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": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import 'Pandas' \n",
    "import pandas as pd \n",
    "\n",
    "# import 'Numpy' \n",
    "import numpy as np\n",
    "\n",
    "# import subpackage of Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# import 'Seaborn' \n",
    "import seaborn as sb\n",
    "\n",
    "# to suppress warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    " \n",
    "# to display the float values upto 6 decimal places     \n",
    "pd.options.display.float_format = '{:.6f}'.format\n",
    "\n",
    "# import train-test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import various functions from statsmodels\n",
    "import statsmodels\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# import StandardScaler to perform scaling\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "\n",
    "# import various functions from sklearn \n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import cohen_kappa_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score \n",
    "from sklearn.model_selection import KFold\n",
    "# import function to perform feature selection\n",
    "from sklearn.feature_selection import RFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1. Load the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <th>MDVP:APQ</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>119.992000</td>\n",
       "      <td>157.302000</td>\n",
       "      <td>74.997000</td>\n",
       "      <td>0.007840</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.005540</td>\n",
       "      <td>0.011090</td>\n",
       "      <td>0.043740</td>\n",
       "      <td>0.426000</td>\n",
       "      <td>0.021820</td>\n",
       "      <td>0.031300</td>\n",
       "      <td>0.029710</td>\n",
       "      <td>0.065450</td>\n",
       "      <td>0.022110</td>\n",
       "      <td>21.033000</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>122.400000</td>\n",
       "      <td>148.650000</td>\n",
       "      <td>113.819000</td>\n",
       "      <td>0.009680</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.004650</td>\n",
       "      <td>0.006960</td>\n",
       "      <td>0.013940</td>\n",
       "      <td>0.061340</td>\n",
       "      <td>0.626000</td>\n",
       "      <td>0.031340</td>\n",
       "      <td>0.045180</td>\n",
       "      <td>0.043680</td>\n",
       "      <td>0.094030</td>\n",
       "      <td>0.019290</td>\n",
       "      <td>19.085000</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>116.682000</td>\n",
       "      <td>131.111000</td>\n",
       "      <td>111.555000</td>\n",
       "      <td>0.010500</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.005440</td>\n",
       "      <td>0.007810</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>0.052330</td>\n",
       "      <td>0.482000</td>\n",
       "      <td>0.027570</td>\n",
       "      <td>0.038580</td>\n",
       "      <td>0.035900</td>\n",
       "      <td>0.082700</td>\n",
       "      <td>0.013090</td>\n",
       "      <td>20.651000</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>116.676000</td>\n",
       "      <td>137.871000</td>\n",
       "      <td>111.366000</td>\n",
       "      <td>0.009970</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.005020</td>\n",
       "      <td>0.006980</td>\n",
       "      <td>0.015050</td>\n",
       "      <td>0.054920</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>0.029240</td>\n",
       "      <td>0.040050</td>\n",
       "      <td>0.037720</td>\n",
       "      <td>0.087710</td>\n",
       "      <td>0.013530</td>\n",
       "      <td>20.644000</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>116.014000</td>\n",
       "      <td>141.781000</td>\n",
       "      <td>110.655000</td>\n",
       "      <td>0.012840</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.006550</td>\n",
       "      <td>0.009080</td>\n",
       "      <td>0.019660</td>\n",
       "      <td>0.064250</td>\n",
       "      <td>0.584000</td>\n",
       "      <td>0.034900</td>\n",
       "      <td>0.048250</td>\n",
       "      <td>0.044650</td>\n",
       "      <td>0.104700</td>\n",
       "      <td>0.017670</td>\n",
       "      <td>19.649000</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",
       "</div>"
      ],
      "text/plain": [
       "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0  phon_R01_S01_1   119.992000    157.302000     74.997000        0.007840   \n",
       "1  phon_R01_S01_2   122.400000    148.650000    113.819000        0.009680   \n",
       "2  phon_R01_S01_3   116.682000    131.111000    111.555000        0.010500   \n",
       "3  phon_R01_S01_4   116.676000    137.871000    111.366000        0.009970   \n",
       "4  phon_R01_S01_5   116.014000    141.781000    110.655000        0.012840   \n",
       "\n",
       "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \\\n",
       "0          0.000070  0.003700  0.005540    0.011090      0.043740   \n",
       "1          0.000080  0.004650  0.006960    0.013940      0.061340   \n",
       "2          0.000090  0.005440  0.007810    0.016330      0.052330   \n",
       "3          0.000090  0.005020  0.006980    0.015050      0.054920   \n",
       "4          0.000110  0.006550  0.009080    0.019660      0.064250   \n",
       "\n",
