Q2_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37b71e69",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import ConfusionMatrixDisplay,confusion_matrix,classification_report,accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier,AdaBoostClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2c43e478",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</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>phon_R01_S01_1</th>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>0.426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_2</th>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>0.626</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_3</th>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>0.482</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_4</th>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>0.517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phon_R01_S01_5</th>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>0.584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "name                                                                      \n",
       "phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "                MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "name                                                               \n",
       "phon_R01_S01_1           0.00007   0.00370   0.00554     0.01109   \n",
       "phon_R01_S01_2           0.00008   0.00465   0.00696     0.01394   \n",
       "phon_R01_S01_3           0.00009   0.00544   0.00781     0.01633   \n",
       "phon_R01_S01_4           0.00009   0.00502   0.00698     0.01505   \n",
       "phon_R01_S01_5           0.00011   0.00655   0.00908     0.01966   \n",
       "\n",
       "                MDVP:Shimmer  MDVP:Shimmer(dB)  ...  Shimmer:DDA      NHR  \\\n",
       "name                                            ...                         \n",
       "phon_R01_S01_1       0.04374             0.426  ...      0.06545  0.02211   \n",
       "phon_R01_S01_2       0.06134             0.626  ...      0.09403  0.01929   \n",
       "phon_R01_S01_3       0.05233             0.482  ...      0.08270  0.01309   \n",
       "phon_R01_S01_4       0.05492             0.517  ...      0.08771  0.01353   \n",
       "phon_R01_S01_5       0.06425             0.584  ...      0.10470  0.01767   \n",
       "\n",
       "                   HNR  status      RPDE       DFA   spread1   spread2  \\\n",
       "name                                                                     \n",
       "phon_R01_S01_1  21.033       1  0.414783  0.815285 -4.813031  0.266482   \n",
       "phon_R01_S01_2  19.085       1  0.458359  0.819521 -4.075192  0.335590   \n",
       "phon_R01_S01_3  20.651       1  0.429895  0.825288 -4.443179  0.311173   \n",
       "phon_R01_S01_4  20.644       1  0.434969  0.819235 -4.117501  0.334147   \n",
       "phon_R01_S01_5  19.649       1  0.417356  0.823484 -3.747787  0.234513   \n",
       "\n",
       "                      D2       PPE  \n",
       "name                                \n",
       "phon_R01_S01_1  2.301442  0.284654  \n",
       "phon_R01_S01_2  2.486855  0.368674  \n",
       "phon_R01_S01_3  2.342259  0.332634  \n",
       "phon_R01_S01_4  2.405554  0.368975  \n",
       "phon_R01_S01_5  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating dataframe df and reading it\n",
    "df=pd.read_csv('https://raw.githubusercontent.com/edyoda/data-science-complete-tutorial/master/Data/epilepsy.data')\n",
    "df.set_index('name',inplace=True)\n",
    "\n",
    "# Getiing top 5 value \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "63d25d2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 23)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 195 entries, phon_R01_S01_1 to phon_R01_S50_6\n",
      "Data columns (total 23 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 1   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 2   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 3   MDVP:Jitter(%)    195 non-null    float64\n",
      " 4   MDVP:Jitter(Abs)  195 non-null    float64\n",
      " 5   MDVP:RAP          195 non-null    float64\n",
      " 6   MDVP:PPQ          193 non-null    float64\n",
      " 7   Jitter:DDP        194 non-null    float64\n",
      " 8   MDVP:Shimmer      195 non-null    float64\n",
      " 9   MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 10  Shimmer:APQ3      195 non-null    float64\n",
      " 11  Shimmer:APQ5      195 non-null    float64\n",
      " 12  MDVP:APQ          195 non-null    float64\n",
      " 13  Shimmer:DDA       195 non-null    float64\n",
      " 14  NHR               195 non-null    float64\n",
      " 15  HNR               195 non-null    float64\n",
      " 16  status            195 non-null    int64  \n",
      " 17  RPDE              195 non-null    float64\n",
      " 18  DFA               195 non-null    float64\n",
      " 19  spread1           195 non-null    float64\n",
      " 20  spread2           195 non-null    float64\n",
      " 21  D2                195 non-null    float64\n",
      " 22  PPE               195 non-null    float64\n",
      "dtypes: float64(22), int64(1)\n",
      "memory usage: 36.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting shape of data\n",
    "print(df.shape)\n",
    "\n",
    "# Getting information of data \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a9b1266",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>193.000000</td>\n",
       "      <td>194.000000</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>0.003468</td>\n",
       "      <td>0.009932</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>0.002765</td>\n",
       "      <td>0.008925</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>0.001880</td>\n",
       "      <td>0.004982</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>0.002700</td>\n",
       "      <td>0.007455</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>0.003960</td>\n",
       "      <td>0.011558</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  193.000000  194.000000    195.000000   \n",
