Parkinson_s Disease Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this project we are going to predict if a patient is suffering from Parkinson's disease based on the audio/ voice measures. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('parkinsons.data')\n",
    "# Reading dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>190</td>\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",
       "      <td>191</td>\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",
       "      <td>192</td>\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",
       "      <td>193</td>\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",
       "      <td>194</td>\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",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 24)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "# The dataset has 195 rows and 24 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                0\n",
       "MDVP:Fo(Hz)         0\n",
       "MDVP:Fhi(Hz)        0\n",
       "MDVP:Flo(Hz)        0\n",
       "MDVP:Jitter(%)      0\n",
       "MDVP:Jitter(Abs)    0\n",
       "MDVP:RAP            0\n",
       "MDVP:PPQ            0\n",
       "Jitter:DDP          0\n",
       "MDVP:Shimmer        0\n",
       "MDVP:Shimmer(dB)    0\n",
       "Shimmer:APQ3        0\n",
       "Shimmer:APQ5        0\n",
       "MDVP:APQ            0\n",
       "Shimmer:DDA         0\n",
       "NHR                 0\n",
       "HNR                 0\n",
       "status              0\n",
       "RPDE                0\n",
       "DFA                 0\n",
       "spread1             0\n",
       "spread2             0\n",
       "D2                  0\n",
       "PPE                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "# There are no null values in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "name                195 non-null object\n",
      "MDVP:Fo(Hz)         195 non-null float64\n",
      "MDVP:Fhi(Hz)        195 non-null float64\n",
      "MDVP:Flo(Hz)        195 non-null float64\n",
      "MDVP:Jitter(%)      195 non-null float64\n",
      "MDVP:Jitter(Abs)    195 non-null float64\n",
      "MDVP:RAP            195 non-null float64\n",
      "MDVP:PPQ            195 non-null float64\n",
      "Jitter:DDP          195 non-null float64\n",
      "MDVP:Shimmer        195 non-null float64\n",
      "MDVP:Shimmer(dB)    195 non-null float64\n",
      "Shimmer:APQ3        195 non-null float64\n",
      "Shimmer:APQ5        195 non-null float64\n",
      "MDVP:APQ            195 non-null float64\n",
      "Shimmer:DDA         195 non-null float64\n",
      "NHR                 195 non-null float64\n",
      "HNR                 195 non-null float64\n",
      "status              195 non-null int64\n",
      "RPDE                195 non-null float64\n",
      "DFA                 195 non-null float64\n",
      "spread1             195 non-null float64\n",
      "spread2             195 non-null float64\n",
      "D2                  195 non-null float64\n",
      "PPE                 195 non-null float64\n",
      "dtypes: float64(22), int64(1), object(1)\n",
      "memory usage: 36.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "# Using df.info we can find the presence of missing values at the same time view the types of columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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",
       "      <td>count</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",
       "      <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",
       "      <td>mean</td>\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",
       "      <td>std</td>\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",
       "      <td>min</td>\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",
       "      <td>25%</td>\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",
       "      <td>50%</td>\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",
       "      <td>75%</td>\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",
       "      <td>max</td>\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "# The describe() method is used for calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
       "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
       "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
       "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',\n",
       "       'spread1', 'spread2', 'D2', 'PPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns\n",
    "# Displaying the column names of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These are the column names as seen in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attribute Information:\n",
    "\n",
    "## Matrix column entries (attributes):\n",
    "### name - ASCII subject name and recording number\n",
    "MDVP:Fo(Hz) - Average vocal fundamental frequency\n",
    "MDVP:Fhi(Hz) - Maximum vocal fundamental frequency\n",
    "MDVP:Flo(Hz) - Minimum vocal fundamental frequency\n",
    "MDVP:Jitter(%),MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP - Several \n",
    "measures of variation in fundamental frequency\n",
    "MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude\n",
    "NHR,HNR - Two measures of ratio of noise to tonal components in the voice\n",
    "status - Health status of the subject (one) - Parkinson's, (zero) - healthy\n",
    "RPDE,D2 - Two nonlinear dynamical complexity measures\n",
    "DFA - Signal fractal scaling exponent\n",
    "spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "190    0\n",
       "191    0\n",
       "192    0\n",
       "193    0\n",
       "194    0\n",
       "Name: status, Length: 195, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'] \n",