       "   MDVP:Shimmer(dB)  Shimmer:APQ3  Shimmer:APQ5  MDVP:APQ  Shimmer:DDA  \\\n",
       "0          0.426000      0.021820      0.031300  0.029710     0.065450   \n",
       "1          0.626000      0.031340      0.045180  0.043680     0.094030   \n",
       "2          0.482000      0.027570      0.038580  0.035900     0.082700   \n",
       "3          0.517000      0.029240      0.040050  0.037720     0.087710   \n",
       "4          0.584000      0.034900      0.048250  0.044650     0.104700   \n",
       "\n",
       "       NHR       HNR  status     RPDE      DFA   spread1  spread2       D2  \\\n",
       "0 0.022110 21.033000       1 0.414783 0.815285 -4.813031 0.266482 2.301442   \n",
       "1 0.019290 19.085000       1 0.458359 0.819521 -4.075192 0.335590 2.486855   \n",
       "2 0.013090 20.651000       1 0.429895 0.825288 -4.443179 0.311173 2.342259   \n",
       "3 0.013530 20.644000       1 0.434969 0.819235 -4.117501 0.334147 2.405554   \n",
       "4 0.017670 19.649000       1 0.417356 0.823484 -3.747787 0.234513 2.332180   \n",
       "\n",
       "       PPE  \n",
       "0 0.284654  \n",
       "1 0.368674  \n",
       "2 0.332634  \n",
       "3 0.368975  \n",
       "4 0.410335  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('parkinsons.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 24)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "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": 53,
   "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>Shimmer:APQ3</th>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <th>MDVP:APQ</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>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>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>0.015664</td>\n",
       "      <td>0.017878</td>\n",
       "      <td>0.024081</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>0.010153</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.016947</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>0.004550</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.007190</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>0.008245</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>0.013080</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>0.012790</td>\n",
       "      <td>0.013470</td>\n",
       "      <td>0.018260</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>0.020265</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>0.029400</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>0.056470</td>\n",
       "      <td>0.079400</td>\n",
       "      <td>0.137780</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",
       "</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:APQ3  Shimmer:APQ5   MDVP:APQ  Shimmer:DDA  \\\n",
       "count        195.000000    195.000000    195.000000 195.000000   195.000000   \n",
       "mean           0.282251      0.015664      0.017878   0.024081     0.046993   \n",
       "std            0.194877      0.010153      0.012024   0.016947     0.030459   \n",
       "min            0.085000      0.004550      0.005700   0.007190     0.013640   \n",
       "25%            0.148500      0.008245      0.009580   0.013080     0.024735   \n",
       "50%            0.221000      0.012790      0.013470   0.018260     0.038360   \n",
       "75%            0.350000      0.020265      0.022380   0.029400     0.060795   \n",
       "max            1.302000      0.056470      0.079400   0.137780     0.169420   \n",
       "\n",
       "             NHR        HNR     status       RPDE        DFA    spread1  \\\n",
       "count 195.000000 195.000000 195.000000 195.000000 195.000000 195.000000   \n",
       "mean    0.024847  21.885974   0.753846   0.498536   0.718099  -5.684397   \n",
       "std     0.040418   4.425764   0.431878   0.103942   0.055336   1.090208   \n",
       "min     0.000650   8.441000   0.000000   0.256570   0.574282  -7.964984   \n",
       "25%     0.005925  19.198000   1.000000   0.421306   0.674758  -6.450096   \n",
       "50%     0.011660  22.085000   1.000000   0.495954   0.722254  -5.720868   \n",
       "75%     0.025640  25.075500   1.000000   0.587562   0.761881  -5.046192   \n",
       "max     0.314820  33.047000   1.000000   0.685151   0.825288  -2.434031   \n",
       "\n",
       "         spread2         D2        PPE  \n",
       "count 195.000000 195.000000 195.000000  \n",
       "mean    0.226510   2.381826   0.206552  \n",
       "std     0.083406   0.382799   0.090119  \n",
       "min     0.006274   1.423287   0.044539  \n",
       "25%     0.174351   2.099125   0.137451  \n",
       "50%     0.218885   2.361532   0.194052  \n",
       "75%     0.279234   2.636456   0.252980  \n",
       "max     0.450493   3.671155   0.527367  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#several data have very less variance,almost negligible.\n",
    "#The total_count is 195 for all variables.\n",
    "#we can see no null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import imblearn\n",
    "from sklearn.datasets import make_classification\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    147\n",
       "0     48\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   0.753846\n",
       "0   0.246154\n",
