Parkinson's dataset analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parkinson's dataset analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the parkinson's dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Parkinsons).<br>\n",
    "In this notebook we'll discuss the following points:\n",
    "* Does every feature is useful to predict the status of a patient?\n",
    "* Which are the best features to predict the status of a patient?\n",
    "\n",
    "\n",
    "Let's start by importing the neccessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All packages are loaded\n"
     ]
    }
   ],
   "source": [
    "# import\n",
    "\n",
    "# data storage\n",
    "import pandas as pd\n",
    "\n",
    "# model selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# metrics\n",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix\n",
    "\n",
    "# build and test models\n",
    "from model import get_X, Tester, get_models\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# some utility\n",
    "from itertools import combinations\n",
    "\n",
    "print(\"All packages are loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 195 entries, 0 to 194\n",
      "Data columns (total 21 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   MDVP:Fo(Hz)       195 non-null    float64\n",
      " 1   MDVP:Fhi(Hz)      195 non-null    float64\n",
      " 2   MDVP:Flo(Hz)      195 non-null    float64\n",
      " 3   MDVP:Jitter(%)    195 non-null    float64\n",
      " 4   MDVP:RAP          195 non-null    float64\n",
      " 5   MDVP:PPQ          195 non-null    float64\n",
      " 6   Jitter:DDP        195 non-null    float64\n",
      " 7   MDVP:Shimmer(dB)  195 non-null    float64\n",
      " 8   Shimmer:APQ3      195 non-null    float64\n",
      " 9   Shimmer:APQ5      195 non-null    float64\n",
      " 10  MDVP:APQ          195 non-null    float64\n",
      " 11  Shimmer:DDA       195 non-null    float64\n",
      " 12  NHR               195 non-null    float64\n",
      " 13  HNR               195 non-null    float64\n",
      " 14  status            195 non-null    int64  \n",
      " 15  RPDE              195 non-null    float64\n",
      " 16  DFA               195 non-null    float64\n",
      " 17  spread1           195 non-null    float64\n",
      " 18  spread2           195 non-null    float64\n",
      " 19  D2                195 non-null    float64\n",
      " 20  PPE               195 non-null    float64\n",
      "dtypes: float64(20), int64(1)\n",
      "memory usage: 32.1 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
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     "metadata": {},
     "output_type": "display_data"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <th>Shimmer:APQ5</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>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.426</td>\n",
       "      <td>0.02182</td>\n",
       "      <td>0.03130</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.626</td>\n",
       "      <td>0.03134</td>\n",
       "      <td>0.04518</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>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.482</td>\n",
       "      <td>0.02757</td>\n",
       "      <td>0.03858</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>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.02924</td>\n",
       "      <td>0.04005</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>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.584</td>\n",
       "      <td>0.03490</td>\n",
       "      <td>0.04825</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 × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  MDVP:RAP  \\\n",
       "0      119.992       157.302        74.997         0.00784   0.00370   \n",
       "1      122.400       148.650       113.819         0.00968   0.00465   \n",
       "2      116.682       131.111       111.555         0.01050   0.00544   \n",
       "3      116.676       137.871       111.366         0.00997   0.00502   \n",
       "4      116.014       141.781       110.655         0.01284   0.00655   \n",
       "\n",
       "   MDVP:PPQ  Jitter:DDP  MDVP:Shimmer(dB)  Shimmer:APQ3  Shimmer:APQ5  ...  \\\n",
       "0   0.00554     0.01109             0.426       0.02182       0.03130  ...   \n",
       "1   0.00696     0.01394             0.626       0.03134       0.04518  ...   \n",
       "2   0.00781     0.01633             0.482       0.02757       0.03858  ...   \n",
       "3   0.00698     0.01505             0.517       0.02924       0.04005  ...   \n",
       "4   0.00908     0.01966             0.584       0.03490       0.04825  ...   \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 21 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer(dB)</th>\n",
       "      <th>Shimmer:APQ3</th>\n",
       "      <th>Shimmer:APQ5</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>195.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>154.228641</td>\n",
       "      <td>197.104918</td>\n",
       "      <td>116.324631</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.003306</td>\n",
       "      <td>0.003446</td>\n",
       "      <td>0.009920</td>\n",
       "      <td>0.282251</td>\n",
       "      <td>0.015664</td>\n",
       "      <td>0.017878</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046993</td>\n",
       "      <td>0.024847</td>\n",
       "      <td>21.885974</td>\n",
       "      <td>0.753846</td>\n",
       "      <td>0.498536</td>\n",
       "      <td>0.718099</td>\n",
       "      <td>-5.684397</td>\n",
       "      <td>0.226510</td>\n",
       "      <td>2.381826</td>\n",
       "      <td>0.206552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>41.390065</td>\n",
       "      <td>91.491548</td>\n",
       "      <td>43.521413</td>\n",
       "      <td>0.004848</td>\n",
       "      <td>0.002968</td>\n",
       "      <td>0.002759</td>\n",
       "      <td>0.008903</td>\n",
       "      <td>0.194877</td>\n",
       "      <td>0.010153</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030459</td>\n",
       "      <td>0.040418</td>\n",
       "      <td>4.425764</td>\n",
       "      <td>0.431878</td>\n",
       "      <td>0.103942</td>\n",
       "      <td>0.055336</td>\n",
       "      <td>1.090208</td>\n",
       "      <td>0.083406</td>\n",
       "      <td>0.382799</td>\n",
       "      <td>0.090119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>88.333000</td>\n",
       "      <td>102.145000</td>\n",
       "      <td>65.476000</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000920</td>\n",
       "      <td>0.002040</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.005700</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013640</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>8.441000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.256570</td>\n",
       "      <td>0.574282</td>\n",
       "      <td>-7.964984</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>1.423287</td>\n",
       "      <td>0.044539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>117.572000</td>\n",
       "      <td>134.862500</td>\n",
       "      <td>84.291000</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.001660</td>\n",
       "      <td>0.001860</td>\n",
       "      <td>0.004985</td>\n",
       "      <td>0.148500</td>\n",
       "      <td>0.008245</td>\n",
       "      <td>0.009580</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024735</td>\n",
       "      <td>0.005925</td>\n",
       "      <td>19.198000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.421306</td>\n",
