Ensemble Techniques Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB as gnb\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "from sklearn import tree\n",
    "from os import system\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the data \n",
    "data = pd.read_csv('parkinsons.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>name</th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phon_R01_S01_1</td>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phon_R01_S01_2</td>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>phon_R01_S01_3</td>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>phon_R01_S01_4</td>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>phon_R01_S01_5</td>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  ...  \\\n",
       "0           0.00007   0.00370   0.00554     0.01109       0.04374  ...   \n",
       "1           0.00008   0.00465   0.00696     0.01394       0.06134  ...   \n",
       "2           0.00009   0.00544   0.00781     0.01633       0.05233  ...   \n",
       "3           0.00009   0.00502   0.00698     0.01505       0.05492  ...   \n",
       "4           0.00011   0.00655   0.00908     0.01966       0.06425  ...   \n",
       "\n",
       "   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
       "0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
       "1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
       "2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
       "3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
       "4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
       "\n",
       "    spread2        D2       PPE  \n",
       "0  0.266482  2.301442  0.284654  \n",
       "1  0.335590  2.486855  0.368674  \n",
       "2  0.311173  2.342259  0.332634  \n",
       "3  0.334147  2.405554  0.368975  \n",
       "4  0.234513  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Eye-balling data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The data has 24 columns with different attributes. \n",
    "#These are explained in the Attribute information below\n",
    "\n",
    "#name - ASCII subject name and recording number\n",
    "\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",
    "\n",
    "#MDVP:Jitter(%), MDVP:Jitter(Abs), MDVP:RAP, MDVP:PPQ, Jitter:DDP \n",
    "#Several measures of variation in fundamental frequency\n",
    "\n",
    "#MDVP:Shimmer, MDVP:Shimmer(dB), Shimmer:APQ3, Shimmer:APQ5, MDVP:APQ, Shimmer:DDA \n",
    "#Several measures of variation in amplitude \n",
    "\n",
    "#NHR,HNR - Two measures of ratio of noise to tonal components in the voice\n",
    "\n",
    "#status - Health status of the subject (one) - Parkinson's, (zero) - healthy\n",
    "\n",
    "#RPDE,D2 - Two nonlinear dynamical complexity measures\n",
    "\n",
    "#DFA - Signal fractal scaling exponent\n",
    "\n",
    "#spread1, spread2, PPE - Three nonlinear measures of fundamental frequency variation \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(195, 24)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#195 different recordings (rows) exist for these 24 attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 24 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   name              195 non-null    object \n",
      " 1   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 2   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 3   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 4   MDVP:Jitter(%)    195 non-null    float64\n",
      " 5   MDVP:Jitter(Abs)  195 non-null    float64\n",
      " 6   MDVP:RAP          195 non-null    float64\n",
      " 7   MDVP:PPQ          195 non-null    float64\n",
      " 8   Jitter:DDP        195 non-null    float64\n",
      " 9   MDVP:Shimmer      195 non-null    float64\n",
      " 10  MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 11  Shimmer:APQ3      195 non-null    float64\n",
      " 12  Shimmer:APQ5      195 non-null    float64\n",
      " 13  MDVP:APQ          195 non-null    float64\n",
      " 14  Shimmer:DDA       195 non-null    float64\n",
      " 15  NHR               195 non-null    float64\n",
      " 16  HNR               195 non-null    float64\n",
      " 17  status            195 non-null    int64  \n",
      " 18  RPDE              195 non-null    float64\n",
      " 19  DFA               195 non-null    float64\n",
      " 20  spread1           195 non-null    float64\n",
      " 21  spread2           195 non-null    float64\n",
      " 22  D2                195 non-null    float64\n",
      " 23  PPE               195 non-null    float64\n",
      "dtypes: float64(22), int64(1), object(1)\n",
      "memory usage: 36.7+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Except for status and name, all other attributes are floating type. \n",
    "#Status is integer and Name is an object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "#Re-Checing the presence of null-values\n",
    "print(data.isnull().values.any())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#No Null Values in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Moving status column to the last\n",
    "df1=data.pop('status') \n",
    "data['status'] = df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping name column as this attribute is not of much significance \n",
    "data = data.drop('name',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>154.228641</td>\n",
       "      <td>41.390065</td>\n",
       "      <td>88.333000</td>\n",
       "      <td>117.572000</td>\n",
       "      <td>148.790000</td>\n",
       "      <td>182.769000</td>\n",
       "      <td>260.105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>197.104918</td>\n",
       "      <td>91.491548</td>\n",
       "      <td>102.145000</td>\n",
       "      <td>134.862500</td>\n",
       "      <td>175.829000</td>\n",
       "      <td>224.205500</td>\n",
       "      <td>592.030000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>116.324631</td>\n",
       "      <td>43.521413</td>\n",
       "      <td>65.476000</td>\n",
       "      <td>84.291000</td>\n",
       "      <td>104.315000</td>\n",
       "      <td>140.018500</td>\n",
       "      <td>239.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.004940</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.033160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.000260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.021440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.003446</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>0.003955</td>\n",
       "      <td>0.019580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.009920</td>\n",
       "      <td>0.008903</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.004985</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.064330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.029709</td>\n",
       "      <td>0.018857</td>\n",
       "      <td>0.009540</td>\n",
       "      <td>0.016505</td>\n",
       "      <td>0.022970</td>\n",
       "      <td>0.037885</td>\n",
       "      <td>0.119080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.282251</td>\n",
       "      <td>0.194877</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.148500</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>1.302000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.015664</td>\n",
       "      <td>0.010153</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.008245</td>\n",
       "      <td>0.012790</td>\n",
       "      <td>0.020265</td>\n",
       "      <td>0.056470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.017878</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>0.013470</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>0.079400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MDVP:APQ</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.024081</td>\n",
       "      <td>0.016947</td>\n",
       "      <td>0.007190</td>\n",
       "      <td>0.013080</td>\n",
       "      <td>0.018260</td>\n",
       "      <td>0.029400</td>\n",
       "      <td>0.137780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>0.030459</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.024735</td>\n",
       "      <td>0.038360</td>\n",
       "      <td>0.060795</td>\n",
       "      <td>0.169420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NHR</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>0.040418</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>0.025640</td>\n",
       "      <td>0.314820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HNR</th>\n",
       "      <td>195.0</td>\n",
       "      <td>21.885974</td>\n",
       "      <td>4.425764</td>\n",
       "      <td>8.441000</td>\n",
       "      <td>19.198000</td>\n",
       "      <td>22.085000</td>\n",
       "      <td>25.075500</td>\n",
       "      <td>33.047000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPDE</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.498536</td>\n",
       "      <td>0.103942</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>0.421306</td>\n",
       "      <td>0.495954</td>\n",
       "      <td>0.587562</td>\n",
