dataset_analyzation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analyzation\n",
    "In this notebook we will analyze the data to obtain the optimum prediction. In the end of the section Logistic regression as well as Neural Network algorithm is implemented using Scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PARKINSON_CSV = './data/parkinsons.csv'\n",
    "df = pd.read_csv(PARKINSON_CSV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Begin by looking at the structure of the csv that contains the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phon_R01_S01_1</td>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phon_R01_S01_2</td>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>phon_R01_S01_3</td>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>phon_R01_S01_4</td>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>phon_R01_S01_5</td>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</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",
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       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>phon_R01_S50_2</td>\n",
       "      <td>174.188</td>\n",
       "      <td>230.978</td>\n",
       "      <td>94.261</td>\n",
       "      <td>0.00459</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00263</td>\n",
       "      <td>0.00259</td>\n",
       "      <td>0.00790</td>\n",
       "      <td>0.04087</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07008</td>\n",
       "      <td>0.02764</td>\n",
       "      <td>19.517</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448439</td>\n",
       "      <td>0.657899</td>\n",
       "      <td>-6.538586</td>\n",
       "      <td>0.121952</td>\n",
       "      <td>2.657476</td>\n",
       "      <td>0.133050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>phon_R01_S50_3</td>\n",
       "      <td>209.516</td>\n",
       "      <td>253.017</td>\n",
       "      <td>89.488</td>\n",
       "      <td>0.00564</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00331</td>\n",
       "      <td>0.00292</td>\n",
       "      <td>0.00994</td>\n",
       "      <td>0.02751</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.01810</td>\n",
       "      <td>19.147</td>\n",
       "      <td>0</td>\n",
       "      <td>0.431674</td>\n",
       "      <td>0.683244</td>\n",
       "      <td>-6.195325</td>\n",
       "      <td>0.129303</td>\n",
       "      <td>2.784312</td>\n",
       "      <td>0.168895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>phon_R01_S50_4</td>\n",
       "      <td>174.688</td>\n",
       "      <td>240.005</td>\n",
       "      <td>74.287</td>\n",
       "      <td>0.01360</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00624</td>\n",
       "      <td>0.00564</td>\n",
       "      <td>0.01873</td>\n",
       "      <td>0.02308</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03804</td>\n",
       "      <td>0.10715</td>\n",
       "      <td>17.883</td>\n",
       "      <td>0</td>\n",
       "      <td>0.407567</td>\n",
       "      <td>0.655683</td>\n",
       "      <td>-6.787197</td>\n",
       "      <td>0.158453</td>\n",
       "      <td>2.679772</td>\n",
       "      <td>0.131728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>phon_R01_S50_5</td>\n",
       "      <td>198.764</td>\n",
       "      <td>396.961</td>\n",
       "      <td>74.904</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00390</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.02296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03794</td>\n",
       "      <td>0.07223</td>\n",
       "      <td>19.020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451221</td>\n",
       "      <td>0.643956</td>\n",
       "      <td>-6.744577</td>\n",
       "      <td>0.207454</td>\n",
       "      <td>2.138608</td>\n",
       "      <td>0.123306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>phon_R01_S50_6</td>\n",
       "      <td>214.289</td>\n",
       "      <td>260.277</td>\n",
       "      <td>77.973</td>\n",
       "      <td>0.00567</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00295</td>\n",
       "      <td>0.00317</td>\n",
       "      <td>0.00885</td>\n",
       "      <td>0.01884</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03078</td>\n",
       "      <td>0.04398</td>\n",
       "      <td>21.209</td>\n",
       "      <td>0</td>\n",
       "      <td>0.462803</td>\n",
       "      <td>0.664357</td>\n",
       "      <td>-5.724056</td>\n",
       "      <td>0.190667</td>\n",
       "      <td>2.555477</td>\n",
       "      <td>0.148569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "               name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0    phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "1    phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "2    phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "3    phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "4    phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "..              ...          ...           ...           ...             ...   \n",
       "190  phon_R01_S50_2      174.188       230.978        94.261         0.00459   \n",
