CENG313_HW2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_1vtEELiOVI2"
   },
   "source": [
    "# CENG313 Introduction to Data Science\n",
    "## Assignment 2 - Analyzing a Toy Covid Data: Predict Clinical Cases\n",
    "## Due: January 22 by 11:59pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "wLVgN0J3OVI4",
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, f1_score, roc_curve, auc\n",
    "from IPython.display import clear_output\n",
    "from sklearn.neighbors import KNeighborsClassifier,KNeighborsRegressor\n",
    "from sklearn.decomposition import PCA\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9CdHPlYyOVI5"
   },
   "source": [
    "### Dataset Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "sTTvWZT6QwCh"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CBOdxD95OVI6"
   },
   "source": [
    "#### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "p0lQndneOVI6",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xlrd in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (2.0.1)\n"
     ]
    },
    {
     "data": {
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       "<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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Clinical classification</th>\n",
       "      <th>Days from symptoms to hospital</th>\n",
       "      <th>No comorbidity</th>\n",
       "      <th>Cardiovascular and\\ncerebrovascular disease</th>\n",
       "      <th>Endocrine system disease</th>\n",
       "      <th>Malignant tumor</th>\n",
       "      <th>Respiratory system disease</th>\n",
       "      <th>Digestive system disease</th>\n",
       "      <th>Renal disease</th>\n",
       "      <th>Liver disease</th>\n",
       "      <th>Fever</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Chest tightness</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Diarrhea</th>\n",
       "      <th>Others</th>\n",
       "      <th>Clinical outcome</th>\n",
       "      <th>Hospital stay, days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>47</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>15</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Aggravated,turn to ICU</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>13</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>57</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>13</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>58</td>\n",
       "      <td>Heavy</td>\n",
       "      <td>1</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Aggravated,transfer to superior hospital</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>54</td>\n",
       "      <td>Ordinary</td>\n",
       "      <td>10</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age Clinical classification  Days from symptoms to hospital  \\\n",
       "0    Male   47                   Heavy                              15   \n",
       "1  Female   53                Ordinary                              13   \n",
       "2  Female   57                Ordinary                              13   \n",
       "3    Male   58                   Heavy                               1   \n",
       "4    Male   54                Ordinary                              10   \n",
       "\n",
       "  No comorbidity Cardiovascular and\\ncerebrovascular disease  \\\n",
       "0              Y                                           N   \n",
       "1              Y                                           N   \n",
       "2              Y                                           N   \n",
       "3              N                                           N   \n",
       "4              Y                                           N   \n",
       "\n",
       "  Endocrine system disease Malignant tumor Respiratory system disease  \\\n",
       "0                        N               N                          N   \n",
       "1                        N               N                          N   \n",
       "2                        N               N                          N   \n",
       "3                        Y               N                          N   \n",
       "4                        N               N                          N   \n",
       "\n",
       "  Digestive system disease Renal disease Liver disease Fever Cough  \\\n",
       "0                        N             N             N     Y     Y   \n",
       "1                        N             N             N     Y     Y   \n",
       "2                        N             N             N     Y     Y   \n",
       "3                        N             N             N     N     N   \n",
       "4                        N             N             N     Y     Y   \n",
       "\n",
       "  Chest tightness Fatigue Diarrhea Others  \\\n",
       "0               Y       Y        Y      N   \n",
       "1               Y       N        N      N   \n",
       "2               N       N        N      N   \n",
       "3               N       N        N      N   \n",
       "4               Y       N        N      N   \n",
       "\n",
       "                           Clinical outcome  Hospital stay, days  \n",
       "0                    Aggravated,turn to ICU                  NaN  \n",
       "1                    Improved and discharge                  6.0  \n",
       "2                    Improved and discharge                  8.0  \n",
       "3  Aggravated,transfer to superior hospital                  NaN  \n",
       "4                    Improved and discharge                  5.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data from given xls (\"COVID19 inpatient cases data.xls\") (2nd sheet, not first sheet)\n",
    "#look the first 5 records of data\n",
    "!pip install --upgrade xlrd\n",
    "\n",
    "main_df = pd.read_excel(\"COVID19 inpatient cases data.xls\", sheet_name=1)\n",
    "\n",
    "main_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "S2iOx3P-OVI6",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "COLUMN NAMES:\n",
      " Index(['Gender', 'Age', 'Clinical classification',\n",
      "       'Days from symptoms to hospital', 'No comorbidity',\n",
      "       'Cardiovascular and\\ncerebrovascular disease',\n",
      "       'Endocrine system disease', 'Malignant tumor',\n",
      "       'Respiratory system disease', 'Digestive system disease',\n",
      "       'Renal disease', 'Liver disease', 'Fever', 'Cough', 'Chest tightness',\n",
      "       'Fatigue', 'Diarrhea', 'Others', 'Clinical outcome',\n",
      "       'Hospital stay, days'],\n",
      "      dtype='object')\n",
      "NOT NULL COUNT:\n",
      " Gender                                         73\n",
      "Age                                            73\n",
      "Clinical classification                        73\n",
      "Days from symptoms to hospital                 73\n",
      "No comorbidity                                 73\n",
      "Cardiovascular and\\ncerebrovascular disease    73\n",
      "Endocrine system disease                       73\n",
      "Malignant tumor                                73\n",
      "Respiratory system disease                     73\n",
      "Digestive system disease                       73\n",
      "Renal disease                                  73\n",
      "Liver disease                                  73\n",
      "Fever                                          73\n",
      "Cough                                          73\n",
      "Chest tightness                                73\n",
      "Fatigue                                        73\n",
      "Diarrhea                                       73\n",
      "Others                                         73\n",
      "Clinical outcome                               73\n",
      "Hospital stay, days                            69\n",
      "dtype: int64\n",
      "DATA TYPES:\n",
      " Gender                                          object\n",
      "Age                                              int64\n",
      "Clinical classification                         object\n",
      "Days from symptoms to hospital                   int64\n",
      "No comorbidity                                  object\n",
      "Cardiovascular and\\ncerebrovascular disease     object\n",
      "Endocrine system disease                        object\n",
      "Malignant tumor                                 object\n",
      "Respiratory system disease                      object\n",
      "Digestive system disease                        object\n",
      "Renal disease                                   object\n",
      "Liver disease                                   object\n",
      "Fever                                           object\n",
      "Cough                                           object\n",
      "Chest tightness                                 object\n",
      "Fatigue                                         object\n",
      "Diarrhea                                        object\n",
      "Others                                          object\n",
      "Clinical outcome                                object\n",
      "Hospital stay, days                            float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# check column names, not null count and data types\n",
    "print(\"COLUMN NAMES:\\n\",main_df.columns)\n",
    "\n",
    "nulls=main_df.isnull().sum()\n",
    "print(\"NOT NULL COUNT:\\n\",len(main_df)-nulls)\n",
    "\n",
    "print(\"DATA TYPES:\\n\",main_df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ilPbfwYPOVI7"
   },
   "source": [
    "#### Dataset preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A75ipWg5OVI7"
   },
   "source": [
    "##### Datasets for FIM and Rule extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "8uQoiAqcOVI7",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fever</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Chest tightness</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Diarrhea</th>\n",
       "      <th>Others</th>\n",
       "      <th>Covid19</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Fever Cough Chest tightness Fatigue Diarrhea Others Covid19\n",
       "0      Y     Y               Y       Y        Y      N       Y\n",
       "1      Y     Y               Y       N        N      N       Y\n",
       "2      Y     Y               N       N        N      N       Y\n",
       "3      N     N               N       N        N      N       Y\n",
       "4      Y     Y               Y       N        N      N       Y\n",
       "..   ...   ...             ...     ...      ...    ...     ...\n",
       "68     Y     Y               N       N        N      N       Y\n",
       "69     N     Y               N       Y        N      N       Y\n",
       "70     N     Y               N       N        N      N       Y\n",
       "71     Y     Y               N       N        N      N       Y\n",