       "      <td>0.685151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFA</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.718099</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>0.574282</td>\n",
       "      <td>0.674758</td>\n",
       "      <td>0.722254</td>\n",
       "      <td>0.761881</td>\n",
       "      <td>0.825288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread1</th>\n",
       "      <td>195.0</td>\n",
       "      <td>-5.684397</td>\n",
       "      <td>1.090208</td>\n",
       "      <td>-7.964984</td>\n",
       "      <td>-6.450096</td>\n",
       "      <td>-5.720868</td>\n",
       "      <td>-5.046192</td>\n",
       "      <td>-2.434031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spread2</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.226510</td>\n",
       "      <td>0.083406</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>0.174351</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>0.279234</td>\n",
       "      <td>0.450493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>195.0</td>\n",
       "      <td>2.381826</td>\n",
       "      <td>0.382799</td>\n",
       "      <td>1.423287</td>\n",
       "      <td>2.099125</td>\n",
       "      <td>2.361532</td>\n",
       "      <td>2.636456</td>\n",
       "      <td>3.671155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PPE</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.206552</td>\n",
       "      <td>0.090119</td>\n",
       "      <td>0.044539</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.194052</td>\n",
       "      <td>0.252980</td>\n",
       "      <td>0.527367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>195.0</td>\n",
       "      <td>0.753846</td>\n",
       "      <td>0.431878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count        mean        std         min         25%  \\\n",
       "MDVP:Fo(Hz)       195.0  154.228641  41.390065   88.333000  117.572000   \n",
       "MDVP:Fhi(Hz)      195.0  197.104918  91.491548  102.145000  134.862500   \n",
       "MDVP:Flo(Hz)      195.0  116.324631  43.521413   65.476000   84.291000   \n",
       "MDVP:Jitter(%)    195.0    0.006220   0.004848    0.001680    0.003460   \n",
       "MDVP:Jitter(Abs)  195.0    0.000044   0.000035    0.000007    0.000020   \n",
       "MDVP:RAP          195.0    0.003306   0.002968    0.000680    0.001660   \n",
       "MDVP:PPQ          195.0    0.003446   0.002759    0.000920    0.001860   \n",
       "Jitter:DDP        195.0    0.009920   0.008903    0.002040    0.004985   \n",
       "MDVP:Shimmer      195.0    0.029709   0.018857    0.009540    0.016505   \n",
       "MDVP:Shimmer(dB)  195.0    0.282251   0.194877    0.085000    0.148500   \n",
       "Shimmer:APQ3      195.0    0.015664   0.010153    0.004550    0.008245   \n",
       "Shimmer:APQ5      195.0    0.017878   0.012024    0.005700    0.009580   \n",
       "MDVP:APQ          195.0    0.024081   0.016947    0.007190    0.013080   \n",
       "Shimmer:DDA       195.0    0.046993   0.030459    0.013640    0.024735   \n",
       "NHR               195.0    0.024847   0.040418    0.000650    0.005925   \n",
       "HNR               195.0   21.885974   4.425764    8.441000   19.198000   \n",
       "RPDE              195.0    0.498536   0.103942    0.256570    0.421306   \n",
       "DFA               195.0    0.718099   0.055336    0.574282    0.674758   \n",
       "spread1           195.0   -5.684397   1.090208   -7.964984   -6.450096   \n",
       "spread2           195.0    0.226510   0.083406    0.006274    0.174351   \n",
       "D2                195.0    2.381826   0.382799    1.423287    2.099125   \n",
       "PPE               195.0    0.206552   0.090119    0.044539    0.137451   \n",
       "status            195.0    0.753846   0.431878    0.000000    1.000000   \n",
       "\n",
       "                         50%         75%         max  \n",
       "MDVP:Fo(Hz)       148.790000  182.769000  260.105000  \n",
       "MDVP:Fhi(Hz)      175.829000  224.205500  592.030000  \n",
       "MDVP:Flo(Hz)      104.315000  140.018500  239.170000  \n",
       "MDVP:Jitter(%)      0.004940    0.007365    0.033160  \n",
       "MDVP:Jitter(Abs)    0.000030    0.000060    0.000260  \n",
       "MDVP:RAP            0.002500    0.003835    0.021440  \n",
       "MDVP:PPQ            0.002690    0.003955    0.019580  \n",
       "Jitter:DDP          0.007490    0.011505    0.064330  \n",
       "MDVP:Shimmer        0.022970    0.037885    0.119080  \n",
       "MDVP:Shimmer(dB)    0.221000    0.350000    1.302000  \n",
       "Shimmer:APQ3        0.012790    0.020265    0.056470  \n",
       "Shimmer:APQ5        0.013470    0.022380    0.079400  \n",
       "MDVP:APQ            0.018260    0.029400    0.137780  \n",
       "Shimmer:DDA         0.038360    0.060795    0.169420  \n",
       "NHR                 0.011660    0.025640    0.314820  \n",
       "HNR                22.085000   25.075500   33.047000  \n",
       "RPDE                0.495954    0.587562    0.685151  \n",
       "DFA                 0.722254    0.761881    0.825288  \n",
       "spread1            -5.720868   -5.046192   -2.434031  \n",
       "spread2             0.218885    0.279234    0.450493  \n",
       "D2                  2.361532    2.636456    3.671155  \n",
       "PPE                 0.194052    0.252980    0.527367  \n",
       "status              1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().transpose()\n",
    "#This is a summary table to show the spread of all attributes\n",
    "\n",
    "#The mean of average vocal fundamental frequency is 154.2 Hz. With cases ranging from 88 Hz to 260 Hz.\n",
    "#The mean of average maximum vocal fundamental frequency in Dataset is 197 Hz with max frequency being 592 Hz and min of 102 Hz.\n",
    "#The mean of average minimum vocal fundamental frequency in Dataset is 116 Hz with max frequency being 239 Hz and min of 65 Hz\n",
    "\n",
    "#A Uni-Variate and Bi-Variate analysis of attrbitues to follow.\n",
    "\n",
    "#A noteoworthy point is that over 75% of recordings in the dataset are of the patients suffering from PD (Status attribute)\n",
    "#The non-PD patients are under-represented in the dataset (esp. since way less than 75 % the population suffers from PD )\n",
    "#Source: https://www.parkinson.org/Understanding-Parkinsons/Statistics#:~:text=Who%20Has%20Parkinson's%3F&text=Approximately%2060%2C000%20Americans%20are%20diagnosed,are%20diagnosed%20before%20age%2050. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    147\n",
       "0     48\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.status.value_counts()\n",
    "# Out of 195 people in dataset, 147 people are affected with Parkinson's Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadb2fe508>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Univariate Analysis for attributes\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(16,10)) \n",
    "sns.boxplot(x='spread1',data=data, ax=ax[0],orient='v') \n",
    "sns.boxplot(x='spread2',data=data, ax=ax[1],orient='v')\n",
    "sns.boxplot(x='PPE',data=data,ax=ax[2],orient='v')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The above figure shows the box plot of the frequency variation. \n",
    "#All the three variations have few outliers. \n",
    "#Generally speaking, decision trees are able to handle outliers. It is very unlikely that decision tree will create a leaf to isolate them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadb439488>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Uni-variate analysis of vocal fundamental frequencies (MDVP)\n",
    "\n",
    "fig, ax = plt.subplots(1,3,figsize=(16,8)) \n",
    "sns.distplot(data['MDVP:Flo(Hz)'],ax=ax[0]) \n",
    "sns.distplot(data['MDVP:Fo(Hz)'],ax=ax[1]) \n",
    "sns.distplot(data['MDVP:Fhi(Hz)'],ax=ax[2])\n",
    "\n",
    "#As seen from the measures of vocal fundamental frequency shown below:\n",
    "# 1. There is a positive skewness for minimum vocal fundemental frequency with more high values between 75Hz and 125Hhz. \n",
    "# 2. The average vocal frequency is almost normally distributed with more values ranging 115Hz and 130Hz. \n",