       "191  phon_R01_S50_3      209.516       253.017        89.488         0.00564   \n",
       "192  phon_R01_S50_4      174.688       240.005        74.287         0.01360   \n",
       "193  phon_R01_S50_5      198.764       396.961        74.904         0.00740   \n",
       "194  phon_R01_S50_6      214.289       260.277        77.973         0.00567   \n",
       "\n",
       "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
       "0             0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
       "1             0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
       "2             0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
       "3             0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
       "4             0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
       "..                ...       ...       ...         ...           ...  ...   \n",
       "190           0.00003   0.00263   0.00259     0.00790       0.04087  ...   \n",
       "191           0.00003   0.00331   0.00292     0.00994       0.02751  ...   \n",
       "192           0.00008   0.00624   0.00564     0.01873       0.02308  ...   \n",
       "193           0.00004   0.00370   0.00390     0.01109       0.02296  ...   \n",
       "194           0.00003   0.00295   0.00317     0.00885       0.01884  ...   \n",
       "\n",
       "     Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
       "0        0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
       "1        0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
       "2        0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
       "3        0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
       "4        0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
       "..           ...      ...     ...     ...       ...       ...       ...   \n",
       "190      0.07008  0.02764  19.517       0  0.448439  0.657899 -6.538586   \n",
       "191      0.04812  0.01810  19.147       0  0.431674  0.683244 -6.195325   \n",
       "192      0.03804  0.10715  17.883       0  0.407567  0.655683 -6.787197   \n",
       "193      0.03794  0.07223  19.020       0  0.451221  0.643956 -6.744577   \n",
       "194      0.03078  0.04398  21.209       0  0.462803  0.664357 -5.724056   \n",
       "\n",
       "      spread2        D2       PPE  \n",
       "0    0.266482  2.301442  0.284654  \n",
       "1    0.335590  2.486855  0.368674  \n",
       "2    0.311173  2.342259  0.332634  \n",
       "3    0.334147  2.405554  0.368975  \n",
       "4    0.234513  2.332180  0.410335  \n",
       "..        ...       ...       ...  \n",
       "190  0.121952  2.657476  0.133050  \n",
       "191  0.129303  2.784312  0.168895  \n",
       "192  0.158453  2.679772  0.131728  \n",
       "193  0.207454  2.138608  0.123306  \n",
       "194  0.190667  2.555477  0.148569  \n",
       "\n",
       "[195 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              195 non-null    object \n",
      " 1   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 2   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 3   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 4   MDVP:Jitter(%)    195 non-null    float64\n",
      " 5   MDVP:Jitter(Abs)  195 non-null    float64\n",
      " 6   MDVP:RAP          195 non-null    float64\n",
      " 7   MDVP:PPQ          195 non-null    float64\n",
      " 8   Jitter:DDP        195 non-null    float64\n",
      " 9   MDVP:Shimmer      195 non-null    float64\n",
      " 10  MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 11  Shimmer:APQ3      195 non-null    float64\n",
      " 12  Shimmer:APQ5      195 non-null    float64\n",
      " 13  MDVP:APQ          195 non-null    float64\n",
      " 14  Shimmer:DDA       195 non-null    float64\n",
      " 15  NHR               195 non-null    float64\n",
      " 16  HNR               195 non-null    float64\n",
      " 17  status            195 non-null    int64  \n",
      " 18  RPDE              195 non-null    float64\n",
      " 19  DFA               195 non-null    float64\n",
      " 20  spread1           195 non-null    float64\n",
      " 21  spread2           195 non-null    float64\n",
      " 22  D2                195 non-null    float64\n",
      " 23  PPE               195 non-null    float64\n",
      "dtypes: float64(22), int64(1), object(1)\n",
      "memory usage: 36.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.228641</td>\n",
       "      <td>197.104918</td>\n",
       "      <td>116.324631</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.003446</td>\n",
       "      <td>0.009920</td>\n",
       "      <td>0.029709</td>\n",
       "      <td>0.282251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>21.885974</td>\n",
       "      <td>0.753846</td>\n",
       "      <td>0.498536</td>\n",
       "      <td>0.718099</td>\n",
       "      <td>-5.684397</td>\n",
       "      <td>0.226510</td>\n",
       "      <td>2.381826</td>\n",
       "      <td>0.206552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.390065</td>\n",
       "      <td>91.491548</td>\n",
