Covid_19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f85670a8",
   "metadata": {},
   "source": [
    "# Team ID: 19\n",
    "\n",
    "# Mohamed Magdy Mostafa &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;18P5160\n",
    "# Khaled Medhat Mahmoud &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;18P3557"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166b69e5",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2cc9d0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import preprocessing\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb4db71",
   "metadata": {},
   "source": [
    "### Reading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9122dcf",
   "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>Unnamed: 0</th>\n",
       "      <th>location</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>vis_wuhan</th>\n",
       "      <th>from_wuhan</th>\n",
       "      <th>symptom1</th>\n",
       "      <th>symptom2</th>\n",
       "      <th>symptom3</th>\n",
       "      <th>symptom4</th>\n",
       "      <th>symptom5</th>\n",
       "      <th>symptom6</th>\n",
       "      <th>diff_sym_hos</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>137</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>116</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  location  country  gender   age  vis_wuhan  from_wuhan  \\\n",
       "0           0       104        8       1  66.0          1           0   \n",
       "1           1       101        8       0  56.0          0           1   \n",
       "2           2       137        8       1  46.0          0           1   \n",
       "3           3       116        8       0  60.0          1           0   \n",
       "4           4       116        8       1  58.0          0           0   \n",
       "\n",
       "   symptom1  symptom2  symptom3  symptom4  symptom5  symptom6  diff_sym_hos  \\\n",
       "0        14        31        19        12         3         1             8   \n",
       "1        14        31        19        12         3         1             0   \n",
       "2        14        31        19        12         3         1            13   \n",
       "3        14        31        19        12         3         1             0   \n",
       "4        14        31        19        12         3         1             0   \n",
       "\n",
       "   result  \n",
       "0       1  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw = pd.read_csv(\"data.csv\")\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce74c374",
   "metadata": {},
   "source": [
    "# Investigating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ed9c7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.drop(\"Unnamed: 0\",axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc47dee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(863, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2dfa0be",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 863 entries, 0 to 862\n",
      "Data columns (total 14 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   location      863 non-null    int64  \n",
      " 1   country       863 non-null    int64  \n",
      " 2   gender        863 non-null    int64  \n",
      " 3   age           863 non-null    float64\n",
      " 4   vis_wuhan     863 non-null    int64  \n",
      " 5   from_wuhan    863 non-null    int64  \n",
      " 6   symptom1      863 non-null    int64  \n",
      " 7   symptom2      863 non-null    int64  \n",
      " 8   symptom3      863 non-null    int64  \n",
      " 9   symptom4      863 non-null    int64  \n",
      " 10  symptom5      863 non-null    int64  \n",
      " 11  symptom6      863 non-null    int64  \n",
      " 12  diff_sym_hos  863 non-null    int64  \n",
      " 13  result        863 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 94.5 KB\n"
     ]
    }
   ],
   "source": [
    "raw.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf6c0b8",
   "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>location</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>vis_wuhan</th>\n",
       "      <th>from_wuhan</th>\n",
       "      <th>symptom1</th>\n",
       "      <th>symptom2</th>\n",
       "      <th>symptom3</th>\n",
       "      <th>symptom4</th>\n",
       "      <th>symptom5</th>\n",
       "      <th>symptom6</th>\n",
       "      <th>diff_sym_hos</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.00000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "      <td>863.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>76.645423</td>\n",
       "      <td>16.995365</td>\n",
       "      <td>0.849363</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>0.181924</td>\n",
       "      <td>0.107764</td>\n",
       "      <td>12.13905</td>\n",
       "      <td>28.002317</td>\n",
       "      <td>18.298957</td>\n",
       "      <td>11.840093</td>\n",
       "      <td>2.993048</td>\n",
       "      <td>0.998841</td>\n",
       "      <td>0.995365</td>\n",
       "      <td>0.125145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.200264</td>\n",
       "      <td>7.809951</td>\n",
       "      <td>0.726062</td>\n",
       "      <td>15.079203</td>\n",
       "      <td>0.386005</td>\n",
       "      <td>0.310261</td>\n",
       "      <td>3.99787</td>\n",
       "      <td>7.473231</td>\n",
       "      <td>2.864064</td>\n",
       "      <td>1.183771</td>\n",
       "      <td>0.127251</td>\n",
       "      <td>0.034040</td>\n",
       "      <td>2.358767</td>\n",
       "      <td>0.331075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>87.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>110.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.00000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>138.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location     country      gender         age   vis_wuhan  from_wuhan  \\\n",
       "count  863.000000  863.000000  863.000000  863.000000  863.000000  863.000000   \n",
       "mean    76.645423   16.995365    0.849363   49.400000    0.181924    0.107764   \n",
       "std     39.200264    7.809951    0.726062   15.079203    0.386005    0.310261   \n",
       "min      0.000000    0.000000    0.000000    2.000000    0.000000    0.000000   \n",
       "25%     45.000000   11.000000    0.000000   40.000000    0.000000    0.000000   \n",
       "50%     87.000000   18.000000    1.000000   49.400000    0.000000    0.000000   \n",
       "75%    110.000000   24.000000    1.000000   57.000000    0.000000    0.000000   \n",
       "max    138.000000   33.000000    2.000000   96.000000    1.000000    1.000000   \n",
       "\n",
       "        symptom1    symptom2    symptom3    symptom4    symptom5    symptom6  \\\n",
       "count  863.00000  863.000000  863.000000  863.000000  863.000000  863.000000   \n",
       "mean    12.13905   28.002317   18.298957   11.840093    2.993048    0.998841   \n",
       "std      3.99787    7.473231    2.864064    1.183771    0.127251    0.034040   \n",
       "min      0.00000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%     14.00000   31.000000   19.000000   12.000000    3.000000    1.000000   \n",
       "50%     14.00000   31.000000   19.000000   12.000000    3.000000    1.000000   \n",
       "75%     14.00000   31.000000   19.000000   12.000000    3.000000    1.000000   \n",
       "max     24.00000   31.000000   19.000000   12.000000    3.000000    1.000000   \n",
       "\n",
       "       diff_sym_hos      result  \n",
       "count    863.000000  863.000000  \n",
       "mean       0.995365    0.125145  \n",
       "std        2.358767    0.331075  \n",
