001615</td>\n",
       "      <td>0.028040</td>\n",
       "      <td>0.24182</td>\n",
       "      <td>...</td>\n",
       "      <td>1.335189</td>\n",
       "      <td>1.385580</td>\n",
       "      <td>1.281551</td>\n",
       "      <td>1.367171</td>\n",
       "      <td>1.319055</td>\n",
       "      <td>1.367095</td>\n",
       "      <td>1.343193</td>\n",
       "      <td>1.374330</td>\n",
       "      <td>1.383364</td>\n",
       "      <td>1.456409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>PARK-40</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45376</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.002724</td>\n",
       "      <td>0.002258</td>\n",
       "      <td>0.064605</td>\n",
       "      <td>0.58002</td>\n",
       "      <td>...</td>\n",
       "      <td>1.327629</td>\n",
       "      <td>1.349928</td>\n",
       "      <td>1.461323</td>\n",
       "      <td>1.350599</td>\n",
       "      <td>1.346363</td>\n",
       "      <td>1.415338</td>\n",
       "      <td>1.361937</td>\n",
       "      <td>1.331923</td>\n",
       "      <td>1.423062</td>\n",
       "      <td>1.307353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>PARK-40</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.34706</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>0.002060</td>\n",
       "      <td>0.019914</td>\n",
       "      <td>0.17087</td>\n",
       "      <td>...</td>\n",
       "      <td>1.440003</td>\n",
       "      <td>1.327946</td>\n",
       "      <td>1.531764</td>\n",
       "      <td>1.471198</td>\n",
       "      <td>1.445675</td>\n",
       "      <td>1.415942</td>\n",
       "      <td>1.304214</td>\n",
       "      <td>1.395653</td>\n",
       "      <td>1.315691</td>\n",
       "      <td>1.422534</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>240 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID  Recording  Status  Gender  Jitter_rel  Jitter_abs  Jitter_RAP  \\\n",
       "0    CONT-01          1       0       1     0.25546    0.000015    0.001467   \n",
       "1    CONT-01          2       0       1     0.36964    0.000022    0.001932   \n",
       "2    CONT-01          3       0       1     0.23514    0.000013    0.001353   \n",
       "3    CONT-02          1       0       0     0.29320    0.000017    0.001105   \n",
       "4    CONT-02          2       0       0     0.23075    0.000015    0.001073   \n",
       "..       ...        ...     ...     ...         ...         ...         ...   \n",
       "235  PARK-39          2       1       0     0.57585    0.000037    0.003701   \n",
       "236  PARK-39          3       1       0     0.23322    0.000015    0.001270   \n",
       "237  PARK-40          1       1       0     0.26862    0.000022    0.001354   \n",
       "238  PARK-40          2       1       0     0.45376    0.000037    0.002724   \n",
       "239  PARK-40          3       1       0     0.34706    0.000028    0.001746   \n",
       "\n",
       "     Jitter_PPQ  Shim_loc  Shim_dB  ...    Delta3    Delta4    Delta5  \\\n",
       "0      0.001673  0.030256  0.26313  ...  1.407701  1.417218  1.380352   \n",
       "1      0.002245  0.023146  0.20217  ...  1.331232  1.227338  1.213377   \n",
       "2      0.001546  0.019338  0.16710  ...  1.412304  1.324674  1.276088   \n",
       "3      0.001444  0.024716  0.20892  ...  1.501200  1.534170  1.323993   \n",
       "4      0.001404  0.013119  0.11607  ...  1.508468  1.334511  1.610694   \n",
       "..          ...       ...      ...  ...       ...       ...       ...   \n",
       "235    0.005149  0.016868  0.14928  ...  1.116409  1.104511  1.099866   \n",
       "236    0.001497  0.017923  0.16720  ...  1.107477  1.083859  1.101819   \n",
       "237    0.001615  0.028040  0.24182  ...  1.335189  1.385580  1.281551   \n",
       "238    0.002258  0.064605  0.58002  ...  1.327629  1.349928  1.461323   \n",
       "239    0.002060  0.019914  0.17087  ...  1.440003  1.327946  1.531764   \n",
       "\n",
       "       Delta6    Delta7    Delta8    Delta9   Delta10   Delta11   Delta12  \n",
       "0    1.420670  1.451240  1.440295  1.403678  1.405495  1.416705  1.354610  \n",
       "1    1.352739  1.354242  1.365692  1.322870  1.314549  1.318999  1.323508  \n",
       "2    1.429634  1.455996  1.368882  1.438053  1.388910  1.305469  1.305402  \n",
       "3    1.496442  1.472926  1.643177  1.551286  1.638346  1.604008  1.621456  \n",
       "4    1.685021  1.417614  1.574895  1.640088  1.533666  1.297536  1.382023  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "235  1.080320  1.154057  1.117423  1.167076  1.132436  1.107824  1.109144  \n",
       "236  1.114161  1.090095  1.140705  1.126667  1.158444  1.096073  1.141835  \n",
       "237  1.367171  1.319055  1.367095  1.343193  1.374330  1.383364  1.456409  \n",
       "238  1.350599  1.346363  1.415338  1.361937  1.331923  1.423062  1.307353  \n",
       "239  1.471198  1.445675  1.415942  1.304214  1.395653  1.315691  1.422534  \n",
       "\n",
       "[240 rows x 48 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Pandas DataFrame through importing a csv file\n",
    "# Read CSV file\n",
    "parkinson_data = pd.read_csv(\"Parkinson.csv\", delimiter = \",\", header = 0)\n",
    "parkinson_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "59f3515b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID            0.0\n",
       "Recording     0.0\n",
       "Status        0.0\n",
       "Gender        0.0\n",
       "Jitter_rel    0.0\n",
       "Jitter_abs    0.0\n",
       "Jitter_RAP    0.0\n",
       "Jitter_PPQ    0.0\n",
       "Shim_loc      0.0\n",
       "Shim_dB       0.0\n",
       "Shim_APQ3     0.0\n",
       "Shim_APQ5     0.0\n",
       "Shi_APQ11     0.0\n",
       "HNR05         0.0\n",
       "HNR15         0.0\n",
       "HNR25         0.0\n",
       "HNR35         0.0\n",
       "HNR38         0.0\n",
       "RPDE          0.0\n",
       "DFA           0.0\n",
       "PPE           0.0\n",
       "GNE           0.0\n",
       "MFCC0         0.0\n",
       "MFCC1         0.0\n",
       "MFCC2         0.0\n",
       "MFCC3         0.0\n",
       "MFCC4         0.0\n",
       "MFCC5         0.0\n",
       "MFCC6         0.0\n",
       "MFCC7         0.0\n",
       "MFCC8         0.0\n",
       "MFCC9         0.0\n",
       "MFCC10        0.0\n",
       "MFCC11        0.0\n",
       "MFCC12        0.0\n",
       "Delta0        0.0\n",
       "Delta1        0.0\n",
       "Delta2        0.0\n",
       "Delta3        0.0\n",
       "Delta4        0.0\n",
       "Delta5        0.0\n",
       "Delta6        0.0\n",
       "Delta7        0.0\n",
       "Delta8        0.0\n",
       "Delta9        0.0\n",
       "Delta10       0.0\n",
       "Delta11       0.0\n",
       "Delta12       0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the missing distribution\n",
    "parkinson_data.isnull().sum()/len(parkinson_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01b2ff29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of case is 120\n",
      "The number of control is 120\n"
     ]
    }
   ],
   "source": [
    "# How many case and control\n",
    "n_case = len(parkinson_data[parkinson_data[\"Status\"] == 1]) \n",
    "n_control = len(parkinson_data[parkinson_data[\"Status\"] == 0])\n",
    "\n",
    "print(\"The number of case is\", n_case)\n",
    "print(\"The number of control is\", n_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4415e8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of class label - status\n",
    "# First approach\n",
    "plt.bar([\"Cases (PD)\", \"Controls (healthy)\"], [n_case, n_control]) # Make barplot\n",
    "plt.title(\"Barplot of status\") # Add title\n",
    "plt.ylabel(\"Number of subjects\") # Modify y-axis label\n",
    "plt.show()\n",
    "\n",
    "# Second approach\n",
    "# parkinson_plot = parkinson_data[\"Status\"].value_counts().plot.bar(rot = 0) # Make barplot\n",
    "# plt.title(\"Barplot of status\") # Add title\n",
    "# parkinson_plot.set_xticklabels([\"Healthy\",\"Parkinson disease\"]) # Modify x-axis tick labels\n",
    "# plt.ylabel(\"Number of subjects\") # Modify y-axis label\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a231bce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of binary feature - gender\n",
    "gender_plot = parkinson_data[\"Gender\"].value_counts().plot.bar(rot = 0) # Make barplot\n",
    "plt.title(\"Barplot of gender\") # Add title\n",
    "gender_plot.set_xticklabels([\"Man\",\"Woman\"]) # Modify x-axis tick labels\n",
    "plt.ylabel(\"Number of subjects\") # Modify y-axis label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d4994b3c",
   "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>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>Shim_APQ3</th>\n",
       "      <th>Shim_APQ5</th>\n",
       "      <th>Shi_APQ11</th>\n",
       "      <th>HNR05</th>\n",
       "      <th>...</th>\n",
       "      <th>Delta3</th>\n",
       "      <th>Delta4</th>\n",
       "      <th>Delta5</th>\n",
       "      <th>Delta6</th>\n",
       "      <th>Delta7</th>\n",
       "      <th>Delta8</th>\n",
       "      <th>Delta9</th>\n",
       "      <th>Delta10</th>\n",
       "      <th>Delta11</th>\n",
       "      <th>Delta12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.583987</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>0.336832</td>\n",
       "      <td>0.021499</td>\n",
       "      <td>0.023468</td>\n",
       "      <td>0.028671</td>\n",
       "      <td>59.328203</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343238</td>\n",
       "      <td>1.348914</td>\n",
       "      <td>1.337717</td>\n",
       "      <td>1.341786</td>\n",
       "      <td>1.340895</td>\n",
       "      <td>1.343978</td>\n",
       "      <td>1.341472</td>\n",
       "      <td>1.331433</td>\n",
       "      <td>1.346381</td>\n",
       "      <td>1.346144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.535769</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.003373</td>\n",
       "      <td>0.004449</td>\n",
       "      <td>0.023213</td>\n",
       "      <td>0.205905</td>\n",
       "      <td>0.013787</td>\n",
       "      <td>0.014402</td>\n",
       "      <td>0.016610</td>\n",
       "      <td>14.541946</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198174</td>\n",
       "      <td>0.212008</td>\n",
       "      <td>0.204978</td>\n",
       "      <td>0.209407</td>\n",
       "      <td>0.213689</td>\n",
       "      <td>0.211364</td>\n",
       "      <td>0.204085</td>\n",
       "      <td>0.211297</td>\n",
       "      <td>0.221484</td>\n",
       "      <td>0.208819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.148010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.007444</td>\n",
       "      <td>0.064989</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>0.006459</td>\n",
       "      <td>22.224723</td>\n",
       "      <td>...</td>\n",
       "      <td>0.766458</td>\n",
       "      <td>0.840133</td>\n",
       "      <td>0.741690</td>\n",
       "      <td>0.759689</td>\n",
       "      <td>0.764649</td>\n",
       "      <td>0.762798</td>\n",
       "      <td>0.811942</td>\n",
       "      <td>0.777012</td>\n",
       "      <td>0.643132</td>\n",
       "      <td>0.748411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.298260</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.024336</td>\n",
       "      <td>0.211785</td>\n",
       "      <td>0.012910</td>\n",
       "      <td>0.014985</td>\n",
       "      <td>0.019180</td>\n",
       "      <td>50.558623</td>\n",
       "      <td>...</td>\n",
       "      <td>1.208624</td>\n",
       "      <td>1.221101</td>\n",
       "      <td>1.197868</td>\n",
       "      <td>1.194186</td>\n",
       "      <td>1.194138</td>\n",
       "      <td>1.191881</td>\n",
       "      <td>1.189515</td>\n",
       "      <td>1.192397</td>\n",
       "      <td>1.202525</td>\n",
       "      <td>1.206559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.481455</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.002870</td>\n",
       "      <td>0.032960</td>\n",
       "      <td>0.287885</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>0.019897</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>59.742330</td>\n",
       "      <td>...</td>\n",
       "      <td>1.351015</td>\n",
       "      <td>1.342237</td>\n",
       "      <td>1.337233</td>\n",
       "      <td>1.338437</td>\n",
       "      <td>1.344416</td>\n",
       "      <td>1.335517</td>\n",
       "      <td>1.349243</td>\n",
       "      <td>1.333345</td>\n",
       "      <td>1.347297</td>\n",
       "      <td>1.331212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.681685</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.003991</td>\n",
       "      <td>0.045475</td>\n",
       "      <td>0.399860</td>\n",
       "      <td>0.025784</td>\n",
       "      <td>0.027900</td>\n",
       "      <td>0.033757</td>\n",
       "      <td>68.686365</td>\n",
       "      <td>...</td>\n",
       "      <td>1.488878</td>\n",
       "      <td>1.470287</td>\n",
       "      <td>1.485597</td>\n",
       "      <td>1.496845</td>\n",
       "      <td>1.490389</td>\n",
       "      <td>1.495355</td>\n",
       "      <td>1.475330</td>\n",
       "      <td>1.472589</td>\n",
       "      <td>1.506674</td>\n",
       "      <td>1.475235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.838200</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.043843</td>\n",
       "      <td>0.065199</td>\n",
       "      <td>0.192600</td>\n",
       "      <td>1.747600</td>\n",
       "      <td>0.113240</td>\n",
       "      <td>0.120760</td>\n",
       "      <td>0.142440</td>\n",
       "      <td>101.206326</td>\n",
       "      <td>...</td>\n",
       "      <td>1.860588</td>\n",
       "      <td>2.038241</td>\n",
       "      <td>1.785984</td>\n",
       "      <td>1.988090</td>\n",
       "      <td>1.872799</td>\n",
       "      <td>1.920131</td>\n",
       "      <td>1.943331</td>\n",
       "      <td>1.949679</td>\n",
       "      <td>1.918392</td>\n",
       "      <td>1.930103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Jitter_rel  Jitter_abs  Jitter_RAP  Jitter_PPQ    Shim_loc     Shim_dB  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     0.583987    0.000044    0.003172    0.003532    0.038428    0.336832   \n",
       "std      0.535769    0.000045    0.003373    0.004449    0.023213    0.205905   \n",
       "min      0.148010    0.000007    0.000678    0.001036    0.007444    0.064989   \n",
       "25%      0.298260    0.000019    0.001551    0.001867    0.024336    0.211785   \n",
       "50%      0.481455    0.000035    0.002337    0.002870    0.032960    0.287885   \n",
       "75%      0.681685    0.000056    0.003678    0.003991    0.045475    0.399860   \n",
       "max      6.838200    0.000550    0.043843    0.065199    0.192600    1.747600   \n",
       "\n",
       "        Shim_APQ3   Shim_APQ5   Shi_APQ11       HNR05  ...      Delta3  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  ...  240.000000   \n",
       "mean     0.021499    0.023468    0.028671   59.328203  ...    1.343238   \n",
       "std      0.013787    0.014402    0.016610   14.541946  ...    0.198174   \n",
       "min      0.003344    0.004103    0.006459   22.224723  ...    0.766458   \n",
       "25%      0.012910    0.014985    0.019180   50.558623  ...    1.208624   \n",
       "50%      0.018571    0.019897    0.024909   59.742330  ...    1.351015   \n",
       "75%      0.025784    0.027900    0.033757   68.686365  ...    1.488878   \n",
       "max      0.113240    0.120760    0.142440  101.206326  ...    1.860588   \n",
       "\n",
       "           Delta4      Delta5      Delta6      Delta7      Delta8      Delta9  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     1.348914    1.337717    1.341786    1.340895    1.343978    1.341472   \n",
       "std      0.212008    0.204978    0.209407    0.213689    0.211364    0.204085   \n",
       "min      0.840133    0.741690    0.759689    0.764649    0.762798    0.811942   \n",
       "25%      1.221101    1.197868    1.194186    1.194138    1.191881    1.189515   \n",
       "50%      1.342237    1.337233    1.338437    1.344416    1.335517    1.349243   \n",
       "75%      1.470287    1.485597    1.496845    1.490389    1.495355    1.475330   \n",
       "max      2.038241    1.785984    1.988090    1.872799    1.920131    1.943331   \n",
       "\n",
       "          Delta10     Delta11     Delta12  \n",
       "count  240.000000  240.000000  240.000000  \n",
       "mean     1.331433    1.346381    1.346144  \n",
       "std      0.211297    0.221484    0.208819  \n",
       "min      0.777012    0.643132    0.748411  \n",
       "25%      1.192397    1.202525    1.206559  \n",
       "50%      1.333345    1.347297    1.331212  \n",
       "75%      1.472589    1.506674    1.475235  \n",
       "max      1.949679    1.918392    1.930103  \n",
       "\n",
       "[8 rows x 44 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check other continuous feature distributions\n",
    "# Remove columns ‘ID’ and ‘Recording’ as they can not be considered as features\n",
    "parkinson_data.drop(columns = [\"ID\", \"Recording\", \"Status\", \"Gender\"]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "66e10452",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>Shim_APQ3</th>\n",
       "      <th>Shim_APQ5</th>\n",
       "      <th>Shi_APQ11</th>\n",
       "      <th>HNR05</th>\n",
       "      <th>HNR15</th>\n",
       "      <th>HNR25</th>\n",
       "      <th>HNR35</th>\n",
       "      <th>HNR38</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>PPE</th>\n",
       "      <th>GNE</th>\n",
       "      <th>MFCC0</th>\n",
       "      <th>MFCC1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.583987</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.003172</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>0.038428</td>\n",
       "      <td>0.336832</td>\n",
       "      <td>0.021499</td>\n",
       "      <td>0.023468</td>\n",
       "      <td>0.028671</td>\n",
       "      <td>59.328203</td>\n",
       "      <td>63.674583</td>\n",
       "      <td>74.198478</td>\n",
       "      <td>80.187240</td>\n",
       "      <td>81.505404</td>\n",
       "      <td>0.310517</td>\n",
       "      <td>0.612911</td>\n",
       "      <td>0.271958</td>\n",
       "      <td>0.917801</td>\n",
       "      <td>1.345764</td>\n",
       "      <td>1.302292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.535769</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.003373</td>\n",
       "      <td>0.004449</td>\n",
       "      <td>0.023213</td>\n",
       "      <td>0.205905</td>\n",
       "      <td>0.013787</td>\n",
       "      <td>0.014402</td>\n",
       "      <td>0.016610</td>\n",
       "      <td>14.541946</td>\n",