       "      <td>43.521413</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.008903</td>\n",
       "      <td>0.018857</td>\n",
       "      <td>0.194877</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030459</td>\n",
       "      <td>0.040418</td>\n",
       "      <td>4.425764</td>\n",
       "      <td>0.431878</td>\n",
       "      <td>0.103942</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>1.090208</td>\n",
       "      <td>0.083406</td>\n",
       "      <td>0.382799</td>\n",
       "      <td>0.090119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>88.333000</td>\n",
       "      <td>102.145000</td>\n",
       "      <td>65.476000</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.009540</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>8.441000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>0.574282</td>\n",
       "      <td>-7.964984</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>1.423287</td>\n",
       "      <td>0.044539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>117.572000</td>\n",
       "      <td>134.862500</td>\n",
       "      <td>84.291000</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.004985</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.148500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024735</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>19.198000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.421306</td>\n",
       "      <td>0.674758</td>\n",
       "      <td>-6.450096</td>\n",
       "      <td>0.174351</td>\n",
       "      <td>2.099125</td>\n",
       "      <td>0.137451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.790000</td>\n",
       "      <td>175.829000</td>\n",
       "      <td>104.315000</td>\n",
       "      <td>0.004940</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.022970</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038360</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>22.085000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495954</td>\n",
       "      <td>0.722254</td>\n",
       "      <td>-5.720868</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>2.361532</td>\n",
       "      <td>0.194052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>182.769000</td>\n",
       "      <td>224.205500</td>\n",
       "      <td>140.018500</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.003955</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.037885</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060795</td>\n",
       "      <td>0.025640</td>\n",
       "      <td>25.075500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587562</td>\n",
       "      <td>0.761881</td>\n",
       "      <td>-5.046192</td>\n",
       "      <td>0.279234</td>\n",
       "      <td>2.636456</td>\n",
       "      <td>0.252980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.105000</td>\n",
       "      <td>592.030000</td>\n",
       "      <td>239.170000</td>\n",
       "      <td>0.033160</td>\n",
       "      <td>0.000260</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.019580</td>\n",
       "      <td>0.064330</td>\n",
       "      <td>0.119080</td>\n",
       "      <td>1.302000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169420</td>\n",
       "      <td>0.314820</td>\n",
       "      <td>33.047000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.685151</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-2.434031</td>\n",
       "      <td>0.450493</td>\n",
       "      <td>3.671155</td>\n",
       "      <td>0.527367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "count   195.000000    195.000000    195.000000      195.000000   \n",
       "mean    154.228641    197.104918    116.324631        0.006220   \n",
       "std      41.390065     91.491548     43.521413        0.004848   \n",
       "min      88.333000    102.145000     65.476000        0.001680   \n",
       "25%     117.572000    134.862500     84.291000        0.003460   \n",
       "50%     148.790000    175.829000    104.315000        0.004940   \n",
       "75%     182.769000    224.205500    140.018500        0.007365   \n",
       "max     260.105000    592.030000    239.170000        0.033160   \n",
       "\n",
       "       MDVP:Jitter(Abs)    MDVP:RAP    MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
       "count        195.000000  195.000000  195.000000  195.000000    195.000000   \n",
       "mean           0.000044    0.003306    0.003446    0.009920      0.029709   \n",
       "std            0.000035    0.002968    0.002759    0.008903      0.018857   \n",
       "min            0.000007    0.000680    0.000920    0.002040      0.009540   \n",
       "25%            0.000020    0.001660    0.001860    0.004985      0.016505   \n",
       "50%            0.000030    0.002500    0.002690    0.007490      0.022970   \n",
       "75%            0.000060    0.003835    0.003955    0.011505      0.037885   \n",
       "max            0.000260    0.021440    0.019580    0.064330      0.119080   \n",
       "\n",
       "       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \\\n",
       "count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   \n",