       "min       -5.000000    0.000000  \n",
       "25%        0.000000    0.000000  \n",
       "50%        0.000000    0.000000  \n",
       "75%        1.000000    0.000000  \n",
       "max       15.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da311d56",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location \t 139\n",
      "country \t 34\n",
      "symptom1 \t 25\n",
      "symptom2 \t 32\n",
      "symptom3 \t 20\n",
      "symptom4 \t 13\n",
      "symptom5 \t 4\n",
      "symptom6 \t 2\n",
      "diff_sym_hos \t 18\n"
     ]
    }
   ],
   "source": [
    "featureList = ['location','country','symptom1','symptom2','symptom3','symptom4','symptom5','symptom6','diff_sym_hos']\n",
    "for i in featureList:\n",
    "    print(f\"{i} \\t {raw[i].nunique()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f0415c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender = raw['gender'].value_counts()\n",
    "gender.plot.pie(title ='Total Gender percentage', ylabel='', autopct='%1.1f%%');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93e16388",
   "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>location</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>vis_wuhan</th>\n",
       "      <th>from_wuhan</th>\n",
       "      <th>symptom1</th>\n",
       "      <th>symptom2</th>\n",
       "      <th>symptom3</th>\n",
       "      <th>symptom4</th>\n",
       "      <th>symptom5</th>\n",
       "      <th>symptom6</th>\n",
       "      <th>diff_sym_hos</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>128</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     location  country  gender   age  vis_wuhan  from_wuhan  symptom1  \\\n",
       "223        56       18       1  35.0          0           0         6   \n",
       "240       128       18       1  65.0          0           0         6   \n",
       "\n",
       "     symptom2  symptom3  symptom4  symptom5  symptom6  diff_sym_hos  result  \n",
       "223        31        19        12         3         1            -1       0  \n",
       "240        31        19        12         3         1            -5       0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw[raw.diff_sym_hos < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9be8f04",
   "metadata": {},
   "source": [
    "### Dropping the two rows where diff_sym_hos is negative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d05e1c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw.drop(240,axis = 0, inplace=True)\n",
    "raw.drop(223,axis = 0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5f80e4",
   "metadata": {},
   "source": [
    "### By observing this sample, we noticed that each country has a specific location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8358e373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location  country\n",
       "0         0           1\n",
       "1         18          7\n",
       "2         14          1\n",
       "3         1           1\n",
       "4         11          1\n",
       "5         25          8\n",
       "6         11          4\n",
       "7         15          1\n",
       "8         32          1\n",
       "9         12         11\n",
       "10        4          17\n",
       "11        25          3\n",
       "12        12         15\n",
       "13        8          18\n",
       "14        5           1\n",
       "15        27          1\n",
       "16        11          1\n",
       "17        11          1\n",
       "18        11          1\n",
       "19        32         11\n",
       "20        25          1\n",
       "21        25          2\n",
       "22        18         10\n",
       "23        8           1\n",
       "24        9           1\n",
       "25        11          4\n",
       "26        13          1\n",
       "27        11          6\n",
       "28        12          2\n",
       "29        8           1\n",
       "30        18          2\n",
       "31        8           3\n",
       "32        18          2\n",
       "33        8           5\n",
       "34        15          2\n",
       "35        8           3\n",
       "36        12          1\n",
       "37        18          1\n",
       "38        33          2\n",
       "39        8           1\n",
       "40        8           2\n",
       "41        8           2\n",
       "42        8           4\n",
       "43        12          1\n",
       "44        18         43\n",
       "45        13         78\n",
       "46        8           5\n",
       "47        8           1\n",
       "48        3           2\n",
       "49        18          4\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_showed = raw.groupby([\"location\",\"country\"]).gender.count()\n",
    "gender_showed.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4cda6ed1",
   "metadata": {
    "scrolled": true
   },
   "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>location</th>\n",
       "      <th>country</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>vis_wuhan</th>\n",
       "      <th>from_wuhan</th>\n",
       "      <th>symptom1</th>\n",
       "      <th>symptom2</th>\n",
       "      <th>symptom3</th>\n",
       "      <th>symptom4</th>\n",
       "      <th>symptom5</th>\n",
       "      <th>symptom6</th>\n",
       "      <th>diff_sym_hos</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>116</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   location  country  gender   age  vis_wuhan  from_wuhan  symptom1  symptom2  \\\n",
       "0       104        8       1  66.0          1           0        14        31   \n",
       "1       101        8       0  56.0          0           1        14        31   \n",
       "2       137        8       1  46.0          0           1        14        31   \n",
       "3       116        8       0  60.0          1           0        14        31   \n",
       "4       116        8       1  58.0          0           0        14        31   \n",
       "\n",
       "   symptom3  symptom4  symptom5  symptom6  diff_sym_hos  result  \n",
       "0        19        12         3         1             8       1  \n",
       "1        19        12         3         1             0       0  \n",
       "2        19        12         3         1            13       0  \n",
       "3        19        12         3         1             0       0  \n",
       "4        19        12         3         1             0       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = raw.copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c73b3b7",
   "metadata": {},
   "source": [
    "### Dropping the gender feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c27bae3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"gender\",axis = 1, inplace=True) #Dropping gender seems like the optimal solution to the three genders problem\n",
    "                                         #so we don't affect the data results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10aa3d15",
   "metadata": {},
   "source": [
    "### One hot encoding and merging of the features \"location\" and \"country\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "042bd679",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_columns = ['location','country']\n",
    "for column in categorical_columns:\n",
    "    tempdf = pd.get_dummies(df[column], prefix=column)\n",
    "    df = pd.merge(\n",
    "        left=df,\n",
    "        right=tempdf,\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "    df = df.drop(columns=column)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f57e94",