       "mean           0.000044    0.003306    0.003468    0.009932      0.029709   \n",
       "std            0.000035    0.002968    0.002765    0.008925      0.018857   \n",
       "min            0.000007    0.000680    0.000920    0.002040      0.009540   \n",
       "25%            0.000020    0.001660    0.001880    0.004982      0.016505   \n",
       "50%            0.000030    0.002500    0.002700    0.007455      0.022970   \n",
       "75%            0.000060    0.003835    0.003960    0.011558      0.037885   \n",
       "max            0.000260    0.021440    0.019580    0.064330      0.119080   \n",
       "\n",
       "       MDVP:Shimmer(dB)  ...  Shimmer:DDA         NHR         HNR      status  \\\n",
       "count        195.000000  ...   195.000000  195.000000  195.000000  195.000000   \n",
       "mean           0.282251  ...     0.046993    0.024847   21.885974    0.753846   \n",
       "std            0.194877  ...     0.030459    0.040418    4.425764    0.431878   \n",
       "min            0.085000  ...     0.013640    0.000650    8.441000    0.000000   \n",
       "25%            0.148500  ...     0.024735    0.005925   19.198000    1.000000   \n",
       "50%            0.221000  ...     0.038360    0.011660   22.085000    1.000000   \n",
       "75%            0.350000  ...     0.060795    0.025640   25.075500    1.000000   \n",
       "max            1.302000  ...     0.169420    0.314820   33.047000    1.000000   \n",
       "\n",
       "             RPDE         DFA     spread1     spread2          D2         PPE  \n",
       "count  195.000000  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
       "mean     0.498536    0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
       "std      0.103942    0.055336    1.090208    0.083406    0.382799    0.090119  \n",
       "min      0.256570    0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
       "25%      0.421306    0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
       "50%      0.495954    0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
       "75%      0.587562    0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
       "max      0.685151    0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
       "\n",
       "[8 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting mean , max & min value , std \n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f217a69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MDVP:Fo(Hz)         0.000000\n",
       "MDVP:Fhi(Hz)        0.000000\n",
       "MDVP:Flo(Hz)        0.000000\n",
       "MDVP:Jitter(%)      0.000000\n",
       "MDVP:Jitter(Abs)    0.000000\n",
       "MDVP:RAP            0.000000\n",
       "MDVP:PPQ            0.010256\n",
       "Jitter:DDP          0.005128\n",
       "MDVP:Shimmer        0.000000\n",
       "MDVP:Shimmer(dB)    0.000000\n",
       "Shimmer:APQ3        0.000000\n",
       "Shimmer:APQ5        0.000000\n",
       "MDVP:APQ            0.000000\n",
       "Shimmer:DDA         0.000000\n",
       "NHR                 0.000000\n",
       "HNR                 0.000000\n",
       "status              0.000000\n",
       "RPDE                0.000000\n",
       "DFA                 0.000000\n",
       "spread1             0.000000\n",
       "spread2             0.000000\n",
       "D2                  0.000000\n",
       "PPE                 0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isna().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cc38df0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping null \n",
    "df=df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c930221",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>...</th>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.400327</td>\n",
       "      <td>0.602803</td>\n",
       "      <td>-0.106765</td>\n",
       "      <td>-0.373000</td>\n",
       "      <td>-0.066314</td>\n",
       "      <td>-0.101633</td>\n",
       "      <td>-0.066332</td>\n",
       "      <td>-0.087435</td>\n",
       "      <td>-0.062556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.066798</td>\n",
       "      <td>-0.083763</td>\n",
       "      <td>-0.013346</td>\n",
       "      <td>0.041347</td>\n",
       "      <td>-0.371439</td>\n",
       "      <td>-0.448063</td>\n",
       "      <td>-0.400615</td>\n",
       "      <td>-0.228277</td>\n",
       "      <td>0.201440</td>\n",
       "      <td>-0.359729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <td>0.400327</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.084839</td>\n",
       "      <td>0.105580</td>\n",
       "      <td>-0.025727</td>\n",
       "      <td>0.100042</td>\n",
       "      <td>0.094352</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>0.046498</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007741</td>\n",
       "      <td>-0.000869</td>\n",
       "      <td>0.166263</td>\n",
       "      <td>-0.029451</td>\n",
       "      <td>-0.108345</td>\n",
       "      <td>-0.342681</td>\n",
       "      <td>-0.072252</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>0.182949</td>\n",
       "      <td>-0.065420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <td>0.602803</td>\n",
       "      <td>0.084839</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.136072</td>\n",
       "      <td>-0.275528</td>\n",
       "      <td>-0.096635</td>\n",
       "      <td>-0.092359</td>\n",
       "      <td>-0.096603</td>\n",
       "      <td>-0.141103</td>\n",
       "      <td>-0.115316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102637</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>-0.106473</td>\n",
       "      <td>0.201967</td>\n",
       "      <td>-0.410294</td>\n",
       "      <td>-0.054428</td>\n",
       "      <td>-0.394259</td>\n",
       "      <td>-0.246411</td>\n",
       "      <td>-0.079596</td>\n",
       "      <td>-0.339180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <td>-0.106765</td>\n",
       "      <td>0.105580</td>\n",
       "      <td>-0.136072</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.935258</td>\n",