       "Name: status, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='status', ylabel='count'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sb.countplot(df['status'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 is higher than 0 hence,people with parkinson have higher representation.\n",
    "#Data is highly imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q4. Check for missing vaues and take necessary measures by dropping observation or imputing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "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": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('name',1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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FNUmE23igkUVFGUxLUc/i8UxLSWDN3Fw2VNbz1zcsUs8q8dS5Lg3dFXg6y8y+PPoxBfVJhOnqG2TL0WY+uCDf61LC3nVLijh2qpv9Jzu8LkVi3Lkai093+UgHMsZ4iLzPpkOnGBhyXKkgOKdrywsxgxc1c5l47FyXhr4f+Pp3U1OORLqNBxpJSYijYlY2x051e11OWCvITGb5jCw2VNbzp1fP97ociWHB3lD2f8ws08wSzOxlM2sacdlI5D0bDzRy+dxckuLjvC4lIly3pIjK2naqmxWa4p1g5yO41jn3V2b2UaAG+ATwKvCTkFUmEedoUxfHm7u594rZXpcStkbPaXB6mIl/eX4fa+flBb2dO1eVTWpdEtuCvaHsdPePG4HHnHPNwXzIzK43s/1mVmVmXx1nnXVmtsPMKs1sY5D1SBg6PaKm2geCl5ueRGFmEpW17V6XIjEs2CB41sz2ARXAy2aWD/Se7QNmFgd8B7gBKAfuMLPyUetkAd8FbnbOLWH4TEMi1GsHGpmZm8rMXA0rcT7Ki6dx7FQXnZqjQDwS7DDUXwXWABXOuQGgC7jlHB9bCVQ55w475/qBx8f4zJ3AU86544Hv03A+xUv4GBzy89aR5vO6vCHDlkzPxDE8f4OIF85nxrHFwCfN7G7g48C151i/BKge8bom8N5IC4BsM/utmW0LbPsMZnafmW01s62NjZrQIxy9W9NGZ98ga+cqCM5X8bRkslITdHlIPBNUY7GZ/RiYC+wAhgJvO+CRs31sjPdGj7AVD1wKXA2kAJvMbLNz7sD7PuTcg8CDABUVFRqlKwy9WdUEwJq5uR5XEnnMjCXFmWw+0kzfwBBJCepxJVMr2F5DFUC5c+58fgnXADNGvC4FasdYp8k51wV0mdlrwFLgABJR3jjURHlxJjlpmqR+IsqnT+ONQ6fYf7KDS0qzvC5HYkywl4Z2A0Xnue0twHwzm21micDtwDOj1vkVcIWZxZtZKrAK2Hue30c81tM/xDvHWlk7T2cDEzUzN5W0xDj2qJ1APBDsGUEesMfM3gb6Tr/pnLt5vA845wbN7IvABiAOeNg5V2lm9weWP+Cc22tmLwA7AT/DE+DsnuC+iEe2Hmumf8jP5WoonjCfGYuLM9l1oo3BIT/xcefTfCdyYYINgm9MZOPOufXA+lHvPTDq9b8C/zqR7Ut4eKPqFPE+Y+UsDad8IcqnZ7L1WAuHGrtYWKShvGTqBNt9dCNwFEgIPN8CvBPCuiSCvHmoieVlWaQlBft3hYxlbn46ifE+9tS1eV2KxJhgxxr6PPBz4PuBt0qAp0NUk0SQtu4Bdp1o43J1G71gCXE+FhZmsKeugyFNYSlTKNgLkX8MrAXaAZxzB4GCUBUlkWPT4VM4h24kmyQXl0yjq2+QQ42dXpciMSTYIOgL3B0MgJnFc+Y9ARKD3jzUREpCHMtmZHldSlRYVJRBSkIc249rLmOZOsEGwUYz+xrDk9hfA/wMeDZ0ZUmkeKOqictm55AYr14ukyE+zsfFJdPYU9dO38DQuT8gMgmC/en9KtAI7AK+wHBPoL8JVVESGerbejnU2MVa3U08qZaXZTEw5DTkhEyZoLp5OOf8ZvY08LRzToP9CDB8WQjUPjDZynJSyUlLZHt1CytmZntdjsSAc01eb2b2DTNrAvYB+82s0cy+PjXlSTh7o+oUWakJlBdnel1KVDEzls3I4nBjF209A16XIzHgXJeGvsRwb6HLnHO5zrkchoeBWGtmfx7q4iR8OefYdKiJNXNy8fnGGl9QLsSyGVk44N3qVq9LkRhwriC4G7jDOXfk9BvOucPAXYFlEqOOnuqmtq1Xw0qESF56EjOyU9hR3cr5jfUocv7OFQQJzrmm0W8G2gkSxlhfYsTp9oHL1VAcMitmZlPf3kt1S4/XpUiUO1cQ9E9wmUS5Nw+doigzmTl5mpYyVJaVZpEU7+Otw6e8LkWi3Ll6DS01s7H6sBmQHIJ6JEw8+tbxcZf5neO3+xpYUJjBY29Xj7ueXJikhDiWl2Wx5WgLN1xcTLrGcpIQOesZgXMuzjmXOcYjwzmnS0MxqqG9j67+Iebkp3tdStRbNTuXIb9j2zHdaSyho9tB5bydHgdnTr4uC4VaYWYys/PSeOvIKfxqNJYQURDIeTvc1EVOWiLZqZqWciqsnpNLa/cAB+o7vC5FopSCQM6L3zmONHUyV2cDU6a8OJPM5Hg2H1GjsYSGgkDOS21rD70DfrUPTKE4n3HZrBwOnOykob3X63IkCikI5LwcbuwCULfRKbZqTi4JccZrBzXUl0w+BYGcl0ONnRRkJJGRrE5jUyk9KZ6KWTnsqG6ltVu38MjkUhBI0Ab9fo6e6tJlIY9cERjOY+MBnRXI5FIQSNBqmnsYGHJqKPZIVmoiFTNz2Hq0hermbq/LkSiiWxUlaIeaOjFgttoHPHPVogLeOd7Cnz2+nY9fOuOCtnXnqrJJqkoinc4IJGiHG7sozkomNVF/P3hlWkoCq+fksv14K3VtGoxOJoeCQIIyMOTneHM3c/PUPuC1dQvzSU6I47mddRqiWiaFgkCCcuxUN0N+p4biMJCaGM+Hyws53NSleY1lUoQ0CMzsejPbb2ZVZvbVs6x3mZkNmdnHQ1mPTNzhxk58BrNyU70uRYCVs3Ioykzm1ztr6R0Y8rociXAhCwIziwO+A9wAlAN3mFn5OOv9b2BDqGqRC3eosZPS7FSSEuK8LkUYvtv4o8tL6Ogd5IXd9V6XIxEulGcEK4Eq59xh51w/8Dhwyxjr/QnwC6AhhLXIBegdGOJEa49GGw0zM3JSWTsvj7ePNnOwQQPSycSFMghKgJGzltQE3nuPmZUAHwUeCGEdcoGOnurC72Cu2gfCzocXF1KQkcTPt9XQ2TfodTkSoUIZBDbGe6O7OPwH8BXn3FkvcprZfWa21cy2Njbqrsqpdrixi3ifUZaj9oFwkxjv45OXzaCnf4ifb6vWnAUyIaEMghpg5B0vpUDtqHUqgMfN7CjwceC7Znbr6A055x50zlU45yry8/NDVK6M53BjJ2U5qSTEqZNZOCqelsJNl0znwMlOtRfIhITyzqAtwHwzmw2cAG4H7hy5gnNu9unnZvZD4NfOuadDWJOcp+6+Qeraerl6caHXpchZXDY7h/r2Xl6vaqIwM4lLZ+Z4XZJEkJAFgXNu0My+yHBvoDjgYedcpZndH1iudoEIcLipCwcaXygC3HhxMY0dfTy9vZa89CRm5uqYSXBCeq7vnFvvnFvgnJvrnPvHwHsPjBUCzrnPOOd+Hsp65PxVNXSSFO+jNFvtA+EuzmfcsbKMrNQEHtl0jHpNYiNB0kVfGZdzjoMNHczJTyfON1bbv4SblMQ4Prt2NglxxsOvH6Gps8/rkiQCKAhkXM1d/bR0DzC/QN1GI0lOWiL3rJ2N3zn+7+tHaNFENnIOCgIZ18GGTgAFQQQqyEzmnrWz6Rsc4uHXj9DeO+B1SRLGFAQyrqqGTrJTE8hJS/S6FJmA6VkpfGbNLDp6B3n49SN064YzGYeCQMY05HccauxkXkEGZmofiFRluWl8es1Mmrv6+eGmo/RpgDoZg4JAxlTT0k3foF+XhaLA3Px07lhZRm1rD49sPsbAkN/rkiTMKAhkTAcbhqel1PhC0WFxcSYfv7SUo01dPPb2cYb8GopCfkdBIGOqauikNDuFlEQNOx0tls3I5qal09lX38Ev3qnBrzCQAAWBnKGtZ4Dq5m7mFWR4XYpMstVzcrm2vJAd1a1849lKTXUpQGjHGpIItelQEw51G41WVy7Ip6d/iEc2HaMsJ5V7r5jjdUniMQWBnOGVfQ0kJ/iYoWGno5KZcd1FRaQlxfOP6/cyOy9NgwrGOF0akvfx+x2v7GtkfkGGhpWIYj4zvvnJpSyZnsmfPradvXXtXpckHlIQyPvsOtFGU2cfi4rUPhDtUhPjeejuy0hPjufeH22lpUtDUcQqBYG8z8v7GvAZLChUEMSComnJ/ODuCho7+vjSEzvUkyhGKQjkfV7Zd5IVZdmkJan5KFZcUprF395czsYDjXz71SqvyxEPKAjkPSfbe9l9op0PLS7wuhSZYneuLOOjy0v4998c4PWDTV6XI1NMQSDveXVfAwBXL1IPklhjZvzjRy9ifkE6f/r4dho0qU1MURDIe17e10BJVgoLCnX/QCxKTYznu59aQXf/IH/5851qL4ghCgIBoHdgiNcPNvGhRQUabTSGzSvI4H/cuJjXDjTyyKajXpcjU0QtggLA5sOn6BkYUvtADHn0reNjvu8zY2FhBv/w3F5augcozEwedxt3rioLVXkyhXRGIABsqKwnNTGONXNyvS5FPGZm/P6KEpLifTyxpZpBDVsd9RQEwsCQnxd21/PhxYUkJ2i0UYGM5AQ+tqKU+vZeXtxz0utyJMQUBMKmQ6do6R7g9y4p9roUCSOLijNZOTuH16uaqArMXy3RSUEgPLezjvSkeK5ckO91KRJmbryomLz0JH7xTg09/ZrmMlopCGLcwJCfFyrruaZcl4XkTInxPm6rKKWjd4Bnd9Z6XY6EiIIgxr1R1URbzwC/d7EuC8nYSrNTuWphATuqW9l1os3rciQEFAQx7rmddWQkxXPFgjyvS5Ewtm5hAaXZKTy9/QTtvQNelyOTLKRBYGbXm9l+M6sys6+OsfxTZrYz8HjTzJaGsh55v/5BPxsq67lmSSFJ8bosJOOL8xmfuHQGg34/T71Toykuo0zIgsDM4oDvADcA5cAdZlY+arUjwJXOuUuAvwceDFU9cqY3qppo7x3kI+otJEHIz0ji+iVFHDjZydtHm70uRyZRKM8