    "# 3. The high vocal frequency does not have any skewness, but some range of values are at the right most tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadb5ae2c8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAHgCAYAAADNOLJ/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzde3Cc53Xn+d/pezeAboAACIAXiJRIXSjbsmxKSmxrEku+ZXOxs7F3baUmSuKKZmrj2kl5tibObMU7SWWrxrtT8exWPLXrHcdRTaKJPU489thybMeWE9txaEqyJYuiJEIURYIECBD3Bvrez/7RDRKiAOHWb7/d/X4/VSoCjQb7UCWI+OE8zznmnBMAAAAAAF4J+V0AAAAAAKCzETwBAAAAAJ4ieAIAAAAAPEXwBAAAAAB4iuAJAAAAAPAUwRMAAAAA4KlIM19sYGDAHTp0qJkvCQDoYE888cQV59yg33W0M/5uBgA00kZ/Nzc1eB46dEiPP/54M18SANDBzOxlv2tod/zdDABopI3+buaoLQAAAADAUwRPAAAAAICnCJ4AAAAAAE8RPAEAAAAAniJ4AgAAAAA8RfAEAAAAAHiK4AkAAAAA8BTBEwAAAADgKYInAAAAAMBTBE8AAAAAgKcIngAAAAAATxE8AQAAAACeIngCAAAAADxF8AQAAAAAeCqylSeZ2TlJS5IqksrOueNmtkfS5yQdknRO0v/gnJvzpkwAAAAAQLvaTsfz7c65Nzrnjtff/5ikbznnjkr6Vv19AAAAAABeYTdHbd8r6eH62w9Let/uywEAAAAAdJqtBk8n6Rtm9oSZPVR/bMg5NyFJ9V/3elEgAAAAAKC9bemOp6S3OucumdleSd80s+e2+gL1oPqQJI2Oju6gRAAAAABAO9tS8HTOXar/OmVmX5R0t6TLZjbinJswsxFJUxt87qclfVqSjh8/7hpT9u49cuL8a378gXsIyQAAAPDXZt+z7gTf58IPmx61NbMuM+tZfVvSuyQ9I+nLkh6sP+1BSV/yqkgAAAAAQPvaSsdzSNIXzWz1+Y845/7GzE5K+ryZfVjSeUkf8K5MAAAAAEC72jR4OufOSrpjncdnJN3vRVEAAAAAgM6xm3UqAAAAAABsiuAJAAAAAPAUwRMAAAAA4CmCJwAAAADAUwRPAAAAAICnCJ4AAAAAAE8RPAEAAAAAniJ4AgAAAAA8RfAEAAAAAHiK4AkAAAAA8BTBEwAAAADgKYInAAAAAMBTBE8AAAAAgKcIngAAAAAATxE8AQAAAACeIngCAAAAADxF8AQAAAAAeIrgCQAAAADwFMETAAAAAOApgicAAAAAwFMETwAAAACApwieAAAAAABPETwBAAAAAJ4ieAIAAAAAPEXwBAAAAAB4iuAJAAAAAPAUwRMAAAAA4CmCJwAAAADAUxG/CwAAAACwdcVyVePzK5qYz2tyIa9ULKzb92d0oC+pkJnf5QHrIngCAAAAbSBXrOgHZ6/o+2MzypUqkqSueET5YkXfHbuiTDKqdx0b0p2jfT5XCrwawRMAAABoYcVyVd8dm9b3zlxRoVzVrcM9uvvQHu3vS6onEVWuWNFzk4v6x7Mz+sIT44qEQ3r9/ozfZQOvQPAEAAAAWpBzTk+Nz+tvnpnUQq6k2/el9fZb9mpfb/IVz0vGwrpztE+378voT7//kj5/8oIS0ZCO7u1Z9/d95MT5htb5wD2jDf390JkYLgQAAAC0mJ+ML+gD/88P9LmTF9QVC+u37r1Rv3rPDa8KnWvFIiE9+NOHNNgT15//48u6OJdrYsXAayN4AgAAAC3i0nxOv/uFp/VLn/qeXrqyrF++c7/+p7cf0eGBri19fjIW1m+89ZAS0bAefWbC42qBreOoLQAAAOCzU5cW9B+/+5L+21OXZCb91r036iP3HdFXntp+eOxJRPUzNw/qK09P6KUry1sOrYCXCJ4AAABAkznn9MLlrL5+alJfe2ZSpycWlYqF9Ws/fUi/8dZDOrgntavf/65De/Sd56f12HNTOvy2ww2qGtg5gicAAADgIeecLs7n9PT4gp4eX9BPLs7rJ+MLWsyXZSa9ebRPv/8Lx/T+Nx1QJhVtyGtGwyHde3RAX3tmUudnVzS6yyAL7BbBEwAAAGgg55xOXVrUD16c0clzs3ri5TnNLBclSdGw6dbhtH7hjn2640BGb79lr/amE57UcffhPfq7F2pdzwffcsiT1wC2iuAJAAAANMCVbEFffPKi/ssTF/TC5awk6Yb+lH7mlkHdOdqnN+zP6NaRHsUj4abUE4+E9bYjA/rGs5d1cS6n/X0bT8QFvEbwBAAAAHahXKnqz/7hnP74my9opVjRGw/26n//5dfpnbcNedbN3KqfurFf335uSj+6MEfwhK8IngAAAMAO/fjCvD72V0/ruckl3XfrXn3s527VzUM9fpd1VSIa1pG93Xr20qJ+/vUjMjO/S0JAETwBAACAHfjms5f12488qYGumP7ff/pmvevYUEsGu2MjaT03uaSJhbz29dL1hD9CfhcAAAAAtJu/fnJc//zPn9BtI2l99X++V+++fbglQ6ck3TqSlkk6dWnR71IQYHQ8AQAA0BYeOXG+4b/nA/eM7qiOf/3Fn+gtN/Xr0792XN3xyCs+1mq64xHd0N+l0xOLeuexIb/LQUDR8QQAAAC26ImXZ/XxLz2jn71lUH/663e9InS2stv3pTW5mNdMtuB3KQgogicAAACwBTPZgn77L36kfb1J/V8fvFOJaHPWojTCbSNpSdKzExy3hT8IngAAAMAmqlWn3/ncjzW7UtR/+NU3KZOM+l3StuzpimkkkyB4wjcETwAAAGATn/7uWX33zBX9b794TK/bn/G7nB05NpLW+ZkVLeVLfpeCACJ4AgAAAK/h8mJe//e3zugdtw3pgbu3P4yoVdw2kpaTNDaV9bsUBBDBEwAAAHgN/+7rz6tUqer3f+G2ll2ZshXDmYTikZDOz674XQoCiOAJAAAAbOCZiwv6wpPj+o23HtYN/V1+l7MrITMd7EsRPOELgicAAACwDuec/vArz2pPKqaP3HfE73IaYrQ/pcmFvAqlit+lIGAIngAAAMA6vvHsZf3wpVl99F03K51orym2Gxndk5KTdGEu53cpCBiCJwAAAHAd55z+w2NjOjzQpf/x+EG/y2mYg30pmcRxWzQdwRMAAAC4zhMvz+mp8QX95lsPKRLunG+Zk7GwBnviOj+77HcpCJjO+SoCAAAAGuQz33tJmWRUv/LmA36X0nA39NcGDFWd87sUBAjBEwAAAFjj/MyKvn5qUr96z6hSsYjf5TTc6J4u5UtVXVkq+F0KAoTgCQBACzGz95jZ82Y2ZmYfW+fjcTP7XP3jJ8zsUP3xqJk9bGY/MbPTZvZ7za4d6BSf/YeXFDLTr/30Ib9L8cQNe1KSuOeJ5iJ4AgDQIswsLOlTkn5O0jFJHzKzY9c97cOS5pxzRyR9UtIn6o9/QFLcOfd6SW+W9M9WQymArVvMl/T5kxf0i3fs03Am4Xc5nujvjikVC+tlgieaiOAJAEDruFvSmHPurHOuKOkvJb33uue8V9LD9be/IOl+MzNJTlKXmUUkJSUVJS02p2ygc/z1E+NaLlb0m2897HcpnjEzje5J6fwMwRPNQ/AEAKB17Jd0Yc374/XH1n2Oc64saUFSv2ohdFnShKTzkv6dc252vRcxs4fM7HEze3x6erqxfwKgzX3xRxd1bCSt1x/I+F2Kp0b3pDSdLWilWPa7FAQEwRMAgNZh6zx2/djJjZ5zt6SKpH2SDkv6l2Z243ov4pz7tHPuuHPu+ODg4G7qBTrK2emsnhpf0C/fef3PezrPwfo9z4vzOZ8rQVAQPAEAaB3jktZuqj8g6dJGz6kfq81ImpX0gKS/cc6VnHNTkr4v6bjnFQMd5L/++JLMpF+8Y5/fpXhuKF27v3p5Ie9zJQgKgicAAK3jpKSjZnbYzGKSPijpy9c958uSHqy//X5J33bOOdWO195nNV2SfkrSc02qG2h7zjl96ccX9Zab+jt2qNBa3fGIuuMRTS6yUgXNQfAEAKBF1O9sfkTS1yWdlvR559wpM/tDM/ul+tM+I6nfzMYkfVTS6sqVT0nqlvSMagH2s865p5v6BwDa2I8uzOvlmRW9742df8x21XA6ocuLdDzRHJ23ERcAgDbmnHtU0qPXPfbxNW/nVVudcv3nZdd7HMDW/NcfXVQ8EtJ7XjfsdylNM5SO68RLs6o6p5Ctd30caBw6ngAAAAi0UqWqrzw9oXccG1JPIup3OU0znEmoXHWaXS76XQoCgOAJAACAQPv+2BXNLhcDdcxWujZgaJIBQ2gCgicAAAAC7W9PX1YqFta9Rwf8LqWp9vYkZBL3PNEUBE8AAAAElnNO3z49pbcdGVAiGva7nKaKRULa0xXTJMETTUDwBAAAQGA9N7mkSwt53X/bXr9L8cVwhsm2aA6CJwAAAALr289NSZLefkswg+dQOqGZbFGlStXvUtDhCJ4AAAAIrL89fVlvOJDR3vqgnaAZTifkJE0tFvwuBR2O4AkAAIBAyhbK+vGFed13azC7ndKaybYct4XHthw8zSxsZj8ys6/U3z9sZifM7IyZfc7MYt6VCQAAADTWC5NLck56x21Dfpfim/7umCIh454nPLedjue/kHR6zfufkPRJ59xRSXOSPtzIwgAAAAAvPTe5qKF0XLfvS/tdim9CZtqbjtPxhOe2FDzN7ICkn5f0H+vvm6T7JH2h/pSHJb3PiwIBAACARitXqzozldV9t+5V7Vvb4BpOJ3R5geAJb2214/nvJf0rSavjrvolzTvnyvX3xyXtb3BtAAAAgCfOz6yoUK4GdprtWkPphJYKZS0Xyps/GdihyGZPMLNfkDTlnHvCzH529eF1nuo2+PyHJD0kSaOjozssEwAAAGics1eWZZLG53J65MR5v8vx1WBPXJJ0JVtQV3zTeADsyFY6nm+V9Etmdk7SX6p2xPbfS+o1s9X/Mg9IurTeJzvnPu2cO+6cOz44ONiAkgEAAIDdOTud1b7epBLRsN+l+G6gqxY8Z7JFnytBJ9s0eDrnfs85d8A5d0jSByV92zn3q5Iek/T++tMelPQlz6oEAAAAGqRYrurCbE43DnT5XUpL6OuKKWTSlWV2ecI7u9nj+buSPmpmY6rd+fxMY0oCAAAAvHN+dkUV53TjIMFTksIhU18qRscTntrWIW7n3Hckfaf+9llJdze+JAAAAMA7Z6ezCpl0qJ/guaq/O6YrWTqe8M5uOp4AAABA2zl7ZVn7e5OKc7/zqv7uuGayRTm37rxQYNcIngAAAAiMQrmi8bkV3TjY7XcpLWWgK6ZipaolVqrAIwRPAAAABMbLMyuqOukwg4Veob+bybbwFsETAAAAgXF2elkhk27oT/ldSksZuBo8uecJbxA8AQAAEBgvXcnqQF9K8Qj3O9fKJKMKm+kKHU94hOAJAACAQCiUKro4n2ONyjrCIdOeLibbwjsETwAAAATChbmcqo41Khvp745pZpngCW8QPAEAABAI52dXJEkH+7jfuZ6B+kqVKitV4AGCJwAAAALhwuyKBnviSsa437me/u6YylWnxVzJ71LQgQieAAAA6HjOOV2YW9HoHrqdG+nvqk+2XWbAEBqP4AkAAICON7Nc1EqxolGO2W5ooDsmSQwYgicIngAAAOh4F1bvd9Lx3FA6GVUkZJphpQo8QPAEAABAxzs/u6J4JKS96bjfpbSskJn6u1mpAm8QPAEAANDxLsyu6EBfUiEzv0tpaf1dcTqe8ATBEwAAAB2tWK5qcjHPYKEtGOiOaXaZlSpoPIInAAAAOtr4/IqqjvudW9HXFVPFsVIFjUfwBAAAQEe7MJuTJCbabsGeVG2y7dwKwRONRfAEAABARzs/u6L+rphS8YjfpbS8vqvBk3ueaCyCJwAAADqWc04XZle437lFmVRUEsETjUfwBAAAQMdayJWULZR1gOC5JdFwSOlERPPLHLVFYxE8AQAA0LEuzeclSft7kz5X0j56UzHN0vFEgxE8AQAA0LEuLeRkkobTCb9LaRt9qajmCZ5oMIInAAAAOtal+ZwGe+KKRfi2d6v6UjEt5EqqVNnlicbhKxAAAAAd6+J8jmO229TXFVPViV2eaCiCJwAAADrSYr6kpXxZ+wie28JKFXiB4HmdK0sFlSpVv8sAAADALk3M5ySJ4LlNfVdXqtDxROOwRXeNS/M5/cljY4qETN94dlJvv2WvfvOthxUKmd+lAQAAYJsu1ifa7sswWGg7MqmoTHQ80Vh0PNdY/eI6ti+tiYW8/uirp/Xc5JLPVQEAAGAnLs3nNNAdUzwa9ruUthIJhZRORjW3TPBE4xA811gpViRJ77l9WP/n+98gSZpayvtZEgAAAHbo0nyOY7Y71JuKctQWDUXwXGM1eKZiEQ12145kTC8V/CwJAAAAO7BcKGs+V2Ki7Q7tScU4aouGIniukSuWFQmZomHTQE9tmteVLF9wAAAA7ebSAoOFdqM3FdNirqRylaGbaAyC5xorxYqSsbDMTKlYRF2xMB1PAACANnTp6mAhgudO9KWicpIWc2W/S0GHIHiusVKsKBW7dvl8sCeuK1mCJwAAQLu5NJ9TXyqqZIzBQjvR11U7/TfLgCE0CMFzjVrwvLZhZqA7TscTAACgDTFYaHf6UrXgOc89TzQIezzXWCmWNdAdlyQ9cuK8cqWKXp5d0SMnzr/ieQ/cM+pHeQAAANiCQrmimeWi7hzt87uUtpVJsssTjUXHc43cdUdtu+MRZfOcawcAAGgnlxdrJ9ZGMgmfK2lf4ZApk2SlChqH4FnnnNNK6ZVHbXsSEeVKFaZ5AQAAtJHJhdpgoeE0wXM3+rpimuOOJxqE4FlXrFRVqbpXdDx74lFJ0nKh4ldZAAAA2KbJxZzikZB6U1G/S2lrfakoR23RMATPupViLVy+4qhtotb9XMpzxAAAAKBdTC4UNJROyMz8LqWtZZIxLeXLqlSd36WgAxA863LrBc94LXhyzxMAAKA9OOc0uZjTMPc7d6336i5PmjDYPYJn3WrHM3ndHU9JyhYIngAAAO1gIVdSvlTlfmcDZJK1o8rzBE80AMGzbqVYC5drO55d9Y7nEsETAACgLUwu1gYLMdF293rrwXOB4IkGIHjWXet4Xgue0XBIiWiIo7YAAABt4nJ9ou0QHc9dy9SHMy0wYAgNQPCsW2+4kCR1x6N0PAEAANrExGJevamoEtHw5k/Ga4pHwkpEQxy1RUMQPOtyxbJikZAioVf+K+lJRJRlqi0AAEBbmFzIc7+zgXqTMY7aoiEInnUrxYpS6/xkrDseYbgQAABAGyhXqrqSLTDRtoEyySjBEw1B8KxbKVZedcxWqu3yXOKOJwAAQMubzhZUdaLj2UC9qajmVwie2D2CZ91KsazUmlUqq3riERXKVZUqVR+qAgAAwFZN1gcLETwbJ5OMKleqqFjme2HsDsGzLleqvGKi7aqruzzpegIAALS0yYW8IiFTf3fc71I6Rm9qdZcnk22xOwTPug2P2rLLEwAAoC1MLua1Nx1XOGR+l9IxMsmYJHZ5YvcInpKqzim34R3P2k956HgCAAC0tsuLeQ31cMy2kTLJ1V2eBE/sDsFTUqFUlZOUXOeO57WOJ19sAAAArSpXrGgxX9YQ9zsbKp2MyCR2eWLXCJ6qDRaS9JpHbVmpAgAA0LqmlmqDhfamud/ZSJFQSN2JCEdtsWsET9Xud0rrB89wyJSKhTlqCwAA0MKmFguSpL0ctW243mSUo7bYNYKn1gTP6KuDp1TrerLLEwAAoHVdXsorGrarU1jROJlklKO22DWCp9YetX31HU+ptlKFo7YAAACta2qpoL09CYWMibaN1puKaSFXlHPO71LQxgieeu2jtpLUk4gSPAEAAFrY1GJee3u43+mFTDKqUqW2BQLYKYKnpFypIpOU2CB4dscj3PEEAABoUUy09dbqShWO22I3CJ6qHbVNRMMbHs3ojkdUrFRVKPNTHgAAgFbDRFtvrd6bZbItdoPgqdpR242O2UrXjuCucLwAAACg5TDR1lt0PNEIBE/VjmckXyN4rn6Mc+0AAACth4m23uqKRxQOGStVsCsET23e8bwaPEsETwCAt8zsPWb2vJmNmdnH1vl43Mw+V//4CTM7tOZjbzCzH5jZKTP7iZnR/kEgTC0y0dZLIbP6SpWi36WgjRE8VbvjudEqFUlKRul4AgC8Z2ZhSZ+S9HOSjkn6kJkdu+5pH5Y055w7IumTkj5R/9yIpD+X9M+dc7dL+llJtCcQCFNLTLT1WiYZ5Y4ndoXgqVrH8zWP2kbpeAIAmuJuSWPOubPOuaKkv5T03uue815JD9ff/oKk+83MJL1L0tPOuackyTk345zjLy50PCbaNkcvwRO7FPjgWak6FcrVTYYL1bqhdDwBAB7bL+nCmvfH64+t+xznXFnSgqR+STdLcmb2dTN70sz+VRPqBXzHRNvmSCejWsqVVXXO71LQpjY+XxoQK8Xafs5UdOPgGQ2bwmZ0PAEAXlvvgtr13+Vt9JyIpLdJukvSiqRvmdkTzrlvvepFzB6S9JAkjY6O7qpgwG9MtG2OTDKqinNaLpTVk2CIE7Yv8B3PfKkqSa951NbMlIiF6XgCALw2LungmvcPSLq00XPq9zozkmbrj/+dc+6Kc25F0qOS3rTeizjnPu2cO+6cOz44ONjgPwLQXEy0bY7VlSoct8VOBT54Fiu14BkLbxw8pVpHlI4nAMBjJyUdNbPDZhaT9EFJX77uOV+W9GD97fdL+rZzzkn6uqQ3mFmqHkh/RtKzTaob8M3UEhNtmyFdD56LBE/sUOCP2hbL9eAZee0MnqTjCQDwmHOubGYfUS1EhiX9qXPulJn9oaTHnXNflvQZSf/JzMZU63R+sP65c2b2x6qFVyfpUefcV335gwBNNL1U0I0DXX6X0fHoeGK3Ah88S/WOZzT82j8lS0bDyhbKzSgJABBgzrlHVTsmu/axj695Oy/pAxt87p+rtlIFCIRCqaKFXEmDrFLxXCoWVjhkWsjx/TB2hqO22+l4ctQWAACgZUxna4OFCJ7eC5kpnYhoMU/HEzsT+OB5reO5SfCMhq9OwAUAAID/ppfqwbOb4NkMGXZ5YhcCHzyvDRfavONZKFXZXQQAANAippcKCpnUT/BsijTBE7sQ+OBZ2upR22hYTlKe47YAAAAtYTpb0J6uuMIhJto2QyYZ1WKuJEcjBjuwafA0s4SZ/dDMnjKzU2b2B/XHD5vZCTM7Y2afq499bzvFrR61re/5ZLItAABAa6itUqHb2SyZZFTlqtMK3w9jB7bS8SxIus85d4ekN0p6j5n9lKRPSPqkc+6opDlJH/auTO+UKk5hs01/UpaM1oMnHU8AAADfVapOs9kig4WaKJ1gpQp2btPg6Wqy9Xej9X+cpPskfaH++MOS3udJhR4rlquKRjY/nnE1ePITHgAAAN/NLhdVcY7BQk20ustzkeCJHdjSHU8zC5vZjyVNSfqmpBclzTvnVse8jkva702J3ipWqpsOFpLWHLWl4wkAAOC7qxNt6Xg2zWrwXGClCnZgS8HTOVdxzr1R0gFJd0u6bb2nrfe5ZvaQmT1uZo9PT0/vvFKPlCrVTe93SgRPAACAVsIOz+brTkQUMo7aYme2NdXWOTcv6TuSfkpSr5lF6h86IOnSBp/zaefccefc8cHBwd3U6oliubrpRFuJo7YAAACtZHopr3QiokT9ezR4L2SmdCLKUVvsyFam2g6aWW/97aSkd0g6LekxSe+vP+1BSV/yqkgvbbXjGQ2HFA0bwRMAAKAFTC8VNEC3s+nY5Ymd2krHc0TSY2b2tKSTkr7pnPuKpN+V9FEzG5PUL+kz3pXpna12PKVa15OjtgAAAP5yzrFKxSeZZFQLufLmTwSuE9nsCc65pyXduc7jZ1W779nWShWn7sTWgmciGmZvEQAAgM+WCmUVylUm2vogk4zq+cklOedktvlmCGDVtu54dqLaVNutfdGkYnQ8AQAA/HZtom3C50qCJ52MqlipKl+q+l0K2kzgg2dpm0dt8wRPAAAAX7FKxT9XV6pwzxPbFPjgWdzicCGptlKFo7YAAAD+mloqKB4JKZ3Y9NYYGixT/3dO8MR2BTp4OudUqlQV22rwZLgQAACA764sFTTYE+eOoQ/S9Y4nK1WwXYEOnpWqU9Vp60dtY2