       "      <td>0.990308</td>\n",
       "      <td>0.974104</td>\n",
       "      <td>0.990309</td>\n",
       "      <td>0.767399</td>\n",
       "      <td>0.802896</td>\n",
       "      <td>...</td>\n",
       "      <td>0.756514</td>\n",
       "      <td>0.744918</td>\n",
       "      <td>0.906811</td>\n",
       "      <td>-0.727884</td>\n",
       "      <td>0.355399</td>\n",
       "      <td>0.098116</td>\n",
       "      <td>0.690928</td>\n",
       "      <td>0.380236</td>\n",
       "      <td>0.429830</td>\n",
       "      <td>0.719109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <td>-0.373000</td>\n",
       "      <td>-0.025727</td>\n",
       "      <td>-0.275528</td>\n",
       "      <td>0.935258</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.922751</td>\n",
       "      <td>0.897142</td>\n",
       "      <td>0.922752</td>\n",
       "      <td>0.700846</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>...</td>\n",
       "      <td>0.645927</td>\n",
       "      <td>0.694730</td>\n",
       "      <td>0.834742</td>\n",
       "      <td>-0.654049</td>\n",
       "      <td>0.436213</td>\n",
       "      <td>0.174904</td>\n",
       "      <td>0.732451</td>\n",
       "      <td>0.381336</td>\n",
       "      <td>0.303092</td>\n",
       "      <td>0.744991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <td>-0.066314</td>\n",
       "      <td>0.100042</td>\n",
       "      <td>-0.096635</td>\n",
       "      <td>0.990308</td>\n",
       "      <td>0.922751</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758212</td>\n",
       "      <td>0.789464</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735876</td>\n",
       "      <td>0.743606</td>\n",
       "      <td>0.919274</td>\n",
       "      <td>-0.722339</td>\n",
       "      <td>0.338044</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>0.646316</td>\n",
       "      <td>0.320029</td>\n",
       "      <td>0.424047</td>\n",
       "      <td>0.669057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <td>-0.101633</td>\n",
       "      <td>0.094352</td>\n",
       "      <td>-0.092359</td>\n",
       "      <td>0.974104</td>\n",
       "      <td>0.897142</td>\n",
       "      <td>0.957121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957123</td>\n",
       "      <td>0.796449</td>\n",
       "      <td>0.838157</td>\n",
       "      <td>...</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.762046</td>\n",
       "      <td>0.844012</td>\n",
       "      <td>-0.731760</td>\n",
       "      <td>0.327855</td>\n",
       "      <td>0.196297</td>\n",
       "      <td>0.714687</td>\n",
       "      <td>0.404053</td>\n",
       "      <td>0.409698</td>\n",
       "      <td>0.768204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <td>-0.066332</td>\n",
       "      <td>0.100015</td>\n",
       "      <td>-0.096603</td>\n",
       "      <td>0.990309</td>\n",
       "      <td>0.922752</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.957123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.758186</td>\n",
       "      <td>0.789433</td>\n",
       "      <td>...</td>\n",
       "      <td>0.735859</td>\n",
       "      <td>0.743587</td>\n",
       "      <td>0.919301</td>\n",
       "      <td>-0.722288</td>\n",
       "      <td>0.337982</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>0.646315</td>\n",
       "      <td>0.319997</td>\n",
       "      <td>0.423996</td>\n",
       "      <td>0.669063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <td>-0.087435</td>\n",
       "      <td>0.005164</td>\n",
       "      <td>-0.141103</td>\n",
       "      <td>0.767399</td>\n",
       "      <td>0.700846</td>\n",
       "      <td>0.758212</td>\n",
       "      <td>0.796449</td>\n",
       "      <td>0.758186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987164</td>\n",
       "      <td>...</td>\n",
       "      <td>0.949841</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.720967</td>\n",
       "      <td>-0.835952</td>\n",
       "      <td>0.444032</td>\n",
       "      <td>0.160499</td>\n",
       "      <td>0.651883</td>\n",
       "      <td>0.450566</td>\n",
       "      <td>0.505347</td>\n",
       "      <td>0.690998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <td>-0.062556</td>\n",
       "      <td>0.046498</td>\n",
       "      <td>-0.115316</td>\n",
       "      <td>0.802896</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.789464</td>\n",
       "      <td>0.838157</td>\n",
       "      <td>0.789433</td>\n",
       "      <td>0.987164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.960779</td>\n",
       "      <td>0.962987</td>\n",
       "      <td>0.743362</td>\n",
       "      <td>-0.828633</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>0.165608</td>\n",
       "      <td>0.649745</td>\n",
       "      <td>0.452798</td>\n",
       "      <td>0.510674</td>\n",
       "      <td>0.692418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <td>-0.083748</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>-0.148028</td>\n",
       "      <td>0.744907</td>\n",
       "      <td>0.694713</td>\n",
       "      <td>0.743598</td>\n",
       "      <td>0.762033</td>\n",
       "      <td>0.743579</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.962983</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715017</td>\n",
       "      <td>-0.827330</td>\n",
       "      <td>0.431596</td>\n",
       "      <td>0.151840</td>\n",
       "      <td>0.607574</td>\n",
       "      <td>0.400194</td>\n",
       "      <td>0.465317</td>\n",
       "      <td>0.641892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <td>-0.061097</td>\n",
       "      <td>-0.007597</td>\n",
       "      <td>-0.097769</td>\n",
       "      <td>0.723923</td>\n",
       "      <td>0.646612</td>\n",
       "      <td>0.708445</td>\n",
       "      <td>0.785559</td>\n",
       "      <td>0.708424</td>\n",
       "      <td>0.982786</td>\n",
       "      <td>0.973634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.948999</td>\n",
       "      <td>0.959891</td>\n",