   "metadata": {},
   "source": [
    "### Splitting the data into features and target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3ba559f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.loc[:, df.columns != 'result']\n",
    "y = df.result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1a02378",
   "metadata": {},
   "source": [
    "###  Normalizing data and selecting number of features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d7deefc4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "d = preprocessing.normalize(x)\n",
    "\n",
    "names = x.columns.values.tolist()\n",
    "scaled_df = pd.DataFrame(d, columns = names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "be0d12d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(861, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new = SelectKBest(chi2, k = 14).fit_transform(scaled_df, y)\n",
    "X_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9462d09",
   "metadata": {},
   "source": [
    "### Splitting data into 80% Train , 10% Validation and 10% Test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a5bbdc88",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_both, y_train, y_both = train_test_split(X_new, y, test_size = 0.2, random_state=3)\n",
    "X_test, X_valid, y_test, y_valid = train_test_split(X_both, y_both, test_size = 0.5, random_state=3)"
   ]
  },
  {
   "attachments": {
    "confusion.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "2419cc1e",
   "metadata": {},
   "source": [
    "# Confusion Matrix Explanation\n",
    "![confusion.png](attachment:confusion.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6676f68b",
   "metadata": {},
   "source": [
    "#### _TP (True Positive)_: when the actual is positive and we preditcted correctly\n",
    "#### _TN (True Negative)_: when the actual is negative and we predicted correctly\n",
    "#### _FN (False Negative)_: when the actual is negative and we predicted incorrectly\n",
    "#### _FP (False Positive)_: when the actual is positive and we predicted incorrectly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaedbc49",
   "metadata": {},
   "source": [
    "# KNeighbors Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e985362d",
   "metadata": {},
   "source": [
    "### Validation with a random N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b70ae432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[77  4]\n",
      " [ 3  3]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.95      0.96        81\n",
      "           1       0.43      0.50      0.46         6\n",
      "\n",
      "    accuracy                           0.92        87\n",
      "   macro avg       0.70      0.73      0.71        87\n",
      "weighted avg       0.93      0.92      0.92        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d3b82da",
   "metadata": {},
   "source": [
    "The True Positive is **77** out of **81** and the True Negative is **3** out of **6**\n",
    "\n",
    "Which results in an accuracy of **0.92**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3a329d5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4615384615384615"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c07148d",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.46** for the positive class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e7318ed",
   "metadata": {},
   "source": [
    "### Finding best N by Cross Val score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e93f92e7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best N is 1 with value 0.9244671781756182\n"
     ]
    }
   ],
   "source": [
    "neighbors = np.arange(1, 10)\n",
    "y_list = []\n",
    "# Loop over K values\n",
    "for i, k in enumerate(neighbors):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    y_pred = cross_val_score(knn, X_train, y_train, cv=10).mean()\n",
    "    y_list.append(y_pred)\n",
    "    if y_pred >= max(y_list):\n",
    "        max_N = k\n",
    "    \n",
    "print(f\"The best N is {max_N} with value {max(y_list)}\")    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6813699d",
   "metadata": {},
   "source": [
    "### Validation with the best N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e581bf6b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[76  5]\n",
      " [ 0  6]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.94      0.97        81\n",
      "           1       0.55      1.00      0.71         6\n",
      "\n",
      "    accuracy                           0.94        87\n",
      "   macro avg       0.77      0.97      0.84        87\n",
      "weighted avg       0.97      0.94      0.95        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1, algorithm = 'brute')\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f196144c",
   "metadata": {},
   "source": [
    "The True Positive is **76** out of **81** and the True Negative is **6** out of **6**\n",
    "\n",
    "Which results in an accuracy of **0.94**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5825fc5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7058823529411764"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b16aa10",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.71** for the positive class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "821b2fdf",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e88d875",
   "metadata": {},
   "source": [
    "### Validation before tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "639ccf04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr = LogisticRegression(solver='lbfgs')\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ea809cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[80  1]\n",
      " [ 6  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96        81\n",
      "           1       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.92        87\n",
      "   macro avg       0.47      0.49      0.48        87\n",
      "weighted avg       0.87      0.92      0.89        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8b6b089",
   "metadata": {},
   "source": [
    "The True Positive is **80** out of **81** and the True Negative is **0** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.92**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5b069501",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3554d7ad",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f63fb840",
   "metadata": {},
   "source": [
    "### Tuning the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8159e499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 7 folds for each of 36 candidates, totalling 252 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1000, penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR = LogisticRegression()\n",
    "LRparam_grid = {\n",
    "    'C': [0.01, 0.1, 1, 10, 100, 1000],\n",
    "    'penalty': ['none','l1', 'l2'],\n",
    "    'solver': ['lbfgs', 'liblinear']\n",
    "}\n",
    "LR_search = GridSearchCV(LR, param_grid = LRparam_grid, refit = True, verbose = 1, cv=7)\n",
    "\n",
    "# fitting the model for grid search \n",
    "LR_search.fit(X_train , y_train)\n",
    "\n",
    "paralist = []\n",
    "paralist = list(LR_search.best_params_.values())\n",
    "\n",
    "lr = LogisticRegression(C= paralist[0] , penalty= paralist[1], solver= paralist[2])\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db966abb",
   "metadata": {},
   "source": [
    "### Validation after tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3eca557",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[80  1]\n",