       "      <td>0.674758</td>\n",
       "      <td>-6.450096</td>\n",
       "      <td>0.174351</td>\n",
       "      <td>2.099125</td>\n",
       "      <td>0.137451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>148.790000</td>\n",
       "      <td>175.829000</td>\n",
       "      <td>104.315000</td>\n",
       "      <td>0.004940</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002690</td>\n",
       "      <td>0.007490</td>\n",
       "      <td>0.221000</td>\n",
       "      <td>0.012790</td>\n",
       "      <td>0.013470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038360</td>\n",
       "      <td>0.011660</td>\n",
       "      <td>22.085000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.495954</td>\n",
       "      <td>0.722254</td>\n",
       "      <td>-5.720868</td>\n",
       "      <td>0.218885</td>\n",
       "      <td>2.361532</td>\n",
       "      <td>0.194052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>182.769000</td>\n",
       "      <td>224.205500</td>\n",
       "      <td>140.018500</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.003955</td>\n",
       "      <td>0.011505</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.020265</td>\n",
       "      <td>0.022380</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060795</td>\n",
       "      <td>0.025640</td>\n",
       "      <td>25.075500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.587562</td>\n",
       "      <td>0.761881</td>\n",
       "      <td>-5.046192</td>\n",
       "      <td>0.279234</td>\n",
       "      <td>2.636456</td>\n",
       "      <td>0.252980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>260.105000</td>\n",
       "      <td>592.030000</td>\n",
       "      <td>239.170000</td>\n",
       "      <td>0.033160</td>\n",
       "      <td>0.021440</td>\n",
       "      <td>0.019580</td>\n",
       "      <td>0.064330</td>\n",
       "      <td>1.302000</td>\n",
       "      <td>0.056470</td>\n",
       "      <td>0.079400</td>\n",
       "      <td>...</td>\n",
       "      <td>0.169420</td>\n",
       "      <td>0.314820</td>\n",
       "      <td>33.047000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.685151</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-2.434031</td>\n",
       "      <td>0.450493</td>\n",
       "      <td>3.671155</td>\n",
       "      <td>0.527367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)    MDVP:RAP  \\\n",
       "count   195.000000    195.000000    195.000000      195.000000  195.000000   \n",
       "mean    154.228641    197.104918    116.324631        0.006220    0.003306   \n",
       "std      41.390065     91.491548     43.521413        0.004848    0.002968   \n",
       "min      88.333000    102.145000     65.476000        0.001680    0.000680   \n",
       "25%     117.572000    134.862500     84.291000        0.003460    0.001660   \n",
       "50%     148.790000    175.829000    104.315000        0.004940    0.002500   \n",
       "75%     182.769000    224.205500    140.018500        0.007365    0.003835   \n",
       "max     260.105000    592.030000    239.170000        0.033160    0.021440   \n",
       "\n",
       "         MDVP:PPQ  Jitter:DDP  MDVP:Shimmer(dB)  Shimmer:APQ3  Shimmer:APQ5  \\\n",
       "count  195.000000  195.000000        195.000000    195.000000    195.000000   \n",
       "mean     0.003446    0.009920          0.282251      0.015664      0.017878   \n",
       "std      0.002759    0.008903          0.194877      0.010153      0.012024   \n",
       "min      0.000920    0.002040          0.085000      0.004550      0.005700   \n",
       "25%      0.001860    0.004985          0.148500      0.008245      0.009580   \n",
       "50%      0.002690    0.007490          0.221000      0.012790      0.013470   \n",
       "75%      0.003955    0.011505          0.350000      0.020265      0.022380   \n",
       "max      0.019580    0.064330          1.302000      0.056470      0.079400   \n",
       "\n",
       "       ...  Shimmer:DDA         NHR         HNR      status        RPDE  \\\n",
       "count  ...   195.000000  195.000000  195.000000  195.000000  195.000000   \n",
       "mean   ...     0.046993    0.024847   21.885974    0.753846    0.498536   \n",
       "std    ...     0.030459    0.040418    4.425764    0.431878    0.103942   \n",
       "min    ...     0.013640    0.000650    8.441000    0.000000    0.256570   \n",
       "25%    ...     0.024735    0.005925   19.198000    1.000000    0.421306   \n",
       "50%    ...     0.038360    0.011660   22.085000    1.000000    0.495954   \n",
       "75%    ...     0.060795    0.025640   25.075500    1.000000    0.587562   \n",
       "max    ...     0.169420    0.314820   33.047000    1.000000    0.685151   \n",
       "\n",
       "              DFA     spread1     spread2          D2         PPE  \n",
       "count  195.000000  195.000000  195.000000  195.000000  195.000000  \n",
       "mean     0.718099   -5.684397    0.226510    2.381826    0.206552  \n",
       "std      0.055336    1.090208    0.083406    0.382799    0.090119  \n",
       "min      0.574282   -7.964984    0.006274    1.423287    0.044539  \n",
       "25%      0.674758   -6.450096    0.174351    2.099125    0.137451  \n",
       "50%      0.722254   -5.720868    0.218885    2.361532    0.194052  \n",
       "75%      0.761881   -5.046192    0.279234    2.636456    0.252980  \n",
       "max      0.825288   -2.434031    0.450493    3.671155    0.527367  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data loaded\n",
    "file = \"parkinsons.csv\"\n",
    "data = pd.read_csv(file)\n",
    "\n",
    "#drop of unuseful and repeat column\n",
    "data.drop([\"name\", \"MDVP:Jitter(Abs)\", \"MDVP:Shimmer\"], axis=1, inplace=True)\n",
    "\n",
    "#data inspection\n",
    "display(data.info())\n",
    "display(data.head())\n",
    "display(data.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds one of 195 voice recording from these individuals (\"name\" column). The main aim of the data is to discriminate healthy people from those with PD, according to \"status\" column which is set to 0 for healthy and 1 for PD.\n",
    "<i><b>UCI Machine learning repository</b></i>\n",
    "\n",
    "There are 147 measurements with parkinson and 48 one without parkinson. Here is the description of each feature\n",
    "<table>\n",
    "<tr>\n",
    "<th>Features</th>\n",
    "<th>Description</th>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:F0 (Hz)</td>\n",
    "<td>Average vocal fundamental frequency</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:Fhi (Hz)</td>\n",
    "<td>Maximum vocal fundamental frequency</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:Flo (Hz)</td>\n",
    "<td>Minimum vocal fundamental frequency</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:Jitter(%)</td>\n",
    "<td>MDVP jitter in percentage</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:RAP</td>\n",
    "<td>MDVP relative amplitude perturbation</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:PPQ</td>\n",
    "<td>MDVP five-point period perturbation quotient</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Jitter:DDP</td>\n",
    "<td>Average absolute difference of differences between jitter cycles</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:Shimmer(dB</td>\n",
    "<td>MDVP local shimmer in dB</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Shimmer:APQ3</td>\n",
    "<td>Three-point amplitude perturbation quotient</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Shimmer:APQ5</td>\n",
    "<td>Five-point amplitude perturbation quotient</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>MDVP:APQ11</td>\n",