       "mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   \n",
       "std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   \n",
       "min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   \n",
       "25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   \n",
       "50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   \n",
       "75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   \n",
       "max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   \n",
       "\n",
       "             RPDE         DFA     spread1     spread2          D2         PPE  \n",
       "count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
       "mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
       "std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n",
       "min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
       "25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
       "50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
       "75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
       "max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-numeric columns:\n",
      "Index(['name'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Drop the 'status' column\n",
    "X = df.drop('status', axis=1)\n",
    "\n",
    "# Check for non-numeric columns\n",
    "non_numeric_columns = X.select_dtypes(exclude=['number']).columns\n",
    "\n",
    "# Display non-numeric columns\n",
    "print(\"Non-numeric columns:\")\n",
    "print(non_numeric_columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Colleration to the Status Column\n",
    "Understanding data correlation is crucial in data analysis and machine learning for several reasons:\n",
    "\n",
    "- Identifying Relationships Between Variables:\n",
    "Correlation coefficients quantify the strength and direction of relationships between pairs of variables. A correlation close to 1 indicates a strong positive relationship, while a correlation close to -1 indicates a strong negative relationship.\n",
    "\n",
    "- Feature Selection:\n",
    "In machine learning, especially in predictive modeling, identifying highly correlated features can be important. Redundant features (highly correlated) might not provide additional information, and including them in a model could lead to overfitting.\n",
    "\n",
    "- Multicollinearity:\n",
    "In regression analysis, multicollinearity occurs when two or more independent variables are highly correlated. This can lead to issues in estimating the individual contribution of each variable to the dependent variable.\n",
    "\n",
    "- Data Cleaning:\n",
    "Identifying strong correlations can sometimes highlight errors in the data. For example, if two seemingly unrelated variables have a high correlation, it could be a sign of data quality issues or a need for further investigation.\n",
    "\n",
    "- Visualization:\n",
    "Visualizing the correlation matrix using heatmaps helps in quickly identifying patterns and relationships in large datasets. Heatmaps make it easy to spot strong correlations and focus attention on specific areas of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the name column first\n",
    "df = df.drop('name', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>0.426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>0.517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</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>190</th>\n",
       "      <td>174.188</td>\n",
       "      <td>230.978</td>\n",
       "      <td>94.261</td>\n",
       "      <td>0.00459</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00263</td>\n",
       "      <td>0.00259</td>\n",
       "      <td>0.00790</td>\n",
       "      <td>0.04087</td>\n",
       "      <td>0.405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.07008</td>\n",
       "      <td>0.02764</td>\n",
       "      <td>19.517</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448439</td>\n",
       "      <td>0.657899</td>\n",
       "      <td>-6.538586</td>\n",
       "      <td>0.121952</td>\n",
       "      <td>2.657476</td>\n",
       "      <td>0.133050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>209.516</td>\n",
       "      <td>253.017</td>\n",
       "      <td>89.488</td>\n",
       "      <td>0.00564</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00331</td>\n",
       "      <td>0.00292</td>\n",
       "      <td>0.00994</td>\n",
       "      <td>0.02751</td>\n",
       "      <td>0.263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04812</td>\n",
       "      <td>0.01810</td>\n",
       "      <td>19.147</td>\n",
       "      <td>0</td>\n",
       "      <td>0.431674</td>\n",
       "      <td>0.683244</td>\n",
       "      <td>-6.195325</td>\n",
       "      <td>0.129303</td>\n",
       "      <td>2.784312</td>\n",
       "      <td>0.168895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>174.688</td>\n",
       "      <td>240.005</td>\n",
       "      <td>74.287</td>\n",
       "      <td>0.01360</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00624</td>\n",
       "      <td>0.00564</td>\n",
       "      <td>0.01873</td>\n",