IVgJVzrnDzrl+4HHglpErOOfedM61BF5uBkpDWI+M8svtJ5iWksAH5qm3kARn1Zxc5uWns35XHac6+7wuRyZJKIOgBKge8bom8N54Pgc8H8J6ZIS2ngE2VNZzy7LpJMarqUiC4zPjY5eWEuczfratRncdR4lQ/gYYa+SyMS8smtlVDAfBV8ZZfp+ZbTWzrY2NjZNYYux69t1a+gb9fOLSGV6XIhFmWkoCNy8t4XhzN99/7bDX5cgkCGUQ1AAjf8uUAmd0RDazS4CHgFucc6fG2pBz7kHnXIVzriI/X5cxJsPPttWwqCiDi0oyvS5FItDS0mlcXDKNf3/pALvVpTTihTIItgDzzWy2mSUCtwPPjFzBzMqAp4BPO+cOhLAWGWFPbTvvVrfyiYoZGnJaJsTMuGXZdHLSEvnzJ3bQO6C7jiNZyILAOTcIfBHYAOwFnnTOVZrZ/WZ2f2C1rwO5wHfNbIeZbQ1VPfI7P958lOQEHx9fobZ5mbjUxHj+z8cv4WBDJ//24n6vy5ELENL5CJxz64H1o957YMTze4F7Q1mDvF9b9wC/3H6CW5eVMC01wetyJMKtW1jAXavLeOj1I1y9uJDVGsY8Iqm7SIz52bZqegf8fHrNTK9LkSjxtRsXMys3jS8/sYOWrn6vy5EJUBDEkIEhPw+/foSVs3JYMn2a1+VIlEhNjOdbty+nqbOfP39yh+Y6jkAKghjyzI5aatt6uX/dHK9LkShzcek0/udN5fx2fyPf23jI63LkPCkIYoTf7/j+a4dYWJjBVQs1L7FMvrtWlXHT0un824v72Xx4zJ7gEqYUBDFi/e46Dpzs5A/XzVWXUQkJM+Off/9iZuWm8cVHt3OitcfrkiRICoIYMDjk55svHmBBYTo3LZ3udTkSxdKT4vn+py+lb2CIz/9oK939g16XJEFQEMSAn2+r4XBTF3957ULifDobkNCaX5jBt+5czr76dr78xLtqPI4ACoIo19YzwL9u2M+KsiyuKS/0uhyJEVctLOBrNy7mhcp6vvmSBg0IdyG9oUy8980X99PS3c+P7lmptgGZUp/7wGyqGjr59qtVFGQmcfeaWV6XJONQEESx7cdb+PHmY9y1eiYXlei+AZlaZsY/3HoRTZ39fP1XlWQmJ3Dr8rONRC9e0aWhKNXdP8ifP7GD4mkp/OV1C70uR2JUfJyPb9+5nDVzcvmLn73Lb/ac9LokGYOCIAo55/jGM5Uca+7m325bSmayxhQS7yQnxPGDP6jgoumZ/NGj7/CSwiDsKAii0E82H+PJrTX80bq5GgRMwkJ6Ujw/umcli4syuP8n2/jVjhNelyQjKAiizKv7Gvi7Z/dw9aIC/uIaXRKS8JGVmshPP7+aipnZfOmJHfxk8zGvS5IABUEUef1gE1/4yTYWF2fyH7cvw6d7BiTMnD4zuGphAX/z9G7+af1ehnSfgefUayiMPPrW8Ql/dkd1K794p4b89CQeuWclGWoXkCkw0f+zVy0soLt/kAdfO8z++g6+dcdypqXo/6xXdEYQ4Qb9fp7fXceTW6uZkZ3K56+YQ3ZaotdliZxVnM+4eWkJty4r4Y2qJj76nTeorNXcx15REESwmpZuHth4iP8+2MTK2Tncs3YWKYlxXpclErSVs3P46b2r6Owb5NbvvMH3Nx7SkBQeUBBEoJPtvTyx5Tjf++0hOnoH+dSqMm5dVkJ8nA6nRJ5Vc3LZ8KUPcvWiQv75+X186qG3ONLU5XVZMUVtBBHC7xxHT3Wx+XAzlSfaSIjzccX8PNYtLCA5QWcBEtmy0xL53l0r+Nm2Gv7+2T1c9++v8YUr5/BH6+bpLHcKKAjCXH17LzuOt/JuTSttPQMkxfu4cmE+a+fmkZY09uG7kEZnEa+YGbdVzGDdgnz+af1e/uuVKn65/QRfvmYBtywr0ci5IaQgCENtPQPsrGllR3UrdW29+AzmF2Rw/ZIiFhdnkhivS0ASvQoyk/mP25fzycvK+F+/3sOXn3yX77xaxZc+vIAbLy5WIISAORdZDTMVFRVu69atXpcx6Tp6B/jGM5Vsr27lSGMXDpiRncKyGVlcXJpF+jh//YtEujtXlY27zO93vFBZz7+/dICDDZ2UZqdw95qZ3FYxg6xU9Y47H2a2zTlXMeYyBYF3+gf9vHagkV/uOMFv9pykb9BPbloiy2ZksXRGFnnpSV6XKBJyZwuC04b8jg2V9fzwzaO8faSZ5AQf15QXcdMlxVy5MJ+keLUjnMvZgkB/Zk4xv9+x5Wgzz+6s5bmddbR0D5CTlsjtl80gNTGe0uwUzRsgMkqcz7jx4mJuvLiYPbXt/PStYzy/u55n360lIymetfPyWDs/j7Vzc5mdl6afofOkM4Ip4JzjneOt/HpnLet31XGyvY/kBB/Xlhdx6/LpXDE/n4Q4nxp5Rc7DkN9xuLGTXSfaqGropLVnAICUhDhKslIozkomPz2J7LREslMTmZaScM72hWDOTiKVzgg8MDDk551jLby8r4HndtZxorWHxHgf6xbk85Gl07l6UcG4vX5E5NzifMb8wgzmF2bgnKO5q5+qxk5OtPRQ19bLm4dOvW8cI59BZkoCmckJZCTHk5GcQGZy/O9epyTQ0tVPVmpCzJ1RhPQ3kZldD/wnEAc85Jz7l1HLLbD8RqAb+Ixz7p1Q1hQqzjmON3fzelUTrx1o5I2qU3T2DZIQZ1wxP5+/uHYB15QXagwgkRAwM3LTk8hNT4LZw+8N+R1tPQO0dPfT0tVPc3c/rd0DtPcO0NDRx6HGTnoH/O/bzrdePkhivI+CjCQKMpIozEymMDOZ/MDzgowkiqclU5KdQmpi9PwhF7I9MbM44DvANUANsMXMnnHO7Rmx2g3A/MBjFfC9wNew5vc7qlu62V/fwYGTHeyobmNHdQtNnf0AlGSlcPOy6Vy5IJ/L5+bql7+IB+J8Rk5aIjlpiZA/9jr9g346egdo7x2ko3eA+YUZNLT30tDRx8n2Xg6c7OD1qiY6egfP+GxuWiKlOamUZqcwIzvwNfC6JCslom70DGWkrQSqnHOHAczsceAWYGQQ3AI84oYbKjabWZaZFTvn6kJV1JDfMej3MzjkGBxyDJx+7vfTOzBER+8gHb2DdPYN/8fo6B2kuaufurZealuHTznr23rpH/rdXxJz8tK4ckEBy8uyWD0nh7n56TF3aikSiRLjfb87k2D8NoLu/kEa2vto6Oijrq2HmpYealq6qW7uofJEGy9W1jMw9P721tNnFKfDaOQjMzmB1MQ4khPiSE2Me+95ckIc8T7D5zPifUbciK+h/J0SyiAoAapHvK7hzL/2x1qnBJj0IHhuZx1ffOwdJtI2HuczijKTmZ6VzLIZWRRfnMycvDQWBK5Pqo+/SHRLTYxnVl48s/LSxlw+5Hc0dPRS09JDdXP3e18bO/to6ernUGMnLV39dPUPTbgGn8H9V87lr65fNOFtjCeUv8HGiq/Rv4aDWQczuw+4L/Cy08z2n2cteUDTeX7mfQ5fyIcnzwXvR5jQfoQX7UfApyapkAs07n585V/gKxPf7szxFoQyCGqAGSNelwK1E1gH59yDwIMTLcTMto7XbSqSaD/Ci/YjvGg/Ji6Ug9ZsAeab2WwzSwRuB54Ztc4zwN02bDXQFsr2AREROVPIzgicc4Nm9kVgA8PdRx92zlWa2f2B5Q8A6xnuOlrFcPfRz4aqHhERGVtIWzmdc+sZ/mU/8r0HRjx3wB+HsoaACV9WCjPaj/Ci/Qgv2o8JirghJkREZHJpYHsRkRgXNUFgZg+bWYOZ7R5nuZnZt8ysysx2mtmKqa4xGEHsxzozazOzHYHH16e6xmCY2Qwze9XM9ppZpZn92RjrhP0xCXI/wv6YmFmymb1tZu8G9uPvxlgnrI9HkPsQ9sfiNDOLM7PtZvbrMZZN7bFwzkXFA/ggsALYPc7yG4HnGb53YTXwltc1T3A/1gG/9rrOIPajGFgReJ4BHADKI+2YBLkfYX9MAv/G6YHnCcBbwOpIOh5B7kPYH4sRtX4ZeHSseqf6WETNGYFz7jWg+SyrvDechXNuM5BlZsVTU13wgtiPiOCcq3OBAQSdcx3AXobvGh8p7I9JkPsR9gL/xp2BlwmBx+gGwrA+HkHuQ0Qws1Lg94CHxlllSo9F1ARBEMYbziISrQmcHj9vZku8LuZczGwWsJzhv+BGiqhjcpb9gAg4JoFLETuABuAl51zEHY8g9gEi4FgA/wH8FeAfZ/mUHotYCoKghrOIAO8AM51zS4H/Ap72tpyzM7N04BfAl5xz7aMXj/GRsDwm59iPiDgmzrkh59wyhu/gX2lmF41aJeyPRxD7EPbHwsw+AjQ457adbbUx3gvZsYilIAhqOItw55xrP3167Ibv00gwszyPyxqTmSUw/Mvzp865p8ZYJSKOybn2I5KOCYBzrhX4LXD9qEURcTxg/H2IkGOxFrjZzI4CjwMfMrOfjFpnSo9FLAVBVAxnYWZFZsPj0ZrZSoaP4SlvqzpToMb/C+x1zn1znNXC/pgEsx+RcEzMLN/MsgLPU4APA/tGrRbWxyOYfYiEY+Gc+2vnXKlzbhbDQ++84py7a9RqU3osomb8ZDN7jOEeA3lmVgP8LcONSbgIGs4iiP34OPCHZjYI9AC3u0A3gzCzFvg0sCtwTRfga0AZRNQxCWY/IuGYFAM/suEJo3zAk865X1tkDfkSzD5EwrEYk5fHQncWi4jEuFi6NCQiImNQEIiIxDgFgYhIjFMQiIjEOAWBiEiMUxCITICZDQVGt6wMDGfwZTPzBZZdY2bbzGxX4OuHvK5X5Gyi5j4CkSnWExjqADMrYHgUyWkM3/fRBNzknKsNDIGwgTAbs0dkJN1HIDIBZtbpnEsf8XoOsAXIG3kDU+Au1yZgunOub+orFTk3XRoSmQTOucMM/zwVjFr0MWC7QkDCmS4NiUye940YGRgC+X8D13pTjkhwdEYgMgkCl4aGGB4n//TEI78E7nbOHfKyNpFzURCIXCAzywceAL7tnHOBETKfA/7aOfeGp8WJBEGNxSITYGZDwC6GR4YdBH4MfNM55zezvwH+Gjg44iPXOucapr5SkXNTEIiIxDhdGhIRiXEKAhGRGKcgEBGJcQoCEZEYpyAQEYlxCgIRkRinIBARiXEKAhGRGPf/AXH7x0cw7oJkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "for i in df.columns:\n",
    "    print(sb.distplot(df[i]))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Most of the plots are right skewed."