       "      <td>15.621195</td>\n",
       "      <td>16.442566</td>\n",
       "      <td>17.187765</td>\n",
       "      <td>17.395830</td>\n",
       "      <td>0.065639</td>\n",
       "      <td>0.077780</td>\n",
       "      <td>0.229851</td>\n",
       "      <td>0.033911</td>\n",
       "      <td>0.209207</td>\n",
       "      <td>0.235640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.148010</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.007444</td>\n",
       "      <td>0.064989</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>0.006459</td>\n",
       "      <td>22.224723</td>\n",
       "      <td>26.274029</td>\n",
       "      <td>33.156101</td>\n",
       "      <td>36.494025</td>\n",
       "      <td>36.908213</td>\n",
       "      <td>0.162762</td>\n",
       "      <td>0.411359</td>\n",
       "      <td>0.004127</td>\n",
       "      <td>0.847307</td>\n",
       "      <td>0.770151</td>\n",
       "      <td>0.725522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.298260</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>0.001867</td>\n",
       "      <td>0.024336</td>\n",
       "      <td>0.211785</td>\n",
       "      <td>0.012910</td>\n",
       "      <td>0.014985</td>\n",
       "      <td>0.019180</td>\n",
       "      <td>50.558623</td>\n",
       "      <td>54.237537</td>\n",
       "      <td>63.879230</td>\n",
       "      <td>69.823899</td>\n",
       "      <td>70.435013</td>\n",
       "      <td>0.263217</td>\n",
       "      <td>0.556756</td>\n",
       "      <td>0.040811</td>\n",
       "      <td>0.891408</td>\n",
       "      <td>1.208843</td>\n",
       "      <td>1.149207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.481455</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.002337</td>\n",
       "      <td>0.002870</td>\n",
       "      <td>0.032960</td>\n",
       "      <td>0.287885</td>\n",
       "      <td>0.018571</td>\n",
       "      <td>0.019897</td>\n",
       "      <td>0.024909</td>\n",
       "      <td>59.742330</td>\n",
       "      <td>64.844019</td>\n",
       "      <td>75.457858</td>\n",
       "      <td>80.992704</td>\n",
       "      <td>82.354119</td>\n",
       "      <td>0.305054</td>\n",
       "      <td>0.606502</td>\n",
       "      <td>0.246486</td>\n",
       "      <td>0.912809</td>\n",
       "      <td>1.340906</td>\n",
       "      <td>1.294779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.681685</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.003678</td>\n",
       "      <td>0.003991</td>\n",
       "      <td>0.045475</td>\n",
       "      <td>0.399860</td>\n",
       "      <td>0.025784</td>\n",
       "      <td>0.027900</td>\n",
       "      <td>0.033757</td>\n",
       "      <td>68.686365</td>\n",
       "      <td>73.439434</td>\n",
       "      <td>85.652177</td>\n",
       "      <td>92.330509</td>\n",
       "      <td>93.500429</td>\n",
       "      <td>0.349273</td>\n",
       "      <td>0.661144</td>\n",
       "      <td>0.405402</td>\n",
       "      <td>0.946479</td>\n",
       "      <td>1.491018</td>\n",
       "      <td>1.474178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.838200</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>0.043843</td>\n",
       "      <td>0.065199</td>\n",
       "      <td>0.192600</td>\n",
       "      <td>1.747600</td>\n",
       "      <td>0.113240</td>\n",
       "      <td>0.120760</td>\n",
       "      <td>0.142440</td>\n",
       "      <td>101.206326</td>\n",
       "      <td>109.651117</td>\n",
       "      <td>120.712830</td>\n",
       "      <td>128.289325</td>\n",
       "      <td>129.985236</td>\n",
       "      <td>0.535953</td>\n",
       "      <td>0.784376</td>\n",
       "      <td>0.908395</td>\n",
       "      <td>0.987292</td>\n",
       "      <td>1.949103</td>\n",
       "      <td>1.835651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Jitter_rel  Jitter_abs  Jitter_RAP  Jitter_PPQ    Shim_loc     Shim_dB  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     0.583987    0.000044    0.003172    0.003532    0.038428    0.336832   \n",
       "std      0.535769    0.000045    0.003373    0.004449    0.023213    0.205905   \n",
       "min      0.148010    0.000007    0.000678    0.001036    0.007444    0.064989   \n",
       "25%      0.298260    0.000019    0.001551    0.001867    0.024336    0.211785   \n",
       "50%      0.481455    0.000035    0.002337    0.002870    0.032960    0.287885   \n",
       "75%      0.681685    0.000056    0.003678    0.003991    0.045475    0.399860   \n",
       "max      6.838200    0.000550    0.043843    0.065199    0.192600    1.747600   \n",
       "\n",
       "        Shim_APQ3   Shim_APQ5   Shi_APQ11       HNR05       HNR15       HNR25  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     0.021499    0.023468    0.028671   59.328203   63.674583   74.198478   \n",
       "std      0.013787    0.014402    0.016610   14.541946   15.621195   16.442566   \n",
       "min      0.003344    0.004103    0.006459   22.224723   26.274029   33.156101   \n",
       "25%      0.012910    0.014985    0.019180   50.558623   54.237537   63.879230   \n",
       "50%      0.018571    0.019897    0.024909   59.742330   64.844019   75.457858   \n",
       "75%      0.025784    0.027900    0.033757   68.686365   73.439434   85.652177   \n",
       "max      0.113240    0.120760    0.142440  101.206326  109.651117  120.712830   \n",
       "\n",
       "            HNR35       HNR38        RPDE         DFA         PPE         GNE  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean    80.187240   81.505404    0.310517    0.612911    0.271958    0.917801   \n",
       "std     17.187765   17.395830    0.065639    0.077780    0.229851    0.033911   \n",
       "min     36.494025   36.908213    0.162762    0.411359    0.004127    0.847307   \n",
       "25%     69.823899   70.435013    0.263217    0.556756    0.040811    0.891408   \n",
       "50%     80.992704   82.354119    0.305054    0.606502    0.246486    0.912809   \n",
       "75%     92.330509   93.500429    0.349273    0.661144    0.405402    0.946479   \n",
       "max    128.289325  129.985236    0.535953    0.784376    0.908395    0.987292   \n",
       "\n",
       "            MFCC0       MFCC1  \n",
       "count  240.000000  240.000000  \n",
       "mean     1.345764    1.302292  \n",
       "std      0.209207    0.235640  \n",
       "min      0.770151    0.725522  \n",
       "25%      1.208843    1.149207  \n",
       "50%      1.340906    1.294779  \n",
       "75%      1.491018    1.474178  \n",
       "max      1.949103    1.835651  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display feature distributions fully by dataframe segmentation\n",
    "parkinson_feature = parkinson_data.drop(columns = [\"ID\", \"Recording\", \"Status\", \"Gender\"])\n",
    "parkinson_feature.iloc[:, 0:20].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "71a101d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MFCC2</th>\n",
       "      <th>MFCC3</th>\n",
       "      <th>MFCC4</th>\n",
       "      <th>MFCC5</th>\n",
       "      <th>MFCC6</th>\n",
       "      <th>MFCC7</th>\n",
       "      <th>MFCC8</th>\n",
       "      <th>MFCC9</th>\n",
       "      <th>MFCC10</th>\n",
       "      <th>MFCC11</th>\n",
       "      <th>MFCC12</th>\n",
       "      <th>Delta0</th>\n",
       "      <th>Delta1</th>\n",
       "      <th>Delta2</th>\n",
       "      <th>Delta3</th>\n",
       "      <th>Delta4</th>\n",
       "      <th>Delta5</th>\n",
       "      <th>Delta6</th>\n",
       "      <th>Delta7</th>\n",
       "      <th>Delta8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.289089</td>\n",
       "      <td>1.349270</td>\n",
       "      <td>1.354317</td>\n",
       "      <td>1.338350</td>\n",
       "      <td>1.334774</td>\n",
       "      <td>1.339938</td>\n",
       "      <td>1.354771</td>\n",
       "      <td>1.346205</td>\n",
       "      <td>1.346094</td>\n",
       "      <td>1.355388</td>\n",
       "      <td>1.338195</td>\n",
       "      <td>1.338939</td>\n",
       "      <td>1.335040</td>\n",
       "      <td>1.342467</td>\n",
       "      <td>1.343238</td>\n",
       "      <td>1.348914</td>\n",
       "      <td>1.337717</td>\n",
       "      <td>1.341786</td>\n",
       "      <td>1.340895</td>\n",
       "      <td>1.343978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.259544</td>\n",
       "      <td>0.215538</td>\n",
       "      <td>0.212376</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>0.207402</td>\n",
       "      <td>0.216467</td>\n",
       "      <td>0.214952</td>\n",
       "      <td>0.203527</td>\n",
       "      <td>0.210776</td>\n",
       "      <td>0.215601</td>\n",
       "      <td>0.204511</td>\n",
       "      <td>0.222206</td>\n",
       "      <td>0.223335</td>\n",
       "      <td>0.227232</td>\n",
       "      <td>0.198174</td>\n",
       "      <td>0.212008</td>\n",
       "      <td>0.204978</td>\n",
       "      <td>0.209407</td>\n",
       "      <td>0.213689</td>\n",
       "      <td>0.211364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.569471</td>\n",
       "      <td>0.727609</td>\n",
       "      <td>0.771259</td>\n",
       "      <td>0.611540</td>\n",
       "      <td>0.829087</td>\n",
       "      <td>0.653565</td>\n",
       "      <td>0.839535</td>\n",
       "      <td>0.823632</td>\n",
       "      <td>0.813607</td>\n",
       "      <td>0.823157</td>\n",
       "      <td>0.844357</td>\n",
       "      <td>0.620845</td>\n",
       "      <td>0.647406</td>\n",
       "      <td>0.628107</td>\n",
       "      <td>0.766458</td>\n",
       "      <td>0.840133</td>\n",
       "      <td>0.741690</td>\n",
       "      <td>0.759689</td>\n",
       "      <td>0.764649</td>\n",
       "      <td>0.762798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.122922</td>\n",
       "      <td>1.212101</td>\n",
       "      <td>1.218042</td>\n",
       "      <td>1.199275</td>\n",
       "      <td>1.204434</td>\n",
       "      <td>1.188848</td>\n",
       "      <td>1.220585</td>\n",
       "      <td>1.221013</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>1.204163</td>\n",
       "      <td>1.197492</td>\n",
       "      <td>1.198202</td>\n",
       "      <td>1.182790</td>\n",
       "      <td>1.196205</td>\n",
       "      <td>1.208624</td>\n",
       "      <td>1.221101</td>\n",
       "      <td>1.197868</td>\n",
       "      <td>1.194186</td>\n",
       "      <td>1.194138</td>\n",
       "      <td>1.191881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.298645</td>\n",
       "      <td>1.338791</td>\n",
       "      <td>1.342907</td>\n",
       "      <td>1.331794</td>\n",
       "      <td>1.323245</td>\n",
       "      <td>1.321939</td>\n",
       "      <td>1.340258</td>\n",
       "      <td>1.334706</td>\n",
       "      <td>1.338213</td>\n",
       "      <td>1.356528</td>\n",
       "      <td>1.338452</td>\n",
       "      <td>1.333607</td>\n",
       "      <td>1.341507</td>\n",
       "      <td>1.323866</td>\n",
       "      <td>1.351015</td>\n",
       "      <td>1.342237</td>\n",
       "      <td>1.337233</td>\n",
       "      <td>1.338437</td>\n",
       "      <td>1.344416</td>\n",
       "      <td>1.335517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.479907</td>\n",
       "      <td>1.496511</td>\n",
       "      <td>1.497181</td>\n",
       "      <td>1.473760</td>\n",
       "      <td>1.452817</td>\n",
       "      <td>1.486387</td>\n",
       "      <td>1.482438</td>\n",
       "      <td>1.486456</td>\n",
       "      <td>1.466926</td>\n",
       "      <td>1.515044</td>\n",
       "      <td>1.474081</td>\n",
       "      <td>1.482893</td>\n",
       "      <td>1.485977</td>\n",
       "      <td>1.491861</td>\n",
       "      <td>1.488878</td>\n",
       "      <td>1.470287</td>\n",
       "      <td>1.485597</td>\n",
       "      <td>1.496845</td>\n",
       "      <td>1.490389</td>\n",
       "      <td>1.495355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.928430</td>\n",
       "      <td>1.857084</td>\n",
       "      <td>1.840846</td>\n",
       "      <td>1.976153</td>\n",
       "      <td>2.000783</td>\n",
       "      <td>2.016728</td>\n",
       "      <td>1.918429</td>\n",
       "      <td>2.039576</td>\n",
       "      <td>2.071291</td>\n",
       "      <td>1.983561</td>\n",
       "      <td>2.029982</td>\n",
       "      <td>2.028056</td>\n",
       "      <td>2.021287</td>\n",
       "      <td>1.979858</td>\n",
       "      <td>1.860588</td>\n",
       "      <td>2.038241</td>\n",
       "      <td>1.785984</td>\n",
       "      <td>1.988090</td>\n",
       "      <td>1.872799</td>\n",
       "      <td>1.920131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MFCC2       MFCC3       MFCC4       MFCC5       MFCC6       MFCC7  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     1.289089    1.349270    1.354317    1.338350    1.334774    1.339938   \n",
       "std      0.259544    0.215538    0.212376    0.216225    0.207402    0.216467   \n",
       "min      0.569471    0.727609    0.771259    0.611540    0.829087    0.653565   \n",
       "25%      1.122922    1.212101    1.218042    1.199275    1.204434    1.188848   \n",
       "50%      1.298645    1.338791    1.342907    1.331794    1.323245    1.321939   \n",
       "75%      1.479907    1.496511    1.497181    1.473760    1.452817    1.486387   \n",
       "max      1.928430    1.857084    1.840846    1.976153    2.000783    2.016728   \n",
       "\n",
       "            MFCC8       MFCC9      MFCC10      MFCC11      MFCC12      Delta0  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     1.354771    1.346205    1.346094    1.355388    1.338195    1.338939   \n",
       "std      0.214952    0.203527    0.210776    0.215601    0.204511    0.222206   \n",
       "min      0.839535    0.823632    0.813607    0.823157    0.844357    0.620845   \n",
       "25%      1.220585    1.221013    1.229496    1.204163    1.197492    1.198202   \n",
       "50%      1.340258    1.334706    1.338213    1.356528    1.338452    1.333607   \n",
       "75%      1.482438    1.486456    1.466926    1.515044    1.474081    1.482893   \n",
       "max      1.918429    2.039576    2.071291    1.983561    2.029982    2.028056   \n",
       "\n",
       "           Delta1      Delta2      Delta3      Delta4      Delta5      Delta6  \\\n",
       "count  240.000000  240.000000  240.000000  240.000000  240.000000  240.000000   \n",
       "mean     1.335040    1.342467    1.343238    1.348914    1.337717    1.341786   \n",
       "std      0.223335    0.227232    0.198174    0.212008    0.204978    0.209407   \n",
       "min      0.647406    0.628107    0.766458    0.840133    0.741690    0.759689   \n",
       "25%      1.182790    1.196205    1.208624    1.221101    1.197868    1.194186   \n",
       "50%      1.341507    1.323866    1.351015    1.342237    1.337233    1.338437   \n",
       "75%      1.485977    1.491861    1.488878    1.470287    1.485597    1.496845   \n",
       "max      2.021287    1.979858    1.860588    2.038241    1.785984    1.988090   \n",
       "\n",
       "           Delta7      Delta8  \n",
       "count  240.000000  240.000000  \n",
       "mean     1.340895    1.343978  \n",
       "std      0.213689    0.211364  \n",
       "min      0.764649    0.762798  \n",
       "25%      1.194138    1.191881  \n",
       "50%      1.344416    1.335517  \n",
       "75%      1.490389    1.495355  \n",
       "max      1.872799    1.920131  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parkinson_feature.iloc[:, 20:40].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee28d6e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta9</th>\n",
       "      <th>Delta10</th>\n",
       "      <th>Delta11</th>\n",
       "      <th>Delta12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>240.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.341472</td>\n",
       "      <td>1.331433</td>\n",
       "      <td>1.346381</td>\n",
       "      <td>1.346144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.204085</td>\n",
       "      <td>0.211297</td>\n",
       "      <td>0.221484</td>\n",
       "      <td>0.208819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.811942</td>\n",
       "      <td>0.777012</td>\n",
       "      <td>0.643132</td>\n",
       "      <td>0.748411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.189515</td>\n",
       "      <td>1.192397</td>\n",
       "      <td>1.202525</td>\n",
       "      <td>1.206559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.349243</td>\n",
       "      <td>1.333345</td>\n",
       "      <td>1.347297</td>\n",
       "      <td>1.331212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.475330</td>\n",
       "      <td>1.472589</td>\n",
       "      <td>1.506674</td>\n",
       "      <td>1.475235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.943331</td>\n",
       "      <td>1.949679</td>\n",
       "      <td>1.918392</td>\n",
       "      <td>1.930103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Delta9     Delta10     Delta11     Delta12\n",
       "count  240.000000  240.000000  240.000000  240.000000\n",
       "mean     1.341472    1.331433    1.346381    1.346144\n",
       "std      0.204085    0.211297    0.221484    0.208819\n",
       "min      0.811942    0.777012    0.643132    0.748411\n",
       "25%      1.189515    1.192397    1.202525    1.206559\n",
       "50%      1.349243    1.333345    1.347297    1.331212\n",
       "75%      1.475330    1.472589    1.506674    1.475235\n",
       "max      1.943331    1.949679    1.918392    1.930103"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parkinson_feature.iloc[:, 40:44].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a140bb",
   "metadata": {},
   "source": [
    "### b. Considering each record as an individual sample, please train a decision tree classifier (max_depth = 3) to predict the status of each sample. Please plot your decision tree. (15%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e45cc0a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.metrics import classification_report \n",
    "from sklearn.metrics import roc_auc_score, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3bd46406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The used features are:\n",
      " ['Gender', 'Jitter_rel', 'Jitter_abs', 'Jitter_RAP', 'Jitter_PPQ', 'Shim_loc', 'Shim_dB', 'Shim_APQ3', 'Shim_APQ5', 'Shi_APQ11', 'HNR05', 'HNR15', 'HNR25', 'HNR35', 'HNR38', 'RPDE', 'DFA', 'PPE', 'GNE', 'MFCC0', 'MFCC1', 'MFCC2', 'MFCC3', 'MFCC4', 'MFCC5', 'MFCC6', 'MFCC7', 'MFCC8', 'MFCC9', 'MFCC10', 'MFCC11', 'MFCC12', 'Delta0', 'Delta1', 'Delta2', 'Delta3', 'Delta4', 'Delta5', 'Delta6', 'Delta7', 'Delta8', 'Delta9', 'Delta10', 'Delta11', 'Delta12']\n",
