Parkinson's Disease Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b60e7222",
   "metadata": {},
   "source": [
    "# Parkinson's Disease Detection using Support Vector Machine Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e5d61be",
   "metadata": {},
   "source": [
    "## Importing the libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "804c331b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa402d3a",
   "metadata": {},
   "source": [
    "## Importint Dataset and Performing exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dc623bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset on to a dataframe\n",
    "parkinson = pd.read_csv('Parkinson disease prediction.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e130266",
   "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",
       "      <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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "   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",
       "   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",
       "    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 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the first five records\n",
    "parkinson.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "40c2cc27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 24)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
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    }
   ],
   "source": [
    "# Finding the number of records and attributes in this dataset \n",
    "parkinson.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dee99648",
   "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": [
    "# Checking for potential missing values in the dataset\n",
    "# If non null entries is equal to 195 then no missing values\n",
    "parkinson.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "91e592f5",
   "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>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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the statistical measures about the dataset\n",
    "parkinson.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f262dab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    147\n",
       "0     48\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the distribution of status variable (0 -> no disease, 1 -> Parkinson's positive)\n",
    "parkinson['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2703596",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KIIT\\AppData\\Local\\Temp\\ipykernel_19164\\2448695016.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  parkinson.groupby('status').mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>181.937771</td>\n",
       "      <td>223.636750</td>\n",
       "      <td>145.207292</td>\n",
       "      <td>0.003866</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.001925</td>\n",
       "      <td>0.002056</td>\n",
       "      <td>0.005776</td>\n",
       "      <td>0.017615</td>\n",
       "      <td>0.162958</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013305</td>\n",
       "      <td>0.028511</td>\n",
       "      <td>0.011483</td>\n",
       "      <td>24.678750</td>\n",
       "      <td>0.442552</td>\n",
       "      <td>0.695716</td>\n",
       "      <td>-6.759264</td>\n",
       "      <td>0.160292</td>\n",
       "      <td>2.154491</td>\n",
       "      <td>0.123017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>145.180762</td>\n",
       "      <td>188.441463</td>\n",
       "      <td>106.893558</td>\n",
       "      <td>0.006989</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.003900</td>\n",
       "      <td>0.011273</td>\n",
       "      <td>0.033658</td>\n",
       "      <td>0.321204</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027600</td>\n",
       "      <td>0.053027</td>\n",
       "      <td>0.029211</td>\n",
       "      <td>20.974048</td>\n",
       "      <td>0.516816</td>\n",
       "      <td>0.725408</td>\n",
       "      <td>-5.333420</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>2.456058</td>\n",
       "      <td>0.233828</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "status                                                            \n",
       "0        181.937771    223.636750    145.207292        0.003866   \n",
       "1        145.180762    188.441463    106.893558        0.006989   \n",
       "\n",
       "        MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
       "status                                                                   \n",