    "<td>MDVP 11-point amplitude perturbation quotient</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Shimmer:DDA</td>\n",
    "<td>Average absolute differences between the amplitudes of consecutive periods</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>NHR</td>\n",
    "<td>Noise-to-harmonics rati</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>HNR</td>\n",
    "<td>Harmonics-to-noise ratio</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>RPDE</td>\n",
    "<td>Recurrence period density entropy measure</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>D2</td>\n",
    "<td>Correlation dimension</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>DFA</td>\n",
    "<td>Signal fractal scaling exponent of detrended fluctuation analysis</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Spread1</td>\n",
    "<td>Two nonlinear measures of fundamental</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>Spread2</td>\n",
    "<td>Frequency variation</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>PPE</td>\n",
    "<td>Pitch period entropy</td>\n",
    "</tr>\n",
    "</table>\n",
    "\n",
    "Let's get a look on how the features are correlate between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WE have 12 features correlate over 90%:, MDVP:RAP, MDVP:APQ, PPE, spread1, Jitter:DDP, Shimmer:APQ3, Shimmer:APQ5, NHR, Shimmer:DDA, MDVP:PPQ, MDVP:Jitter(%), MDVP:Shimmer(dB).\n",
      "\n",
      "Here is their correlations:\n",
      "\n",
      "corr(MDVP:Jitter(%),MDVP:RAP) = 99.03%\n",
      "corr(MDVP:Jitter(%),MDVP:PPQ) = 97.43%\n",
      "corr(MDVP:Jitter(%),Jitter:DDP) = 99.03%\n",
      "corr(MDVP:Jitter(%),NHR) = 90.7%\n",
      "corr(MDVP:RAP,MDVP:PPQ) = 95.73%\n",
      "corr(MDVP:RAP,Jitter:DDP) = 100.0%\n",
      "corr(MDVP:RAP,NHR) = 91.95%\n",
      "corr(MDVP:PPQ,Jitter:DDP) = 95.73%\n",
      "corr(Jitter:DDP,NHR) = 91.95%\n",
      "corr(MDVP:Shimmer(dB),Shimmer:APQ3) = 96.32%\n",
      "corr(MDVP:Shimmer(dB),Shimmer:APQ5) = 97.38%\n",
      "corr(MDVP:Shimmer(dB),MDVP:APQ) = 96.1%\n",
      "corr(MDVP:Shimmer(dB),Shimmer:DDA) = 96.32%\n",
      "corr(Shimmer:APQ3,Shimmer:APQ5) = 96.01%\n",
      "corr(Shimmer:APQ3,Shimmer:DDA) = 100.0%\n",
      "corr(Shimmer:APQ5,MDVP:APQ) = 94.91%\n",
      "corr(Shimmer:APQ5,Shimmer:DDA) = 96.01%\n",
      "corr(spread1,PPE) = 96.24%\n",
      "\n",
      "Features's correlation with the status:\n",
      "\n",
      "corr(MDVP:Fo(Hz),status) = -38.35%\n",
      "corr(MDVP:Fhi(Hz),status) = -16.61%\n",
      "corr(MDVP:Flo(Hz),status) = -38.02%\n",
      "corr(MDVP:Jitter(%),status) = 27.82%\n",
      "corr(MDVP:RAP,status) = 26.67%\n",
      "corr(MDVP:PPQ,status) = 28.87%\n",
      "corr(Jitter:DDP,status) = 26.66%\n",
      "corr(MDVP:Shimmer(dB),status) = 35.07%\n",
      "corr(Shimmer:APQ3,status) = 34.76%\n",
      "corr(Shimmer:APQ5,status) = 35.11%\n",
      "corr(MDVP:APQ,status) = 36.43%\n",
      "corr(Shimmer:DDA,status) = 34.76%\n",
      "corr(NHR,status) = 18.94%\n",
      "corr(HNR,status) = -36.15%\n",
      "corr(RPDE,status) = 30.86%\n",
      "corr(DFA,status) = 23.17%\n",
      "corr(spread1,status) = 56.48%\n",
      "corr(spread2,status) = 45.48%\n",
      "corr(D2,status) = 34.02%\n",
      "corr(PPE,status) = 53.1%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vizualisation of correlations between each features and status\n",
    "\n",
    "# target\n",
    "target = \"status\"\n",
    "\n",
    "# features names\n",
    "features = list(data.columns)\n",
    "features.remove(\"status\")\n",
    "\n",
    "# correlations matrix\n",
    "corr = data.corr()\n",
    "\n",
    "# heatmat plot of the correlations\n",
    "sns.heatmap(corr, square=True, cmap=\"Greys\")\n",
    "\n",
    "# all pair of features\n",
    "pair_feature = list(combinations(features, 2))\n",
    "\n",
    "# couples of features correlate over 90%\n",
    "highly_corr = [(f1, f2) for (f1, f2) in pair_feature if corr.loc[f1, f2]>=0.9 ]\n",
    "\n",
    "# set of features correlate over 90%\n",
    "highly_corr_set = set()\n",
    "for f1, f2 in highly_corr:\n",
    "    highly_corr_set.add(f1)\n",
    "    highly_corr_set.add(f2)\n",
    "\n",
    "print(\"WE have \"+str(len(highly_corr_set))+\" features correlate over 90%:\", *highly_corr_set, sep=\", \", end=\".\\n\\n\")\n",
    "print(\"Here is their correlations:\")\n",
    "print()\n",
    "for f1, f2 in highly_corr:\n",
    "    print(\"corr({},{}) = {}%\".format(f1, f2, round(corr.loc[f1, f2]*100,2)))\n",
    "    \n",
    "# Features's correlation with the status\n",
    "print()\n",
    "print(\"Features's correlation with the status:\")\n",
    "print()\n",
    "for f in features:\n",
    "    print(\"corr({},status) = {}%\".format(f, round(corr.loc[f, \"status\"]*100,2)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3M8sYJ34zs4xx4jczyxgnfjOzjHHiNzPLGCd+M7OMceI3M8sYJ34zs4xx4jczyxgnfjOzjHHiNzPLGCd+M7OMceI3M8sYJ34zs4wZlc/cBZA0F/gmUAf8c0Rc1md+E3AzMAdYD5waEc9XOk6zSujqyrNpeyedXUEuJ3Z05UEQAd35Qll9TggY1yA2b88jQZ3EjnyQzwcNdTlygob6HF3dwbaubuokxjXmEGJrZzeN9XVMmdhILqfetvP5YP2WTjq7Ss+3sWdUJn5JdcA1wHFAO/CIpGUR8VRRtXOAjRFxkKQFwOXAqZWP1ixdXV15OrZsY/P2bvL5PJ1dwVX3P8vZ757BRXetpH3jVlomj+eK+bM5cPI4XtnczVX3PcMn3n8QWzu7uXDpG3W+cephjG/Icd6tj+603ITGOi5d9hQdm7dzw1mtHLz/JHI5kc8Hq1/exLk3t/XWL55vY9No7eo5ElgTEWsjohO4HZjXp8484Kbk/VLgWEneE63mvLJ5O13d0L5hK3W5Oj7+/Uc5ac7U3qQP0L5xKxcuXYnI8fFbV3DSnKls3LKjN+n31Pn0HY/zyqbOXZbbsGUH5x/zVto3buXcm9tYv6UTgPVbOnuTfk/94vk2No3WxH8gsK5ouj0pK1knIrqA14ApfVckaaGkNkltHR0dKYVrtvsGu2/u6M7THcGExjpyKiTfvcc39CbjHu0bt9Id0Tt/QmNdyToTGutKlu09vqF3urOrG4DOru6S6+iZb2PTaE38pY7cYwh1iIglEdEaEa3Nzc0jEpzZSBjsvtlQl6NO4vXObvIBLZPH84etO2iZPH6nei2Tx1Mn9c5/vbO7ZJ3XO7tLlv1h647e6cb6wj+Hxvq6kuvomW9j02hN/O3A1KLpFuDF/upIqgf2AjZUJDqzCtpvjybq66Bln/F057u57owjuGvFOi4/aXZvUu7pqw/yXHfmHO5asY7JExu4Yv7Odb5x6mHsN6lxl+X2mdjA9Q8+19uHP2ViIwBTJjZyw1mtO9Uvnm9jkyJ2OUiuuiSRPwMcC7wAPAKcHhGriup8EvjTiDg/Obn7VxFxykDrbW1tjba2tgHbnr7ont2O9/nLTtjtZSxdu/t73I3fYSrnkcrtmwOO6okgpzKjeiJoyPUd1ZOnTnhUT+0Y9C9lVI7qiYguSRcA91IYznljRKyStBhoi4hlwHeAWyStoXCkv6B6EZulq74+x+T6cYOuv9eEITQysXRxLieaJzUNYYU2Wo3KxA8QEcuB5X3KLi56vw04udJxmZmNdaO1j9/MzFLixG9mljFO/GZmGePEb2aWMaNyOGdaJHUA/1Gm2r7AqxUIZzTytpf3akTMHenG++ybWf49lOLP4w0DfRaD3jczlfgHQ1JbRLRWO45q8LaPjm0fTbGMBv483jBSn4W7eszMMsaJ38wsY5z4d7Wk2gFUkbd9dBhNsYwG/jzeMCKfhfv4zcwyxkf8ZmYZ48RvZpYxTvxmZhnjxG9mljGZSvxz584NCo9n9Muvob5S4X3TrxF4DVqmEv+rr/qqbxudvG9aJWUq8ZuZmRO/mVnmOPGbmWVMVRO/pLmSVktaI2lRiflNku5I5j8saXqf+dMkbZb0uUrFbGY21lUt8UuqA64BjgdmAadJmtWn2jnAxog4CLgSuLzP/CuBn6Ydq5lZLanmEf+RwJqIWBsRncDtwLw+deYBNyXvlwLHShKApL8E1gKrKhSvmVlNqK9i2wcC64qm24Gj+qsTEV2SXgOmSNoKXAQcB7ibxzJh+qJ7dnuZ5y87IYVIbKyr5hG/SpT1vQihvzr/CFwZEZvLNiItlNQmqa2jo2MIYZqlw/umVUs1E387MLVougV4sb86kuqBvYANFL4ZfFXS88Cngf8p6YJSjUTEkohojYjW5ubmkd0Cs2HwvmnVUs2unkeAmZJmAC8AC4DT+9RZBpwN/BqYD9wfhQcI/LeeCpIuBTZHxNWVCNrMbKyrWuJP+uwvAO4F6oAbI2KVpMVAW0QsA74D3CJpDYUj/QXVitfMrFZU84ifiFgOLO9TdnHR+23AyWXWcWkqwZmZ1ShfuWtmljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMVVN/JLmSlotaY2kRSXmN0m6I5n/sKTpSfmRkh5PXk9I+milYzczG6uqlvgl1QHXAMcDs4DTJM3qU+0cYGNEHARcCVyelD8JtEbEYcBc4NvJM3nNzKyMah7xHwmsiYi1EdEJ3A7M61NnHnBT8n4pcKwkRcTrEdGVlI8DoiIRm5nVgGom/gOBdUXT7UlZyTpJon8NmAIg6ShJq4DfAecX/SPYiaSFktoktXV0dIzwJpgNnfdNq5ZqJn6VKOt75N5vnYh4OCLeCfwZ8HlJ40o1EhFLIqI1Ilqbm5uHFbDZSPK+adVSzcTfDkwtmm4BXuyvTtKHvxewobhCRDwNbAEOSS1SM7MaUs3E/wgwU9IMSY3AAmBZnzrLgLOT9/OB+yMikmXqAST9CXAw8HxlwjYzG9uqNhImIrokXQDcC9QBN0bEKkmLgbaIWAZ8B7hF0hoKR/oLksXfCyyStAPIA5+IiFcrvxVmZmNPVYdARsRyYHmfsouL3m8DTi6x3C3ALakHaGZWg3zlrplZxjjxm5lljBO/mVnGOPGbmWWME7+ZWcY48ZuZZYwTv5lZxjjxm5lljBO/mVnGOPGbmWWME7+ZWcY48ZuZZYwTv5lZxjjxm5llTFUTv6S5klZLWiNpUYn5TZLuSOY/LGl6Un6cpBWSfpf8/PNKx25mNlZVLfFLqgOuAY