       "72     Y     N               Y       N        N      N       Y\n",
       "\n",
       "[73 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "symptom_cols = ['Fever', 'Cough','Chest tightness', 'Fatigue', 'Diarrhea', 'Others']\n",
    "# create df_symtoms dataframe including given columns\n",
    "# add \"Covid19\" columns assign all entries as \"Y\"\n",
    "df_symptoms=main_df[symptom_cols].copy()\n",
    "df_symptoms[\"Covid19\"]='Y'\n",
    "df_symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "_ykZQpA1OVI7",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cardiovascular and\\ncerebrovascular disease</th>\n",
       "      <th>Endocrine system disease</th>\n",
       "      <th>Malignant tumor</th>\n",
       "      <th>Respiratory system disease</th>\n",
       "      <th>Digestive system disease</th>\n",
       "      <th>Renal disease</th>\n",
       "      <th>Liver disease</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Illness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>47</td>\n",
       "      <td>Male</td>\n",
       "      <td>Heavy ill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>57</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ordinary ill</td>\n",
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       "    <tr>\n",
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       "      <td>N</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>Heavy ill</td>\n",
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       "      <th>4</th>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>Ordinary ill</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>59</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ordinary ill</td>\n",
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       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
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       "      <td>N</td>\n",
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       "      <td>Ordinary ill</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
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       "      <td>N</td>\n",
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       "      <td>Female</td>\n",
       "      <td>Ordinary ill</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cardiovascular and\\ncerebrovascular disease Endocrine system disease  \\\n",
       "0                                            N                        N   \n",
       "1                                            N                        N   \n",
       "2                                            N                        N   \n",
       "3                                            N                        Y   \n",
       "4                                            N                        N   \n",
       "..                                         ...                      ...   \n",
       "68                                           N                        N   \n",
       "69                                           N                        Y   \n",
       "70                                           Y                        N   \n",
       "71                                           Y                        N   \n",
       "72                                           Y                        N   \n",
       "\n",
       "   Malignant tumor Respiratory system disease Digestive system disease  \\\n",
       "0                N                          N                        N   \n",
       "1                N                          N                        N   \n",
       "2                N                          N                        N   \n",
       "3                N                          N                        N   \n",
       "4                N                          N                        N   \n",
       "..             ...                        ...                      ...   \n",
       "68               N                          N                        N   \n",
       "69               N                          N                        N   \n",
       "70               N                          N                        N   \n",
       "71               N                          N                        N   \n",
       "72               N                          N                        N   \n",
       "\n",
       "   Renal disease Liver disease  Age  Gender       Illness  \n",
       "0              N             N   47    Male     Heavy ill  \n",
       "1              N             N   53  Female  Ordinary ill  \n",
       "2              N             N   57  Female  Ordinary ill  \n",
       "3              N             N   58    Male     Heavy ill  \n",
       "4              N             N   54    Male  Ordinary ill  \n",
       "..           ...           ...  ...     ...           ...  \n",
       "68             N             N   30  Female  Ordinary ill  \n",
       "69             N             N   59  Female  Ordinary ill  \n",
       "70             N             N   87  Female  Ordinary ill  \n",
       "71             N             N   56  Female  Ordinary ill  \n",
       "72             N             N   54  Female  Ordinary ill  \n",
       "\n",
       "[73 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_cols = ['Cardiovascular and\\ncerebrovascular disease', 'Endocrine system disease','Malignant tumor','Respiratory system disease',\n",
    "                'Digestive system disease','Renal disease','Liver disease']\n",
    "other_cols= ['Age','Gender']\n",
    "# create df_diseases dataframe consisting records of given cols(disease_cols and other_cols) from main dataframe\n",
    "# add also \"Clinicial classification\" attribute from main dataframe calling  this column as \"Illness\" in your df_diseases dataframe\n",
    "# add \" ill\" string to all records in \"illness\" attribute\n",
    "df_diseases=main_df[disease_cols].copy()\n",
    "df_diseases[other_cols]=main_df[other_cols]\n",
    "df_diseases['Illness']=main_df['Clinical classification']\n",
    "df_diseases['Illness']=df_diseases['Illness']+\" ill\"\n",
    "df_diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "vnVr1MBWOVI8",
    "scrolled": true,
    "tags": []
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   "outputs": [
    {
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      "text/plain": [
       "   Cardiovascular and\\ncerebrovascular disease Endocrine system disease  \\\n",
       "0                                            N                        N   \n",
       "1                                            N                        N   \n",
       "2                                            N                        N   \n",
       "3                                            N                        Y   \n",
       "4                                            N                        N   \n",
       "..                                         ...                      ...   \n",
       "68                                           N                        N   \n",
       "69                                           N                        Y   \n",
       "70                                           Y                        N   \n",
       "71                                           Y                        N   \n",
       "72                                           Y                        N   \n",
       "\n",
       "   Malignant tumor Respiratory system disease Digestive system disease  \\\n",
       "0                N                          N                        N   \n",
       "1                N                          N                        N   \n",
       "2                N                          N                        N   \n",
       "3                N                          N                        N   \n",
       "4                N                          N                        N   \n",
       "..             ...                        ...                      ...   \n",
       "68               N                          N                        N   \n",
       "69               N                          N                        N   \n",
       "70               N                          N                        N   \n",
       "71               N                          N                        N   \n",
       "72               N                          N                        N   \n",
       "\n",
       "   Renal disease Liver disease  Gender       Illness Age Group  \n",
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       "4              N             N    Male  Ordinary ill    Adults  \n",
       "..           ...           ...     ...           ...       ...  \n",
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       "69             N             N  Female  Ordinary ill    Adults  \n",
       "70             N             N  Female  Ordinary ill   Elderly  \n",
       "71             N             N  Female  Ordinary ill    Adults  \n",
       "72             N             N  Female  Ordinary ill    Adults  \n",
       "\n",
       "[73 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# categorize ages as \"Elderly\" (Age>=65),\n",
    "#                        \"Adults\" (18<= Age <65),\n",
    "#                        \"Teens\" (12=<Age<18),\n",
    "#                        \"Children\" (1<Age<11),\n",
    "#                        \"Infants\" (Age<=1)\n",
    "# in new column \"Age Group\", drop \"Age\" column in df_disease dataframe.\n",
    "age_group=[(0,\"Infants\"),(1,\"Children\"),(12,\"Teens\"),(18,\"Adults\"),(65,\"Elderly\")]\n",
    "age_bins = [-1, 1, 11, 18, 65, float('inf')]\n",
    "age_labels = ['Infants', 'Children', 'Teens', 'Adults', 'Elderly']\n",
    "\n",
    "age_group_column=pd.cut(df_diseases['Age'], bins=age_bins, labels=age_labels)\n",
    "df_diseases['Age Group']=age_group_column\n",
    "df_diseases=df_diseases.drop(columns='Age', axis=1)\n",
    "\n",
    "df_diseases"
   ]
  },
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   "execution_count": 7,
   "metadata": {
    "id": "NDv9GtmROVI8",
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    {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Cardiovascular and\\ncerebrovascular disease Endocrine system disease  \\\n",
       "0                                            N                        N   \n",
       "1                                            N                        N   \n",
       "2                                            N                        N   \n",
       "3                                            N                        Y   \n",
       "4                                            N                        N   \n",
       "..                                         ...                      ...   \n",
       "68                                           N                        N   \n",