       "0               0.000023  0.001925  0.002056    0.005776      0.017615   \n",
       "1               0.000051  0.003757  0.003900    0.011273      0.033658   \n",
       "\n",
       "        MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA       NHR        HNR  \\\n",
       "status                    ...                                               \n",
       "0               0.162958  ...  0.013305     0.028511  0.011483  24.678750   \n",
       "1               0.321204  ...  0.027600     0.053027  0.029211  20.974048   \n",
       "\n",
       "            RPDE       DFA   spread1   spread2        D2       PPE  \n",
       "status                                                              \n",
       "0       0.442552  0.695716 -6.759264  0.160292  2.154491  0.123017  \n",
       "1       0.516816  0.725408 -5.333420  0.248133  2.456058  0.233828  \n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the dataset on the status variable\n",
    "parkinson.groupby('status').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b8bb3f",
   "metadata": {},
   "source": [
    "## Data pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39b3d1b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating the features (attributes) and the target (status) variables\n",
    "X = parkinson.drop(columns = ['name', 'status'],axis = 1)\n",
    "Y = parkinson['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3a60676f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     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)  ...  MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE  \\\n",
      "0               0.426  ...   0.02971      0.06545  0.02211  21.033  0.414783   \n",
      "1               0.626  ...   0.04368      0.09403  0.01929  19.085  0.458359   \n",
      "2               0.482  ...   0.03590      0.08270  0.01309  20.651  0.429895   \n",
      "3               0.517  ...   0.03772      0.08771  0.01353  20.644  0.434969   \n",
      "4               0.584  ...   0.04465      0.10470  0.01767  19.649  0.417356   \n",
      "..                ...  ...       ...          ...      ...     ...       ...   \n",
      "190             0.405  ...   0.02745      0.07008  0.02764  19.517  0.448439   \n",
      "191             0.263  ...   0.01879      0.04812  0.01810  19.147  0.431674   \n",
      "192             0.256  ...   0.01667      0.03804  0.10715  17.883  0.407567   \n",
      "193             0.241  ...   0.01588      0.03794  0.07223  19.020  0.451221   \n",
      "194             0.190  ...   0.01373      0.03078  0.04398  21.209  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 22 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "89e95786",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2589ee2b",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "457d05ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e7f58c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 22) (156, 22) (39, 22)\n"
     ]
    }
   ],
   "source": [
    "# Finding size of training and test data\n",
    "print(X.shape, X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90591321",
   "metadata": {},
   "source": [
    "## Performing data standardization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "72ae01f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an instance of StandardScaler dependency\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "01d76641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63239631, -0.02731081, -0.87985049, ..., -0.97586547,\n",
       "        -0.55160318,  0.07769494],\n",
       "       [-1.05512719, -0.83337041, -0.9284778 , ...,  0.3981808 ,\n",
       "        -0.61014073,  0.39291782],\n",
       "       [ 0.02996187, -0.29531068, -1.12211107, ..., -0.43937044,\n",
       "        -0.62849605, -0.50948408],\n",
       "       ...,\n",
       "       [-0.9096785 , -0.6637302 , -0.160638  , ...,  1.22001022,\n",
       "        -0.47404629, -0.2159482 ],\n",
       "       [-0.35977689,  0.19731822, -0.79063679, ..., -0.17896029,\n",
       "        -0.47272835,  0.28181221],\n",
       "       [ 1.01957066,  0.19922317, -0.61914972, ..., -0.716232  ,\n",
       "         1.23632066, -0.05829386]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f5bb58c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        -3.38233172e-01,  4.69404093e-01,  1.41615028e+00,\n",
       "         8.07181356e-01,  1.25910147e+00,  3.01185643e-01,\n",
       "         9.63235768e-01],\n",
       "       [-7.35633088e-01, -5.86160109e-01, -2.83768173e-01,\n",
       "         2.21029873e-02,  3.63192830e-01,  2.05332005e-01,\n",