4HZgGnSZrVp9o5wMaIOAi4Erg8KX8V+EhE/CmFJ3T53vxmZoNUzSP+I4E1EbE2IjqB24F5ferMA25K3i8FjpWkiHgsInqez7sKGCepqSJRW03J54OOTdt5YePrdGzaTj4f1Q7JLHXVfALXgcC6oul24Kj+6iSPanwNmELhiL/HScBjEbE9xVitBuXzweqXN3HuzW20b9xKy+Tx3HBWKwfvP4lcTtUOzyw1ZY/4JR0g6cuSfpS8/qekKSPQdqm/rL6HWwPWkfROCt0/5/XbiLRQUpukto6OjiEFarVp/ZbO3qQP0L5xK+fe3Mb6LZ0Vad/7plXLgIlf0vuA3wLdwPcodLs0AfdLmiFpOH3r7cDUoukW4MX+6kiqB/ai8NB1JLUAPwbOiojn+mskIpZERGtEtDY3Nw8jXKs1nV3dvUm/R/vGrXR2dVekfe+bVi3lunquAE6MiMeKyu6W9GPgCQqJd6geAWZKmgG8ACwATu9TZxmFk7e/BuYD90dESNobuAf4fEQ8NIwYLMMa6+tomTx+p+TfMnk8jfV1VYzKLH3lunr26JP0AYiIx4GXgY8NteGI6AIuAO4FngbujIhVkhZLOjGp9h1giqQ1wGeAniGfFwAHAV+U9Hjy2m+osVg2TZnYyA1ntdIyeTxAbx//lImNVY7MLF3ljvglaXJEbOxTuA/QFRH54TQeEcuB5X3KLi56vw04ucRyXwa+PJy2zXI5MbN5D+48713s6M7TUJdjvz2afGLXal65xH8l8HNJnwMeTcrmUDihemWagZmlLZ8Pnu3Y7FE9ljkDJv6IWCLpReBLwDspjKh5CvhyRPxLBeIzS01/o3p+/In30DypNi4Lmb7ont2q//xlJ6QUiY0mZcfxR8RPJP0mIl4tV9dsLKn2qB6zaik3nPMjkjqAlZLaJb27QnGZpU5S74ndHi2TxyO5m8dqW7lRPV8B/ltEvJnCFbL/lH5IZpVRJ7j8pNk7jeq5/KTZ1DnvW40r19XTFRH/DhARD0uaVIGYzCqiO+Cmf/s9X/zwLPYe38Aftu7gpn/7PZeeeEi1QzNLVbnEv5+kz/Q3HRFfTycss/TVCc5+9wwuumtl76geH/FbFpRL/DcAkwaYNhuzcrlcySP+r3x0drVDM0tVueGc/wggaV+P6rFaM2ViI3933MG7jOP3lbtW6wZM/JI+DHwX2CEpD5wSEf9WkcjMUpbLiYP3n8SPP/EeOru6aayvY8rERl+8ZTWvXFfP/0dhVM+/SzoK+CrwvvTDMquMXE41c7GW2WCVG86506ge3L9vZjbmeVSPZVo+H6zf0umuHssUj+qxzPKjFy2rBjWqx6wWZeEmbWallH3mbo9khE+/00Mhaa6k1ZLWSFpUYn6TpDuS+Q9Lmp6UT5H0gKTNkq4ebhyWTb5Jm2XVoBM/8GdlpneLpDrgGuB4YBZwmqRZfaqdA2yMiIMo3P//8qR8G/BF4HPDicGyraE+V/ImbQ31u/NnYTb2DHoPj4hLBpoegiOBNRGxNiI6gduBeX3qzKPwgHeApcCxkhQRWyLiXyn8AzAbkrocXHnKoTvdpO3KUw6lznnfaly5C7hmAl8D3gr8DvhcRLwwQm0fCKwrmm4HjuqvTkR0SXoNmAL4KmIbts6uPA31Ob407xAmNNbxemc3DfU5Orui2qGZparcqJ4bgZuBXwEnAlcBfzVCbZcaNtH3L24wdQZuRFoILASYNm3a7ixqNS6fhwt+8NhO/fwtk8dzx8KjK9K+902rlnJfaidFxA0RsToirgCmj2Db7cDUoukW4MX+6kiqB/YCNuxOIxGxJCJaI6K1ubl5GOFarenKR8mTu935yhzxe9+0ail3xD9O0uG8ceQ9vng6Ih7td8nyHgFmSpoBvAAsAE7vU2cZcDbwa2A+cH9E+Hu4jYiGXOEJXH2P+Os9ht9qXLnE/1/A1/uZDuDPh9pw0md/AXAvUAfcGBGrJC0G2iJiGfAd4BZJaygc6S/oWV7S88CeQKOkvwQ+GBFPDTUey576uhzXnH44G7bs6O3j32diA/U+u2s1rlziPy4idqTVeEQsB5b3Kbu46P024OR+lp2eVlyWDd2RZ9uOPF+8+8neK3f/18mH0u0vlVbjyh3avCDpBknvl59AbTUmn4fP/vCJna7c/ewPnyBfoT5+s2opl/jfAbQBFwPrJH0juT2z2ZjX3d/JXR/xW40bMPFHxPqI+HZEvJ/CBVe/B74h6TlJX6lIhBWSzwcdm7bzwsbX6di03Ud9GVCXnNwt1jJ5PHX+cms1bneu3H2RwsnW64BNwN+mFVSl9dyl8aPXPsR7Ln+Aj177EKtf3uTkX+NygstPmr3TlbuXnzQbD+qxWlfu5C6SxgEfAU4D3gP8DPg88PN0Q6sc36Uxm7oDnv2v1/jBuUeTjyAncf9TL/EnUyZUOzSzVJW7ZcMPgA9QuHL3B8DpyUibmuK7NGbThMYcf/aWfTn9ht/0jur59l/PYUKjh3NabSt3xH8vcF5EbKpEMNXSUJ/jg7P246Q5U9l7fAN/2LqDu1as810aa1xnV3DeLSt2+qZ33i0rWHr+u6ocmVm6yj2I5SZJByf3FHl7Uvw0cENErE49ugppqhefOvZtfPzWFb1HftedOYemenf21rLtXfl+vunlqxSRWWUMeEgr6V3Ag8BmYAmFRy9uAR6QVJk7WVXA5m3dXHXfM3zxw7O4Y+HRfPHDs7jqvmfYvM1dPbWsvr9RPT67azWuXFfPxcBpEfFgUdn/lnQ/cAmFh6iMeRKc/e4ZXHTXyt4jfo/uqH0NdTmuPOVQ/u7OJ3p/71eecigNvmWD1bhyif+tfZI+ABHxfyQtSSekyougN+lD4ev+RXetrNjtea06uvJ5Dth7HLcvPJrufCRH+kF33l09VtvKJf6BTupuGclAqqnat+e16hjXkGP9lh20b9jae5O2ln3GM2ViQ7VDM0tVucQ/VdK3SpSLwtOxakJ9P7fndV9vbdvRFby6aftON2m7Yv5s9mwqe3mL2ZhWrjPzQmBFiVcb8PfphlY5TfU5rj3jiJ2u4Lz2jCNo8nDOmrYjH1y4dOcuvguXrmSHv+lZjSs7nLNSgVRTZ3eexnrxvY8dSU6QD+jKd7Oj2329tay/m7T5Vh1W68oN51w20Gu4jUuaK2m1pDWSFpWY3yTpjmT+w5KmF837fFK+WtKHhhNHPuDrP3+G5zo207FpO891bObrP3+Gbv/917TGulzJ4Zwe1WO1rlxn5ruAdcBtwMOUfmDBNpkAAA0WSURBVPj5kEiqA64BjqPwbN1HJC3r8xStc4CNEXGQpAXA5cCpkmZReBrXO4E3A7+U9LaIGNLA+6b6HB97z4zer/09fb3u6qltExrFt/96Tu/Vu2/cssHndqy2lUv8b6KQmE+j8Dzce4DbImLVCLR9JLAmItYCSLodmAcUJ/55wKXJ+6XA1ckDYeYBt0fEduD3yaMZj6TwbN7d1lgPzZOa+NK8Q3pHdzRPaqLR5/hqWj6geVLjTsM5G+uFe3qs1pW7H393RPwsIs4GjgbWAA9K+tQItH0ghW8TPdrZdaRQb52I6AJeA6YMclkAJC2U1CapraOjo2Qg43L17Dm+npn778Gb9hrHzP33YM/x9YzLOfPXsj0aG+jOBz3PXYko9Pvv0ViZ4ZyD2TfN0lC2LyPpZ/8r4Fbgk8C3gB+NQNulvk/3Pdbqr85gli0URiyJiNaIaG1ubi4ZSFNTPXs1NvSuVMBejQ00eVhfTWtoqGPfCU29V2jnBPtOaKKhoa4i7Q9m3zRLQ7nbMt8EHAL8FPjHiHhyBNtuB6YWTbcAL/ZTp11SPbAXsGGQy+6WpqZ6DnSiz5yGhjoOnOz771u2lDvi/2vgbcD/C/ybpD8mr02S/jjMth8BZkqaIamRwsnaviOFlgFnJ+/nA/dHRCTlC5JvIzOAmcBvhxmPmVkmlBvHn9qwlojoknQBhXv+1wE3RsQqSYuBtohYRuFRj7ckJ283UPjnQFLvTgongruATw51RI+ZWdZUtW8jIpYDy/uUXVz0fhtwcj/LfgWoqQe+m5lVggeqm5lljBO/mVnGOPGbmWWME7+ZWcY48ZuZZYwTv5lZxjjxm5lljBO/mVnGOPGbmWWME7+ZWcY48ZuZZYwTv5lZxjjxm5lljBO/mVnGVCXxS9pH0i8kPZv8nNxPvbOTOs9KOruo/CuS1knaXLmozcxqQ7WO+BcB90XETOC+ZHonkvYBLgGOAo4ELin6B/EvSZmZme2maiX+ecBNyfubgL8sUedDwC8iYkNEbAR+AcwFiIjfRMRLFYnUzKzGVCvx79+TuJOf+5WocyCwrmi6PSkzM7NhSO3Ri5J+CbypxKwvDHYVJcpiCHEsBBYCTJs2bXcXN0uN902rltQSf0R8oL95kl6WdEBEvCTpAOCVEtXagWOKpluAB4cQxxJgCUBra+tu/+MwS4v3TauWanX1LAN6RumcDdxdos69wAclTU5O6n4wKTMzs2GoVuK/DDhO0rPAcck0klol/TNARGwAvgQ8krwWJ2VI+qqkdmCCpHZJl1ZhG8zMxqTUunoGEhHrgWNLlLcBf1s0fSNwY4l6fw/8fZoxmpnVKl+5a2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGVOV4ZxmNjpNX3TPbi/z/GUnpBCJpclH/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMVW5gEvSPsAdwHTgeeCUiNhYot7ZwD8kk1+OiJskTQB+CLwV6Ab+JSIWVSJuM9uVL/oae6p1xL8IuC8iZgL3JdM7Sf45XAIcBRwJXJI8ghHgaxHxduBw4D2Sjq9M2GZmY1+1Ev884Kbk/U3AX5ao8yHgFxGxIfk28AtgbkS8HhEPAEREJ/AohQexm5nZIFTrXj37R8RLABHxkqT9StQ5EFhXNN2elPWStDfwEeCbaQVqZiNvd7uH3DU0slJL/JJ+CbypxKwvDHYVJcqiaP31wG3AtyJi7QBxLAQWAkybNm2QTZulz/umVUtqiT8iPtDfPEkvSzogOdo/AHilRLV24Jii6RbgwaLpJcCzEfGNMnEsSerS2toaA9U1qyTvm1Yt1erjXwacnbw/G7i7RJ17gQ9Kmpyc1P1gUoakLwN7AZ+uQKxmZjWlWon/MuA4Sc8CxyXTSGqV9M8AEbEB+BLwSPJaHBEbJLVQ6C6aBTwq6XFJf1uNjTAzG4uqcnI3ItYDx5YobwP+tmj6RuDGPnXaKd3/b2Y1arReKzBa4yrHT+AyM2NoSbwS7aTxj8K3bDAzyxhFZGcwgaQO4D/KVNsXeLUC4YxG3vbyXo2IuSPdeJ99M8u/h1L8ebxhoM9i0PtmphL/YEhqi4jWasdRDd720bHtoymW0cCfxxtG6rNwV4+ZWcY48ZuZZYwT/66WVDuAKvK2jw6jKZbRwJ/HG0bks3Afv5lZxviI38wsYzKb+CXNlbRa0hpJpR4E0yTpjmT+w5KmVz7KdAxi2/9GUkdyO4yauSWGpBslvSLpyX7mS9K3ks9lpaQjUohhyPudpM8n5aslfWikY6u0oX4WkqZIekDSZklXVzrutAzj8zhO0gpJv0t+/nnZxiIicy+gDngOeAvQCDwBzOpT5xPA9cn7BcAd1Y67gtv+N8DV1Y41hW3/f4AjgCf7mf8XwE8p3BLkaODhKnz2Jfc7CvemegJoAmYk66mr9mdapc9iIvBe4Pxa2U+H+XkcDrw5eX8I8EK59rJ6xH8ksCYi1kbhKV63U3gqWLHip4QtBY6VVAv3CBrMttekiPgVsGGAKvOAm6PgN8DeyW3DR8pw9rt5wO0RsT0ifg+sSdY3Vg35s4iILRHxr8C2yoWbuuF8Ho9FxItJ+SpgnKSmgRrLauIv+3Sv4joR0QW8BkypSHTpGsy2A5yUdHcslTS1MqFV3WA/mzTX399+l3ZslZblv8FSRurzOAl4LCK2D9RYVhP/gE/32o06Y9FgtutfgOkRMRv4JW8cZdS6tH/nw9nvam1/zPLfYCnD/jwkvRO4HDivXGNZTfztQPFRbAvwYn91ksc87sXA3QRjRdltj4j1RUcMNwBzKhRbtQ1mv0h7/f3td2nHVmlZ/hssZVifR/Kckh8DZ0XEc+Uay2rifwSYKWmGpEYKJ0qW9alT/JSw+cD9kZw9GePKbnuffu0TgacrGF81LQPOSkb3HA28FhEvjeD6h7PfLQMWJCM7ZgAzgd+OYGyVluW/wVKG/HlI2hu4B/h8RDw0qNaqfTa7imfR/wJ4hsKZ9C8kZYuBE5P344AfUjiJ9lvgLdWOuYLb/k8UThI9ATwAvL3aMY/Qdt8GvATsoHD0dA6FkSHnJ/MFXJN8Lr8DWkfTfkfhyXPPAauB46v9eVb5s3iewtHu5uR3OavS8Y+WzwP4B2AL8HjRa7+B2vKVu2ZmGZPVrh4zs8xy4jczyxgnfjOzjHHiNzPLGCd+M7OMceLPAEmfljRhpOqZFZMUkm4pmq5P7u76k2S6526vj0l6VtK9kt5dNO+2PuvbN6nfJOnB5I6VT0h6SNLBJdq/VNILyZ1kn5R04kDlybyFkv49ebVJOialj2dUcuLPhk8Dg0nog61nVmwLcIik8cn0ccALfercERGHR8RM4DLgR5LeAfwIOK7PAcd8YFm8cfX4GRFxKIVbh1zRTwxXRsRhwMnAjZJy/ZVL+jCF2xq8NyLeDiwEbpU0lu99tFuc+GuMpImS7kmOkJ6UdAnwZuABSQ8kda5LjnJWSfrHpOx/lKi3uWi98yV9L3l/crLuJyT9qsKbaKPTT4ETkvenUbhYrqSIeIDCIwQXRsQfgV8BHymqsqCf5X8FHDRQEBHxNNAF7DtA+UXAhRHxajLvUeC7wCcHWnctceKvPXOBFyPi0Ig4BPgGhXt+vD8i3p/U+UJEtAKzgfdJmh0R3ypRrz8XAx9KjsJOLFPXsuF2CreUGEdhv3q4TP1Hgbcn72+jkOyR9GbgbRSuGO/rIxSuqEbS4uKumx6SjgLyQMcA5e8EVvRZtI3CMw8yob7aAdiI+x3wNUmXAz+JiP9b4jECp0haSOH3fwCFHX7lbrTxEPA9SXdS+KpuGRcRK5MnQp0GLB/EIsU75U+AayXtCZwCLI2I7qL535e0lcJtGj6VtHdxn/X9naQzgU3AqRERyX7fX/lA8dQ8J/4aExHPSJpD4b4f/yTp58Xzkxt8fQ74s4jYmHTfjOtvdUXve+tExPnJEdQJwOOSDouI9SO5HTYmLQO+BhxD+fvmH05y87+I2CrpZ8BHKRz5/12fumdERFuZ9V0ZEV8bZPlTFO44e39R2REUjvozwV09NSb5qvx6RNxK4Y/wCApHO5OSKntSOBn3mqT9geOLFi+uB/CypHckJ8o+WtTGWyPi4eSo61V2vp2sZdeNwOKI+N1AlSS9j8IJ1RuKim8DPgPsD/wmtQgLvgpcLmlKEs9hFPbvb6fc7qjhI/7a86fAFZLyFO5C+XHgXcBPJb0UEe+X9BiFu2+updBt02NJcT1gEYWv4euAJ4E9knpXSJpJ4evxfRTu4mkZFxHtwDf7mX2qpPdSGDX2e+Ck5IRrj59TGLXznRjEnSMlLQbaIqLvrYsHE+ey5ADpIRXua/8m4NCI6CizaM3w3TnNLLOSxP9dCr0fZw7mn04tcOI3M8sY9/GbmWWME7+ZWcY48ZuZZYwTv5lZxjjxm5lljBO