EVy1WVKpxpBwAA8MvUUp7BQj7pSURlkhbyBE9sT6CDZ6niJGnrR23rC4o5WgAAAOCPQqmixXyZ+50+CYdMPYkI3w9j2wIdPIv1zuWWj9rGONMOAADgp+ksg4X8lk5GOWqLbQt28CzXgmc0srX7AXQ8AQAA/MVEW/9lklG+H8a2BTp4lrbd8awHzxW+0AAAAPwwvVRQyKT+LoKnX9IET+xAoIPntY7n1v41pOh4AgAA+GpqqaD+rrjCISba+iWTiKpQrrLfHtsS6OC53Y5nIkbwBAAA8NN0tsAxW59lWKmCHQh08FwdLrTdqbbzHLUFAABoukrVaYbg6bvVXZ6sVMF2BDt41o/abnWPZzhkikdCdDwBAAB8MLtcVNUxWMhvdDyxE4EOntvd4ynVup4ETwAAgOabXspLkvYSPH2VTrBiENsX6OC53T2eUm2y7UKu6FVJAAAA2MDqKpWBboKnnyLhkLrjES3kyn6XgjYS7OC5zaO2kpSg4wkAAOCLqaWC0omIEvW5G/BPJhnlqC22JdDBs1SpKmTa1jjuVIzgCQAA4Acm2rYOdnliuwIdPIuV6ra6nVLtjidTbQEAAJrLOafpJYJnq8gkIwRPbEugg2epXN3WYCGpdsdzni8yAACApppaKqhQrmqwJ+F3KZCUSUSVK1WuXl0DNhPo4FmsVLc1WEiSUtGwiuWq8qWKR1UBAADgei9OZSVJgwwWaglpVqpgmwIdPEvlHRy1jdXGR8+tMNkWAACgWcama8GTVSqtYXWX50Ke4ImtCXbwrLhtH7VNxWpT1LjnCQAA0DxjU1nFIyH11HdIwl9XgycdT2xRoIPnTo7aJuvBk44nAABA84xNZTXYE5fZ1rcRwDsctcV2BTp4lipVRbd51PQk0RkAACAASURBVHa147lAxxMAAKBpxqayHLNtIdFwiDWD2JZAB89iuapoeHs/NUtdvePJFxkAAEAzLOZLmloqMNG2xWTY5YltCHbw3MlUW47aAgAANNXYFIOFWlE6EeWoLbYs0MGzVNn+VNtoOKRENMRPdwAAAJpkNXgOEjxbCh1PbEegg2ftqO32/xX0JmOaW6bjCQAA0AwvTmUVC4fUl4r5XQrWSCejWi5W2G+PLQls8CxXq6o6bbvjKUm9qajm+ekOAABAU4xNZXV4oEvhEBNtW8nqSpWpxYLPlaAdBDZ4lspOknbW8UxFNc8dTwAAgKZ4cTqrI3u7/S4D11kNnhMLOZ8rQTsIbPAsVqqStO3hQpLUl4ppnqm2AAAAnsuXKjo/u6KbCJ4tJ52sbXuYXMz7XAnaQWCDZ2k1eEa2f2SjNxVlnQoAAEATnJtZVtWJjmcLyiRWO54ET2wusMGzWK4Fz50dtY1pIVeUc67RZQEAAGCN1Ym2RwYJnq0mHg0rEQ1pkuCJLQhs8Czt4qhtbzKqUsVpucgELwAAAC+NTWVlJt042OV3KVhHJhnljie2JLDB8+odzx1MtV0d5c2AIQAAAG+NTWV1sC+lRDTsdylYRyYZpeOJLQls8Czt4qhtJlU7z86AIQAAAG+NTTHRtpWlE1HueGJLAhs8i5Xa/cydTrWVpDk6ngAAAJ6pVJ3OXlkmeLawTDKq6Wzh6vwUYCOBDZ5XO547OmpLxxMAAMBr43MrKparDBZqYZlkVM5JU0t0PfHaAhs8d7PH89pRWzqeAAAAXlmdaHvTXgYLtap0svZ9Mfc8sZnABs/VqbbRnezxTK4OF6LjCQAA4JVrq1R6fK4EG8kk2eWJrQls8CyWqwqZFLbtB89YJKSuWFhzBE8AAADPjE1lNdAdv3raDK0nQ8cTWxTc4FmpKhoOyXYQPCWpNxXTfI6jtgAAAF4Zm87qCMdsW1q83pCh44nNBDZ4lirVHe3wXNWbinLUFgAAwCPOOVaptAEz03AmocnFnN+loMUFNngWy9Ud7fBc1ZeKMVwIAADAI9NLBS3ly0y0bQMjmSQdT2wqsMGzVHE7mmi7KkPHEwAAwDNXBwvtZbBQqxvOJLjjiU0FNngWd3nUti8V1XyO4AkAAOCFsenV4EnHs9WNZBKaWiqoXN8aAawnuMGzXFU0vLPBQlJtpcr8SlHVqmtgVQAAAJBqHc/ueERD6bjfpWATw5mEKlWnK1muoWFjgQ2epUp1V0dte1NRVZ20lC83sCoAAABIteB5097uHW8gQPOMZBKSpIkFBgxhY4ENnsVyVdFdTbWNSRIrVQAAADwwNpVlsFCbGE4nJbHLE68tsMFztx3Pvvoi4zkGDAEAADTUYr6kqaUC9zvbxLWOJ8ETGwts8CxWGtTxZKUKAABAQ12baEvwbAe9qajikZAmFwme2Fhgg2epvLt1Kr31jicrVQAAABqL4NlezEwjmQQdT7ymQAbPUqWqinOK7uqoLR1PAAAAL7w4lVUsHNLBvqTfpWCLRjJJTTJcCK8hkMEzX6pIkmK7WKeSTkQkcccTAACg0camsjo80KXILpoEaC46nthMIL+ac/XguZs7npFwSOlERAs5gicAAEAjjU1nOWbbZoYzCV1ezLPjHhsKZPDMF6uStKs7nlJtwNAcR20BAAAaJl+q6MLsim4ieLaVkUxCpYrTleWC36WgRQUyeK52PHd7fKMvFWW4EAAAQAO9dGVZVcdgoXYznGGXJ15boIPnbu54SlImFWO4EAAAQANdnWg7SPBsJ+zyxGaCGTyL9TueDeh4MlwIAACgcc5cXlI4ZLpxsMvvUrANw/XgSccTGwlk8Fydarvb4LmnK6bZZTqeAAAAjfLc5JIO9aeUiIb9LgXbsCcVUywcouOJDQUyeDZiqq0kDXTHlS2UrwZZAAAA7M4Ll5d0y3CP32Vgm0Ih01Amzi5PbCiYwbO4esdzt8EzJkm6kmV6FwAAwG7lihW9PLuim4cInu1oJJ2k44kNBTN4Xj1qu7vhQgPdcUnSlSzHbQEAAHZrbCor56RbCJ5taTiT0OQiwRPrC2TwbNQdz/7V4LlExxMAAGC3nr+8JEm6maO2bWkkk9DEQl7OOb9LQQsieO7C6lHbGRblAgAA7NoLl5cUi4R0w56U36VgB4YzCRXLVbY+YF2BDJ65UkVhM4VDHLUFALQWM3uPmT1vZmNm9rF1Ph43s8/VP37CzA5d9/FRM8ua2f/SrJqBRnl+cklHBrsV2WVzAP64tsuTAUN4tUB+VeeKVUV2eb9TkhLRsLrjEU1z1BYA0ABmFpb0KUk/J+mYpA+Z2bHrnvZhSXPOuSOSPinpE9d9/JOSvuZ1rYAXmGjb3oYzSUns8sT6ghk8S5VdT7RdNdAd0wy7PAEAjXG3pDHn3FnnXFHSX0p673XPea+kh+tvf0HS/WZmkmRm75N0VtKpJtULNMxCrqSJhTzBs41d63gSPPFqgQye+VJl1zs8Vw10xxkuBABolP2SLqx5f7z+2LrPcc6VJS1I6jezLkm/K+kPmlAn0HAv1AcLMdG2fQ10xxUOGR1PrCuQwTNXrOx6lcqq/u4Yw4UAAI2y3l9O14+H3Og5fyDpk8657KYvYvaQmT1uZo9PT0/voEyg8Z6fZKJtuwuHTEM9cTqeWFcwg2epsuuJtqsGuuMMFwIANMq4pINr3j8g6dJGzzGziKSMpFlJ90j6P8zsnKTfkfSvzewj672Ic+7Tzrnjzrnjg4ODjf0TADv0wuUldccj2lc/ron2NNKb1OQiw4XwahG/C/BDI4Nnf3dccytFlStVJrABAHbrpKSjZnZY0kVJH5T0wHXP+bKkByX9QNL7JX3b1Zbm3bv6BDP7N5Kyzrk/aUbRQCM8P7mkm4e6Vb+yjDY1nEno9KVFv8tACwpkUso3cLjQYHdMzkmzK3Q9AQC7U7+z+RFJX5d0WtLnnXOnzOwPzeyX6k/7jGp3OsckfVTSq1auAO3GOcdE2w4xkk5oYiGv2s/DgGuC2fEsVhRv4HAhSbqyVNTeHo6GAAB2xzn3qKRHr3vs42vezkv6wCa/x7/xpDjAI9PZguZWSrqZwUJtbziTUK5U0WKurEwq6nc5aCGbpi8zO2hmj5nZaTM7ZWb/ov74HjP7ppmdqf/a5325jdHoo7aSGDAEAACwQ6uDhZho2/5G6rs8J7jnietsJX2VJf1L59xtkn5K0m/Xl1l/TNK3nHNHJX1LbXTUJ1+qNnC4UEySdCVL8AQAANiJZ+t3Am8bSftcCXZrmF2e2MCm6cs5N+Gce7L+9pJqd07265ULrB+W9D6vimy0fKmR61SuHbUFAADA9j07sah9mYT6umJ+l4JdGqkHT3Z54nrbavuZ2SFJd0o6IWnIOTch1cKppL2NLs4LzrnaUdsG3fFMJyKKhUO6wlFbAACAHTk9sahj++h2doLBnrhCRscTr7bl4UJm1i3pryT9jnNucaujrs3sIUkPSdLo6OhOamyoUsWpUnW7mmr7yInzr3g/GQvr5EuzeuTEeT1wj/9/RgAAgHaRL1X04vSy3n37sN+loAGi4ZAGe+KaXOCOJ15pS+nLzKKqhc6/cM79df3hy2Y2Uv/4iKSp9T631ZZU50oVSWrYHU9J6o5HlC2UG/b7AQAABMULl5dUqTod435nxxjOJOl44lW2MtXWVNsZdto598drPrS6wFr1X7/U+PIaL0/wBAAAaBmrg4U4ats5RtIJ7njiVbaSvt4q6Z9Kus/Mflz/57+T9G8lvdPMzkh6Z/39lpcrrgbPxgwXkurBM0/wBAAA2K5nJxbVHY/oYF/K71LQIMMZgidebdM7ns6570naKKXd39hyvOfFUduueETLhYqccw37PQEAAILg2UuLum2kR6FQ45oC8NdIJqGlQllL+ZJ6ElG/y0GLaFz6ahOrwTPWoKm2ktSdiKjinPKlasN+TwAAgE5XrbraRFvud3aU1V2elxfpeuKawAXPfNGLO55hSeKeJwAAwDacn13RcrHC/c4OM5JJSmKlCl4pcMHz2lHbRt7xrB0hIHgCAABs3emJ+mChkYzPlaCRRuodT4In1gpc8Fw9DtvoqbYSwRMAAGA7np1YVDhkOjrU7XcpaKChdEJm0sU5dnnimsAFT2+GC3HUFgAAYLuevbSomwa7lIiG/S4FDRSLhDTUk9DFeYInrglw8GzcUduueEQmsVIFAABgG55lsFDHOtCX1Pjcit9loIUELniuDheKNbDjGTJTKhbWMh1PAACALZldLmpiIc9goQ5VC550PHFN4ILnascz0sDgKdVWqnDUFgAAYGt+cnFBknT7PgYLdaIDfSlNLuRVrrBuEDWBDJ6xcEjhBi8p7o5HtJQvNfT3BAAA6FRPX5iXJL3+AMGzEx3oS6pcdbq8VPC7FLSI4AXPYkWJaOP/2OlEVEvc8QQAANiSp8YXdONgl9KJqN+lwAP7+2q7PMdnueeJmsAFz3ypomSs8ZPTMsmoFvMlVaqu4b83AABAp3l6fF53HOj1uwx45EBfSpK454mrAhc8c6WKJyO708moqk6ayXKcAAAA4LVMLuQ1tVTQGzhm27H29SYkETxxTfCCZ7GipAfBM5OsHROZWMg3/PcGAADoJE+N1+53voGOZ8eKR8IaSsdZqYKrghc8Pep4EjwBAAC25qkL84qETLezSqWjHehL6eI8HU/UBC545kvedDzT9eA5ucAXFwAAwGt5enxBtwz3eNIMQOtglyfWCmDwrHoyXKgrFlY4ZJpYpOMJAACwEeecnh6f55htABzoS+rSfI7hm5AUwOCZ86jjaWZKJyKa5KgtAADAhs7NrGgxX9YdDBbqePt7U7VdnjRmoCAGz6I3dzyl2j1PgicAAMDGnmawUGAcWN3lyXFbKIDBs7bH05s/djoZ1SQ/0QEAANjQUxcWlIiGdPNQt9+lwGPXgieTbRHA4OnVUVup1vGcWMjLOc6xAwAArOfp8Xndvi+jSDhw34YGzr7eWvC8SMcTCljwdM55HjyL5armVkqe/P4AAADtrFyp6plLC7qDY7aBkIiGtbcnzlFbSApY8CyUq3JOinsUPNOJ1V2efHEBAABc7/TEkvKlqt44SvAMigN9SY3Pc9QWAQue+VJFkjzteEpiwBAAAMA6Tp6blSTddajP50rQLPv7UnQ8ISlgwTO3Gjw92OMpXQueEwRPAACAV3n85Vkd6EtqJJP0uxQ0Cbs8sSpYwbPobcezOxFROGR0PAEAAK7jnNPJc3O669Aev0tBEx3oS6pUcZpa4vvjoAtW8Kx3PL3a4xky01BPnI4nAADAdV6eWdH0UkHHOWYbKAf6UpKYbIuABc98qSrJu6O2kjScSWhykS8sAACAta7d76TjGSSruzwvsMsz8AIWPL09aitJI5kkHU8AAIDrPH5uTplkVEcGu/0uBU10oC8ps1rHG8EWqODp9R1Pqd7xXMjLOS5QAwAArDr58qzuOtSnUMj8LgVNFI+EtS+TJHgiYMHz6lRb7/7YI5mEVooVLebLnr0GAABAO5nJFnR2elnHOWYbSIcGUjo3s+x3GfBZIIOnV8OFpFrHU2KXJwAAwKrHX56TxP7OoBrd00XHE8EKnvkmBM+RevCcWGDAEAAAgCQ9fm5WsUhIr9uf8bsU+OBQf0qzy0Ut5kt+lwIfBSp4NueOZ21yFx1PAACAmpPn5vTGA72KR7z7Hgyt64b+LknSebqegRas4NmEjufenrjMxGRbAAAASdlCWc9cXNBdhzlmG1SHBmq7PLnnGWyBC56xSEhhD6epRcMhDXbH6XgCAABI+scXZ1SuOr31yIDfpcAno3tqwZN7nsEWqOCZL1Y8PWa7aqQ3qYvz3PEEAAD43tgVJaNhvfkGOp5BlYpFtLcnrpfpeAZaoIJnrtSc4HmwL6kLc/xEBwAA4O/PTOvuw3u43xlwh/q7dI6OZ6AFKnjmS1UlY97/T290T0oX53KqVJ3nrwUAANCqLs3ndHZ6Wfce5Zht0N3Qn6LjGXCBCp65UsXTwUKrRvekVK46VqoAAIBA+96ZK5Kke48O+lwJ/HZDf0qXFwtXt0wgeAIVPPOlipJR7//Iqxeoz89ynAAAAATX35+Z1t6euG4e6va7FPjs6koVvj8OrEAFz1yxOR3Pg/XgeYEvLAAAEFDVqtM/vDijtx0ZkJl3GwXQHg7VgycrVYIrWMGzScOFRjIJhUOmC7MctQUAAMH07MSiZpeLehv3OyFptH91pQrBM6gCFzwTTRguFAmHtL83yVECAAAQWN+t3+98G/s7ISmTjKovFWWybYAFKng2a4+nJB3cQ/AEAADB9d0z07p1uEd70wm/S0GLuKG/S+cJnoEVqODZrKO2Um3A0Di7PAEAQAAt5ks6eW5W/+RmptnimkP9Ke54BljwgmcTjtpKtQFDV7JFLRfKTXk9AACAVvHYc1MqVZzeffuQ36WghdzQ36VL8zkVyqxUCaLABM9ypap8qaquWKQpr7e6UuUCXU8AABAw3zh1WYM9cd15sM/vUtBCbuhPqeqk8TkGcAZRYILncn1ZbVe8SR3PvvouT86xAwCAAMmXKvrO81N657EhhUKsUcE1hwZqK1Vemua4bRAFJ3jWj7x2xZvd8eQnOgAAIDi+P3ZFy8WK3nWMY7Z4pSN7uyVJY9NZnyuBHwieHulNRdUTj+gCk20BAECAfOPUZfXEI3rLTaxRwSulE1Ht7YlrbIrgGUTBCZ71o7bdTTpqa2Y6sCfFShUAABAYlarT356+rLffulexSGC+zcQ2HB3q1hmCZyAF5v8Iqx3PVJOGC0nSKLs8AQBAgDx+blYzy0W9+/Zhv0tBizoy2K0Xp7JyzvldCposMMEzWw+e3U06aivV7nlemF3hCwsAAATC109dViwS0s/cwv5OrO/IUI+yhbImF/N+l4ImC0zwbPYdT6kWPAvlqqaXCk17TQAAAD9Uqk5fe2ZC9x4ZaOoP+tFejgzWBwxx3DZwAvN/hWvBszl3PCXpQH2y7fnZFe1NJ5r2ugAAAM32gxdnNLGQ1//687ddfeyRE+d9rAit6OhQLXieuZzVvUfpjAdJcDqeV4cLNbfjKUkX5rjnCQAAOttfPzmudCKid9zGGhVsrL8rpt5UlJUqARSc4Fkoy0xKRpvX8dzfm5SZdH6GXZ4AAKBzZQtlfe2ZSf3CHfuUaOL3Wmg/ZqYjg90au0zwDJrABM9soayuWERm1rTXTETDGk4n9PLMctNeEwAAoNm+9pMJ5UoV/cqb9vtdCtrA0aFuOp4BFJjguVwoN/V+56obB7v04hWCJwAA6Fx/9eS4Dg906U2jfX6XgjZw02C3ZpeLmskygDNIghM8i5WmTrRddeNAt85Os6sIAAB0pvG5Ff3j2Vn993fub+rJMrSvo0M9kphsGzTBCZ6Fsi+jvW8a7NJSvqxpfqIDAAA60BefvChJet+dHLPF1hzZW1+pwnHbQAlU8EzF/DhqW/vCOjvNcVsAANBZypWq/vMPz+stN/XrYH2aP7CZfZmEumJhnWHAUKAEJnhmCxVfOp43DnZJIngCAIDO841nL+vSQl6//pZDfpeCNmJmumlvt16k4xkogQmeteFCzQ+e+zJJJaIhneULCwAAdJjPfv8lHdyT1P3s7sQ2HRnspuMZMIEJnitFf4JnKGQ6PMBPdAAAQGf5yfiCTp6b06+/5bDCIYYKYXuODHVrcjGvpXzJ71LQJM1PYj6p7fH0/o7nIyfOv+qxSMj01PiC568NAADQLJ/9/kvqioX1geMH/C4FbejmvbXJts9PLun4oT0+V4NmCETwLFeqypeqvnQ8JWmgO65nLi6oUK4oHmn+gCMAAIDNrPfD840s5kv60o8v6e7De/SVpyY8rAqd6vb9aUnSqUuLBM+ACMRR2+ViRZJ8GS4kSYM9cTlJ52dWfHl9AACARjpxdkZV5/TTN/X7XQra1HA6of6umE5d4lRgUAQjeBbKkuRbx3OwOy5J3PMEAABtb6VY1j+8OKNj+9IaqH+PA2yXmenYvrSeubjodylokkAEz5Wiv8FzoDsmSXqRlSoAAKDNfW/sigrlqu6/lUm22J3X7c/ohctLKpQrfpeCJghE8MwWav8xN2O40Hri0bDSiQi7PAEAmzKz95jZ82Y2ZmYfW+fjcTP7XP3jJ8zsUP3xd5rZE2b2k/qv9zW7dnS+5UKt2/n6/RkNZxJ+l4M2d/u+tMpVx1qVgAhE8PT7qK0kDfTEdfYKX1QAgI2ZWVjSpyT9nKRjkj5kZseue9qHJc05545I+qSkT9QfvyLpF51zr5f0oKT/1JyqESTfPTOtUrmq+2/d63cp6ACv25eRJD1zkXueQRCI4JmtB0+/hgtJtXueL05l5ZzzrQYAQMu7W9KYc+6sc64o6S8lvfe657xX0sP1t