       "69                                           N                        Y   \n",
       "70                                           Y                        N   \n",
       "71                                           Y                        N   \n",
       "72                                           Y                        N   \n",
       "\n",
       "   Malignant tumor Respiratory system disease Digestive system disease  \\\n",
       "0                N                          N                        N   \n",
       "1                N                          N                        N   \n",
       "2                N                          N                        N   \n",
       "3                N                          N                        N   \n",
       "4                N                          N                        N   \n",
       "..             ...                        ...                      ...   \n",
       "68               N                          N                        N   \n",
       "69               N                          N                        N   \n",
       "70               N                          N                        N   \n",
       "71               N                          N                        N   \n",
       "72               N                          N                        N   \n",
       "\n",
       "   Renal disease Liver disease  Gender_Female  Gender_Male  Illness_Heavy ill  \\\n",
       "0              N             N          False         True               True   \n",
       "1              N             N           True        False              False   \n",
       "2              N             N           True        False              False   \n",
       "3              N             N          False         True               True   \n",
       "4              N             N          False         True              False   \n",
       "..           ...           ...            ...          ...                ...   \n",
       "68             N             N           True        False              False   \n",
       "69             N             N           True        False              False   \n",
       "70             N             N           True        False              False   \n",
       "71             N             N           True        False              False   \n",
       "72             N             N           True        False              False   \n",
       "\n",
       "    Illness_Ordinary ill  Age Group_Infants  Age Group_Children  \\\n",
       "0                  False              False               False   \n",
       "1                   True              False               False   \n",
       "2                   True              False               False   \n",
       "3                  False              False               False   \n",
       "4                   True              False               False   \n",
       "..                   ...                ...                 ...   \n",
       "68                  True              False               False   \n",
       "69                  True              False               False   \n",
       "70                  True              False               False   \n",
       "71                  True              False               False   \n",
       "72                  True              False               False   \n",
       "\n",
       "    Age Group_Teens  Age Group_Adults  Age Group_Elderly  \n",
       "0             False              True              False  \n",
       "1             False              True              False  \n",
       "2             False              True              False  \n",
       "3             False              True              False  \n",
       "4             False              True              False  \n",
       "..              ...               ...                ...  \n",
       "68            False              True              False  \n",
       "69            False              True              False  \n",
       "70            False             False               True  \n",
       "71            False              True              False  \n",
       "72            False              True              False  \n",
       "\n",
       "[73 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert \"Gender\", \"Illness\", \"Age Group\" attributes to dummies for one-hot encoding, \\\n",
    "#  then drop these attributes from df_diseases\n",
    "df_diseases=pd.get_dummies(df_diseases,columns=['Gender','Illness','Age Group'])\n",
    "df_diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "FTqhNMfyOVI8",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
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       "      <td>True</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fever  Cough  Chest tightness  Fatigue  Diarrhea  Others  Covid19\n",
       "0    True   True             True     True      True   False     True\n",
       "1    True   True             True    False     False   False     True\n",
       "2    True   True            False    False     False   False     True\n",
       "3   False  False            False    False     False   False     True\n",
       "4    True   True             True    False     False   False     True\n",
       "..    ...    ...              ...      ...       ...     ...      ...\n",
       "68   True   True            False    False     False   False     True\n",
       "69  False   True            False     True     False   False     True\n",
       "70  False   True            False    False     False   False     True\n",
       "71   True   True            False    False     False   False     True\n",
       "72   True  False             True    False     False   False     True\n",
       "\n",
       "[73 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert all data to bool (True or False) (by replacing or mapping attribute values ) for df_symptoms dataframe\n",
    "df_symptoms=df_symptoms.replace({'Y':True, 'N':False})\n",
    "df_symptoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "La_AAXVEOVI9",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cardiovascular and\\ncerebrovascular disease  Endocrine system disease  \\\n",
       "0                                         False                     False   \n",
       "1                                         False                     False   \n",
       "2                                         False                     False   \n",
       "3                                         False                      True   \n",
       "4                                         False                     False   \n",
       "..                                          ...                       ...   \n",
       "68                                        False                     False   \n",
       "69                                        False                      True   \n",
       "70                                         True                     False   \n",
       "71                                         True                     False   \n",
       "72                                         True                     False   \n",
       "\n",
       "    Malignant tumor  Respiratory system disease  Digestive system disease  \\\n",
       "0             False                       False                     False   \n",
       "1             False                       False                     False   \n",
       "2             False                       False                     False   \n",
       "3             False                       False                     False   \n",
       "4             False                       False                     False   \n",
       "..              ...                         ...                       ...   \n",
       "68            False                       False                     False   \n",
       "69            False                       False                     False   \n",
       "70            False                       False                     False   \n",
       "71            False                       False                     False   \n",
       "72            False                       False                     False   \n",
       "\n",
       "    Renal disease  Liver disease  Gender_Female  Gender_Male  \\\n",
       "0           False          False          False         True   \n",
       "1           False          False           True        False   \n",
       "2           False          False           True        False   \n",
       "3           False          False          False         True   \n",
       "4           False          False          False         True   \n",
       "..            ...            ...            ...          ...   \n",
       "68          False          False           True        False   \n",
       "69          False          False           True        False   \n",
       "70          False          False           True        False   \n",
       "71          False          False           True        False   \n",
       "72          False          False           True        False   \n",
       "\n",
       "    Illness_Heavy ill  Illness_Ordinary ill  Age Group_Infants  \\\n",
       "0                True                 False              False   \n",
       "1               False                  True              False   \n",
       "2               False                  True              False   \n",
       "3                True                 False              False   \n",
       "4               False                  True              False   \n",
       "..                ...                   ...                ...   \n",
       "68              False                  True              False   \n",
       "69              False                  True              False   \n",
       "70              False                  True              False   \n",
       "71              False                  True              False   \n",
       "72              False                  True              False   \n",
       "\n",
       "    Age Group_Children  Age Group_Teens  Age Group_Adults  Age Group_Elderly  \n",
       "0                False            False              True              False  \n",
       "1                False            False              True              False  \n",
       "2                False            False              True              False  \n",
       "3                False            False              True              False  \n",
       "4                False            False              True              False  \n",
       "..                 ...              ...               ...                ...  \n",
       "68               False            False              True              False  \n",