      "\n",
      "\n",
      "(216, 45) (24, 45) (216,) (24,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into training and testing sets: 90% for training; 10% for testing\n",
    "y = parkinson_data[\"Status\"].values\n",
    "# Drop columns that are not features\n",
    "X = parkinson_data.drop(columns = [\"ID\", \"Recording\", \"Status\"]).values\n",
    "feature_used = parkinson_data.drop(columns = [\"ID\", \"Recording\", \"Status\"]).columns.tolist()\n",
    "print(\"The used features are:\\n\",feature_used)\n",
    "\n",
    "# Set random_state to keep the spliting is the same every time\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.10, random_state = 0)\n",
    "print(\"\\n\")\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "eec45db2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a tree\n",
    "# sklearn DecisionTreeClassifier--describe clearly--parameters, attributes\n",
    "# https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html \n",
    "tree = DecisionTreeClassifier(criterion = \"gini\", # Set Gini to measure the quality of a split\n",
    "                              class_weight = \"balanced\", # Set balanced weights associated with classes\n",
    "                              random_state = 0, # Controls the randomness of the estimator \n",
    "                              max_depth = 3) # Set 3 as the maximum depth of the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6f902db2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 1 0 1 0 0 0 1 0]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.83        11\n",
      "           1       0.91      0.77      0.83        13\n",
      "\n",
      "    accuracy                           0.83        24\n",
      "   macro avg       0.84      0.84      0.83        24\n",
      "weighted avg       0.84      0.83      0.83        24\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8006993006993007\n"
     ]
    }
   ],
   "source": [
    "# Train the tree\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "# Use the trained classifier to predict the status of each sample in the test set\n",
    "y_pred = tree.predict(X_test) \n",
    "print(y_pred)\n",
    "\n",
    "# Evaluate the prediciton performance\n",
    "print(classification_report(y_test, y_pred))\n",
    "# To understand the report\n",
    "# support: the number of true instances for each label\n",
    "# macro avg: Calculate metrics for each label and find their unweighted mean. This does not take label imbalance into account\n",
    "# weighted avg: Calculate metrics for each label, and find their weighted mean\n",
    "\n",
    "# Predict class probabilities of the input samples X\n",
    "# The predicted class probability is the fraction of samples of the same class in a leaf\n",
    "y_proba = tree.predict_proba(X_test)[:,1]\n",
    "\n",
    "# Create true and false positive rates\n",
    "fpr_dt, tpr_dt, threshold_dt = roc_curve(y_test, y_proba)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.figure(figsize = (10, 6)) \n",
    "plt.title(\"Receiver Operating Characteristic\") \n",
    "plt.plot(fpr_dt, tpr_dt) \n",
    "plt.plot([0, 1], ls = \"--\") \n",
    "plt.plot([0, 0], [1, 0], c = \"0.5\") \n",
    "plt.plot([1, 1], c = \"0.5\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()\n",
    "\n",
    "# Calculate area under the curve\n",
    "auc_lr = roc_auc_score(y_test, y_proba) \n",
    "print(auc_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f391c43e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 1.36897895e-15 0.00000000e+00\n",
      " 0.00000000e+00 7.17450061e-02 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 1.10750585e-01 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 1.34368677e-01 0.00000000e+00 0.00000000e+00\n",
      " 6.19561079e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 6.35746534e-02\n",
      " 0.00000000e+00]\n",
      "\n",
      "\n",
      "[32 29 13  9 43  6  0 25 26 27 28 30 31 34 35 36 37 38 39 40 41 42 33 24\n",
      " 22 21  1  2  3  4  5  7  8 10 11 12 14 15 16 17 18 19 20 23 44]\n",
      "\n",
      "\n",
      "['Gender', 'Jitter_rel', 'Jitter_abs', 'Jitter_RAP', 'Jitter_PPQ', 'Shim_loc', 'Shim_dB', 'Shim_APQ3', 'Shim_APQ5', 'Shi_APQ11', 'HNR05', 'HNR15', 'HNR25', 'HNR35', 'HNR38', 'RPDE', 'DFA', 'PPE', 'GNE', 'MFCC0', 'MFCC1', 'MFCC2', 'MFCC3', 'MFCC4', 'MFCC5', 'MFCC6', 'MFCC7', 'MFCC8', 'MFCC9', 'MFCC10', 'MFCC11', 'MFCC12', 'Delta0', 'Delta1', 'Delta2', 'Delta3', 'Delta4', 'Delta5', 'Delta6', 'Delta7', 'Delta8', 'Delta9', 'Delta10', 'Delta11', 'Delta12']\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance\n",
    "# Obtain feature importance values \n",
    "importances = tree.feature_importances_ \n",
    "print(importances)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Return the indices of an array sorted by descending order\n",
    "indices = np.argsort(-importances) \n",
    "\n",
    "# Keep feature_used list consistent with indices\n",
    "print(indices)\n",
    "print(\"\\n\")\n",
    "print(feature_used)\n",
    "print(\"\\n\")\n",
    "\n",
    "# Plot feature importance figure\n",
    "plt.figure(figsize = (10, 8)) \n",
    "plt.bar(np.array(feature_used)[indices], importances[indices]) \n",
    "plt.ylabel(\"Feature Importance\")\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0dcd926a",
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"206\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta11 ≤ 1.236</text>\n",
       "<text text-anchor=\"start\" x=\"219\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.265</text>\n",
       "<text text-anchor=\"start\" x=\"209.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 100</text>\n",
       "<text text-anchor=\"start\" x=\"180\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [15.853, 84.785]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
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       "<title>1&#45;&gt;2</title>\n",
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       "<title>5</title>\n",
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       "<text text-anchor=\"start\" x=\"371.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Shim_dB ≤ 0.167</text>\n",
       "<text text-anchor=\"start\" x=\"396.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"387.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"365.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5.945, 0.0]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;5 -->\n",
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       "<title>1&#45;&gt;5</title>\n",
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       "<title>3</title>\n",
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       "<text text-anchor=\"start\" x=\"43\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.112</text>\n",
       "<text text-anchor=\"start\" x=\"37.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 66</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3.963, 62.579]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
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       "<title>2&#45;&gt;3</title>\n",
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       "<title>4</title>\n",
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       "<text text-anchor=\"start\" x=\"220\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.454</text>\n",
       "<text text-anchor=\"start\" x=\"214.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 34</text>\n",
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       "</g>\n",
       "<!-- 2&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>2&#45;&gt;4</title>\n",
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       "<title>6</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M480.5,-53C480.5,-53 368.5,-53 368.5,-53 362.5,-53 356.5,-47 356.5,-41 356.5,-41 356.5,-12 356.5,-12 356.5,-6 362.5,0 368.5,0 368.5,0 480.5,0 480.5,0 486.5,0 492.5,-6 492.5,-12 492.5,-12 492.5,-41 492.5,-41 492.5,-47 486.5,-53 480.5,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"395.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"386.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"364.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
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       "<!-- 5&#45;&gt;6 -->\n",
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       "<title>5&#45;&gt;6</title>\n",
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       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M634.5,-53C634.5,-53 522.5,-53 522.5,-53 516.5,-53 510.5,-47 510.5,-41 510.5,-41 510.5,-12 510.5,-12 510.5,-6 516.5,0 522.5,0 522.5,0 634.5,0 634.5,0 640.5,0 646.5,-6 646.5,-12 646.5,-12 646.5,-41 646.5,-41 646.5,-47 640.5,-53 634.5,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"549.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"540.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
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       "<title>5&#45;&gt;7</title>\n",
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       "<title>9</title>\n",
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       "<text text-anchor=\"start\" x=\"565.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"556.5\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
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       "<title>10</title>\n",
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       "<text text-anchor=\"start\" x=\"702.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Shi_APQ11 ≤ 0.063</text>\n",
       "<text text-anchor=\"start\" x=\"728\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.288</text>\n",
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      ],
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       "<graphviz.files.Source at 0x7fd67f7f6850>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the decision tree\n",
    "tree_data = export_graphviz(tree,\n",
    "                            out_file = None, \n",
    "                            feature_names = feature_used, \n",
    "                            filled = True,\n",
    "                            rounded = True, \n",
    "                            special_characters = True)\n",
    "graph = graphviz.Source(tree_data)\n",
    "display(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "29413472",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "# Obtain the depth of the tree\n",
    "print(tree.get_depth()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cff8502",
   "metadata": {},
   "source": [
    "### c. As discussed in class, Grid Search can help us to tune the model parameters to find the optimal solution. Please tune your decision tree classifier to improve the predictive performance. (15%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fe1c5510",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import packages\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "150ca866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model using Grid Search with cross-validation\n",
    "# The goal is to identify the best hyper-parameters of the model\n",
    "tree = DecisionTreeClassifier(criterion = \"gini\", \n",
    "                              class_weight = \"balanced\", \n",
    "                              random_state = 0,\n",
    "                              max_features = None) # max_features=n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a5c726a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                              random_state=0),\n",
       "             param_grid={'max_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_leaf': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'min_samples_split': [2, 3, 4, 5]},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune the parameters of max_depth, min_samples_leaf, and min_samples_split\n",
    "# I have tried \"max_leaf_nodes\":[30, 50, 100, 200, 500], however, it does not improve the AUC area, \n",
    "# so I give up this parameter.\n",
    "grid = GridSearchCV(tree, \n",
    "                    param_grid = {\"max_depth\":[2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "                                  \"min_samples_leaf\":[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
    "                                  \"min_samples_split\":[2, 3, 4, 5]},\n",
    "                    scoring = \"accuracy\",\n",
    "                    cv = 5, \n",
    "                    return_train_score = True)\n",
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "936ed80f",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.786127</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.822933</td>\n",
       "      <td>0.023745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002135</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_leaf': 1, 'min_s...</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698732</td>\n",
       "      <td>0.052421</td>\n",
       "      <td>321</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.826590</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.786127</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.822933</td>\n",
       "      <td>0.023745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_leaf': 1, 'min_s...</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698732</td>\n",
       "      <td>0.052421</td>\n",
       "      <td>321</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.826590</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.786127</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.822933</td>\n",
       "      <td>0.023745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001896</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_leaf': 1, 'min_s...</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698732</td>\n",
       "      <td>0.052421</td>\n",
       "      <td>321</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.826590</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.786127</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.822933</td>\n",
       "      <td>0.023745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001997</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 2, 'min_samples_leaf': 2, 'min_s...</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.698732</td>\n",
       "      <td>0.052421</td>\n",
       "      <td>321</td>\n",
       "      <td>0.837209</td>\n",
       "      <td>0.826590</td>\n",
       "      <td>0.849711</td>\n",
       "      <td>0.786127</td>\n",
       "      <td>0.809249</td>\n",
       "      <td>0.821777</td>\n",
       "      <td>0.022224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>0.003285</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 9, 'min_...</td>\n",
       "      <td>0.772727</td>\n",
       "      <td>0.697674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.712685</td>\n",
       "      <td>0.039373</td>\n",
       "      <td>301</td>\n",
       "      <td>0.872093</td>\n",
       "      <td>0.867052</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.884393</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.883089</td>\n",
       "      <td>0.011923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>0.002885</td>\n",
       "      <td>0.000242</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 10, 'min...</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731184</td>\n",
       "      <td>0.039325</td>\n",
       "      <td>235</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.878613</td>\n",
       "      <td>0.890173</td>\n",
       "      <td>0.876139</td>\n",
       "      <td>0.015923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>0.002996</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 10, 'min...</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731184</td>\n",
       "      <td>0.039325</td>\n",
       "      <td>235</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.878613</td>\n",
       "      <td>0.890173</td>\n",
       "      <td>0.876139</td>\n",
       "      <td>0.015923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0.002668</td>\n",
       "      <td>0.000231</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 10, 'min...</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731184</td>\n",
       "      <td>0.039325</td>\n",
       "      <td>235</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.878613</td>\n",
       "      <td>0.890173</td>\n",
       "      <td>0.876139</td>\n",
       "      <td>0.015923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0.002564</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>{'max_depth': 10, 'min_samples_leaf': 10, 'min...</td>\n",
       "      <td>0.795455</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731184</td>\n",
       "      <td>0.039325</td>\n",
       "      <td>235</td>\n",
       "      <td>0.860465</td>\n",
       "      <td>0.855491</td>\n",
       "      <td>0.895954</td>\n",
       "      <td>0.878613</td>\n",
       "      <td>0.890173</td>\n",
       "      <td>0.876139</td>\n",
       "      <td>0.015923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0         0.002694      0.000455         0.000502        0.000199   \n",
       "1         0.002135      0.000143         0.000431        0.000111   \n",
       "2         0.002450      0.000279         0.000408        0.000110   \n",
       "3         0.001896      0.000083         0.000284        0.000038   \n",
       "4         0.001997      0.000283         0.000333        0.000125   \n",
       "..             ...           ...              ...             ...   \n",
       "355       0.003285      0.000382         0.000526        0.000158   \n",
       "356       0.002885      0.000242         0.000406        0.000073   \n",
       "357       0.002996      0.000405         0.000470        0.000066   \n",
       "358       0.002668      0.000231         0.000344        0.000064   \n",
       "359       0.002564      0.000261         0.000284        0.000062   \n",
       "\n",
       "    param_max_depth param_min_samples_leaf param_min_samples_split  \\\n",
       "0                 2                      1                       2   \n",
       "1                 2                      1                       3   \n",
       "2                 2                      1                       4   \n",
       "3                 2                      1                       5   \n",
       "4                 2                      2                       2   \n",
       "..              ...                    ...                     ...   \n",
       "355              10                      9                       5   \n",
       "356              10                     10                       2   \n",
       "357              10                     10                       3   \n",
       "358              10                     10                       4   \n",