       "      <td>0.02308</td>\n",
       "      <td>0.256</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03804</td>\n",
       "      <td>0.10715</td>\n",
       "      <td>17.883</td>\n",
       "      <td>0</td>\n",
       "      <td>0.407567</td>\n",
       "      <td>0.655683</td>\n",
       "      <td>-6.787197</td>\n",
       "      <td>0.158453</td>\n",
       "      <td>2.679772</td>\n",
       "      <td>0.131728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>198.764</td>\n",
       "      <td>396.961</td>\n",
       "      <td>74.904</td>\n",
       "      <td>0.00740</td>\n",
       "      <td>0.00004</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00390</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.02296</td>\n",
       "      <td>0.241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03794</td>\n",
       "      <td>0.07223</td>\n",
       "      <td>19.020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.451221</td>\n",
       "      <td>0.643956</td>\n",
       "      <td>-6.744577</td>\n",
       "      <td>0.207454</td>\n",
       "      <td>2.138608</td>\n",
       "      <td>0.123306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>214.289</td>\n",
       "      <td>260.277</td>\n",
       "      <td>77.973</td>\n",
       "      <td>0.00567</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00295</td>\n",
       "      <td>0.00317</td>\n",
       "      <td>0.00885</td>\n",
       "      <td>0.01884</td>\n",
       "      <td>0.190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03078</td>\n",
       "      <td>0.04398</td>\n",
       "      <td>21.209</td>\n",
       "      <td>0</td>\n",
       "      <td>0.462803</td>\n",
       "      <td>0.664357</td>\n",
       "      <td>-5.724056</td>\n",
       "      <td>0.190667</td>\n",
       "      <td>2.555477</td>\n",
       "      <td>0.148569</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>195 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0        119.992       157.302        74.997         0.00784   \n",
       "1        122.400       148.650       113.819         0.00968   \n",
       "2        116.682       131.111       111.555         0.01050   \n",
       "3        116.676       137.871       111.366         0.00997   \n",
       "4        116.014       141.781       110.655         0.01284   \n",
       "..           ...           ...           ...             ...   \n",
       "190      174.188       230.978        94.261         0.00459   \n",
       "191      209.516       253.017        89.488         0.00564   \n",
       "192      174.688       240.005        74.287         0.01360   \n",
       "193      198.764       396.961        74.904         0.00740   \n",
       "194      214.289       260.277        77.973         0.00567   \n",
       "\n",
       "     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
       "0             0.00007   0.00370   0.00554     0.01109       0.04374   \n",
       "1             0.00008   0.00465   0.00696     0.01394       0.06134   \n",
       "2             0.00009   0.00544   0.00781     0.01633       0.05233   \n",
       "3             0.00009   0.00502   0.00698     0.01505       0.05492   \n",
       "4             0.00011   0.00655   0.00908     0.01966       0.06425   \n",
       "..                ...       ...       ...         ...           ...   \n",
       "190           0.00003   0.00263   0.00259     0.00790       0.04087   \n",
       "191           0.00003   0.00331   0.00292     0.00994       0.02751   \n",
       "192           0.00008   0.00624   0.00564     0.01873       0.02308   \n",
       "193           0.00004   0.00370   0.00390     0.01109       0.02296   \n",
       "194           0.00003   0.00295   0.00317     0.00885       0.01884   \n",
       "\n",
       "     MDVP:Shimmer(dB)  ...  Shimmer:DDA      NHR     HNR  status      RPDE  \\\n",
       "0               0.426  ...      0.06545  0.02211  21.033       1  0.414783   \n",
       "1               0.626  ...      0.09403  0.01929  19.085       1  0.458359   \n",
       "2               0.482  ...      0.08270  0.01309  20.651       1  0.429895   \n",
       "3               0.517  ...      0.08771  0.01353  20.644       1  0.434969   \n",
       "4               0.584  ...      0.10470  0.01767  19.649       1  0.417356   \n",
       "..                ...  ...          ...      ...     ...     ...       ...   \n",
       "190             0.405  ...      0.07008  0.02764  19.517       0  0.448439   \n",
       "191             0.263  ...      0.04812  0.01810  19.147       0  0.431674   \n",
       "192             0.256  ...      0.03804  0.10715  17.883       0  0.407567   \n",
       "193             0.241  ...      0.03794  0.07223  19.020       0  0.451221   \n",
       "194             0.190  ...      0.03078  0.04398  21.209       0  0.462803   \n",
       "\n",
       "          DFA   spread1   spread2        D2       PPE  \n",
       "0    0.815285 -4.813031  0.266482  2.301442  0.284654  \n",
       "1    0.819521 -4.075192  0.335590  2.486855  0.368674  \n",
       "2    0.825288 -4.443179  0.311173  2.342259  0.332634  \n",
       "3    0.819235 -4.117501  0.334147  2.405554  0.368975  \n",