       "      <td>0.656570</td>\n",
       "      <td>-0.814920</td>\n",
       "      <td>0.396942</td>\n",
       "      <td>0.214747</td>\n",
       "      <td>0.645012</td>\n",
       "      <td>0.457585</td>\n",
       "      <td>0.501556</td>\n",
       "      <td>0.700765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <td>-0.066798</td>\n",
       "      <td>0.007741</td>\n",
       "      <td>-0.102637</td>\n",
       "      <td>0.756514</td>\n",
       "      <td>0.645927</td>\n",
       "      <td>0.735876</td>\n",
       "      <td>0.802846</td>\n",
       "      <td>0.735859</td>\n",
       "      <td>0.949841</td>\n",
       "      <td>0.960779</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896181</td>\n",
       "      <td>0.692664</td>\n",
       "      <td>-0.801422</td>\n",
       "      <td>0.448348</td>\n",
       "      <td>0.157430</td>\n",
       "      <td>0.670719</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>0.535212</td>\n",
       "      <td>0.719671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <td>-0.083763</td>\n",
       "      <td>-0.000869</td>\n",
       "      <td>-0.148017</td>\n",
       "      <td>0.744918</td>\n",
       "      <td>0.694730</td>\n",
       "      <td>0.743606</td>\n",
       "      <td>0.762046</td>\n",
       "      <td>0.743587</td>\n",
       "      <td>0.987572</td>\n",
       "      <td>0.962987</td>\n",
       "      <td>...</td>\n",
       "      <td>0.896181</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.715025</td>\n",
       "      <td>-0.827334</td>\n",
       "      <td>0.431593</td>\n",
       "      <td>0.151850</td>\n",
       "      <td>0.607578</td>\n",
       "      <td>0.400178</td>\n",
       "      <td>0.465312</td>\n",
       "      <td>0.641904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NHR</th>\n",
       "      <td>-0.013346</td>\n",
       "      <td>0.166263</td>\n",
       "      <td>-0.106473</td>\n",
       "      <td>0.906811</td>\n",
       "      <td>0.834742</td>\n",
       "      <td>0.919274</td>\n",
       "      <td>0.844012</td>\n",
       "      <td>0.919301</td>\n",
       "      <td>0.720967</td>\n",
       "      <td>0.743362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.692664</td>\n",
       "      <td>0.715025</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.715837</td>\n",
       "      <td>0.368259</td>\n",
       "      <td>-0.132850</td>\n",
       "      <td>0.538909</td>\n",
       "      <td>0.315831</td>\n",
       "      <td>0.471325</td>\n",
       "      <td>0.550427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNR</th>\n",
       "      <td>0.041347</td>\n",
       "      <td>-0.029451</td>\n",
       "      <td>0.201967</td>\n",
       "      <td>-0.727884</td>\n",
       "      <td>-0.654049</td>\n",
       "      <td>-0.722339</td>\n",
       "      <td>-0.731760</td>\n",
       "      <td>-0.722288</td>\n",
       "      <td>-0.835952</td>\n",
       "      <td>-0.828633</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.801422</td>\n",
       "      <td>-0.827334</td>\n",
       "      <td>-0.715837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.600515</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>-0.669196</td>\n",
       "      <td>-0.430786</td>\n",
       "      <td>-0.595321</td>\n",
       "      <td>-0.688676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPDE</th>\n",
       "      <td>-0.371439</td>\n",
       "      <td>-0.108345</td>\n",
       "      <td>-0.410294</td>\n",
       "      <td>0.355399</td>\n",
       "      <td>0.436213</td>\n",
       "      <td>0.338044</td>\n",
       "      <td>0.327855</td>\n",
       "      <td>0.337982</td>\n",
       "      <td>0.444032</td>\n",
       "      <td>0.406784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448348</td>\n",
       "      <td>0.431593</td>\n",
       "      <td>0.368259</td>\n",
       "      <td>-0.600515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117896</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.467334</td>\n",
       "      <td>0.234777</td>\n",
       "      <td>0.539678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFA</th>\n",
       "      <td>-0.448063</td>\n",
       "      <td>-0.342681</td>\n",
       "      <td>-0.054428</td>\n",
       "      <td>0.098116</td>\n",
       "      <td>0.174904</td>\n",
       "      <td>0.063601</td>\n",
       "      <td>0.196297</td>\n",
       "      <td>0.063544</td>\n",
       "      <td>0.160499</td>\n",
       "      <td>0.165608</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157430</td>\n",
       "      <td>0.151850</td>\n",
       "      <td>-0.132850</td>\n",
       "      <td>-0.010869</td>\n",
       "      <td>-0.117896</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.195518</td>\n",
       "      <td>0.162144</td>\n",
       "      <td>-0.166319</td>\n",
       "      <td>0.271564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread1</th>\n",
       "      <td>-0.400615</td>\n",
       "      <td>-0.072252</td>\n",
       "      <td>-0.394259</td>\n",
       "      <td>0.690928</td>\n",
       "      <td>0.732451</td>\n",
       "      <td>0.646316</td>\n",
       "      <td>0.714687</td>\n",
       "      <td>0.646315</td>\n",
       "      <td>0.651883</td>\n",
       "      <td>0.649745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670719</td>\n",
       "      <td>0.607578</td>\n",
       "      <td>0.538909</td>\n",
       "      <td>-0.669196</td>\n",
       "      <td>0.585191</td>\n",
       "      <td>0.195518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.646920</td>\n",
       "      <td>0.488723</td>\n",
       "      <td>0.961909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread2</th>\n",
       "      <td>-0.228277</td>\n",
       "      <td>0.004140</td>\n",
       "      <td>-0.246411</td>\n",
       "      <td>0.380236</td>\n",
       "      <td>0.381336</td>\n",
       "      <td>0.320029</td>\n",
       "      <td>0.404053</td>\n",
       "      <td>0.319997</td>\n",
       "      <td>0.450566</td>\n",