       "359              10                     10                       5   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "0    {'max_depth': 2, 'min_samples_leaf': 1, 'min_s...           0.772727   \n",
       "1    {'max_depth': 2, 'min_samples_leaf': 1, 'min_s...           0.772727   \n",
       "2    {'max_depth': 2, 'min_samples_leaf': 1, 'min_s...           0.772727   \n",
       "3    {'max_depth': 2, 'min_samples_leaf': 1, 'min_s...           0.772727   \n",
       "4    {'max_depth': 2, 'min_samples_leaf': 2, 'min_s...           0.772727   \n",
       "..                                                 ...                ...   \n",
       "355  {'max_depth': 10, 'min_samples_leaf': 9, 'min_...           0.772727   \n",
       "356  {'max_depth': 10, 'min_samples_leaf': 10, 'min...           0.795455   \n",
       "357  {'max_depth': 10, 'min_samples_leaf': 10, 'min...           0.795455   \n",
       "358  {'max_depth': 10, 'min_samples_leaf': 10, 'min...           0.795455   \n",
       "359  {'max_depth': 10, 'min_samples_leaf': 10, 'min...           0.795455   \n",
       "\n",
       "     split1_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0             0.744186  ...         0.698732        0.052421              321   \n",
       "1             0.744186  ...         0.698732        0.052421              321   \n",
       "2             0.744186  ...         0.698732        0.052421              321   \n",
       "3             0.744186  ...         0.698732        0.052421              321   \n",
       "4             0.744186  ...         0.698732        0.052421              321   \n",
       "..                 ...  ...              ...             ...              ...   \n",
       "355           0.697674  ...         0.712685        0.039373              301   \n",
       "356           0.720930  ...         0.731184        0.039325              235   \n",
       "357           0.720930  ...         0.731184        0.039325              235   \n",
       "358           0.720930  ...         0.731184        0.039325              235   \n",
       "359           0.720930  ...         0.731184        0.039325              235   \n",
       "\n",
       "     split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0              0.837209            0.826590            0.855491   \n",
       "1              0.837209            0.826590            0.855491   \n",
       "2              0.837209            0.826590            0.855491   \n",
       "3              0.837209            0.826590            0.855491   \n",
       "4              0.837209            0.826590            0.849711   \n",
       "..                  ...                 ...                 ...   \n",
       "355            0.872093            0.867052            0.895954   \n",
       "356            0.860465            0.855491            0.895954   \n",
       "357            0.860465            0.855491            0.895954   \n",
       "358            0.860465            0.855491            0.895954   \n",
       "359            0.860465            0.855491            0.895954   \n",
       "\n",
       "     split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0              0.786127            0.809249          0.822933         0.023745  \n",
       "1              0.786127            0.809249          0.822933         0.023745  \n",
       "2              0.786127            0.809249          0.822933         0.023745  \n",
       "3              0.786127            0.809249          0.822933         0.023745  \n",
       "4              0.786127            0.809249          0.821777         0.022224  \n",
       "..                  ...                 ...               ...              ...  \n",
       "355            0.884393            0.895954          0.883089         0.011923  \n",
       "356            0.878613            0.890173          0.876139         0.015923  \n",
       "357            0.878613            0.890173          0.876139         0.015923  \n",
       "358            0.878613            0.890173          0.876139         0.015923  \n",
       "359            0.878613            0.890173          0.876139         0.015923  \n",
       "\n",
       "[360 rows x 23 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain grid results\n",
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9974d102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visulization of grid search\n",
    "scores.plot(x = \"param_max_depth\", y = \"mean_train_score\", yerr = \"std_train_score\") \n",
    "scores.plot(x = \"param_max_depth\", y = \"mean_test_score\", yerr = \"std_test_score\",\n",
    "            ax = plt.gca()) # ax = plt.gca(), keep plot at the samp axis\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c88221da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight='balanced', max_depth=9, random_state=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain the best classifier\n",
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "78b410c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.77      0.91      0.83        11\n",
      "           1       0.91      0.77      0.83        13\n",
      "\n",
      "    accuracy                           0.83        24\n",
      "   macro avg       0.84      0.84      0.83        24\n",
      "weighted avg       0.84      0.83      0.83        24\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8391608391608392\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the prediciton performance\n",
    "# Use the best model to predict\n",
    "y_pred_grid = grid.best_estimator_.predict(X_test) \n",
    "print(classification_report(y_test, y_pred_grid))\n",
    "\n",
    "# Predict class probabilities of the input samples X\n",
    "# The predicted class probability is the fraction of samples of the same class in a leaf\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test)[:, 1]\n",
    "\n",
    "# Create true and false positive rates\n",
    "fpr_dt, tpr_dt, threshold_dt = roc_curve(y_test, y_proba)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.figure(figsize = (10, 6)) \n",
    "plt.title(\"Receiver Operating Characteristic\") \n",
    "plt.plot(fpr_dt, tpr_dt) \n",
    "plt.plot([0, 1], ls = \"--\") \n",
    "plt.plot([0, 0], [1, 0], c = \"0.5\") \n",
    "plt.plot([1, 1], c = \"0.5\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()\n",
    "\n",
    "# Calculate area under the curve\n",
    "auc_lr = roc_auc_score(y_test, y_proba) \n",
    "print(auc_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "17f3c2d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000000e+00 0.00000000e+00 1.35743412e-16 1.41509434e-02\n",
      " 0.00000000e+00 0.00000000e+00 4.50815115e-03 0.00000000e+00\n",
      " 0.00000000e+00 1.06092213e-01 2.32946299e-02 0.00000000e+00\n",
      " 1.59918677e-02 6.16725563e-02 0.00000000e+00 1.72154913e-02\n",
      " 2.26542372e-02 8.63202874e-02 0.00000000e+00 2.96784200e-02\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 2.86777157e-02 1.80834248e-02 0.00000000e+00\n",
      " 0.00000000e+00 1.14735112e-01 1.54945008e-17 0.00000000e+00\n",
      " 3.78743773e-01 0.00000000e+00 1.40205200e-02 0.00000000e+00\n",
      " 2.87584834e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
      " 0.00000000e+00 0.00000000e+00 0.00000000e+00 3.54021732e-02\n",
      " 0.00000000e+00]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Feature importance based on the best model\n",
    "# Obtain feature importance value\n",
    "importances = grid.best_estimator_.feature_importances_ \n",
    "print(importances)\n",
    "\n",
    "# Return the indices of an array sorted by descending order\n",
    "indices = np.argsort(-importances) \n",
    "\n",
    "# Plot feature importance figure\n",
    "plt.figure(figsize = (10, 8))\n",
    "plt.bar(np.array(feature_used)[indices], importances[indices]) \n",
    "plt.ylabel(\"Feature Importance (best model)\") \n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4a802e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
       " -->\n",
       "<!-- Title: Tree Pages: 1 -->\n",
       "<svg width=\"1921pt\" height=\"997pt\"\n",
       " viewBox=\"0.00 0.00 1921.00 997.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 993)\">\n",
       "<title>Tree</title>\n",
       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-993 1917,-993 1917,4 -4,4\"/>\n",
       "<!-- 0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0</title>\n",
       "<path fill=\"#ffffff\" stroke=\"#000000\" d=\"M1151,-989C1151,-989 1023,-989 1023,-989 1017,-989 1011,-983 1011,-977 1011,-977 1011,-933 1011,-933 1011,-927 1017,-921 1023,-921 1023,-921 1151,-921 1151,-921 1157,-921 1163,-927 1163,-933 1163,-933 1163,-977 1163,-977 1163,-983 1157,-989 1151,-989\"/>\n",
       "<text text-anchor=\"start\" x=\"1041.5\" y=\"-973.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta0 ≤ 1.334</text>\n",
       "<text text-anchor=\"start\" x=\"1058\" y=\"-958.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.5</text>\n",
       "<text text-anchor=\"start\" x=\"1041\" y=\"-943.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 216</text>\n",
       "<text text-anchor=\"start\" x=\"1019\" y=\"-928.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [108.0, 108.0]</text>\n",
       "</g>\n",
       "<!-- 1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>1</title>\n",
       "<path fill=\"#6cb6ec\" stroke=\"#000000\" d=\"M1016.5,-885C1016.5,-885 873.5,-885 873.5,-885 867.5,-885 861.5,-879 861.5,-873 861.5,-873 861.5,-829 861.5,-829 861.5,-823 867.5,-817 873.5,-817 873.5,-817 1016.5,-817 1016.5,-817 1022.5,-817 1028.5,-823 1028.5,-829 1028.5,-829 1028.5,-873 1028.5,-873 1028.5,-879 1022.5,-885 1016.5,-885\"/>\n",
       "<text text-anchor=\"start\" x=\"891.5\" y=\"-869.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC10 ≤ 1.432</text>\n",
       "<text text-anchor=\"start\" x=\"908.5\" y=\"-854.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.325</text>\n",
       "<text text-anchor=\"start\" x=\"899\" y=\"-839.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 106</text>\n",
       "<text text-anchor=\"start\" x=\"869.5\" y=\"-824.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [21.798, 84.785]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0&#45;&gt;1</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1040.5039,-920.9465C1027.5092,-911.4293 1013.2663,-900.9978 999.8379,-891.163\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1001.7363,-888.2151 991.6006,-885.13 997.6002,-893.8624 1001.7363,-888.2151\"/>\n",
       "<text text-anchor=\"middle\" x=\"995.4171\" y=\"-906.137\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n",
       "</g>\n",
       "<!-- 32 -->\n",
       "<g id=\"node33\" class=\"node\">\n",
       "<title>32</title>\n",
       "<path fill=\"#eca36e\" stroke=\"#000000\" d=\"M1211.5,-885C1211.5,-885 1068.5,-885 1068.5,-885 1062.5,-885 1056.5,-879 1056.5,-873 1056.5,-873 1056.5,-829 1056.5,-829 1056.5,-823 1062.5,-817 1068.5,-817 1068.5,-817 1211.5,-817 1211.5,-817 1217.5,-817 1223.5,-823 1223.5,-829 1223.5,-829 1223.5,-873 1223.5,-873 1223.5,-879 1217.5,-885 1211.5,-885\"/>\n",
       "<text text-anchor=\"start\" x=\"1087.5\" y=\"-869.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">HNR35 ≤ 67.698</text>\n",
       "<text text-anchor=\"start\" x=\"1103.5\" y=\"-854.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.334</text>\n",
       "<text text-anchor=\"start\" x=\"1094\" y=\"-839.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 110</text>\n",
       "<text text-anchor=\"start\" x=\"1064.5\" y=\"-824.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [86.202, 23.215]</text>\n",
       "</g>\n",
       "<!-- 0&#45;&gt;32 -->\n",
       "<g id=\"edge32\" class=\"edge\">\n",
       "<title>0&#45;&gt;32</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1104.3542,-920.9465C1108.701,-912.4169 1113.422,-903.153 1117.963,-894.2424\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1121.1846,-895.629 1122.6068,-885.13 1114.9478,-892.4505 1121.1846,-895.629\"/>\n",
       "<text text-anchor=\"middle\" x=\"1130.3306\" y=\"-905.207\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n",
       "</g>\n",
       "<!-- 2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>2</title>\n",
       "<path fill=\"#5eafea\" stroke=\"#000000\" d=\"M821.5,-781C821.5,-781 678.5,-781 678.5,-781 672.5,-781 666.5,-775 666.5,-769 666.5,-769 666.5,-725 666.5,-725 666.5,-719 672.5,-713 678.5,-713 678.5,-713 821.5,-713 821.5,-713 827.5,-713 833.5,-719 833.5,-725 833.5,-725 833.5,-769 833.5,-769 833.5,-775 827.5,-781 821.5,-781\"/>\n",
       "<text text-anchor=\"start\" x=\"700.5\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta11 ≤ 1.236</text>\n",
       "<text text-anchor=\"start\" x=\"713.5\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.265</text>\n",
       "<text text-anchor=\"start\" x=\"704\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 100</text>\n",
       "<text text-anchor=\"start\" x=\"674.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [15.853, 84.785]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>1&#45;&gt;2</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M881.1497,-816.9465C862.5473,-807.0252 842.0822,-796.1105 822.9633,-785.9138\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"824.4644,-782.7477 813.9937,-781.13 821.1702,-788.9242 824.4644,-782.7477\"/>\n",
       "</g>\n",
       "<!-- 29 -->\n",
       "<g id=\"node30\" class=\"node\">\n",
       "<title>29</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1001,-781C1001,-781 889,-781 889,-781 883,-781 877,-775 877,-769 877,-769 877,-725 877,-725 877,-719 883,-713 889,-713 889,-713 1001,-713 1001,-713 1007,-713 1013,-719 1013,-725 1013,-725 1013,-769 1013,-769 1013,-775 1007,-781 1001,-781\"/>\n",
       "<text text-anchor=\"start\" x=\"899.5\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC7 ≤ 1.29</text>\n",
       "<text text-anchor=\"start\" x=\"916\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"907\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 6</text>\n",
       "<text text-anchor=\"start\" x=\"885\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [5.945, 0.0]</text>\n",
       "</g>\n",
       "<!-- 1&#45;&gt;29 -->\n",
       "<g id=\"edge29\" class=\"edge\">\n",
       "<title>1&#45;&gt;29</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M945,-816.9465C945,-808.776 945,-799.9318 945,-791.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"948.5001,-791.13 945,-781.13 941.5001,-791.13 948.5001,-791.13\"/>\n",
       "</g>\n",
       "<!-- 3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>3</title>\n",
       "<path fill=\"#46a3e7\" stroke=\"#000000\" d=\"M634.5,-677C634.5,-677 499.5,-677 499.5,-677 493.5,-677 487.5,-671 487.5,-665 487.5,-665 487.5,-621 487.5,-621 487.5,-615 493.5,-609 499.5,-609 499.5,-609 634.5,-609 634.5,-609 640.5,-609 646.5,-615 646.5,-621 646.5,-621 646.5,-665 646.5,-665 646.5,-671 640.5,-677 634.5,-677\"/>\n",
       "<text text-anchor=\"start\" x=\"521.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta0 ≤ 1.313</text>\n",
       "<text text-anchor=\"start\" x=\"530.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.112</text>\n",
       "<text text-anchor=\"start\" x=\"525\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 66</text>\n",
       "<text text-anchor=\"start\" x=\"495.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [3.963, 62.579]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>2&#45;&gt;3</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M690.079,-712.9465C672.8583,-703.1599 653.9367,-692.4066 636.2046,-682.3294\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"637.4791,-679.028 627.0557,-677.13 634.0204,-685.1139 637.4791,-679.028\"/>\n",
       "</g>\n",
       "<!-- 14 -->\n",
       "<g id=\"node15\" class=\"node\">\n",
       "<title>14</title>\n",
       "<path fill=\"#a3d1f3\" stroke=\"#000000\" d=\"M817.5,-677C817.5,-677 682.5,-677 682.5,-677 676.5,-677 670.5,-671 670.5,-665 670.5,-665 670.5,-621 670.5,-621 670.5,-615 676.5,-609 682.5,-609 682.5,-609 817.5,-609 817.5,-609 823.5,-609 829.5,-615 829.5,-621 829.5,-621 829.5,-665 829.5,-665 829.5,-671 823.5,-677 817.5,-677\"/>\n",
       "<text text-anchor=\"start\" x=\"710.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">PPE ≤ 0.424</text>\n",
       "<text text-anchor=\"start\" x=\"713.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.454</text>\n",
       "<text text-anchor=\"start\" x=\"708\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 34</text>\n",
       "<text text-anchor=\"start\" x=\"678.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [11.89, 22.206]</text>\n",
       "</g>\n",
       "<!-- 2&#45;&gt;14 -->\n",
       "<g id=\"edge14\" class=\"edge\">\n",
       "<title>2&#45;&gt;14</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M750,-712.9465C750,-704.776 750,-695.9318 750,-687.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"753.5001,-687.13 750,-677.13 746.5001,-687.13 753.5001,-687.13\"/>\n",
       "</g>\n",
       "<!-- 4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>4</title>\n",
       "<path fill=\"#42a2e6\" stroke=\"#000000\" d=\"M465.5,-573C465.5,-573 330.5,-573 330.5,-573 324.5,-573 318.5,-567 318.5,-561 318.5,-561 318.5,-517 318.5,-517 318.5,-511 324.5,-505 330.5,-505 330.5,-505 465.5,-505 465.5,-505 471.5,-505 477.5,-511 477.5,-517 477.5,-517 477.5,-561 477.5,-561 477.5,-567 471.5,-573 465.5,-573\"/>\n",
       "<text text-anchor=\"start\" x=\"362.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">DFA ≤ 0.65</text>\n",
       "<text text-anchor=\"start\" x=\"361.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.087</text>\n",
       "<text text-anchor=\"start\" x=\"356\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 65</text>\n",
       "<text text-anchor=\"start\" x=\"326.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2.972, 62.579]</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;4 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>3&#45;&gt;4</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M511.6631,-608.9465C495.9058,-599.2497 478.6063,-588.6039 462.3613,-578.6069\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"463.8122,-575.3902 453.4612,-573.13 460.1435,-581.3518 463.8122,-575.3902\"/>\n",
       "</g>\n",
       "<!-- 13 -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>13</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M623,-565.5C623,-565.5 511,-565.5 511,-565.5 505,-565.5 499,-559.5 499,-553.5 499,-553.5 499,-524.5 499,-524.5 499,-518.5 505,-512.5 511,-512.5 511,-512.5 623,-512.5 623,-512.5 629,-512.5 635,-518.5 635,-524.5 635,-524.5 635,-553.5 635,-553.5 635,-559.5 629,-565.5 623,-565.5\"/>\n",