/mVnGOPGbmWXM/w+YBr37ZdSjiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mlmFO8mZmGeYkb2aWYU7yZmYZ5iRvZpZhTvJmZhnmJG9mlmFO8mZmGeYkb2aWYU7yZmYZ5iRvZpZhTvJmZhnmJG9mlmFO8mZmGeYkb2aWYU7yZmYZ5iRvZpZhI/IZrwCS5gOXAXng6xFxUdn+ccANwDxgA/CuiHik0XGaNUJvb4Enn9/GxLYcW3uDnkKRfE605XNEBFv7ioxvyVEM6CkUac2JlnyOLb0F8jnRmhPjWnMUItjaU6QQwfjWPPtOGkcupyHrLxaDDZt66Okr0NaSZ+qktqqOs+YbkUleUh64HDga6AZWSFoWEQ+WFHsfsDEiDpK0CLgYeFfjozWrr97eAv/95PM8sv5ZZrXvyZk3raR74xY6pkzgkoVzmdiW55u//BPvPGwa5y1dtd2+z/9oDeuf38YlC+ey7x5tbO4p8P5v3TdQ5upTOzl4/8k7TdjFYrDmiedYfEPXLh1nI8NI7a45HFgbEesioge4GVhQVmYBcH26vBQ4SpKvOMucJ5/fxlk3reTVfzV1IMEDdG/cwnlLV/H0pl4Wv/HAgQRfuu/MI18ysNy9cStPb+rdrsziG7rYsKlnp/Vv2NQzkOB35TgbGUZqkp8GPFqy3p1uq1gmIvqAZ4Cp5SeStERSl6Su9evX1ylcs11X7bXZVwy6N26hkH4t1b1xCxPb8uRzqrhv7wmt25Wb2JbfoUxPX2Gncfb0FSqee6jjbGQYqUm+Uos8dqMMEXFVRHRGRGd7e3tNgjOrhWqvzZac6JgygXz6tVTHlAls7ilQKEbFfX/Z0rtduc09hR3KtLVsn/jLtbXkK557qONsZBipSb4bmF6y3gE8NlgZSS3AXsDTDYnOrIH222McV5wyj/v+uIErT5k3kHD7+933mdTK1T9bxyUL5+6w78q7Hx5Y7pgynn0mtW5X5upTO5k6qW2n9U+d1MbVp3bu8nE2MozID16BFcBsSbOAPwOLgHeXlVkGnAbcAywE7oyIHVryZqNda2uel+23B/tMbGViW47vnPHX9BaK5EpG17z/LQcxviXZVzq65rJFh5IrG11zy5IjKASMb81VNbomlxMH7z+Z7579eo+uGYVGZJKPiD5J5wC3kwyhvCYiVku6EOiKiGXAN4AbJa0lacEval7EZvXV2ppn2pSJtTnZpF0/JJcT7ZPH1aZ+a6gRmeQBImI5sLxs2wUly1uBExodl5nZaDJS++TNzKwGnOTNzDLMSd7MLMOc5M3MMkxjadShpPXAH4coti/wVAPCGYn83of2VETMr3XlVV6bI8FoukbGWqwVr80xleSrIakrIjqbHUcz+L2Pzfe+K0bT98mxJtxdY2aWYU7yZmYZ5iS/o6uaHUAT+b3bUEbT98mx4j55M7NMc0vezCzDnOTNzDLMSd7MLMOc5M3MMmxMJfn58+cHySMC/fJrd1914WvTrxq8KhpTSf6pp0bLHc421vjatHoZU0nezGyscZI3M8uwpiZ5SfMlrZG0VtL5Ffa/UdK9kvokLSzbd5qk36ev0xoXtZnZ6NG0JC8pD1wOHAvMAU6SNKes2J+A04FvlR27D/Ap4LXA4cCnJE2pd8xmZqNNM1vyhwNrI2JdRPQANwMLSgtExCMRsQoolh37t8B/RcTTEbER+C+g5nN8m5mNds1M8tOAR0vWu9Nt9T7WzGzMaGli3aqwbdCxnrt7rKQlwBKAGTNmVHl6s/rb1Wtz5vk/2OU6Hrnobbt8jGVLM1vy3cD0kvUO4LFaHxsRV0VEZ0R0tre371agZvXga9MaoZlJfgUwW9IsSW3AImBZlcfeDhwjaUr6gesx6TYzMyvRtCQfEX3AOSTJ+SHg1ohYLelCSccBSHqNpG7gBOBrklanxz4NfIbkD8UK4MJ0m5mZlWhmnzwRsRxYXrbtgpLlFSRdMZWOvQa4pq4BmpmNcr7j1cwsw5zkzcwyzEnezCzDnOTNzDLMSd7MLMOc5M3MMsxJ3swsw5zkzcwyzEnezCzDnOTNzDLMSd7MLMOc5M3MMsxJ3swsw5zkzcwyzEnezCzDnOTNzDKsqUle0nxJayStlXR+hf3jJN2S7v+VpJnp9lZJ10t6QNJDkj7W6NjNzEaDpiV5SXngcuBYYA5wkqQ5ZcXeB2yMiIOAS4GL0+0nAOMi4lXAPOCM/j8AZruiWAzWP7eNP2/czPrntlEsRrNDMqupZj7+73BgbUSsA5B0M7AAeLCkzALg0+nyUuArkgQEMElSCzAB6AGebVDclhHFYrDmiedYfEMX3Ru30DFlAlef2snB+08ml1OzwzOriWZ210wDHi1Z7063VSyTPvj7GWAqScLfBDwO/An4dz/I23bVhk09AwkeoHvjFhbf0MWGTT1NjsysdpqZ5Cs1lcr/Vx6szOFAATgAmAV8SNKBFSuRlkjqktS1fv364cRrGdPTVxhI8P26N26hp6/QkPp9bVojNDPJdwPTS9Y7gMcGK5N2zewFPA28G/hRRPRGxJPAz4HOSpVExFUR0RkRne3t7TV+CzaatbXk6ZgyYbttHVMm0NaSb0j9vjatEZqZ5FcAsyXNktQGLAKWlZVZBpyWLi8E7oyIIOmieYsSk4AjgP9uUNyWEVMntXH1qZ0Dib6/T37qpLYmR2ZWO0374DUi+iSdA9wO5IFrImK1pAuBrohYBnwDuFHSWpIW/KL08MuBa4HfknTpXBsRqxr+JmxUy+XEwftP5rtnv56evgJtLXmmTmrzh66WKc0cXUNELAeWl227oGR5K8lwyfLjnq+03WxX5XKiffK4ZodhVje+49XMLMOc5M3MMsxJ3swsw5zkzcwyzEnezCzDnOTNzDJsl5J8euPSP0h6Wb0CMjOz2tlpkpf0nyXLC4A7gXcA35N0en1DMzOz4RrqZqi/Kln+KPCWiPiDpH2BO4Dr6hWYmZkN31DdNaWzQrZExB8AIuIpoFi3qMzMrCaGaskfIulZkvlhxkl6UUT8TzqhWGOm6jMzs9220yQfEYMl8onAGbUPx8zMaqmqCcokzQZenq7eGxHdwD11i8rMzGpiqNE1e6cjbH4MnA68F/ippCvTudznNyBGMzPbTUO15L8M/Ab4h4goAqQP0v4E8H3gYGB2XSM0M7PdNlSSPyIi3lO6IX0y02ckPQm8vm6RmZnZsA01hHJnj8h5JiJ+P5zKJc2XtEbSWknnV9g/TtIt6f5fSZpZsm+upHskrZb0gKTxw4nFzCyLhkryP5d0QdpFM0DSJxjmB6+S8iSP8TsWmAOcJGlOWbH3ARsj4iDgUuDi9NgW4CbgzIh4BXAk0DuceMzMsmioJH8u8CpgraTbJC2V9DBwKHDOMOs+HFgbEesioge4GVhQVmYBcH26vBQ4Kv2DcwywKiLuB4iIDRFRGGY8ZmaZM9Q4+WeBEyS9hKS1LeCjEfFwDeqeBjxast4NvHawMumDv58BpgIvBULS7UA7cHNEfL5SJZKWAEsAZsyYUYOwzWrD16Y1wlBDKPeT9EXgMuB1wN01SvBQub8/qizTAvwNcHL69Z2SjqpUSURcFRGdEdHZ3t4+nHjNasrXpjXCUN01NwCbSIZS7gF8qYZ1dwPTS9Y7gMcGK5P2w+8FPJ1u/2lEPBURm4HlwGE1jM3MLBOGSvIvioiPR8TtEXEuMLeGda8AZqdz1LcBi4BlZWWWAaelywuBO9MhnLcDcyVNTJP/m4AHaxibjRHFYrD+uW38eeNm1j+3jWKx/J9Js9FtqHHykjSFF7pN8qXrEfH07lac9rGfQ5Kw88A1EbFa0oVAV0QsA74B3ChpLUkLflF67EZJXyD5QxHA8oj4we7GYmNTsRiseeI5Ft/QRffGLXRMmcDVp3Zy8P6TyeV2NnrYbPQYKsnvBaxk+77xe9OvARw4nMojYjlJV0vptgtKlrcCJwxy7E0kwyjNdsuGTT0DCR6ge+MWFt/QxXfPfj3tk8c1OTqz2hhqdM3MBsVh1nA9fYWBBN+ve+MWevo8GteyY5cf5C3pJZI+Lum39QjIrFEk0TFlwnbbOqZMoOzeP7NRraokL+nFkv5Z0q+B1ST/AZxU18jM6iwvuPj4uQOJvmPKBC4+fi5553jLkJ1210haTJLMO4BbgX8CvhcR/9qA2MzqKpfLcf0v/sAn3z6HvSe08pctvVz/iz/wuXfWchCZWXMN9cHr5SRz1Lw7IroAJHmMmWXC1EltfPCtB7P4xpLRNe/pZOqktmaHZlYzQyX5A0hGt3xB0v4krfnWukdl1gDFYtDaIj6z4JVMbMuzuadAa4soFsNDKC0zhhpd8xRwBXCFpA6ScepPSnoI+G5E/K8GxGhWF08+v43Tr12x3QibjikTuPWMv+aAvSfs5Eiz0aPq0TUR0R0R/x4R84C/B7bVLyyz+usrFCsOoewrFJsUkVntVZ3kJb2ofzki1gBfq0tEZg2Sz1UeQpl3V41lyFB98qW+AbytZP3rwNtrG45Z4+QEX3n3q9m4qXegT37KpFac4y1Lqk7yEfG2snUneBvV8rkcvX1FPvm93w6Mrrn0xEPI53b5HkGzEWvIq1lSzne3WlZ98Nb7t5u75oO33t/kiMxqa8gkHxFF4H5JfnSNZcrWQeau2drnD14tO6rtrnkxsDqd1mBT/8aIOK4uUZk1QF7imDn7cfy86QN3vN628tFMTWsw8/xdm4H7kYveNnQhG1WqTfKexsAyZ/KEPOce9VLOumnlQJ/8FafMY/KEfLNDM6uZqj5hioifAo8ArenyCl6YV95sVHpuS2EgwUPSVXPWTSt5bounGrbsqHYWysXAUl4YGz8N+M/hVi5pvqQ1ktZKOr/C/nGSbkn3/0rSzLL9MyQ9L+nDw43Fxp6+YlTsky/4EYCWIdWOFXs/8HrgWYCI+D2w33AqlpQnmQDtWGAOcJKkOWXF3gdsjIiDgEuBi8v2Xwr8cDhx2NjV4puhbAyoNslvi4ie/pX04dnDbe4cDqyNiHXpuW8GFpSVWQBcny4vBY5S+kQHSX8PrCOZ395sl+UGmU/eOd6ypNoPXn8q6X8BEyQdDZwNfH+YdU8DHi1Z7wZeO1iZ9MHfzwBTJW0BPgocDey0q0bSEmAJwIwZHgVqLygEFeeTv+Adr2hI/b42rRGqbcmfD6wHHgDOIHn49ieGWXel9lL5fweDlflX4NKIeH6oSiLiqojojIjO9vb23QjTsqolJ97/5oNoyye/Bm35HO9/80G0NKgp72vTGqGqlnx6Q9TV6atWuoHpJesdwGODlOlOu4j2Ap4mafEvlPR5YG+gKGlrRHylhvFZxrW1iHwut920BleeMo+2FvfXWHZUO7rm7ZLuk/S0pGclPSfp2WHWvQKYLWmWpDaSueqXlZVZBpyWLi8E7ozEGyJiZkTMBL4I/G8neNtVW3qKnFk2hPLMm1aypcd3vFp2VNsn/0XgH4AHIqIm48vSPvZzgNuBPHBNRKyWdCHQFRHLSGa+vFHSWpIW/KJa1G0GUPAQShsDqk3yjwK/rVWC7xcRy0n690u3XVCyvJXk8YM7O8enaxmTjR3988mXPxnKQygtS6pN8h8Blkv6KSVPhIqIL9QlKrMG6B9C+dHbVg30yV98/Fw807BlSbVJ/nPA88B4wI+yt0woDjKE8lMNGkJp1gjVJvl9IuKYukZi1mD5nDjtdbN2aMk3agilWSNUm+R/IumYiPhxXaMxa6CcVLEl/9m/f1WzQzOrmWqT/PuBj0jaBvSS3KQUEbFn3SIza4DFbzhw4OlQ/Y//M8uSam+GmlzvQMwara9YpLUlx2cWvHLgQd6tLTkKRY+Tt+yo+kHekuYCM0uPiYj/qENMZg0RAed8674dhlDesuSIJkZlVltVJXlJ1wBzSWZ87G/mBOAkb6NWMSrfDFWs7e0gZk1VbUv+iIgon+vdbFTL53KD3AzlgfKWHdVezfdUeKCH2ajWlhdXnjJvu/nkrzxlHm1ZepK3jXnVtuSvJ0n0/0Nyx2v/6Jq5dYuswYrFYMOmHnr6CrS15Jk6qY2cx0tnmpTMRFn6wWtbi5B/7JYh1Sb5a4D3kMwnn7mhB8VisOaJ51h8Q9fAULqrT+3k4P0nO9Fn2NbeIv94XZc/eLVMq7a75k8RsSwi/hARf+x/1TWyBtqwqWcgwUPy4dviG7rYsKlniNSH8MsAABJNSURBVCNtNBvsQd59noXSMqTalvx/S/oWySP/Sicoy8Tomp6+QsVf9p6+QpMiskZoGWQWSk9rYFlSbZKfQJLcS+evycwQyraWfMVf9raWfBOjsnob15Lj2tM76d64daBPvmPKeMa1eHSNZUe1d7y+tx6VS5oPXEby0JCvR8RFZfvHATcA84ANwLsi4pH0YeIXkcyI2QOcFxF37m4cUye1cfWpnTv0yU+d5Ak3s6wYwba+2OHxf8UdHjVsNnrtNMlL+khEfF7Sl9nxIdtExAd2t2JJeeBy4GiSZ7mukLQsIh4sKfY+YGNEHCRpEXAx8C7gKeAdEfGYpFeSPF1q2u7GksuJg/efzHfPfr1H14whfYXgS3f8brsJyr50x+/4tKcatgwZqiX/UPq1qw51Hw6sjYh1AJJuBhYApUl+AfDpdHkp8BVJioj7SsqsBsZLGhcR29hNuZxonzxudw+30Uhw9psPYuOmXgDa8jnOfvNBHkJpmbLTJB8R30+/Xl+HuqeRPFawXzfw2sHKpM+EfQaYStKS73c8cN9wEryNTXmJLT2F7bprLlk4l5yzvGVIVZ8wSXqppKsk/VjSnf2vYdZd6TepvEtop2UkvYKkC+eMQSuRlkjqktS1fv363QrUsqm3GJy3dNV2Q2fPW7qK3gYNofS1aY1Q7eia7wBXAl8HajWusBuYXrLeATw2SJluSS3AXsDTAJI6gO8Cp0bEw4NVEhFXAVcBdHZ2Dvrb6ztex57iIOPkiw1K8tVem2bDUW2S74uIK2pc9wpgtqRZwJ+BRcC7y8osA04D7gEWAndGREjaG/gB8LGI+PlwA/Edr2NTPieOmbMfx8+bPvDB620rHyXvn7llyFCja/ZJF78v6WySlnPpzVBP727FaR/7OSQjY/LANRGxWtKFQFdELAO+AdwoaS1JC35Revg5wEHAJyV9Mt12TEQ8uTuxPLVpW8U7Xv/j7Nex3+Txu/sWbYRrzYtz3zKbs75578Af9ytOPoxWT1BmGTJUS34lSR94/1X/4bL9Bw6n8ohYDiwv23ZByfJW4IQKx30W+Oxw6i61tbfyHa9bezM3TY+V6CvEQIKH5Gd+1jfv5VbPXWMZMlSSfxfwaEQ8DiDpNJLRLI/wwtDGUS8vccYbZrKwcwb5nCgUg6Vdf8INumzrLQbte4zbbpz8lXc/7LlrLFOGSvJXAm8FkPRG4N+Ac4FDST4wWljX6BpkYluOtx/awXuvW/HCv+2nzGNim29vz7JxLTk+Mv/ggRE2/UMo2zytgWXIUFdzvqTf/V3AVRFxW0R8kqRPPBM29xT5cnrn4y1LjuCTb5/Dl+/4HZt73F2TZcWoPITSj/+zLBmqJZ+X1BIRfcBRwJJdOHbUkOC0183io7e90KK7+Pi5vvMx4/oKg0w1XHCSt+wYqiX/beCnkr4HbAH+L4Ckg4Bn6hxbw0QwkOAh+UX/6G2rcIMu2/LpVMOlkme8+q+7ZcdOk3xEfA74EHAd8DcRA2kvR9I3nwnFGOSmGGf5TGvNiUsWzt3uGa+XLJxLq5O8ZciQXS4R8csK235Xn3CaI6/KD4/Iu78m07b2Ffn8j9ZsN7rm8z9aw2WLDm12aGY1k5l+9eHI5cSlJx7CB2+9f6BP/tITD/HdrhnXkhPrn9/GGTeuHNg21p8MNfP8H+zyMY9c9LY6RGK14iRP0l0zeUIL1733cHKCYkBfsUC4uybTcml3TfkQSv9xtyxxkifprtm4qZfzlq7c7pd9ygQ/GSrLtg3SXfNFd9dYhjjJM/iUszf79vZMax2ku8YfvFqW+NY+oNDkKWetOVpbclxx8mHbja654uTDaPUdr5YhbsmT3N5eaXSNb2/Pts09BW68549ce/prBuYsuvpn6zjnLZm5mdvMSR6SURZfe888zrjxhT75r71n3pgeZTEWtObEL9Zt4NaV3QPbOqZM4J/fOruJUZnVlpM8sNf4Vjb1FrYbXdPaIvYa39rs0KyOxrUm3TXl88mPa/V/cJYdTvJAa2ueAyaP58nnt9FXDMblxH57jKO1Nd/s0KyO9hzXyqaJ2/9xb2sRe47zH3fLjqY2WSTNl7RG0lpJ51fYP07SLen+X0maWbLvY+n2NZL+drixtLbmmTZlIn81dRLTpkx0gh8DWlvzvHjyeCa05sjnxITWHC+ePN4/e8uUprXkJeWBy4GjSR7YvULSsoh4sKTY+4CNEXGQpEXAxcC7JM0heRTgK4ADgJ9IemlE1Ooh4zZG9P9xN8uqZrbkDwfWRsS6iOgBbgYWlJVZAFyfLi8FjpKkdPvNEbEtIv4ArE3PZ2ZmJZqZ5KcBj5asd6fbKpZJ57R/Bpha5bFmZmNeM5N8pfGJ5XcfDVammmOTE0hLJHVJ6lq/fv0uhmhWP742rRGameS7gekl6x3AY4OVkdQC7AU8XeWxAETEVRHRGRGd7e3tNQrdbPh8bVojNDPJrwBmS5olqY3kg9RlZWWWAaelywuBO9MHlywDFqWjb2YBs4FfNyhuM7NRo2mjayKiT9I5wO1AHrgmIlZLuhDoiohlwDeAGyWtJWnBL0qPXS3pVuBBoA94v0fWmJntqKk3Q0XEcmB52bYLSpa3AicMcuzngM/VNUAzs1HOd7yaWcPt6hOo/PSp3edJOszMMsxJ3swsw5zkzcwyzEnezCzDnOTNzDLMSd7MLMOc5M3MMsxJ3swsw3wzlJmNeLt68xT4Bqp+bsmbmWWYk7yZWYY5yZuZZZj75M1sWHanv9waxy15M7MMc5I3M8uwpnTXSNoHuAWYCTwCnBgRGyuUOw34RLr62Yi4XtJE4DvAS4AC8P2IOL8RcZvZ6OE56xPNasmfD9wREbOBO9L17aR/CD4FvBY4HPiUpCnp7n+PiJcBrwZeL+nYxoRtZja6NCvJLwCuT5evB/6+Qpm/Bf4rIp5OW/n/BcyPiM0RcRdARPQA9wIdDYjZzGzUadbomv0j4nGAiHhc0n4VykwDHi1Z7063DZC0N/AO4LJ6BWpmVkuN7kaqW5KX9BPgRRV2fbzaU1TYFiXnbwG+DXwpItbtJI4lwBKAGTNmVFm1Wf352rRGUEQMXarWlUprgCPTVvyLgbsj4uCyMielZc5I17+Wlvt2un4N8HxEfGAX6l0P/HGIYvsCT1X/bjLF731oT0XE/FpXXuW1ORKMpmtkrMVa8dpsVpK/BNgQERdJOh/YJyI+UlZmH2AlcFi66V5gXkQ8LemzwMuBEyKiWOPYuiKis5bnHC383sfme98Vo+n75FgTzfrg9SLgaEm/B45O15HUKenrABHxNPAZYEX6ujBN8B0kXT5zgHsl/UbSPzXjTZiZjXRN+eA1IjYAR1XY3gX8U8n6NcA1ZWW6qdxfb2ZmZXzH646uanYATeT3bkMZTd8nx0qT+uTNzKwx3JI3M8uwMZvkJc2XtEbS2nSET/n+cZJuSff/StLMxkdZH1W899MlrU8/1M7MB9uSrpH0pKTfDrJfkr6Ufl9WSTqsUrks2d3fA0lHS1op6YH061tKjrk7PWf/9VPpZsdGxTlT0paSWK4sOWZeGv/a9Odek8/6hhHrySVx/kZSUdKh6b7d/55GxJh7AXngYeBAoA24H5hTVuZs4Mp0eRFwS7PjbuB7Px34SrNjrcN7fyPJkNzfDrL/74AfknywfwTwq2bHPAKuhYq/ByTzRh2QLr8S+HPJMXcDnSMkzpk7+Xn/Gvjr9Of9Q+DYZsZaVuZVwLpafE/Hakv+cGBtRKyLZP6bm0nm0ylVOr/OUuCoWv2lb7Jq3nsmRcTPgKd3UmQBcEMkfgnsnd6sl1W7/XsQEfdFxGPp9tXAeEnjRlqcg50w/bnuGRH3RJJFb6DyHFrNivUkkjv6h22sJvkh58UpLRMRfcAzwNSGRFdf1bx3gOPTLoulkqY3JrSmq/Z7kxW1+j04HrgvIraVbLs27Vb4ZA0aR8ONc5ak+yT9VNIbSsp3D3HOZsTa713smOR363s6VpP8TufF2YUyo1E17+v7wMyImAv8hBdaHVmX1Z/5YIb9eyDpFcDFwBkl+0+OiFcBb0hf72linI8DMyLi1cC/AN+StGeV59wdtfievhbYHBGlnx3t9vd0rCb5bqC0ddoBPDZYGSWToe3Fzv/VHy2GfO8RsaGkVXY1MK9BsTVbNddFlgzr9yC9+/y7wKkR8XD/ARHx5/Trc8C3SLowmhJnRGyL5OZLImIlSX/5S9PypVOU1+pnXYvcsoiyVvxwvqdjNcmvAGZLmiWpjeSbuqyszDLgtHR5IXBn2nc32g353sv6oY8DHmpgfM20DDg1HWVzBPBMpFNiZ9Ru/x4omeb7B8DHIuLn/YUltUjaN11uBd4OVBzN1KA42yXl03gOBGaTfKD5OPCcpCPSro9Tge8NM85hxZrGmANOIOnLJ902vO9prT4BH20vkpEUvyP5y/7xdNuFwHHp8niSxwyuJfkU/sBmx9zA9/5vJB+m3Q/cBbys2THX6H1/m+Tf916S1tT7gDOBM9P9Ai5Pvy8PUMMRIiP1tbu/BySP5dwE/KbktR8wiWRiwVXpNXQZkG9inMeXXMv3Au8oOWcnSbJ8GPgK6c2hzYo13Xck8Muy8w3re+o7Xs3MMmysdteYmY0JTvJmZhnmJG9mlmFO8mZmGeYkb2aWYU7yY4Ckf5Y0sVblLPskfVzS6nRqi99Ieq2kR/rHa5eVPa7SbIvNlI6P75V0Rtn2R9KZJ++X9GNJL0q37yXpBkkPp69vSppSclyhZAbI8nHvI5qHUI4Bkh4hGfO906fBV1vOsk3SXwNfAI6MiG1pYm8DfsEIuz4k5SOiUGH72SSTfBUi4siS7Y+QvgdJ/xvYIyI+IGkpyWyVn07L/StwaEQsSNefj4g96v6G6sAt+YyRNEnSD9KWym8lfQo4ALhL0l1pmSskdaUttX9Nt32gQrnnS867UNJ16fIJ6bnvl/SzBr9Fq78XA09FOrVFRDwVL8w4ea6ke9PW8Mtg4PkDX0mXr0uvr7skrZP0JiXz+D/Uf/2k5Z6XdLGSueh/IulwJXOmr5N0XFomL+kSSSvS/yjOSLcfmZ7/WyQ3rVVyEvAhoEPSYBOP/Qw4SNJBJFN3fKZk34XAIZIO3uXv3gjjJJ8984HHIuKQiHgl8EWSuTPeHBFvTst8PCI6gbnAmyTNjYgvVSg3mAuAv42IQ0imPbBs+TEwXdLvJH1V0ptK9j0VEYcBVwAfHuT4KcBbgA+STHZ3KfAK4FVKH4JBchfn3RExD3gO+CxwNPBOkgQLyR3Jz0TEa4DXAIslzUr3HU5yHc8BkLRc0gHp8nTgRRHxa+BWkhkdK3k7yR+JOcBvSv8jSJfvA16ebhqfNox+KakWUxI3jJN89jwAvDVtJb0hIp6pUOZESfeSXMSvILnId8XPgeskLSZ5SIJlSEQ8T9KyXQKsB26RdHq6+z/SrytJHshRyfcj6Qd+AHgiIh6IiCLJLfn9x/QAP0qXHwB+GhG96XJ/mWNI5hL6DfArkul4Z6f7fh0RfyiJ+e9K/ttYRJLcIZkD5qSy+O5Kz7knyRQeovIMlKWzRc5IG0bvBr4o6SWDvPcRp6XZAVhtRcTvJM0jmT/j3yT9uHR/2hL6MPCaiNiY/gs9frDTlSwPlImIM5VMh/o24DeSDo10pj/LhrQlezdwt6QHeGFCrf7ZSQsMnj/6yxRLlvvX+4/pjRc+EBwoFxFFJTMzQpJkz42I20tPLulIknlzBnMSsL+kk9P1AyTNjojfp+tvLv1cQdJq4NWScukfo/6JwuaSzHdD/x+QiFgn6W6SJ2MNzLw5krklnzHpv6ybI+Im4N9JHnf3HDA5LbInyS/IM5L2B44tOby0HMATkl6eXvDvLKnjJRHxq4i4AHiK7adWtVFO0sGSZpdsOhT4YxNCuR04S8nMi0h6qaRJOzsg7UOfFBHTImJmRMwkaa0vGuyYiFhL8l/tJ0o2fwK4IyL+JGmK0qdepR9Cvx54cBjvq6Hcks+eVwGXSCqSzLZ4FslzLH8o6fGIeLOk+0j+dV5H0vXS76rScsD5wP8heYrNb4H+0QWXpElAwB0kM/xZduwBfFnJdMJ9JLMlLiHpw26kr5N03dwrSSRdRxX7wyUtB/6JpBX/3bLdt5F023ym/LgS/0jynteSzO++AnhHuu/lwNfS36kccFFEjJok7yGUZmYl0v8GlpN0FS1vdjzD5SRvZpZh7pM3M8swJ3kzswxzkjczyzAneTOzDHOSNzPLMCd5M7MMc5I3M8uw/w+2VHmGTzKRXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of the relations between each feature and target \n",
    "for feature in features:\n",
    "    sns.pairplot(data.loc[:, [target, feature]])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "MDVP:Jitter(%), MDVP:Shimmer(dB) and MDVP:RAP are the features highly correlate with more than 2 others features.\n",
    "    spread1 and PPE are the features correlate with the status over 50% (56.48% and 53.1% respectively), on average all the others are under 30%; so there is no feature who can well explain the status.\n",
    "    \n",
    "Let's take a look at the pairplot:\n",
    "\n",
    "    - MDVP:Fo(Hz): All measurements with parkinson are under 250 Hz and those without parkinson are generally over 200hz\n",
    "    - MDVP:Fhi(Hz): Measurements with parkinson seem to form two groups, one under 300 Hz and the others over 400 Hz. Measurements without parkinson are majoritary under 300 Hz.\n",
    "    - MDVP:Flo(Hz): All patients with parkison are under 200 Hz. Patients without parkinson form two groups, one under 140 Hz and one other over 180 Hz\n",
    "    - MDVP:jitter(%): Measurements with parkinson are between 0% and 0.O4%; and those without parkinson are between 0% and 0.02%\n",
    "    - MDVP RAP: Measurements with parkinson are between 0 and 0.O3; and those without parkinson are between 0 and 0.01\n",
    "    - MDVP PPQ: it's the same thig like MDVP RAP\n",
    "    - Jitter DDP: Measurements with parkinson are between 0 and 0.O8; and those without parkinson are between 0 and 0.02\n",
    "    - MDVP Shimmer(db): Measurements with parkinson are between 0.1 and 1.2; and those without parkinson are between 0.1 and 0.4\n",
    "    - Shimmer APQ3: Measurements with parkinson are between 0 and 0.O6; and those without parkinson are between 0 and 0.02\n",
    "    - Shimmer APQ5: Measurements with parkinson are between 0 and 0.O8; and those without parkinson are between 0 and 0.02\n",
    "    - MDVP APQ: Measurements with parkinson are between 0 and 0.10; and those without parkinson are between 0 and 0.05\n",
    "    - Shimmer DDA: Measurements with parkinson are between 0 and 0.15; and those without parkinson are between 0 and 0.05\n",
    "    - NHR: Measurements with parkinson are between 0 and 0.3 and are more concentrate under 0.1; and those without parkinson are between 0 and 0.1\n",
    "    - HNR: Measurements with parkinson are under 30 those wthout parkinso are over 17\n",
    "    - RPDE: All the measurements are under 0.7\n",
    "    - DFA: Measurements with parkinson are over 0.55 and those without parkinson are over 0.60\n",
    "    - spread1: Measurements with parkinson are over -7 and those without parkison are between -3 and -5\n",
    "    - spread2: Measurements with parkinson are over 0.05 and those without parkinson are under 0.3\n",
    "    - D2: Measurements with parkinson are over 1.6 and those without parkinson are between 1.5 and 3\n",