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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9ExHPx/zGGTDmtfRM+I7yDbwPwCJJ32zgSJhHOAMGgCCcAQNAEAIMAEEIMAAEIcAAEIQAA0CQ/wPnPCv+bRZ5AAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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u+3LL/0nZpWMXOrF4gpZj1mOSXlig2W2SHjOzL7n7nKT/Tif5bnH3ryr7i5MHzGyvsktZvmyBPiTpTmXvFqTskMcdLcu/LOmP0v0HlJ3MfFxZiN+h7FtH7kuHcrZK+liq4RRlJ0xP5nAOBhhX3cPASHuwDym7APw3S9rGK5XtTa8ruN/3SnqDu99UZL8YHOxZYyCY2ZnK9mC/VlZQS7/47r0v5D8UU5AxSX9VcJ8YIOxZA0AA7FkDQACENQAEQFgDQACENQAEQFgDQAD/DzLzzzciFogRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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U3T/kDR3bvtXdn+4z3kfc/Stl2s3stJld6e4nC83XSrq3zz6A2nHkjs3gLkl/5e7rnRY5x8zeouxI+nOF5rsl/Z2kx9z91PBKlJTd0e+z7QvAZnadsrv5rfefAzBUHLkjeXko39Hl5feY2ZskXSDpcUnvcveHC6//S953usy+zOzzyu64uNB34/PNS7pI0rH8ft0vkvRqd//ZAGMBG8JdIYEhyH/Lztcl/bu7d/uYJTA0hDsABMQ5dwAIiHAHgIAIdwAIiHAHgIAIdwAIiHAHgID+Hxp1oFF9bkvkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BBoAkBBgAkhBgAEhCgAEgCQEGgCQEGACS/B9yY3iT8OxBOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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3j01rf7hGm+p2k5K2RsQHZyjzxarbhyRVn6nX/b3+iAjbd6s4K7+xfPjkcluYZzgDxnS3Svp0RIy/XCPbl0q6TsXb7im3q3hL/2RE7GlmUNvvtY8vdHymiiB8oZltdMhDki62PVzWdYrts2Zou1M/vZ69S9IZU9ebVbyLmMklKs7Sp5wlqacWJEJ3EMD4GRGxJyJumuHpq8uvUj2u4psMH4iI6qUkv6LiemqtD99OMO0a8LUqrgFvl/QlSR+KiMmWJtGGiNgn6cOSbre9Q0UgL5+h+T0q3ikoIl5S8Q/SPeWHcE9Pazu173aoeOfwmarnLpZ0X6fmgP7BamhAG8qwfV9EvNBi//MkfSwiru1oYegLBDDQhvKrd4fKDzBb6b9S0hMRsbujhaEvEMAAkIRrwACQhAAGgCQEMAAkIYABIAkBDABJ/h9I22CPylLuNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAANyklEQVR4nO3da2ye91mA8euu3dG06TRmoyqk1dLJY1M7pK7LykbRVk3tCN3UUbYvRSIpQpRxcEMQQkA/cBB8AASsTUWh2ikR6zhoVGhTSNYiApMQLUlJ63bZwc0yaIA1ccXWLlk7Jzcfnsed5zmJX7+H+32T6ydZsd/n9P+/tq88fmw/jsxEkjR4F1QPQJLOVwZYkooYYEkqYoAlqYgBlqQi452sPDk5mRs2bOjTUCTp3DM5OcmePXv2ZOampcs6CvCGDRvYt29f70YmSeeBiJhc7nEvQUhSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBXp6G/CDZvt27czOzvb9+McOXIEgPXr1/f9WL0yNTXF9PR09TAkncFIB3h2dpYDTx7k5MWv7utxxo5/DYD/fXE0nq6x489VD0HSCoxGUc7g5MWv5sQbbu7rMdZ8fhdA34/TKwvjlTTcvAYsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQYS4O3bt7N9+/ZBHEoaen4+aMH4IA4yOzs7iMNII8HPBy3wEoQkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEsjYG5ujjvvvJO5ubmeb9vNvoddp3Nbbv1+Pj8GWBoBO3bsYGZmhp07d/Z82272Pew6ndty6/fz+THA0pCbm5tj9+7dZCa7d+/u6EzsbNt2s+9h1+ncllu/38/PeE/3dhpHjhzhxIkTbN26taf7nZ2d5YKXsqf7PBdc8M2vMzv7fM+fb/XG7Owsa9asWfH6O3bs4NSpUwCcPHmSnTt3sm3btp5s282+h12nc1tu/czs6/Nz1jPgiLgjIvZFxL6jR4/27MCSVubhhx9mfn4egPn5eR566KGebdvNvoddp3Nbbv1+Pz9nPQPOzPuB+wE2bty4qtPN9evXA3D33XevZvPT2rp1K/sPfbWn+zwXnLrolUy99rKeP9/qjU6/MrnxxhvZtWsX8/PzjI+Pc9NNN/Vs2272Pew6ndty62dmX58frwFLQ27Lli1ccEHzqTo2NsbmzZt7tm03+x52nc5tufX7/fwYYGnITUxMsGnTJiKCTZs2MTEx0bNtu9n3sOt0bsut3+/nZyDfhJPUnS1btnD48OFVnYGdbdtu9j3sOp3bcuv38/kxwNIImJiY4J577unLtt3se9h1Orfl1u/n8+MlCEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqcj4IA4yNTU1iMNII8HPBy0YSICnp6cHcRhpJPj5oAVegpCkIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqYgBlqQiBliSihhgSSpigCWpiAGWpCIGWJKKGGBJKmKAJamIAZakIgZYkooYYEkqYoAlqch49QC6NXb8OdZ8flefjzEH0Pfj9MrY8eeAy6qHIeksRjrAU1NTAznOkSPzAKxfPypRu2xgz42k1RvpAE9PT1cPQZJWzWvAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBUxwJJUxABLUhEDLElFDLAkFTHAklTEAEtSEQMsSUUMsCQVMcCSVMQAS1IRAyxJRQywJBWJzFz5yhFHga/0bzh9NQkcqx7EAJwv8wTnei46F+d5DCAzNy1d0FGAR1lE7MvMjdXj6LfzZZ7gXM9F58s8F3gJQpKKGGBJKnI+Bfj+6gEMyPkyT3Cu56LzZZ7AeXQNWJKGzfl0BixJQ8UAS1KRkQ9wRGyKiC9ExGxE/PoyyyMi7mmXPxER1y5a9pGIeDYinhzsqFdntXONiCsi4p8i4mBEPBURWwc/+s50MdeLIuLRiHi8nevvDH70K9fNx2+7fCwi/iMiPj24Ua9Ol5+rhyNiJiIORMS+wY68jzJzZF+AMeBp4LXAK4DHgauWrHMz8A9AAG8FHlm07O3AtcCT1XPp51yBdcC17euXAl9cuu0wvXQ51wDWtq9fCDwCvLV6Tr2e56LlvwI8AHy6ej79nCtwGJisnkevX0b9DPg6YDYzD2XmS8BfAe9dss57gZ3Z+DfgVRGxDiAz/wV4bqAjXr1VzzUz/yczHwPIzOeBg8D6QQ6+Q93MNTPzhXadC9uXYf1Oc1cfvxFxOfBu4EODHPQqdTXXc9WoB3g98F+L3n6G7w7LStYZBT2Za0RsAN5Ec2Y4rLqaa/tl+QHgWeChzBzWuXb7Pv0g8GvAqT6Nr5e6nWsCn4mI/RFxR99GOWCjHuBY5rGlZzsrWWcUdD3XiFgLfBL45cz8eg/H1mtdzTUzT2bmNcDlwHUR8cbeDq9nVj3PiHgP8Gxm7u/9sPqi24/f6zPzWuDHgF+MiLf3cnBVRj3AzwBXLHr7cuC/V7HOKOhqrhFxIU18P56Zf9fHcfZCT96vmfl/wF7gu26CMiS6mef1wC0RcZjmy/l3RsRf9m+oXevqfZqZC/8+CzxIc0lj9FVfhO7mBRgHDgFX8u0L+1cvWefdfOeF/UeXLN/AaHwTbtVzbd/eCXyweh4DmOv3Aa9qX18DfBZ4T/Wcej3PJevcwPB/E66b9+klwKWLXv9XYFP1nHrxMr7CTg+lzJyPiF8C9tB8l/UjmflURHygXf7nwC6a767OAseBn17YPiI+QfPBOxkRzwC/lZkfHuwsVqbLuV4P/BQw014bBfjNzNw1wCmsWJdzXQfsiIgxmq/w/iYzh/JHtLr9+B0lXc71MuDBiIAm5A9k5u4BT6Ev/FVkSSoy6teAJWlkGWBJKmKAJamIAZakIgZYkooYYK1KRNzV3m3sifYOVT/U3rFqcpl1b1nu7leVIuJNEZER8aNLHj/ZzufJiPjbiLi4ffzyiPj7iPhSRByKiHsj4nvaZde12xxo78J2a8WcNHr8MTR1LCLeBvwJcENmvthG9xU0PyC/MTOH5s+KR8RYZp5c5vE/BN4GPJ2Zty96/IXMXNu+/nFgP/CnNPfOuC8zP9r+jPH9wAuZubWN9Evtz7quo/klg+/PzPl+z0+jzTNgrcY64FhmvgiQmcey/VVRYDoiHmvv3foGgIi4PSLubV//WETcF839iQ9FxDuiuS/zwYj42MIBIuKFiPiD9uYrD7dnmXvbbW5p1xmLiD+KiH9vz8R/rn38hnb/DwAzSwcfzU/0vx+4HXhXRFx0mnl+FpgC3gl8MzM/2s73JLAN2BwRazPz+KLYXsRo3mtEBQywVuMzwBUR8cWI+LOIeMeiZceyuWnKfcCvnmb776WJ2jbgUzRnmFcDPxgR17TrXALszcw3A88DvwfcBNwK/G67zs8AX8vMtwBvAX42Iq5sl10H3JWZVwEs+g1AaH4z8MuZ+TTNvSJuXjrAiBinufHLTDu277jpTTY3MzpME2jaSzBPtet/wLNfrYQBVseyud/um4E7gKPAX0fE7e3ihRv97Ke5z8ZyPpXNta8Z4KuZOZOZp4CnFm3zErDw66YzwD9n5rfa1xfWeRfNWegBmksEE8Dr2mWPZuaXF435mkXHv43mBja0/962aNmadn/7gP8EPkxzb4LlzmpfvntXZj6SmVfT/EfwG2c4q5ZeNtL3glCd9svwvcDeiJgBtrSLXmz/PcnpP74W1jm16PWFtxe2+VZ++xsUL6+Xmafas1NoAjidmXsW7zwibgC+sdyB2+u376O5k9hd7T4mIuLSbG5Wf2JJrGnPbN+35LFX0tyj4AuLH8/MgxHxDeCNNBGXTsszYHUsIl4fEa9b9NA1wFcKhrIH+Pn2VptExA9ExCVn2eZG4PHMvCIzN2Tma2hu0/njZ9jmH4GLI2Jze5wx4I+BezPzRERcufCfQkS8Bng9zeUJ6YwMsFZjLc0dxz4XEU8AVwG/XTCODwGfAx6L5g+r/gWnOetedA34Npr7yS72SeAnT3eQ9kz8VuD9EfElYA44lZm/367yI8Dj7TEeBH5hmH4SRMPLH0OTOhQRPwx8AviJHJ2/SKEhZIAlqYiXICSpiAGWpCIGWJKKGGBJKmKAJamIAZakIv8PPbKdkjXsrxsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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WUl6cN/wF6td8fdVJnuL3gKPA4xExTf1vhl1TvOKVVphXUZOWUP3u8R7gr0opd2bn0anFEpakRJ6OkKRElrAkJbKEJSmRJSxJiSxhSUpkCUtSov8HUApJGlS+CuEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAJ8UlEQVR4nO3dX4jl91nH8c+zM4FuiVq6m8Y4qS5lLsQKagglIkiRpCwF8UIvvHEXb0QvJmu8q4jipTeFZLwoAYVdUUHwD1K3KwkogmB1t0Y3JcEeS4rZtiaZYNKya2Vmv17Msdkd9s9snT3POXNeLxhyZs6Z83vy3S/vPfM7c87WGCMAzN6R7gEAlpUAAzQRYIAmAgzQRIABmqzey42PHz8+Tpw4cZ9GATicLl269NYY46G9X7+nAJ84cSIXL148uKkAlkBVfeVWX3cKAqCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCb39G/CMf82NzczmUy6x/i2K1euJEnW1taaJ7nZ+vp6NjY2usdgyQnwITOZTPLSy69k5/0f7B4lSbJy9Z0kyde/NT9bbeXq290jQBIBPpR23v/BXPvBT3aPkSQ5+ur5JJmbeZL3ZoJuzgEDNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzSZSYA3Nzezubk5i0MBh8CyNGN1FgeZTCazOAxwSCxLM5yCAGgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAe5ga2srTz/9dLa2tg78vgUY4A7Onj2by5cv59y5cwd+3wIMcBtbW1u5cOFCxhi5cOHCgT8KXj3Qe7uNK1eu5Nq1azlz5swsDrfUJpNJjvzP6B5jrh3573czmXzDfpxjk8kkR48e7R4jZ8+ezfXr15MkOzs7OXfuXJ555pkDu/+7PgKuql+qqotVdfHNN988sAMDzLsXX3wx29vbSZLt7e288MILB3r/d30EPMZ4PsnzSfL4449/Rw+t1tbWkiTPPvvsd/Lt3IMzZ87k0pf/s3uMuXb9fd+d9Y88bD/OsXn56eTJJ5/M+fPns729ndXV1Tz11FMHev/OAQPcxunTp3PkyG4mV1ZWcurUqQO9fwEGuI1jx47l5MmTqaqcPHkyx44dO9D7n8mTcACL6vTp03nttdcO/NFvIsAAd3Ts2LE899xz9+W+nYIAaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZHUWB1lfX5/FYYBDYlmaMZMAb2xszOIwwCGxLM1wCgKgiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATVa7B+DgrVx9O0dfPd89RpJk5epWkszNPMnu+iQPd48BAnzYrK+vd49wkytXtpMka2vzFLyH526dWE4CfMhsbGx0jwDsk3PAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCY1xtj/javeTPKV+zdOkuR4krfu8zEWifW4mfV4j7W42Tyvxw+MMR7a+8V7CvAsVNXFMcbj3XPMC+txM+vxHmtxs0VcD6cgAJoIMECTeQzw890DzBnrcTPr8R5rcbOFW4+5OwcMsCzm8REwwFIQYIAmLQGuqt+vqjeq6uXbXP/xqnqnql6afvzmrGecpar6cFX9TVW9UlVfrKozt7hNVdVzVTWpqn+tqsc6Zr3f9rkWS7M/qup9VfWPVfUv0/X47VvcZin2RrLv9Vic/THGmPlHkp9M8liSl29z/ceTfLZjtqb1eCTJY9PL35Xk35L80J7bfDLJ55JUkieSfL577sa1WJr9Mf3zfnB6+YEkn0/yxDLujXtYj4XZHy2PgMcYf5fk7Y5jz6MxxtfGGF+YXv5GkleSrO252c8kOTd2/UOSD1TVIzMe9b7b51osjemf9zennz4w/dj7zPlS7I1k3+uxMOb5HPCPT3/M+FxVfbR7mFmpqhNJfiy7f7PfaC3Jf9zw+es55GG6w1okS7Q/qmqlql5K8kaSF8YYS7039rEeyYLsj3kN8Bey+9rpH0mymeQveseZjap6MMmfJvnVMca7e6++xbcs7N/8d3OXtViq/THG2Blj/GiSR5N8rKp+eM9Nlmpv7GM9FmZ/zGWAxxjv/t+PGWOM80keqKrjzWPdV1X1QHaD84djjD+7xU1eT/LhGz5/NMlXZzHbrN1tLZZxfyTJGOO/kvxtkpN7rlqavXGj263HIu2PuQxwVX1vVdX08seyO+dW71T3z/T/9feSvDLG+PRtbvaXSU5Nn/F+Isk7Y4yvzWzIGdnPWizT/qiqh6rqA9PLR5M8meTVPTdbir2R7G89Fml/rHYctKr+OLvPVB6vqteT/FZ2T6ZnjPGZJD+X5FeqajvJtSQ/P6ZPbx5SP5HkF5Jcnp7bSpJfT/L9ybfX5Hx2n+2eJLma5BdnP+ZM7Gctlml/PJLkbFWtZDckfzLG+GxV/XKydHsj2d96LMz+8FJkgCZzeQoCYBkIMEATAQZoIsAATQQYoIkAs1Cqamf6DldfnL7U9Neq6sj0uqeq6lJVXZ7+96e654U7afk9YPh/uDZ9GWqq6kNJ/ijJ92T3d8nfSvLTY4yvTl+e+tc5xO+JwOLze8AslKr65hjjwRs+/0iSf0py/MZftp++EuqtJN83xvjW7CeFu3MKgoU2xvhydvfxh/Zc9bNJ/ll8mWdOQXAY3PRuYNO3H/ydJJ/oGQf2xyNgFtr0FMROdt8bNlX1aJI/T3JqjPHvnbPB3QgwC6uqHkrymSS/O8YY03fJ+qsknxpj/H3rcLAPnoRjoVTVTpLL2X33vO0kf5Dk02OM61X1G0k+leRLN3zLJ8YYb8x+Urg7AQZo4hQEQBMBBmgiwABNBBigiQADNBFggCYCDNDkfwF2myTPdnmwXQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "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": [
    "for i in df.columns:\n",
    "    print(sb.boxplot(df[i]))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Almost all variables have outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q7. Are there any strong correlations among the independent features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop('status',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vif=pd.DataFrame()\n",
    "vif['Features']=x.columns\n",
    "vif['VIF']=[variance_inflation_factor(x.values,i) for i in range(x.shape[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Features</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shimmer:APQ3</td>\n",
       "      <td>51152986.518916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shimmer:DDA</td>\n",
       "      <td>51126303.411585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jitter:DDP</td>\n",
       "      <td>3103948.971369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MDVP:RAP</td>\n",
       "      <td>3101964.447447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MDVP:Shimmer</td>\n",
       "      <td>2653.656644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MDVP:Jitter(%)</td>\n",
       "      <td>506.560577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DFA</td>\n",
       "      <td>414.914997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spread1</td>\n",
       "      <td>384.369565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shimmer:APQ5</td>\n",
       "      <td>342.841149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MDVP:Shimmer(dB)</td>\n",
       "      <td>303.102407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MDVP:PPQ</td>\n",
       "      <td>273.803581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MDVP:APQ</td>\n",
       "      <td>184.053597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PPE</td>\n",
       "      <td>133.491270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HNR</td>\n",