       "<text text-anchor=\"start\" x=\"538\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"529\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"507\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 3&#45;&gt;13 -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>3&#45;&gt;13</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M567,-608.9465C567,-598.2621 567,-586.4254 567,-575.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"570.5001,-575.5421 567,-565.5422 563.5001,-575.5422 570.5001,-575.5421\"/>\n",
       "</g>\n",
       "<!-- 5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>5</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M286,-469C286,-469 166,-469 166,-469 160,-469 154,-463 154,-457 154,-457 154,-413 154,-413 154,-407 160,-401 166,-401 166,-401 286,-401 286,-401 292,-401 298,-407 298,-413 298,-413 298,-457 298,-457 298,-463 292,-469 286,-469\"/>\n",
       "<text text-anchor=\"start\" x=\"178\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Jitter_abs ≤ 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"197\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"184\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 43</text>\n",
       "<text text-anchor=\"start\" x=\"162\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 43.402]</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;5 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>4&#45;&gt;5</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M341.6808,-504.9465C325.4952,-495.1599 307.711,-484.4066 291.0448,-474.3294\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"292.8141,-471.3092 282.4458,-469.13 289.1921,-477.2993 292.8141,-471.3092\"/>\n",
       "</g>\n",
       "<!-- 8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>8</title>\n",
       "<path fill=\"#58ace9\" stroke=\"#000000\" d=\"M465.5,-469C465.5,-469 330.5,-469 330.5,-469 324.5,-469 318.5,-463 318.5,-457 318.5,-457 318.5,-413 318.5,-413 318.5,-407 324.5,-401 330.5,-401 330.5,-401 465.5,-401 465.5,-401 471.5,-401 477.5,-407 477.5,-413 477.5,-413 477.5,-457 477.5,-457 477.5,-463 471.5,-469 465.5,-469\"/>\n",
       "<text text-anchor=\"start\" x=\"358.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">PPE ≤ 0.197</text>\n",
       "<text text-anchor=\"start\" x=\"361.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.232</text>\n",
       "<text text-anchor=\"start\" x=\"356\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 22</text>\n",
       "<text text-anchor=\"start\" x=\"326.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2.972, 19.178]</text>\n",
       "</g>\n",
       "<!-- 4&#45;&gt;8 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>4&#45;&gt;8</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M398,-504.9465C398,-496.776 398,-487.9318 398,-479.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"401.5001,-479.13 398,-469.13 394.5001,-479.13 401.5001,-479.13\"/>\n",
       "</g>\n",
       "<!-- 6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>6</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M124,-357.5C124,-357.5 12,-357.5 12,-357.5 6,-357.5 0,-351.5 0,-345.5 0,-345.5 0,-316.5 0,-316.5 0,-310.5 6,-304.5 12,-304.5 12,-304.5 124,-304.5 124,-304.5 130,-304.5 136,-310.5 136,-316.5 136,-316.5 136,-345.5 136,-345.5 136,-351.5 130,-357.5 124,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"39\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"30\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"8\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 1.009]</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>5&#45;&gt;6</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M174.2649,-400.9465C155.8573,-388.8301 135.1987,-375.232 117.0249,-363.2696\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"118.6009,-360.1168 108.3237,-357.5422 114.7522,-365.9638 118.6009,-360.1168\"/>\n",
       "</g>\n",
       "<!-- 7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>7</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M286,-357.5C286,-357.5 166,-357.5 166,-357.5 160,-357.5 154,-351.5 154,-345.5 154,-345.5 154,-316.5 154,-316.5 154,-310.5 160,-304.5 166,-304.5 166,-304.5 286,-304.5 286,-304.5 292,-304.5 298,-310.5 298,-316.5 298,-316.5 298,-345.5 298,-345.5 298,-351.5 292,-357.5 286,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"197\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"184\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 42</text>\n",
       "<text text-anchor=\"start\" x=\"162\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 42.393]</text>\n",
       "</g>\n",
       "<!-- 5&#45;&gt;7 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>5&#45;&gt;7</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M226,-400.9465C226,-390.2621 226,-378.4254 226,-367.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"229.5001,-367.5421 226,-357.5422 222.5001,-367.5422 229.5001,-367.5421\"/>\n",
       "</g>\n",
       "<!-- 9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>9</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M440,-357.5C440,-357.5 328,-357.5 328,-357.5 322,-357.5 316,-351.5 316,-345.5 316,-345.5 316,-316.5 316,-316.5 316,-310.5 322,-304.5 328,-304.5 328,-304.5 440,-304.5 440,-304.5 446,-304.5 452,-310.5 452,-316.5 452,-316.5 452,-345.5 452,-345.5 452,-351.5 446,-357.5 440,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"355\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"346\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"324\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1.982, 0.0]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>8&#45;&gt;9</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M393.4159,-400.9465C391.9776,-390.2621 390.3842,-378.4254 388.9235,-367.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"392.3759,-366.9858 387.573,-357.5422 385.4385,-367.9197 392.3759,-366.9858\"/>\n",
       "</g>\n",
       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#43a2e6\" stroke=\"#000000\" d=\"M617.5,-365C617.5,-365 482.5,-365 482.5,-365 476.5,-365 470.5,-359 470.5,-353 470.5,-353 470.5,-309 470.5,-309 470.5,-303 476.5,-297 482.5,-297 482.5,-297 617.5,-297 617.5,-297 623.5,-297 629.5,-303 629.5,-309 629.5,-309 629.5,-353 629.5,-353 629.5,-359 623.5,-365 617.5,-365\"/>\n",
       "<text text-anchor=\"start\" x=\"510.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">DFA ≤ 0.651</text>\n",
       "<text text-anchor=\"start\" x=\"513.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.093</text>\n",
       "<text text-anchor=\"start\" x=\"508\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 20</text>\n",
       "<text text-anchor=\"start\" x=\"478.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 19.178]</text>\n",
       "</g>\n",
       "<!-- 8&#45;&gt;10 -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>8&#45;&gt;10</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M447.7705,-400.9465C461.8115,-391.3395 477.214,-380.8009 491.7068,-370.8848\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"493.841,-373.6655 500.1177,-365.13 489.8882,-367.8883 493.841,-373.6655\"/>\n",
       "</g>\n",
       "<!-- 11 -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>11</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M448,-253.5C448,-253.5 336,-253.5 336,-253.5 330,-253.5 324,-247.5 324,-241.5 324,-241.5 324,-212.5 324,-212.5 324,-206.5 330,-200.5 336,-200.5 336,-200.5 448,-200.5 448,-200.5 454,-200.5 460,-206.5 460,-212.5 460,-212.5 460,-241.5 460,-241.5 460,-247.5 454,-253.5 448,-253.5\"/>\n",
       "<text text-anchor=\"start\" x=\"363\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"354\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"332\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;11 -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>10&#45;&gt;11</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M498.2649,-296.9465C479.8573,-284.8301 459.1987,-271.232 441.0249,-259.2696\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"442.6009,-256.1168 432.3237,-253.5422 438.7522,-261.9638 442.6009,-256.1168\"/>\n",
       "</g>\n",
       "<!-- 12 -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>12</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M610,-253.5C610,-253.5 490,-253.5 490,-253.5 484,-253.5 478,-247.5 478,-241.5 478,-241.5 478,-212.5 478,-212.5 478,-206.5 484,-200.5 490,-200.5 490,-200.5 610,-200.5 610,-200.5 616,-200.5 622,-206.5 622,-212.5 622,-212.5 622,-241.5 622,-241.5 622,-247.5 616,-253.5 610,-253.5\"/>\n",
       "<text text-anchor=\"start\" x=\"521\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"508\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 19</text>\n",
       "<text text-anchor=\"start\" x=\"486\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 19.178]</text>\n",
       "</g>\n",
       "<!-- 10&#45;&gt;12 -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>10&#45;&gt;12</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M550,-296.9465C550,-286.2621 550,-274.4254 550,-263.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"553.5001,-263.5421 550,-253.5422 546.5001,-263.5422 553.5001,-263.5421\"/>\n",
       "</g>\n",
       "<!-- 15 -->\n",
       "<g id=\"node16\" class=\"node\">\n",
       "<title>15</title>\n",
       "<path fill=\"#fdf7f2\" stroke=\"#000000\" d=\"M808.5,-573C808.5,-573 673.5,-573 673.5,-573 667.5,-573 661.5,-567 661.5,-561 661.5,-561 661.5,-517 661.5,-517 661.5,-511 667.5,-505 673.5,-505 673.5,-505 808.5,-505 808.5,-505 814.5,-505 820.5,-511 820.5,-517 820.5,-517 820.5,-561 820.5,-561 820.5,-567 814.5,-573 808.5,-573\"/>\n",
       "<text text-anchor=\"start\" x=\"687.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC10 ≤ 1.381</text>\n",
       "<text text-anchor=\"start\" x=\"704.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.499</text>\n",
       "<text text-anchor=\"start\" x=\"699\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 23</text>\n",
       "<text text-anchor=\"start\" x=\"669.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [11.89, 11.103]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;15 -->\n",
       "<g id=\"edge15\" class=\"edge\">\n",
       "<title>14&#45;&gt;15</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M747.0531,-608.9465C746.346,-600.776 745.5806,-591.9318 744.8397,-583.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"748.3028,-582.791 743.9536,-573.13 741.3288,-583.3945 748.3028,-582.791\"/>\n",
       "</g>\n",
       "<!-- 28 -->\n",
       "<g id=\"node29\" class=\"node\">\n",
       "<title>28</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M971,-565.5C971,-565.5 851,-565.5 851,-565.5 845,-565.5 839,-559.5 839,-553.5 839,-553.5 839,-524.5 839,-524.5 839,-518.5 845,-512.5 851,-512.5 851,-512.5 971,-512.5 971,-512.5 977,-512.5 983,-518.5 983,-524.5 983,-524.5 983,-553.5 983,-553.5 983,-559.5 977,-565.5 971,-565.5\"/>\n",
       "<text text-anchor=\"start\" x=\"882\" y=\"-550.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"869\" y=\"-535.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 11</text>\n",
       "<text text-anchor=\"start\" x=\"847\" y=\"-520.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 11.103]</text>\n",
       "</g>\n",
       "<!-- 14&#45;&gt;28 -->\n",
       "<g id=\"edge28\" class=\"edge\">\n",
       "<title>14&#45;&gt;28</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M802.7174,-608.9465C821.5598,-596.775 842.7168,-583.1084 861.2966,-571.1066\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"863.4099,-573.9082 869.9107,-565.5422 859.6117,-568.0283 863.4099,-573.9082\"/>\n",
       "</g>\n",
       "<!-- 16 -->\n",
       "<g id=\"node17\" class=\"node\">\n",
       "<title>16</title>\n",
       "<path fill=\"#f4ccaf\" stroke=\"#000000\" d=\"M796,-469C796,-469 668,-469 668,-469 662,-469 656,-463 656,-457 656,-457 656,-413 656,-413 656,-407 662,-401 668,-401 668,-401 796,-401 796,-401 802,-401 808,-407 808,-413 808,-413 808,-457 808,-457 808,-463 802,-469 796,-469\"/>\n",
       "<text text-anchor=\"start\" x=\"686.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta4 ≤ 1.275</text>\n",
       "<text text-anchor=\"start\" x=\"695.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.468</text>\n",
       "<text text-anchor=\"start\" x=\"690\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 19</text>\n",
       "<text text-anchor=\"start\" x=\"664\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [11.89, 7.065]</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;16 -->\n",
       "<g id=\"edge16\" class=\"edge\">\n",
       "<title>15&#45;&gt;16</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M738.0531,-504.9465C737.346,-496.776 736.5806,-487.9318 735.8397,-479.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"739.3028,-478.791 734.9536,-469.13 732.3288,-479.3945 739.3028,-478.791\"/>\n",
       "</g>\n",
       "<!-- 27 -->\n",
       "<g id=\"node28\" class=\"node\">\n",
       "<title>27</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M950,-461.5C950,-461.5 838,-461.5 838,-461.5 832,-461.5 826,-455.5 826,-449.5 826,-449.5 826,-420.5 826,-420.5 826,-414.5 832,-408.5 838,-408.5 838,-408.5 950,-408.5 950,-408.5 956,-408.5 962,-414.5 962,-420.5 962,-420.5 962,-449.5 962,-449.5 962,-455.5 956,-461.5 950,-461.5\"/>\n",
       "<text text-anchor=\"start\" x=\"865\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"856\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"834\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 4.037]</text>\n",
       "</g>\n",
       "<!-- 15&#45;&gt;27 -->\n",
       "<g id=\"edge27\" class=\"edge\">\n",
       "<title>15&#45;&gt;27</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M791.0979,-504.9465C808.923,-492.8301 828.9279,-479.232 846.5265,-467.2696\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"848.6497,-470.0584 854.9524,-461.5422 844.7145,-464.2692 848.6497,-470.0584\"/>\n",
       "</g>\n",
       "<!-- 17 -->\n",
       "<g id=\"node18\" class=\"node\">\n",
       "<title>17</title>\n",
       "<path fill=\"#9acdf2\" stroke=\"#000000\" d=\"M796,-365C796,-365 668,-365 668,-365 662,-365 656,-359 656,-353 656,-353 656,-309 656,-309 656,-303 662,-297 668,-297 668,-297 796,-297 796,-297 802,-297 808,-303 808,-309 808,-309 808,-353 808,-353 808,-359 802,-365 796,-365\"/>\n",
       "<text text-anchor=\"start\" x=\"679.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">HNR05 ≤ 53.003</text>\n",
       "<text text-anchor=\"start\" x=\"695.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.442</text>\n",
       "<text text-anchor=\"start\" x=\"694\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 9</text>\n",
       "<text text-anchor=\"start\" x=\"664\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2.972, 6.056]</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;17 -->\n",
       "<g id=\"edge17\" class=\"edge\">\n",
       "<title>16&#45;&gt;17</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M732,-400.9465C732,-392.776 732,-383.9318 732,-375.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"735.5001,-375.13 732,-365.13 728.5001,-375.13 735.5001,-375.13\"/>\n",
       "</g>\n",
       "<!-- 24 -->\n",
       "<g id=\"node25\" class=\"node\">\n",
       "<title>24</title>\n",
       "<path fill=\"#e88f4f\" stroke=\"#000000\" d=\"M1096,-365C1096,-365 968,-365 968,-365 962,-365 956,-359 956,-353 956,-353 956,-309 956,-309 956,-303 962,-297 968,-297 968,-297 1096,-297 1096,-297 1102,-297 1108,-303 1108,-309 1108,-309 1108,-353 1108,-353 1108,-359 1102,-365 1096,-365\"/>\n",
       "<text text-anchor=\"start\" x=\"986.5\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta0 ≤ 1.323</text>\n",
       "<text text-anchor=\"start\" x=\"995.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.183</text>\n",
       "<text text-anchor=\"start\" x=\"990\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 10</text>\n",
       "<text text-anchor=\"start\" x=\"964\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8.917, 1.009]</text>\n",
       "</g>\n",
       "<!-- 16&#45;&gt;24 -->\n",
       "<g id=\"edge24\" class=\"edge\">\n",
       "<title>16&#45;&gt;24</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M808.3647,-404.1792C811.2749,-403.0921 814.1608,-402.0288 817,-401 859.2784,-385.6805 906.7705,-370.0547 946.3445,-357.4642\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"947.4904,-360.7727 955.9646,-354.4137 945.3744,-354.1002 947.4904,-360.7727\"/>\n",
       "</g>\n",
       "<!-- 18 -->\n",
       "<g id=\"node19\" class=\"node\">\n",
       "<title>18</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M764,-253.5C764,-253.5 652,-253.5 652,-253.5 646,-253.5 640,-247.5 640,-241.5 640,-241.5 640,-212.5 640,-212.5 640,-206.5 646,-200.5 652,-200.5 652,-200.5 764,-200.5 764,-200.5 770,-200.5 776,-206.5 776,-212.5 776,-212.5 776,-241.5 776,-241.5 776,-247.5 770,-253.5 764,-253.5\"/>\n",
       "<text text-anchor=\"start\" x=\"677\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"670\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"648\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 5.047]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;18 -->\n",
       "<g id=\"edge18\" class=\"edge\">\n",
       "<title>17&#45;&gt;18</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M724.1415,-296.9465C721.6759,-286.2621 718.9443,-274.4254 716.4402,-263.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"719.7842,-262.499 714.1251,-253.5422 712.9634,-264.0731 719.7842,-262.499\"/>\n",
       "</g>\n",
       "<!-- 19 -->\n",
       "<g id=\"node20\" class=\"node\">\n",
       "<title>19</title>\n",
       "<path fill=\"#eeac7c\" stroke=\"#000000\" d=\"M934,-261C934,-261 806,-261 806,-261 800,-261 794,-255 794,-249 794,-249 794,-205 794,-205 794,-199 800,-193 806,-193 806,-193 934,-193 934,-193 940,-193 946,-199 946,-205 946,-205 946,-249 946,-249 946,-255 940,-261 934,-261\"/>\n",
       "<text text-anchor=\"start\" x=\"811\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Jitter_RAP ≤ 0.003</text>\n",
       "<text text-anchor=\"start\" x=\"833.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.378</text>\n",
       "<text text-anchor=\"start\" x=\"832\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"802\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2.972, 1.009]</text>\n",
       "</g>\n",
       "<!-- 17&#45;&gt;19 -->\n",
       "<g id=\"edge19\" class=\"edge\">\n",
       "<title>17&#45;&gt;19</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M777.1864,-296.9465C789.815,-287.4293 803.6567,-276.9978 816.7068,-267.163\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"818.8325,-269.9437 824.7121,-261.13 814.6195,-264.3534 818.8325,-269.9437\"/>\n",
       "</g>\n",
       "<!-- 20 -->\n",
       "<g id=\"node21\" class=\"node\">\n",
       "<title>20</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M923,-157C923,-157 811,-157 811,-157 805,-157 799,-151 799,-145 799,-145 799,-101 799,-101 799,-95 805,-89 811,-89 811,-89 923,-89 923,-89 929,-89 935,-95 935,-101 935,-101 935,-145 935,-145 935,-151 929,-157 923,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"813.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC11 ≤ 1.211</text>\n",