      " [ 1  5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99        81\n",
      "           1       0.83      0.83      0.83         6\n",
      "\n",
      "    accuracy                           0.98        87\n",
      "   macro avg       0.91      0.91      0.91        87\n",
      "weighted avg       0.98      0.98      0.98        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbd2eb1e",
   "metadata": {},
   "source": [
    "The True Positive is **80** out of **81** and the True Negative is **5** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.98**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cd2b78b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a1cd97",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.83** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "744242ee",
   "metadata": {},
   "source": [
    "# Naïve Bayes Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05f4482",
   "metadata": {},
   "source": [
    "### Validation before tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "01b3e9f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50fd03ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[81  0]\n",
      " [ 4  2]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.98        81\n",
      "           1       1.00      0.33      0.50         6\n",
      "\n",
      "    accuracy                           0.95        87\n",
      "   macro avg       0.98      0.67      0.74        87\n",
      "weighted avg       0.96      0.95      0.94        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1188dde",
   "metadata": {},
   "source": [
    "The True Positive is **81** out of **81** and the True Negative is **2** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.95**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f174ef49",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce57231",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.50** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5af162",
   "metadata": {},
   "source": [
    "### Tuning the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0b7d0f87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n"
     ]
    }
   ],
   "source": [
    "nb_classifier = GaussianNB()\n",
    "\n",
    "params_NB = {'var_smoothing': np.logspace(0,-9, num=100)}\n",
    "gs_NB = GridSearchCV(estimator=nb_classifier, param_grid = params_NB, verbose=1, scoring='accuracy') \n",
    "gs_NB.fit(X_train, y_train)\n",
    "\n",
    "var_value = gs_NB.best_params_.get('var_smoothing')\n",
    "gnb = GaussianNB(var_smoothing = var_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c73e46",
   "metadata": {},
   "source": [
    "### Validation after tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "63b15bda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[78  3]\n",
      " [ 1  5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.96      0.97        81\n",
      "           1       0.62      0.83      0.71         6\n",
      "\n",
      "    accuracy                           0.95        87\n",
      "   macro avg       0.81      0.90      0.84        87\n",
      "weighted avg       0.96      0.95      0.96        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)\n",
    "y_pred = gnb.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffca1fcc",
   "metadata": {},
   "source": [
    "The True Positive is **78** out of **81** and the True Negative is **5** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.95**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5734e3d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7142857142857143"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44ecc1b",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.71** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eca5493",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22838f0b",
   "metadata": {},
   "source": [
    "### Validation before tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1d2c0a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import export_graphviz\n",
    "from six import StringIO\n",
    "from IPython.display import Image\n",
    "import pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "32f00108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(random_state=3)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree = DecisionTreeClassifier(random_state=3)\n",
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "176439ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[76  5]\n",
      " [ 1  5]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.94      0.96        81\n",
      "           1       0.50      0.83      0.62         6\n",
      "\n",
      "    accuracy                           0.93        87\n",
      "   macro avg       0.74      0.89      0.79        87\n",
      "weighted avg       0.95      0.93      0.94        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = dtree.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8b36a4",
   "metadata": {},
   "source": [
    "The True Positive is **76** out of **81** and the True Negative is **5** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.93**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1a8da6a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.625"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32f6fed7",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.625** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d949900",
   "metadata": {},
   "source": [
    "### Tuning the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d1e64d59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 7 folds for each of 196 candidates, totalling 1372 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4, max_features=4,\n",
       "                       random_state=3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtreeparam_grid = {\n",
    "    'max_depth': np.arange(1,15),\n",
    "    'max_features': np.arange(1,15),\n",
    "    'random_state': [3]\n",
    "}\n",
    "dtree_search = GridSearchCV(dtree, param_grid = dtreeparam_grid, refit = True, verbose = 1, cv=7)\n",
    "dtree_search.fit(X_train , y_train)\n",
    "\n",
    "paralist = []\n",
    "paralist = list(dtree_search.best_params_.values())\n",
    "\n",
    "dtree = DecisionTreeClassifier(criterion = \"entropy\", max_depth= paralist[0], max_features =paralist[1], random_state=3)\n",
    "dtree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec9e1c98",
   "metadata": {},
   "source": [
    "### Validation after tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78fb1964",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[79  2]\n",
      " [ 0  6]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        81\n",
      "           1       0.75      1.00      0.86         6\n",
      "\n",
      "    accuracy                           0.98        87\n",
      "   macro avg       0.88      0.99      0.92        87\n",
      "weighted avg       0.98      0.98      0.98        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = dtree.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1254e3",
   "metadata": {},
   "source": [
    "The True Positive is **79** out of **81** and the True Negative is **6** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.98**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "36bc2454",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f70adc",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.86** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c992a020",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "export_graphviz(dtree, out_file = dot_data, rounded = True, filled = True, special_characters = True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "graph.write_png(\"dtree.png\")\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e3ea9e",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99929123",