       "      <td>132.110508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MDVP:Jitter(Abs)</td>\n",
       "      <td>122.716536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>D2</td>\n",
       "      <td>112.654094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MDVP:Fo(Hz)</td>\n",
       "      <td>102.242806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RPDE</td>\n",
       "      <td>55.614407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spread2</td>\n",
       "      <td>23.076221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MDVP:Flo(Hz)</td>\n",
       "      <td>18.052014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NHR</td>\n",
       "      <td>15.997170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MDVP:Fhi(Hz)</td>\n",
       "      <td>8.880351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features             VIF\n",
       "10      Shimmer:APQ3 51152986.518916\n",
       "13       Shimmer:DDA 51126303.411585\n",
       "7         Jitter:DDP  3103948.971369\n",
       "5           MDVP:RAP  3101964.447447\n",
       "8       MDVP:Shimmer     2653.656644\n",
       "3     MDVP:Jitter(%)      506.560577\n",
       "17               DFA      414.914997\n",
       "18           spread1      384.369565\n",
       "11      Shimmer:APQ5      342.841149\n",
       "9   MDVP:Shimmer(dB)      303.102407\n",
       "6           MDVP:PPQ      273.803581\n",
       "12          MDVP:APQ      184.053597\n",
       "21               PPE      133.491270\n",
       "15               HNR      132.110508\n",
       "4   MDVP:Jitter(Abs)      122.716536\n",
       "20                D2      112.654094\n",
       "0        MDVP:Fo(Hz)      102.242806\n",
       "16              RPDE       55.614407\n",
       "19           spread2       23.076221\n",
       "2       MDVP:Flo(Hz)       18.052014\n",
       "14               NHR       15.997170\n",
       "1       MDVP:Fhi(Hz)        8.880351"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif.sort_values(by='VIF',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#High correlation between independent variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q8. Split dataset into training & test dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop('status',1)\n",
    "y=df['status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,train_size=0.8,random_state=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q9. Create a default decision tree model using criterion = Entropy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier(criterion='entropy')\n",
    "dt.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest_pred=dt.predict(xtest)\n",
    "ytest_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      1.00      0.74         7\n",
      "           1       1.00      0.84      0.92        32\n",
      "\n",
      "    accuracy                           0.87        39\n",
      "   macro avg       0.79      0.92      0.83        39\n",
      "weighted avg       0.93      0.87      0.88        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ytest_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f1 score 88%"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lets use gridsearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 23)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'criterion':['gini','entropy'],\n",
    "       'max_depth':np.arange(1,11,2),\n",
    "       'min_samples_leaf':np.arange(1,10)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': array([1, 3, 5, 7, 9]),\n",
       "                         'min_samples_leaf': array([1, 2, 3, 4, 5, 6, 7, 8, 9])},\n",
       "             scoring='f1_weighted')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt=DecisionTreeClassifier()\n",
    "grd=GridSearchCV(dt,param_grid=params,cv=5,scoring='f1_weighted')\n",
    "grd.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_leaf=3)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#best features\n",
    "bst_feat=grd.best_estimator_\n",
    "bst_feat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_leaf=3)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_fin=DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_leaf=3)\n",
    "dt_fin.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred_fin=dt_fin.predict(xtest)\n",
    "ypred_fin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      1.00      0.78         7\n",
      "           1       1.00      0.88      0.93        32\n",
      "\n",
      "    accuracy                           0.90        39\n",
      "   macro avg       0.82      0.94      0.86        39\n",
      "weighted avg       0.93      0.90      0.91        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ypred_fin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f1_score 91% .\n",
    "#We have achieved better performance after regularization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The regularization parameter is a control on your fitting parameters.\n",
    "#It helps avoiding overfit models.\n",
    "#It also helps to reduce error with better performance ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier()\n",
    "rf.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "params={'n_estimators':np.arange(100,200)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=6, estimator=RandomForestClassifier(),\n",
