Ensemble_parkinson_mini_proj.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dear Participant,\n",
    "\n",
    "Parkinson’s Disease (PD) is a degenerative neurological disorder marked by decreased dopamine levels in the brain. It manifests itself through a deterioration of movement, including the presence of tremors and stiffness. There is commonly a marked effect on speech, including dysarthria (difficulty articulating sounds), hypophonia (lowered volume), and monotone (reduced pitch range). Additionally, cognitive impairments and changes in mood can occur, and risk of dementia is increased.\n",
    "\n",
    "Traditional diagnosis of Parkinson’s Disease involves a clinician taking a neurological history of the patient and observing motor skills in various situations. Since there is no definitive laboratory test to diagnose PD, diagnosis is often difficult, particularly in the early stages when motor effects are not yet severe. Monitoring progression of the disease over time requires repeated clinic visits by the patient. An effective screening process, particularly one that doesn’t require a clinic visit, would be beneficial. Since PD patients exhibit characteristic vocal features, voice recordings are a useful and non-invasive tool for diagnosis. If machine learning algorithms could be applied to a voice recording dataset to accurately diagnosis PD, this would be an effective screening step prior to an appointment with a clinician.\n",
    "\n",
    "Use the provided dataset in order to do your analysis."
   ]
  },
  {
   "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required library\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1. Load the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('parkinsons.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              195 non-null    object \n",
      " 1   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 2   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 3   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 4   MDVP:Jitter(%)    195 non-null    float64\n",
      " 5   MDVP:Jitter(Abs)  195 non-null    float64\n",
      " 6   MDVP:RAP          195 non-null    float64\n",
      " 7   MDVP:PPQ          195 non-null    float64\n",
      " 8   Jitter:DDP        195 non-null    float64\n",
      " 9   MDVP:Shimmer      195 non-null    float64\n",
      " 10  MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 11  Shimmer:APQ3      195 non-null    float64\n",
      " 12  Shimmer:APQ5      195 non-null    float64\n",
      " 13  MDVP:APQ          195 non-null    float64\n",
      " 14  Shimmer:DDA       195 non-null    float64\n",
      " 15  NHR               195 non-null    float64\n",
      " 16  HNR               195 non-null    float64\n",
      " 17  status            195 non-null    int64  \n",
      " 18  RPDE              195 non-null    float64\n",
      " 19  DFA               195 non-null    float64\n",
      " 20  spread1           195 non-null    float64\n",
      " 21  spread2           195 non-null    float64\n",
      " 22  D2                195 non-null    float64\n",
      " 23  PPE               195 non-null    float64\n",
      "dtypes: float64(22), int64(1), object(1)\n",
      "memory usage: 36.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q2. Use the .describe() method on the dataset and state any insights you may come across."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MDVP:Jitter(Abs)'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 23)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop('name',axis=1)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the features have a very low variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q3. Check for class imbalance. Do people with Parkinson's have greater representation in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df['status'])\n",
    "plt.text(x=-0.05,y=df['status'].value_counts()[0]+1,s=round(df['status'].value_counts(normalize=True)[0]*100,2))\n",
    "plt.text(x=0.95,y=df['status'].value_counts()[1]+1,s=round(df['status'].value_counts(normalize=True)[1]*100,2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4. Check for missing vaues and take necessary measures by dropping observation or imputing them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q5. Plot the distribution of all the features. State any observations you can make based on the distribution plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(11,2,figsize=(15,20))\n",
    "\n",
    "for figu,a in zip(df.drop('status',axis=1),ax.flatten()):\n",
    "    figur=sns.histplot(df[figu],ax=a)\n",
    "    figur.set_xlabel(f\"{figu}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q6. Check for outliers in the data. Are there any variables with high amount of outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 22 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(11,2,figsize=(15,20))\n",
    "\n",
    "for figu,a in zip(df.drop('status',axis=1),ax.flatten()):\n",
    "    figur=sns.boxplot(df[figu],ax=a)\n",
    "    figur.set_xlabel(f\"{figu}\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are outliers present in the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q7. Are there any strong correlations among the independent features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('status',axis=1)\n",
    "y=df['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <td>102.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <td>8.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <td>18.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <td>506.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <td>122.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <td>3101964.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <td>273.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <td>3103948.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <td>2653.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <td>303.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <td>51152986.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <td>342.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <td>184.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <td>51126303.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NHR</th>\n",