       "69               False            False              True              False  \n",
       "70               False            False             False               True  \n",
       "71               False            False              True              False  \n",
       "72               False            False              True              False  \n",
       "\n",
       "[73 rows x 16 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# same for df_diseases dataframe\n",
    "df_diseases=df_diseases.replace({'Y':True, 'N':False})\n",
    "df_diseases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "063E2VQ-OVI9",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (0.23.1)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (1.11.4)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (1.26.3)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (2.0.3)\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (1.4.0)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (3.7.2)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from mlxtend) (1.2.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (9.4.0)\n",
      "Requirement already satisfied: pyparsing<3.1,>=2.3.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2023.3)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from scikit-learn>=1.0.2->mlxtend) (3.2.0)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\fuuya\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7->matplotlib>=3.0.0->mlxtend) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "# install mlxtend library\n",
    "!pip install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "C5cCtW5HOVI9",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "</table>\n",
       "<p>73 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Fever  Cough  Chest tightness  Fatigue  Diarrhea  Others  Gender_Female  \\\n",
       "0    True   True             True     True      True   False          False   \n",
       "1    True   True             True    False     False   False           True   \n",
       "2    True   True            False    False     False   False           True   \n",
       "3   False  False            False    False     False   False          False   \n",
       "4    True   True             True    False     False   False          False   \n",
       "..    ...    ...              ...      ...       ...     ...            ...   \n",
       "68   True   True            False    False     False   False           True   \n",
       "69  False   True            False     True     False   False           True   \n",
       "70  False   True            False    False     False   False           True   \n",
       "71   True   True            False    False     False   False           True   \n",
       "72   True  False             True    False     False   False           True   \n",
       "\n",
       "    Gender_Male  Illness_Heavy ill  Illness_Ordinary ill  Age Group_Infants  \\\n",
       "0          True               True                 False              False   \n",
       "1         False              False                  True              False   \n",
       "2         False              False                  True              False   \n",
       "3          True               True                 False              False   \n",
       "4          True              False                  True              False   \n",
       "..          ...                ...                   ...                ...   \n",
       "68        False              False                  True              False   \n",
       "69        False              False                  True              False   \n",
       "70        False              False                  True              False   \n",
       "71        False              False                  True              False   \n",
       "72        False              False                  True              False   \n",
       "\n",
       "    Age Group_Children  Age Group_Teens  Age Group_Adults  Age Group_Elderly  \n",
       "0                False            False              True              False  \n",
       "1                False            False              True              False  \n",
       "2                False            False              True              False  \n",
       "3                False            False              True              False  \n",
       "4                False            False              True              False  \n",
       "..                 ...              ...               ...                ...  \n",
       "68               False            False              True              False  \n",
       "69               False            False              True              False  \n",
       "70               False            False             False               True  \n",
       "71               False            False              True              False  \n",
       "72               False            False              True              False  \n",
       "\n",
       "[73 rows x 15 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create \"df_symptoms_extended\" concatanating df_symptoms with df_diseases dummy attributes of \"Gender\", \"Illness\", \"Age Group\" and their records.\n",
    "# drop \"Covid19\" attribute in extended dataframe\n",
    "selected_diseases_columns=df_diseases.columns[df_diseases.columns.str.contains('Gender|Illness|Age Group')]\n",
    "short_diseases=df_diseases[selected_diseases_columns]\n",
    "\n",
    "df_symptoms_extended = pd.concat([df_symptoms, short_diseases],axis=1)\n",
    "df_symptoms_extended=df_symptoms_extended.drop(\"Covid19\", axis=1)\n",
    "df_symptoms_extended"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PQY2D1EUOVI-"
   },
   "source": [
    "#### Fill mising values with KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GkWP6zd5OVI-"
   },
   "source": [
    "The usage of KNNImputer is not allowed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "jQUeNTTmOVI-",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing percentage of Hospital stay, days:  4 / 73 = % 5.47945205479452\n"
     ]
    }
   ],
   "source": [
    "# show missing record as (number of missings/total) (should be only \"Hospital stay, days\")\n",
    "missing_number = main_df[\"Hospital stay, days\"].isnull().sum()\n",
    "\n",
    "print(\"Missing percentage of Hospital stay, days: \",missing_number,\"/\",len(main_df),\"= %\",100*missing_number/len(main_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "SiH1fYz5OVI_",
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Clinical classification</th>\n",
       "      <th>Days from symptoms to hospital</th>\n",
       "      <th>No comorbidity</th>\n",
       "      <th>Cardiovascular and\\ncerebrovascular disease</th>\n",
       "      <th>Endocrine system disease</th>\n",
       "      <th>Malignant tumor</th>\n",
       "      <th>Respiratory system disease</th>\n",
       "      <th>Digestive system disease</th>\n",
       "      <th>Renal disease</th>\n",
       "      <th>Liver disease</th>\n",
       "      <th>Fever</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Chest tightness</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Diarrhea</th>\n",
       "      <th>Others</th>\n",
       "      <th>Clinical outcome</th>\n",
       "      <th>Hospital stay, days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Aggravated,turn to ICU</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Aggravated,transfer to superior hospital</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2</td>\n",
       "      <td>59</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2</td>\n",
       "      <td>87</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Clinical classification  Days from symptoms to hospital  \\\n",
       "0        1   47                        1                              15   \n",
       "1        2   53                        2                              13   \n",
       "2        2   57                        2                              13   \n",
       "3        1   58                        1                               1   \n",
       "4        1   54                        2                              10   \n",
       "..     ...  ...                      ...                             ...   \n",
       "68       2   30                        2                              13   \n",
       "69       2   59                        2                              10   \n",
       "70       2   87                        2                              10   \n",
       "71       2   56                        2                              20   \n",
       "72       2   54                        2                               7   \n",
       "\n",
       "    No comorbidity  Cardiovascular and\\ncerebrovascular disease  \\\n",
       "0                1                                            0   \n",
       "1                1                                            0   \n",
       "2                1                                            0   \n",
       "3                0                                            0   \n",
       "4                1                                            0   \n",
       "..             ...                                          ...   \n",
       "68               1                                            0   \n",
       "69               0                                            0   \n",
       "70               0                                            1   \n",
       "71               0                                            1   \n",
       "72               0                                            1   \n",
       "\n",
       "    Endocrine system disease  Malignant tumor  Respiratory system disease  \\\n",
       "0                          0                0                           0   \n",
       "1                          0                0                           0   \n",
       "2                          0                0                           0   \n",
       "3                          1                0                           0   \n",
       "4                          0                0                           0   \n",
       "..                       ...              ...                         ...   \n",
       "68                         0                0                           0   \n",
       "69                         1                0                           0   \n",
       "70                         0                0                           0   \n",
       "71                         0                0                           0   \n",
       "72                         0                0                           0   \n",
       "\n",
       "    Digestive system disease  Renal disease  Liver disease  Fever  Cough  \\\n",
       "0                          0              0              0      1      1   \n",