    "    - PPE: Measurements with parkison are over 0.1 and those without parkinson are under 0.3\n",
    "    \n",
    "We can see that there are some good relations between the status and the feature which can help for prediction.\n",
    "Let's build some models.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ## Models for prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the status variable is a binary target, we'll perform some classification model on the data; in our case we'll the fifth classifiers below:\n",
    "\n",
    "* Logistic regression\n",
    "* K-Nearest Neighbors (KNN)\n",
    "* Support Vector Classifier (SVC)\n",
    "* AdaBoost\n",
    "* XgBoost\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All is ready!\n"
     ]
    }
   ],
   "source": [
    "# target vector\n",
    "y = data.loc[:, target]\n",
    "    \n",
    "print(\"All is ready!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Let's make some test to select the best features to predict the status of a patient.\n",
    "We'll make five selections and evaluate all the models on them to see which give us the best performance. Our score metric is the roc_auc and a 10-folds cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For the first test we'll use all the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X shape:(195, 20)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y shape:(195,)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "  Roc_auc_score: 0.8483333333333333 (84.833%)\n",
      "\n",
      "\n",
      "KNN\n",
      "  Best parameter: {'n_neighbors': 6}\n",
      "  Best roc_auc_score: 0.8977142857142857 (89.771%)\n",
      "\n",
      "\n",
      "SVC\n",
      "  Best parameter: {'degree': 1}\n",
      "  Best roc_auc_score: 0.8135238095238095 (81.352%)\n",
      "\n",
      "\n",
      "AdaBoost\n",
      "  Best parameter: {'n_estimators': 4}\n",
      "  Best roc_auc_score: 0.8320952380952382 (83.21%)\n",
      "\n",
      "\n",
      "XgBoost\n",
      "  Roc_auc_score: 0.9128095238095237 (91.281%)\n",
      "\n",
      "\n",
      "All tests are done\n"
     ]
    }
   ],
   "source": [
    "# data to build our models\n",
    "X = get_X(data, features)\n",
    "\n",
    "# models\n",
    "models = get_models()\n",
    "\n",
    "\n",
    "display(\"X shape:\"+str(X.shape), \"y shape:\"+str(y.shape))\n",
    "\n",
    "tester = Tester(models, X, y)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this test XgBoost give us the best Roc_auc_score of 91.281%.\n"
     ]
    }
   ],
   "source": [
    "best_model = tester.best_model()\n",
    "print(\"For this test {} give us the best Roc_auc_score of {}%.\".format(best_model.name, round(best_model.score*100,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For this test we'll use the second member of the couples of highly_corr plus the others features (those not in highly_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 12 features selected:\n",
      "MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz), MDVP:APQ, Shimmer:DDA, NHR, HNR, RPDE, DFA, spread2, D2, PPE\n"
     ]
    }
   ],
   "source": [
    "#features selection\n",
    "exclude = list(zip(*highly_corr))[0]\n",
    "\n",
    "features_selected1 = [feature for feature in features if feature not in exclude ]\n",
    "print(\"We have \"+str(len(features_selected1))+\" features selected:\")\n",
    "print(*features_selected1, sep=\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X shape:(195, 12)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y shape:(195,)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "  Roc_auc_score: 0.8434285714285714 (84.343%)\n",
      "\n",
      "\n",
      "KNN\n",
      "  Best parameter: {'n_neighbors': 3}\n",
      "  Best roc_auc_score: 0.903095238095238 (90.31%)\n",
      "\n",
      "\n",
      "SVC\n",
      "  Best parameter: {'degree': 1}\n",
      "  Best roc_auc_score: 0.8109999999999999 (81.1%)\n",
      "\n",
      "\n",
      "AdaBoost\n",
      "  Best parameter: {'n_estimators': 4}\n",
      "  Best roc_auc_score: 0.8684761904761904 (86.848%)\n",
      "\n",
      "\n",
      "XgBoost\n",
      "  Roc_auc_score: 0.9168095238095237 (91.681%)\n",
      "\n",
      "\n",
      "All tests are done\n"
     ]
    }
   ],
   "source": [
    "# data to build our models\n",
    "X = get_X(data, features_selected1)\n",
    "\n",
    "# models refresh\n",
    "models = get_models()\n",
    "\n",
    "\n",
    "display(\"X shape:\"+str(X.shape), \"y shape:\"+str(y.shape))\n",
    "\n",
    "tester = Tester(models, X, y)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this test XgBoost give us the best Roc_auc_score of 91.681%.\n"
     ]
    }
   ],
   "source": [
    "best_model = tester.best_model()\n",
    "print(\"For this test {} give us the best Roc_auc_score of {}%.\".format(best_model.name, round(best_model.score*100,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For this test we'll use the first member of the couples of highly_corr plus the others features (those not in highly_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 11 features selected:\n",
      "spread1, MDVP:Flo(Hz), DFA, MDVP:Fhi(Hz), spread2, MDVP:Fo(Hz), RPDE, D2, HNR, MDVP:Jitter(%), MDVP:Shimmer(dB)\n"
     ]
    }
   ],
   "source": [
    "#features selection\n",
    "exclude = list(zip(*highly_corr))[1]\n",
    "\n",
    "features_selected2 = [feature for feature in features if feature not in exclude ]\n",
    "features_selected2 = list(set(features_selected2))\n",
    "\n",
    "print(\"We have \"+str(len(features_selected2))+\" features selected:\")\n",
    "print(*features_selected2, sep=\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X shape:(195, 11)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y shape:(195,)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "  Roc_auc_score: 0.8524761904761904 (85.248%)\n",
      "\n",
      "\n",
      "KNN\n",
      "  Best parameter: {'n_neighbors': 7}\n",
      "  Best roc_auc_score: 0.9039523809523808 (90.395%)\n",
      "\n",
      "\n",
      "SVC\n",
      "  Best parameter: {'degree': 1}\n",
      "  Best roc_auc_score: 0.8186666666666665 (81.867%)\n",
      "\n",
      "\n",
      "AdaBoost\n",
      "  Best parameter: {'n_estimators': 8}\n",
      "  Best roc_auc_score: 0.8527619047619048 (85.276%)\n",
      "\n",
      "\n",
      "XgBoost\n",
      "  Roc_auc_score: 0.9116666666666667 (91.167%)\n",
      "\n",
      "\n",
      "All tests are done\n"
     ]
    }
   ],
   "source": [
    "# data to build our models\n",
    "X = get_X(data, features_selected2)\n",
    "\n",
    "# models refresh\n",
    "models = get_models()\n",
    "\n",
    "\n",
    "display(\"X shape:\"+str(X.shape), \"y shape:\"+str(y.shape))\n",
    "\n",
    "tester = Tester(models, X, y)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this test XgBoost give us the best Roc_auc_score of 91.167%.\n"
     ]
    }
   ],
   "source": [
    "best_model = tester.best_model()\n",
    "print(\"For this test {} give us the best Roc_auc_score of {}%.\".format(best_model.name, round(best_model.score*100,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fourth selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For this test we'll use the features outside of highly_corr_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 8 features selected:\n",
      "DFA, MDVP:Flo(Hz), MDVP:Fhi(Hz), spread2, MDVP:Fo(Hz), RPDE, D2, HNR\n"
     ]
    }
   ],
   "source": [
    "#features selection\n",
    "features_selected3 = set(features) - highly_corr_set\n",
    "features_selected3 = list(features_selected3)\n",
    "\n",
    "print(\"We have \"+str(len(features_selected3))+\" features selected:\")\n",