       "      <td>0.452798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.501220</td>\n",
       "      <td>0.400178</td>\n",
       "      <td>0.315831</td>\n",
       "      <td>-0.430786</td>\n",
       "      <td>0.467334</td>\n",
       "      <td>0.162144</td>\n",
       "      <td>0.646920</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.524845</td>\n",
       "      <td>0.641415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.201440</td>\n",
       "      <td>0.182949</td>\n",
       "      <td>-0.079596</td>\n",
       "      <td>0.429830</td>\n",
       "      <td>0.303092</td>\n",
       "      <td>0.424047</td>\n",
       "      <td>0.409698</td>\n",
       "      <td>0.423996</td>\n",
       "      <td>0.505347</td>\n",
       "      <td>0.510674</td>\n",
       "      <td>...</td>\n",
       "      <td>0.535212</td>\n",
       "      <td>0.465312</td>\n",
       "      <td>0.471325</td>\n",
       "      <td>-0.595321</td>\n",
       "      <td>0.234777</td>\n",
       "      <td>-0.166319</td>\n",
       "      <td>0.488723</td>\n",
       "      <td>0.524845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.475093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPE</th>\n",
       "      <td>-0.359729</td>\n",
       "      <td>-0.065420</td>\n",
       "      <td>-0.339180</td>\n",
       "      <td>0.719109</td>\n",
       "      <td>0.744991</td>\n",
       "      <td>0.669057</td>\n",
       "      <td>0.768204</td>\n",
       "      <td>0.669063</td>\n",
       "      <td>0.690998</td>\n",
       "      <td>0.692418</td>\n",
       "      <td>...</td>\n",
       "      <td>0.719671</td>\n",
       "      <td>0.641904</td>\n",
       "      <td>0.550427</td>\n",
       "      <td>-0.688676</td>\n",
       "      <td>0.539678</td>\n",
       "      <td>0.271564</td>\n",
       "      <td>0.961909</td>\n",
       "      <td>0.641415</td>\n",
       "      <td>0.475093</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "MDVP:Fo(Hz)          1.000000      0.400327      0.602803       -0.106765   \n",
       "MDVP:Fhi(Hz)         0.400327      1.000000      0.084839        0.105580   \n",
       "MDVP:Flo(Hz)         0.602803      0.084839      1.000000       -0.136072   \n",
       "MDVP:Jitter(%)      -0.106765      0.105580     -0.136072        1.000000   \n",
       "MDVP:Jitter(Abs)    -0.373000     -0.025727     -0.275528        0.935258   \n",
       "MDVP:RAP            -0.066314      0.100042     -0.096635        0.990308   \n",
       "MDVP:PPQ            -0.101633      0.094352     -0.092359        0.974104   \n",
       "Jitter:DDP          -0.066332      0.100015     -0.096603        0.990309   \n",
       "MDVP:Shimmer        -0.087435      0.005164     -0.141103        0.767399   \n",
       "MDVP:Shimmer(dB)    -0.062556      0.046498     -0.115316        0.802896   \n",
       "Shimmer:APQ3        -0.083748     -0.000879     -0.148028        0.744907   \n",
       "Shimmer:APQ5        -0.061097     -0.007597     -0.097769        0.723923   \n",
       "MDVP:APQ            -0.066798      0.007741     -0.102637        0.756514   \n",
       "Shimmer:DDA         -0.083763     -0.000869     -0.148017        0.744918   \n",
       "NHR                 -0.013346      0.166263     -0.106473        0.906811   \n",
       "HNR                  0.041347     -0.029451      0.201967       -0.727884   \n",
       "RPDE                -0.371439     -0.108345     -0.410294        0.355399   \n",
       "DFA                 -0.448063     -0.342681     -0.054428        0.098116   \n",
       "spread1             -0.400615     -0.072252     -0.394259        0.690928   \n",
       "spread2             -0.228277      0.004140     -0.246411        0.380236   \n",
       "D2                   0.201440      0.182949     -0.079596        0.429830   \n",
       "PPE                 -0.359729     -0.065420     -0.339180        0.719109   \n",
       "\n",
       "                  MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  \\\n",
       "MDVP:Fo(Hz)              -0.373000 -0.066314 -0.101633   -0.066332   \n",
       "MDVP:Fhi(Hz)             -0.025727  0.100042  0.094352    0.100015   \n",
       "MDVP:Flo(Hz)             -0.275528 -0.096635 -0.092359   -0.096603   \n",
       "MDVP:Jitter(%)            0.935258  0.990308  0.974104    0.990309   \n",
       "MDVP:Jitter(Abs)          1.000000  0.922751  0.897142    0.922752   \n",
       "MDVP:RAP                  0.922751  1.000000  0.957121    1.000000   \n",
       "MDVP:PPQ                  0.897142  0.957121  1.000000    0.957123   \n",
       "Jitter:DDP                0.922752  1.000000  0.957123    1.000000   \n",
       "MDVP:Shimmer              0.700846  0.758212  0.796449    0.758186   \n",
       "MDVP:Shimmer(dB)          0.714286  0.789464  0.838157    0.789433   \n",
       "Shimmer:APQ3              0.694713  0.743598  0.762033    0.743579   \n",
       "Shimmer:APQ5              0.646612  0.708445  0.785559    0.708424   \n",
       "MDVP:APQ                  0.645927  0.735876  0.802846    0.735859   \n",
       "Shimmer:DDA               0.694730  0.743606  0.762046    0.743587   \n",
       "NHR                       0.834742  0.919274  0.844012    0.919301   \n",
       "HNR                      -0.654049 -0.722339 -0.731760   -0.722288   \n",
       "RPDE                      0.436213  0.338044  0.327855    0.337982   \n",
       "DFA                       0.174904  0.063601  0.196297    0.063544   \n",
       "spread1                   0.732451  0.646316  0.714687    0.646315   \n",
       "spread2                   0.381336  0.320029  0.404053    0.319997   \n",
       "D2                        0.303092  0.424047  0.409698    0.423996   \n",
       "PPE                       0.744991  0.669057  0.768204    0.669063   \n",
       "\n",
       "                  MDVP:Shimmer  MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA  \\\n",
       "MDVP:Fo(Hz)          -0.087435         -0.062556  ... -0.066798    -0.083763   \n",
       "MDVP:Fhi(Hz)          0.005164          0.046498  ...  0.007741    -0.000869   \n",
       "MDVP:Flo(Hz)         -0.141103         -0.115316  ... -0.102637    -0.148017   \n",