    "# The Status column is the target column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# status - Health status of the subject (one) - Parkinson's, (zero) - healthy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.figure(figsize=(10, 6))\n",
    "df.status.hist()\n",
    "plt.xlabel('status')\n",
    "plt.ylabel('Frequencies')\n",
    "plt.plot()\n",
    "# The dataset has high number of patients effected with Parkinson's disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=\"status\",y=\"NHR\",data=df);\n",
    "# The patients effected with Parkinson's disease have high NHR that is the measures of ratio of noise to tonal components in the voice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=\"status\",y=\"HNR\",data=df);\n",
    "# The patients effected with Parkinson's disease have high HNR that is the measures of ratio of noise to tonal components in the voice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=\"status\",y=\"RPDE\",data=df);\n",
    "# The nonlinear dynamical complexity measure RPDE is high in the patients effected with Parkinson's disease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rows=3\n",
    "cols=7\n",
    "fig, ax=plt.subplots(nrows=rows,ncols=cols,figsize=(16,4))\n",
    "col=df.columns\n",
    "index=1\n",
    "for i in range(rows):\n",
    "    for j in range(cols):\n",
    "        sns.distplot(df[col[index]],ax=ax[i][j])\n",
    "        index=index+1\n",
    "        \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A distribution plot displays a distribution and range of a set of numeric values plotted against a dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['name'],axis=1,inplace=True)\n",
    "# Removing  name column for machine learning algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>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>MDVP:APQ</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</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",
       "      <td>0</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>0.426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02971</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</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",
       "      <td>1</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>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04368</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</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",
       "      <td>2</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>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03590</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</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",
       "      <td>3</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>0.517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03772</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</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",
       "      <td>4</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>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04465</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  MDVP:Jitter(Abs)  \\\n",
       "0      119.992       157.302        74.997         0.00784           0.00007   \n",
       "1      122.400       148.650       113.819         0.00968           0.00008   \n",
       "2      116.682       131.111       111.555         0.01050           0.00009   \n",
       "3      116.676       137.871       111.366         0.00997           0.00009   \n",
       "4      116.014       141.781       110.655         0.01284           0.00011   \n",
       "\n",
       "   MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  MDVP:Shimmer(dB)  ...  \\\n",
       "0   0.00370   0.00554     0.01109       0.04374             0.426  ...   \n",
       "1   0.00465   0.00696     0.01394       0.06134             0.626  ...   \n",
       "2   0.00544   0.00781     0.01633       0.05233             0.482  ...   \n",
       "3   0.00502   0.00698     0.01505       0.05492             0.517  ...   \n",
       "4   0.00655   0.00908     0.01966       0.06425             0.584  ...   \n",
       "\n",
       "   MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE       DFA   spread1  \\\n",
       "0   0.02971      0.06545  0.02211  21.033  0.414783  0.815285 -4.813031   \n",
       "1   0.04368      0.09403  0.01929  19.085  0.458359  0.819521 -4.075192   \n",
       "2   0.03590      0.08270  0.01309  20.651  0.429895  0.825288 -4.443179   \n",
       "3   0.03772      0.08771  0.01353  20.644  0.434969  0.819235 -4.117501   \n",
       "4   0.04465      0.10470  0.01767  19.649  0.417356  0.823484 -3.747787   \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",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(labels=['status'],axis=1)\n",
    "Y=df['status']\n",
    "X.head()\n",
    "### Spitting the dataset into x and y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<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>MDVP:APQ</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</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",
       "      <td>0</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>0.426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.02971</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</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",
       "      <td>1</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>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04368</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</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",
       "      <td>2</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>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03590</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</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",