       "             param_grid={'n_estimators': array([100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112,\n",
       "       113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,\n",
       "       126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138,\n",
       "       139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151,\n",
       "       152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,\n",
       "       165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,\n",
       "       178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190,\n",
       "       191, 192, 193, 194, 195, 196, 197, 198, 199])},\n",
       "             scoring='f1_weighted')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grd_rf=GridSearchCV(rf,param_grid=params,cv=6,scoring='f1_weighted')\n",
    "grd_rf.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=140)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grd_rf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 140}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grd_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Optimal number of trees is 140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=140)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_fin=RandomForestClassifier(n_estimators=140)\n",
    "rf_fin.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_test_rf=rf_fin.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.71      0.83         7\n",
      "           1       0.94      1.00      0.97        32\n",
      "\n",
      "    accuracy                           0.95        39\n",
      "   macro avg       0.97      0.86      0.90        39\n",
      "weighted avg       0.95      0.95      0.95        39\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ypred_test_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#f1_score 95%\n",
    "#very high performance and the best of above models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Features</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>PPE</td>\n",
       "      <td>0.123712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>spread1</td>\n",
       "      <td>0.112904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MDVP:Fo(Hz)</td>\n",
       "      <td>0.088676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>spread2</td>\n",
       "      <td>0.064271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MDVP:Jitter(Abs)</td>\n",
       "      <td>0.060116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jitter:DDP</td>\n",
       "      <td>0.057209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MDVP:Fhi(Hz)</td>\n",
       "      <td>0.048859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NHR</td>\n",
       "      <td>0.045377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MDVP:APQ</td>\n",
       "      <td>0.045008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MDVP:Flo(Hz)</td>\n",
       "      <td>0.044904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MDVP:RAP</td>\n",
       "      <td>0.040733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>D2</td>\n",
       "      <td>0.035014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MDVP:PPQ</td>\n",
       "      <td>0.029507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MDVP:Jitter(%)</td>\n",
       "      <td>0.027382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>RPDE</td>\n",
       "      <td>0.026215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>HNR</td>\n",
       "      <td>0.024504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>DFA</td>\n",
       "      <td>0.024009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Shimmer:APQ3</td>\n",
       "      <td>0.022009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Shimmer:DDA</td>\n",
       "      <td>0.021037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MDVP:Shimmer</td>\n",
       "      <td>0.020590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shimmer:APQ5</td>\n",
       "      <td>0.020466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MDVP:Shimmer(dB)</td>\n",
       "      <td>0.017498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Features    score\n",
       "21               PPE 0.123712\n",
       "18           spread1 0.112904\n",
       "0        MDVP:Fo(Hz) 0.088676\n",
       "19           spread2 0.064271\n",
       "4   MDVP:Jitter(Abs) 0.060116\n",
       "7         Jitter:DDP 0.057209\n",
       "1       MDVP:Fhi(Hz) 0.048859\n",
       "14               NHR 0.045377\n",
       "12          MDVP:APQ 0.045008\n",
       "2       MDVP:Flo(Hz) 0.044904\n",
       "5           MDVP:RAP 0.040733\n",
       "20                D2 0.035014\n",
       "6           MDVP:PPQ 0.029507\n",
       "3     MDVP:Jitter(%) 0.027382\n",
       "16              RPDE 0.026215\n",
       "15               HNR 0.024504\n",
       "17               DFA 0.024009\n",
       "10      Shimmer:APQ3 0.022009\n",
       "13       Shimmer:DDA 0.021037\n",
       "8       MDVP:Shimmer 0.020590\n",
       "11      Shimmer:APQ5 0.020466\n",
       "9   MDVP:Shimmer(dB) 0.017498"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_imp=pd.DataFrame({'Features':xtrain.columns,'score':rf_fin.feature_importances_})\n",
    "f_imp.sort_values(by='score',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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