       "MDVP:Jitter(%)        0.767399          0.802896  ...  0.756514     0.744918   \n",
       "MDVP:Jitter(Abs)      0.700846          0.714286  ...  0.645927     0.694730   \n",
       "MDVP:RAP              0.758212          0.789464  ...  0.735876     0.743606   \n",
       "MDVP:PPQ              0.796449          0.838157  ...  0.802846     0.762046   \n",
       "Jitter:DDP            0.758186          0.789433  ...  0.735859     0.743587   \n",
       "MDVP:Shimmer          1.000000          0.987164  ...  0.949841     0.987572   \n",
       "MDVP:Shimmer(dB)      0.987164          1.000000  ...  0.960779     0.962987   \n",
       "Shimmer:APQ3          0.987572          0.962983  ...  0.896179     1.000000   \n",
       "Shimmer:APQ5          0.982786          0.973634  ...  0.948999     0.959891   \n",
       "MDVP:APQ              0.949841          0.960779  ...  1.000000     0.896181   \n",
       "Shimmer:DDA           0.987572          0.962987  ...  0.896181     1.000000   \n",
       "NHR                   0.720967          0.743362  ...  0.692664     0.715025   \n",
       "HNR                  -0.835952         -0.828633  ... -0.801422    -0.827334   \n",
       "RPDE                  0.444032          0.406784  ...  0.448348     0.431593   \n",
       "DFA                   0.160499          0.165608  ...  0.157430     0.151850   \n",
       "spread1               0.651883          0.649745  ...  0.670719     0.607578   \n",
       "spread2               0.450566          0.452798  ...  0.501220     0.400178   \n",
       "D2                    0.505347          0.510674  ...  0.535212     0.465312   \n",
       "PPE                   0.690998          0.692418  ...  0.719671     0.641904   \n",
       "\n",
       "                       NHR       HNR      RPDE       DFA   spread1   spread2  \\\n",
       "MDVP:Fo(Hz)      -0.013346  0.041347 -0.371439 -0.448063 -0.400615 -0.228277   \n",
       "MDVP:Fhi(Hz)      0.166263 -0.029451 -0.108345 -0.342681 -0.072252  0.004140   \n",
       "MDVP:Flo(Hz)     -0.106473  0.201967 -0.410294 -0.054428 -0.394259 -0.246411   \n",
       "MDVP:Jitter(%)    0.906811 -0.727884  0.355399  0.098116  0.690928  0.380236   \n",
       "MDVP:Jitter(Abs)  0.834742 -0.654049  0.436213  0.174904  0.732451  0.381336   \n",
       "MDVP:RAP          0.919274 -0.722339  0.338044  0.063601  0.646316  0.320029   \n",
       "MDVP:PPQ          0.844012 -0.731760  0.327855  0.196297  0.714687  0.404053   \n",
       "Jitter:DDP        0.919301 -0.722288  0.337982  0.063544  0.646315  0.319997   \n",
       "MDVP:Shimmer      0.720967 -0.835952  0.444032  0.160499  0.651883  0.450566   \n",
       "MDVP:Shimmer(dB)  0.743362 -0.828633  0.406784  0.165608  0.649745  0.452798   \n",
       "Shimmer:APQ3      0.715017 -0.827330  0.431596  0.151840  0.607574  0.400194   \n",
       "Shimmer:APQ5      0.656570 -0.814920  0.396942  0.214747  0.645012  0.457585   \n",
       "MDVP:APQ          0.692664 -0.801422  0.448348  0.157430  0.670719  0.501220   \n",
       "Shimmer:DDA       0.715025 -0.827334  0.431593  0.151850  0.607578  0.400178   \n",
       "NHR               1.000000 -0.715837  0.368259 -0.132850  0.538909  0.315831   \n",
       "HNR              -0.715837  1.000000 -0.600515 -0.010869 -0.669196 -0.430786   \n",
       "RPDE              0.368259 -0.600515  1.000000 -0.117896  0.585191  0.467334   \n",
       "DFA              -0.132850 -0.010869 -0.117896  1.000000  0.195518  0.162144   \n",
       "spread1           0.538909 -0.669196  0.585191  0.195518  1.000000  0.646920   \n",
       "spread2           0.315831 -0.430786  0.467334  0.162144  0.646920  1.000000   \n",
       "D2                0.471325 -0.595321  0.234777 -0.166319  0.488723  0.524845   \n",
       "PPE               0.550427 -0.688676  0.539678  0.271564  0.961909  0.641415   \n",
       "\n",
       "                        D2       PPE  \n",
       "MDVP:Fo(Hz)       0.201440 -0.359729  \n",
       "MDVP:Fhi(Hz)      0.182949 -0.065420  \n",
       "MDVP:Flo(Hz)     -0.079596 -0.339180  \n",
       "MDVP:Jitter(%)    0.429830  0.719109  \n",
       "MDVP:Jitter(Abs)  0.303092  0.744991  \n",
       "MDVP:RAP          0.424047  0.669057  \n",
       "MDVP:PPQ          0.409698  0.768204  \n",
       "Jitter:DDP        0.423996  0.669063  \n",
       "MDVP:Shimmer      0.505347  0.690998  \n",
       "MDVP:Shimmer(dB)  0.510674  0.692418  \n",
       "Shimmer:APQ3      0.465317  0.641892  \n",
       "Shimmer:APQ5      0.501556  0.700765  \n",
       "MDVP:APQ          0.535212  0.719671  \n",
       "Shimmer:DDA       0.465312  0.641904  \n",
       "NHR               0.471325  0.550427  \n",
       "HNR              -0.595321 -0.688676  \n",
       "RPDE              0.234777  0.539678  \n",
       "DFA              -0.166319  0.271564  \n",
       "spread1           0.488723  0.961909  \n",
       "spread2           0.524845  0.641415  \n",
       "D2                1.000000  0.475093  \n",
       "PPE               0.475093  1.000000  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('status',axis=1).corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0df274d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#chech whether data s balanced or not\n",
    "df['status'].value_counts(normalize=True).plot(kind='bar');\n",
    "#we see that "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68ae138a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
       "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
       "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
       "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',\n",
       "       'spread1', 'spread2', 'D2', 'PPE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9cbc5ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#target and feature data segregation\n",
    "target='status'\n",
    "features=['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',\n",
    "       'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',\n",
    "       'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',\n",