   "metadata": {},
   "source": [
    "### Validation before tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "201efbf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8a152132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[81  0]\n",
      " [ 6  0]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      1.00      0.96        81\n",
      "           1       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.93        87\n",
      "   macro avg       0.47      0.50      0.48        87\n",
      "weighted avg       0.87      0.93      0.90        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = svc.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d93cf1",
   "metadata": {},
   "source": [
    "The True Positive is **81** out of **81** and the True Negative is **0** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.97**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0b150c34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b52071a1",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5d7e58",
   "metadata": {},
   "source": [
    "### Tuning the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7fbaae6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 348 candidates, totalling 1392 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1, degree=21, kernel='poly', random_state=3)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svcparam_grid = {\n",
    "    'kernel': ['poly','rbf','sigmoid'],\n",
    "    'degree': np.arange(1,30),\n",
    "    'random_state':[3],\n",
    "    'C': np.arange(0,2),\n",
    "    'gamma':['scale', 'auto']\n",
    "}\n",
    "svc_search = GridSearchCV(svc, param_grid = svcparam_grid, refit = True, verbose = 1, cv=4)\n",
    "svc_search.fit(X_train , y_train)\n",
    "svc_search.best_params_\n",
    "\n",
    "paralist = []\n",
    "paralist = list(svc_search.best_params_.values())\n",
    "\n",
    "svc = SVC(C =paralist[0], degree= paralist[1], gamma=paralist[2], kernel = paralist[3], random_state=3)\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3156970",
   "metadata": {},
   "source": [
    "### Validation after tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f7adb088",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[79  2]\n",
      " [ 0  6]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99        81\n",
      "           1       0.75      1.00      0.86         6\n",
      "\n",
      "    accuracy                           0.98        87\n",
      "   macro avg       0.88      0.99      0.92        87\n",
      "weighted avg       0.98      0.98      0.98        87\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = svc.predict(X_valid)\n",
    "print(confusion_matrix(y_valid, y_pred))\n",
    "print(classification_report(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c18830",
   "metadata": {},
   "source": [
    "The True Positive is **79** out of **81** and the True Negative is **6** out of **6**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.97**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8177ef18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571428571428571"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_valid, y_pred, average='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb9569d",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **86** for the positive class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9383b29e",
   "metadata": {},
   "source": [
    "# Testing Algorithms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232efa59",
   "metadata": {},
   "source": [
    "### Testing KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d550bff5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[69  5]\n",
      " [ 3  9]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.93      0.95        74\n",
      "           1       0.64      0.75      0.69        12\n",
      "\n",
      "    accuracy                           0.91        86\n",
      "   macro avg       0.80      0.84      0.82        86\n",
      "weighted avg       0.91      0.91      0.91        86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb7c681",
   "metadata": {},
   "source": [
    "The True Positive is **69** out of **74** and the True Negative is **9** out of **12**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.91**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "833e2f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6923076923076924"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a90f564a",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.69** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cb529f95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA2u0lEQVR4nO3deXhU5dn48e9NEghJJmyBBMEQRBI2BSGiKFrUakFUtNparfvP2rr2fdva2retVbR2s26tVqlbrRaXtlq0VKvW3VIWRURkExMIeyZAlklClvv3xzkzTNY5CZlMMnN/ritX5uz3mcB5znOf5zyPqCrGGGMSV59YB2CMMSa2rCAwxpgEZwWBMcYkOCsIjDEmwVlBYIwxCS451gF0VFZWlubl5cU6DGOM6VVWrFhRqqpDW1vW6wqCvLw8li9fHuswjDGmVxGR4raWWWrIGGMSnBUExhiT4KwgMMaYBGcFgTHGJDgrCIwxJsFFrSAQkUdFZJeIrG5juYjIfSKyUURWicjUaMVijDGmbdGsETwOzG5n+RxgrPtzFfD7KMZijDGmDVF7j0BV3xaRvHZWmQc8oU4/2EtEZKCIDFfV7dGKyRhjehNVpWRPNet2VLBuZwWTRw5k5tisLj9OLF8oGwFsCZsucee1KAhE5CqcWgO5ubndEpwxxnSnsqr9rN1RzrodFazfWcHaHRVs2FlJZW19aJ2rZ42Ju4JAWpnX6ig5qroAWABQWFhoI+kYY3qtwP561u+sZP0O52IfvOiXVtaG1hmYlkJBto8vTx1BQY6PcTk+xmb7yExNiUpMsSwISoBDw6ZHAttiFIsxxnSpuoZGikqrmlzs1+2oYMueAMGBIVNT+jB2mI9ZBUMpyPaFLvpDff0Qae1eOTpiWRAsAq4TkaeBY4B99nzAGNPbqCpb91aHLvbBO/1Nu6vY39AIQB+B0VnpTBqRyblTR1KQ41z0cwenkdSn+y74bYlaQSAiC4FZQJaIlAA/BVIAVPVBYDFwOrARCACXRysWY4zpCnuq9je7wy9nfbM8/vABqRTk+PhC2F3+mKEZpKYkxTDy9kWz1dAFEZYrcG20jm+MMZ1Vvb+BDbsOpHOCF/7dFQfy+AP6p1CQ4+Oco0aE7vDzs30M6B+dPH409bpuqI0xpqvUNzRS5K9i3Y5K1u0oD93tF5cdyOP3S+7D2OwMThw7lIKcDApyMinI9pGd2b15/GiygsAYE/dUle37aljXrKXOZ7sqm+Tx87LSGT88k7OPGhFK64wakt4j8vjRZAWBMSau7A00zeOvd1/Gqqg5kMfPyXTy+CeMzQpd8A8f1rPz+NFkBYExpleq3t/Axl2VrN1RfuCiv7OCneUH8vi+1GTG5fiYN+UQ94KfSX52BgPT+sYw8p7HCgJjTI/m5PEDLe7wi/xVoTx+3+Q+jB2WwfFjspyHtm57/JzM1LjJ40eTFQTGmB5BVdlRXnPgYu9e8DfsqmR/vZPHF4G8IekUZPs4c/IhjHNb64wanEZykvWq31meCgIR6QNMBg4BqoFPVHVnNAMzxsSvfYE61u102uE7v52f8rA8fnZmP/KzfVw6Y1Sopc7hwzLo3zcx8/jR1G5BICJjgB8AXwQ2ALuBVCBfRALAQ8AfVbUx2oEaY3qfmjonjx+8uw9e8HeU14TW8fVLpiDHucMvyPFRkO20xx+Ubnn87hKpRnA7zjgB33RfAAsRkWHAhcDFwB+jE54xpjdoaFSK/VUtLvhF/ioag3n8pD6MGZbBjDFDQhf8ghwfwwdYHj/W2i0I2ns7WFV3Afd0dUDGmJ5LVdlZXhtK6wRb6mzYWUltWB5/1OA0CnJ8nHHkcCetk5NB3pB0y+P3UJ1+WCwip6rqq10ZjDGm59hXXde0pY57t7+vui60zlBfP8bl+Lj42FGhljqHD8sgra+1Q+lNDuav9Qhgo8QY08sF8/jrd1Y0Se1s33cgj5/h5vFPP2I449w+dQpyfAy2PH5ciPSweFFbi4AhXR+OMSZaGhqVzWUBp6XOjkrW7Sx38/gBGtxEfkqSMGZoBseMHhy6w8/P9jFiYH/L48exSDWCE4CLgMpm8wWYHpWIjDEHRVXZVVEbemAbvMPfsKuCmroDefzcwWnkZzt3+fnZzkU/LyudFMvjJ5xIBcESIKCqbzVfICLrohOSMcar8pq60Ju24Rf+vYEDefysDCePf+H0UaEXsMZmWx7fHBCp1dCcdpad2PXhGGNaU1vfwGe7qtx0TmVoQJSte6tD66T3TSI/x8ecSTmhHH5Bto8hGf1iGLnpDeyWwJgepDGYx2/24Pbz0qoWefxpowZx4TG5oTz+yEGWxzedYwWBMTGgquyuDMvjB/vV2VlJdV1DaL1DB/enIDuT2RNzQg9v84ak0zfZ8vim61hBYEyUVdTUsX6n281CWN86e5rk8fuSn+3ja9MPDd3h52f7SO9n/0VN9Nm/MmO6yP76Rj7bXRk2sLnzE57HT+ubRH62jy9NzAm11MnP8ZFleXwTQ54LAhG5RVVvaWvamETR2KiU7Klm7Y7yFnn8ejePn9zHyeNPdfP4wYv+iIH96RPnwx6a3qcjNYIVEaaNiTu7K2rD7vDLWbezkg07KwjsP5DHHzmoP+NyfJw6IdtpqZPj47CsDMvjm17Dc0Ggqi+2N21Mb1ZZW3+giwX3Z/3OCvxV+0PrDE7vS0G2j68WHhq64Odn+8iwPL7p5SJ1MfFbQNtarqo3dHlExkTR/vpGNpVWNrnYr91RQcmeA3n8/ilOe/xTxg8LDYhSkONjqM/y+CY+RbqVWd4tURjTxRobla17q0PdJAdTO5t2H8jjJ/URDstKZ8qhAznfvcsfl5PJyEGWxzeJJdKbxU0GnBGRdFWtim5IxnRMaWUt63dUNLnob9hZQVVYHn/EwP4U5Pg4ZXx26A7/sKHp9Eu2YQ+N8Tpm8QycbqczgFwRmYwzatk10QzOmHBVbh4/vHnm+p0VlFYeyOMPSkuhIMfHedNGugOi+MjPzsCXmhLDyI3p2bw+5boH+BKwCEBVPxIR62vIREVdQyOfl1aFBkRZu6OCdTvL2VJ2II+fmtKH/GwfJxUMCz24LcjxMTSjn3WzYEwHdaTV0JZm/8Ea2lrXGC9Unfb4ze/wP9tdSV3DgTz+6Kx0jhw5kK9MOzTUkVru4DTL4xvTRbwWBFtE5DhARaQvcAPwafTCMvGmrGo/a3eUh7pMdvL4lVTW1ofWGTGwP/nZGcwqGEZBTgYF2ZkcNjSd1BTL4xsTTV4Lgm8B9wIjgK3AK8C10QrK9F6B/fWs31nZ4uFtaWVtaJ2BaSkUZPv48tQRoTv8/BwfmZbHNyYmPBUEqloKfL2jOxeR2TgFSBLwsKr+otnyAcCTOGMfJwN3qupjHT2O6X51DY0UBfP4Ow9c9DeXBVD3zZN+yU4ef1bB0FBLnYIcH8N8lsc3pifx2mroMJwL+rE4L5j9B/hfVd3UzjZJwP3AqUAJsExEFqnqmrDVrgXWqOqZIjIUWCciT6nq/lZ2aWJA1WmPH7rYu3f6m3ZXsb/BGfawj8DorHQmHpLJl48a6aR1cjLJHZxGkuXxjenxvKaG/oxzUT/Hnf4asBA4pp1tpgMbg4WFiDwNzAPCCwIFfOLcHmYAZUB98x2Z7rGnan+LO/z1OyqoCMvjDx+QSkGOjy/kDw11sXD4sAzL4xvTi3ktCERV/xQ2/aSIXBdhmxHAlrDpEloWHL/DaZK6DfAB56tqY4uDi1wFXAWQm5vrMWTTlur9DWzYdeAOP9h75q6KA3n8zNRkxuVkcvZRI0IDouRn+xjQ3/L4xsSbSH0NDXY/viEiNwFP49zFnw/8I8K+W8sJNO+36EvASuBkYAzwqoi8o6rlTTZSXQAsACgsLGyz7yPTVH1DI0X+qtAYt8ELfnGzPP7Y7Axmjs1yBzZ3+tbJzrQ8vjGJIlKNYAXOxTt4Rfhm2DIFbmtn2xLg0LDpkTh3/uEuB36hqgpsFJHPgXHA0ghxmTCqyvZ9NU36xl+7o4LPdlU2yePnDUln/HDnLj/48HbUkHTL4xuT4CL1NTT6IPa9DBgrIqNxmpx+Dbiw2TqbgVOAd0QkGygA2nwAbWBvYH+TC37wc0XNgTx+TmYq+Tk+ThibFbrgWx7fGNOWjoxQNgmYAKQG56nqE22tr6r17nOEV3Cajz6qqp+IyLfc5Q/i1CgeF5GPcWodP3Cbqia8mroGNuysdF7CCnt4u7P8QB7fl5rMuBwf86Yc4l7wM8nPzmBgWt8YRm6M6W28Nh/9KTALpyBYDMwB3gXaLAgAVHWxu374vAfDPm8DTutQxHGmvqGR4rJAKJ0TfHhb7K/C7S2Zvsl9OHxoBsePyXJa6rgPb3MyUy2Pb4w5aF5rBOcBk4EPVfVyN43zcPTCij+qyo7ymgMXe/eCv2FXJfvrnTy+uHn8gmwfZ04+JNRSJ29IGslJNuyhMSY6vBYE1araKCL1IpIJ7AIOi2Jcvdq+QJ2bwy9vkssvD8vjZ2f2Iz/bx6UzRrkDm2dy+LAM+ve1PL4xpnt5LQiWi8hA4A84LYkqsZY91DU0NnlgG/y8o7wmtI6vXzIFOT7OcO/wC7Kdu/xB6ZbHN8b0DF77GgoOQPOgiLwMZKrqquiF1Ttc+9QH/GvNTgD6JvVhzLAMZowZ4t7hO611hg+wPL4xpmeL9ELZ1PaWqeoHXR9S77GqZB8n5g/l5jPGkzck3fL4xpheKVKN4DftLFOcN4ITUk1dAzvKa/j6MbkcPswX63CMMabTIr1QdlJ3BdLbbC4LAJA7JC3GkRhjzMGxXEYnFZVWAU5zT2OM6c2sIOikYI1glNUIjDG9nBUEnVTkr2JA/xTrzsEY0+t5KgjEcZGI3OxO54rI9OiG1rMV+wNWGzDGxAWvNYIHgBnABe50Bc6IZQnLKQjs+YAxpvfzWhAco6rXAjUAqroHSNicSF1DI1v3VjNqsNUIjDG9n9eCoM4djF4B3IHmWwwpmSi27qmmoVEtNWSMiQteC4L7gOeBYSLyM5wuqO+IWlQ9XHGoxZClhowxvZ/XvoaeEpEVOKOJCXC2qn4a1ch6sGJ/8B0CqxEYY3o/rwPT3As8o6oJ/YA4qNgfoH9KEkN9/WIdijHGHDSvqaEPgB+LyEYR+bWIFEYzqJ6u2F/FqCFp1quoMSYueCoIVPWPqno6MB1YD/xSRDZENbIerNgfINdaDBlj4kRH3yw+HBgH5AFruzyaXqCxUSkuC5CXZQ+KjTHxweubxcEawHzgE2Caqp4Z1ch6qJ0VNeyvb7QagTEmbngdqvJzYIaqlkYzmN6gqNRpOmq9jhpj4kWkEcrGqepanPGJc0UkN3x5Io5QtrnMaTpqL5MZY+JFpBrBd4CraH2ksoQcoazIHyAlSRg+IDXWoRhjTJeINELZVe7HOapaE75MRBLySljsr2LkoDQbn9gYEze8Xs3e9zgv7ln308aYeBPpGUEOMALoLyJH4XQvAZAJJNzVUFUp9gcoHDUo1qEYY0yXifSM4EvAZcBI4K6w+RXA/0Upph6rrGo/lbX11tmcMSauRHpG8EfgjyJyrqr+tZti6rGK/DZOsTEm/kRKDV2kqk8CeSLynebLVfWuVjaLWweajlqNwBgTPyI9LA5e8TIAXys/7RKR2SKyzu2s7qY21pklIitF5BMReasDsXe7otIAInDo4P6xDsUYY7pMpNTQQ+7vWzu6Y3dEs/uBU4ESYJmILFLVNWHrDMQZD3m2qm4WkWEdPU532lwW4JAB/emXnBTrUIwxpst47WvoVyKSKSIpIvK6iJSKyEURNpsObFTVTaq6H3gamNdsnQuBv6nqZgBV3dXRE+hORf4q62PIGBN3vL5HcJqqlgNn4Nzd5wM3RthmBLAlbLrEnRcuHxgkIm+KyAoRuaS1HYnIVSKyXESW796922PIXW+zP0BelhUExpj44rUgSHF/nw4sVNUyD9u0NmqLNptOBqYBc3Gaqv5ERPJbbKS6QFULVbVw6NChHkPuWhU1dfir9pM72B4UG2Pii9feR18UkbVANXCNiAwFaiJsUwIcGjY9EtjWyjqlqloFVInI28BknMFvepRif7DXUasRGGPii9cRym4CZgCFqloHVNEy39/cMmCsiIwWkb7A14BFzdb5O3CCiCSLSBpwDPBpR06guwQLglwrCIwxccbr4PUpwMXAie44vW8BD7a3jarWi8h1wCtAEvCoqn4iIt9ylz+oqp+KyMvAKqAReFhVV3f6bKKo2N4hMMbEKa+pod/jPCd4wJ2+2J13ZXsbqepiYHGzeQ82m/418GuPccRMcWmArIy+ZPTz+pUZY0zv4PWqdrSqTg6b/reIfBSNgHqq4rIqqw0YY+KS11ZDDSIyJjghIocBDdEJqWey7qeNMfHKa43gRuANEdmE0yx0FHB51KLqYWrqGti+r4ZR1nTUGBOHIhYEblPRfThvCg/DKQjWqmptlGPrMbaUuU1H7WUyY0wcajc1JCJXAp8AvwVWAnmq+lEiFQIQ1nTUupcwxsShSDWC/wEmqupu97nAU7R8FyDuFfmdpqN59rDYGBOHIj0s3q+quwFUdRPQL/oh9TybywL4UpMZmJYSeWVjjOllItUIRorIfW1Nq+oN0QmrZynyB8gbko77Mp0xxsSVSAVB8x5GV0QrkJ5ss7+KiSMGxDoMY4yJCi9jFie0+oZGSvZUM/fI4bEOxRhjoiJSq6EFIjKpjWXpInKFiHw9OqH1DNv21lDfqPYOgTEmbkVKDT0A3CwiRwCrgd1AKjAWyAQexWlJFLeCLYbsrWJjTLyKlBpaCXxVRDKAQmA4zpgEn6rquuiHF3vFfut11BgT3zx1MaGqlcCb0Q2lZyr2B0hN6cMwX0K2nDXGJACvnc4lrCJ/gNzBafTpY01HjTHxyQqCCDZb99PGmDjXoYJARBLqitjYqE7309bHkDEmjnkqCETkOBFZgzuesIhMFpEHImzW6+2qqKW2vpFRWQlV/hljEozXGsHdwJcAP4CqfgScGK2geopQ01GrERhj4pjn1JCqbmk2K+5HKNvsdj9tvY4aY+KZ1xHKtojIcYCKSF/gBtw0UTwr8leR3Ec4ZGBqrEMxxpio8Voj+BZwLTACKAGmANdEKaYeo7gswMhB/UlOssZVxpj45bVGUKCqTfoUEpHjgfe6PqSeo9hfRa6lhYwxcc7rre5vPc6LG6pO09E862PIGBPn2q0RiMgM4DhgqIh8J2xRJpAUzcBibU+gjoqaehun2BgT9yKlhvoCGe56vrD55cB50QqqJyi2cYqNMQkiUu+jbwFvicjjqlrcTTH1CMVu01HrftoYE++8PiwOiMivgYk44xEAoKonRyWqHqDYH0AEDrXUkDEmznl9WPwUsBYYDdwKFAHLohRTj1DsryInM5XUlLh+FGKMMZ4LgiGq+ghQp6pvqeoVwLFRjCvmissClhYyxiQErwVBnft7u4jMFZGjgJFRiqlHKPZX2TjFxpiE4LUguF1EBgDfBb4HPAz8T6SNRGS2iKwTkY0iclM76x0tIg0i0iNaIlXW1lNauZ9RWVYjMMbEP69DVb7kftwHnAShN4vbJCJJwP3AqTjdUiwTkUWquqaV9X4JvNKx0KMnNE6x1QiMMQmg3RqBiCSJyAUi8j0RmeTOO0NE3gd+F2Hf04GNqrpJVfcDTwPzWlnveuCvwK6Ohx8dm63pqDEmgUSqETwCHAosBe4TkWJgBnCTqr4QYdsRQHjX1SXAMeEriMgI4BzgZODotnYkIlcBVwHk5uZGOOzBK7KCwBiTQCIVBIXAkaraKCKpQClwuKru8LDv1kZ712bT9wA/UNUGkbYHh1fVBcACgMLCwub76HKby6oYkt4XX2pKtA9ljDExF6kg2K+qjQCqWiMi6z0WAuDUAA4Nmx4JbGu2TiHwtFsIZAGni0i9h9pGVBWVBsi12oAxJkFEKgjGicgq97MAY9xpAVRVj2xn22XAWBEZDWwFvgZcGL6Cqo4OfhaRx4GXYl0IAGwuCzB99OBYh2GMMd0iUkEwvrM7VtV6EbkOpzVQEvCoqn4iIt9ylz/Y2X1HU219A9v2VVuvo8aYhBGp07mD6mhOVRcDi5vNa7UAUNXLDuZYXWVLWTWqkGfvEBhjEoSNwdhM6B0C637aGJMgrCBoJtR01FJDxpgE4bkgEJH+IlIQzWB6gs3+Knz9khmc3jfWoRhjTLfwVBCIyJnASuBld3qKiCyKYlwxU+R3mo62916DMcbEE681gltwuozYC6CqK4G8aAQUa5vLAjY8pTEmoXgtCOpVdV9UI+kB6hsa2VJmL5MZYxKL16EqV4vIhUCSiIwFbgDej15YsbF9Xw31jUqeFQTGmATitUZwPc54xbXAn3G6o/6fKMUUM0Vu09Fc637aGJNAvNYIClT1R8CPohlMrBW7TUftZTJjTCLxWiO4S0TWishtIjIxqhHFULG/ir7Jfcj2pcY6FGOM6TaeCgJVPQmYBewGFojIxyLy42gGFgvF/gCjBqfRp481HTXGJA7PL5Sp6g5VvQ/4Fs47BTdHK6hYKfYHbDAaY0zC8fpC2XgRuUVEVuMMUfk+zvgCcUNVKS6rsj6GjDEJx+vD4seAhcBpqtp8cJm4sKuilpq6RqsRGGMSjqeCQFWPjXYgsVYcGqfYagTGmMTSbkEgIs+q6ldF5GOajjfsZYSyXiX4DoH1OmqMSTSRagTfdn+fEe1AYm2zP0BSH2HEoP6xDsUYY7pVuw+LVXW7+/EaVS0O/wGuiX543afIX8WIgf1JSbIhGowxicXrVe/UVubN6cpAYm1zmTUdNcYkpnYLAhG52n0+UCAiq8J+PgdWdU+I3aOotMoKAmNMQor0jODPwD+BnwM3hc2vUNWyqEXVzfYG9lNeU2/jEBhjElKkgkBVtUhErm2+QEQGx0thEBynONdaDBljEpCXGsEZwAqc5qPhnfAocFiU4upWxW7T0bwsqxEYYxJPuwWBqp7h/h7dPeHERrHVCIwxCcxrX0PHi0i6+/kiEblLRHKjG1r3KfYHyMlMJTUlKdahGGNMt/PafPT3QEBEJgPfB4qBP0Utqm5W7K+ycYqNMQmrI4PXKzAPuFdV7wV80QurexWXBWycYmNMwvLa+2iFiPwQuBg4QUSSgJTohdV9qmrr2V1Ra53NGWMSltcawfk4A9dfoao7gBHAr6MWVTc60Ouo1QiMMYnJ61CVO4CngAEicgZQo6pPRDWybrK5LNjrqNUIjDGJyWuroa8CS4GvAF8F/isi53nYbraIrBORjSJyUyvLvx7WbcX77sPobhV6mcxqBMaYBOX1GcGPgKNVdReAiAwFXgP+0tYG7nOE+3E6rCsBlonIIlVdE7ba58AXVHWPiMwBFgDHdPw0Oq/YH2BQWgoD+sfFIw9jjOkwr88I+gQLAZffw7bTgY2quklV9wNP47Q6ClHV91V1jzu5hBiMg1zst3GKjTGJzWuN4GUReQVn3GJwHh4vjrDNCGBL2HQJ7d/t/z+cDu5aEJGrgKsAcnO79j22Yn+AwrxBXbpPY4zpTbyOWXyjiHwZmInT39ACVX0+wmbSyjxtZR4ichJOQTCzjeMvwEkbUVhY2Oo+OqO2voFt+6oZNaTbKyLGGNNjRBqzeCxwJzAG+Bj4nqpu9bjvEuDQsOmRwLZWjnEk8DAwR1X9HvfdJUr2VKNq4xQbYxJbpDz/o8BLwLk4PZD+tgP7XgaMFZHRItIX+BqwKHwFt7+ivwEXq+r6Duy7SxzoddQKAmNM4oqUGvKp6h/cz+tE5AOvO1bVehG5DngFSAIeVdVPRORb7vIHgZuBIcADIgJOVxaFHT2JzjrQ66g9LDbGJK5IBUGqiBzFgXx///BpVW23YFDVxTR7qOwWAMHPVwJXdjTorlLsD5DeN4msjL6xCsEYY2IuUkGwHbgrbHpH2LQCJ0cjqO4SbDrq1kaMMSYhRRqY5qTuCiQWiv0BCnLiphNVY4zpFK8vlMWdhkZly56AvUxmjEl4CVsQbNtbTV2DWq+jxpiEl7AFweYy637aGGPAe++j4o5VfLM7nSsi06MbWnQVue8QWGrIGJPovNYIHgBmABe40xU4PYv2Wpv9Afom92F4ZmqsQzHGmJjy2uncMao6VUQ+BHC7je7Vje+L/FUcOqg/ffpY01FjTGLzWiOoc8cXUAiNR9AYtai6QbE/QJ6lhYwxxnNBcB/wPDBMRH4GvAvcEbWookxV2VwWsFHJjDEG791QPyUiK4BTcLqXOFtVP41qZFG0u7KWwP4GqxEYYwweCwK3l9AA8GL4PFXdHK3AoqnYxik2xpgQrw+L/4HzfECAVGA0sA6YGKW4oipYEFiNwBhjvKeGjgifFpGpwDejElE3KPZX0UdgxMD+sQ7FGGNirlNvFrvdTx/dxbF0m2J/gBGD+tM3OWFfrDbGmBCvzwi+EzbZB5gK7I5KRN2g2F/FKBuMxhhjAO81Al/YTz+cZwbzohVUtBWXBayPIWOMcUWsEbgvkmWo6o3dEE/U7Q3sZ2+gzgoCY4xxtVsjEJFkVW3ASQXFhWCLIetszhhjHJFqBEtxCoGVIrIIeA6oCi5U1b9FMbaoKLbup40xpgmv7xEMBvw4YxQH3ydQoPcVBKVOOZY72AoCY4yByAXBMLfF0GoOFABBGrWooqi4LMAwXz/S+notA00s1NXVUVJSQk1NTaxDMaZXSU1NZeTIkaSkpHjeJtLVMAnIoGkBENQ7CwJ/lb1R3AuUlJTg8/nIy8tDxLoKN8YLVcXv91NSUsLo0aM9bxepINiuqvMPLrSepdgf4MT8obEOw0RQU1NjhYAxHSQiDBkyhN27O/aaV6T3COLqf2Fgfz27KmrJswfFvYIVAsZ0XGf+30QqCE7pXCg9U3DA+lxLDRljTEi7BYGqlnVXIN2hqDTY66jVCExkGRkZoc+LFy9m7NixbN68mVtuuYW0tDR27drV6rptOf3009m7d2+768yaNYvly5e3mP/4449z3XXXeQ++A+68807GjRvHpEmTmDx5Mk888US7sXTG8uXLueGGGwCora3li1/8IlOmTOGZZ57hyiuvZM2aNQe1/3vuuScUN0B9fT1ZWVn88Ic/bLJeXl4epaWloek333yTM844IzT9z3/+k8LCQsaPH8+4ceP43ve+d1BxAaxYsYIjjjiCww8/nBtuuAHVlo9X6+rquPTSSzniiCMYP348P//5z1usc9ZZZzFp0qTQ9O9+9zsee+yxg44POtnpXG+1ucxpOmr9DJmOeP3117n++ut5+eWXyc3NBSArK4vf/OY3HdrP4sWLGThwYBQibJ+q0tjY+siyDz74IK+++ipLly5l9erVvP32261eqA5WYWEh9913HwAffvghdXV1rFy5kvPPP5+HH36YCRMmeN5XQ0NDk+n6+noeffRRLrzwwtC8f/3rXxQUFPDss896Pp/Vq1dz3XXX8eSTT/Lpp5+yevVqDjvsMM9xteXqq69mwYIFbNiwgQ0bNvDyyy+3WOe5556jtraWjz/+mBUrVvDQQw9RVFQUWv63v/2txc3GFVdcEfpOD1ZCFQRF/gAD01IYkOa9WZWJvVtf/ITzH/pPl/7c+uInno79zjvv8I1vfIN//OMfjBkzJjT/iiuu4JlnnqGsrGWl+cknn2T69OlMmTKFb37zm6ELV/jd6G233ca4ceM49dRTueCCC7jzzjtD2z/33HNMnz6d/Px83nnnndD8LVu2MHv2bAoKCrj11ltD8++66y4mTZrEpEmTuOeeewAoKipi/PjxXHPNNUydOpUtW7Zw2WWXMWnSJI444gjuvvtuAO644w4eeOABMjMzARgwYACXXnppi3O6+uqrKSwsZOLEifz0pz8Nzb/pppuYMGECRx55ZOju+bnnngvVLk488UTgwJ33rl27uOiii1i5ciVTpkzhs88+a1Lz+Ne//sWMGTOYOnUqX/nKV6isrAx9d/Pnz2fmzJk899xzTWL797//zdSpU0lOPtD2ZeHChXz7298mNzeXJUuWtPKXbelXv/oVP/rRjxg3bhwAycnJXHPNNZ62bcv27dspLy9nxowZiAiXXHIJL7zwQov1RISqqirq6+uprq6mb9++ob9JZWUld911Fz/+8Y+bbJOWlkZeXh5Lly49qBjB+wtlcWGzP8Aoe5HMeFRbW8u8efN48803QxeHoIyMDK644gruvffeJhflTz/9lGeeeYb33nuPlJQUrrnmGp566ikuueSS0DrLly/nr3/9Kx9++CH19fVMnTqVadOmhZbX19ezdOlSFi9ezK233sprr70GELprT0tL4+ijj2bu3LmICI899hj//e9/UVWOOeYYvvCFLzBo0CDWrVvHY489xgMPPMCKFSvYunUrq1evBmDv3r1UVFRQUVHRpIBry89+9jMGDx5MQ0MDp5xyCqtWrWLkyJE8//zzrF27FhEJpb3mz5/PK6+8wogRI1qkwoYNG8bDDz/MnXfeyUsvvdRkWWlpKbfffjuvvfYa6enp/PKXv+Suu+7i5ptvBpz28e+++26L2N57770m3191dTWvv/46Dz30EHv37mXhwoXMmDEj4jmuXr2a7373uxHXe+ONN/jf//3fFvPT0tJ4//33m8zbunUrI0eODE2PHDmSrVu3ttj2vPPO4+9//zvDhw8nEAhw9913M3jwYAB+8pOf8N3vfpe0tJbXrsLCQt555x2mT58eMe72JFRBUOSvYmruoFiHYTrop2fGZiC8lJQUjjvuOB555BHuvffeFstvuOEGpkyZ0uTi8frrr7NixQqOPtoZrqO6upphw4Y12e7dd99l3rx59O/vDIx05plnNln+5S9/GYBp06Y1SQ+ceuqpDBkyJLTOu+++i4hwzjnnkJ6eHpr/zjvvcNZZZzFq1CiOPfZYAA477DA2bdrE9ddfz9y5cznttNOorKz03MLk2WefZcGCBdTX17N9+3bWrFnDhAkTSE1N5corr2Tu3LmhXPvxxx/PZZddxle/+tXQuXixZMkS1qxZw/HHHw/A/v37m1zAzz///Fa32759O+PHjw9Nv/TSS5x00kmkpaVx7rnnctttt3H33XeTlJTU6vl2tJXNSSedxMqVKz2t21paqrXjLV26lKSkJLZt28aePXs44YQT+OIXv0h5eTkbN27k7rvvbvJvIWjYsGGsXbu2Q/G3JqqpIRGZLSLrRGSjiNzUynIRkfvc5avckc+iYn99I9v2VlsfQ8azPn368Oyzz7Js2TLuuOOOFssHDhzIhRdeyAMPPBCap6pceumlrFy5kpUrV7Ju3TpuueWWJttFyln369cPgKSkJOrr60Pzm19ARKTdfQULB4BBgwbx0UcfMWvWLO6//36uvPJKMjMzSU9PZ9OmTe3G8/nnn3PnnXfy+uuvs2rVKubOnUtNTQ3JycksXbqUc889lxdeeIHZs2cDznOH22+/nS1btjBlyhT8fn+7+w9SVU499dTQd7dmzRoeeeSRVs8nXP/+/Zu8gb5w4UJee+018vLymDZtGn6/nzfeeAOAIUOGsGfPntC6ZWVlZGVlATBx4kRWrFgRMc433niDKVOmtPg57rjjWqw7cuRISkpKQtMlJSUccsghLdb785//zOzZs0lJSWHYsGEcf/zxLF++nP/85z+sWLGCvLw8Zs6cyfr165k1a1Zou5qamtANxcGIWkHgdl99PzAHmABcICLNnwjNAca6P1cBv49WPCV7AjSq9TpqOiYtLY2XXnqJp556qslFKeg73/kODz30UOiCfcopp/CXv/wl1KKorKyM4uLiJtvMnDmTF198kZqaGiorK/nHP/7hKZZXX32VsrIyqqureeGFFzj++OM58cQTeeGFFwgEAlRVVfH8889zwgkntNi2tLSUxsbG0B3yBx98AMAPf/hDrr32WsrLywEoLy9nwYIFTbYtLy8nPT2dAQMGsHPnTv75z38CTu563759nH766dxzzz2hu+TPPvuMY445hvnz55OVlcWWLVs8nd+xxx7Le++9x8aNGwEIBAKsX78+4nbjx48PbVNeXs67777L5s2bKSoqoqioiPvvv5+FCxcCTkuoP/3pT4Dz0PnJJ5/kpJNOAuDGG2/kjjvuCB2zsbGRu+66q8XxgjWC5j/N00IAw4cPx+fzsWTJElSVJ554gnnzWg7lkpuby7///W9UlaqqKpYsWcK4ceO4+uqr2bZtG0VFRbz77rvk5+fz5ptvhrZbv359k5ZEnRXN1NB0YKOqbgIQkadxBrMJbyc2D3hCnduaJSIyUESGq+r2rg7Geh01nTV48GBefvllTjzxxNDdY1BWVhbnnHNO6OHrhAkTuP322znttNNobGwkJSWF+++/n1GjRoW2OfrooznrrLOYPHkyo0aNorCwkAEDBkSMY+bMmVx88cVs3LiRCy+8kMLCQgAuu+yyUI74yiuv5KijjmqRRti6dSuXX355qPVQsHni1VdfTWVlJUcffTQpKSmkpKS0yJNPnjyZo446iokTJ3LYYYeFUjcVFRXMmzePmpoaVDX0Hdx4441s2LABVeWUU05h8uTJvPXWWxHPb+jQoTz++ONccMEF1NbWAnD77beTn5/f7nZz5szh4osvBpzWNSeffHKoVgUwb948vv/971NbW8tPfvITrr76aiZPnoyqMnv2bC666CIAjjzySO655x4uuOACAoEAIsLcuXMjxh3J73//ey677DKqq6uZM2cOc+bMAWDRokUsX76c+fPnc+2113L55ZczadIkVJXLL7+cI488MuK+33vvvSYP7ztNVaPyA5wHPBw2fTHwu2brvATMDJt+HShsZV9XAcuB5bm5udoZyz736zf+uEx3V9R0anvTvdasWRPrEKKqoqJCVVWrqqp02rRpumLFihhH1LudffbZun79+liH0a0++OADveiii1pd1tr/H2C5tnG9jmaNwEtHdZ46s1PVBcACgMLCwk41ci7MG0xh3uDObGpMl7vqqqtYs2YNNTU1XHrppUydGjdjP8XEL37xC7Zv387YsWNjHUq3KS0t5bbbbuuSfUWzICgBDg2bHgls68Q6xsSdP//5z7EOIa4UFBRQUFAQ6zC61amnntpl+4pmq6FlwFgRGS0ifYGvAYuarbMIuMRtPXQssE+j8HzA9E4ahTdcjYl3nfl/E7UagarWi8h1wCs44xo8qqqfiMi33OUPAouB04GNQAC4PFrxmN4lNTUVv9/PkCFDrBdSYzxSdzyC1NTUDm0nve2uq7CwULuqIyzTc9kIZcZ0TlsjlInIClUtbG2bhHqz2PQeKSkpHRphyRjTeQnV6ZwxxpiWrCAwxpgEZwWBMcYkuF73sFhEdgPFEVdsXRZQGnGt+GLnnBjsnBPDwZzzKFUd2tqCXlcQHAwRWd7WU/N4ZeecGOycE0O0ztlSQ8YYk+CsIDDGmASXaAXBgsirxB0758Rg55wYonLOCfWMwBhjTEuJViMwxhjTjBUExhiT4OKyIBCR2SKyTkQ2ishNrSwXEbnPXb5KRHr9qCAezvnr7rmuEpH3RWRyLOLsSpHOOWy9o0WkQUTO6874osHLOYvILBFZKSKfiEjkMSJ7OA//tgeIyIsi8pF7zr26F2MReVREdonI6jaWd/31q62hy3rrD06X158BhwF9gY+ACc3WOR34J84IaccC/4113N1wzscBg9zPcxLhnMPW+zdOl+fnxTrubvg7D8QZFzzXnR4W67i74Zz/D/il+3koUAb0jXXsB3HOJwJTgdVtLO/y61c81gimAxtVdZOq7geeBuY1W2ce8IQ6lgADRWR4dwfahSKes6q+r6p73MklOKPB9WZe/s4A1wN/BXZ1Z3BR4uWcLwT+pqqbAVS1t5+3l3NWwCfOwBUZOAVBffeG2XVU9W2cc2hLl1+/4rEgGAFsCZsuced1dJ3epKPn8/9w7ih6s4jnLCIjgHOAB7sxrmjy8nfOBwaJyJsiskJELum26KLDyzn/DhiPM8ztx8C3VbWxe8KLiS6/fsXjeAStDWfVvI2sl3V6E8/nIyIn4RQEM6MaUfR5Oed7gB+oakOcjHLm5ZyTgWnAKUB/4D8iskRV10c7uCjxcs5fAlYCJwNjgFdF5B1VLY9ybLHS5deveCwISoBDw6ZH4twpdHSd3sTT+YjIkcDDwBxV9XdTbNHi5ZwLgafdQiALOF1E6lX1hW6JsOt5/bddqqpVQJWIvA1MBnprQeDlnC8HfqFOAn2jiHwOjAOWdk+I3a7Lr1/xmBpaBowVkdEi0hf4GrCo2TqLgEvcp+/HAvtUdXt3B9qFIp6ziOQCfwMu7sV3h+EinrOqjlbVPFXNA/4CXNOLCwHw9m/778AJIpIsImnAMcCn3RxnV/JyzptxakCISDZQAGzq1ii7V5dfv+KuRqCq9SJyHfAKTouDR1X1ExH5lrv8QZwWJKcDG4EAzh1Fr+XxnG8GhgAPuHfI9dqLe270eM5xxcs5q+qnIvIysApoBB5W1VabIfYGHv/OtwGPi8jHOGmTH6hqr+2eWkQWArOALBEpAX4KpED0rl/WxYQxxiS4eEwNGWOM6QArCIwxJsFZQWCMMQnOCgJjjElwVhAYY0yCs4IgAbg9b64M+8lrZ93KLjje4yLyuXusD0RkRif28bCITHA//1+zZe8fbIzufoLfy2q398qBEdafIiKnd+I4w0XkJffzLBHZJyIfisinIvLTTuzvrGAvnCJydvB7cqfni8gXO7rPVo7xuETordXtxsJzE2T33F/ysF6rvW+KyJ0icrLX4xnvrCBIDNWqOiXsp6gbjnmjqk4BbgIe6ujGqnqlqq5xJ/+v2bLjDj484MD3Mgmnk69rI6w/Baf9dkd9B/hD2PQ7qnoUzpvPF4nItI7sTFUXqeov3MmzgQlhy25W1dc6EWNP8jgwu5X5v8X592S6mBUECUhEMkTkdfdu/WMRadFrp3sX+3bYHfMJ7vzTROQ/7rbPiUhGhMO9DRzubvsdd1+rReR/3HnpIvIPcfqSXy0i57vz3xSRQhH5BdDfjeMpd1ml+/uZ8Dt09y72XBFJEpFfi8gycfpr/6aHr+U/uB13ich0ccZs+ND9XeC+1TofON+N5Xw39kfd43zY2vfoOhd4uflMtxuIFcAYt7axxI33eREZ5MZyg4iscec/7c67TER+JyLHAWcBv3ZjGhO8kxeROSLybNh3M0tEXnQ/d+hvKCI3u+e4WkQWiDTpuOki9ztaLSLT3fW9fi+taqv3TVUtBoaISE5H9mc86K4+tu0ndj9AA06nXCuB53HeKM90l2XhvKEYfLmw0v39XeBH7uckwOeu+zaQ7s7/AXBzK8d7HLfvf+ArwH9xOkL7GEjH6Sr4E+AonIvkH8K2HeD+fhMoDI8pbJ1gjOcAf3Q/98XpkbE/cBXwY3d+P2A5MLqVOCvDzu85YLY7nQkku5+/CPzV/XwZ8Luw7e8ALnI/D8Tpzye92TFGAyvCpmcBL7mfhwBFwEScN4G/4M6fD9zjft4G9Aseo3kc4d91+LT7N94c9rf6PXBRJ/+Gg8Pm/wk4M+xv9Af384m4/ee39b00O/dCnLee2/o3m0cr/fHj1KzOjfX/qXj7ibsuJkyrqtVJ0wAgIinAHSJyIk43BCOAbGBH2DbLgEfddV9Q1ZUi8gWcNMR77k1hX5w76db8WkR+DOzG6e30FOB5de6CEZG/ASfg3CnfKSK/xLlIvNOB8/oncJ+I9MNJJbytqtUichpwZFiOewAwFvi82fb9RWQlzkVnBfBq2Pp/FJGxOL06prRx/NOAs0Tke+50KpBL0759hrvfQbgTRORDnO/+FzidiA1U1eBoYn/EKZjAKSCeEpEXgBfaiKMFdbpmeBk4U0T+AswFvg905G8YdJKIfB9IAwbjFOIvussWusd7W0QyxXnO0tb3Eh7fcuBKr+cTZhdwSCe2M+2wgiAxfR1nJKdpqlonIkU4/1lD3P/YJ+JcQP4kIr8G9gCvquoFHo5xo6r+JTghbTzAVNX1bo78dODnIvIvVZ3v5SRUtUZE3sTphvh83IsSTn8z16vqKxF2Ua2qU0RkAPASzjOC+3D6rnlDVc8R58H6m21sLzh3p+vaOwbNvlucZwRnhHbiHL8tc3Huts8CfiIiE9tZt7lncM6pDFimqhVuWsfr3xARSQUewKmdbRGRW2h6Ps37qFHa+F7E6RDuYKXifKemC9kzgsQ0ANjlFgInAaOaryAio9x1/gA8gjN03hLgeBEJ5vzTRCTf4zHfBs52t0nHSeu8IyKHAAFVfRK40z1Oc3VuzaQ1T+N0unUCTsdkuL+vDm4jIvnuMVulqvuAG4DvudsMALa6iy8LW7UCJ0UW9ApwfTBnLiJHtbL79Tg1jja5x98j7nMY4GLgLRHpAxyqqm/g3M0PxEmrhWseU7g3cb7Pb+AUCtDxv2Hwol/qPkto3pIo+ExnJk4vmPvw9r10Vj7QazvR66msIEhMTwGFIrIcp3awtpV1ZgEr3RTGucC9qrob58K4UERW4VxUxnk5oKp+gJN3XorzzOBhVf0QOAJY6qZofgTc3srmC4BV4j4sbuZfOHfMr6kzlCE4Yy6sAT4QpwniQ0So/bqxfITTzfGvcGon7+E8Pwh6A5gQfFiMU3NIcWNb7U43328V8FnwwtuOS3HSaatwWifNd4/9pDi9an4I3K2qe5tt9zRwo/tQdkyzYzfg1HTmuL/p6N/QPd4fcJ7vvICTMgy3R5zmvA/ipADBw/ciTkOAh1s7pji9b/4HKBCREhH5f+78FJyGB8vbitd0jvU+akyUicg5OGm4H8c6lt7M/R6nqupPYh1LvLFnBMZEmao+LyJDYh1HHEgGfhPrIOKR1QiMMSbB2TMCY4xJcFYQGGNMgrOCwBhjEpwVBMYYk+CsIDDGmAT3/wH+562r10IutwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(knn, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "74d35729",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8412162162162162"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test.to_numpy(), y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd6936a",
