A_C_SH_SD-Model2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **FINAL PROJECT : SLEEP DISORDER CLASSIFICATION  👨🏽‍💻**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Name         : Lintang Nagari\n",
    "* Email        : unggullintangg@gmail.com\n",
    "* Linkedin     : <a href='https://www.linkedin.com/in/lintangnagari/'>Lintang Nagari</a>\n",
    "* Github       : <a href='https://github.com/lnt-ngr'>lnt-ngr</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# split data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# preprocess\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "# model\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "# evaluation\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Person ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Sleep Duration</th>\n",
       "      <th>Quality of Sleep</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Stress Level</th>\n",
       "      <th>BMI Category</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>Daily Steps</th>\n",
       "      <th>Sleep Disorder</th>\n",
       "      <th>BloodPressure_Upper_Value</th>\n",
       "      <th>BloodPressure_Lower_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>77</td>\n",
       "      <td>4200</td>\n",
       "      <td>None</td>\n",
       "      <td>126.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>Normal</td>\n",
       "      <td>75</td>\n",
       "      <td>10000</td>\n",
       "      <td>None</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>Normal</td>\n",
       "      <td>75</td>\n",
       "      <td>10000</td>\n",
       "      <td>None</td>\n",
       "      <td>125.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>70</td>\n",
       "      <td>8000</td>\n",
       "      <td>None</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Male</td>\n",
       "      <td>29</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>70</td>\n",
       "      <td>8000</td>\n",
       "      <td>None</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>370</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>7000</td>\n",
       "      <td>Sleep Apnea</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>371</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>7000</td>\n",
       "      <td>Sleep Apnea</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>372</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>7000</td>\n",
       "      <td>Sleep Apnea</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>373</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>7000</td>\n",
       "      <td>Sleep Apnea</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>374</td>\n",
       "      <td>Female</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>7000</td>\n",
       "      <td>Sleep Apnea</td>\n",
       "      <td>140.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Person ID  Gender  Age         Occupation  Sleep Duration  \\\n",
       "0            1    Male   27  Software Engineer             6.1   \n",
       "1            2    Male   28             Doctor             6.2   \n",
       "2            3    Male   28             Doctor             6.2   \n",
       "3            8    Male   29             Doctor             7.8   \n",
       "4            9    Male   29             Doctor             7.8   \n",
       "..         ...     ...  ...                ...             ...   \n",
       "354        370  Female   59              Nurse             8.1   \n",
       "355        371  Female   59              Nurse             8.0   \n",
       "356        372  Female   59              Nurse             8.1   \n",
       "357        373  Female   59              Nurse             8.1   \n",
       "358        374  Female   59              Nurse             8.1   \n",
       "\n",
       "     Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
       "0                   6                       42             6   Overweight   \n",
       "1                   6                       60             8       Normal   \n",
       "2                   6                       60             8       Normal   \n",
       "3                   7                       75             6       Normal   \n",
       "4                   7                       75             6       Normal   \n",
       "..                ...                      ...           ...          ...   \n",
       "354                 9                       75             3   Overweight   \n",
       "355                 9                       75             3   Overweight   \n",
       "356                 9                       75             3   Overweight   \n",
       "357                 9                       75             3   Overweight   \n",
       "358                 9                       75             3   Overweight   \n",
       "\n",
       "     Heart Rate  Daily Steps Sleep Disorder  BloodPressure_Upper_Value  \\\n",
       "0            77         4200           None                      126.0   \n",
       "1            75        10000           None                      125.0   \n",