       "      <td>3</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>0.517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03772</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</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",
       "      <td>4</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>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04465</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  MDVP:Jitter(Abs)  \\\n",
       "0      119.992       157.302        74.997         0.00784           0.00007   \n",
       "1      122.400       148.650       113.819         0.00968           0.00008   \n",
       "2      116.682       131.111       111.555         0.01050           0.00009   \n",
       "3      116.676       137.871       111.366         0.00997           0.00009   \n",
       "4      116.014       141.781       110.655         0.01284           0.00011   \n",
       "\n",
       "   MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  MDVP:Shimmer(dB)  ...  \\\n",
       "0   0.00370   0.00554     0.01109       0.04374             0.426  ...   \n",
       "1   0.00465   0.00696     0.01394       0.06134             0.626  ...   \n",
       "2   0.00544   0.00781     0.01633       0.05233             0.482  ...   \n",
       "3   0.00502   0.00698     0.01505       0.05492             0.517  ...   \n",
       "4   0.00655   0.00908     0.01966       0.06425             0.584  ...   \n",
       "\n",
       "   MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE       DFA   spread1  \\\n",
       "0   0.02971      0.06545  0.02211  21.033  0.414783  0.815285 -4.813031   \n",
       "1   0.04368      0.09403  0.01929  19.085  0.458359  0.819521 -4.075192   \n",
       "2   0.03590      0.08270  0.01309  20.651  0.429895  0.825288 -4.443179   \n",
       "3   0.03772      0.08771  0.01353  20.644  0.434969  0.819235 -4.117501   \n",
       "4   0.04465      0.10470  0.01767  19.649  0.417356  0.823484 -3.747787   \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",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()\n",
    "# Displaying X head "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.head()\n",
    "# Displaying Y head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(156, 22) (39, 22) (156,) (39,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=40)\n",
    "print(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape)\n",
    "# Splitting the data into x_train, y_train, x_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  0.8782051282051282\n",
      "Model accuracy on test is:  0.8461538461538461\n",
      "--------------------------------------------------\n",
      "confusion_matrix train is:  [[ 25  15]\n",
      " [  4 112]]\n",
      "confusion_matrix test is:  [[ 5  3]\n",
      " [ 3 28]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Rahul\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:764: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    }
   ],
   "source": [
    "log_reg = LogisticRegression().fit(X_train, Y_train)\n",
    "\n",
    "#predict on train \n",
    "train_preds = log_reg.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds))\n",
    "\n",
    "#predict on test\n",
    "test_preds = log_reg.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds))\n",
    "print('-'*50)\n",
    "\n",
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  1.0\n",
      "Model accuracy on test is:  0.8974358974358975\n",
      "confusion_matrix train is:  [[ 40   0]\n",
      " [  0 116]]\n",
      "confusion_matrix test is:  [[ 6  2]\n",
      " [ 2 29]]\n"
     ]
    }
   ],
   "source": [
    "RF=RandomForestClassifier().fit(X_train,Y_train)\n",
    "#predict on train \n",
    "train_preds2 = RF.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds2))\n",
    "\n",
    "#predict on test\n",
    "test_preds2 = RF.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds2))\n",
    "\n",
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds2))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 / 39\n"
     ]
    }
   ],
   "source": [
    "# Wrong Predictions made.\n",
    "print((Y_test !=test_preds2).sum(),'/',((Y_test == test_preds2).sum()+(Y_test != test_preds2).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KappaScore is:  0.685483870967742\n"
     ]
    }
   ],
   "source": [
    "# Kappa Score\n",
    "print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let us go ahead and compare the predicted and actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "        1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1], dtype=int64),\n",
       " 96     1\n",
       " 5      1\n",
       " 116    1\n",
       " 35     0\n",
       " 178    1\n",
       " 185    0\n",
       " 54     1\n",
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       " 90     1\n",
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       " 81     1\n",
       " 111    1\n",
       " 67     1\n",
       " Name: status, dtype: int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_preds2,Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Saving the actual and predicted values to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf=pd.DataFrame(data=[test_preds2,Y_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddf.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Above 0 means Predicted Value and 1 is True Value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random forest model performs better compared to other models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random forest model gives us an accuracy of 94 percent compared to logistic regression which gave us 84 percent accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying other machine learning models to see if there is improvement in accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  1.0\n",