       "        -2.70355840e-02,  2.05049832e-01, -5.04363961e-01,\n",
       "        -5.68132808e-01, -4.19085677e-01, -4.71318931e-01,\n",
       "        -5.66585326e-01, -4.18671179e-01,  1.42736188e-02,\n",
       "        -5.39856099e-01,  5.80200971e-01, -6.35698393e-01,\n",
       "         1.40403962e-01, -6.10707386e-01, -1.48125916e+00,\n",
       "         1.72702062e-01],\n",
       "       [-8.11248106e-01, -5.54498924e-01, -1.41381545e-01,\n",
       "         1.04469119e+00,  1.16799512e+00,  6.11329378e-01,\n",
       "         1.27962902e+00,  6.08865132e-01,  3.19290152e-01,\n",
       "         3.45188689e-01,  3.77596749e-01,  2.29528919e-01,\n",
       "         2.34246937e-01,  3.78051167e-01, -3.66707140e-01,\n",
       "        -5.99747201e-01,  3.47314967e-01,  1.75277084e+00,\n",
       "         1.20632129e+00, -3.36901642e-01, -1.11573342e+00,\n",
       "         1.57096123e+00],\n",
       "       [-1.24402268e-01, -8.29942503e-02, -8.42210287e-01,\n",
       "        -5.22995685e-01, -4.41609463e-01, -6.76662289e-01,\n",
       "        -5.18651970e-01, -6.74940735e-01, -6.01868187e-01,\n",
       "        -5.77266023e-01, -7.88091685e-01, -5.94210655e-01,\n",
       "        -7.28904365e-02, -7.88210355e-01, -3.61932945e-01,\n",
       "         6.10931054e-01, -8.24455015e-01, -7.54396866e-01,\n",
       "         9.29299002e-02,  3.18141033e-01,  5.76470743e-01,\n",
       "         5.18816272e-03],\n",
       "       [ 1.09378440e+00,  2.32462230e-01, -7.42590243e-01,\n",
       "         1.06581905e+00, -3.92083168e-02,  1.31832480e+00,\n",
       "         6.52171266e-01,  1.32079384e+00,  5.97262724e-01,\n",
       "         5.27852988e-01,  8.22256709e-01,  4.80179365e-01,\n",
       "         2.53585216e-01,  8.21704049e-01, -4.51687811e-01,\n",
       "        -3.15656422e-01, -7.84138455e-02,  4.68199043e-01,\n",
       "        -1.10748178e-01,  6.32072795e-01,  8.60326128e-01,\n",
       "        -1.85434800e-01],\n",
       "       [ 1.57463982e+00,  7.77040959e-01, -8.04476632e-01,\n",
       "         2.24930400e-01, -4.41609463e-01,  2.33331824e-01,\n",
       "         1.86429426e-01,  2.33060142e-01, -3.94885533e-01,\n",
       "        -2.48470284e-01, -3.27992144e-01, -3.15574965e-01,\n",
       "        -5.55209867e-01, -3.28087760e-01,  2.84346156e+00,\n",
       "        -5.42991758e-01, -4.63136299e-01, -7.78552242e-01,\n",
       "         2.26006912e-02, -5.34232072e-01,  6.91043111e-01,\n",
       "        -6.82722000e-01],\n",
       "       [-1.11483912e+00, -8.99104469e-01, -4.77176455e-01,\n",
       "         3.98178815e-01,  7.65593976e-01,  4.85330193e-01,\n",
       "         2.83458976e-01,  4.87487123e-01,  7.32400159e-01,\n",
       "         4.18254408e-01,  8.71663371e-01,  5.66568597e-01,\n",
       "         6.75614718e-01,  8.71113187e-01, -3.41881327e-01,\n",
       "        -3.34156815e-01,  1.55342667e+00,  1.10764554e+00,\n",
       "         6.93342979e-01,  2.45323176e-01, -1.01768833e+00,\n",
       "         8.11136847e-01],\n",
       "       [ 1.39454805e+00,  4.17479294e-01, -4.30415482e-01,\n",
       "        -7.93107192e-02, -8.44010610e-01,  2.82331507e-01,\n",
       "        -2.72843777e-01,  2.79743992e-01, -2.05864184e-01,\n",
       "        -7.49391997e-02, -2.02931530e-01, -1.65914747e-01,\n",
       "        -2.92436781e-01, -2.03535559e-01, -3.55249073e-01,\n",
       "        -3.37606040e-01, -1.91668108e+00,  5.22225108e-02,\n",
       "        -1.08529670e+00, -6.53046431e-01,  1.90695289e+00,\n",
       "        -1.12578869e+00],\n",
       "       [ 2.23398106e+00,  6.77775144e-01,  3.30873141e+00,\n",
       "        -1.28359848e+00, -1.28665187e+00, -1.06865975e+00,\n",
       "        -9.77925173e-01, -1.06941926e+00, -7.92600136e-01,\n",
       "        -8.32996041e-01, -7.12437734e-01, -6.35580146e-01,\n",
       "        -9.41975449e-01, -7.13067291e-01, -9.22423436e-01,\n",
       "         7.61129160e-01, -7.34449158e-01, -1.25432806e+00,\n",
       "        -1.44681476e+00, -1.84692800e+00, -2.31823791e-02,\n",
       "        -1.44809578e+00],\n",
       "       [-9.88738697e-01, -8.15642747e-01, -3.34337129e-01,\n",
       "         3.89727673e-01,  7.65593976e-01,  6.25329287e-01,\n",
       "         3.86957162e-01,  6.25204479e-01,  6.95424872e-02,\n",
       "        -1.10066950e-02,  2.04673432e-01,  7.79967782e-04,\n",
       "        -1.25217545e-01,  2.05119185e-01, -1.74784502e-01,\n",
       "        -7.54021664e-01,  5.53243497e-01,  1.24321677e+00,\n",
       "         7.95714122e-01, -1.11358128e+00,  2.86930432e-01,\n",
       "         8.73480381e-01],\n",