       "2            75        10000           None                      125.0   \n",
       "3            70         8000           None                      120.0   \n",
       "4            70         8000           None                      120.0   \n",
       "..          ...          ...            ...                        ...   \n",
       "354          68         7000    Sleep Apnea                      140.0   \n",
       "355          68         7000    Sleep Apnea                      140.0   \n",
       "356          68         7000    Sleep Apnea                      140.0   \n",
       "357          68         7000    Sleep Apnea                      140.0   \n",
       "358          68         7000    Sleep Apnea                      140.0   \n",
       "\n",
       "     BloodPressure_Lower_Value  \n",
       "0                         83.0  \n",
       "1                         80.0  \n",
       "2                         80.0  \n",
       "3                         80.0  \n",
       "4                         80.0  \n",
       "..                         ...  \n",
       "354                       95.0  \n",
       "355                       95.0  \n",
       "356                       95.0  \n",
       "357                       95.0  \n",
       "358                       95.0  \n",
       "\n",
       "[359 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Dataset/sleep_health.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n",
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\_encoders.py:828: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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>Person ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Sleep Duration</th>\n",
       "      <th>Quality of Sleep</th>\n",
       "      <th>Physical Activity Level</th>\n",
       "      <th>Stress Level</th>\n",
       "      <th>BMI Category</th>\n",
       "      <th>Heart Rate</th>\n",
       "      <th>...</th>\n",
       "      <th>Nurse</th>\n",
       "      <th>Engineer</th>\n",
       "      <th>Accountant</th>\n",
       "      <th>Scientist</th>\n",
       "      <th>Teacher</th>\n",
       "      <th>Lawyer</th>\n",
       "      <th>Salesperson</th>\n",
       "      <th>Underweight</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6</td>\n",
       "      <td>42</td>\n",
       "      <td>6</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>Normal</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "      <td>60</td>\n",
       "      <td>8</td>\n",
       "      <td>Normal</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>370</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>371</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>372</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>373</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>374</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>Nurse</td>\n",
       "      <td>8.1</td>\n",
       "      <td>9</td>\n",
       "      <td>75</td>\n",
       "      <td>3</td>\n",
       "      <td>Overweight</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>359 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Person ID  Gender  Age         Occupation  Sleep Duration  \\\n",
       "0            1       1   27  Software Engineer             6.1   \n",
       "1            2       1   28             Doctor             6.2   \n",
       "2            3       1   28             Doctor             6.2   \n",
       "3            8       1   29             Doctor             7.8   \n",
       "4            9       1   29             Doctor             7.8   \n",
       "..         ...     ...  ...                ...             ...   \n",
       "354        370       0   59              Nurse             8.1   \n",
       "355        371       0   59              Nurse             8.0   \n",
       "356        372       0   59              Nurse             8.1   \n",
       "357        373       0   59              Nurse             8.1   \n",
       "358        374       0   59              Nurse             8.1   \n",
       "\n",
       "     Quality of Sleep  Physical Activity Level  Stress Level BMI Category  \\\n",
       "0                   6                       42             6   Overweight   \n",
       "1                   6                       60             8       Normal   \n",
       "2                   6                       60             8       Normal   \n",
       "3                   7                       75             6       Normal   \n",
       "4                   7                       75             6       Normal   \n",
       "..                ...                      ...           ...          ...   \n",
       "354                 9                       75             3   Overweight   \n",
       "355                 9                       75             3   Overweight   \n",
       "356                 9                       75             3   Overweight   \n",
       "357                 9                       75             3   Overweight   \n",
       "358                 9                       75             3   Overweight   \n",
       "\n",