       "<text text-anchor=\"start\" x=\"838\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"829\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"807\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [2.972, 0.0]</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;20 -->\n",
       "<g id=\"edge20\" class=\"edge\">\n",
       "<title>19&#45;&gt;20</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M869.0177,-192.9465C868.782,-184.776 868.5269,-175.9318 868.2799,-167.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"871.7715,-167.0249 867.9845,-157.13 864.7744,-167.2268 871.7715,-167.0249\"/>\n",
       "</g>\n",
       "<!-- 23 -->\n",
       "<g id=\"node24\" class=\"node\">\n",
       "<title>23</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1077,-149.5C1077,-149.5 965,-149.5 965,-149.5 959,-149.5 953,-143.5 953,-137.5 953,-137.5 953,-108.5 953,-108.5 953,-102.5 959,-96.5 965,-96.5 965,-96.5 1077,-96.5 1077,-96.5 1083,-96.5 1089,-102.5 1089,-108.5 1089,-108.5 1089,-137.5 1089,-137.5 1089,-143.5 1083,-149.5 1077,-149.5\"/>\n",
       "<text text-anchor=\"start\" x=\"990\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"983\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"961\" y=\"-104.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 1.009]</text>\n",
       "</g>\n",
       "<!-- 19&#45;&gt;23 -->\n",
       "<g id=\"edge23\" class=\"edge\">\n",
       "<title>19&#45;&gt;23</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M919.443,-192.9465C937.0351,-180.8301 956.7785,-167.232 974.147,-155.2696\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"976.2125,-158.0969 982.4628,-149.5422 972.2419,-152.332 976.2125,-158.0969\"/>\n",
       "</g>\n",
       "<!-- 21 -->\n",
       "<g id=\"node22\" class=\"node\">\n",
       "<title>21</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M773,-53C773,-53 661,-53 661,-53 655,-53 649,-47 649,-41 649,-41 649,-12 649,-12 649,-6 655,0 661,0 661,0 773,0 773,0 779,0 785,-6 785,-12 785,-12 785,-41 785,-41 785,-47 779,-53 773,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"688\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"679\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"657\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;21 -->\n",
       "<g id=\"edge21\" class=\"edge\">\n",
       "<title>20&#45;&gt;21</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M814.1156,-88.9777C798.8856,-79.1798 782.3194,-68.5222 767.2536,-58.8298\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"768.8816,-55.7154 758.5779,-53.2485 765.0943,-61.6024 768.8816,-55.7154\"/>\n",
       "</g>\n",
       "<!-- 22 -->\n",
       "<g id=\"node23\" class=\"node\">\n",
       "<title>22</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M927,-53C927,-53 815,-53 815,-53 809,-53 803,-47 803,-41 803,-41 803,-12 803,-12 803,-6 809,0 815,0 815,0 927,0 927,0 933,0 939,-6 939,-12 939,-12 939,-41 939,-41 939,-47 933,-53 927,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"842\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"833\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"811\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [1.982, 0.0]</text>\n",
       "</g>\n",
       "<!-- 20&#45;&gt;22 -->\n",
       "<g id=\"edge22\" class=\"edge\">\n",
       "<title>20&#45;&gt;22</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M868.4103,-88.9777C868.7519,-80.7364 869.1187,-71.887 869.4657,-63.5153\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"872.974,-63.3849 869.8913,-53.2485 865.98,-63.0949 872.974,-63.3849\"/>\n",
       "</g>\n",
       "<!-- 25 -->\n",
       "<g id=\"node26\" class=\"node\">\n",
       "<title>25</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1088,-253.5C1088,-253.5 976,-253.5 976,-253.5 970,-253.5 964,-247.5 964,-241.5 964,-241.5 964,-212.5 964,-212.5 964,-206.5 970,-200.5 976,-200.5 976,-200.5 1088,-200.5 1088,-200.5 1094,-200.5 1100,-206.5 1100,-212.5 1100,-212.5 1100,-241.5 1100,-241.5 1100,-247.5 1094,-253.5 1088,-253.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1003\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"994\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 9</text>\n",
       "<text text-anchor=\"start\" x=\"972\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [8.917, 0.0]</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;25 -->\n",
       "<g id=\"edge25\" class=\"edge\">\n",
       "<title>24&#45;&gt;25</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1032,-296.9465C1032,-286.2621 1032,-274.4254 1032,-263.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1035.5001,-263.5421 1032,-253.5422 1028.5001,-263.5422 1035.5001,-263.5421\"/>\n",
       "</g>\n",
       "<!-- 26 -->\n",
       "<g id=\"node27\" class=\"node\">\n",
       "<title>26</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1242,-253.5C1242,-253.5 1130,-253.5 1130,-253.5 1124,-253.5 1118,-247.5 1118,-241.5 1118,-241.5 1118,-212.5 1118,-212.5 1118,-206.5 1124,-200.5 1130,-200.5 1130,-200.5 1242,-200.5 1242,-200.5 1248,-200.5 1254,-206.5 1254,-212.5 1254,-212.5 1254,-241.5 1254,-241.5 1254,-247.5 1248,-253.5 1242,-253.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1155\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1148\" y=\"-223.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1126\" y=\"-208.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 1.009]</text>\n",
       "</g>\n",
       "<!-- 24&#45;&gt;26 -->\n",
       "<g id=\"edge26\" class=\"edge\">\n",
       "<title>24&#45;&gt;26</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1082.4254,-296.9465C1100.3669,-284.8301 1120.5026,-271.232 1138.2162,-259.2696\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1140.3687,-262.0393 1146.6972,-253.5422 1136.4511,-256.2382 1140.3687,-262.0393\"/>\n",
       "</g>\n",
       "<!-- 30 -->\n",
       "<g id=\"node31\" class=\"node\">\n",
       "<title>30</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M987,-669.5C987,-669.5 875,-669.5 875,-669.5 869,-669.5 863,-663.5 863,-657.5 863,-657.5 863,-628.5 863,-628.5 863,-622.5 869,-616.5 875,-616.5 875,-616.5 987,-616.5 987,-616.5 993,-616.5 999,-622.5 999,-628.5 999,-628.5 999,-657.5 999,-657.5 999,-663.5 993,-669.5 987,-669.5\"/>\n",
       "<text text-anchor=\"start\" x=\"902\" y=\"-654.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"893\" y=\"-639.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"871\" y=\"-624.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 29&#45;&gt;30 -->\n",
       "<g id=\"edge30\" class=\"edge\">\n",
       "<title>29&#45;&gt;30</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M940.4159,-712.9465C938.9776,-702.2621 937.3842,-690.4254 935.9235,-679.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"939.3759,-678.9858 934.573,-669.5422 932.4385,-679.9197 939.3759,-678.9858\"/>\n",
       "</g>\n",
       "<!-- 31 -->\n",
       "<g id=\"node32\" class=\"node\">\n",
       "<title>31</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1141,-669.5C1141,-669.5 1029,-669.5 1029,-669.5 1023,-669.5 1017,-663.5 1017,-657.5 1017,-657.5 1017,-628.5 1017,-628.5 1017,-622.5 1023,-616.5 1029,-616.5 1029,-616.5 1141,-616.5 1141,-616.5 1147,-616.5 1153,-622.5 1153,-628.5 1153,-628.5 1153,-657.5 1153,-657.5 1153,-663.5 1147,-669.5 1141,-669.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1056\" y=\"-654.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1047\" y=\"-639.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"1025\" y=\"-624.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [4.954, 0.0]</text>\n",
       "</g>\n",
       "<!-- 29&#45;&gt;31 -->\n",
       "<g id=\"edge31\" class=\"edge\">\n",
       "<title>29&#45;&gt;31</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M990.8412,-712.9465C1007.0035,-700.9403 1025.1242,-687.4791 1041.1205,-675.5962\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1043.3299,-678.3151 1049.2702,-669.5422 1039.1556,-672.6958 1043.3299,-678.3151\"/>\n",
       "</g>\n",
       "<!-- 33 -->\n",
       "<g id=\"node34\" class=\"node\">\n",
       "<title>33</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1196,-773.5C1196,-773.5 1084,-773.5 1084,-773.5 1078,-773.5 1072,-767.5 1072,-761.5 1072,-761.5 1072,-732.5 1072,-732.5 1072,-726.5 1078,-720.5 1084,-720.5 1084,-720.5 1196,-720.5 1196,-720.5 1202,-720.5 1208,-726.5 1208,-732.5 1208,-732.5 1208,-761.5 1208,-761.5 1208,-767.5 1202,-773.5 1196,-773.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1111\" y=\"-758.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1102\" y=\"-743.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 5</text>\n",
       "<text text-anchor=\"start\" x=\"1080\" y=\"-728.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 5.047]</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;33 -->\n",
       "<g id=\"edge33\" class=\"edge\">\n",
       "<title>32&#45;&gt;33</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1140,-816.9465C1140,-806.2621 1140,-794.4254 1140,-783.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1143.5001,-783.5421 1140,-773.5422 1136.5001,-783.5422 1143.5001,-783.5421\"/>\n",
       "</g>\n",
       "<!-- 34 -->\n",
       "<g id=\"node35\" class=\"node\">\n",
       "<title>34</title>\n",
       "<path fill=\"#ea9c63\" stroke=\"#000000\" d=\"M1393.5,-781C1393.5,-781 1250.5,-781 1250.5,-781 1244.5,-781 1238.5,-775 1238.5,-769 1238.5,-769 1238.5,-725 1238.5,-725 1238.5,-719 1244.5,-713 1250.5,-713 1250.5,-713 1393.5,-713 1393.5,-713 1399.5,-713 1405.5,-719 1405.5,-725 1405.5,-725 1405.5,-769 1405.5,-769 1405.5,-775 1399.5,-781 1393.5,-781\"/>\n",
       "<text text-anchor=\"start\" x=\"1260\" y=\"-765.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Shi_APQ11 ≤ 0.063</text>\n",
       "<text text-anchor=\"start\" x=\"1285.5\" y=\"-750.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.288</text>\n",
       "<text text-anchor=\"start\" x=\"1276\" y=\"-735.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 105</text>\n",
       "<text text-anchor=\"start\" x=\"1246.5\" y=\"-720.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [86.202, 18.168]</text>\n",
       "</g>\n",
       "<!-- 32&#45;&gt;34 -->\n",
       "<g id=\"edge34\" class=\"edge\">\n",
       "<title>32&#45;&gt;34</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1199.5936,-816.9465C1216.7202,-807.1599 1235.5384,-796.4066 1253.1735,-786.3294\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1255.3266,-789.1303 1262.2725,-781.13 1251.8536,-783.0526 1255.3266,-789.1303\"/>\n",
       "</g>\n",
       "<!-- 35 -->\n",
       "<g id=\"node36\" class=\"node\">\n",
       "<title>35</title>\n",
       "<path fill=\"#ea975c\" stroke=\"#000000\" d=\"M1389.5,-677C1389.5,-677 1254.5,-677 1254.5,-677 1248.5,-677 1242.5,-671 1242.5,-665 1242.5,-665 1242.5,-621 1242.5,-621 1242.5,-615 1248.5,-609 1254.5,-609 1254.5,-609 1389.5,-609 1389.5,-609 1395.5,-609 1401.5,-615 1401.5,-621 1401.5,-621 1401.5,-665 1401.5,-665 1401.5,-671 1395.5,-677 1389.5,-677\"/>\n",
       "<text text-anchor=\"start\" x=\"1272.5\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC6 ≤ 1.187</text>\n",
       "<text text-anchor=\"start\" x=\"1285.5\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.254</text>\n",
       "<text text-anchor=\"start\" x=\"1276\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 102</text>\n",
       "<text text-anchor=\"start\" x=\"1250.5\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [86.202, 15.14]</text>\n",
       "</g>\n",
       "<!-- 34&#45;&gt;35 -->\n",
       "<g id=\"edge35\" class=\"edge\">\n",
       "<title>34&#45;&gt;35</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1322,-712.9465C1322,-704.776 1322,-695.9318 1322,-687.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1325.5001,-687.13 1322,-677.13 1318.5001,-687.13 1325.5001,-687.13\"/>\n",
       "</g>\n",
       "<!-- 58 -->\n",
       "<g id=\"node59\" class=\"node\">\n",
       "<title>58</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1598,-677C1598,-677 1486,-677 1486,-677 1480,-677 1474,-671 1474,-665 1474,-665 1474,-621 1474,-621 1474,-615 1480,-609 1486,-609 1486,-609 1598,-609 1598,-609 1604,-609 1610,-615 1610,-621 1610,-621 1610,-665 1610,-665 1610,-671 1604,-677 1598,-677\"/>\n",
       "<text text-anchor=\"start\" x=\"1497\" y=\"-661.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">RPDE ≤ 0.373</text>\n",
       "<text text-anchor=\"start\" x=\"1513\" y=\"-646.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1504\" y=\"-631.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"1482\" y=\"-616.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 3.028]</text>\n",
       "</g>\n",
       "<!-- 34&#45;&gt;58 -->\n",
       "<g id=\"edge58\" class=\"edge\">\n",
       "<title>34&#45;&gt;58</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1394.0362,-712.9465C1416.7153,-702.2255 1441.8484,-690.3444 1464.8671,-679.4628\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1466.3665,-682.6255 1473.9113,-675.1874 1463.3748,-676.297 1466.3665,-682.6255\"/>\n",
       "</g>\n",
       "<!-- 36 -->\n",
       "<g id=\"node37\" class=\"node\">\n",
       "<title>36</title>\n",
       "<path fill=\"#7abdee\" stroke=\"#000000\" d=\"M1258,-573C1258,-573 1130,-573 1130,-573 1124,-573 1118,-567 1118,-561 1118,-561 1118,-517 1118,-517 1118,-511 1124,-505 1130,-505 1130,-505 1258,-505 1258,-505 1264,-505 1270,-511 1270,-517 1270,-517 1270,-561 1270,-561 1270,-567 1264,-573 1258,-573\"/>\n",
       "<text text-anchor=\"start\" x=\"1148.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Delta2 ≤ 1.119</text>\n",
       "<text text-anchor=\"start\" x=\"1157.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.372</text>\n",
       "<text text-anchor=\"start\" x=\"1156\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 4</text>\n",
       "<text text-anchor=\"start\" x=\"1126\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 3.028]</text>\n",
       "</g>\n",
       "<!-- 35&#45;&gt;36 -->\n",
       "<g id=\"edge36\" class=\"edge\">\n",
       "<title>35&#45;&gt;36</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1280.088,-608.9465C1268.485,-599.519 1255.7779,-589.1946 1243.7741,-579.4415\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1245.9744,-576.7196 1236.0061,-573.13 1241.5602,-582.1524 1245.9744,-576.7196\"/>\n",
       "</g>\n",
       "<!-- 39 -->\n",
       "<g id=\"node40\" class=\"node\">\n",
       "<title>39</title>\n",
       "<path fill=\"#e99355\" stroke=\"#000000\" d=\"M1443.5,-573C1443.5,-573 1300.5,-573 1300.5,-573 1294.5,-573 1288.5,-567 1288.5,-561 1288.5,-561 1288.5,-517 1288.5,-517 1288.5,-511 1294.5,-505 1300.5,-505 1300.5,-505 1443.5,-505 1443.5,-505 1449.5,-505 1455.5,-511 1455.5,-517 1455.5,-517 1455.5,-561 1455.5,-561 1455.5,-567 1449.5,-573 1443.5,-573\"/>\n",
       "<text text-anchor=\"start\" x=\"1322.5\" y=\"-557.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC0 ≤ 1.803</text>\n",
       "<text text-anchor=\"start\" x=\"1335.5\" y=\"-542.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.218</text>\n",
       "<text text-anchor=\"start\" x=\"1330\" y=\"-527.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 98</text>\n",
       "<text text-anchor=\"start\" x=\"1296.5\" y=\"-512.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [85.211, 12.112]</text>\n",
       "</g>\n",
       "<!-- 35&#45;&gt;39 -->\n",
       "<g id=\"edge39\" class=\"edge\">\n",
       "<title>35&#45;&gt;39</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1338.3719,-608.9465C1342.4726,-600.4169 1346.9264,-591.153 1351.2104,-582.2424\"/>\n",
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       "</g>\n",
       "<!-- 37 -->\n",
       "<g id=\"node38\" class=\"node\">\n",
       "<title>37</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1104,-461.5C1104,-461.5 992,-461.5 992,-461.5 986,-461.5 980,-455.5 980,-449.5 980,-449.5 980,-420.5 980,-420.5 980,-414.5 986,-408.5 992,-408.5 992,-408.5 1104,-408.5 1104,-408.5 1110,-408.5 1116,-414.5 1116,-420.5 1116,-420.5 1116,-449.5 1116,-449.5 1116,-455.5 1110,-461.5 1104,-461.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1019\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1010\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"988\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 36&#45;&gt;37 -->\n",
       "<g id=\"edge37\" class=\"edge\">\n",
       "<title>36&#45;&gt;37</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1146.1941,-504.9465C1129.3392,-492.9403 1110.4419,-479.4791 1093.7601,-467.5962\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1095.4367,-464.4933 1085.2611,-461.5422 1091.3753,-470.1947 1095.4367,-464.4933\"/>\n",
       "</g>\n",
       "<!-- 38 -->\n",
       "<g id=\"node39\" class=\"node\">\n",
       "<title>38</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1258,-461.5C1258,-461.5 1146,-461.5 1146,-461.5 1140,-461.5 1134,-455.5 1134,-449.5 1134,-449.5 1134,-420.5 1134,-420.5 1134,-414.5 1140,-408.5 1146,-408.5 1146,-408.5 1258,-408.5 1258,-408.5 1264,-408.5 1270,-414.5 1270,-420.5 1270,-420.5 1270,-449.5 1270,-449.5 1270,-455.5 1264,-461.5 1258,-461.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1171\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1164\" y=\"-431.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 3</text>\n",
       "<text text-anchor=\"start\" x=\"1142\" y=\"-416.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 3.028]</text>\n",
       "</g>\n",
       "<!-- 36&#45;&gt;38 -->\n",
       "<g id=\"edge38\" class=\"edge\">\n",
       "<title>36&#45;&gt;38</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1196.6195,-504.9465C1197.4414,-494.2621 1198.3519,-482.4254 1199.1866,-471.5742\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1202.6809,-471.7812 1199.9583,-461.5422 1195.7015,-471.2442 1202.6809,-471.7812\"/>\n",
       "</g>\n",
       "<!-- 40 -->\n",
       "<g id=\"node41\" class=\"node\">\n",
       "<title>40</title>\n",
       "<path fill=\"#e89050\" stroke=\"#000000\" d=\"M1443.5,-469C1443.5,-469 1300.5,-469 1300.5,-469 1294.5,-469 1288.5,-463 1288.5,-457 1288.5,-457 1288.5,-413 1288.5,-413 1288.5,-407 1294.5,-401 1300.5,-401 1300.5,-401 1443.5,-401 1443.5,-401 1449.5,-401 1455.5,-407 1455.5,-413 1455.5,-413 1455.5,-457 1455.5,-457 1455.5,-463 1449.5,-469 1443.5,-469\"/>\n",
       "<text text-anchor=\"start\" x=\"1318.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC10 ≤ 1.195</text>\n",
       "<text text-anchor=\"start\" x=\"1335.5\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.189</text>\n",
       "<text text-anchor=\"start\" x=\"1330\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 96</text>\n",
       "<text text-anchor=\"start\" x=\"1296.5\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [85.211, 10.093]</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;40 -->\n",
       "<g id=\"edge40\" class=\"edge\">\n",