78g6X4zM+fcj5xzl+qPn5KUMDMu4KFhlvIl/eDsjO442Ku9abqd2L3RPSn1xCM6dYl7nkEQiODZEh3P7rgW82XNLBd9qwEA0PL2S7qw5v3x+mPrPsc5V5a0IOn60aK/IulHzrnCei9iZg+Z2eNm9vj09HRDCkfne+z5KZUrTvfdQrcTjREKmW7bl9YzTLYNhE2Dp5n9qZlNmdkzax7bY2bfNLMz9V/7vC1zd1bXqfh1x1OqrVSRxD1PAMBrsXUeu/6ozGs+x8xuV+347T/b6EWcc592zh13zh0fHBzcUaEIlomFnE6cndU9N+7RQA+NdDTO6/ZldHpiUZUqpwI73VY6nn8m6T3XPfYxSd9yzh2V9K36+y2rFTqeqytVzrJSBQCwsXFJB9e8f0DSpY2eY2YRSRlJs/X3D0j6oqRfc8696Hm1CATnnP7bU5eUjIX1jtuYZIvGun1fWvlSle+RA2DT4Omc+3vV/0JbY+39koclva/BdTXUcqEsMynlY8czk4oqEQ3pzBRfVACADZ2UdNTMDptZTNIHJX35uud8WbXhQZL0fknfds45M+uV9FVJv+ec+37TKkbHe3p8QedmVvSuY8NKxfz7IT460+v21wcMcdy24+30jueQc25Ckuq/tvRh/2yhrK5YRGbrnU5qjpCZbh7q0fOTS77VAABobfU7mx+R9HVJpyV93jl3ysz+0Mx+qf60z0jqN7MxSR/VtVNHH5F0RNLvm9mP6/+09N/PaH2FckVfe2ZC+3oTOn6opW9WoU3dNNileCSkZy4yYKjTef5jKzN7SNJDkjQ6Our1y61ruVBWV9y/bueqW4Z69NjzDHEAAGzMOfeopEeve+zja97OS/rAOp/3R5L+yPMCESjffPayFvNlPXD3qEI+/gAfnSsSDunWkbRO0fHseDvteF42sxFJqv86tdETW2GAwXKxoq4WOBpyy3CPrmQLmsmuO2QQAACgZZydzuofXpzRT9/Yr9H+Lr/LQQd73b60Tl1aZO1gh9tp8Fx7v+RBSV9qTDneqHU8/Q+etw6nJYnjtgAAoKUVShX91ZPj6u+K6d23D/tdDjrc6/dntJQv60W2P3S0raxT+c+SfiDpFjMbN7MPS/q3kt5pZmckvbP+fstqmaO2wz2SpOcIngAAoIU9+syk5ldKev+bDygWCcTad/jorsN7JEknz10/zxSdZNM2oHPuQxt86P4G1+KZbKGi/b0Jv8vQYE9c/V0xOp4AAKBlnZ5Y1Mlzs7r36IBu4IgtmuDGgS4NdMd08qVZfehuf2bCwHuB+BHWSrE1jtpKta7nc5cJngAAoPXMLhf1X564oH2ZBDs70TRmpuM37NEP6Xh2tEAEz+VCuWX2Tt081KMzl5dUrXJ5GgAAtI5SpapHfviyJOmBe25QNByIbxPRIu46vEfjczlNLOT8LgUeCcT/UbKFsrpb4I6nJN063KOVYkUX5lb8LgUAAOCqrz49oUvzeX3gzQe1pyvmdzkImLsP1e55/vAlup6dquODZ7lSVb5UbamjthIDhgAAQOv4ixMv64fnZvVPjg7qtpG03+UggG4b6VF3PMKAoQ7W8cFzuViRJHW3SPC8eagWPBkwBAAAWsHfvTCtj3/plG4e6tY7j3GvE/6IhEN60w19dDw7WMcHz5ViWZJa5o5nVzyi0T0pgicAAPDd85NL+u2/eFJH93brQ3eNKhwyv0tCgN19qE8vXM5qbrnodynwQMcHz+VCLXi2wh7PVbcM9+i5yUW/ywAAAAE2sZDTb/7ZSXXFw/rsb9yleLR1vldCMN1Vv+f5+MtzPlcCL7RGG9BD2UJrHbWVagOGvv3clPKlihL8Tx4AAOzAIyfO7/hzs4Wy/r+/P6vFfEm/de+Neuy56QZWBuzMHQd7FQuHdPLcLMe+O1DrpDGPXOt4+v9HXf0LYnqpoErV6U++PaZ9vUlJ0gP3sCwXAAB4L1es6LPff0nzuaJ+/S2Hr34vAvgtEQ3rDQcy3PPsUB1/1DZbD56t1PEcTiek/7+9+46Pq7rz//86M6Peu6xiS5blItu422AbjMEGAyGUkKWEBTZsgEBCNtnku+GXfPPlm918N5vdJARSCEkIARI6CRCcAC4U2xgXcK9ylaxmybKq1WbO748Zg6LItmyVmbl6Px+PeWjanfl8dKR77+eec88FqhraghyJiIiIDCftnV5+9/5Bahrb+dycURSmxwU7JJG/MWd0KluPNNBwojPYocgAc3zh+cnkQqEzpDU9IYpIj4vy47pAroiIiAyNtk4vv11zkPL6Vm6clf/xTPsioeSS8Zl4fZZ39mj4t9M4vvAMxXM8XcaQmxxD2bHWYIciIiIiw8CJDi+Prz5AeX0rN88eyaTcpGCHJNKrqfkppMVFsmxHdbBDkQHm+MIzlM7x7C4/JZaqhjY6vb5ghyIiIiIO1tzexW9W76fyeBufmzOKiTkqOiV0uV2GS8ZnsnJ3jfaTHWZYFJ7GhNZQW4D81Bi81lKp4bYiIiIySOpbOvjlO/s42tTOreePZMKIxGCHJHJGi0qyY4tcUQAAIABJREFUaGrrYv1BTTLkJI4vPJvbu4iL9GBMaF0QOT8lFoCyehWeIiIiMvCqGtv45bv7aOno4vPzChmXraJTwsOFxelEelws21ET7FBkADm+8Gxt94ZcbydAYkwESTERlNXrPE8REREZWIfqWnjs3X0A3HVREaPSNHuthI/YSA/zitJ4a2cV1tpghyMDxPGFZ3NHV0hNLNRdfoomGBIREZGBtauqkcdXHyAu0sPdFxV9fBk3kXCyqCSLsmMn2FvTHOxQZIA4vvBsae8KuYmFTspPjaW+tfPja42KiIiI9MdHh+t5eu0hMhOiuXtBESlxkcEOSeScXDo+C4C3NLutYwyTwjP0htpCt/M81espIiIi/bS6tJYXNpZTkB7HP88vDNkRXyJ9kZ0UzXl5SSo8HcTxhWdzu5e4yNBc8eYkx+Ay6DxPEREROWfWWpbtrOb1rZVMzEnkjgsKiIoIzYPuImfjyskj2FR2nFINt3UExxeeoTzUNtLjIjsxmvJjmtlWREREzp7PWl7bUsmKXTXMGJXCTbNG4nE7fvdOhonPTM/D4zI8u+5wsEORAeD4NVN9SwepIXx+Q15qLGX1rfh8mrFLRERE+s7rs7y4sZy1++uYPyad66fl4naF1uXjRPojIyGKyyZm8dKH5bR3eYMdjvSTowvPtk4vTe1dpMeHbuGZnxJLe5eP/bUaQiAiIiJ909bp5fcfHGJT2XEuK8niiknZIXfNcpGBcNOskdS3dvLGdp3rGe4cXXgea+kAIDUuKsiRnNqoNP8EQ6tL64IciYiIiISDxrZObnt8Hburmrhmag4Xj8tU0SmONX9MOnkpMRpu6wCOLjzrmv2FZ1oI93imx0eRHh+lGbtERETkjGqb27n5sbV8eKieG2flM6cwLdghiQwql8tw06x81uyr42BtS7DDkX5wduHZ0g4Q0kNtAUpGJLJ2fx0NJzqDHYqIiIiEqPL6Vv7h0ffZd7SZX98+k/PykoMdksiQ+OzMfNwuwzPr1esZzpxdeJ7s8QzhobYAJTmJdPksb++uCXYoIiIiEoJKa5r47KPvU9vcztN3zuHicZnBDklkyGQlRnNZSRbPfHCYhlZ11IQrZxeegR7P1BDv8cxLiSEjIYo3ddK0iIiI9LCl/DifffR9Or2W5+6+gJkFqcEOSWTIfWVRMU3tXfzinX3BDkXOkcMLzw4i3S4SQvQ6nie5jGHRhCze3l2jqaJFRETkY2v21XLzY2uJj/bw0hcvYMKIxGCHJBIU47MTuW5qLr9dfYCqhrZghyPnwNmFZ3MHafGRYTHT22UTs2jp8LJmn2a3FREREXhjexV3/HY9uSkxvHjPXEalxQU7JJGg+urisfis5eEVe4MdipyD0O4K7Ke65vaQntG2u7lFacRFunlzezULdd6GiIjIsPbChjL+7aUtTMlP5rd3zCI5Njz2Z0QGwh8+OPUkQjNGpfLsusNkJ0aTHt/3eVxumTNyIEKTfnB2j2dLR0hfw7O7KI+bi8dlsmxnNT6fDXY4IiIiEiS/fm8/33hxC/PGpPP0nXNUdIp0s3BcBh6Xi6VbK7FW+8zhxNmFZ3MH6XHhs7K+bGIWR5vaWb2vNtihiIiIyBCz1vI/b+zmP17fyZWTs/n17TOJC/F5KkSGWkJ0BItKsthV1cT6g/XBDkfOgmMLT2stdS3hM9QW4PKJ2eQkRfM/b+7RERwREZFhxOez/O9XtvHTlaXcNCufR26eTpTHHeywRELS3KI0ijPjeX1rBTWNmmgoXDi28Gzt8NLW6SPtLMZ+B1t0hJuvLCpmc9lx3tqhS6uIiIgMBx1dPr7y3CaeXnuYexYU8Z/XT8btCv2JEUWCxWUMn5mRR4TbxXMbyujy+oIdkvSBYwvPuuYOAFLDaKgtwGem5zE6PY4fvrkHr871FBERcbQTHV7uemoDr22u4N+WjOebV4wPi9n4RYItMTqCz0zPo7KhjVc3V2i0YBhwbuHZ0g5AehgNtQXwuF187bKx7K5u4rXNFcEOR0RERAbJ8dYO/vE3H/DOnqP85/WT+eLFRcEOSSSsTBiRyMXjMthwqF6TDYUB5xaegR7PtDCZ1ba7KyeNYGJOIj96aw9tnd5ghyMiIiIDrOxYK5/5xRq2lDfw05unc/NsXepB5FwsnpDF3KI0Vu+r480d1So+Q5hzC89Aj2c4TS50kstleOCKCRw+1sr3Xt8Z7HBERERkAG0tb+D6X6zhaFM7T905m6vOGxHskETCljGGqyaPYFZBKu/sOcrSrZU6XS1EObbwrA3jHk+A+cXpfOHCQp5ae0hDbkVERBxi5a4abnzsfSLdLl764lzmjE4LdkgiYc8YwzVTc7hgtL/n84k1B2ht7wp2WNKDYy8Odaylg9hINzGR4TsV+f9aMp6Nh+r5+gubOVDbQnovM/TeMkdDc0RERMLBM+sO8+0/bWN8dgK/vWMWmYnRwQ5JxDFcxnD1lBxykqN5ZVMFP3u7lBtnjWRkamywQ5MAx/Z41jWH1zU8exPhdvHILdNxGcMz6w5rqmgREZEw5PNZ/ueN3Tzw8lbmj0nnubsvUNEpMkhmjErlrotGYy388p19LN1aSUeX9qFDgWN7POtaOsJ2mG13uckx3DAjj6fWHuLtPUdZNCEr2CGJiIg43h8+ODwgn3Oiw8vzG8rYXd3EzFEpLJqQxaubdAqNyGDKS4nl/kuL+ev2KlaV1rKzspFDdS0UZyUM2Hdo1OHZc3CPZwdpYXYNz1OZMCKRKXlJvL27hqqGtmCHIyIiIn1Q1dDGz94uZW9NE5+eksN103Jxu3SNTpGhEB3h5tqpudw5vxCA3645yNNrD3GspSPIkQ1fzi08W8J/qG13nzovh5gINy99WK6ZukRERELc5vLj/OKdUjq9Pr5w4WjOH52GMSo6RYZaUUY8X7m0mMtLsiitaeahZXtYtrNaw2+DwJGFp7XW3+PZy2Q84SouysPVU3I4cvwEq0trgx2OiIiI9MLrsyzdWslz68vISYrhvoVjGJUWF+ywRIY1j9vFgnGZfHXxWEpyElmxq4aHlu1h65EGXfdzCDmy8Gw80UWXzzpmqO1Jk3OTmDAikeW7qmlq6wx2OCIiItLN8dYOfr1qP6tKazl/dBp3XlhIYnREsMMSkYCkmAhumjWSf76wkOgIN8+sO8zjqw9Q3ahT2YaCIwvPupZ2AEcNtQX/NYqumJhNl9fy3l71eoqIiISKrUcaeHjFXiob2vjsjDw+PSUHj8uRu1kiYW90ejz3LRzD1eeN4MjxEzyyYi+vb6mgvcsb7NAczZGz2tYFThp2wqy2PaUnRDFtZDJr99cxvzg92OGIiIgMax1dPv68pYINh+rJS4nhxpn5jjrVR8Sp3C7DBUXpTM5L5q0dVazeV8f2ykaunZrL2AGc/VY+4chDcXXNzuzxPGnhuEx81vLunqPBDkVERGTYOnL8BD9duZeNh+pZMDaDuy8qUtEpEmbiozxcNy2Puy8aTYTLxRNrDvLChjJOdKj3c6A5ssezttnf45nu0JV/WnwU00amsO7AMaoa2shO0kWoRUREhorPWlaX1vLm9mriotx8fn4hRRnxwQ5LRPphVFocX7pkDG/vruGdPUfZX9vCZ2fkMVr/2wPGkT2eJ6/PkxLrzB5P+KTX8+dvlwY7FBERkWGjqa2T3605yF+2VTEuO4H7LylW0SniEBFuF4tLsrn7oiI8LsNvVh3gL1srdSnDAeLIHs+65nYSoz1EesKnrv7DB4fP6v2pcZHMGJXCs+vLuG/hGLIS1espIiIymHZXNfHixjI6vD6umZrD7IJUXZtTxIHyU2P58iXFLN1WyXultRw61srNs0eSFKNZqvsjfCqzs1Db0uHYYbbdLRibiddn+dW7+4MdioiIiGN1+Xz8ZWslv3v/IAnREdx78RjmFKap6BRxsEiPi2un5nLTrHyqGtt4ZMVe9tY0BTussObIHs/apnZSHXYNz96kxkVyzZQcfv/BYe5dOGZY5CwiIjKUjrV08Oz6w5TXn2BOYSpXTh5BhNuRx+1FztnZjtwLJ+flJZOdFM0fPjjME6sPcsn4TBaOzwx2WGHJcWtOay2lNc2MzogLdihD4t6FRbR1eXl81YFghyIiIuIoW4808MiKvdQ2t3PL7JFcMzVXRafIMJSZEM29F49han4yy3fV8MSagx9fRUP6znFrz6NN7dS1dDBhRGKwQxkSYzITuGJSNr9bc5CGE53BDkdERCTstXV6+dNHR3hm3WEyE6L48sJiJuUmBTssEQmiSI+LG2bkcd3UXA7WtnD1I6vYUn482GGFFccVntsrGwEoGSaFJ8C9F4+hqb2LJ9ccDHYoIiIiYa20polrf7aadQePcVFxOnddVESKTmUREcAYw6zCVO5eUIQxhhsefZ8XNpQFO6yw4bjCc0eFv/CckDN8Cs9JuUksmpDFo+/so6qhLdjhiIiIhB1rLc+vL+PqR1ZztKmdO+YWsGTSCNwuTSAkIn8rNzmG1748n5mjUvjGi1v4zivb6OjyBTuskOe4yYV2VDaSnxpDYvTwmu74f39qAot//C7/8foOfnrLdODMJ3rfMmfkOX3X6T73XD8zlL5PRESGl6a2Tr79p228sqmCC0an8dBNU1m+sybYYYlICEuNi+TJz8/mv/66i1+9d4CdlY387HPTyUzQJQ5PxXE9njsrGofVMNuTRqXFcd/FY/jzlkpW7a0NdjgiIiJhYWt5A1c/sorXNlfwr4vH8vQ/z9G1sUWkTzxuF9+6qoSHb57G1iP+dcmHh+uDHVbIclTh2drRxYG6lmEzsVBPdy8YTUFaLN95ZRvtXd5B/S6vz3KgtoVdVY00t3cN6neJiIgMNGstj686wPW/WE17l49n77qAL19arKG1InLWPj0lh5e/OI9Ij4ubfrmWZ9Y59/Iy/eGooba7qpqwdnhNLNRddISb/3vNJG5/fB0/fmsvI1NjB/w7tpY38OLGcnZVNdLa8UlxmxoXydT8ZG6YkUekx1HHM0RExGHqWzr4xoubWbazhkUTMvnvG6ZoAiER6ZeSnERe+9J8vvzMRzzw8la2lDfw4KdLiPK4gx1ayHBU4XlyYqGSYTSxUE8LxmZw06x8Hn1nH5eOz+TSCVkD8rk+n+Wx9/bzP2/sxuM2jM9OZMKIROKjPJQda2V/bTMrdtVw3c9X89CNUynOShiQ7xURERlIa0pr+drzm6lraec7nyrhn+YVYIx6OUWk/5JjI3nin2bzwzd38/O397GrqpFffG4G2Ukavg9OKzwrG0mM9pCbHBPsUILqe9dNptNreenDcnwWFk3I7NdGtaapja89t5lVpbVcMSmbmaNSiYn85OhNYXocF43NYEdFA0u3VXHVI6v4f9dN5oYZeQORjoiISL+1d3n54Zt7+NV7+ylMi+NXt81jcp6uzSkiA8vtMvyvJeOZlJvE11/YzKceWcUvbp3OrILUYIcWdM4qPCsaKclJHFZHLk814+u0kckcrGth5e4aapvbWVySRXp81Fl/bnl9K0+vPURrh5drp+YyqyDllL/fkpwk8lNjeX5DGd94YTPv76tjxqiUj18fqBlovT7L9ooG1h04xvMbyrh4XAaXjM9kUk4SLgeem6NZfUVE+mdvdRNfeXYTOyob+dyckXzrqgnERjpqF0hEQsyVk0cwJjOeu5/ayM2PreXrl4/jrgtHO3Jfta8cs9b1+iy7qhq5ebZ2xAFcxnDdtFySYiJ4b+9Rtlc0MG1kCvPGpJPdx9n6NpUd5+UPy4mP8nDPgiJy+tCTnBAdwW0XFPDU2kO8/GE5LgPTRqaccbm+2nionjd3VNHU1kVqXCSJMRH8ZPleHlq2lxmjUnjk5ml9ilNERJzPWsuT7x/i/y3dSXyUh1/fNpNFJQNzCoqIyJmMzUrgT/fN45svbeH7f9nFu3uO8qN/mDpsh946pvA8UNtCW6dv2E4s1BuXMSyakMWcwlTe3XOUDw4cY+OhekYkRTMtP5kLi9PJ72UCot1VTfxh3WG2HWmgIC2OW+aMJD6q738qEW4Xt84ZxZNrD/LixnKMMUzNT+53PqtKa1m6tZJRabFcPy2X4qwEbj1/FHXN7SzdVsX3l+7kqoff40c3TmXhuMx+f5+IiISvsmOt/H9/3Mp7e2tZOC6D/7rhPF1fT0SGXFJMBD//3HSe31DGg6/uYMlP3uX/XF3CtVNzh9UoTXBQ4bmzUhMLnUpCdARXnZfDgnGZbCk/zqay4yzdVsXSbVWMyYznwuJ04iI9eK1l/9Fm3theTaTHxcJxmSwcn4HHdfaz1EZ6XNx2fgG/e/8gL2wow2X6Nyz0nd01vLGjmok5idw4K/9vYkqLj+Ifzx/FvKI07v39h/zTb9dz38IivrpoLB63ZtgVERlOurw+frPqAD9etge3Mfz7NRO59fxRw24HT0RChzGGG2eNZFZBKl9/YTNffW4zr2yq4HvXTR5Wc9M4pvDcUdlIhNtQnKnZVE8lPsrD3KJ05halU9vcTlyUhxW7qvn92sN0+Xy4XYa4KA9fWjiG5JgIYs+il7M3kR4Xt19QwBNrDvD8hjIuGpvBlZNHnNVnWGtZvrOa5btqmJKXxA0z8k95jbXRGfH86b55PPjqdn62ch8bDtbz8M3TdCFwEZFh4t09R/mP13ewp7qZRROy+PdrJzIiafjs1IlIaBudEc8L98zlqfcP8oM3drP4R+9w38Ix3Dm/kOgI5192xRGFZ5fXxxvbqyjJSdI1JPsoPT6KW+aM5M75hb2+froJbc7GJ8XnQe5/5iM6unxcOy23T8taa/nvN3azfFcN00emcP30XFxnOGIdHeHm+585j9mFqXzrj9u46uH3+M/rz2OxzukREXGs3VVNfP8vO1m5+ygjU2N59NYZXD4xS72cIhJy3C7DHfMKuXRCFv/+5x389xu7eWbdYR64YgJXTMp29ORDjig8X/7wCPuPtvDordODHYr0IirCze1zC3hjexX/8twm9h9t5l8WjT3tP5a1lv94fSe/WXWAWQWpXDM1p9ei83QF8l0Xjea59WV84ckNTMhO4FNTckiJ/eQC4eE0I2yn18ehOv/1Uts7fSTFRJAcG8HF4zIcMZlSOM3cG06xnqvhkKM4w87KRh5evpe/bKsiIcrDA1eM5455Bbpgu4iEvPzUWB67bSarS2v57ms7uO8PHzI+O4H7Ly1myURnFqBhX3i2d3l5aNkepuQlcfnE7GCHI6cQHeHmqTvn8O0/beXhFaXsq23he9dOIrlbIXhSeX0rD766nWU7a7hjbgHFmfHndNQ6KzGa+xaOYXVpLct3VfPQsj3MK0pnfnF62Eyjv7e6iWfXH2ZHRSNdPovL+Cdvau/yAfDCxnKWTMzm8/MLmD7y1Je6ERFxCq/PsmJXDU+tPcS7e46SEOXhy5eM4fPzCkmJ+/ttiohIKJs3Jp3X75/Pa1sqeGRFKff+/kOKMuL4x/NHcf2MPBKjI4Id4oAJj73v0/j92sNUNLTxgxumaKc7xEV6XPzXZ86jKCOe7/91Fyt31fAPM/O57YJRREW4aWjt5L29R3lo2V4Avn3VBO6cX8gz68rO+TvdLsNFYzOYnJfEX7dV8faeo7y/v465ReksmZRNaojupJTWNPGT5aX8eUsFEW4XMwtSGZsZT0F6HNERbto6vRxr6aDD6+PZdYd5fWslcwpT+eYV4wf08jVDyVqLz3LKc3hDlbUWr7XnNAlXOLHW0trhpbWjK2wO3IhzWGvZWdnEn7dU8MqmCo4cP0FWYhRfWzyW2y8oICnWOTtmIjL8eNwurpuWx6en5PLnLRU8vvogD762gx+8sZurJo/gU1NymFuURkSYT5rZr70HY8wS4CeAG/i1tfb7AxJVH7W0d/GzlaXMLUpjfnH6UH61nCNjDHcvKGLBuAwee3c/T689xBNrDv7NexZNyOTBT08kL+XvL/VyrlJiI7l59kgWNraxYmc1K3fXMPf7y7lhRh53zh9NYXrcgH1Xf+w72szDy/fy6uYKYiLc3LOgiNTYSOJ6TPQUHeEmJzmGW+aM5F8WFfP8+jJ+unIf1/18DVdMyub+S4uZEOKXFmrt6GLV3lrW7j/Gyt01VDW0caLTS4TbEB3hJiU2kqKMOIoy4pg2MiUkzt9u7ehid1UjpTXNHKhtoaGti7ZOL16fJSHKEzjXPJELx6QzqzA1rDcQnV4fpTXN7KxspKqxjdrmdto6fXxv6U6SYyPITY5hdmEqi0uymF2QqhmkB9CZtq3GmCjgSWAGUAfcaK09GHjtAeBOwAvcb619YwhDH1CtHV18cOAYq/bWsnJ3DfuPtuB2GeYWpfHtqyawqCQrrP/HRER6crsM10zN5ZqpuWwpP87Taw+xdGsVL2wsJzk2gkvGZTK/OJ15Y9LDcvJMY609twWNcQN7gMVAObAeuNlau+NUy8ycOdNu2LDhnL6vp8N1rfz76zt4a0c1f7x37ln38gzU5Dnh7HTnag3G76e376tsOMFbO6qJ8rhIjI4gJzmG8/KS/qb3ejBiqW5so6qhjT9+dIQOr4/ZhalcNy2XKyeNGPIj522dXpbvrOHlD8tZubuGKI+b2+aO4q4LR5MWH9Xn8+1a2rv49XsHeOzdfbR0eLlgdBqfn1/IgrEZIVG0AVQcP8HyXTUs31nNmn11dHT5iIlwkx4fSXZSDEkxHto7fZzo9FLd2EZ5/QkskBDlYeH4TC6bmMWFxRkkxQxdG9U0trFydw1vbq/mvdJaOrp8eFyGkWmxpMdFER3hJsJjONbcQVuXj73VTXQFCtGLxmVw6fhMFo7LDIshgMdaOlixq4bHVx1gb00TnV5LlMdFfkos6QmRpMVFMTE3kYrjJzhY28q6g8fo6PKfc7xwXAaLS7JZMC7jrK7721/GmI3W2plD9oWDrC/bVmPMvcB51tp7jDE3AddZa280xpQAzwCzgRxgGTDWWus93XcO5Lb5XNW3dLDvaDOlNc1sOdLAlvLj7Kr0/y9FelzMLkhlyaRsrpiUTVp81JDEpP0EETmdoZrzoK3Ty3t7a3l9SwXv7DlKfWsnAKPSYpmcm8SUvGTGZicwOj2O3OSYkDg39FTb5v7sHcwGSq21+wNf8CxwDXDKwnMg1DS28ciKUp5Zdxi3y/CNy8eF7dBCgRFJMdx2QcGQf29WYjRfXTyWf718LM+vL+Plj47wwMtb+dYftzI+O5Hpo5KZnJvEiKQYspOiSYmNJNLtItLjIsJtcLtMn4d2W2vp9Frau7wcb+2krqWDqoY2dlQ0sPVIAxsP1dPY1kVWYhT3LCji8/MLST+HHau4KA9fWVTMHXMLeGb9YX635iBfeHIDMRFuZhWmcv7oVArT4hiRHEN2YjTRES4i3Cdvfc/ndHl2+SwdXT4aTnRS39rB0aZ2Smua2VPdxJbyBnZVNQH+leWtc0axaEImMwtSeXFjea+f2dbpJTclhhU7a1i2s5pXN1dgDIzNTGBmQQpjsxLITY4hNyWGhGgPUR43UREuojwuIt2uM+Z0MubWdi8tHV0ca+mgvP4E5fWt7KxsYsOhYxyqawUgNzmGW+eMwmIpSIvrtaflljkjaWnvYnVpLSt21bB8Vw2vb6nEZWBKfjITcxIZl5VAUUY8qfGRpMRGkhDtweNy4XGZQd9YdHl9dHh9NLd3UdfcQW1zO4ePtbLtSCPbKxrYdqQBn4XEaA/TR6ZQkpNIYXrc3wwj7nmw4729R3lrRw0rdlXzp00VeFyGCSMSmZKfxKScJLKToslMiCYtPhKPy/hzdZu/68WXj/Vl23oN8GDg/ovAT43/j/0a4FlrbTtwwBhTGvi89wc76Ka2ThpOdNLptXR6fXR0+ej0+uj0Wlo7umhs66LhRCeNgVvDiU5qmtqpOH6CyoY2Gk50fvxZCdEezstL4gsXjeaC0WnMLkwdFpcZEBHpTXSEm8UlWSwuycLns+yobGR1aS2byo7z0eHj/HlL5cfvjfK4yEqMJiMhioz4KP/PhCjS4iOJj/IQHeEmJsJNbKSb6Aj/zePy79d63GbQLz/Vny1/LtD95LtyYE7/wjmzp9Ye4pl1h7lxVj73X1oclt3MEjoyE6L50iXF3LdwDNuONPLWjio+PHycP31UwdNrT3202wQm+TlZJ5wcOPDx+IGPH/uLzt64DBRnJrBkUjZXT8lhblH6gJzfmBQbwT0LirhzfiFv7z7Kqr1HWbOvjh/8dfdplztZUH+cQo+w/y4L2/2uv4A71QCK9PhIxmcn8sAVuVw6IYuijLg+FbrREW4un5jN5ROz8fosGw/Vs3Z/HRsO1fPKpgqa27tOu/zHPb0WfNZi8Rebtpf8ekqLi2RmQQq3zhnF3DFplIxIxBhzxl6QuCgPl03M5rKJ2fh8lm0VDSzbWcOa0lpe2VRBU9upYzYG3MbfDt1ncbbdftmnbZcebdKd1+c/h7Y3STERTMpN5EsLx7C4JJst5cf71D5xUR6WTBrBkkkj6PL62Hionrf3HGVz2XFeOc3/UFykm+3fXXLGzx+m+rJt/fg91touY0wDkBZ4fm2PZft2/ap++tV7B3h4+d4+vTfK4yIpJoL0+CjyUmKYWZBCQVocRRnxjM6IIz8lNiSO2IuIhBqXyzApN4lJuUkfP1fX7D/Iv7+2hQO1LVQ3tnG0qZ19R5tZe6CO462dp/nETyTFRLD5/1w2WKED/Rtq+1ngcmvtPwce/yMw21r75R7vuwu4K/BwHHD6vd+hkw7UBjuIAaacwoMTcwJn5qWcQt8oa21GsIMYKH3ZthpjtgfeUx54vA9/z+Z3gfettU8Hnv8NsNRa+1Iv3xNq22an/V2epLzCi/IKH07MCZyTV6/b5v70eJYD+d0e5wEVPd9krX0MeKwf3zMojDEbnHReECincOHEnMCZeSknCYK+bFtPvqfcGOMBkoBjfVwWCL1ts1P/LpVXeFFe4cOJOYFz8zqpPzNLB8STAAAJu0lEQVSOrAeKjTGFxphI4Cbg1YEJS0REZFjqy7b1VeD2wP0bgBXWP3zpVeAmY0yUMaYQKAbWDVHcIiIip3XOPZ6B80q+BLyBf8r3x6212wcsMhERkWHmVNtWY8x3gQ3W2leB3wBPBSYPOoa/OCXwvufxT0TUBdx3phltRUREhkq/phW01i4Flg5QLEMtZIYYDSDlFB6cmBM4My/lJEOut22rtfY73e63AZ89xbLfA743qAEODqf+XSqv8KK8wocTcwLn5gX0Y3IhERERERERkb4IjavKi4iIiIiIiGM5rvA0xiwxxuw2xpQaY77Zy+tRxpjnAq9/YIwp6PbaA4HndxtjLh/KuM/kXPMyxhQYY04YYzYFbo8Odeyn0oecLjLGfGiM6TLG3NDjtduNMXsDt9t7Lhss/czJ262dQmairj7k9DVjzA5jzBZjzHJjzKhur4VrO50up5BsJ+hTXvcYY7YGYl9ljCnp9lrIrv/E2YwxB7v9XW4IdjznyhjzuDGmxhizrdtzqcaYtwLrwLeMMSnBjPFcnCKvB40xR7qtC68MZoxnyxiTb4xZaYzZaYzZboz5SuD5sG6v0+QV7u0VbYxZZ4zZHMjr/waeLwzs8+4N7ANHBjvWs3GavJ4wxhzo1l5Tgx3rgLHWOuaGfyKGfcBoIBLYDJT0eM+9wKOB+zcBzwXulwTeHwUUBj7HHeycBiCvAmBbsHM4x5wKgPOAJ4Ebuj2fCuwP/EwJ3E8J55wCrzUHO4dzzGkhEBu4/8Vuf3vh3E695hSq7XQWeSV2u/9p4K+B+yG7/tPN+TfgIJAe7DgGII+LgOndt7nAD4BvBu5/E/ivYMc5QHk9CHw92LH1I6cRwPTA/QRgT2A9GNbtdZq8wr29DBAfuB8BfACcDzwP3BR4/lHgi8GOdYDyeqLnPqJTbk7r8ZwNlFpr91trO4BngWt6vOca4HeB+y8ClxpjTOD5Z6217dbaA0Bp4PNCQX/yClVnzMlae9BauwXw9Vj2cuAta+0xa2098BawZCiCPoP+5BSq+pLTSmtta+DhWvzXDoTwbqdT5RTK+pJXY7eHccDJk/xDef0nEhaste/in2W4u+7b5t8B1w5pUAPgFHmFNWttpbX2w8D9JmAnkEuYt9dp8gpr1q858DAicLPAJfj3eSE82+tUeTmW0wrPXKCs2+Ny/v4f7uP3WGu7gAYgrY/LBkt/8gIoNMZ8ZIx5xxhz4WAH20f9+X2Halv1N65oY8wGY8xaY0yorDzPNqc7gb+c47JDpT85QWi2E/QxL2PMfcaYffiP7N9/NsuKDBILvGmM2WiMuSvYwQywLGttJfiLAiAzyPEMpC8FTkd4PNyGpHZn/KcmTcPf2+SY9uqRF4R5exlj3MaYTUAN/gPZ+4DjgX1eCNPtVs+8rLUn2+t7gfb6sTEmKoghDiinFZ699fD1PHJwqvf0Zdlg6U9elcBIa+004GvAH4wxiQMc37noz+87VNuqv3GNtNbOBG4BHjLGFA1MWP3S55yMMbcCM4H/Pttlh1h/coLQbCfoY17W2p9Za4uAfwO+fTbLigySedba6cAVwH3GmIuCHZCc0S+AImAq/v2MHwY3nHNjjIkHXgL+pceIkLDWS15h317WWq+1dir+EUizgQm9vW1oo+q/nnkZYyYBDwDjgVn4T1f6tyCGOKCcVniWA/ndHucBFad6jzHGAyThH0LSl2WD5ZzzCgydqwOw1m7Ef4Ro7KBHfGb9+X2Halv1Ky5rbUXg537gbfxHKoOtTzkZYxYB3wI+ba1tP5tlg6A/OYVqO8HZ/76f5ZNhSaHaVjIMdPufqgH+iLOGeVcbY0YABH7WBDmeAWGtrQ7sMPuAXxGGbWaMicBfnP3eWvty4Omwb6/e8nJCe51krT2Of9t7PpAc2OeFMN9udctrSWDItA3se/yWMG6vnpxWeK4HigOzXEXin2Sn56yTrwInZ9e8AVhhrbWB528y/tlhC4FiYN0QxX0m55yXMSbDGOMGMMaMxp/X/iGK+3T6ktOpvAFcZoxJCQwXuSzwXLCdc06BXKIC99OBecCOQYu0786YkzFmGvBL/AVa94102LbTqXIK4XaCvuVV3O3hVcDewP1QXv+Jgxlj4owxCSfv419PbDv9UmGl+7b5duCVIMYyYE4WZwHXEWZtFpgD4zfATmvtj7q9FNbtdaq8HNBeGcaY5MD9GGAR/vNXV+Lf54XwbK/e8trV7eCHwX+AOKza67QGa9aiYN2AK/HP4rUP+Fbgue/i34EEiAZewD95xjpgdLdlvxVYbjdwRbBzGYi8gM8A2/HPWPkhcHWwczmLnGbh74lpAeqA7d2W/Xwg11Lgn4KdS39zAuYCWwPttBW4M9i5nEVOy4BqYFPg9qoD2qnXnEK5nfqY108C64NN+DfYE7stG7LrP92ce8M/C/PmwG37yb/bcLwBz+AfxtgZWM/fiX+uheX4D/IsB1KDHecA5fVUYB24BX+xNiLYcZ5lTvPxD8vc0m09f2W4t9dp8gr39joP+CgQ/zbgO4HnR+Pf5y3Fvw8cFexYByivFYH22gY8TWDmWyfcTCBBERERERERkUHhtKG2IiIiIiIiEmJUeIqIiIiIiMigUuEpIiIiIiIig0qFp4iIiIiIiAwqFZ4iIiIiIiIyqFR4igwhY4w1xvyw2+OvG2MeDNx/0Bjz9R7vPxi4XiTGGK8xZpMxZpsx5rWT134SERGRc2eMae7x+A5jzE8D9x80xrQaYzJ7e7+2zSJ9p8JTZGi1A9efLCbP0glr7VRr7STgGHDfwIYmIiIivagF/vUUr2nbLNJHKjxFhlYX8Bjw1X5+zvtAbv/DERERkTN4HLjRGJN6hvdp2yxyGio8RYbez4DPGWOSenntq4EhO5uMMZuAnJ5vMMa4gUuBVwc5ThERkeEgpse297s9Xm/GX3x+5VQfoG2zyJmp8BQZYtbaRuBJ4P5eXv5xYMjOVGvtVKCi22sxgQ1iHZAKvDX40YqIiDjeiR7b3u/08p6HgduNMYk9nte2WaSPVHiKBMdDwJ1A3FkscyKwQRwFRKLzSERERIaEtfY48Afg3h4vadss0kcqPEWCwFp7DHgef/F5tss24O8t/boxJmKgYxMREZFe/Qi4G/D0fEHbZpEzU+EpEjw/BM5ldlustR8Bm4GbBjQiERER6ZW1thb4IxB1ite1bRY5DWOtDXYMIiIiIiIi4mDq8RQREREREZFBpcJTREREREREBpUKTxERERERERlUKjxFRERERERkUKnwFBERERERkUGlwlNEREREREQGlQpPERERERERGVQqPEVERERERGRQ/f/PLSa50MVzFgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(16,8)) \n",
    "sns.distplot(data['NHR'],ax=ax[0]) \n",
    "sns.distplot(data['HNR'],ax=ax[1])\n",
    "\n",
    "#The measure of tonal component of frequency is shown below. \n",
    "#The value NHR is right skewed and there are lot of observations in the area, but they seem to be with very minimal values. \n",
    "#The maximum number of observations is between 0 and 0.04. \n",
    "#The value HNR looks like normally distributed, but in a first look there seems to be a slight negative skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadba46988>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,3,figsize=(16,8)) \n",
    "sns.distplot(data['MDVP:Shimmer'],ax=ax[0,0]) \n",
    "sns.distplot(data['MDVP:Shimmer(dB)'],ax=ax[0,1]) \n",
    "sns.distplot(data['Shimmer:APQ3'],ax=ax[0,2]) \n",
    "sns.distplot(data['Shimmer:APQ5'],ax=ax[1,0]) \n",
    "sns.distplot(data['MDVP:APQ'],ax=ax[1,1]) \n",
    "sns.distplot(data['Shimmer:DDA'],ax=ax[1,2])\n",
    "\n",
    "#For all of the below graphs, we can observe that the measure of variation in amplitude is positively skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadc34b188>"
      ]
     },
     "execution_count": 20,
     "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": [
    "##Bi-Variate Analysis\n",
    "\n",
    "sns.distplot( data[data.status == 0]['spread1'], color = 'r')\n",
    "sns.distplot( data[data.status == 1]['spread1'], color = 'g')\n",
    "\n",
    "#Spread1 is normally distributed between people who have Parkinson's disease and people who are normal. \n",
    "#People with spread1 between - 8.5 and -7.5 are more in numbers and they are normal. \n",