       "     Heart Rate  ...  Nurse Engineer  Accountant  Scientist  Teacher  Lawyer  \\\n",
       "0            77  ...    0.0      0.0         0.0        0.0      0.0     1.0   \n",
       "1            75  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "2            75  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "3            70  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "4            70  ...    0.0      0.0         0.0        0.0      0.0     0.0   \n",
       "..          ...  ...    ...      ...         ...        ...      ...     ...   \n",
       "354          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "355          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "356          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "357          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "358          68  ...    0.0      0.0         1.0        0.0      0.0     0.0   \n",
       "\n",
       "     Salesperson  Underweight  Normal  Overweight  \n",
       "0            0.0          0.0     0.0         1.0  \n",
       "1            0.0          1.0     0.0         0.0  \n",
       "2            0.0          1.0     0.0         0.0  \n",
       "3            0.0          1.0     0.0         0.0  \n",
       "4            0.0          1.0     0.0         0.0  \n",
       "..           ...          ...     ...         ...  \n",
       "354          0.0          0.0     0.0         1.0  \n",
       "355          0.0          0.0     0.0         1.0  \n",
       "356          0.0          0.0     0.0         1.0  \n",
       "357          0.0          0.0     0.0         1.0  \n",
       "358          0.0          0.0     0.0         1.0  \n",
       "\n",
       "[359 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop coloumn NA\n",
    "df = df.dropna()\n",
    "\n",
    "#Handling categorical variables/columns\n",
    "df['Gender'] = LabelEncoder().fit_transform(df['Gender'])\n",
    "\n",
    "#OneHotEncoder\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['Sleep Disorder']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Sleep Apnea'] = df_enc[0]\n",
    "df['insomnia'] = df_enc[1]\n",
    "df['None'] = df_enc[2]\n",
    "\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['Occupation']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Software Engineer'] = df_enc[0]\n",
    "df['Doctor'] = df_enc[1]\n",
    "df['Manager'] = df_enc[2]\n",
    "df['Nurse'] = df_enc[3]\n",
    "df['Engineer'] = df_enc[4]\n",
    "df['Accountant'] = df_enc[5]\n",
    "df['Scientist'] = df_enc[6]\n",
    "df['Teacher'] = df_enc[7]\n",
    "df['Lawyer'] = df_enc[8]\n",
    "df['Salesperson'] = df_enc[9]\n",
    "\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "st_enc = enc.fit_transform(df[['BMI Category']])\n",
    "df_enc = pd.DataFrame(st_enc)\n",
    "df['Underweight'] = df_enc[0]\n",
    "df['Normal'] = df_enc[1]\n",
    "df['Overweight'] = df_enc[2]\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Target and Predictor\n",
    "X = df.drop(['Gender', 'Occupation', 'BMI Category', 'Sleep Disorder'], axis=1)\n",
    "y = df['Sleep Disorder']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data to train and  test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.18, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Lintang\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['None', 'None', 'None', 'None', 'None', 'Insomnia', 'None', 'None',\n",
       "       'None', 'None', 'None', 'Sleep Apnea', 'Insomnia', 'None', 'None',\n",
       "       'Sleep Apnea', 'None', 'None', 'None', 'Sleep Apnea', 'None',\n",
       "       'None', 'None', 'None', 'Sleep Apnea', 'Sleep Apnea',\n",
       "       'Sleep Apnea', 'None', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'Sleep Apnea', 'None', 'None', 'None', 'Insomnia',\n",
       "       'None', 'None', 'Insomnia', 'None', 'None', 'None', 'None', 'None',\n",
       "       'None', 'None', 'None', 'Sleep Apnea', 'Insomnia', 'None', 'None',\n",
       "       'Sleep Apnea', 'None', 'None', 'None', 'None', 'Insomnia',\n",
       "       'Insomnia', 'Insomnia', 'None', 'None'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediksi\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.8462\n",
      "precision: 0.8958\n",
      "recall: 0.7644\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1d071533b20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAGwCAYAAABB4NqyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMcElEQVR4nO3deVxU9f4/8NewOIAMICibIODCpqIpilC5VGqYhWmGaaXX5WakZpaaWkqaIHrdLbcU0K9e5KdJZq4tYIaaEKZXue4o5YIbgijbzOf3BzG3EUYZZoYZZ17Px+M8Hp7POZ9z3jMjw5v353POkQghBIiIiIjMiIWhAyAiIiJqaEyAiIiIyOwwASIiIiKzwwSIiIiIzA4TICIiIjI7TICIiIjI7DABIiIiIrNjZegAqOEpFApcuXIFMpkMEonE0OEQEZGGhBAoLi6Gp6cnLCz0V8soLS1FeXm51sdp1KgRbGxsdBCR7jABMkNXrlyBt7e3ocMgIiIt5efnw8vLSy/HLi0thZ+PPa4VyLU+lru7Oy5evGhUSRATIDMkk8kAAE+HToaVldTA0RCRLkkOnzB0CNQAKlGBg9il/D7Xh/LyclwrkONSti8cZPWvMhUVK+DTOQ/l5eVMgMiwqoe9rKyksLIynv+MRKQ9icTa0CFQQ/jrIVYNMY3BXiaBvaz+51HAOKdaMAEiIiIiteRCAbkWTw2VC4XugtEhJkBERESklgICCtQ/A9Kmrz7xMngiIiIyO6wAERERkVoKKKDNIJZ2vfWHCRARERGpJRcCclH/YSxt+uoTh8CIiIjI7LACRERERGqZ6iRoJkBERESklgICchNMgDgERkRERGaHFSAiIiJSi0NgREREZHZ4FRgRERGRiWAFiIiIiNRS/LVo098YMQEiIiIiteRaXgWmTV99YgJEREREaskFtHwavO5i0SXOASIiIiKzwwoQERERqcU5QERERGR2FJBADolW/Y0Rh8CIiIjI7LACRERERGopRNWiTX9jxASIiIiI1JJrOQSmTV994hAYERERmR1WgIiIiEgtU60AMQEiIiIitRRCAoXQ4iowLfrqE4fAiIiIyOywAkRERERqcQiMiIiIzI4cFpBrMWAk12EsusQEiIiIiNQSWs4BEpwDRERERGQcWAEiIiIitTgHiIiIiMyOXFhALrSYA2Skj8LgEBgREREZjZUrVyIkJAQODg5wcHBAeHg4du/erdw+YsQISCQSlaVbt24an4cVICIiIlJLAQkUWtRLFNCsBOTl5YV58+ahdevWAIDk5GRERUUhJycHbdu2BQC8+OKLSExMVPZp1KiRxnExASIiIiK1GnoO0Msvv6yyPnfuXKxcuRKHDx9WJkBSqRTu7u71jgngEBgRERE1gKKiIpWlrKzssX3kcjlSUlJQUlKC8PBwZXt6ejpcXV3h7++PMWPGoKCgQON4mAARERGRWtWToLVZAMDb2xuOjo7KJT4+Xu05T5w4AXt7e0ilUowdOxbbt29HcHAwACAyMhKbNm3Cjz/+iIULF+Lo0aN47rnn6pRQ/R2HwIiIiEitqjlAWjwM9a+++fn5cHBwULZLpVK1fQICAnDs2DEUFhZi27ZtGD58ODIyMhAcHIzo6Gjlfu3atUNoaCh8fHzw3XffYeDAgXWOiwkQERER6V31VV110ahRI+Uk6NDQUBw9ehRLly7F6tWra+zr4eEBHx8fnD17VqN4mAARERGRWgotnwWm6VVgtRFCqB3iunXrFvLz8+Hh4aHRMZkAERERkVra3whRswRo+vTpiIyMhLe3N4qLi5GSkoL09HTs2bMH9+7dQ2xsLAYNGgQPDw/k5eVh+vTpaNq0KV599VWNzsMEiIiIiNRSwKJB7wN0/fp1vPXWW7h69SocHR0REhKCPXv2oHfv3njw4AFOnDiBDRs2oLCwEB4eHujVqxe2bNkCmUym0XmYABEREZHRWLdundpttra22Lt3r07OwwSIiIiI1JILCeRCixshatFXn5gAERERkVpyLSdBy3UwCVofeCNEIiIiMjusABEREZFaCmEBhRZXgSk0vAqsoTABIiIiIrU4BEZERERkIlgBIiIiIrUU0O5KLoXuQtEpJkBERESklvY3QjTOwSbjjIqIiIhIj1gBIiIiIrW0fxaYcdZamAARERGRWgpIoIA2c4B4J2giIiJ6wrACRAaRl5cHPz8/5OTkoGPHjoYO54m04YutcHctqdG+Y08AVqzrZoCISF/4WZuf/sNvYvC7N+DsWoFLZ2ywaqYn/vOrvaHDoieASSdAI0aMQGFhIdLS0gwdSr15e3vj6tWraNq0qaFDeWKNn9YfFhb/uxGXr/cdJMzcjwOHfA0XFOkFP2vz0uOVOxj72RWsmN4cJ39tjJfeuoXPN13EmJ4BuPFnI0OHZzK0vxGicVaAjDMqUrK0tIS7uzusrEw6V9Wru0U2uFNoq1zCOv+BP6/JcPyUm6FDIx3jZ21eBv7zJvb+2xl7Nrsg/5wNVs1qjhtXrNH/7VuGDs2kKIRE68UYmU0C1LNnT0yYMAFTpkyBs7Mz3N3dERsbq7JPbGwsWrRoAalUCk9PT0yYMEG57c6dO3j77bfRpEkT2NnZITIyEmfPnlVuT0pKgpOTE3bu3ImAgADY2dnhtddeQ0lJCZKTk+Hr64smTZpg/PjxkMvlyn6+vr6Ii4vDyJEjIZPJ0KJFC6xZs0a5PS8vDxKJBMeOHQMAyOVyjBo1Cn5+frC1tUVAQACWLl2qnzfNBFlZyfH8sxew98fWgJFOzCPd4Gdt2qysFWgTch/ZGTKV9uwMGYJDaw6DEj3MbBIgAEhOTkbjxo1x5MgRzJ8/H7Nnz8b+/fsBAFu3bsXixYuxevVqnD17FmlpaWjfvr2y74gRI5CVlYUdO3bg0KFDEEKgX79+qKioUO5z//59LFu2DCkpKdizZw/S09MxcOBA7Nq1C7t27cLGjRuxZs0abN26VSWuhQsXIjQ0FDk5OYiJicG7776L//73v7W+BoVCAS8vL6SmpuLUqVOYOXMmpk+fjtTUVLWvu6ysDEVFRSqLuYrokg/7xuXYl97a0KGQnvGzNm0OznJYWgGFN1Wr44U3rNDEtdJAUZkmxV9DYPVdjPVGiGY1rhISEoJZs2YBANq0aYMVK1bghx9+QO/evXH58mW4u7vjhRdegLW1NVq0aIGuXbsCAM6ePYsdO3bgl19+QUREBABg06ZN8Pb2RlpaGgYPHgwAqKiowMqVK9GqVSsAwGuvvYaNGzfi+vXrsLe3R3BwMHr16oWffvoJ0dHRyrj69euHmJgYAMDUqVOxePFipKenIzAwsMZrsLa2xmeffaZc9/PzQ2ZmJlJTU/H666/X+rrj4+NV+pizF587i6M5zXH7jp2hQyE942dtHh5+0LhEAhjpszefWNo/Dd44EyDjjEpPQkJCVNY9PDxQUFAAABg8eDAePHiAli1bYsyYMdi+fTsqK6v+isjNzYWVlRXCwsKUfV1cXBAQEIDc3Fxlm52dnTL5AQA3Nzf4+vrC3t5epa36nLXFJZFI4O7uXmOfv1u1ahVCQ0PRrFkz2NvbY+3atbh8+bLa/adNm4a7d+8ql/z8fLX7mjLXpvfwVMhV7P6hjaFDIT3jZ236im5bQl4JNGmmWu1xbFqJOzfM6m97qiezSoCsra1V1iUSCRSKqse0eXt74/Tp0/jiiy9ga2uLmJgYdO/eHRUVFRAP/4nxFyEEJJL/zS2o7fiPOmdd4npYamoqPvjgA4wcORL79u3DsWPH8I9//APl5eVqX7dUKoWDg4PKYo769jqHwrs2OPKbl6FDIT3jZ236KisscPa4HTp1L1Zp79S9GKeyGhsoKtMkh0TrxRgxTf4bW1tbvPLKK3jllVfw3nvvITAwECdOnEBwcDAqKytx5MgR5RDYrVu3cObMGQQFBTVojD///DMiIiKUQ2YAcP78+QaN4UkkkQj06XUO+zNaQaEwq7zf7PCzNh9fr2mKycvycea4LXKzGqPfm7fg2rwC321wMXRoJsVUh8CYAP0lKSkJcrkcYWFhsLOzw8aNG2FrawsfHx+4uLggKioKY8aMwerVqyGTyfDxxx+jefPmiIqKatA4W7dujQ0bNmDv3r3w8/PDxo0bcfToUfj5+TVoHE+aTu2vwK1ZyV9XBJEp42dtPjJ2NIGsiRzDPrgOZ9dKXDptg0/e9EMB7wFEdcAE6C9OTk6YN28eJk2aBLlcjvbt2+Pbb7+Fi0vVXxKJiYl4//330b9/f5SXl6N79+7YtWtXjeErfRs7diyOHTuG6OhoSCQSvPHGG4iJicHu3bsbNI4nTfbx5ugzeLihw6AGwM/avOxMboqdybxRrD7JAa2GseSP38UgJELdBBcyWUVFRXB0dESPbp/AysrG0OEQkQ5Jfjlm6BCoAVSKCqTjG9y9e1dv8zqrf1d8crgPbOzr/8d+6b0KfN5tn15jrQ9WgIiIiEgtU30YqnFGRURERKRHrAARERGRWgISKLSYAyR4GTwRERE9aTgERkRERGQiWAEiIiIitRRCAoWo/zCWNn31iQkQERERqVX9VHdt+hsj44yKiIiISI9YASIiIiK1OARGREREZkcBCyi0GDDSpq8+GWdURERERHrEChARERGpJRcSyLUYxtKmrz4xASIiIiK1THUOEIfAiIiISC0hLKDQYhEa3gl65cqVCAkJgYODAxwcHBAeHo7du3f/LR6B2NhYeHp6wtbWFj179sTJkyc1fl1MgIiIiMhoeHl5Yd68ecjKykJWVhaee+45REVFKZOc+fPnY9GiRVixYgWOHj0Kd3d39O7dG8XFxRqdhwkQERERqSWHROsFAIqKilSWsrKyWs/38ssvo1+/fvD394e/vz/mzp0Le3t7HD58GEIILFmyBDNmzMDAgQPRrl07JCcn4/79+9i8ebNGr4sJEBEREamlEP+bB1S/peo43t7ecHR0VC7x8fGPPbdcLkdKSgpKSkoQHh6Oixcv4tq1a+jTp49yH6lUih49eiAzM1Oj18VJ0ERERKR3+fn5cHBwUK5LpVK1+544cQLh4eEoLS2Fvb09tm/fjuDgYGWS4+bmprK/m5sbLl26pFE8TICIiIhIrerJzNr0B6Cc1FwXAQEBOHbsGAoLC7Ft2zYMHz4cGRkZyu0SieqVZUKIGm2PwwSIiIiI1FJAAgW0uAy+Hn0bNWqE1q1bAwBCQ0Nx9OhRLF26FFOnTgUAXLt2DR4eHsr9CwoKalSFHodzgIiIiMioCSFQVlYGPz8/uLu7Y//+/cpt5eXlyMjIQEREhEbHZAWIiIiI1GroO0FPnz4dkZGR8Pb2RnFxMVJSUpCeno49e/ZAIpFg4sSJiIuLQ5s2bdCmTRvExcXBzs4OQ4cO1eg8TICIiIhILV3NAaqr69ev46233sLVq1fh6OiIkJAQ7NmzB7179wYATJkyBQ8ePEBMTAzu3LmDsLAw7Nu3DzKZTKPzMAEiIiIio7Fu3bpHbpdIJIiNjUVsbKxW52ECRERERGopoOWzwLSYQK1PTICIiIhILaHlVWCCCRARERE9afg0eCIiIiITwQoQERERqdXQV4E1FCZAREREpBaHwIiIiIhMBCtAREREpJYhngXWEJgAERERkVocAiMiIiIyEawAERERkVqmWgFiAkRERERqmWoCxCEwIiIiMjusABEREZFaploBYgJEREREaglodym70F0oOsUEiIiIiNQy1QoQ5wARERGR2WEFiIiIiNQy1QoQEyAiIiJSy1QTIA6BERERkdlhBYiIiIjUMtUKEBMgIiIiUksICYQWSYw2ffWJQ2BERERkdlgBIiIiIrUUkGh1I0Rt+uoTEyAiIiJSy1TnAHEIjIiIiMwOK0BERESklqlOgmYCRERERGqZ6hAYEyAiIiJSy1QrQJwDRERERGaHFSAzZnnsDCwljQwdBunZ7guHDR0CNaC+nh0NHQKZGKHlEJixVoCYABEREZFaAoAQ2vU3RhwCIyIiIrPDChARERGppYAEEt4JmoiIiMwJrwIjIiIiMhFMgIiIiEit6hsharNoIj4+Hl26dIFMJoOrqysGDBiA06dPq+wzYsQISCQSlaVbt24anYcJEBEREaklhPaLJjIyMvDee+/h8OHD2L9/PyorK9GnTx+UlJSo7Pfiiy/i6tWrymXXrl0anYdzgIiIiMho7NmzR2U9MTERrq6uyM7ORvfu3ZXtUqkU7u7u9T4PK0BERESkVvUkaG0WACgqKlJZysrK6nT+u3fvAgCcnZ1V2tPT0+Hq6gp/f3+MGTMGBQUFGr0uJkBERESklq4SIG9vbzg6OiqX+Pj4OpxbYNKkSXjmmWfQrl07ZXtkZCQ2bdqEH3/8EQsXLsTRo0fx3HPP1TmpAjgERkRERI+gEBJIdPA0+Pz8fDg4OCjbpVLpY/uOGzcOx48fx8GDB1Xao6Ojlf9u164dQkND4ePjg++++w4DBw6sU1xMgIiIiEjvHBwcVBKgxxk/fjx27NiBAwcOwMvL65H7enh4wMfHB2fPnq3z8ZkAERERkVr1uZLr4f6a7S8wfvx4bN++Henp6fDz83tsn1u3biE/Px8eHh51Pg/nABEREZFaVQmQNnOANDvfe++9h//7v//D5s2bIZPJcO3aNVy7dg0PHjwAANy7dw8fffQRDh06hLy8PKSnp+Pll19G06ZN8eqrr9b5PKwAERERkdFYuXIlAKBnz54q7YmJiRgxYgQsLS1x4sQJbNiwAYWFhfDw8ECvXr2wZcsWyGSyOp+HCRARERGp1dDPAhOPKRnZ2tpi79699Y6nGhMgIiIiUkv8tWjT3xhxDhARERGZHVaAiIiISK2GHgJrKEyAiIiISD0THQNjAkRERETqaVkBgpFWgDgHiIiIiMwOK0BERESkVkPfCbqhMAEiIiIitUx1EjSHwIiIiMjssAJERERE6gmJdhOZjbQCxASIiIiI1DLVOUAcAiMiIiKzwwoQERERqccbIRIREZG5MdWrwOqUAC1btqzOB5wwYUK9gyEiIiJqCHVKgBYvXlyng0kkEiZAREREpsZIh7G0UacE6OLFi/qOg4iIiIyQqQ6B1fsqsPLycpw+fRqVlZW6jIeIiIiMidDBYoQ0ToDu37+PUaNGwc7ODm3btsXly5cBVM39mTdvns4DJCIiItI1jROgadOm4ffff0d6ejpsbGyU7S+88AK2bNmi0+CIiIjI0CQ6WIyPxpfBp6WlYcuWLejWrRskkv+9qODgYJw/f16nwREREZGBmeh9gDSuAN24cQOurq412ktKSlQSIiIiIiJjpXEC1KVLF3z33XfK9eqkZ+3atQgPD9ddZERERGR4JjoJWuMhsPj4eLz44os4deoUKisrsXTpUpw8eRKHDh1CRkaGPmIkIiIiQzHRp8FrXAGKiIjAL7/8gvv376NVq1bYt28f3NzccOjQIXTu3FkfMRIRERHpVL2eBda+fXskJyfrOhYiIiIyMkJULdr0N0b1SoDkcjm2b9+O3NxcSCQSBAUFISoqClZWfLYqERGRSTHRq8A0zlj+85//ICoqCteuXUNAQAAA4MyZM2jWrBl27NiB9u3b6zxIIiIiIl3SeA7Q6NGj0bZtW/zxxx/47bff8NtvvyE/Px8hISH45z//qY8YiYiIyFCqJ0FrsxghjStAv//+O7KystCkSRNlW5MmTTB37lx06dJFp8ERERGRYUlE1aJNf2OkcQUoICAA169fr9FeUFCA1q1b6yQoIiIiMhImeh+gOiVARUVFyiUuLg4TJkzA1q1b8ccff+CPP/7A1q1bMXHiRCQkJOg7XiIiIiKt1WkIzMnJSeUxF0IIvP7668o28dc1bi+//DLkcrkewiQiIiKDMNEbIdYpAfrpp5/0HQcREREZI3O+DL5Hjx76joOIiIiowdT7zoX379/H5cuXUV5ertIeEhKidVBERERkJMy5AvR3N27cwD/+8Q/s3r271u2cA0RERGRCTDQB0vgy+IkTJ+LOnTs4fPgwbG1tsWfPHiQnJ6NNmzbYsWOHPmIkIiIiMxEfH48uXbpAJpPB1dUVAwYMwOnTp1X2EUIgNjYWnp6esLW1Rc+ePXHy5EmNzqNxAvTjjz9i8eLF6NKlCywsLODj44M333wT8+fPR3x8vKaHIyIiImPWwHeCzsjIwHvvvYfDhw9j//79qKysRJ8+fVBSUqLcZ/78+Vi0aBFWrFiBo0ePwt3dHb1790ZxcXGdz6PxEFhJSQlcXV0BAM7Ozrhx4wb8/f3Rvn17/Pbbb5oejoiIiIyYru4EXVRUpNIulUohlUpr7L9nzx6V9cTERLi6uiI7Oxvdu3eHEAJLlizBjBkzMHDgQABAcnIy3NzcsHnzZrzzzjt1iqted4KuLkV17NgRq1evxp9//olVq1bBw8ND08MR6V27LkWIXXsa/3foN+y+cAThvW8bOiTSgW+TXTD2+QC86t8er/q3x8SX2+DojzLl9js3rPCviS3wxlNt8UrLEEwf2hJ/XmhkwIhJH/oPv4nkw7n49sJxrNhzBu263jN0SKSGt7c3HB0dlUtdR43u3r0LoKroAgAXL17EtWvX0KdPH+U+UqkUPXr0QGZmZp3jqdccoKtXrwIAZs2ahT179qBFixZYtmwZ4uLiND2cyRgxYgQkEgnmzZun0p6WlqZyE0lqeDZ2ClzItcOXsb6GDoV0qJlHBUZOv4Llu89g+e4z6PB0MWL/4Ye80zYQAvhspB+uXmqE2MQL+GLfabh5lePj6NYova/x1x4ZqR6v3MHYz67g38tcEdPHH/850hifb7qIZs3LH9+Z6k5Hj8LIz8/H3bt3lcu0adMef2ohMGnSJDzzzDNo164dAODatWsAADc3N5V93dzclNvqQuMhsGHDhin//dRTTyEvLw///e9/0aJFCzRt2lTTw5kUGxsbJCQk4J133lF5WCwZVlaGE7IynAwdBulYtz6q5fR/fHwNOzc0xX+z7WBlJZCb3Rirf/ovfANKAQDj4v9AdEg7/LTdCZHDWAU0BQP/eRN7/+2MPZtdAACrZjVH557F6P/2LSTGc0TC2Dg4OMDBwUGjPuPGjcPx48dx8ODBGtseLi4IITQqOGj9p5CdnR06depk9skPALzwwgtwd3d/ZFlv27ZtaNu2LaRSKXx9fbFw4UKV7b6+voiLi8PIkSMhk8nQokULrFmzRmWfP//8E9HR0WjSpAlcXFwQFRWFvLw8fbwkoieCXA6kpzmh7L4FgkJLUFFe9SXYSKpQ7mNpCVhbC5w8am+oMEmHrKwVaBNyH9kZMpX27AwZgkNL1PSi+pDgf/OA6rXU87zjx4/Hjh078NNPP8HLy0vZ7u7uDgA1qj0FBQU1qkKPUqcK0KRJk+p8wEWLFtV5X1NjaWmJuLg4DB06FBMmTFD5wAAgOzsbr7/+OmJjYxEdHY3MzEzExMTAxcUFI0aMUO63cOFCzJkzB9OnT8fWrVvx7rvvonv37ggMDMT9+/fRq1cvPPvsszhw4ACsrKzw+eef48UXX8Tx48fRqFHNOQ5lZWUoKytTrj88EY3oSXUx1wYTX26D8jIL2DZWYOa6i/DxL0NlBeDmVY718R54P+EP2Ngp8PXqZrhdYI3b1+t9/1cyIg7OclhaAYU3VT/PwhtWaOJaaaCoSBeEEBg/fjy2b9+O9PR0+Pn5qWz38/ODu7s79u/fj6eeegoAUF5ejoyMDI0eyl6nb4KcnJw6HYxzXYBXX30VHTt2xKxZs7Bu3TqVbYsWLcLzzz+PTz/9FADg7++PU6dOYcGCBSoJUL9+/RATEwMAmDp1KhYvXoz09HQEBgYiJSUFFhYW+Oqrr5Tvd2JiIpycnJCenq4yKaxafHw8PvvsMz29YiLD8WpVhi/3n0ZJkSUOfueEf73vgwVfn4WPfxk+/eoiFk1qgdeC28PCUuCpZ4vR5Tkm/6ZGPHR1kkQCo73x3hOrgR+G+t5772Hz5s345ptvIJPJlJUeR0dH2NraQiKRYOLEiYiLi0ObNm3Qpk0bxMXFwc7ODkOHDq3zefgwVD1ISEjAc889hw8//FClPTc3F1FRUSptTz/9NJYsWQK5XA5LS0sAqo8TkUgkcHd3R0FBAYCqKtK5c+cgk6mWfUtLS3H+/Pla45k2bZpKFa+oqAje3t71f4FERsK6kUBzv6oJr/4dHuD0MTukfdUM78//A21CHmDl96dRUmSBigoJnFzkmPBSG/iH3Ddw1KQLRbctIa8EmjRTrfY4Nq3EnRus8ulUA98JeuXKlQCAnj17qrQnJiYqiwVTpkzBgwcPEBMTgzt37iAsLAz79u2r8bvxUfi/RA+6d++Ovn37Yvr06SqVndomaImH/3wBYG1trbIukUigUFTNZVAoFOjcuTM2bdpUo1+zZs1qjUfdvRaITFFFuerUxsYOVT87f15ohLO/22H45LpfJULGq7LCAmeP26FT92Jk7nFUtnfqXoxDex0f0ZOMXW2/Fx8mkUgQGxuL2NjYep+HCZCezJs3Dx07doS/v7+yLTg4uMZM9szMTPj7+yurP4/TqVMnbNmyBa6urhrPpjdXNnZyePqUKtfdvMvQMqgExXetcOMKE8Mn1fp4D3R5rgjNPCvw4J4F0r9xwvFMe3y+qaoSeuBbRzi6yOHavBwXc22waqYXwl+8i849636nWDJuX69pisnL8nHmuC1ysxqj35u34Nq8At9tcDF0aKbFRJ8FxgRIT9q3b49hw4Zh+fLlyrYPP/wQXbp0wZw5cxAdHY1Dhw5hxYoV+PLLL+t83GHDhmHBggWIiorC7Nmz4eXlhcuXL+Prr7/G5MmTa0y8JqBN+xLM/3eucv2dTy4DAPZvbYpFU1oZKizSUuENKywY74PbBVawk8nhF1SKzzedR+ceVTfCu33dGqtjm6PwphWcXSvxwuDbGDrxuoGjJl3K2NEEsiZyDPvgOpxdK3HptA0+edMPBX/yhpe6pKs7QRsbJkB6NGfOHKSmpirXO3XqhNTUVMycORNz5syBh4cHZs+erTJM9jh2dnY4cOAApk6dioEDB6K4uBjNmzfH888/z4qQGieOOCCyZZihwyAdm7Qo/5HbB4y+iQGjbzZQNGQoO5ObYmcyb8NCmmMCpCNJSUk12nx8fFBaWqrSNmjQIAwaNEjtcWq7n8+xY8dU1t3d3ZGcnFyfMImIiDRjokNg9boR4saNG/H000/D09MTly5dAgAsWbIE33zzjU6DIyIiIgPT0aMwjI3GCdDKlSsxadIk9OvXD4WFhZDL5QAAJycnLFmyRNfxEREREemcxgnQ8uXLsXbtWsyYMUPlyqXQ0FCcOHFCp8ERERGRYWn1GAwtJ1Drk8ZzgC5evKi89fTfSaVSlJTw+StEREQmpYHvBN1QNK4A+fn51ZiUCwC7d+9GcHCwLmIiIiIiY2Gic4A0rgBNnjwZ7733HkpLSyGEwK+//op///vfiI+Px1dffaWPGImIiIh0SuME6B//+AcqKysxZcoU3L9/H0OHDkXz5s2xdOlSDBkyRB8xEhERkYHwRoh/M2bMGIwZMwY3b96EQqGAq6urruMiIiIiY2Ci9wHS6kaITZvy7ptERET05NE4AfLz86vxRPO/u3DhglYBERERkRHR9lJ2U6kATZw4UWW9oqICOTk52LNnDyZPnqyruIiIiMgYcAisyvvvv19r+xdffIGsrCytAyIiIiLSt3o9C6w2kZGR2LZtm64OR0RERMaA9wF6tK1bt8LZ2VlXhyMiIiIjwMvg//LUU0+pTIIWQuDatWu4ceMGvvzyS50GR0RERKQPGidAAwYMUFm3sLBAs2bN0LNnTwQGBuoqLiIiIiK90SgBqqyshK+vL/r27Qt3d3d9xURERETGwkSvAtNoErSVlRXeffddlJWV6SseIiIiMiLVc4C0WYyRxleBhYWFIScnRx+xEBERETUIjecAxcTE4MMPP8Qff/yBzp07o3HjxirbQ0JCdBYcERERGQEjreJoo84J0MiRI7FkyRJER0cDACZMmKDcJpFIIISARCKBXC7XfZRERERkGCY6B6jOCVBycjLmzZuHixcv6jMeIiIiIr2rcwIkRFUK5+Pjo7dgiIiIyLjwRojAI58CT0RERCbI3IfAAMDf3/+xSdDt27e1CoiIiIhI3zRKgD777DM4OjrqKxYiIiIyMhwCAzBkyBC4urrqKxYiIiIyNiY6BFbnGyFy/g8RERGZCo2vAiMiIiIzYqIVoDonQAqFQp9xEBERkRHiHCAiIiIyPyZaAdL4YahERERETzomQERERKSe0MGioQMHDuDll1+Gp6cnJBIJ0tLSVLaPGDECEolEZenWrZtG52ACRERERGpVzwHSZtFUSUkJOnTogBUrVqjd58UXX8TVq1eVy65duzQ6B+cAERERkVGJjIxEZGTkI/eRSqVwd3ev9zlYASIiIiL1dDQEVlRUpLKUlZVpFVZ6ejpcXV3h7++PMWPGoKCgQKP+TICIiIhILV0NgXl7e8PR0VG5xMfH1zumyMhIbNq0CT/++CMWLlyIo0eP4rnnntMoqeIQGBEREeldfn4+HBwclOtSqbTex4qOjlb+u127dggNDYWPjw++++47DBw4sE7HYAJERERE6unoPkAODg4qCZAueXh4wMfHB2fPnq1zHyZAREREpN4TcCPEW7duIT8/Hx4eHnXuwwSIiIiIjMq9e/dw7tw55frFixdx7NgxODs7w9nZGbGxsRg0aBA8PDyQl5eH6dOno2nTpnj11VfrfA4mQERERKSW5K9Fm/6aysrKQq9evZTrkyZNAgAMHz4cK1euxIkTJ7BhwwYUFhbCw8MDvXr1wpYtWyCTyep8DiZAREREpJ4BhsB69uwJIdR33Lt3rxYBVWECRERERGqZ6tPgeR8gIiIiMjusABEREZF6T8BVYPXBBIiIiIgezUiTGG1wCIyIiIjMDitAREREpJapToJmAkRERETqmegcIA6BERERkdlhBYiIiIjU4hAYERERmR8OgRERERGZBlaAzJiitAwKicLQYZCe9Wv/nKFDoAZU9lIrQ4dADaCyohTY+02DnItDYERERGR+THQIjAkQERERqWeiCRDnABEREZHZYQWIiIiI1OIcICIiIjI/HAIjIiIiMg2sABEREZFaEiEgEfUv42jTV5+YABEREZF6HAIjIiIiMg2sABEREZFavAqMiIiIzA+HwIiIiIhMAytAREREpBaHwIiIiMj8mOgQGBMgIiIiUstUK0CcA0RERERmhxUgIiIiUo9DYERERGSOjHUYSxscAiMiIiKzwwoQERERqSdE1aJNfyPEBIiIiIjU4lVgRERERCaCFSAiIiJSj1eBERERkbmRKKoWbfobIw6BERERkVE5cOAAXn75ZXh6ekIikSAtLU1luxACsbGx8PT0hK2tLXr27ImTJ09qdA4mQERERKSe0MGioZKSEnTo0AErVqyodfv8+fOxaNEirFixAkePHoW7uzt69+6N4uLiOp+DQ2BERESkliGuAouMjERkZGSt24QQWLJkCWbMmIGBAwcCAJKTk+Hm5obNmzfjnXfeqdM5WAEiIiIi9arvA6TNAqCoqEhlKSsrq1c4Fy9exLVr19CnTx9lm1QqRY8ePZCZmVnn4zABIiIiIr3z9vaGo6OjcomPj6/Xca5duwYAcHNzU2l3c3NTbqsLDoERERGRWroaAsvPz4eDg4OyXSqVaheXRKKyLoSo0fYoTICIiIhIPR3dB8jBwUElAaovd3d3AFWVIA8PD2V7QUFBjarQo3AIjIiIiJ4Yfn5+cHd3x/79+5Vt5eXlyMjIQERERJ2PwwoQERERqWWIq8Du3buHc+fOKdcvXryIY8eOwdnZGS1atMDEiRMRFxeHNm3aoE2bNoiLi4OdnR2GDh1a53MwASIiIiL1DPA0+KysLPTq1Uu5PmnSJADA8OHDkZSUhClTpuDBgweIiYnBnTt3EBYWhn379kEmk9X5HEyAiIiIyKj07NkT4hGJk0QiQWxsLGJjY+t9DiZAREREpJYhhsAaAhMgIiIiUs9EnwbPq8CIiIjI7LACRERERGpxCIyIiIjMj0JULdr0N0JMgIiIiEg9zgEiIiIiMg2sABEREZFaEmg5B0hnkegWEyAiIiJSzwB3gm4IHAIjIiIis8MKEBEREanFy+CJiIjI/PAqMCIiIiLTwAoQERERqSURAhItJjJr01efmAARERGReoq/Fm36GyEOgREREZHZYQWIiIiI1OIQGBEREZkfE70KjAkQERERqcc7QRMRERGZBlaAiIiISC3eCZroCdZ/+E0MfvcGnF0rcOmMDVbN9MR/frU3dFikY6+PuoSIF27Ay+8+ykstkPu7I9YvboU/8+wMHRrpga20HKMGZOOZpy6hiewBzl52wfIt4Tid18zQoZkWDoEZhkQiQVpamqHDoCdYj1fuYOxnV/DvZa6I6eOP/xxpjM83XUSz5uWGDo10rF1oIXamNMekYZ0x458dYWkpMHf1MUht5YYOjfRg8vCf0Tn4T8St64GRsQORdao5Fn6wC02dSgwdGj0BDJoAFRQU4J133kGLFi0glUrh7u6Ovn374tChQ4YM65E2b94MS0tLjB071tChUB0N/OdN7P23M/ZsdkH+ORusmtUcN65Yo//btwwdGunYzHc74PtvPHD5fGNcPGOPRZ8GwtWzDG2Ciw0dGulYI+tK9OiUh9Vbu+L4WQ/8ecMRSd92xrVbMkT1zDV0eCZFotB+MUYGTYAGDRqE33//HcnJyThz5gx27NiBnj174vbt24YM65HWr1+PKVOmICUlBffv3zd0OPQYVtYKtAm5j+wMmUp7doYMwaH8K9HUNbavBAAU3+Vov6mxtFDA0lKgvMJSpb2s3ArtW18zUFQmqnoITJvFCBksASosLMTBgweRkJCAXr16wcfHB127dsW0adPw0ksvqe33559/Ijo6Gk2aNIGLiwuioqKQl5ensk9iYiKCgoJgY2ODwMBAfPnll8pteXl5kEgkSElJQUREBGxsbNC2bVukp6c/Nua8vDxkZmbi448/RmBgILZu3aqyPSkpCU5OTkhLS4O/vz9sbGzQu3dv5OfnK/eJjY1Fx44dsXHjRvj6+sLR0RFDhgxBcfH//kIVQmD+/Plo2bIlbG1t0aFDB5VzyeVyjBo1Cn5+frC1tUVAQACWLl2qNu6ysjIUFRWpLObCwVkOSyug8KbqL8DCG1Zo4lppoKioYQiMmXwO/8l2xKVznO9lah6UNcJ/zrni7f45cHEsgYVEgd5hZxHkVwBnxweGDo+eAAZLgOzt7WFvb4+0tDSUlZXVqc/9+/fRq1cv2Nvb48CBAzh48CDs7e3x4osvory8aj7H2rVrMWPGDMydOxe5ubmIi4vDp59+iuTkZJVjTZ48GR9++CFycnIQERGBV155BbduPXpIZP369XjppZfg6OiIN998E+vWras1xrlz5yI5ORm//PILioqKMGTIEJV9zp8/j7S0NOzcuRM7d+5ERkYG5s2bp9z+ySefIDExEStXrsTJkyfxwQcf4M0330RGRgYAQKFQwMvLC6mpqTh16hRmzpyJ6dOnIzU1tda44+Pj4ejoqFy8vb0f/2abmIf/AJFIYLQ35yLdiJlxFn7+JUiYGmzoUEhP4tb3BABs+9e/sX9lIgY+fwo//NoKCoXEsIGZGqGDxQgZrC5sZWWFpKQkjBkzBqtWrUKnTp3Qo0cPDBkyBCEhIbX2SUlJgYWFBb766itIJFX/wRMTE+Hk5IT09HT06dMHc+bMwcKFCzFw4EAAgJ+fH06dOoXVq1dj+PDhymONGzcOgwYNAgCsXLkSe/bswbp16zBlypRaz61QKJCUlITly5cDAIYMGYJJkybh3LlzaN26tXK/iooKrFixAmFhYQCA5ORkBAUF4ddff0XXrl1VjiWTVQ3LvPXWW/jhhx8wd+5clJSUYNGiRfjxxx8RHh4OAGjZsiUOHjyI1atXo0ePHrC2tsZnn32mPKefnx8yMzORmpqK119/vUbs06ZNw6RJk5TrRUVFZpMEFd22hLwSaNJMtdrj2LQSd25wWMRUjZ12BmE9b2LKiKdw67qNocMhPblywwET/9UfNo0qYGdbgdt37TDznz/g6k3Z4ztTnZnqozAMPgfoypUr2LFjB/r27Yv09HR06tQJSUlJte6fnZ2Nc+fOQSaTKStIzs7OKC0txfnz53Hjxg3k5+dj1KhRyu329vb4/PPPcf78eZVjVScXQFUyFhoaitxc9RPn9u3bh5KSEkRGRgIAmjZtij59+mD9+vUq+1Ufq1pgYCCcnJxUju3r66tMfgDAw8MDBQUFAIBTp06htLQUvXv3VnkNGzZsUHkNq1atQmhoKJo1awZ7e3usXbsWly9frjV2qVQKBwcHlcVcVFZY4OxxO3TqrjoJtlP3YpzKamygqEh/BN6dfgYRz9/AtFEdcf1PW0MHRA2gtNwat+/awd6uDF3b/olfjvkYOiR6Ahj8T+DqeTK9e/fGzJkzMXr0aMyaNQsjRoyosa9CoUDnzp2xadOmGtuaNWuG0tJSAFXDYNUVmGqWlpY1+jysuqpUm/Xr1+P27duws/vf/UQUCgVycnIwZ84clePXdpy/t1lbW9fYplAolMcEgO+++w7NmzdX2U8qlQIAUlNT8cEHH2DhwoUIDw+HTCbDggULcOTIkce+RnP09ZqmmLwsH2eO2yI3qzH6vXkLrs0r8N0GF0OHRjoWM+MMevYrwOz32+FBiSWauFQNr5fcs0J52eO/A+jJ0qXtH5BA4PJ1JzRvdhfvDv4Vl685Ynemv6FDMy0meh8ggydADwsODlZ7359OnTphy5YtcHV1rbWK4ejoiObNm+PChQsYNmzYI89z+PBhdO/eHQBQWVmJ7OxsjBs3rtZ9b926hW+++QYpKSlo27atsl2hUODZZ5/F7t270b9/f+WxsrKylMNdp0+fRmFhIQIDAx/72oGq1y+VSnH58mX06NGj1n1+/vlnREREICYmRtn2cIWL/idjRxPImsgx7IPrcHatxKXTNvjkTT8U/NnI0KGRjvUfcgUAMD/xmEr7ok8C8f03HgaIiPSpsW05xrx6FM2alKC4RIoDv/nhq7RQyOVGf4u7J4sAoM2l7MaZ/xguAbp16xYGDx6MkSNHIiQkBDKZDFlZWZg/fz6ioqJq7TNs2DAsWLAAUVFRmD17Nry8vHD58mV8/fXXmDx5Mry8vBAbG4sJEybAwcEBkZGRKCsrQ1ZWFu7cuaMyD+aLL75AmzZtEBQUhMWLF+POnTsYOXJkrefduHEjXFxcMHjwYFhYqP5g9e/fH+vWrVMmQNbW1hg/fjyWLVsGa2trjBs3Dt26dVMmRI8jk8nw0Ucf4YMPPoBCocAzzzyDoqIiZGZmwt7eHsOHD0fr1q2xYcMG7N27F35+fti4cSOOHj0KPz+/Op3DHO1MboqdyU0NHQbpWb/2vQwdAjWg9KyWSM9qaegwTJ6pzgEyWAJkb2+PsLAwLF68GOfPn0dFRQW8vb0xZswYTJ8+vdY+dnZ2OHDgAKZOnYqBAweiuLgYzZs3x/PPP6+sCI0ePRp2dnZYsGABpkyZgsaNG6N9+/aYOHGiyrHmzZuHhIQE5OTkoFWrVvjmm2/QtGntvyDXr1+PV199tUbyA1TNY4qOjsb169eVMU6dOhVDhw7FH3/8gWeeeabGPKHHmTNnDlxdXREfH48LFy7AyckJnTp1Ur4vY8eOxbFjxxAdHQ2JRII33ngDMTEx2L17t0bnISIiMlcSIYw0NdOTvLw8+Pn5IScnBx07dtTpsZOSkjBx4kQUFhbq9Li6VlRUBEdHR/REFKwk1o/vQE80SxdnQ4dADeh+t1aGDoEaQGVFKQ7tnYW7d+/q7cKW6t8Vz3X8GFaW0nofp1Jehh+PzdNrrPVhdHOAiIiIyIiY6CRozhQjIiIis2N2CZCvry+EEDof/gKAESNGGP3wFxERkUYUOliMkNklQERERFR31VeBabNoIjY2FhKJRGVxd3fX+eviHCAiIiIyKm3btsX333+vXK/LzYw1xQSIiIiI1NPRJOiioiKVZqlUqnzCwcOsrKz0UvX5Ow6BERERkXrVCZA2CwBvb284Ojoql/j4eLWnPHv2LDw9PeHn54chQ4bgwoULOn9ZrAARERGR3uXn56vcB0hd9ScsLAwbNmyAv78/rl+/js8//xwRERE4efIkXFx09wxHJkBERESkno6GwBwcHOp0I8TIyEjlv9u3b4/w8HC0atUKycnJKo+00hYTICIiIlJPAUCiZX8tVD/S6uzZs9od6CGcA0RERERqNfRl8A8rKytDbm4uPDw8dPSKqjABIiIiIqPx0UcfISMjAxcvXsSRI0fw2muvoaioCMOHD9fpeTgERkREROo18LPA/vjjD7zxxhu4efMmmjVrhm7duuHw4cPw8fGpfwy1YAJERERE6ikEINEiAVJo1jclJaX+59IAh8CIiIjI7LACREREROo18BBYQ2ECRERERI+gZQIE40yAOARGREREZocVICIiIlKPQ2BERERkdhQCWg1jaXgVWEPhEBgRERGZHVaAiIiISD2hqFq06W+EmAARERGRepwDRERERGaHc4CIiIiITAMrQERERKQeh8CIiIjI7AhomQDpLBKd4hAYERERmR1WgIiIiEg9DoERERGR2VEoAGhxLx+Fcd4HiENgREREZHZYASIiIiL1OARGREREZsdEEyAOgREREZHZYQWIiIiI1DPRR2EwASIiIiK1hFBAaPFEd2366hMTICIiIlJPCO2qOJwDRERERGQcWAEiIiIi9YSWc4CMtALEBIiIiIjUUygAiRbzeIx0DhCHwIiIiMjssAJERERE6nEIjIiIiMyNUCggtBgCM9bL4DkERkRERGaHFSAiIiJSj0NgREREZHYUApCYXgLEITAiIiIyO6wAERERkXpCANDmPkDGWQFiAkRERERqCYWA0GIITDABIiIioieOUEC7ChAvgyciIiKqky+//BJ+fn6wsbFB586d8fPPP+v0+EyAiIiISC2hEFovmtqyZQsmTpyIGTNmICcnB88++ywiIyNx+fJlnb0uJkBERESknlBov2ho0aJFGDVqFEaPHo2goCAsWbIE3t7eWLlypc5eFucAmaHqCWmVqNDq3lb0ZBCKckOHQA2osqLU0CFQA6isrPqcG2KCsba/KypRAQAoKipSaZdKpZBKpTX2Ly8vR3Z2Nj7++GOV9j59+iAzM7P+gTyECZAZKi4uBgAcxC4DR0IN4rahA6AGtdfQAVBDKi4uhqOjo16O3ahRI7i7u+PgNe1/V9jb28Pb21ulbdasWYiNja2x782bNyGXy+Hm5qbS7ubmhmvXrmkdSzUmQGbI09MT+fn5kMlkkEgkhg6nwRQVFcHb2xv5+flwcHAwdDikR/yszYe5ftZCCBQXF8PT01Nv57CxscHFixdRXq59FVkIUeP3TW3Vn797eP/ajqENJkBmyMLCAl5eXoYOw2AcHBzM6ovSnPGzNh/m+Fnrq/LzdzY2NrCxsdH7ef6uadOmsLS0rFHtKSgoqFEV0gYnQRMREZHRaNSoETp37oz9+/ertO/fvx8RERE6Ow8rQERERGRUJk2ahLfeeguhoaEIDw/HmjVrcPnyZYwdO1Zn52ACRGZDKpVi1qxZjx13picfP2vzwc/aNEVHR+PWrVuYPXs2rl69inbt2mHXrl3w8fHR2Tkkwlgf0kFERESkJ5wDRERERGaHCRARERGZHSZAREREZHaYABHVU15eHiQSCY4dO2boUIgMRiKRIC0tzdBhEGmMCRAZxIgRIzBgwABDh6EVb29v5dUJpHsjRoyARCLBvHnzVNrT0tLM6g7mhlRQUIB33nkHLVq0gFQqhbu7O/r27YtDhw4ZOjS1Nm/eDEtLS51eLk2miQkQUT1ZWlrC3d0dVla8m4S+2NjYICEhAXfu3DF0KGZp0KBB+P3335GcnIwzZ85gx44d6NmzJ27fNt4HzK1fvx5TpkxBSkoK7t+/b+hwyIgxASKD69mzJyZMmIApU6bA2dkZ7u7uNR6QFxsbq/wr1NPTExMmTFBuu3PnDt5++200adIEdnZ2iIyMxNmzZ5Xbk5KS4OTkhJ07dyIgIAB2dnZ47bXXUFJSguTkZPj6+qJJkyYYP3485HK5sp+vry/i4uIwcuRIyGQytGjRAmvWrFFuf3gITC6XY9SoUfDz84OtrS0CAgKwdOlS/bxpZuKFF16Au7s74uPj1e6zbds2tG3bFlKpFL6+vli4cKHK9sd9jgDw559/Ijo6Gk2aNIGLiwuioqKQl5enj5f0xCgsLMTBgweRkJCAXr16wcfHB127dsW0adPw0ksvqe1Xl/cyMTERQUFBsLGxQWBgIL788kvltuqfq5SUFERERMDGxgZt27ZFenr6Y2POy8tDZmYmPv74YwQGBmLr1q0q26u/C9LS0uDv7w8bGxv07t0b+fn5yn1iY2PRsWNHbNy4Eb6+vnB0dMSQIUOUD5EGqp5JNX/+fLRs2RK2trbo0KGDyrn4XfCEEEQGMHz4cBEVFSWEEKJHjx7CwcFBxMbGijNnzojk5GQhkUjEvn37hBBC/L//9/+Eg4OD2LVrl7h06ZI4cuSIWLNmjfJYr7zyiggKChIHDhwQx44dE3379hWtW7cW5eXlQgghEhMThbW1tejdu7f47bffREZGhnBxcRF9+vQRr7/+ujh58qT49ttvRaNGjURKSoryuD4+PsLZ2Vl88cUX4uzZsyI+Pl5YWFiI3NxcIYQQFy9eFABETk6OEEKI8vJyMXPmTPHrr7+KCxcuiP/7v/8TdnZ2YsuWLQ3wjpqe6v8jX3/9tbCxsRH5+flCCCG2b98uqr+6srKyhIWFhZg9e7Y4ffq0SExMFLa2tiIxMVF5nMd9jiUlJaJNmzZi5MiR4vjx4+LUqVNi6NChIiAgQJSVlTX46zYWFRUVwt7eXkycOFGUlpaq3Q+A2L59uxCibu/lmjVrhIeHh9i2bZu4cOGC2LZtm3B2dhZJSUlCiP/9XHl5eYmtW7eKU6dOidGjRwuZTCZu3rz5yJg//fRT8dprrwkhhFi+fLno3r27yvbq74LQ0FCRmZkpsrKyRNeuXUVERIRyn1mzZgl7e3sxcOBAceLECXHgwAHh7u4upk+frtxn+vTpIjAwUOzZs0ecP39eJCYmCqlUKtLT04UQ/C54UjABIoN4OAF65plnVLZ36dJFTJ06VQghxMKFC4W/v78yofm7M2fOCADil19+UbbdvHlT2NraitTUVCFE1ZceAHHu3DnlPu+8846ws7MTxcXFyra+ffuKd955R7nu4+Mj3nzzTeW6QqEQrq6uYuXKlUKImglQbWJiYsSgQYMe93ZQLf7+f6Rbt25i5MiRQgjVBGjo0KGid+/eKv0mT54sgoODleuP+xzXrVsnAgIChEKhUO5TVlYmbG1txd69e/Xy2p4UW7duFU2aNBE2NjYiIiJCTJs2Tfz+++8q+/w9AarLe+nt7S02b96scow5c+aI8PBwIcT/fq7mzZun3F5RUSG8vLxEQkKC2ljlcrnw9vYWaWlpQgghbty4IaytrcXZs2eV+1R/Fxw+fFjZlpubKwCII0eOCCGqEiA7OztRVFSk3Gfy5MkiLCxMCCHEvXv3hI2NjcjMzFQ5/6hRo8Qbb7yhNj5+FxgfDoGRUQgJCVFZ9/DwQEFBAQBg8ODBePDgAVq2bIkxY8Zg+/btqKysBADk5ubCysoKYWFhyr4uLi4ICAhAbm6uss3Ozg6tWrVSrru5ucHX1xf29vYqbdXnrC0uiUQCd3f3Gvv83apVqxAaGopmzZrB3t4ea9euxeXLlzV5K6gWCQkJSE5OxqlTp1Tac3Nz8fTTT6u0Pf300zh79qzKcOajPsfs7GycO3cOMpkM9vb2sLe3h7OzM0pLS3H+/Hk9virjN2jQIFy5cgU7duxA3759kZ6ejk6dOiEpKanW/R/3Xt64cQP5+fkYNWqUcru9vT0+//zzGu91eHi48t9WVlYIDQ1V+Zl+2L59+1BSUoLIyEgAVU8U79OnD9avX6+yX/WxqgUGBsLJyUnl2L6+vpDJZMr1v38fnTp1CqWlpejdu7fKa9iwYYPKa+B3gfHj7E0yCtbW1irrEokECoUCQNXVVqdPn8b+/fvx/fffIyYmBgsWLEBGRgaEmie5CCFUrhSq7fiPOmdd4npYamoqPvjgAyxcuBDh4eGQyWRYsGABjhw58ohXTnXRvXt39O3bF9OnT8eIESOU7Q9/ztVtD3vU56hQKNC5c2ds2rSpRr9mzZrpIPonW/U8md69e2PmzJkYPXo0Zs2apfI5VHvce1laWgoAWLt2rcofLUDVRQWP86ir/9avX4/bt2/Dzs5OJZ6cnBzMmTNH5fi1Hedx3xd///8CAN999x2aN2+usl/188j4XfBkYAJETwRbW1u88soreOWVV/Dee+8hMDAQJ06cQHBwMCorK3HkyBFEREQAAG7duoUzZ84gKCioQWP8+eefERERgZiYGGWbuVcQdGnevHno2LEj/P39lW3BwcE4ePCgyn6ZmZnw9/ev0y9UAOjUqRO2bNkCV1dXODg46DRmUxQcHKz2vj+Pey8dHR3RvHlzXLhwAcOGDXvkeQ4fPozu3bsDACorK5GdnY1x48bVuu+tW7fwzTffICUlBW3btlW2KxQKPPvss9i9ezf69++vPFZWVha6du0KADh9+jQKCwsRGBj42NcOVL1+qVSKy5cvo0ePHrXuw++CJwMTIDJ6SUlJkMvlCAsLg52dHTZu3AhbW1v4+PgorzIZM2YMVq9eDZlMho8//hjNmzdHVFRUg8bZunVrbNiwAXv37oWfnx82btyIo0ePws/Pr0HjMFXt27fHsGHDsHz5cmXbhx9+iC5dumDOnDmIjo7GoUOHsGLFCpWrih5n2LBhWLBgAaKiojB79mx4eXnh8uXL+PrrrzF58mR4eXnp4+UYvVu3bmHw4MEYOXIkQkJCIJPJkJWVhfnz56v92arLexkbG4sJEybAwcEBkZGRKCsrQ1ZWFu7cuYNJkyYpj/XFF1+gTZs2CAoKwuLFi3Hnzh2MHDmy1vNu3LgRLi4uGDx4MCwsVGd29O/fH+vWrVMmQNbW1hg/fjyWLVsGa2trjBs3Dt26dVMmRI8jk8nw0Ucf4YMPPoBCocAzzzyDoqIiZGZmwt7eHsOHD+d3wROCc4DI6Dk5OWHt2rV4+umnERISgh9++AHffvstXFxcAFRdUtu5c2f0798f4eHhEEJg165dNcrY+jZ27FgMHDgQ0dHRCAsLw61bt1T+AiTtzZkzR2WIq1OnTkhNTUVKSgratWuHmTNnYvbs2bUOz6hjZ2eHAwcOoEWLFhg4cCCCgoIwcuRIPHjwwKwrQvb29ggLC8PixYvRvXt3tGvXDp9++inGjBmDFStW1NqnLu/l6NGj8dVXXyEpKQnt27dHjx49kJSUVCM5mDdvHhISEtChQwf8/PPP+Oabb9C0adNaz7t+/Xq8+uqrNZIfoGoe086dO3H9+nVljFOnTsXQoUMRHh4OW1tbpKSkaPTezJkzBzNnzkR8fDyCgoLQt29ffPvtt8rXwO+CJ4NEqJtEQURE1MDy8vLg5+eHnJwcdOzYUafHTkpKwsSJE1FYWKjT49KTiRUgIiIiMjtMgIiIiMjscAiMiIiIzA4rQERERGR2mAARERGR2WECRERERGaHCRARERGZHSZAREREZHaYABGRQcTGxqrc6G7EiBEYMGBAg8eRl5cHiUSCY8eOqd3H19cXS5YsqfMxk5KS4OTkpHVsEolE7XO3iEg7TICISGnEiBGQSCSQSCSwtrZGy5Yt8dFHH6GkpETv5166dCmSkpLqtG9dkhYiokfhw1CJSMWLL76IxMREVFRU4Oeff8bo0aNRUlKClStX1ti3oqJCZ89cc3R01MlxiIjqghUgIlIhlUrh7u4Ob29vDB06FMOGDVMOw1QPW61fvx4tW7aEVCqFEAJ3797FP//5T7i6usLBwQHPPfccfv/9d5Xjzps3D25ubpDJZBg1ahRKS0tVtj88BKZQKJCQkIDWrVtDKpWiRYsWmDt3LgAoHzr51FNPQSKRoGfPnsp+iYmJCAoKgo2NDQIDA2s8Gf7XX3/FU089BRsbG4SGhiInJ0fj92jRokVo3749GjduDG9vb8TExODevXs19ktLS4O/vz9sbGzQu3dv5Ofnq2z/9ttv0blzZ9jY2KBly5b47LPPUFlZqXE8RKQ5JkBE9Ei2traoqKhQrp87dw6pqanYtm2bcgjqpZdewrVr17Br1y5kZ2ejU6dOeP7553H79m0AQGpqKmbNmoW5c+ciKysLHh4eNRKTh02bNg0JCQn49NNPcerUKWzevBlubm4AqpIYAPj+++9x9epVfP311wCAtWvXYsaMGZg7dy5yc3MRFxeHTz/9FMnJyQCAkpIS9O/fHwEBAcjOzkZsbCw++ugjjd8TCwsLLFu2DP/5z3+QnJyMH3/8EVOmTFHZ5/79+5g7dy6Sk5Pxyy+/oKioCEOGDFFu37t3L958801MmDABp06dwurVq5GUlKRM8ohIzwQR0V+GDx8uoqKilOtHjhwRLi4u4vXXXxdCCDFr1ixhbW0tCgoKlPv88MMPwsHBQZSWlqocq1WrVmL16tVCCCHCw8PF2LFjVbaHhYWJDh061HruoqIiIZVKxdq1a2uN8+LFiwKAyMnJUWn39vYWmzdvVmmbM2eOCA8PF0IIsXr1auHs7CxKSkqU21euXFnrsf7Ox8dHLF68WO321NRU4eLiolxPTEwUAMThw4eVbbm5uQKAOHLkiBBCiGeffVbExcWpHGfjxo3Cw8NDuQ5AbN++Xe15iaj+OAeIiFTs3LkT9vb2qKysREVFBaKiorB8+XLldh8fHzRr1ky5np2djXv37sHFxUXlOA8ePMD58+cBALm5uRg7dqzK9vDwcPz000+1xpCbm4uysjI8//zzdY77xo0byM/Px6hRozBmzBhle2VlpXJ+UW5uLjp06AA7OzuVODT1008/IS4uDqdOnUJRUREqKytRWlqKkpISNG7cGABgZWWF0NBQZZ/AwEA4OTkhNzcXXbt2RXZ2No4ePapS8ZHL5SgtLcX9+/dVYiQi3WMCREQqevXqhZUrV8La2hqenp41JjlX/4KvplAo4OHhgfT09BrHqu+l4La2thr3USgUAKqGwcLCwlS2WVpaAgCEDp79fOnSJfTr1w9jx47FnDlz4OzsjIMHD2LUqFEqQ4VA1WXsD6tuUygU+OyzzzBw4MAa+9jY2GgdJxE9GhMgIlLRuHFjtG7dus77d+rUCdeuXYOVlRV8fX1r3ScoKAiHDx/G22+/rWw7fPiw2mO2adMGtra2+OGHHzB69Oga2xs1agSgqmJSzc3NDc2bN8eFCxcwbNiwWo8bHByMjRs34sGDB8ok61Fx1CYrKwuVlZVYuHAhLCyqplGmpqbW2K+yshJZWVno2rUrAOD06dMoLCxEYGAggKr37fTp0xq910SkO0yAiEgrL7zwAsLDwzFgwAAkJCQgICAAV65cwa5duzBgwACEhobi/fffx/DhwxEaGopnnnkGmzZtwsmTJ9GyZctaj2ljY4OpU6diypQpaNSoEZ5++mncuHEDJ0+exKhRo+Dq6gpbW1vs2bMHXl5esLGxgaOjI2JjYzFhwgQ4ODggMjISZWVlyMrKwp07dzBp0iQMHToUM2bMwKhRo/DJJ58gLy8P//rXvzR6va1atUJlZSWWL1+Ol19+Gb/88gtWrVpVYz9ra2uMHz8ey5Ytg7W1NcaNG4du3bopE6KZM2eif//+8Pb2xuDBg2FhYYHjx4/jxIkT+PzzzzX/IIhII7wKjIi0IpFIsGvXLnTv3h0jR46Ev78/hgwZgry8POVVW9HR0Zg5cyamTp2Kzp0749KlS3j33XcfedxPP/0UH374IWbOnImgoCBER0ejoKAAQNX8mmXLlmH16tXw9PREVFQUAGD06NH46quvkJSUhPbt26NHjx5ISkpSXjZvb2+Pb7/9FqdOncJTTz2FGTNmICEhQaPX27FjRyxatAgJCQlo164dNm3ahPj4+Br72dnZYerUqRg6dCjCw8Nha2uLlJQU5fa+ffti586d2L9/P7p06YJu3bph0aJF8PHx0SgeIqofidDFoDgRERHRE4QVICIiIjI7TICIiIjI7DABIiIiIrPDBIiIiIjMDhMgIiIiMjtMgIiIiMjsMAEiIiIis8MEiIiIiMwOEyAiIiIyO0yAiIiIyOwwASIiIiKz8/8BuOSU4lFz8uIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Evaluation\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print('accuracy: ' + str(round(accuracy,4)))\n",
    "\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "print('precision: ' + str(round(precision,4)))\n",
    "\n",
    "recall =  recall_score(y_test, y_pred, average='macro')\n",
    "print('recall: ' + str(round(recall,4)))\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "conf_matrix\n",
    "\n",
    "ConfusionMatrixDisplay(confusion_matrix=conf_matrix, display_labels=model.classes_).plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}