       "1                          0              0              0      1      1   \n",
       "2                          0              0              0      1      1   \n",
       "3                          0              0              0      0      0   \n",
       "4                          0              0              0      1      1   \n",
       "..                       ...            ...            ...    ...    ...   \n",
       "68                         0              0              0      1      1   \n",
       "69                         0              0              0      0      1   \n",
       "70                         0              0              0      0      1   \n",
       "71                         0              0              0      1      1   \n",
       "72                         0              0              0      1      0   \n",
       "\n",
       "    Chest tightness  Fatigue  Diarrhea  Others  \\\n",
       "0                 1        1         1       0   \n",
       "1                 1        0         0       0   \n",
       "2                 0        0         0       0   \n",
       "3                 0        0         0       0   \n",
       "4                 1        0         0       0   \n",
       "..              ...      ...       ...     ...   \n",
       "68                0        0         0       0   \n",
       "69                0        1         0       0   \n",
       "70                0        0         0       0   \n",
       "71                0        0         0       0   \n",
       "72                1        0         0       0   \n",
       "\n",
       "                            Clinical outcome  Hospital stay, days  \n",
       "0                     Aggravated,turn to ICU                  NaN  \n",
       "1                     Improved and discharge                  6.0  \n",
       "2                     Improved and discharge                  8.0  \n",
       "3   Aggravated,transfer to superior hospital                  NaN  \n",
       "4                     Improved and discharge                  5.0  \n",
       "..                                       ...                  ...  \n",
       "68                    Improved and discharge                 24.0  \n",
       "69                    Improved and discharge                 16.0  \n",
       "70                    Improved and discharge                 14.0  \n",
       "71                    Improved and discharge                 12.0  \n",
       "72                    Improved and discharge                  8.0  \n",
       "\n",
       "[73 rows x 20 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy dataframe df1 to df2\n",
    "# convert bools and strings to numeric attributes ('Male':1, 'Female':2, 'Heavy':1, 'Ordinary':2, 'Y':1, 'N':0)\n",
    "value_changes={'Male': 1, 'Female': 2, 'Heavy': 1, 'Ordinary': 2, 'Y': 1, 'N': 0}\n",
    "\n",
    "df2=main_df.copy()\n",
    "df2=df2.replace(value_changes)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "qANOmj5WOVI_",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def min_max_scale(df):\n",
    "    numeric_columns = df.select_dtypes(include=['number']).columns\n",
    "    non_numeric_columns = df.select_dtypes(include=['object']).columns\n",
    "    scaler = MinMaxScaler()\n",
    "    df_min_max_scaled = pd.DataFrame(scaler.fit_transform(df[numeric_columns]), columns=df[numeric_columns].columns)\n",
    "    \n",
    "    df_min_max_scaled=pd.concat([df_min_max_scaled, df[non_numeric_columns]], axis=1)\n",
    "    return  df_min_max_scaled\n",
    "\n",
    "def inv_min_max_scale(df, scaled_val):\n",
    "    numeric_columns = df.select_dtypes(include=['number']).columns\n",
    "    non_numeric_columns = df.select_dtypes(include=['object']).columns\n",
    "    scaled_numeric_features = scaled_val[numeric_columns]\n",
    "    \n",
    "    scaler = MinMaxScaler()\n",
    "    first_features = pd.DataFrame(scaler.fit(df[numeric_columns]).inverse_transform(scaled_numeric_features),columns=scaled_numeric_features.columns)\n",
    "    df_inv_min_max_scaled = pd.concat([original_numeric_features, df[non_numeric_columns]], axis=1)\n",
    "    \n",
    "    return df_inv_min_max_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "RCnjJ6IzOVI_",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# min_max_scale for scaling missing atttribute (you will need for \"nan\" values)\n",
    "df2_without_nan = df2['Hospital stay, days'].dropna().values.reshape(-1, 1)\n",
    "\n",
    "# scale other nonmissing attributes using MinMaxScaler in sklearn\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(df2_without_nan)\n",
    "\n",
    "# fill missing values based on KNeighborsRegressor regression in sklearn (for k=7)\n",
    "df2['Hospital stay, days_scaled'] = df2['Hospital stay, days'].apply(lambda x: scaler.transform([[x]])[0][0] if not np.isnan(x) else np.nan)\n",
    "X_train = df2[df2['Hospital stay, days'].notna()][other_cols]\n",
    "y_train = df2[df2['Hospital stay, days'].notna()]['Hospital stay, days_scaled']\n",
    "X_test = df2[df2['Hospital stay, days'].isna()][other_cols]\n",
    "regressor = KNeighborsRegressor(n_neighbors=7)\n",
    "regressor.fit(X_train, y_train)\n",
    "df2.loc[df2['Hospital stay, days'].isna(), 'Hospital stay, days_scaled'] = regressor.predict(X_test)\n",
    "\n",
    "# fill df1 missings with given values\n",
    "# don't forget to convert original range\n",
    "df2['Hospital stay, days_filled'] = df2['Hospital stay, days_scaled'].apply(lambda x: scaler.inverse_transform([[x]])[0][0] if not np.isnan(x) else np.nan)\n",
    "main_df['Hospital stay, days'].fillna(df2['Hospital stay, days_filled'], inplace=True)\n",
    "print( main_df['Hospital stay, days'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "elk34epDOVJA"
   },
   "source": [
    "#### Dataset for classification task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "LkcLqNFwOVJA",
    "tags": []
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Days from symptoms to hospital</th>\n",
       "      <th>No comorbidity</th>\n",
       "      <th>Cardiovascular and\\ncerebrovascular disease</th>\n",
       "      <th>Endocrine system disease</th>\n",
       "      <th>Malignant tumor</th>\n",
       "      <th>Respiratory system disease</th>\n",
       "      <th>Digestive system disease</th>\n",
       "      <th>Renal disease</th>\n",
       "      <th>Liver disease</th>\n",
       "      <th>Fever</th>\n",
       "      <th>Cough</th>\n",
       "      <th>Chest tightness</th>\n",
       "      <th>Fatigue</th>\n",
       "      <th>Diarrhea</th>\n",
       "      <th>Others</th>\n",
       "      <th>Clinical outcome</th>\n",
       "      <th>Hospital stay, days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>15</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Aggravated,turn to ICU</td>\n",
       "      <td>16.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Aggravated,transfer to superior hospital</td>\n",
       "      <td>15.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1</td>\n",
       "      <td>87</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Improved and discharge</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender  Age  Days from symptoms to hospital  No comorbidity  \\\n",
       "0        0   47                              15               1   \n",
       "1        1   53                              13               1   \n",
       "2        1   57                              13               1   \n",
       "3        0   58                               1               0   \n",
       "4        0   54                              10               1   \n",
       "..     ...  ...                             ...             ...   \n",
       "68       1   30                              13               1   \n",
       "69       1   59                              10               0   \n",
       "70       1   87                              10               0   \n",
       "71       1   56                              20               0   \n",
       "72       1   54                               7               0   \n",
       "\n",
       "    Cardiovascular and\\ncerebrovascular disease  Endocrine system disease  \\\n",
       "0                                             0                         0   \n",
       "1                                             0                         0   \n",
       "2                                             0                         0   \n",
       "3                                             0                         1   \n",
       "4                                             0                         0   \n",
       "..                                          ...                       ...   \n",
       "68                                            0                         0   \n",
       "69                                            0                         1   \n",
       "70                                            1                         0   \n",
       "71                                            1                         0   \n",
       "72                                            1                         0   \n",
       "\n",
       "    Malignant tumor  Respiratory system disease  Digestive system disease  \\\n",
       "0                 0                           0                         0   \n",
       "1                 0                           0                         0   \n",
       "2                 0                           0                         0   \n",
       "3                 0                           0                         0   \n",
       "4                 0                           0                         0   \n",
       "..              ...                         ...                       ...   \n",
       "68                0                           0                         0   \n",
       "69                0                           0                         0   \n",
       "70                0                           0                         0   \n",
       "71                0                           0                         0   \n",
       "72                0                           0                         0   \n",
       "\n",
       "    Renal disease  Liver disease  Fever  Cough  Chest tightness  Fatigue  \\\n",
       "0               0              0      1      1                1        1   \n",
       "1               0              0      1      1                1        0   \n",
       "2               0              0      1      1                0        0   \n",
       "3               0              0      0      0                0        0   \n",
       "4               0              0      1      1                1        0   \n",
       "..            ...            ...    ...    ...              ...      ...   \n",