       "<title>39&#45;&gt;40</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1372,-504.9465C1372,-496.776 1372,-487.9318 1372,-479.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1375.5001,-479.13 1372,-469.13 1368.5001,-479.13 1375.5001,-479.13\"/>\n",
       "</g>\n",
       "<!-- 55 -->\n",
       "<g id=\"node56\" class=\"node\">\n",
       "<title>55</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1598,-469C1598,-469 1486,-469 1486,-469 1480,-469 1474,-463 1474,-457 1474,-457 1474,-413 1474,-413 1474,-407 1480,-401 1486,-401 1486,-401 1598,-401 1598,-401 1604,-401 1610,-407 1610,-413 1610,-413 1610,-457 1610,-457 1610,-463 1604,-469 1598,-469\"/>\n",
       "<text text-anchor=\"start\" x=\"1492.5\" y=\"-453.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC0 ≤ 1.877</text>\n",
       "<text text-anchor=\"start\" x=\"1513\" y=\"-438.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1504\" y=\"-423.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 2</text>\n",
       "<text text-anchor=\"start\" x=\"1482\" y=\"-408.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 2.019]</text>\n",
       "</g>\n",
       "<!-- 39&#45;&gt;55 -->\n",
       "<g id=\"edge55\" class=\"edge\">\n",
       "<title>39&#45;&gt;55</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1427.6644,-504.9465C1443.5149,-495.2497 1460.9168,-484.6039 1477.2579,-474.6069\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1479.5067,-477.3342 1486.2106,-469.13 1475.8537,-471.363 1479.5067,-477.3342\"/>\n",
       "</g>\n",
       "<!-- 41 -->\n",
       "<g id=\"node42\" class=\"node\">\n",
       "<title>41</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1263,-357.5C1263,-357.5 1151,-357.5 1151,-357.5 1145,-357.5 1139,-351.5 1139,-345.5 1139,-345.5 1139,-316.5 1139,-316.5 1139,-310.5 1145,-304.5 1151,-304.5 1151,-304.5 1263,-304.5 1263,-304.5 1269,-304.5 1275,-310.5 1275,-316.5 1275,-316.5 1275,-345.5 1275,-345.5 1275,-351.5 1269,-357.5 1263,-357.5\"/>\n",
       "<text text-anchor=\"start\" x=\"1178\" y=\"-342.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1169\" y=\"-327.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1147\" y=\"-312.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 1.009]</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;41 -->\n",
       "<g id=\"edge41\" class=\"edge\">\n",
       "<title>40&#45;&gt;41</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1317.9728,-400.9465C1298.5749,-388.72 1276.7834,-374.9847 1257.68,-362.9437\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1259.4362,-359.9135 1249.1102,-357.5422 1255.7036,-365.8353 1259.4362,-359.9135\"/>\n",
       "</g>\n",
       "<!-- 42 -->\n",
       "<g id=\"node43\" class=\"node\">\n",
       "<title>42</title>\n",
       "<path fill=\"#e88e4e\" stroke=\"#000000\" d=\"M1440.5,-365C1440.5,-365 1305.5,-365 1305.5,-365 1299.5,-365 1293.5,-359 1293.5,-353 1293.5,-353 1293.5,-309 1293.5,-309 1293.5,-303 1299.5,-297 1305.5,-297 1305.5,-297 1440.5,-297 1440.5,-297 1446.5,-297 1452.5,-303 1452.5,-309 1452.5,-309 1452.5,-353 1452.5,-353 1452.5,-359 1446.5,-365 1440.5,-365\"/>\n",
       "<text text-anchor=\"start\" x=\"1328\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">RPDE ≤ 0.269</text>\n",
       "<text text-anchor=\"start\" x=\"1336.5\" y=\"-334.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.174</text>\n",
       "<text text-anchor=\"start\" x=\"1331\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 95</text>\n",
       "<text text-anchor=\"start\" x=\"1301.5\" y=\"-304.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [85.211, 9.084]</text>\n",
       "</g>\n",
       "<!-- 40&#45;&gt;42 -->\n",
       "<g id=\"edge42\" class=\"edge\">\n",
       "<title>40&#45;&gt;42</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1372.3274,-400.9465C1372.406,-392.776 1372.491,-383.9318 1372.5734,-375.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1376.0754,-375.1632 1372.6718,-365.13 1369.0757,-375.0958 1376.0754,-375.1632\"/>\n",
       "</g>\n",
       "<!-- 43 -->\n",
       "<g id=\"node44\" class=\"node\">\n",
       "<title>43</title>\n",
       "<path fill=\"#edaa79\" stroke=\"#000000\" d=\"M1425.5,-261C1425.5,-261 1290.5,-261 1290.5,-261 1284.5,-261 1278.5,-255 1278.5,-249 1278.5,-249 1278.5,-205 1278.5,-205 1278.5,-199 1284.5,-193 1290.5,-193 1290.5,-193 1425.5,-193 1425.5,-193 1431.5,-193 1437.5,-199 1437.5,-205 1437.5,-205 1437.5,-249 1437.5,-249 1437.5,-255 1431.5,-261 1425.5,-261\"/>\n",
       "<text text-anchor=\"start\" x=\"1296\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">Shi_APQ11 ≤ 0.023</text>\n",
       "<text text-anchor=\"start\" x=\"1325.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.37</text>\n",
       "<text text-anchor=\"start\" x=\"1316\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 29</text>\n",
       "<text text-anchor=\"start\" x=\"1286.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [21.798, 7.065]</text>\n",
       "</g>\n",
       "<!-- 42&#45;&gt;43 -->\n",
       "<g id=\"edge43\" class=\"edge\">\n",
       "<title>42&#45;&gt;43</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1368.0884,-296.9465C1366.8971,-288.6863 1365.6063,-279.7374 1364.3588,-271.0875\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1367.8144,-270.5279 1362.9226,-261.13 1360.8861,-271.5272 1367.8144,-270.5279\"/>\n",
       "</g>\n",
       "<!-- 50 -->\n",
       "<g id=\"node51\" class=\"node\">\n",
       "<title>50</title>\n",
       "<path fill=\"#e6853f\" stroke=\"#000000\" d=\"M1602.5,-261C1602.5,-261 1467.5,-261 1467.5,-261 1461.5,-261 1455.5,-255 1455.5,-249 1455.5,-249 1455.5,-205 1455.5,-205 1455.5,-199 1461.5,-193 1467.5,-193 1467.5,-193 1602.5,-193 1602.5,-193 1608.5,-193 1614.5,-199 1614.5,-205 1614.5,-205 1614.5,-249 1614.5,-249 1614.5,-255 1608.5,-261 1602.5,-261\"/>\n",
       "<text text-anchor=\"start\" x=\"1485.5\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">MFCC7 ≤ 1.211</text>\n",
       "<text text-anchor=\"start\" x=\"1502.5\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.06</text>\n",
       "<text text-anchor=\"start\" x=\"1493\" y=\"-215.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 66</text>\n",
       "<text text-anchor=\"start\" x=\"1463.5\" y=\"-200.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [63.413, 2.019]</text>\n",
       "</g>\n",
       "<!-- 42&#45;&gt;50 -->\n",
       "<g id=\"edge50\" class=\"edge\">\n",
       "<title>42&#45;&gt;50</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1426.0449,-296.9465C1441.1495,-287.2497 1457.7324,-276.6039 1473.3046,-266.6069\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1475.3116,-269.4777 1481.836,-261.13 1471.53,-263.5871 1475.3116,-269.4777\"/>\n",
       "</g>\n",
       "<!-- 44 -->\n",
       "<g id=\"node45\" class=\"node\">\n",
       "<title>44</title>\n",
       "<path fill=\"#e68743\" stroke=\"#000000\" d=\"M1255.5,-157C1255.5,-157 1120.5,-157 1120.5,-157 1114.5,-157 1108.5,-151 1108.5,-145 1108.5,-145 1108.5,-101 1108.5,-101 1108.5,-95 1114.5,-89 1120.5,-89 1120.5,-89 1255.5,-89 1255.5,-89 1261.5,-89 1267.5,-95 1267.5,-101 1267.5,-101 1267.5,-145 1267.5,-145 1267.5,-151 1261.5,-157 1255.5,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"1148.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">PPE ≤ 0.537</text>\n",
       "<text text-anchor=\"start\" x=\"1151.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.088</text>\n",
       "<text text-anchor=\"start\" x=\"1146\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 22</text>\n",
       "<text text-anchor=\"start\" x=\"1116.5\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [20.807, 1.009]</text>\n",
       "</g>\n",
       "<!-- 43&#45;&gt;44 -->\n",
       "<g id=\"edge44\" class=\"edge\">\n",
       "<title>43&#45;&gt;44</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1302.3356,-192.9465C1286.4851,-183.2497 1269.0832,-172.6039 1252.7421,-162.6069\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1254.1463,-159.363 1243.7894,-157.13 1250.4933,-165.3342 1254.1463,-159.363\"/>\n",
       "</g>\n",
       "<!-- 47 -->\n",
       "<g id=\"node48\" class=\"node\">\n",
       "<title>47</title>\n",
       "<path fill=\"#59ade9\" stroke=\"#000000\" d=\"M1426,-157C1426,-157 1298,-157 1298,-157 1292,-157 1286,-151 1286,-145 1286,-145 1286,-101 1286,-101 1286,-95 1292,-89 1298,-89 1298,-89 1426,-89 1426,-89 1432,-89 1438,-95 1438,-101 1438,-101 1438,-145 1438,-145 1438,-151 1432,-157 1426,-157\"/>\n",
       "<text text-anchor=\"start\" x=\"1313.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">HNR25 ≤ 80.35</text>\n",
       "<text text-anchor=\"start\" x=\"1325.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.242</text>\n",
       "<text text-anchor=\"start\" x=\"1324\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 7</text>\n",
       "<text text-anchor=\"start\" x=\"1294\" y=\"-96.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 6.056]</text>\n",
       "</g>\n",
       "<!-- 43&#45;&gt;47 -->\n",
       "<g id=\"edge47\" class=\"edge\">\n",
       "<title>43&#45;&gt;47</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1359.3097,-192.9465C1359.624,-184.776 1359.9642,-175.9318 1360.2935,-167.3697\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1363.8003,-167.2572 1360.6873,-157.13 1356.8055,-166.9881 1363.8003,-167.2572\"/>\n",
       "</g>\n",
       "<!-- 45 -->\n",
       "<g id=\"node46\" class=\"node\">\n",
       "<title>45</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1101,-53C1101,-53 981,-53 981,-53 975,-53 969,-47 969,-41 969,-41 969,-12 969,-12 969,-6 975,0 981,0 981,0 1101,0 1101,0 1107,0 1113,-6 1113,-12 1113,-12 1113,-41 1113,-41 1113,-47 1107,-53 1101,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1010\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"999\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 21</text>\n",
       "<text text-anchor=\"start\" x=\"977\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [20.807, 0.0]</text>\n",
       "</g>\n",
       "<!-- 44&#45;&gt;45 -->\n",
       "<g id=\"edge45\" class=\"edge\">\n",
       "<title>44&#45;&gt;45</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1136.1733,-88.9777C1121.2479,-79.1798 1105.013,-68.5222 1090.2486,-58.8298\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1092.0268,-55.8104 1081.7464,-53.2485 1088.1853,-61.6622 1092.0268,-55.8104\"/>\n",
       "</g>\n",
       "<!-- 46 -->\n",
       "<g id=\"node47\" class=\"node\">\n",
       "<title>46</title>\n",
       "<path fill=\"#399de5\" stroke=\"#000000\" d=\"M1255,-53C1255,-53 1143,-53 1143,-53 1137,-53 1131,-47 1131,-41 1131,-41 1131,-12 1131,-12 1131,-6 1137,0 1143,0 1143,0 1255,0 1255,0 1261,0 1267,-6 1267,-12 1267,-12 1267,-41 1267,-41 1267,-47 1261,-53 1255,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1168\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = &#45;0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1161\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1139\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.0, 1.009]</text>\n",
       "</g>\n",
       "<!-- 44&#45;&gt;46 -->\n",
       "<g id=\"edge46\" class=\"edge\">\n",
       "<title>44&#45;&gt;46</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1191.8782,-88.9777C1192.828,-80.6449 1193.8488,-71.6903 1194.8124,-63.2364\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1198.2958,-63.5806 1195.951,-53.2485 1191.3408,-62.7877 1198.2958,-63.5806\"/>\n",
       "</g>\n",
       "<!-- 48 -->\n",
       "<g id=\"node49\" class=\"node\">\n",
       "<title>48</title>\n",
       "<path fill=\"#e58139\" stroke=\"#000000\" d=\"M1415,-53C1415,-53 1303,-53 1303,-53 1297,-53 1291,-47 1291,-41 1291,-41 1291,-12 1291,-12 1291,-6 1297,0 1303,0 1303,0 1415,0 1415,0 1421,0 1427,-6 1427,-12 1427,-12 1427,-41 1427,-41 1427,-47 1421,-53 1415,-53\"/>\n",
       "<text text-anchor=\"start\" x=\"1330\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">gini = 0.0</text>\n",
       "<text text-anchor=\"start\" x=\"1321\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">samples = 1</text>\n",
       "<text text-anchor=\"start\" x=\"1299\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">value = [0.991, 0.0]</text>\n",
       "</g>\n",
       "<!-- 47&#45;&gt;48 -->\n",
       "<g id=\"edge48\" class=\"edge\">\n",
       "<title>47&#45;&gt;48</title>\n",
       "<path fill=\"none\" stroke=\"#000000\" d=\"M1360.9423,-88.9777C1360.6861,-80.7364 1360.411,-71.887 1360.1507,-63.5153\"/>\n",
       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1363.6407,-63.1349 1359.8316,-53.2485 1356.6441,-63.3524 1363.6407,-63.1349\"/>\n",
       "</g>\n",
       "<!-- 49 -->\n",
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       "<title>49</title>\n",
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      "text/plain": [
       "<graphviz.files.Source at 0x7fd67fdedd30>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Visualization decision tree (best model)\n",
    "dot_data = export_graphviz(grid.best_estimator_,\n",
    "                           out_file = None, \n",
    "                           feature_names = feature_used, \n",
    "                           filled = True,\n",
    "                           rounded = True, \n",
    "                           special_characters = True)\n",
    "graph = graphviz.Source(dot_data)\n",
    "display(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "514b154e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    " print(grid.best_estimator_.get_depth())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d382d2",
   "metadata": {},
   "source": [
    "## 4. Indian Liver Patient Dataset (https://archive.ics.uci.edu/ml/datasets/ILPD+%28Indian+Liver+Patient+Dataset%29, please find the data as the ILPD.csv file.) provides the age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos of patients. Please train a KNN classifier and a Logistic Regression classifier to predict class label of the patient. (for KNN classifier please refer to: https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html ) (40%)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b38121c9",
   "metadata": {},
   "source": [
    "### Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "21d73553",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.2</td>\n",
       "      <td>245.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>Male</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>583 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  total Bilirubin  direct Bilirubin  total proteins  albumin  \\\n",
       "0     65  Female              0.7               0.1           187.0     16.0   \n",
       "1     62    Male             10.9               5.5           699.0     64.0   \n",
       "2     62    Male              7.3               4.1           490.0     60.0   \n",
       "3     58    Male              1.0               0.4           182.0     14.0   \n",
       "4     72    Male              3.9               2.0           195.0     27.0   \n",
       "..   ...     ...              ...               ...             ...      ...   \n",
       "578   60    Male              0.5               0.1           500.0     20.0   \n",
       "579   40    Male              0.6               0.1            98.0     35.0   \n",
       "580   52    Male              0.8               0.2           245.0     48.0   \n",
       "581   31    Male              1.3               0.5           184.0     29.0   \n",
       "582   38    Male              1.0               0.3           216.0     21.0   \n",
       "\n",
       "     A/G ratio  SGPT  SGOT  Alkphos  Class  \n",
       "0           18   6.8   3.3     0.90      1  \n",
       "1          100   7.5   3.2     0.74      1  \n",
       "2           68   7.0   3.3     0.89      1  \n",
       "3           20   6.8   3.4     1.00      1  \n",
       "4           59   7.3   2.4     0.40      1  \n",
       "..         ...   ...   ...      ...    ...  \n",
       "578         34   5.9   1.6     0.37      2  \n",
       "579         31   6.0   3.2     1.10      1  \n",
       "580         49   6.4   3.2     1.00      1  \n",
       "581         32   6.8   3.4     1.00      1  \n",
       "582         24   7.3   4.4     1.50      2  \n",
       "\n",
       "[583 rows x 11 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a Pandas DataFrame through importing a csv file\n",
    "# Read CSV file\n",
    "ilpd_data = pd.read_csv(\"ILPD.csv\", delimiter = \",\", header = 0)\n",
    "ilpd_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0b476d4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of case is 416\n",
      "The number of control is 167\n",
      "Proportion of case: 71.36%\n"
     ]
    }
   ],
   "source": [
    "# How many case and control\n",
    "n_case = len(ilpd_data[ilpd_data[\"Class\"] == 1]) \n",
    "n_control = len(ilpd_data[ilpd_data[\"Class\"] == 2])\n",
    "\n",
    "print(\"The number of case is\", n_case)\n",
    "print(\"The number of control is\", n_control)\n",
    "print(\"Proportion of case: %.2f%%\" % (100 * n_case / (n_control + n_case)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d7ec2d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of class label\n",
    "class_plot = ilpd_data[\"Class\"].value_counts().plot.bar(rot = 0) # Make barplot\n",
    "plt.title(\"Barplot of class\") # Add title\n",
    "class_plot.set_xticklabels([\"Cases (liver patients)\", \"Controls (non liver patients)\"]) # Modify x-axis tick labels\n",
    "plt.ylabel(\"Number of subjects\") # Modify y-axis label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d1e6a8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AGAuOPZrV5gG39HVb3bSN39ZJSa5OcvXatWs3pQxJ0kZ0eSDe64DPAp9smuYBF3bdQZLHAJ8D3lxVd0216gRt9ZCGqnOramFVLZwzZ07XMiRJHXS5JPVk4CDgLoCqupEHv91PKck29ALhf1fV55vm25LMbZbPBW5v2lcDe/d13wu4tct+JEmbR5dQWFdV943NJNmaCb7Bj5ckwD8BN1RV/y+3LQWOa6aPA77Q135sku2S7AssAK7sUJ8kaTPpcqL58iR/BeyQ5MXAG4CLOvQ7CPgz4Pok1zZtfwWcBSxJciJwM3AMQFWtSLKE3m823A+c7JVHkjRcXULhNOBE4Hrg9cAXgX/cWKeq+r9MfJ4A4NBJ+iwCFnWoSZI0AF2ekrq++WGdK+gNG62sqo0OH0mStjwbDYUkRwDnAD+k981/3ySvr6ovDbo4SdJwdRk++hDwgqpaBZDkScAlgKEgSTNMl6uPbh8LhMaPePAyUknSDDLpkUKSlzeTK5J8EVhC75zCMfTuapYkzTBTDR+9tG/6NuDgZnotsOtDV5ckbekmDYWqOmGYhUiSRq/L1Uf7Am8C5vev3+HR2ZKkLUyXq48upPe4iouA9QOtRpI0Ul1C4bdV9dGBVyJJGrkuoXB2kjOArwLrxhqrapN+U0GSNP11CYXfo/dguxfy4PBRNfOSpBmkSyi8DPid/sdnS5Jmpi53NH8X2GXAdUiSpoEuRwqPB36Q5Co2PKfgJamSNMN0CYUzBl6FJGla6PJ7CpcPoxBJ0uh1uaP5bh78TeZtgW2Ae6vqsYMsTJI0fF2OFHbqn09yNHDgoAqSJI1Ol6uPNlBVF+I9CpI0I3UZPnp53+yjgIU8OJwkSZpBulx91P+7CvcDNwFHDaQaSUM3/7RLRl3CjHHTWUeMuoRHrMs5BX9XQZJmial+jvOdU/SrqjpzAPVIkkZoqiOFeydo2xE4EXgcYChI0gwz1c9xfmhsOslOwCnACcAFwIcm6ydJ2nJNeU4hyW7AfwdeDSwGnlVVvxxGYZKk4ZvqnMIHgJcD5wK/V1X3DK0qSdJITHXz2luAPYF3ALcmuav5uzvJXcMpT5I0TFOdU9jku50lSVs2P/glSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLUGlgoJDkvye1JvtfXtluSS5Pc2Lzu2rfs9CSrkqxMctig6pIkTW6QRwqfBg4f13YasKyqFgDLmnmS7AccC+zf9PlEkq0GWJskaQIDC4Wq+nfgjnHNR9F72irN69F97RdU1bqq+jGwCjhwULVJkiY27HMKj6+qNQDN6x5N+zzglr71VjdtD5HkpCRXJ7l67dq1Ay1Wkmab6XKiORO01UQrVtW5VbWwqhbOmTNnwGVJ0uwy7FC4LclcgOb19qZ9NbB333p7AbcOuTZJmvWGHQpLgeOa6eOAL/S1H5tkuyT7AguAK4dcmyTNelP+HOcjkeR84BBg9ySrgTOAs4AlSU4EbgaOAaiqFUmWAN8H7gdOrqoHBlWbJGliAwuFqnrVJIsOnWT9RcCiQdUjSdq46XKiWZI0DRgKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqSWoSBJahkKkqTWtAuFJIcnWZlkVZLTRl2PJM0m0yoUkmwFfBz4z8B+wKuS7DfaqiRp9phWoQAcCKyqqh9V1X3ABcBRI65JkmaNrUddwDjzgFv65lcDf9C/QpKTgJOa2XuSrBxSbbPB7sDPR13ExuT9o65AI+D/zc3riZMtmG6hkAnaaoOZqnOBc4dTzuyS5OqqWjjqOqTx/L85PNNt+Gg1sHff/F7ArSOqRZJmnekWClcBC5Lsm2Rb4Fhg6YhrkqRZY1oNH1XV/UneCHwF2Ao4r6pWjLis2cRhOU1X/t8cklTVxteSJM0K0234SJI0QoaCJKllKMxwSSrJZ/rmt06yNsnFG+l3yMbWkbpI8kCSa/v+5g9wXzcl2X1Q258NptWJZg3EvcDTk+xQVb8BXgz8dMQ1aXb5TVUdMOoi1I1HCrPDl4AjmulXAeePLUhyYJJvJPlO8/rU8Z2T7JjkvCRXNev56BE9IkmeneTyJNck+UqSuU378iQfTvLvSW5I8pwkn09yY5K/6et/YdN3RfOUg4n28ZokVzZHJ59snq2mjTAUZocLgGOTbA88A7iib9kPgOdX1TOBdwLvnaD/24HLquo5wAuADyTZccA1a+bYoW/o6N+SbAN8DHhFVT0bOA9Y1Lf+fVX1fOAc4AvAycDTgeOTPK5Z57VN34XAX/a1A5DkacArgYOao5QHgFcP7i3OHA4fzQJVdV0zjvsq4IvjFu8MLE6ygN4jRbaZYBMvAY5M8tZmfntgH+CGwVSsGWaD4aMkT6f3IX9pEujdk7Smb/2xG1avB1ZU1Zqm34/oPfHgF/SC4GXNensDC5r2MYcCzwauavaxA3D7Zn1XM5ShMHssBT4IHAL0f6s6E/h6Vb2sCY7lE/QN8F+qyocPanMIvQ/7502yfF3zur5vemx+6ySHAC8CnldVv06ynN4XlfH7WFxVp2+uomcLh49mj/OA91TV9ePad+bBE8/HT9L3K8Cb0nzlSvLMgVSo2WIlMCfJ8wCSbJNk/03ovzPwyyYQfhd47gTrLANekWSPZh+7JZn0yaB6kKEwS1TV6qo6e4JFfwu8L8n/o3cYP5Ez6Q0rXZfke8289LA0v5XyCuD9Sb4LXAv84SZs4sv0jhiuo/d/8VsT7OP7wDuArzbrXQrMfYSlzwo+5kKS1PJIQZLUMhQkSS1DQZLUMhQkSS1DQZLUMhQkSS1DQZLU+v8o5TG6RgudxAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of binary feature - gender\n",