    "       'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'RPDE', 'DFA',\n",
    "       'spread1', 'spread2', 'D2', 'PPE']\n",
    "X=df[features]\n",
    "y=df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6fe438f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((192, 22), (192,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape,y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "163084e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7c7916a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7712418300653595"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Baseline accyracy\n",
    "y_base_accuracy=y_train.value_counts(normalize=True).max()\n",
    "y_base_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c771be5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=StandardScaler()\n",
    "X_train=pd.DataFrame(scale.fit_transform(X_train),columns=X.columns)\n",
    "X_test=pd.DataFrame(scale.transform(X_test),columns=X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5421abb6",
   "metadata": {},
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "30fed3d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False  True  True  True False  True False  True False  True\n",
      "  True False False  True  True False  True  True  True  True]\n",
      "[1 1 7 1 1 1 5 1 3 1 4 1 1 2 8 1 1 6 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "lm=LogisticRegression()\n",
    "rfe=RFE(lm,n_features_to_select=15)\n",
    "rfe.fit(X_train,y_train)\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71a57200",
   "metadata": {},
   "outputs": [],
   "source": [
    "col=X_train.columns[rfe.support_]\n",
    "X_train=X_train[col]\n",
    "X_test=X_test[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd27efea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b8c79a12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8823529411764706\n",
      "0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "print(lm.score(X_train,y_train))\n",
    "print(lm.score(X_test,y_test))\n",
    "\n",
    "#So, our accuracy improved by roughly 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d94a67c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(X_train.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23e74ca0",
   "metadata": {},
   "source": [
    "# model 2- Decision tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "258a12d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "#model 2- Decision tree\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "000f9412",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(random_state=42))])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    DecisionTreeClassifier(random_state=42)\n",
    ")\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b13256f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "#accuracy scores\n",
    "print(dt.score(X_train,y_train))\n",
    "print(dt.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b8c5d06",
   "metadata": {},
   "source": [
    "# model3 - Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "464bb78c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "#model3 - Random Forest\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "62288571",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('randomforestclassifier',\n",
      "                 RandomForestClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    RandomForestClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "41c36595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87096774 0.87096774 0.93548387 0.96666667 0.9       ]\n"
     ]
    }
   ],
   "source": [
    "cv_acc_scores = cross_val_score(clf,X_train,y_train,cv=5,n_jobs=-1)\n",
    "print(cv_acc_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "144166e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'randomforestclassifier__n_estimators': range(25, 100, 10),\n",
       " 'randomforestclassifier__max_depth': range(10, 21)}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'randomforestclassifier__n_estimators':range(25,100,10),\n",
    "    'randomforestclassifier__max_depth':range(10,21,1)\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c278c0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(10, 21),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2dffccbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 88 candidates, totalling 440 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('randomforestclassifier',\n",
       "                                        RandomForestClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'randomforestclassifier__max_depth': range(10, 21),\n",
       "                         'randomforestclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cec90de0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.8717948717948718\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc9a94d",
   "metadata": {},
   "source": [
    "# Model 4 - Gradiant boosting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7c151b79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n",
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('gradientboostingclassifier',\n",
      "                 GradientBoostingClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "#Model 4 - Gradiant boosting classifier\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    GradientBoostingClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "47d1d320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gradientboostingclassifier__n_estimators': range(25, 100, 10),\n",
       " 'gradientboostingclassifier__max_depth': range(10, 21)}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'gradientboostingclassifier__n_estimators':range(25,100,10),\n",
    "    'gradientboostingclassifier__max_depth':range(10,21,1)\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7602fa1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('gradientboostingclassifier',\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingclassifier__max_depth': range(10, 21),\n",
       "                         'gradientboostingclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cc246a30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 88 candidates, totalling 440 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('gradientboostingclassifier',\n",