       "      <td>16.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNR</th>\n",
       "      <td>132.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPDE</th>\n",
       "      <td>55.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFA</th>\n",
       "      <td>414.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread1</th>\n",
       "      <td>384.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread2</th>\n",
       "      <td>23.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>112.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPE</th>\n",
       "      <td>133.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          VIF\n",
       "MDVP:Fo(Hz)            102.24\n",
       "MDVP:Fhi(Hz)             8.88\n",
       "MDVP:Flo(Hz)            18.05\n",
       "MDVP:Jitter(%)         506.56\n",
       "MDVP:Jitter(Abs)       122.72\n",
       "MDVP:RAP           3101964.45\n",
       "MDVP:PPQ               273.80\n",
       "Jitter:DDP         3103948.97\n",
       "MDVP:Shimmer          2653.66\n",
       "MDVP:Shimmer(dB)       303.10\n",
       "Shimmer:APQ3      51152986.52\n",
       "Shimmer:APQ5           342.84\n",
       "MDVP:APQ               184.05\n",
       "Shimmer:DDA       51126303.41\n",
       "NHR                     16.00\n",
       "HNR                    132.11\n",
       "RPDE                    55.61\n",
       "DFA                    414.91\n",
       "spread1                384.37\n",
       "spread2                 23.08\n",
       "D2                     112.65\n",
       "PPE                    133.49"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "\n",
    "VIF=[round(vif(X.values,i),2) for i in range(X.shape[1])]\n",
    "\n",
    "df_vif=pd.DataFrame(VIF,index=X.columns,columns=['VIF'])\n",
    "df_vif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes there are strong multicolinearity among independent varaibles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q8. Split dataset into training & test dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q9. Create a default decision tree model using criterion = Entropy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import tree\n",
    "from sklearn.tree import export_graphviz\n",
    "import IPython\n",
    "from IPython.display import Image\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', random_state=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier(criterion='entropy',random_state=1)\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels=X.columns\n",
    "dot_plot=tree.export_graphviz(dt,feature_names=labels,class_names=['0','1'])\n",
    "graph=pydotplus.graph_from_dot_data(dot_plot)\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        31\n",
      "           1       1.00      1.00      1.00        99\n",
      "\n",
      "    accuracy                           1.00       130\n",
      "   macro avg       1.00      1.00      1.00       130\n",
      "weighted avg       1.00      1.00      1.00       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "decision_pred=dt.predict(X_train)\n",
    "print(\"Train data report\")\n",
    "print(classification_report(y_train,decision_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.76      0.84        17\n",
      "           1       0.92      0.98      0.95        48\n",
      "\n",
      "    accuracy                           0.92        65\n",
      "   macro avg       0.93      0.87      0.89        65\n",
      "weighted avg       0.92      0.92      0.92        65\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_pred=dt.predict(X_test)\n",
    "print(\"Test data report\")\n",
    "print(classification_report(y_test,decision_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q10.  Use regularization parameters of max_depth, min_sample_leaf to recreate the model. What is the impact on the model accuracy? How does regularization help? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=3, min_samples_leaf=5,\n",
       "                       random_state=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier(criterion='entropy',random_state=1,max_depth=3,min_samples_leaf=5)\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.94      0.85        31\n",
      "           1       0.98      0.92      0.95        99\n",
      "\n",
      "    accuracy                           0.92       130\n",
      "   macro avg       0.88      0.93      0.90       130\n",
      "weighted avg       0.93      0.92      0.93       130\n",
      "\n"
     ]
    }
   ],
   "source": [
    "decision_pred=dt.predict(X_train)\n",
    "print(\"Train data report\")\n",
    "print(classification_report(y_train,decision_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test data report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.76      0.74        17\n",
      "           1       0.91      0.90      0.91        48\n",
      "\n",
      "    accuracy                           0.86        65\n",
      "   macro avg       0.82      0.83      0.82        65\n",
      "weighted avg       0.86      0.86      0.86        65\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "decision_pred=dt.predict(X_test)\n",
    "print(\"Test data report\")\n",
    "print(classification_report(y_test,decision_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of our 2nd model has reduced but its actually good as out train and test results are more close while earlier our model was overfit on train data but didnt perform same on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q11. Implement a Random Forest model. What is the optimal number of trees that gives the best result?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_params=[{'n_estimators':[10,30,50,100,150,200,250,300]}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=RandomForestClassifier(),\n",
       "             param_grid=[{'n_estimators': [10, 30, 50, 100, 150, 200, 250,\n",
       "                                           300]}],\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "gsv=GridSearchCV(estimator=rf,param_grid=tuned_params,cv=3,scoring='roc_auc')\n",
    "gsv.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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