    "print(*features_selected3, sep=\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X shape:(195, 8)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y shape:(195,)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "  Roc_auc_score: 0.8109999999999999 (81.1%)\n",
      "\n",
      "\n",
      "KNN\n",
      "  Best parameter: {'n_neighbors': 8}\n",
      "  Best roc_auc_score: 0.877952380952381 (87.795%)\n",
      "\n",
      "\n",
      "SVC\n",
      "  Best parameter: {'degree': 1}\n",
      "  Best roc_auc_score: 0.8289523809523809 (82.895%)\n",
      "\n",
      "\n",
      "AdaBoost\n",
      "  Best parameter: {'n_estimators': 18}\n",
      "  Best roc_auc_score: 0.791952380952381 (79.195%)\n",
      "\n",
      "\n",
      "XgBoost\n",
      "  Roc_auc_score: 0.9093809523809524 (90.938%)\n",
      "\n",
      "\n",
      "All tests are done\n"
     ]
    }
   ],
   "source": [
    "# data to build our models\n",
    "X = get_X(data, features_selected3)\n",
    "\n",
    "# models refresh\n",
    "models = get_models()\n",
    "\n",
    "\n",
    "display(\"X shape:\"+str(X.shape), \"y shape:\"+str(y.shape))\n",
    "\n",
    "tester = Tester(models, X, y)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this test XgBoost give us the best Roc_auc_score of 90.938%.\n"
     ]
    }
   ],
   "source": [
    "best_model = tester.best_model()\n",
    "print(\"For this test {} give us the best Roc_auc_score of {}%.\".format(best_model.name, round(best_model.score*100,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fifth selection"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "For this test we'll use the member of the couples of highly_corr which has the biggest correlation with the status plus the others features (those not in highly_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 11 features selected:\n",
      "spread1, MDVP:Flo(Hz), DFA, MDVP:Fhi(Hz), spread2, MDVP:Fo(Hz), RPDE, D2, HNR, MDVP:PPQ, MDVP:APQ\n"
     ]
    }
   ],
   "source": [
    "#features selection\n",
    "exclude  = set()\n",
    "for f1, f2 in highly_corr:\n",
    "    if corr.loc[f1, target] <= corr.loc[f2, target]:\n",
    "        exclude.add(f1)\n",
    "    else:\n",
    "        exclude.add(f2)\n",
    "exclude = list(exclude)\n",
    "\n",
    "features_selected4 = [feature for feature in features if feature not in exclude ]\n",
    "features_selected4 = list(set(features_selected4))\n",
    "\n",
    "print(\"We have \"+str(len(features_selected4))+\" features selected:\")\n",
    "print(*features_selected4, sep=\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'X shape:(195, 11)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'y shape:(195,)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "  Roc_auc_score: 0.8538095238095238 (85.381%)\n",
      "\n",
      "\n",
      "KNN\n",
      "  Best parameter: {'n_neighbors': 7}\n",
      "  Best roc_auc_score: 0.9008571428571429 (90.086%)\n",
      "\n",
      "\n",
      "SVC\n",
      "  Best parameter: {'degree': 1}\n",
      "  Best roc_auc_score: 0.8099999999999999 (81.0%)\n",
      "\n",
      "\n",
      "AdaBoost\n",
      "  Best parameter: {'n_estimators': 9}\n",
      "  Best roc_auc_score: 0.8601904761904763 (86.019%)\n",
      "\n",
      "\n",
      "XgBoost\n",
      "  Roc_auc_score: 0.9048095238095237 (90.481%)\n",
      "\n",
      "\n",
      "All tests are done\n"
     ]
    }
   ],
   "source": [
    "# data to build our models\n",
    "X = get_X(data, features_selected4)\n",
    "\n",
    "# models refresh\n",
    "models = get_models()\n",
    "\n",
    "\n",
    "display(\"X shape:\"+str(X.shape), \"y shape:\"+str(y.shape))\n",
    "\n",
    "tester = Tester(models, X, y)\n",
    "tester.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For this test XgBoost give us the best Roc_auc_score of 90.481%.\n"
     ]
    }
   ],
   "source": [
    "best_model = tester.best_model()\n",
    "print(\"For this test {} give us the best Roc_auc_score of {}%.\".format(best_model.name, round(best_model.score*100,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best Model and features"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Xgboost is the best model with the 8 features MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz), MDVP:APQ, Shimmer:DDA, NHR, HNR, RPDE, DFA, spread2, D2, PPE from second selection; it's give us a roc_auc_score of 91.681%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-auc:0.83493\n",
      "Will train until validation_0-auc hasn't improved in 10 rounds.\n",
      "[1]\tvalidation_0-auc:0.94139\n",
      "[2]\tvalidation_0-auc:0.93541\n",
      "[3]\tvalidation_0-auc:0.92703\n",
      "[4]\tvalidation_0-auc:0.92584\n",
      "[5]\tvalidation_0-auc:0.95096\n",
      "[6]\tvalidation_0-auc:0.95096\n",
      "[7]\tvalidation_0-auc:0.94856\n",
      "[8]\tvalidation_0-auc:0.94139\n",
      "[9]\tvalidation_0-auc:0.92943\n",
      "[10]\tvalidation_0-auc:0.94019\n",
      "[11]\tvalidation_0-auc:0.94737\n",
      "[12]\tvalidation_0-auc:0.96890\n",
      "[13]\tvalidation_0-auc:0.96890\n",
      "[14]\tvalidation_0-auc:0.96172\n",
      "[15]\tvalidation_0-auc:0.96412\n",
      "[16]\tvalidation_0-auc:0.96412\n",
      "[17]\tvalidation_0-auc:0.96890\n",
      "[18]\tvalidation_0-auc:0.96890\n",
      "[19]\tvalidation_0-auc:0.96890\n",
      "[20]\tvalidation_0-auc:0.96651\n",
      "[21]\tvalidation_0-auc:0.96172\n",
      "[22]\tvalidation_0-auc:0.95694\n",
      "Stopping. Best iteration:\n",
      "[12]\tvalidation_0-auc:0.96890\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=None, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=0, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=42, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, seed=42, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# model\n",
    "import xgboost as xgb\n",
    "\n",
    "# data to build our models\n",
    "X = get_X(data, features_selected1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
    "\n",
    "clf_xgb = xgb.XGBClassifier(objective=\"binary:logistic\", missing=None, seed=42)\n",
    "clf_xgb.fit(X_train,\n",
    "       y_train,\n",
    "       eval_metric='auc',\n",
    "        early_stopping_rounds=10,\n",
    "       eval_set=[(X_test, y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensitivity: 100.0%\n",
      "Specificity: 63.64%\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf_xgb.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plot_confusion_matrix(clf_xgb,\n",
    "                     X_test,\n",
    "                     y_test,\n",
    "                     display_labels=[\"Healthly\", \"Parkinson\"])\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.show()\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "sensitivity = tp/(tp+fn)\n",
    "specificity = tn/(fp+tn)\n",
    "print(\"Sensitivity: {}%\".format(round(sensitivity*100,2)))\n",
    "print(\"Specificity: {}%\".format(round(specificity*100,2)))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Handsome our model is capable to predict clearly 100% of measurements of patients whom have parkinson and do some misclassification around 37% on healthly one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "All are the features are not useful to predict the status of a patient, nly a few one between 8 and 12 features ca be use to make good prediction since all the best score are so near. From our analysis MDVP:Fo(Hz), MDVP:Fhi(Hz), MDVP:Flo(Hz), MDVP:APQ, Shimmer:DDA, NHR, HNR, RPDE, DFA, spread2, D2, PPE are the best features if we want to make predictions on status."
   ]
  }
 ],
 "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
}