       "68              0              0      1      1                0        0   \n",
       "69              0              0      0      1                0        1   \n",
       "70              0              0      0      1                0        0   \n",
       "71              0              0      1      1                0        0   \n",
       "72              0              0      1      0                1        0   \n",
       "\n",
       "    Diarrhea  Others                          Clinical outcome  \\\n",
       "0          1       0                    Aggravated,turn to ICU   \n",
       "1          0       0                    Improved and discharge   \n",
       "2          0       0                    Improved and discharge   \n",
       "3          0       0  Aggravated,transfer to superior hospital   \n",
       "4          0       0                    Improved and discharge   \n",
       "..       ...     ...                                       ...   \n",
       "68         0       0                    Improved and discharge   \n",
       "69         0       0                    Improved and discharge   \n",
       "70         0       0                    Improved and discharge   \n",
       "71         0       0                    Improved and discharge   \n",
       "72         0       0                    Improved and discharge   \n",
       "\n",
       "    Hospital stay, days  \n",
       "0             16.142857  \n",
       "1              6.000000  \n",
       "2              8.000000  \n",
       "3             15.142857  \n",
       "4              5.000000  \n",
       "..                  ...  \n",
       "68            24.000000  \n",
       "69            16.000000  \n",
       "70            14.000000  \n",
       "71            12.000000  \n",
       "72             8.000000  \n",
       "\n",
       "[73 rows x 19 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# copy df1 to data_df datafarme\n",
    "# split data: label attribute 'Clinical classification' as Y, features as X (including nonmissing features)\n",
    "# convert attribute values to 0 and 1 ('Y':1, 'N':0, 'Male':0, \"Female\":1)\n",
    "data_df=main_df.copy()\n",
    "\n",
    "Y = data_df['Clinical classification']\n",
    "X = data_df.drop('Clinical classification', axis=1)\n",
    "X = X.replace({'Y': 1, 'N': 0, 'Male': 0, 'Female': 1})\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "kF1ov3xYOVJA",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Split dataset as train and test set using sklearn (for random_state=44)\n",
    "# x_train, x_val, y_test, y_test = # to-do\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=44)\n",
    "\n",
    "x_train_numerical = x_train.select_dtypes(include=['number'])\n",
    "x_test_numerical = x_test.select_dtypes(include=['number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "49-65ds2OVJD"
   },
   "source": [
    "### Classification Part: Predicting Clinical Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xyhuUbMhOVJE"
   },
   "source": [
    "#### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "qp0hPcmzOVJE",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7368421052631579\n",
      "Error:  0.26315789473684215\n",
      "Recall:  0.9166666666666666\n",
      "Precision:  0.7333333333333333\n",
      "Sensitivity:  0.9166666666666666\n",
      "Specifity:  0.42857142857142855\n",
      "F1 Measure:  0.8148148148148148\n",
      "Confusion Matrix: \n",
      " [[ 3  4]\n",
      " [ 1 11]]\n"
     ]
    }
   ],
   "source": [
    "# create ID3 Tree using sklearn\n",
    "dt_classifier=DecisionTreeClassifier(random_state=44)\n",
    "\n",
    "# train Decision Tree Classifier\n",
    "dt_classifier.fit(x_train_numerical, y_train)\n",
    "\n",
    "# Predict classes for test data\n",
    "predictions = dt_classifier.predict(x_test_numerical)\n",
    "\n",
    "# calculate accuracy, error, recall, precision, sensitivity, specifity, f1 measure, confusion matrix\n",
    "#           based on your defined functions\n",
    "accuracy=accuracy_score(y_test,predictions)\n",
    "cm=confusion_matrix(y_test,predictions)\n",
    "tp = cm[1, 1]\n",
    "tn = cm[0, 0]\n",
    "fp = cm[0, 1]\n",
    "fn = cm[1, 0]\n",
    "sensitivity=tp/(tp+fn)\n",
    "specificity =tn/(tn+fp)\n",
    "\n",
    "print(\"Accuracy: \",accuracy)\n",
    "print(\"Error: \",1-accuracy)\n",
    "print(\"Recall: \",recall_score(y_test,predictions,pos_label='Ordinary'))\n",
    "print(\"Precision: \",precision_score(y_test,predictions,pos_label='Ordinary'))\n",
    "print(\"Sensitivity: \",sensitivity)\n",
    "print(\"Specifity: \",specificity)\n",
    "print(\"F1 Measure: \",f1_score(y_test,predictions,pos_label='Ordinary'))\n",
    "print(\"Confusion Matrix: \\n\",cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "mFMjtD3BOVJE",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x2000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize your tree using plot_tree inside sklearn and dtreeplt\n",
    "# !pip install dtreeplt\n",
    "!pip install dtreeplt\n",
    "clear_output()\n",
    "\n",
    "temp_X=X.select_dtypes(include=['number'])\n",
    "model = dt_classifier\n",
    "\n",
    "class_names=sorted(Y.unique())\n",
    "feature_names=temp_X.columns.tolist()\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_tree(model, filled=True, feature_names=feature_names, class_names=class_names, rounded=True)\n",
    "plt.title('Decision Tree - sklearn')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nRU9lPAXOVJE"
   },
   "source": [
    "#### KNN Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "xNCppno8OVJE",
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QfAjRaLZmzl9do3uqqnRELCneEDPnr268ovKMxxAAAIDmQohGs7V83bbDnx05rznyGAIAANBcCNFotnp0KqjX85ojjyEAAADNhRCNZmvkwC7Ru2jb4U4qyneYHDmwS+MVlWdGDuwSvQo9hgAAADR9QjSarZYtUvGj8UO3e841Jw6Nli1SjVRR/mnZIhXjh/fe7jkeQwAAAJoCIRrNWo/CthFR3jH1ad89bu84btj2A6LmrrQsHU+9tSIiIjq2bVXj5+OH9/YYAgAA0CTU/NQLzchDry6JiIiT9u8TXz24XyxftyH+9uL78dS8lfH8/FVxwZGDslxhbpv02pKYt/yjKCxoFU98e3S8uXRdLF+3Id5d8XHc8Oi8ePKtFVH8yeYoatc626UCAADAThGi0WyVlaXjn7PLQ7QT9usTowZ1jYiI/XbrHEf98sl4/M0V8cqiNbF/385ZrDJ3lZal48ZH50VExDmf3T26dGhT+RhmHtu3ln0UE5+eH1ces2c2SwUAAICdZpyTZuvFhR/GsrUbo1PbVvHZPbpVHh/QrUOctP+uERFxw7S3slVezpv02pJ4u6IL7RufHVDtZy1apOLyo8qDs4nPzI/iTzZnoUIAAACoP0I0mq1JFaOcx+zTM9q2alntZ5eOGRwtW6Ti8TdXxMsLP8xGeTmttCxdGTCee/juUVhQc1zz+GG9Yq+enWLdhpK48+n5jV0iAAAA1CshGs1SWVk6Jr9WHqKN37fmwvcDunWILx5Q0Y1WMbLIFg+9ujjeWfFxFBa0irMOG7DVc1q0SMXlR+8RERF3PT0/itfrRgMAACB/CdFoll5478NYvm5jdCqoPspZVaYb7QndaNVUXQvtm9voQss4bp9esXevTrFuY0nc+fS7jVUiAAAA1DshGs1Spgtt7NBeNUY5M/p37RBfquhGu36abrSMTBdaUbvWceY2utAyytdGq+hGe2ZBrFm/qREqBAAAgPqX9RDtlltuiYEDB0ZBQUGMGDEipk+fXqvLPfPMM9GqVavYf//9G7ZAmpxqo5zDe2333EsqutGefGtFvKQbrXwttIoutHM/O3C7XWgZx1bpRptobTQAAADyVFZDtPvuuy+uuOKK+MEPfhAvv/xyHH744XH88cfHwoULt3u54uLiOOOMM+Koo45qpEppSqqNcg7uvt1z+3ftEF8+UDdaxoP/WhzvVnShbWsttE9r0SIVV1SsjTZRNxoAAAB5Kqsh2i9/+cs455xz4txzz40hQ4bE9ddfH3379o1bb711u5c7//zz47TTTotRo0Y1UqU0JZNeXRwR5aOcbVolPwUuGb1HtGyRiqfeWhEvvtd8u9Gqr4U2MDrVogstY+zQ8m60jzbaqRMAAID8lLUQbdOmTfHiiy/G2LFjqx0fO3ZsPPvss9u83F133RXvvPNOXHPNNbW6nY0bN8batWurfdF8lZal45+zl0ZExAnDa+7KuTX9urav7EZrzjt1PvivxfHuyo+jc/vWceahA+p02ardaNZGAwAAIB9lLURbuXJllJaWRs+ePasd79mzZyxdunSrl5k3b15873vfi3vvvTdatWpVq9uZMGFCFBUVVX717dt3p2snf72wYHUsX7cxCgtaxWGDt74r59ZcMnqPaNWMu9FKSsuq7chZly60jLFDe8WQ3oXx0caSuGO6bjQAAADyS9Y3FkilUtW+T6fTNY5FRJSWlsZpp50W1157bey55561vv6rr746iouLK78WLVq00zWTvyZlduXcp3ajnBnl3Wi7RUTE9dPeapDactmDr27pQjtjVP8duo6qO3Xe/eyC+PBj3WgAAADkj6yFaN26dYuWLVvW6Dpbvnx5je60iIh169bFCy+8EJdcckm0atUqWrVqFdddd13861//ilatWsVjjz221dtp27ZtFBYWVvuieao6yjm+lqOcVV0yZnC0apGK6fNWxovvra7v8nJWSWlZ3PTo2xGx411oGcfu0zOGZrrRnn63vkoEAACABpe1EK1NmzYxYsSImDp1arXjU6dOjUMPPbTG+YWFhfHaa6/FK6+8Uvl1wQUXxF577RWvvPJKHHLIIY1VOnlq1oLVsSIzyjmo9qOcGX27tI9/G5HpRms+a6M9sBNroX1aKpWKyyvWRrv7Gd1oAAAA5I+sjnNeddVVcccdd8TEiRNj7ty5ceWVV8bChQvjggsuiIjyUcwzzjijvNAWLWLYsGHVvnr06BEFBQUxbNiw6NChQzbvCnlgcsUo57F1HOWs6uLRW7rRXljQ9LvRSkrL4qbHtnShdWxbu7UIt2fs0PJutI83lepGAwAAIG9kNUQ7+eST4/rrr4/rrrsu9t9//3jqqadi8uTJ0b9/+ZpLS5YsiYULF2azRJqI0rJ0TH6tfJRz3A6Mcmb07dI+vnJQ8+lG+8cri2P+yo9jl3roQstIpbbs1Hn3MwtitW40AAAA8kAqnU6ns11EY1q7dm0UFRVFcXGx9dGakRnvrIpTf/tcFLVrHbN+cPQOd6JFRCxavT5G//yJKClLx18uGBUHD+hSj5XmjpLSsjj6l0/GglXr4zvH7RUXHTm43q47nU7HCTc9Ha8vXhsXHTkovnPc3vV23QAAAFAXtc2Ksr47JzSGLaOcPXcqQIuo3o12QxPuRvvHK4tjwar15V1oowbU63WXd6OV77L7u2d1owEAAJD7hGg0eVV35Ry3746PclaVWRvt6bdXxqwmuDZa+Vpo5QHheZ8bFB3qYS20Tzt6SI/Yp0/52mi/nW5tNAAAAHKbEI0mb+b81bHyo41R1K51HDa47rtybs1uu7SPrxzUNyIirp/2Vr1cZy75v4outC4d2sQZo/o3yG3oRgMAACCfCNFo8ia9tjgiykc5W7esv1/5i0cPitYtU/HM26ti5vym041WvQtt9wbpQss4ekiPGLZrYazfVBq/eUo3GgAAALlLiEaTVlqWjikVo5zjh/ep1+tuqt1o97/8QbxX0YX29c80TBdaRiqViiuOKu9G+/2MBbHqo40NensAAACwo4RoNGnPz18VKz/aFJ3bt45DB3Wt9+u/ePTgaN0yFc++syqef3dVvV9/YyspLYtfP/52RDR8F1rGUUN6xL67FsX6TaXx2+nzG/z2AAAAYEcI0WjSJr1asSvn0F71OsqZsWvndvHVym60/N+p8+9VutAaai20TytfG22PiNCNBgAAQO4SotFklZSWxcOvZ0Y562dXzq25qKIbbca7q+K5PO5G21xaFr9+rLwL7fzP7R7t2zR8F1rGmL17xPDdyrvRfmOnTgAAAHKQEI0mq3xXzvJRzlENMMqZUbUb7YY87ka7/+UPYuHq9dG1Q5v4eiN1oWVU60Z79j3daAAAAOQcIRpN1qTXykc5j9unYUY5q7o4z7vRNlfZkfP8Ixq3Cy1j9F7l3WifbLZTJwAAALlHiEaTVFJaVrkr57h9G26UM6NP53Zx8sH5u1Pn/S99EItWfxLdOraJ0xt4R85tqb422nuxUjcaAAAAOUSIRpP0/PzVserjTbFLA49yVnXRkYOjTcsW8dy7q2PGO/nTjba5tCxueryiC+1zg7LShZYxeq8esZ9uNAAAAHJQ9j4tUy9Ky9Ixc/7qWL5uQ/ToVBAjB3aJli1S2S4r6ypHOYc1/ChnRqYb7Q/PvRfXT3srRg0a1Si3u7P+/tL7lV1oX/tMv6zWUt6Ntmd84+5Z8fsZC+K8z+0e3Tq2zWpNVOc1ByC/5Prrdq7XF6HG+pDr9UXkfo25Xl+EGutDrtcXkR81NiQhWh6bMntJXPvgnFhSvKHyWO+igrjmxKFx3LCGH2HMVY09ylnVRaMHxX2zFsXz88u70RqrC25Hla+FltmRM7tdaBlH7tU99uvbOf61aE385ql34/vjhmS7JCp4zQHIL7n+up3r9UWosT7ken0RuV9jrtcXocb6kOv1ReRHjQ3NOGeemjJ7SVx4z0vVfnkjIpYWb4gL73kppsxekqXKsu/5+atjdWaUc/fGDbF6F7WLU0aWr432q2lvRTqdbtTbr6u/vfh+vP9hdtdC+7Tqa6MtiBXrrI2WC7zmAOSXXH/dzvX6ItRYH3K9vojcrzHX64tQY33I9foi8qPGxiBEy0OlZem49sE5sbV4JnPs2gfnRGlZbgc4DeWhV7eMcrZqpFHOqi48clC0adkiZs5fHTNyeKfOTSVl8evHy7vQLjhiULRr0zLLFW1x5J7dY/++nWPD5rL4zVPvZLucZs9rDkB+qe3rdklpWaTT6Ub/Kikti5/kcH1qbB715UONuV6fGptHfXWpsTl8Hkil0+mmfy+rWLt2bRQVFUVxcXEUFhZmu5wdMuOdVXHqb59LPO9P3/xMzo8T1reS0rIY+V+PxuqPN8U95xwSn92jW1bquOYfs+N3M96LkQO7xH3nfSZSqdybEf/zzIXxvb+/Ft06to3p3xmdUyFaRMQTby6Ps+6aFQWtW8T074yJ7p2sjZYtXnMA8kttX7cBoD7l8+eB2mZFOtHy0PJ1G5JPqsN5Tclz75aPcnbp0CY+s3uXrNVxYcVOnTPn5+ZOnZtKtqyFdsERu+dcgBYRcUSVbrTbn9SNlk1ecwDyi9djALKhOfz/k/1VxKmzHp0K6vW8pmTSa4sjIuLYfbIzypnRq6ggTh3ZN3434724ftq8GDWoa051o/3tpffjgzWfRLeObeNrh+TGWmifllkb7ay7ZsU9z78X5x2xe7P8nc4FXnMA8kttX49/8/URcdCAxv+j4wsLVsd5f3gx8bxs1RehxvqQ6/VF5H6NuV5fhBrrQ67XF1H7GpvD5wEhWh4aObBL9C4qiKXFG7Y6k5yK8hBn5MDsdWJlQ9VdOU8Ynv2dQS48cnD8adaimLmgvBvt0MHZGS39tE0lZfHrii60C4/MrbXQPu2IPbvHAf06x8sL18TtT74bPzphaLZLapYyrzmfXkQ0o7m+5gDkqtq+bh81pGe0bNH4f+Q7akjPWr2XzVZ9EWqsD7leX0Tu15jr9UWosT7ken0Rta+xOXweMM6Zh1q2SMU1J5aHCVt7CqUj4poTh2btCZYtM95dFR+u3xxdOrSJQ3LgydurqCBOG9kvInJrp86/vljehda9U9v42iH9sl3OdpV3o+0ZERH3PPdes2gPzkUtW6Ti/CN23+rPMq8yzfE1ByBXVX2v+Gm58Lq9vfeyuVBfhBrrQ67XF5H7NeZ6fRFqrA+5Xl9EftTYWIRoeeq4Yb3j1tMPjF5FNdslDx6wSxw3LPudWI1t8mvZ3ZVzay48clC0adUiZi34MJ7NgbXRNpWUxc1VduQsaJ27XWgZn9ujWxzQr3NsLCmL2598N9vlNFv/WlQcERFtW1V/bhW1ax23nn5gs3zNAchln9uze7RuWfPDTK+igpx43d7We9lcqS9CjfUh1+uLyP0ac72+CDXWh1yvLyI/amwMdufMc6Vl6Zg5f3UsX7chPt5YEt+/f3akUhFTrzwiBvfomO3yGs3m0rIY+Z/T4sP1m+OP5x6SM6OTERE/eeD1uPvZBXHwgF3i/50/Kqtro/3x+YXx/ftfi+6dynfkzIcQLSLiqbdWxBkTZ0bbVi1i+ndGR4/Cpj9rn0veWfFRHPPLJ6MsHfF/Fx0Wn2wujbufXRAPv740jtq7e9x51shslwjApzz06uK45I8vR99d2sX//NvwWL5uY/ToVD5qk0udAlXfy+ZifRFqrA+5Xl9E7teY6/VFqLE+5Hp9EflR446obVZkTbQ817JFqtoWsk+8uSIembMsbnx0Xtx46gFZrKxxzXinfJSza4c2OTeHfeGRg+JPMxfGrAUfxjNvr4rP7pGdgK9qF9qFedKFlnH4Ht3iwH6d46WFa+K2J9+NH29jRIWG8evH3o6ydMTRQ3rE/v06R0RE145t4uHXl8b0eati7YbNUVjQOrtFAlBNpkN//PA+MWpQ7vxx8dM+/V42F6lx5+V6fRG5X2Ou1xehxvqQ6/VF5EeNDSk3Zt6oN5cdtUdERDz46uJ4e/m6LFfTeHJxlDOjZ2FBnFax9tj1WVwb7S8vLooP1nwSPTq1rawnX6RSqbjymPK10e59/r1YvtbaaI3lnRUfxT9e+SAionJ9uoiIPXt2isE9Osam0rKYNmdZtsoDYCvWbyqJx95YHhG5sdkSADQVuZU2sNOG7VoUY4f2jHQ64oZH3852OY1ic2lZTHm9fFfO8fvm5hvFC48YFG1btYgX3vswnn57ZaPf/saS0ri5yo6c+dSFlvHZwd1iRP9dYmNJWdz65DvZLqfZuOnReRVdaD1j2K5F1X6Web5lQmwAcsNjbyyPDZvLon/X9rFPn/xfvgQAcoUQrQm6/OjybrSHXl0c85Y1/W60Ge+sijXrN0e3jrk3ypnRo1o32rxG70b7ywvvx+LiDdGjU9s4dWR+daFllO/UWf67/cfnF+pGawRvL/8oHvjX4oiIyse+qvEV3Q1PvbUy1m7Y3Ki1AbBtk14t/+PGuH17Z3UtVgBoaoRoTdA+fYri2H3Ku9FufKzpd6Nl3igeu0/ujXJWlelGe7GRu9E2lpTGLRVroV2Up11oGZ8d3C0OquhGu+UJ3WgN7abHyrvQjhlaswstonykcw8jnQA55eONJfH4m+WjnLnaoQ8A+Sp3Ewd2yuVHla9d9NCri+OtJtyNtrm0LB6eUzHKmeNrfvQoLIivHdI/IiJ+NbXx1kb7fxVdaD0L28YpedqFllHejVb+u/3HmQtjmW60BlO1C+3yo2p2oWVknneZMBuA7DLKCQANR4jWRA3tUxjH7dOrvBvt0XnZLqfBPFtllPOQgbm/Q8gFR+webVu1iJcWronp8xq+G616F9rgvO5CyzhscNc4eMAusamkLG7VjdZgbnx0XqTTEWO30YWWkelyeGreiij+xEgnQLZl/qgx3ignANQ7IVoTllkbbdJrS5psN9qkV8s7ZY4b1itatsj9N4o9Cgvi9M+Ud6M1xk6d/++F92NJRRfayQf3bdDbaiyf7kZbWqwbrb69vXxdPFjx3LpsO11oERF79OwUe/bsGJtL00Y6AbKs6ijnOKOcAFDvhGhN2JDehXH8sF4VO3U2vW60zaVl8fDr5R/ax+/bJ8vV1N75R+weBa3Lu9GeasButKbYhZZx6KAt3Wi32amz3t346Nu16kLLyHxQm2SXToCsevSN5bGxpCwGGOUEgAYhRGviMl0kk19bEm8ubVrdaM+8vTKKP9kc3Tq2zdldObemR6eCOP2Qhu9G+3+zFsWS4g3Rq7CgyXShZaRSqbhSN1qDmLdsSxfa5VvZkXNrMiOd0410AmTV5Mwo53CjnADQEIRoTVzVbrSmtjba5Iqul+PzZJSzqvMqutFeXrgmnnxrRb1f/4bNpXHz4+UdWheNzu8dObdl1KCuMXJAl4q10Zr+LrSN5cbHyrvQjt2nZ+zTJ7kLLaL6SOdUI50AWfGRUU4AaHBCtGag6tpoTaUbbVNJlVHOHN+Vc2uqd6PNq/dutP/3wqJYura8C+2rBzWtLrSM8rXRyn+3/zRzkW60ejBv2bp4KNOFVrHDb21lRqoz6xQC0LgenbssNpaUxcBuHWJob6OcANAQhGjNwN69CmPcvr0ioul0oz3zzpZRzoMH5M8oZ1XnHzEoClq3iFcW1W832obNpXFLRRfaxU20Cy1j1KCuMXJgl9hUWha36EbbaTdU7Mh53D69Ymgd19IZP7z8Nebpt1dG8XojnQCNLdOhP27fXkY5AaCBCNGaiUxXyaTXlsQbS9dmuZqdl1nzY9y++TfKmdG9U9v4esVOnb+qx260+2aVd6H1LiqIrzaxtdA+rWo32p9nLoolxZ9kuaL89daydZUbA9R2LbSqBvfoFHv17BSbS9PxyJyl9V0eANtRPspZ/ge5fNpsCQDyjRCtmdirV6fKxb/zvRutfJSz/EN6vq/5cd7nyrvR/rVoTTxRD91oGzaXVnZkXTR6cLRt1XS70DIOHdQtDqnoRrv1CTt17qhMF9rxw3rFkB0cA8qMVk+2SydAo3p07rLYVFIWu3frEEN6d8p2OQDQZAnRmpHLjtojUqmIya8tjblL8rcb7Zl3VsbaDSXRvVP+jnJmdO/UNs4YNSAi6mdttPtmLYplazeWd6EdtFs9VJgfrqjYqfPPMxfF4jW60erqzaXrKoOvzI6+O2Jc5S6dRjoBGtOkyg59u3ICQEMSojUje/XqVPkhN5+70TJvFPNxV86tOe9zu0e71i3Lu9He3PFutObYhZYxalBX3Wg74cbHdr4LLSJicI+OsXevTlFSlo6HjXQCNIp1GzZXdrPn42ZLAJBPhGjNzOUV3Wj/nJ2f3WibSsrikYpRzvF5PsqZ0a1j2zhjVGanzrd2uBvtzzMXxrK1G6NPM+tCy8h0o903SzdaXVTtQtuRtdA+LfO8NNIJ0Dgee2N55Sjn3r2McgJAQxKiNTN79tyyNtoN0/KvG+2Zt8tHOXt0ahsH5fkoZ1XfzHSjvV8cj7+5vM6XL+9CK+/Aam5daBmjBnWNz+xup866urFiLbRx+/aKvXvteBdaxriKLoin562MNes37fT1AbB9D1V06I8fbpQTABqaEK0ZyqyNNuX1pTFncX51oz3UxEY5M6p3o9V9bbQ/zVwYy9eVd6F9pRl2oWXoRqubN5du2ZFzZ9ZCq2pQ9y0jnY/MWVYv1wnA1q3bsDmeNMoJAI1GiNYMVetGe/StLFdTe5tKyuKRinWWxg9vetu3Z9ZGe7WO3WgbNpdWrgN28Zjm2YWW8Zndu8ao3bvG5tJ03Py4brQkmef/+H1710sXWkbm9SWzfiEADePRuRWjnN07xF49jXICQEMTojVTmbXRHn59Wby+uDjb5dTK02+viHWZUc7+u2S7nHrXtWPbOOPQunej/fH58i60XTu3i6+M6NuQJeaFKyrW9fp/LyyKD3SjbdMbS9fG5NeWRipVf11oGZmRzmfeNtIJ0JAyHfon2JUTABqFEK2Z2qNnpzihopsrX3bqnPRqeRfauH17R4smNMpZ1XmH7x7t25R3oz32RnI32obNpXHrkxVdaKMHR5tWntKH7N41Dh1U3o12i260bcqsiThu396xVz0vRD2oe8cY0ruwfKTzdSOdAA1h3YbN8VTFKOc4o5wA0Ch84m7GLhszOG+60TaWlFYZ5Wy6bxS7dmwbZ4waEBG160b74/MLY0VFF9q/jWi+a6F92uVHbelGe//D9VmuJvfMXbI2/jm7ogttTP12oWWM37dXREQ8ZJdOgAYxbe6y2FRaFoOMcgJAoxGiNWN79OwUJ1Z0o+X6Tp1Pz1sZ6zaURM/CtjGiX9Mb5azqvM+Vd6O99kFxPDp3291outC2rVo3WsV6cWyR6T5tiC60jHEV66I9+/bK+PBjI50A9S3ToT9+eB+jnADQSHzqbuYuO6q8G+2ROcti9ge5242W2UHw+GFNd5Qzo0uHNnHmoQMiIuL6R9/aZjfavbrQtiuzU+dfdKNVM2fxli60y+t5LbSqdq860lnRRQpA/VhbZZQzs5kLANDwhGjN3OAeneLz+1V0o+Xo2mgbS0pjasW6Sk15lLOqb1asjTb7g7UxbSvdaJ9s2rIj5yVjdKFtzciBXeKwwZmdOnWjZWS60Mbv2zv2bODxnxMqnq+TXhOiAdSnRytGOQf36Bh79uyY7XIAoNnwyZu4dEz5Tp1Tc7Qb7el5K2PdxuYxyplRrRttWs1utHuffy9WflTehfblA3WhbYtutOrmLF4bU15v+C60jMxI5zNGOgHq1aSKXTnH2ZUTABqVEI0Y3KNjZTfa9Tm4NlrmjWJzGOWs6puH7x4d2rSM1xdX70b7ZFNp3PbkuxERcakutO06eECX+OzgblFSlo6b7dQZNzz6VkREnDC8T+zRCItQD+zWIYb2LozSsnQ8/LpuNID6UD7KuTIitnT8AgCNw6dvIqK8G61Fqnynp1zqRttYUhpT55SPcja3N4rb6kbLdKHttku7+LK10BJdcXR5x9VfXng/Fq1uvt1ory8ujodfX1axI+fgRrvd8ZUjnXbpBKgP0+ZUHeW0KycANCYhGhGRu91o098qH+XsVVgQBzaTUc6qqnaj/fqxt+OvLyyqXNPq0jGDo3VLT+EkBw3oEofvUd6Nds0Ds+Mfr3wQM95ZFaVlW9+woanK7MB7YiN1oWVkFrx+9p1VsdpIJ81IaVk6Zryzqtm+5tBwMh36NhQAgMbXKtsFkDsuPWqPeOBfi2Pa3GXx2vvFse9uRdkuacuunPv2alajnBm7dGgTh+/ZLabMXha/mPpW5fGWqYgObT19a+uQgV1i+ryV8dgbK+KxN8p3M+tdVBDXnDg0jhvW9D+EzP6gOB6ZU9GFdlTjdaFFRAzo1iH26VMYry9eG4+8vjROGdmvUW8fsmHK7CVx7YNzYknxhspjzek1h4ZT/MnmmD6vfJSzuWy2BAC5RBsLlQZ17xhf2H/XiNiydlI2bdhcGtOa6ShnxpTZS+Lh2ctqHC9NR1z6x5djymwjckmmzF4Sv3ik5u/z0uINceE9LzWLxzDTvXji8D4xuEfjj/5kNhgw0klzMGX2krjwnpeqBWgRzes1h4aTGeXcwygnAGSFEI1qLh0zuGJttOXx6vtrslrL9HlbRjkP6Nv8RjlLy9Jx7YNzYnsDQNc+OMeI0HZs7zHMHGvqj2H1LrSG35Fza4x00lx4zaGhZf4YoQsNALJDiEY1u3fvGCdlutGyvDbapFcXR0R5F0tzHOWcOX91jU6GqtIRsaR4Q8ycv7rxisozHsOIGyq60D6/X58Y3KNjVmoY0K1DDNvVLp00fV5zaEjlo5zlSxJYDw0AskOIRg2XVHSjPfpG9rrRNmwujWlzl0dE8/1r6/J12/4gtiPnNUfN/TGc/UFxTJ2zLFqkynfgzabKkc5XjbLRdDX31xwa1tQ5y2JzaTr27NmxUTeIAQC2EKJRw+7dO8ZJB5R3o2Vrp86n3loRH20sid5FBXFA385ZqSHbenQqqNfzmqPm/hhmnr/Z7ELLyHRNzHh3Vaz6aGNWa4GG0txfc2hYkzOjnPv2yXIlANB8CdHYqkvH7BEtUhGPvbE8/rVoTaPffuaNYnMd5YyIGDmwS/QuKoht3ftUlO/2NnJgl8YsK68kPYYREa1apGJQ9w6NVlNjmf1BcUybW9GFlqW10Krq37XqSGfNzTKgKXhj6drt/tzrNjuqeH2VUc7hvbJcDQA0X0I0tmpgtw5VutEad6fOqqOc45rxmh8tW6TimhOHRkTUCIEy319z4tBo2UxDxtrY3mOYUVKWjjMmzmxy3VGZ5+0X9t81BnXPbhdaRqZ7YtJri7NcCdS/3z27IK59cE7l91t7zUmH1212zCNzlsbm0nTs1bNTVnZZBgDKCdHYpsvG7BEtW6Ti8TdXxCuN2I2WGeXs04xHOTOOG9Y7bj39wOhVVH30p1dRQdx6+oFx3LDmGzLW1rYew95FBfGTzw+NHp3axhtL18XX7ni+yQRpr71fHNPmLo8WqfI1DnNF5UjnO0Y6aVrufmZ+XPPA6xERccERg+LWr9V8zYmIOHjALl632SFVO/QBgOxple0CyF0DunWIk/bfNf720vtxw7S34q5vjGyU251klLOa44b1jmOG9oqZ81fH8nUboken8lEgnQy1t73H8PA9usepv3ku3li6Lk777fNx7zcPiW4d22a75J1yw6O514UWEdGva/vYd9eieO2D4pjy+tL42iH9s10S7LS7nplf2YF24ZGD4jvH7hWpVCrG7rPlNefjjSXx/ftnxwvvfRhvL1+nk4g6KV6/OZ5+e2VEGOUEgGzTicZ2XTpmcGU32ssLP2zw29uwuTSmzSlfL2lcM92Vc2tatkjFqEFd4wv77xqjBnUVoO2AbT2Gg7p3jD+f95noWdg23ly2Lk777XOxMo+7pF59f01lF9qlOdSFlpHZbTfTVQH5bOLTWwK0i6oEaBHVX3NOO6R/jB3aM9LpiBsffTubJZOHMqOce/cyygkA2SZEY7sGdOsQX6xYG+2GRxt+p84n31oRH28qjV07t2v2o5w0nt27d4w/fbM8SHtr2Ud5HaTdULEj50n77xq751AXWkbVkc58fYwhojxAu+6h8gDt4tGD4ttVArStufzo8g0+Hnx1cby9fF2j1EjTMMkoJwDkDCEaiTLdaE80QjfapFfL3ygeP6zXdj+MQH3bvXvH+PN5o/I6SHv1/TXx6BvLc2ZHzq3p26V9DN+tKMrSEQ+/vjTb5cAOubNKgHbJ6MHxrbHbD9AiIvbpUxTH7lPejXaDbjRqqXj95nh6XvkopxANALJPiEai/l07xJcqd+psuG60DZtL49G55aOc441ykgUDu3WIP583KnoVFsRbyz6KU3/zXKxYlz9BWub5edIBu8bAbh2yXM22ZT4IZkJzyCd3TH83/qNKgPbvY/es9R99Lj9qz4iIeOjVxTFvmW40kj08Z2mUlGVGOXOvuxgAmhshGrVySUU32pNvrYiXGqgb7Yk3t4xy7m+UkywpD9I+E70KC2Le8vKOtHwI0v61aE089sbyaNkiFZeOyc0utIzMSOdz7xrpJL/cMf3d+OmkuRFR3qVdlwAtImJon8I4bp9eFd1oDb9EAvkv88eG8brQACAnCNGolcboRtuy5odRTrJrwKeCtFPzIEi7flr5jpwn7Z/bXWgR5SOd+1WMdE6ZbaST/FA1QLtszOC46pi6BWgZmbXRJr22JN7SjcZ2rFm/KZ6p2JXTZksAkBuEaNTapWP2iJYtUvHUWyvixffqtxut+ihnn3q9btgRmSCtd1FBvF0RpC1ftyHbZW3VK4vWxONvrqjoQsu9HTm3xkgn+eS3T1UJ0I7aI67cwQAtImJI78I4flivip06daOxbY+8vqxylHNQDm4UAwDNkRCNWuvXtX18+cCG2anziTeXx/qKUc79diuq1+uGHVUjSPtNbgZpN1R0oX3xgF1jQI53oWVkQrTn56/K+S4/mrffPPVO/OfkKgHa0XvsdLf0ZUfpRiNZpkP/BF1oAJAzhGjUySWj94hWDdCNNum18pGu8cN7G+Ukp/TvWh6k9SkqiHdWfFwepK3NnSDt5YUfVnahXTI6P7rQIj410mmXTnLU7U++E/81+Y2IiLj8qD12eITz06p2o1kbja358OMqo5zWQwOAnCFEo07Ku9F2i4gtazDtrKqjnN4okovKg7RRW4K03+ZOkJb5AJ5PXWgZmV14J726OMuVQE23P/lOTPjnlgDtymP2rNfrz6yNNvm1JfHmUt1oVPdIxa6cQ3oXxu5GOQEgZwjRqLNLxgyOVi1SMX3eynjxvdU7fX1GOckH/bq2rxaknZIDQdrLCz+MJ/JsLbSqMqH5zPmrc3JMlubrtioB2hVH13+AFhGxd6/CGLevtdHYusoO/X17ZbkSAKAqIRp11rdL+/i3EZlutJ1/4//Qq1vW/DDKSS7LBGm7dm4X7+ZAkJZ5/n3pgF2jf9f86kKLiNhtl/axX9/OUZaOeNguneSIW594J/67IkC78ug944qj6z9Ay6i6NppuNDKMcgJA7hKisUMuHr2lG+2FBTvejfbJptJ4dO7yiPBGkfxQHqR9ZkuQ9pvnYlkWgrSXFn4YT75VsRZaHnahZZyQ2aXzNbt0kn23PPF2/GzKlgAtM3LZUPbuVRjjK54DNzxaP0skkP8embM0SsvSMdQoJwDkHCEaO6Rvl/bxlYN2vhvtiTeXxyebS2O3XdrFcKOc5Im+XaoEaSvLNxto7CAt87z78oH52YWWcXzFqNLzRjrJslueeDv+Z8qbERFx1TENH6BlXHbUHpFKRUx+bWm8sXRto9wmuS3ToT/erpwAkHOEaOywi44s70Z7+u2VMWsHu9Eequg+Gb+vUU7yy6eDtMbsSHvxvQ/jqbdWRKsWqbhkdON80G8ou+3SPvbv2znS6YgpRjrJkpsf3xKg/fsxe1aOWTaGvXp1quzEtjYaqz/eFM++syoidOgDQC4SorHDqnej1X0M5ZNNpfFYxSinv7aSj6oGafMrgrSlxQ0fpGV25PzygbtFv67tG/z2GlpmnG3Sq0Y6aXw3P/52/O/D5QHat8buGZc2YoCWcdmYLd1oc5foRmvOHnl9yyjnwDzbcRkAmgMhGjslszbaM2+vipnz69aN9niVUc59dzXKSX7KBGm77VIepJ3624YN0qp2oV08On/XQqsqM9I5c8HqrO94SvPy68fmVQZo3z52r7hkTHY6O3WjkZFZH9IfFwEgNwnR2Cm77dI+vnJQ34io+6LIk6qs+WGUk3z26SDtlN/MiCXFnzTIbWW6PptKF1pE+evIAf0qRjpfN9JJ4/j1Y/Pi54+UP5++fexeWQ+lL69YG+2fs5fGnMW60ZqjqqOc441yAkBOEqKx0y4ePShat6xbN9r6TSXx2Bvlo5wn7NunIcuDRrHbLluCtAWr1sepv3mu3oO0F99bHdPnrSxfCy2Pd+TcmswHxoeMdNIIbno0twK0iIg9e3aqfB7oRmueHq4Y5dynT2EMMMoJADlJiMZOq9qNVtu10R5/Y0V8srk0+nZpF8N2LWzI8qDRZIK0vl3Kg7RT6jlIy+zI+W8jdou+XZpGF1rG8RXhwSwjnTSwGx+dF7+YWv5/1XeOy40ALSPTjTbldd1ozdEku3ICQM4TolEvLh49OFq3TMWz76yK599dlXj+5MpdOfsY5aRJKQ/SRkXfLu3ivYogbfGanQ/SXliwpQstlz7015ddO7erHOn8p106aSA3TJsXv6wI0L573N5x0ZG59Vzao2enOGF4eXd2XZdIIL+t+mhjzHjXKCcA5DohGvVi187t4quV3WjbH0NZv6kkHn1jWUR4o0jTtGvndvHn80ZFvy7t6y1IyzyvvnJQ0+tCy7BLJw3phmnz4lfTtgRoFx45KMsVbd1lYwZHKhXx8OvL4vXFxdkuh0by8OvLorQsHcN2LYz+XY1yAkCuEqJRby6q6Eab8e6qeG473WiPv7EiNmwui35d2hvlpMnatXO7+NN5n4l+XdrHwtU7F6TNWrA6nn67vAst1zpn6lNmd8JZ762OZUY6qUfXT3urMkD73vG5G6BFlHejnZjpRkv4oxRNR9UOfQAgdwnRqDfVu9G2PYYy6bXFEWFXTpq+8o606kHaBzsQpN1Q2YXWt8l2oUVE9OncLg7MjHS+phuN+vGrqW9VdnJeffzeccERuRugZVx2VHk32iNzlsXsD3SjNXWrPtoYz76zMiJ06ANArhOiUa8ya6M99+7qmPFOzW60qrtyeqNIc9CnIkjr3zUTpM2oU5BWtQvt4tG5/+F/Z42v6MCZ/Jp10dh5v5r6VtxQsdPl98ftHefnQYAWETG4x5ZuNDt1Nn0Pv74sytIR++5aFP26Nt0/lABAUyBEo1716dwuTj64vBtta4siP/bG8tiwuSz6d20f+/QxyknzUDVIW7T6kzjlNzPi/Q/X1+qyma7OrxzUN3bbpel/uBq3b6+IKB/pXFpspJMdVzVA+8G4IXHe5/IjQMu4rGKnTt1oTV+mQ3+cPy4CQM4TolHvLjpycLRp2WKr3WiZBcPH7WuUk+ald1F5kDagIkg79bfPJQZpM+evjmfeXhWtWzaPLrSI8sdpRP9dKnbpNNJJ3aXT6fjlpwK0b35u9yxXVXeDe3SMz++X2alTN1pTtfKjjZXvlXToA0DuE6JR76p2o1VdG+3jjSXx+JtGOWm+eheVbzYwoLIjbftBWnPrQsvIvD5Mti4adZROp+NXU9+qHIH84fj8DNAyLh2zR7RIRUzVjdZkPfz6UqOcAJBHhGg0iItGD4o2LVvE8/NXVy6Wa5QTMh1po2JA1/bx/oflQdqi1TWDtOffXRXPvpPpQmu6O3JuzfGZkc4FHxrppNYqA7TH3o6I8gDt3MPzN0CLqN6Ndr2dOpukTIf++OH+uAgA+UCIRoPoXdQuThlZ3o32q6lvxYx3VsZvp78bERHHD+tllJNmrVdRQfz5vFExsFuHakFaaVk6ZryzKv7xygdx7YNzIiLiqwf1jV07t8tyxY2rd1G7OKj/LhFhpDNbqv4uznhnVZSWpbNdUjWfrq+ktCx+2cQCtIxLjyrvRps2VzdaU7Pyo43x3LtGOQEgn7TKdgE0XRceOSj++PzCmLXgwzj1t89XHv/ri+/H/n07x3HDvGGk+epVVBB/+uZn4tTfPhfzV34cX/j1M9GyZSpWrNtY7byhzbRrc9y+veOF9z6MSa8uiW8cNjDb5TQrU2YviWsfnBNLqnQB9i4qiGtOHJoTr9tbq69j25bx0cbSiIj40QlD45zPNp3fmUHdO8YX9t817n/5g7h+2ltxx5kHZ7sk6smU2eWjnMN3K4q+XYxyAkA+0IlGg/nXojVRspXuhVUfbYoL73kppugwoZkr70j