       "                                        GradientBoostingClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'gradientboostingclassifier__max_depth': range(10, 21),\n",
       "                         'gradientboostingclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5ca685e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fec2ef43",
   "metadata": {},
   "source": [
    "# Model 5 - Adaboost classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "31e34062",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n",
      "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
      "                ('adaboostclassifier', AdaBoostClassifier(random_state=42))])\n"
     ]
    }
   ],
   "source": [
    "#Model 5 - Adaboost classifier\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "clf = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    AdaBoostClassifier(random_state=42)\n",
    ")\n",
    "print(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8b84af8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'adaboostclassifier__n_estimators': range(25, 100, 10)}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = {\n",
    "    'adaboostclassifier__n_estimators':range(25,100,10)\n",
    "}\n",
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cb88a794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('adaboostclassifier',\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'adaboostclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GridSearchCV(\n",
    "    clf,\n",
    "    param_grid=params,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    verbose=1\n",
    ")\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25501254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                                       ('adaboostclassifier',\n",
       "                                        AdaBoostClassifier(random_state=42))]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'adaboostclassifier__n_estimators': range(25, 100, 10)},\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "af21cccd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy =1.0\n",
      "test accuracy =0.7948717948717948\n"
     ]
    }
   ],
   "source": [
    "print('training accuracy ={}'.format(model.score(X_train,y_train)))\n",
    "print('test accuracy ={}'.format(model.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fa7f22",
   "metadata": {},
   "source": [
    "# Best model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "137e28ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(153, 22)\n",
      "(153,)\n",
      "(39, 22)\n",
      "(39,)\n"
     ]
    }
   ],
   "source": [
    "#Best model\n",
    "#model 2- Decision tree\n",
    "X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.2,random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9c2d67a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(random_state=42))])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt = make_pipeline(\n",
    "    StandardScaler(),\n",
    "    DecisionTreeClassifier(random_state=42)\n",
    ")\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9e02ac7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.8974358974358975\n"
     ]
    }
   ],
   "source": [
    "#accuracy scores\n",
    "print(dt.score(X_train,y_train))\n",
    "print(dt.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "90835819",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2=dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "0b5cd8c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy = 1.0\n",
      "test accuracy = 0.9\n",
      "test precision = 0.93\n",
      "test recall = 0.93\n",
      "test error = 0.1\n"
     ]
    }
   ],
   "source": [
    "cnf=confusion_matrix(y_test,y_pred2)\n",
    "print('training accuracy = {}'.format(round(accuracy_score(y_train,dt.predict(X_train)),2)))\n",
    "print('test accuracy = {}'.format(round((cnf[0][0]+cnf[1][1])/(cnf[0][0]+cnf[0][1]+cnf[1][0]+cnf[1][1]),2)))\n",
    "print('test precision = {}'.format(round((cnf[1][1])/(cnf[0][1]+cnf[1][1]),2)))\n",
    "print('test recall = {}'.format(round((cnf[1][1])/(cnf[1][0]+cnf[1][1]),2)))\n",
    "print('test error = {}'.format(round((cnf[0][1]+cnf[1][0])/(cnf[0][0]+cnf[0][1]+cnf[1][0]+cnf[1][1]),2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2702fbe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.82      0.82        11\n",
      "           1       0.93      0.93      0.93        28\n",
      "\n",
      "    accuracy                           0.90        39\n",
      "   macro avg       0.87      0.87      0.87        39\n",
      "weighted avg       0.90      0.90      0.90        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "67d9fdcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1e03d7dc220>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(y_test,y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "02617921",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Feature Importance\n",
    "# Get importances\n",
    "importances = dt.named_steps['decisiontreeclassifier'].feature_importances_\n",
    "\n",
    "# Put importances into a Series\n",
    "feat_imp = pd.Series(importances,index=X_train.columns).sort_values()\n",
    "# plot\n",
    "feat_imp.tail(15).plot(kind='barh')\n",
    "plt.xlabel(\"Gini Importance\")\n",
    "plt.ylabel(\"Feature\")\n",
    "plt.title(\"Decision tree model Feature Importance\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5119c307",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9821941765776485"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_imp.tail(9).sum() # shows that more than 98 % if data is segregated by above top 8 variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2262ad49",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
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   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
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 "nbformat": 4,
 "nbformat_minor": 5
}