    "#People whose spread1 is between -6.5 and -5 are having Parkinson's disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadc0fc048>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploring relationship between variables\n",
    "\n",
    "fig, ax = plt.subplots(1,2,figsize=(16,8))\n",
    "sns.boxplot(x='status',y='NHR',data=data,ax=ax[0])\n",
    "sns.boxplot(x='status',y='HNR',data=data,ax=ax[1])\n",
    "\n",
    "#People who have PD(status equal to one) have higher levels of Noise to Harmonic ratio. \n",
    "#Also, looking at the HNR ratio, people who have Parkinson's have lower levels of HNR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadc1f0988>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploring relationship between variables\n",
    "\n",
    "fig, ax = plt.subplots(1,2,figsize=(16,8))\n",
    "sns.boxplot(x='status',y='MDVP:Flo(Hz)',data=data,palette=\"Set1\",ax=ax[0])\n",
    "sns.boxplot(x='status',y='MDVP:Fo(Hz)',data=data,palette=\"Set1\",ax=ax[1])\n",
    "\n",
    "#When we look the relationship between status and MDVP:Fo(Hz) we can see the median value is around 199 Hz \n",
    "#for people who are normal. \n",
    "\n",
    "#For people who are affected with Parkinsons the median value comes around 145 Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For categorical predictors\n",
    "\n",
    "cols = [\"MDVP:Jitter(%)\",\"MDVP:Jitter(%)\",\"MDVP:RAP\",\"MDVP:PPQ\",\"Jitter:DDP\"]\n",
    "fig, axs = plt.subplots(ncols = 5,figsize=(16,8))\n",
    "fig.tight_layout()\n",
    "for i in range(0,len(cols)):\n",
    "    sns.boxplot(x='status',y=cols[i],data=data, ax = axs[i])\n",
    "    \n",
    "#People who are suffering for PD tend to have higher jitter %. \n",
    "#It seems if the values goes above 0.15 we can confirm the patient is having PD. \n",
    "#The variation of fundamental frequency is in a low range for people who is normal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Correlation comparision with heat map\n",
    "\n",
    "corr = data.corr()\n",
    "sns.set_context(\"notebook\", font_scale=1.0, rc={\"lines.linewidth\": 3.5})\n",
    "plt.figure(figsize=(18,7))\n",
    "\n",
    "# creating a mask so that the correlation values are shown only once\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask, 1)] = True\n",
    "a = sns.heatmap(corr,mask=mask, annot=True, fmt='.2f')\n",
    "rotx = a.set_xticklabels(a.get_xticklabels(), rotation=90)\n",
    "roty = a.set_yticklabels(a.get_yticklabels(), rotation=30)\n",
    "\n",
    "#MDVP:Jitter(%) has a very high correlation with MDVP:Jitter(Abs),MDVP:RAP,MDVP:PPQ,Jitter:DDP\n",
    "\n",
    "#MDVP:Shimmer has a very correlation with MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA \n",
    "#this may be because they are related to each other. \n",
    "#This may be because multi-dimensinal voice programs analysis is closely related with these variables\n",
    "\n",
    "#The target variable status has a weak positive corelation with spread1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Applying Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree\n",
    "#Decision trees can be used to predict both continuous and discrete values \n",
    "#i.e. they work well for both regression and classification tasks.\n",
    "\n",
    "from sklearn import metrics\n",
    "X = data.drop(\"status\",axis=1)\n",
    "Y = data[\"status\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "136\n",
      "59\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting Data into 70% Training data and 30% Testing Data:\n",
    "X_train, X_test, y_train,  y_test = train_test_split(X, Y,train_size=0.7, test_size=0.3, random_state=42)\n",
    "print(len(X_train)),print(len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=6, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=1e-07,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=100, splitter='best')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying decision tree model\n",
    "dt_model = DecisionTreeClassifier(criterion='entropy',max_depth=6,random_state=100,min_samples_leaf=5)\n",
    "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=6,\n",
    "            max_features=None, max_leaf_nodes=None,\n",
    "            min_impurity_split=1e-07, min_samples_leaf=5,\n",
    "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
    "            presort=False, random_state=100, splitter='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=6, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=5, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=100, splitter='best')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8983050847457628"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_model.score(X_test , y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = dt_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  4],\n",
       "       [ 2, 42]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples: 6\n"
     ]
    }
   ],
   "source": [
    "#Count mis-classified one\n",
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Decision Tree has accuracy of 89% on test data (59 records)\n",
    "#There are 6 misclassified samples (FP and FN)\n",
    "#53 samples are correctly classified (TP and TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Imp\n",
      "MDVP:Fo(Hz)       0.113981\n",
      "MDVP:Fhi(Hz)      0.196589\n",
      "MDVP:Flo(Hz)      0.000000\n",
      "MDVP:Jitter(%)    0.000000\n",
      "MDVP:Jitter(Abs)  0.000000\n",
      "MDVP:RAP          0.000000\n",
      "MDVP:PPQ          0.000000\n",
      "Jitter:DDP        0.000000\n",
      "MDVP:Shimmer      0.000000\n",
      "MDVP:Shimmer(dB)  0.073695\n",
      "Shimmer:APQ3      0.000000\n",
      "Shimmer:APQ5      0.000000\n",
      "MDVP:APQ          0.000000\n",
      "Shimmer:DDA       0.056276\n",
      "NHR               0.000000\n",
      "HNR               0.000000\n",
      "RPDE              0.000000\n",
      "DFA               0.000000\n",
      "spread1           0.205876\n",
      "spread2           0.000000\n",
      "D2                0.000000\n",
      "PPE               0.353584\n"
     ]
    }
   ],
   "source": [
    "#Building visual representation of Decision Tree\n",
    "\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "from os import system\n",
    "\n",
    "train_char_label = ['No', 'Yes']\n",
    "pd_tree_regularized = open('pd_tree_regularized.dot','w')\n",
    "dot_data = tree.export_graphviz(dt_model, out_file= pd_tree_regularized , feature_names = list(X_train), class_names = list(train_char_label))\n",
    "\n",
    "pd_tree_regularized.close()\n",
    "\n",
    "print (pd.DataFrame(dt_model.feature_importances_, columns = [\"Imp\"], index = X_train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can also copy the script in the .dot file and paste it at http://webgraphviz.com/ to get tree view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logReg_score:  0.864406779661017\n",
      "Logistic Regression Accuracy:  86.4406779661017\n",
      "Confusion Matrix: \n",
      "[[ 9  6]\n",
      " [ 2 42]]\n"
     ]
    }
   ],
   "source": [
    "#Scaling Data\n",
    "\n",
    "trainingData = preprocessing.scale(X_train)\n",
    "testData = preprocessing.scale(X_test)\n",
    "\n",
    "# Running the logistic regression\n",
    "logReg = LogisticRegression()\n",
    "logReg.fit(trainingData, y_train)\n",
    "pred = logReg.predict(testData)\n",
    "logReg_score = logReg.score(testData, y_test)\n",
    "print('logReg_score: ' , logReg_score)\n",
    "accuracy = logReg.score(testData, y_test) * 100\n",
    "accuracies = {}\n",
    "accuracies['LR'] = accuracy\n",
    "print('Logistic Regression Accuracy: ', accuracy)\n",
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Logistic Regression has accuracy of 86% on test data (59 records)\n",
    "#There are 8 misclassified samples (FP and FN)\n",
    "#51 samples are correctly classified (TP and TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.60      0.58        15\n",
      "           1       0.86      0.84      0.85        44\n",
      "\n",
      "    accuracy                           0.78        59\n",
      "   macro avg       0.71      0.72      0.72        59\n",
      "weighted avg       0.78      0.78      0.78        59\n",
      "\n",
      "Naive Bayes accuracy:  77.96610169491525\n",
      "Confusion Matrix: \n",
      "[[ 9  6]\n",
      " [ 7 37]]\n"
     ]
    }
   ],
   "source": [
    "#Running Naive Bayes\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(trainingData, y_train)\n",
    "expected  = y_test\n",
    "predicted = nb.predict(testData)\n",
    "print(metrics.classification_report(expected, predicted))\n",
    "accuracy = (metrics.accuracy_score(expected, predicted))*100\n",
    "accuracies['NB'] = accuracy\n",
    "print('Naive Bayes accuracy: ', accuracy)\n",
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(expected, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#GaussianNB has accuracy of 78% on test data (59 records)\n",
    "#There are 13 misclassified samples (FP and FN)\n",
    "#46 samples are correctly classified (TP and TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy:  83.05084745762711\n",
      "\n",
      "Confusion Matrix: \n",