      "Model accuracy on test is:  1.0\n"
     ]
    }
   ],
   "source": [
    "#fit the model on train data \n",
    "DT = DecisionTreeClassifier().fit(X,Y)\n",
    "\n",
    "#predict on train \n",
    "train_preds3 = DT.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds3))\n",
    "\n",
    "#predict on test\n",
    "test_preds3 = DT.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix train is:  [[ 40   0]\n",
      " [  0 116]]\n",
      "confusion_matrix test is:  [[ 8  0]\n",
      " [ 0 31]]\n",
      "Wrong predictions out of total\n",
      "--------------------------------------------------\n",
      "0 / 39\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds3))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds3))\n",
    "print('Wrong predictions out of total')\n",
    "print('-'*50)\n",
    "\n",
    "# Wrong Predictions made.\n",
    "print((Y_test !=test_preds3).sum(),'/',((Y_test == test_preds3).sum()+(Y_test != test_preds3).sum()))\n",
    "print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KappaScore is:  1.0\n"
     ]
    }
   ],
   "source": [
    "# Kappa Score\n",
    "print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NB=GaussianNB()\n",
    "NB.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  0.7307692307692307\n",
      "Model accuracy on test is:  0.6923076923076923\n"
     ]
    }
   ],
   "source": [
    "#fit the model on train data \n",
    "NB=GaussianNB()\n",
    "NB.fit(X_train,Y_train)\n",
    "\n",
    "#predict on train \n",
    "train_preds4 = NB.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds4))\n",
    "\n",
    "#predict on test\n",
    "test_preds4 = NB.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix train is:  [[38  2]\n",
      " [40 76]]\n",
      "confusion_matrix test is:  [[ 8  0]\n",
      " [12 19]]\n",
      "Wrong predictions out of total\n",
      "--------------------------------------------------\n",
      "12 / 39\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds4))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds4))\n",
    "print('Wrong predictions out of total')\n",
    "print('-'*50)\n",
    "\n",
    "# Wrong Predictions made.\n",
    "print((Y_test !=test_preds4).sum(),'/',((Y_test == test_preds4).sum()+(Y_test != test_preds4).sum()))\n",
    "print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KappaScore is:  0.3937823834196892\n"
     ]
    }
   ],
   "source": [
    "# Kappa Score\n",
    "print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-NearestNeighbours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  0.9102564102564102\n",
      "Model accuracy on test is:  0.8461538461538461\n"
     ]
    }
   ],
   "source": [
    "#fit the model on train data \n",
    "KNN = KNeighborsClassifier().fit(X_train,Y_train)\n",
    "#predict on train \n",
    "train_preds5 = KNN.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds5))\n",
    "\n",
    "#predict on test\n",
    "test_preds5 = KNN.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix train is:  [[ 30  10]\n",
      " [  4 112]]\n",
      "confusion_matrix test is:  [[ 4  4]\n",
      " [ 2 29]]\n",
      "Wrong predictions out of total\n",
      "--------------------------------------------------\n",
      "6 / 39\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds5))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds5))\n",
    "print('Wrong predictions out of total')\n",
    "print('-'*50)\n",
    "\n",
    "# Wrong Predictions made.\n",
    "print((Y_test !=test_preds5).sum(),'/',((Y_test == test_preds5).sum()+(Y_test != test_preds5).sum()))\n",
    "\n",
    "print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KappaScore is:  0.48\n"
     ]
    }
   ],
   "source": [
    "# Kappa Score\n",
    "print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SupportVectorMachine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on train is:  0.8782051282051282\n",
      "Model accuracy on test is:  0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "#fit the model on train data \n",
    "SVM = SVC(kernel='linear')\n",
    "SVM.fit(X_train, Y_train)\n",
    "\n",
    "#predict on train \n",
    "train_preds6 = SVM.predict(X_train)\n",
    "#accuracy on train\n",
    "print(\"Model accuracy on train is: \", accuracy_score(Y_train, train_preds6))\n",
    "\n",
    "#predict on test\n",
    "test_preds6 = SVM.predict(X_test)\n",
    "#accuracy on test\n",
    "print(\"Model accuracy on test is: \", accuracy_score(Y_test, test_preds6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion_matrix train is:  [[ 23  17]\n",
      " [  2 114]]\n",
      "confusion_matrix test is:  [[ 5  3]\n",
      " [ 1 30]]\n",
      "Wrong predictions out of total\n",
      "--------------------------------------------------\n",
      "recall 0.967741935483871\n",
      "--------------------------------------------------\n",
      "4 / 39\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#Confusion matrix\n",
    "print(\"confusion_matrix train is: \", confusion_matrix(Y_train, train_preds6))\n",
    "print(\"confusion_matrix test is: \", confusion_matrix(Y_test, test_preds6))\n",
    "print('Wrong predictions out of total')\n",
    "print('-'*50)\n",
    "\n",
    "print(\"recall\", metrics.recall_score(Y_test, test_preds6))\n",
    "print('-'*50)\n",
    "\n",
    "# Wrong Predictions made.\n",
    "print((Y_test !=test_preds6).sum(),'/',((Y_test == test_preds6).sum()+(Y_test != test_preds6).sum()))\n",
    "print('-'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KappaScore is:  0.6533333333333333\n"
     ]
    }
   ],
   "source": [
    "# Kappa Score\n",
    "print('KappaScore is: ', metrics.cohen_kappa_score(Y_test,test_preds6))"
   ]
  }
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