   "metadata": {},
   "source": [
    "### Testing LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ed8ae842",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[73  1]\n",
      " [ 4  8]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97        74\n",
      "           1       0.89      0.67      0.76        12\n",
      "\n",
      "    accuracy                           0.94        86\n",
      "   macro avg       0.92      0.83      0.86        86\n",
      "weighted avg       0.94      0.94      0.94        86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066110bf",
   "metadata": {},
   "source": [
    "The True Positive is **74** out of **74** and the True Negative is **8** out of **12**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.95**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1be88f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.761904761904762"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef8308be",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.76** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "088586f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1b98500fcd0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(lr, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d731e26e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8265765765765765"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test.to_numpy(), y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6276a5f6",
   "metadata": {},
   "source": [
    "### Testing GNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2b5f44ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[71  3]\n",
      " [ 2 10]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.96      0.97        74\n",
      "           1       0.77      0.83      0.80        12\n",
      "\n",
      "    accuracy                           0.94        86\n",
      "   macro avg       0.87      0.90      0.88        86\n",
      "weighted avg       0.94      0.94      0.94        86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e7e673",
   "metadata": {},
   "source": [
    "The True Positive is **71** out of **74** and the True Negative is **10** out of **12**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.94**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2b63da49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred, average='binary') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3427e29",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.80** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "528bf1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1b984f62310>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(gnb, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "75224f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8963963963963965"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test.to_numpy(), y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cda6501",
   "metadata": {},
   "source": [
    "### Testing Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fdbd8eaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[73  1]\n",
      " [ 2 10]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.99      0.98        74\n",
      "           1       0.91      0.83      0.87        12\n",
      "\n",
      "    accuracy                           0.97        86\n",
      "   macro avg       0.94      0.91      0.92        86\n",
      "weighted avg       0.96      0.97      0.96        86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = dtree.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bea05a5",
   "metadata": {},
   "source": [
    "The True Positive is **73** out of **74** and the True Negative is **10** out of **12**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.97**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c81005dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8695652173913043"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred, average='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55bbee2a",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.87** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "50dcffd3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1b984f02b80>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(dtree, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e43abe4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.90990990990991"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test.to_numpy(), y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c565c5",
   "metadata": {},
   "source": [
    "### Testing Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "34429b45",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[72  2]\n",
      " [ 4  8]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.97      0.96        74\n",
      "           1       0.80      0.67      0.73        12\n",
      "\n",
      "    accuracy                           0.93        86\n",
      "   macro avg       0.87      0.82      0.84        86\n",
      "weighted avg       0.93      0.93      0.93        86\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = svc.predict(X_test)\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae4a006",
   "metadata": {},
   "source": [
    "The True Positive is **72** out of **74** and the True Negative is **8** out of **12**\n",
    "\n",
    "\n",
    "Which results in an accuracy of **0.93**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "39db9445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7272727272727272"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_test, y_pred, average='binary')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7d380c",
   "metadata": {},
   "source": [
    "Which makes the **F1 score** equal to **0.73** for the positive class\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "328ad2bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x1b984b26040>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics.plot_roc_curve(svc, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c7244026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8198198198198198"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test.to_numpy(), y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b2502aa",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "attachments": {
    "image-3.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "1709bfd6",
   "metadata": {},
   "source": [
    "![image-3.png](attachment:image-3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e920d6",
   "metadata": {},
   "source": [
    "As we can see from this table, the best classifier according to **\"Accuracy\"** is **\"Decision Tree\"**\n",
    "\n",
    "and the best classifier according to **\"F1 Score\"** is **\"Decision Tree\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7c53bff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}