      "[[ 8  7]\n",
      " [ 3 41]]\n",
      "\n",
      "Misclassified samples in KNN: 10\n"
     ]
    }
   ],
   "source": [
    "#Running KNN\n",
    "k_model = KNeighborsClassifier(n_neighbors=5)\n",
    "k_model.fit(X_train, y_train)\n",
    "k_model.score(X_test,y_test)\n",
    "\n",
    "expected  = y_test\n",
    "predicted = k_model.predict(X_test)\n",
    "y_pred = k_model.predict(X_test)\n",
    "accuracy = (metrics.accuracy_score(y_test, predicted))*100\n",
    "\n",
    "accuracies['KNN'] = accuracy\n",
    "print('KNN accuracy: ', accuracy)\n",
    "print('\\nConfusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, predicted))\n",
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('\\nMisclassified samples in KNN: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#KNN has accuracy of 83% on test data (59 records)\n",
    "#There are 10 misclassified samples (FP and FN)\n",
    "#49 samples are correctly classified (TP and TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machines accuracy:  88.13559322033898\n",
      "Confusion Matrix: \n",
      "[[ 8  7]\n",
      " [ 0 44]]\n"
     ]
    }
   ],
   "source": [
    "#Running SVM\n",
    "from sklearn import svm\n",
    "\n",
    "svm = svm.SVC(gamma = 0.025, C = 3)\n",
    "svm.fit(trainingData, y_train)\n",
    "svm.score(testData, y_test)\n",
    "predicted = svm.predict(testData)\n",
    "accuracy = (metrics.accuracy_score(y_test, predicted))*100\n",
    "accuracies['SVM'] = accuracy\n",
    "print('Support Vector Machines accuracy: ', accuracy)\n",
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SVM has accuracy of 88% on test data (59 records)\n",
    "#There are 7 misclassified samples ( All FN)\n",
    "#51 samples are correctly classified (TP and TN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eadd6e8408>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the performace of all 4 Supervised Learning Models as done above. NB performs the worst\n",
    "plt.figure(figsize = (8,5))\n",
    "plt.yticks(np.arange(0,100,10))\n",
    "sns.barplot(x = list(accuracies.keys()), y = list(accuracies.values()))\n",
    "\n",
    "#The graph shows higest accuracy of SVM - however, SVM has a very high number of False Negatives\n",
    "#In healthcare, False Negative needs to be minimized to zero as it means the patient had PD but was not diagnosed\n",
    "#Hence, despite high accuracy, SVM might not be the mdoel fo choice - proceeding to Meta-Classifiers and Ensemble Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Meta-Classifier with LR as Level-2 classifier and KNN, NB and RFC as Level-1 classifiers\n",
    "\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "from sklearn import model_selection\n",
    "\n",
    "clf1 = KNeighborsClassifier(n_neighbors=1)\n",
    "clf2 = LogisticRegression(max_iter=400)\n",
    "clf3 = GaussianNB()\n",
    "clf4 = RandomForestClassifier(random_state=1)\n",
    "\n",
    "sclf = StackingClassifier(classifiers=[clf1, clf3, clf4], meta_classifier = clf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.76 (+/- 0.12) [KNN]\n",
      "Accuracy: 0.84 (+/- 0.10) [LR]\n",
      "Accuracy: 0.70 (+/- 0.17) [Naive Bayes]\n",
      "Accuracy: 0.84 (+/- 0.10) [RF]\n",
      "Accuracy: 0.85 (+/- 0.08) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "#Running a 10-fold cross-validation\n",
    "for clf, label in zip([clf1, clf2, clf3, clf4, sclf], ['KNN', 'LR', 'Naive Bayes', 'RF',\n",
    "                                                 'StackingClassifier']):\n",
    "    scores = model_selection.cross_val_score(clf, X, Y, cv=10, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\"  % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The simple stacked Meta-Classifier has achieved an accuracy of 85%. \n",
    "#We can try with using probabilities of level-1 classifiers to see if the accuracy imprves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.76 (+/- 0.12) [KNN]\n",
      "Accuracy: 0.84 (+/- 0.10) [LR]\n",
      "Accuracy: 0.70 (+/- 0.17) [Naive Bayes]\n",
      "Accuracy: 0.84 (+/- 0.10) [RF]\n",
      "Accuracy: 0.82 (+/- 0.11) [StackingClassifier]\n"
     ]
    }
   ],
   "source": [
    "##Using Probablities - #Running a 10-fold cross-validation\n",
    "sclf = StackingClassifier(classifiers=[clf1, clf3, clf4],\n",
    "                          use_probas=True,\n",
    "                          average_probas=False,\n",
    "                          meta_classifier=clf2)\n",
    "for clf, label in zip([clf1, clf2, clf3, clf4, sclf], ['KNN', 'LR', 'Naive Bayes', 'RF',\n",
    "                                                 'StackingClassifier']):\n",
    "    scores = model_selection.cross_val_score(clf, X, Y, cv=10, scoring='accuracy')\n",
    "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\"  % (scores.mean(), scores.std(), label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Using the probabilities of the first-level classifiers to train the meta-classifier has reduced the accuracy of \n",
    "#meta-classifier. We can see that the StackingClassifier accuracy has gone down from 0.85 to 0.82\n",
    "#PS: The probabilites of the level-1 classifiers are stacked (not everaged) as it is a recommended strategy. \n",
    "#Instead of using LR as meta-classifier; RF was also used, however, the accuracy went further down hence LR was retained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9491525423728814"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Running Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfcl = RandomForestClassifier(n_estimators = 50)\n",
    "rfcl = rfcl.fit(X_train, y_train)\n",
    "y_pred = rfcl.predict(X_test)\n",
    "rfcl.score(X_test , y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "[[12  3]\n",
      " [ 0 44]]\n",
      "Misclassified samples in Random Forest: 3\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, y_pred))\n",
    "\n",
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples in Random Forest: {}'.format(count_misclassified))\n",
    "\n",
    "#The Random Forest Classifier has given the accuracy of 95%. This is highest by-far\n",
    "#Trying with other techniques to see if this can be imporved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp = pd.Series(rfcl.feature_importances_,index=X.columns).sort_values(ascending=False)\n",
    "feature_imp\n",
    "# Creating a bar plot\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "# Adding labels to the plot\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.show()\n",
    "\n",
    "#The Fo(Hz), PPE and spread1 are the most impactful attributes for Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8983050847457628"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Bagging\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "bgcl = BaggingClassifier(base_estimator=dt_model, n_estimators=50, max_samples=.7)\n",
    "bgcl = bgcl.fit(X_train, y_train)\n",
    "y_pred = bgcl.predict(X_test)\n",
    "bgcl.score(X_test , y_test)\n",
    "\n",
    "#With 90% accuracy of Bagging there is no improvement over RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "[[10  5]\n",
      " [ 1 43]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples in Bagging: 6\n"
     ]
    }
   ],
   "source": [
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples in Bagging: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8983050847457628"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#AdaBoosting\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "abcl = AdaBoostClassifier( n_estimators= 50)\n",
    "abcl = abcl.fit(X_train,y_train)\n",
    "y_pred = abcl.predict(X_test)\n",
    "abcl.score(X_test , y_test)\n",
    "\n",
    "#AdaBoosting is a bit higher than Boosting but takes the accuracy down to 90% as compared to RF \n",
    "#hence not the model of choice "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "[[10  5]\n",
      " [ 1 43]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples in Ada Boosting: 6\n"
     ]
    }
   ],
   "source": [
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples in Ada Boosting: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8983050847457628"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gradient Boosting\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "gbcl = GradientBoostingClassifier(n_estimators = 50, learning_rate = 0.05)\n",
    "gbcl = gbcl.fit(X_train,y_train)\n",
    "y_pred = gbcl.predict(X_test)\n",
    "gbcl.score(X_test , y_test)\n",
    "\n",
    "#Gradient Boosting also gives the accuracy of 90% - still lower than RFC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "[[10  5]\n",
      " [ 1 43]]\n"
     ]
    }
   ],
   "source": [
    "print('Confusion Matrix: ')\n",
    "print(metrics.confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Misclassified samples in Gradient Boosting: 6\n"
     ]
    }
   ],
   "source": [
    "count_misclassified = (y_test != y_pred).sum()\n",
    "print('Misclassified samples in Gradient Boosting: {}'.format(count_misclassified))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Of all the above ones Random Forest algorithm gave the maximum accuracy"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}