       "4    0.823484 -3.747787  0.234513  2.332180  0.410335  \n",
       "..        ...       ...       ...       ...       ...  \n",
       "190  0.657899 -6.538586  0.121952  2.657476  0.133050  \n",
       "191  0.683244 -6.195325  0.129303  2.784312  0.168895  \n",
       "192  0.655683 -6.787197  0.158453  2.679772  0.131728  \n",
       "193  0.643956 -6.744577  0.207454  2.138608  0.123306  \n",
       "194  0.664357 -5.724056  0.190667  2.555477  0.148569  \n",
       "\n",
       "[195 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MDVP:Fo(Hz)        -0.383535\n",
       "MDVP:Flo(Hz)       -0.380200\n",
       "HNR                -0.361515\n",
       "MDVP:Fhi(Hz)       -0.166136\n",
       "NHR                 0.189429\n",
       "DFA                 0.231739\n",
       "Jitter:DDP          0.266646\n",
       "MDVP:RAP            0.266668\n",
       "MDVP:Jitter(%)      0.278220\n",
       "MDVP:PPQ            0.288698\n",
       "RPDE                0.308567\n",
       "MDVP:Jitter(Abs)    0.338653\n",
       "D2                  0.340232\n",
       "Shimmer:DDA         0.347608\n",
       "Shimmer:APQ3        0.347617\n",
       "MDVP:Shimmer(dB)    0.350697\n",
       "Shimmer:APQ5        0.351148\n",
       "MDVP:APQ            0.364316\n",
       "MDVP:Shimmer        0.367430\n",
       "spread2             0.454842\n",
       "PPE                 0.531039\n",
       "spread1             0.564838\n",
       "status              1.000000\n",
       "Name: status, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here we count the corr matrix using pearson method\n",
    "df.corr(method='pearson')['status'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.corr(method='pearson')[\"status\"].sort_values().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of 0 (no-parkinson) and 1 (parkinson)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='status', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use of Logistic Linear Regression\n",
    "Logistic Regression (also known as logit model) is often used for classification and predictive analytics. Logistic regression estimates the probability of an event occurring. Since the outcome is a probability, the dependent variable is bounded between 0 and 1 which aligns with the case we are dealing with here.\n",
    "\n",
    "#### **Logistic Regression Formula**\n",
    "\n",
    "![Logistic Function](./img/logistic-func.png)\n",
    "\n",
    "\n",
    "The logistic regression formula is given by:\n",
    "\n",
    "$$\n",
    "\\text{logit}(z) = \\frac{1}{1 + \\exp(-z)}\n",
    "$$\n",
    "\n",
    "And the log-odds (logit) can be expressed in terms of linear regression as follows:\n",
    "\n",
    "$$\n",
    "\\ln\\left(\\frac{z}{1 - z}\\right) = \\beta_0 + \\beta_1 \\cdot X_1 + \\ldots + \\beta_k \\cdot X_k\n",
    "$$\n",
    "\n",
    "\n",
    "#### **Reference**\n",
    "IBM. (n.d.). Logistic Regression. https://www.ibm.com/topics/logistic-regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 89.74358974358975\n",
      "\n",
      "Confusion Matrix:\n",
      "\n",
      "[[ 3  4]\n",
      " [ 0 32]]\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.43      0.60         7\n",
      "           1       0.89      1.00      0.94        32\n",
      "\n",
      "    accuracy                           0.90        39\n",
      "   macro avg       0.94      0.71      0.77        39\n",
      "weighted avg       0.91      0.90      0.88        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# separating dependent variable from independent variables\n",
    "X = df.drop('status', axis=1)\n",
    "y = df['status']\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy * 100}')\n",
    "print(f'\\nConfusion Matrix:\\n\\n{conf_matrix}')\n",
    "print(f'\\nClassification Report:\\n\\n{classification_rep}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "Standardize or normalize the features. Logistic Regression is sensitive to the scale of input features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with feature scaling: 89.74358974358975\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Standardizing the features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "model_scaled = LogisticRegression(max_iter=1000)\n",
    "model_scaled.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Make predictions on the scaled test set\n",
    "y_pred_scaled = model_scaled.predict(X_test_scaled)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy_scaled = accuracy_score(y_test, y_pred_scaled)\n",
    "print(f'Accuracy with feature scaling: {accuracy_scaled * 100}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying out with Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with Neural Network: 94.87179487179486\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "model_nn = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=1000)\n",
    "model_nn.fit(X_train_scaled, y_train)\n",
    "y_pred_nn = model_nn.predict(X_test_scaled)\n",
    "accuracy_nn = accuracy_score(y_test, y_pred_nn)\n",
    "print(f'Accuracy with Neural Network: {accuracy_nn * 100}')"
   ]
  }
 ],
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