    "gender_plot = ilpd_data[\"Gender\"].value_counts().plot.bar(rot = 0) # Make barplot\n",
    "plt.title(\"Barplot of gender\") # Add title\n",
    "gender_plot.set_xticklabels([\"Male\", \"Female\"]) # Modify x-axis tick labels\n",
    "plt.ylabel(\"Number of subjects\") # Modify y-axis label\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ba1e1d68",
   "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>total Bilirubin</th>\n",
       "      <th>direct Bilirubin</th>\n",
       "      <th>total proteins</th>\n",
       "      <th>albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>583.000000</td>\n",
       "      <td>579.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.746141</td>\n",
       "      <td>3.298799</td>\n",
       "      <td>1.486106</td>\n",
       "      <td>290.422680</td>\n",
       "      <td>80.898276</td>\n",
       "      <td>109.910806</td>\n",
       "      <td>6.483190</td>\n",
       "      <td>3.141852</td>\n",
       "      <td>0.947064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.189833</td>\n",
       "      <td>6.209522</td>\n",
       "      <td>2.808498</td>\n",
       "      <td>243.118615</td>\n",
       "      <td>183.063760</td>\n",
       "      <td>288.918529</td>\n",
       "      <td>1.085451</td>\n",
       "      <td>0.795519</td>\n",
       "      <td>0.319592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>175.250000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>0.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>60.250000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>4929.000000</td>\n",
       "      <td>9.600000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  total Bilirubin  direct Bilirubin  total proteins  \\\n",
       "count  583.000000       583.000000        583.000000      582.000000   \n",
       "mean    44.746141         3.298799          1.486106      290.422680   \n",
       "std     16.189833         6.209522          2.808498      243.118615   \n",
       "min      4.000000         0.400000          0.100000       63.000000   \n",
       "25%     33.000000         0.800000          0.200000      175.250000   \n",
       "50%     45.000000         1.000000          0.300000      208.000000   \n",
       "75%     58.000000         2.600000          1.300000      298.000000   \n",
       "max     90.000000        75.000000         19.700000     2110.000000   \n",
       "\n",
       "           albumin    A/G ratio        SGPT        SGOT     Alkphos  \n",
       "count   580.000000   583.000000  583.000000  583.000000  579.000000  \n",
       "mean     80.898276   109.910806    6.483190    3.141852    0.947064  \n",
       "std     183.063760   288.918529    1.085451    0.795519    0.319592  \n",
       "min      10.000000    10.000000    2.700000    0.900000    0.300000  \n",
       "25%      23.000000    25.000000    5.800000    2.600000    0.700000  \n",
       "50%      35.000000    42.000000    6.600000    3.100000    0.930000  \n",
       "75%      60.250000    87.000000    7.200000    3.800000    1.100000  \n",
       "max    2000.000000  4929.000000    9.600000    5.500000    2.800000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check other continuous feature distribution\n",
    "ilpd_data.drop(columns = [\"Class\", \"Gender\"]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ee36338f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 0.000000\n",
       "Gender              0.000000\n",
       "total Bilirubin     0.000000\n",
       "direct Bilirubin    0.000000\n",
       "total proteins      0.001715\n",
       "albumin             0.005146\n",
       "A/G ratio           0.000000\n",
       "SGPT                0.000000\n",
       "SGOT                0.000000\n",
       "Alkphos             0.006861\n",
       "Class               0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the missing distribution\n",
    "ilpd_data.isnull().sum()/len(ilpd_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f12bca80",
   "metadata": {},
   "source": [
    "### k-nearest neighor classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "19d51cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>total Bilirubin</th>\n",
       "      <th>direct Bilirubin</th>\n",
       "      <th>total proteins</th>\n",
       "      <th>albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>187.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>18</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>10.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>699.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>100</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>490.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>68</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>182.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>59</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.4</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Gender  total Bilirubin  direct Bilirubin  total proteins  albumin  \\\n",
       "0   65       1              0.7               0.1           187.0     16.0   \n",
       "1   62       0             10.9               5.5           699.0     64.0   \n",
       "2   62       0              7.3               4.1           490.0     60.0   \n",
       "3   58       0              1.0               0.4           182.0     14.0   \n",
       "4   72       0              3.9               2.0           195.0     27.0   \n",
       "\n",
       "   A/G ratio  SGPT  SGOT  Alkphos  Class  \n",
       "0         18   6.8   3.3     0.90      1  \n",
       "1        100   7.5   3.2     0.74      1  \n",
       "2         68   7.0   3.3     0.89      1  \n",
       "3         20   6.8   3.4     1.00      1  \n",
       "4         59   7.3   2.4     0.40      1  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preprocess variables\n",
    "# Recode Gender as 0 = Male, 1 = Female\n",
    "ilpd_data[\"Gender\"] = ilpd_data[\"Gender\"].apply(lambda x: 1 if x == \"Female\" else 0)\n",
    "\n",
    "# Recode Class as 0 = 2 (non liver patient)\n",
    "ilpd_data[\"Class\"] = ilpd_data[\"Class\"].apply(lambda x: 0 if x == 2 else 1)\n",
    "\n",
    "# Take a peek at first 5 records\n",
    "ilpd_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "77bd44b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>total Bilirubin</th>\n",
       "      <th>direct Bilirubin</th>\n",
       "      <th>total proteins</th>\n",
       "      <th>albumin</th>\n",
       "      <th>A/G ratio</th>\n",
       "      <th>SGPT</th>\n",
       "      <th>SGOT</th>\n",
       "      <th>Alkphos</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>500.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>34</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.1</td>\n",
       "      <td>98.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>31</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>245.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.5</td>\n",
       "      <td>184.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>32</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>216.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24</td>\n",
       "      <td>7.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  Gender  total Bilirubin  direct Bilirubin  total proteins  albumin  \\\n",
       "578   60       0              0.5               0.1           500.0     20.0   \n",
       "579   40       0              0.6               0.1            98.0     35.0   \n",
       "580   52       0              0.8               0.2           245.0     48.0   \n",
       "581   31       0              1.3               0.5           184.0     29.0   \n",
       "582   38       0              1.0               0.3           216.0     21.0   \n",
       "\n",
       "     A/G ratio  SGPT  SGOT  Alkphos  Class  \n",
       "578         34   5.9   1.6     0.37      0  \n",
       "579         31   6.0   3.2     1.10      1  \n",
       "580         49   6.4   3.2     1.00      1  \n",
       "581         32   6.8   3.4     1.00      1  \n",
       "582         24   7.3   4.4     1.50      0  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a peek at last 5 records\n",
    "ilpd_data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fad860d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Gender', 'total Bilirubin', 'direct Bilirubin', 'total proteins', 'albumin', 'A/G ratio', 'SGPT', 'SGOT', 'Alkphos']\n",
      "\n",
      "\n",
      "(437, 10) (146, 10) (437,) (146,)\n"
     ]
    }
   ],
   "source": [
    "# Split data into Training (75%) and Testing (25%) sets\n",
    "y = ilpd_data[\"Class\"].values\n",
    "X = ilpd_data.drop(columns = [\"Class\"]).values\n",
    "feature_used = ilpd_data.drop(columns = [\"Class\"]).columns.tolist()\n",
    "print(feature_used)\n",
    "print(\"\\n\")\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.25, random_state = 0)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0ad78fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impute the missing values by mean\n",
    "# Import packages\n",
    "from sklearn.impute import SimpleImputer\n",
    "imputer = SimpleImputer(missing_values = np.nan, strategy = \"mean\")\n",
    "imputer.fit(X_train)\n",
    "X_train = np.array(imputer.transform(X_train), dtype = np.float32) \n",
    "X_test = np.array(imputer.transform(X_test), dtype = np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "70e25fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.2724998   1.6832508  -0.36378908 ...  0.9000858   0.30893022\n",
      "  -0.47380003]\n",
      " [ 1.0783157  -0.5940885  -0.39459836 ...  1.5271325   0.06235034\n",
      "  -1.0922339 ]\n",
      " [ 1.2644721   1.6832508  -0.39459836 ...  0.2730392  -0.30751935\n",
      "  -0.78301704]\n",
      " ...\n",
      " [ 1.5126806   1.6832508  -0.3791937  ...  1.7062886   2.158279\n",
      "   1.3815012 ]\n",
      " [ 0.95421135 -0.5940885  -0.1481242  ...  0.09388269  0.43222004\n",
      "   0.4538507 ]\n",
      " [-1.1555613  -0.5940885   6.090753   ...  0.9000858  -0.6773893\n",
      "  -1.4014508 ]]\n",
      "[[ 0.64395064 -0.5940885  -0.22514737 ... -0.3540079  -0.55409926\n",
      "  -0.78301704]\n",
      " [ 0.95421135 -0.5940885   0.82236785 ...  0.81050783 -0.18422955\n",
      "  -1.0922339 ]\n",
      " [-0.41093567 -0.5940885  -0.08650566 ...  0.9000858   1.2952492\n",
      "   0.76306766]\n",
      " ...\n",
      " [-1.7140305  -0.5940885  -0.36378908 ...  0.36261722  1.2952492\n",
      "   1.8453265 ]\n",
      " [ 0.8301071   1.6832508  -0.3791937  ...  0.4521953   1.0486697\n",
      "   1.0722842 ]\n",
      " [-0.53503996 -0.5940885  -0.3791937  ... -0.44358593 -1.1705488\n",
      "  -1.4014508 ]]\n"
     ]
    }
   ],
   "source": [
    "# Data scaling\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "scaler = StandardScaler() # Use z-score based scaling\n",
    "\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train) \n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "print(X_train_scaled)\n",
    "print(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c81bbdbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train KNN classifier with Grid Search\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "knn = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9654aa45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'n_neighbors': [5, 10, 15, 20, 25, 30, 35, 40]},\n",
       "             return_train_score=True, scoring='roc_auc')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = GridSearchCV(knn,\n",
    "                    param_grid = {\"n_neighbors\":[5, 10, 15, 20, 25, 30, 35, 40]},\n",
    "                    scoring = \"roc_auc\",\n",
    "                    cv = 5, \n",
    "                    return_train_score = True)\n",
    "grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "036db871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualize tuning process\n",
    "scores = pd.DataFrame(grid.cv_results_)\n",
    "scores.plot(x = \"param_n_neighbors\", y = \"mean_test_score\", \n",
    "            yerr = \"std_test_score\", ax = plt.gca())\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "2c313b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_neighbors': 30}\n"
     ]
    }
   ],
   "source": [
    "# Obtain best model parameters\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0337d64a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
      "[[0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.         1.        ]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.03333333 0.96666667]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.03333333 0.96666667]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]\n",
      " [0.06666667 0.93333333]]\n"
     ]
    }
   ],
   "source": [
    "# Make prediction on test set\n",
    "y_pred = grid.best_estimator_.predict(X_test) # Get predicted labels \n",
    "print(y_pred)\n",
    "\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test) # Get probabilities that belong to each class \n",
    "print(y_proba)\n",
    "\n",
    "y_proba = y_proba[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a37539fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create true and false positive rates\n",
    "fpr_knn, tpr_knn, threshold_knn = roc_curve(y_test, y_proba)\n",
    "\n",
    "# Plot ROC Curve\n",
    "plt.figure(figsize = (10, 6)) \n",
    "plt.title(\"Receiver Operating Characteristic\") \n",
    "plt.plot(fpr_knn, tpr_knn) \n",
    "plt.plot([0, 1], ls = \"--\") \n",
    "plt.plot([0, 0], [1, 0], c = \"0.5\") \n",
    "plt.plot([1, 1], c = \"0.5\")\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "74331209",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5627173913043478\n"
     ]
    }
   ],
   "source": [
    "# Calculate area under the curve\n",
    "auc_knn = roc_auc_score(y_test, y_proba) \n",
    "print(auc_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25dcea0c",
   "metadata": {},
   "source": [
    "### Logistic regression classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "91be5e6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build logistic regression classifer\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "lr = LogisticRegression(max_iter = 10000) # Customize the maximum iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "77a88648",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set parameters for GridSearchCV\n",
    "params = {\"penalty\":[\"l2\"], \n",
    "          \"C\":[0.01, 0.1, 1, 10, 100, 200, 300], \n",
    "          \"class_weight\":[\"balanced\", None]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7c507458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.01, 'class_weight': None, 'penalty': 'l2'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train model with Grid Search\n",
    "grid = GridSearchCV(lr, param_grid = params, scoring = \"roc_auc\", cv = 10)\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "# Print best parameters choosen through GridSearchCV\n",
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "fd4d8742",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make prediction on the test set\n",
    "y_pred = grid.best_estimator_.predict(X_test)\n",
    "y_proba = grid.best_estimator_.predict_proba(X_test)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1e62a5e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.55      0.13      0.21        46\n",
      "           1       0.70      0.95      0.81       100\n",
      "\n",
      "    accuracy                           0.69       146\n",
      "   macro avg       0.62      0.54      0.51       146\n",
      "weighted avg       0.65      0.69      0.62       146\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Output the precision, recall, and f1-score\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6931a65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create true and false positive rates\n",
    "fpr_lr, tpr_lr, threshold_lr = roc_curve(y_test, y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1b75e286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC Curve\n",
    "plt.figure(figsize = (10, 6)) \n",
    "plt.title(\"Receiver Operating Characteristic\") \n",
    "plt.plot(fpr_lr, tpr_lr) \n",
    "plt.plot([0, 1], ls = \"--\") \n",
    "plt.plot([0, 0], [1, 0], c = \"0.5\") \n",
    "plt.plot([1, 1], c = \"0.5\")\n",
    "plt.xlabel(\"False Positive Rate\") \n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "20444c5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7554347826086956\n"
     ]
    }
   ],
   "source": [
    "# Calculate area under the curve\n",
    "auc_lr = roc_auc_score(y_test, y_proba) \n",
    "print(auc_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5b86085e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importance \n",
    "fig, ax = plt.subplots(figsize = (12, 7))\n",
    "fig.subplots_adjust(left = 0.2, right = 0.95, top = 0.95, bottom = 0.08, hspace = 0.5, wspace = 0.15) \n",
    "importances = grid.best_estimator_.coef_[0] # Coefficient measures feature importance\n",
    "indices = np.argsort(abs(importances))[::-1]\n",
    "plt.title(\"Feature importance\")\n",
    "plt.barh(list(range(X.shape[1]))[:], list(importances[indices])[:], align = \"center\") \n",
    "plt.yticks(list(range(X.shape[1]))[:], (np.array(feature_used)[indices]).tolist()[:])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}