       "       [-4.19104054e-01, -3.46448799e-01, -2.83342105e-01,\n",
       "         1.94896341e+00,  1.57039627e+00,  2.31931833e+00,\n",
       "         1.07263265e+00,  2.31749403e+00,  4.33045081e-01,\n",
       "         4.45654053e-01,  4.03844039e-01,  1.95459926e-01,\n",
       "         3.36626061e-01,  4.03270414e-01,  2.29920334e+00,\n",
       "        -4.41082814e-01,  1.44339368e+00, -1.38440051e+00,\n",
       "         7.43045709e-01,  6.50784136e-01,  1.76136925e+00,\n",
       "         6.72911406e-01]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0655d0b4",
   "metadata": {},
   "source": [
    "## Training the model using Support Vector Machine Classifier "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "53f408e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "parkinson_model = svm.SVC(kernel = 'linear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c408cd88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parkinson_model.fit(X_train.values, Y_train.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93deeea7",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f0604e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the accuracy score on training data\n",
    "X_train_prediction = parkinson_model.predict(X_train.values)\n",
    "train_data_accuracy = accuracy_score(Y_train.values, X_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c1fd66c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data :-  0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "# Displaying accuracy score on the training data\n",
    "print(\"Accuracy on training data :- \",train_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3410588d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the accuracy score on testing data\n",
    "X_test_prediction = parkinson_model.predict(X_test.values)\n",
    "test_data_accuracy = accuracy_score( Y_test.values, X_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "95fee7d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on testing data :-  0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "# Displaying accuracy score on the testing data\n",
    "print(\"Accuracy on testing data :- \",test_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "58686eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# displaying the confusion matrix\n",
    "cm = confusion_matrix(Y_test, X_test_prediction)\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bfec8251",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.50      0.62         8\n",
      "           1       0.88      0.97      0.92        31\n",
      "\n",
      "    accuracy                           0.87        39\n",
      "   macro avg       0.84      0.73      0.77        39\n",
      "weighted avg       0.87      0.87      0.86        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# displaying the model's performance\n",
    "print(classification_report(Y_test, X_test_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87606e4e",
   "metadata": {},
   "source": [
    "## Building a predictive system "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f49f04c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The person has Parkinson's disease\n"
     ]
    }
   ],
   "source": [
    "input_data = (117.274,129.916,110.402,0.00752,0.00006,0.00299,0.00469,0.00898,0.02293,0.221,0.01189,0.01459,0.01948,0.03568,0.00681,22.817,0.530529,0.817756,-4.60826,0.290024,2.021591,0.314464)\n",
    "input_np = np.asarray(input_data)\n",
    "input_reshaped = input_np.reshape(1, -1)\n",
    "\n",
    "#standardizing the input data\n",
    "standardized_data = scaler.fit_transform(input_reshaped)\n",
    "\n",
    "\n",
    "prediction = parkinson_model.predict(standardized_data)\n",
    "\n",
    "# printing the prediction\n",
    "if (prediction[0] == 0):\n",
    "    print(\"The person does not have Parkinson's disease\")\n",
    "else:\n",
    "    print(\"The person has Parkinson's disease\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56efb5fd",
   "metadata": {},
   "source": [
    "## Saving the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e4bd547e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "11661e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'Parkinson disease prediction.sav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3eca4e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(parkinson_model, open(filename, 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c8d7d18",
   "metadata": {},
   "source": [
    "# Made By :-\n",
    "\n",
    "## Manav Malhotra"
   ]
  }
 ],
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