7TPTo1DZWr99UI0CLiPjh/bOb5XMls0vdC+99GEuKP8lyNc3HlNlL4sJ7XqoWUEVELC3ekBOv29uqLxOg/duI3ZpUgJZx6ZjBFd1oy+O193WjNRWVo5y60AAgbwjRaBClZenKcbRPy8Rq1z44J+dGhKCxdevYNvGc5vhc6VVUsGWk87WlWa6meci8bm/tNy0XXre3V1/GM2+vbJLPld27d4yT9t81IiJuePSthLPJByvWbYzn55ePco4TogFA3jDOSYOYOX91jU6BqtIRsaR4Q8ycvzpGDeraeIVBjpk5f3Us30oHWkZzfq6MH14x0vnakji7CXYX5Zravm7v8+Mp0bJF469rWVqWjg0lZds9pyk/Vy4ZMzj+75UPYtrc5fHq+2ti+G6ds10SO2FKxa6c+xnlBIC8ohONBrF8Xe121KvtedBUea5s2/HDekcqFfGikc5GUdvfsQ0lZfHxptJG/0oK0Op6P/JNtW40O3Xmvcl25QSAvKQTjQbRo1NBvZ4HTZXnyrZlRjpnLfgwJr+2tEmudZVLavs7dsPJ+8cB/XZp4Gpqennhh3H5fa8knteUnyuXHrVH/N8rH8SjbyyPfy1aE/v17ZztktgBy9dtqBzlPD4HNusAAGpPiEaDGDmwS/QuKoilxRu2un5NKso/II8c2KWxS4Oc4rmyfeP37R2zFnwYk15dLERrYKlU+e/btlYUy/wunrBfn6yMc+66S7v47ylvNOvnysBuHeKkA3aNv7/0Qdzw6LyYeJadOvPRwxW7cu7Xt7NRTgDIM8Y5aRAtW6TimhOHRkT5B5uqMt9fc+LQrHwQg1ziubJ9x+9bPtL50sI1sXiNkc6GMnP+6jj77lmV4VQu/i56rpS7dMwe0bJFKh6r6EYj/0x6LbMrZ68sVwIA1JUQjQZz3LDecevpB0avouqjNb2KCuLW0w+M44wwQER4rmxPz8KCOLh/eWfR5IoPntSv599dFWfdNTPWbyqNzw7uFjecsn/O/i56rlR0o1WsjXb9NDt15pvyUc7VEWFXTgDIR8Y5aVDHDesdxwztVbED4Ybo0al81KapdwpAXXmubNv44b1j5oLVMfm1JXHu4btnu5wm5fl3V8U37p5VGaDdceZBUdC6ZZwwvE/O/i56rkRcWrFT5+NvrohXFq2J/a2Nljcenr000umI/ft2jt12McoJAPlGiEaDa9kiFaMGdc12GZDzPFe27vhhveInD75eOdLZp3O7bJfUJFQN0A7fo1v89ozyAC0i938Xc72+hjagW4f44gG7xl9ffD9umPZW3PWNkdkuiVp6KLMrpy40AMhLxjkByGk9Cgvi4AFGOuvTc++uirPu2nqARn64ZPTgaNkiFY+/uSJeXvhhtsuhFpav2xAzF5SPch5vPTQAyEtCNAByXqZrY5IQbafNeGdVfOOuWfHJ5tL43J7dBWh5KtONFhFxw6PzslwNtTHFKCcA5D0hGgA57/hhvSKVinh54Zr4wC6dO2zGO6vi7Lu3BGi/+foIAVoeu3RMeTfaE2+uiJd0o+W8zCjnCcONcgJAvhKiAZDzehQWxMiKkc5/6kbbIQK0pqd/1w7xpUw32jTdaLls+doNMatylFOIBgD5SogGQF4YX9G9kenmoPaefWdlfOPumfHJ5tI4QoDWpFxS0Y325Fu60XLZPytGOQ/o1zl2tTkKAOQtIRoAeeG4ipHOVxatifc/XJ/tcvLGs2+vjLPvnhUbNpfFkXt1j9sFaE1K/64d4ssHlnejXa8bLWdl1nO0KycA5DchGgB5oUenqiOdS7NcTX549u2VcfbvtgRot50uQGuKLhm9R7RqkYqn3loRL76nGy3XLKsyyjlOiAYAeU2IBkDeyCzI/ZB10RI9UyVAGy1Aa9L6dW0fXz5wt4iIuH7aW1muhk/752tLIp2OOLBf5+hjlBMA8poQDYC8cWzFSOe/Fq2JRauNdG7LM1VGOEfv1T1uM8LZ5F08enC0apGK6fNWxovvrc52OVQxuaJzVhcaAOQ/IRoAeaNHp4I4ZGDFSOds3Whb8/S88gBtY0lZjNm7R9z29RHRtpUAramr3o1mbbRcsWzthpj1nlFOAGgqhGgA5JXxw/tERMQk66LV8PS8lXHO77YEaLeefqAArRm5ZIxutFyTGeUc0X8Xo5wA0AQI0QDIK8ft0ytaGOmsYfq8FZUB2lECtGapb5f28W8jdKPlksyunLrQAKBpEKIBkFe6d2obhwzsGhERk20wEBERT721Is793QuxsaQsjh7SI24RoDVbVddGe2GBbrRsWlq8IWYtKN8tddy+vbJcDQBQH4RoAOSdcRW7dArRKgK0328J0G7+mgCtOevbpX185SDdaLkgs27jiP67RO8io5wA0BQI0QDIO5Ujne8XN+uRzkyAtqmkLI4e0jNu+ZpNBIi46MjybrSn314Zs3SjZc2kV8tDtPFGOQGgyRCiAZB3undqG5/ZvXmPdD5ZI0A7MNq08t86mW60vhERcf20t7JcTfO0tHhDvPBeZpRTiAYATYV32wDkpcwH00nNMER74s3l8c2KAO2YoQI0arp49KBo1SIVz7y9KmbO143W2DLh/kH9d4leRQVZrgYAqC/ecQOQl44bVj7S+er7xbFwVfMZ6XzizeVx3h9ejE0lZTF2aM+4+TQBGjXttsuWbrQbHtWN1tgy4f744brQAKAp8a4bgLzUrWOVkc7ZzaMbrWqAduw+PePXAjS24+LRg6J1S91ojW1J8SfxYsUo5/HDhGgA0JR45w1A3sp0eWQW8G7KHn9zeZz3+y0B2k2nCtDYvqrdaNZGazyTX1saEREHDzDKCQBNjXffAOStzC6dr33QtEc6H39jeZz/+xdjU6kONOrm4tGDo3XLVDz7zqp4/t1V2S6nWcish2ZXTgBoerwDByBvde3YNkYNKh/pbKobDDz2xrI4/w/lAdpx+/SKX592YLRu6b9vamfXzu3iq5XdaPOyXE3Tt3hN+ShnKhVxvBANAJoc78IByGvj9+0TEVu6P5qSx95YFhf84aXYVFoWxw/rFTeddoAAjTq7qKIbbca7q+I53WgN6p+zK0Y5+3eJnoVGOQGgqfFOHIC8duw+PaNli1S89kFxvLfq42yXU28enbslQBu3b6+48VQBGjtm187t4uSDrY3WGCa9ujgiIsbt2yvLlQAADcG7cQDyWteObWPU7k1rpPPRucvignterAzQbjhFgMbOuejIwdGmZYt47t3VMeMd3WgNYfGaT+KlhWuMcgJAE+YdOQB5b1zFB9amMNI5bU55gLa5NB3j9+0tQKNe9KnSjXbDo7rRGkLm9ccoJwA0Xd6VA5D3MiOdsz9Ym9cjndPmLIsL790SoF1/yv4CNOrNhUcO0o3WgDKdsOOH60IDgKbKO3MA8l7Xjm3j0DzfpXNq1QBteO+4QYBGPetjbbQG88GaT+LlzCjnMOuhAUBT5d05AE1CZqRz0qv5F6I98vrSuKhqgHby/tFKgEYDuGh0eTfa8/NXx7PvrMx2OU3GPzOjnAO6RA+jnADQZLXKdgEAUB+O3adX/OD+1+L1xWvjzunvxtA+RTFyYJdo2SKV7dKqKS1Lx8z5q2P5ug3Ro1NBFK/fFJf++eXYXJqOE4b3jusFaDSg3kXt4pSRfeP3M96LX019K1IRsXzdxujRqSDnni+ffq7kWn0RW2r8w4z3IsKunADQ1KXS6XQ620U0prVr10ZRUVEUFxdHYWFhtssBoJ5Mmb0kLvvzK7GppKzyWO+igrjmxKFx3LDcWKNoyuwlce2Dc2JJ8YYaPztxvz7xq6/uJ0CjwS0p/iQO/9njUVJW/S1gLj1ftvZcyaX6IrZeY49ObeO6L+yTMzUCALVT26zIO3UA8t6U2UviwnteqhagRUQsLd4QF97zUkyZnf0Rz0yNWwvQIiKO26enAI1G8a9Fa2oEaBG583zZ1nMlV+qL2HaNK9ZtzJkaAYD6Z5wTgLxWWpaOax+cE1trq84c+85fX43Faz6JFqnsjIKVpdNx/bR5W60xIiIVET+dNDeOG9Y758bVaFoyz5etyYXny/aeK7lQX0RyjamIuPbBOXHM0F6ezwDQxAjRAMhrM+ev3mZ3V8baDSVx3UNzG6miuktHxJLiDTFz/uoYVbHLKDSEpvB8yfX6PJ8BoOkSogGQ15av234gkLF/386x6y7tGriarfvgw0/ilUVrEs+r7X2BHZXrz5faPlc8nwGAbBCiAZDXenQqqNV53z1u76x1hcx4Z1Wc+tvnEs+r7X2BHZXrz5faPlc8nwGAbLCCMQB5beTALtG7qCC2tfJQKsp39Rs5sEtjllVNPtRI85Drv4u5Xl9EftQIADQMIRoAea1li1Rcc+LQiIgaH2oz319z4tCsLvCdDzXSPOT672Ku1xeRHzUCAA0j6yHaLbfcEgMHDoyCgoIYMWJETJ8+fZvn/v3vf49jjjkmunfvHoWFhTFq1Kh4+OGHG7FaAHLRccN6x62nHxi9iqqPT/UqKohbTz8wjhvWO0uVbZEPNdI85PrvYq7XF5EfNQIA9S+VTqe3tkN3o7jvvvvi61//etxyyy1x2GGHxe233x533HFHzJkzJ/r161fj/CuuuCL69OkTo0ePjs6dO8ddd90VP//5z+P555+PAw44oFa3uXbt2igqKori4uIoLCys77sEQBaVlqVj5vzVsXzdhujRqXycKte6QfKhRpqHXP9dzPX6IvKjRgAgWW2zoqyGaIccckgceOCBceutt1YeGzJkSJx00kkxYcKEWl3HPvvsEyeffHL8+Mc/rtX5QjQAAAAAMmqbFWVtnHPTpk3x4osvxtixY6sdHzt2bDz77LO1uo6ysrJYt25ddOmy7YVbN27cGGvXrq32BQAAAAB1kbUQbeXKlVFaWho9e/asdrxnz56xdOnSWl3HL37xi/j444/jq1/96jbPmTBhQhQVFVV+9e3bd6fqBgAAAKD5yfrGAqlU9XUj0ul0jWNb86c//Sl+8pOfxH333Rc9evTY5nlXX311FBcXV34tWrRop2sGAAAAoHlpla0b7tatW7Rs2bJG19ny5ctrdKd92n333RfnnHNO/OUvf4mjjz56u+e2bds22rZtu9P1AgAAANB8Za0TrU2bNjFixIiYOnVqteNTp06NQw89dJuX+9Of/hRnnXVW/PGPf4zx48c3dJkAAAAAkL1OtIiIq666Kr7+9a/HQQcdFKNGjYrf/OY3sXDhwrjgggsionwU84MPPojf//73EVEeoJ1xxhlxww03xGc+85nKLrZ27dpFUVFR1u4HAAAAAE1bVkO0k08+OVatWhXXXXddLFmyJIYNGxaTJ0+O/v37R0TEkiVLYuHChZXn33777VFSUhIXX3xxXHzxxZXHzzzzzLj77rsbu3wAAAAAmolUOp1OZ7uIxrR27dooKiqK4uLiKCwszHY5AAAAAGRRbbOirO/OCQAAAAC5TogGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQQIgGAAAAAAmEaAAAAACQIOsh2i233BIDBw6MgoKCGDFiREyfPn275z/55JMxYsSIKCgoiN133z1uu+22RqoUAAAAgOYqqyHafffdF1dccUX84Ac/iJdffjkOP/zwOP7442PhwoVbPX/+/Pkxbty4OPzww+Pll1+O73//+3HZZZfF3/72t0auHAAAAIDmJJVOp9PZuvFDDjkkDjzwwLj11lsrjw0ZMiROOumkmDBhQo3zv/vd78YDDzwQc+fOrTx2wQUXxL/+9a+YMWNGrW5z7dq1UVRUFMXFxVFYWLjzdwIAAACAvFXbrKhVI9ZUzaZNm+LFF1+M733ve9WOjx07Np599tmtXmbGjBkxduzYaseOPfbYuPPOO2Pz5s3RunXrGpfZuHFjbNy4sfL74uLiiCh/gAAAAABo3jIZUVKfWdZCtJUrV0ZpaWn07Nmz2vGePXvG0qVLt3qZpUuXbvX8kpKSWLlyZfTu3bvGZSZMmBDXXnttjeN9+/bdieoBAAAAaErWrVsXRUVF2/x51kK0jFQqVe37dDpd41jS+Vs7nnH11VfHVVddVfl9WVlZrF69Orp27brd28kna9eujb59+8aiRYtydkRVjTsv1+uLyP0ac72+CDXWh1yvL0KN9SHX64tQY33I9foi1Fgfcr2+CDXWh1yvLyL3a8z1+iLUWB9yvb6I/KixrtLpdKxbty769Omz3fOyFqJ169YtWrZsWaPrbPny5TW6zTJ69eq11fNbtWoVXbt23epl2rZtG23btq12rHPnzjteeA4rLCzM+V9gNe68XK8vIvdrzPX6ItRYH3K9vgg11odcry9CjfUh1+uLUGN9yPX6ItRYH3K9vojcrzHX64tQY33I9foi8qPGutheB1pG1nbnbNOmTYwYMSKmTp1a7fjUqVPj0EMP3eplRo0aVeP8Rx55JA466KCtrocGAAAAAPUhayFaRMRVV10Vd9xxR0ycODHmzp0bV155ZSxcuDAuuOCCiCgfxTzjjDMqz7/gggvivffei6uuuirmzp0bEydOjDvvvDO+9a1vZesuAAAAANAMZHVNtJNPPjlWrVoV1113XSxZsiSGDRsWkydPjv79+0dExJIlS2LhwoWV5w8cODAmT54cV155Zdx8883Rp0+fuPHGG+PLX/5ytu5CTmjbtm1cc801NcZWc4kad16u1xeR+zXmen0RaqwPuV5fhBrrQ67XF6HG+pDr9UWosT7ken0RaqwPuV5fRO7XmOv1RaixPuR6fRH5UWNDSaWT9u8EAAAAgGYuq+OcAAAAAJAPhGgAAAAAkECIBvD/27v3mKrrP47jr+NRJiEyURAIuaiBtzxupsusNGVHzVTUaS63ENDWxCZbthrWZCOFLFoXpXnJQ+oSI0XNa7hEx5YJTRLNCEIHiYJ3BQ1E3r8/fuMkcDjfA/U7n4/+Xo+N9etwBk+Ovvqe8/khEhERERERERngIRoREREREREREZEBHqI9xI4dO4Zp06YhKCgIJpMJu3btUp3UxhdffIHhw4ejZ8+e6NmzJ8aMGYMDBw6ozrJLTk6GyWRq8RYQEKA6q4WwsLA2jSaTCQkJCarT7G7fvo3ExESEhobC09MTzzzzDAoKCpT1GG1j586dmDRpEvr06QOTyYSioiLtGpOTkzFo0CB4eXmhV69eiIqKwk8//aRN34IFC9r8nnz66afd1udKo6PdmEwmfPjhh9o0VldXY8GCBQgKCsJjjz2GyZMno7S01G19qampGDVqFLy9veHv74/o6GiUlJS0uI/KvbjSp3orrjSq3osrjSr34kqf6q0YPZ/R4bpi1Kh6K0Z9qnfiSqMO1xWjRtVbaS01NRUmkwmJiYn223TYy4McNarei1GfDnsxatRhL0aNqvdi9FpU9VaM+nTaiTvxEO0hVldXB4vFgjVr1qhOaVdwcDDS0tJQWFiIwsJCTJgwATNmzMCZM2dUp9kNHToUFy9etL8VFxerTmqhoKCgRV9ubi4AYM6cOYrL/rZw4ULk5uZiy5YtKC4uhtVqRVRUFC5cuKCkx2gbdXV1GDt2LNLS0txc1rLBWWNERATWrFmD4uJi5OfnIywsDFarFZcvX9aiDwAmT57c4vfm/v373dLWzKjxwbaLFy9i06ZNMJlMmD17thaNIoLo6GiUl5dj9+7dOHnyJEJDQxEVFYW6ujq39B09ehQJCQk4fvw4cnNz0djYCKvV2uLzq9yLK32qt+JKI6B2L640qtyLUZ8OWzF6PqPDdcWoUfVWXHlOqPq6YtSow3XFWaMOW3lQQUEB1q9fj+HDh7e4XYe9NGuvUfVejPoA9Xtp1l6jDntx1qjLXpy9FtVhK876dNmJ2wk9EgBITk6O6gyX9OrVSzZu3Kg6Q0REVqxYIRaLRXVGhyxdulQGDBggTU1NqlNEROTOnTtiNptl7969LW63WCyyfPlyRVV/c7aNc+fOCQA5efKkW5tac2W/N2/eFABy+PBh90Q9wFFfTEyMzJgxw+0t7XHlMZwxY4ZMmDDBPUEOtG4sKSkRAHL69Gn7bY2NjeLr6ysbNmxQUChSU1MjAOTo0aNt3qfDXpz1NVO5FRHHjbrtxZXHUeVeWvfpuBURx89ndNjJg5w951K9FZGWfbrtpJmzx1D1daVZc6NOW7l9+7Y88cQTkpubK+PGjZOlS5e2uY/qvbjS2EzFXpz16bKXjjyGqvbSXqMOe3H1taiqrXT0tbIO1xV34Heikdvcv38fWVlZqKurw5gxY1Tn2JWWliIoKAjh4eGYN28eysvLVSe1q6GhAVu3bkVcXBxMJpPqHABAY2Mj7t+/j+7du7e43dPTE/n5+YqqHi0NDQ1Yv349fHx8YLFYVOfY5eXlwd/fHxEREVi0aBFqampUJ7Wruroa+/btQ3x8vOoUu/r6egBosR2z2QwPDw9l27l58yYAwNfXV8nnN2LUp8NW2mvUaS9Gj6PqvbTu020ruj6feZBRo+qttNen006MHkPVOwHaNuq0lYSEBEydOhVRUVFu/bwd4Wqjqr0Y9emwF1cfQ5V7aa9Rl73o/lrU1T7V1xW3Un2KR/8OaPydaKdOnRIvLy8xm83i4+Mj+/btU51kt3//fvn222/l1KlT9v93om/fvnLlyhXVaQ5t375dzGazXLhwQXVKC2PGjJFx48bJhQsXpLGxUbZs2SImk0kiIiJUpz3U34n23XffiZeXl5hMJgkKCpITJ064P04c92VlZcnevXuluLhY9uzZIxaLRYYOHSp//fWXNo0P+uCDD6RXr15y9+5d90W10rqxoaFBQkNDZc6cOXLt2jWpr6+X1NRUASBWq9XtfU1NTTJt2jR59tlnHb5f9V6c9emylfYaddqL0a+ziNq9OOrTZSuuPJ9RvROjRtVbcdany05cfd6qciftNeqylW3btsmwYcPsj42O34nmSqPKvRj16bAXV3+dRdTtxVmjDntx9bWoqq240qf6uqICD9EeETofotXX10tpaakUFBTIO++8I3369JEzZ86oznKotrZW+vbtK+np6apTHLJarfLSSy+pzmijrKxMnn/+eQEgZrNZRo0aJfPnz5fBgwerTnuoD9Fqa2ultLRUfvzxR4mLi5OwsDCprq7Wpu9BVVVV0q1bN9mxY4d7oloxaoyMjJQlS5a4L8gBR42FhYVisVjs25k0aZJMmTJFpkyZ4va+xYsXS2hoqFRWVjp8v+q9OOvTZStGj2EzlXtxpVHlXtrr02ErrjyfUb0To0bVW+nIc0JVO3G1UeVOnDWq3kpFRYX4+/tLUVGR/TbdDtFcbVS1l448hs3cvZeONqrYiyuNqvfSWnuvRVVfW5o56lN9XVGBh2iPCJ0P0VqbOHGivPbaa6oz2hUVFSWvv/666ow2zp8/L126dJFdu3apTmlXbW2tVFVViYjI3Llz5cUXX1Rc9HAforU2cOBAWbVq1f8+qJWO9KWlpf3vgxxw1njs2DEB0OJJlArOGm/cuCE1NTUiIjJ69GhZvHixG8tElixZIsHBwVJeXt7ufVTuxZW+B6nYSmca3b0XVxpV7sWVPtVbeZCj5zO6XFeaGT3nUnVdaeZKn6rrSjNHjbpcV5o5alS1lZycHPuBRPMbADGZTGI2m6WxsdF+X1V76Ujjg9y1l3/S5669dKRR1V460qjTtcXRa1Gdri1Gr5VVX1fcoeu/9wdDiVwjIvY/g66b+vp6nD17Fs8995zqlDZsNhv8/f0xdepU1Snt8vLygpeXF65fv45Dhw5h9erVqpMeKTpv5+rVq6isrERgYKDqlDa+/PJLjBw5Uuufz+Dj4wPgvz93orCwECkpKW75vCKCN954Azk5OcjLy0N4eLhbPq+rOtvnzq10ptHde+lIo4q9dKRP1VYc0fm/yc2MGlV/Dc4+vy7XFUeNul1XHDWq2srEiRPb/E33sbGxGDRoEN5++22YzWa3dDjT2UZ37aUzfe7eS0caVe2lI426XFt0fi0KuNan+rriDjxEe4jV1tairKzM/u/nzp1DUVERfH19ERISorDsb0lJSZgyZQr69euH27dvIysrC3l5eTh48KDqNADAsmXLMG3aNISEhKCmpgbvv/8+bt26hZiYGNVpLTQ1NcFmsyEmJgZdu+o320OHDkFEEBkZibKyMrz11luIjIxEbGyskh6jbVy7dg0VFRWoqqoCAJSUlAAAAgICEBAQoLyxd+/eWLlyJaZPn47AwEBcvXoVGRkZ+PPPPzFnzhzlfb6+vkhOTsbs2bMRGBiI8+fPIykpCX369MHMmTPd0mfU2PzfwFu3biE7Oxvp6elu6+pIY3Z2Nvz8/BASEoLi4mIsXboU0dHRsFqtbulLSEjA119/jd27d8Pb2xuXLl0C8N8nk56engCgdC9GfXV1dcq3YtRYW1urfC+u/DoD6vbiSp/qrRg9n9HhuuKsUYetOOvTYSdGjc1UX1eMGlVvxdvbG8OGDWtxm5eXF3r37m2/XfVejBpV78WoT4e9uPLrDKjdiyuNqvdi9FpU9Vac9aneiVJu/943+tccOXJEALR5i4mJUZ1mFxcXJ6GhoeLh4SF+fn4yceJE+f7771Vn2b388ssSGBgo3bp1k6CgIJk1a5aWP6/t0KFDAkBKSkpUpzi0fft26d+/v3h4eEhAQIAkJCTIjRs3lPUYbcNmszl8/4oVK7RovHv3rsycOVOCgoLEw8NDAgMDZfr06W79QZ3O+u7cuSNWq1X8/PykW7duEhISIjExMVJRUeG2PqPGZuvWrRNPT09lvx+NGj/99FMJDg62P47vvvuu1NfXu63PURsAsdls9vuo3ItRnw5bMWrUYS+u/DqLqNuLK32qt2L0fEaH64qzRh224qxPh50YNTZTfV0xalS9FUda/xwqHfbirFGHvTjr02Uvzhqbqd5La60bVe/F6LWo6q0469NxJ+5iEhHpyKEbERERERERERHR/5suqgOIiIiIiIiIiIh0x0M0IiIiIiIiIiIiAzxEIyIiIiIiIiIiMsBDNCIiIiIiIiIiIgM8RCMiIiIiIiIiIjLAQzQiIiIiIiIiIiIDPEQjIiIiIiIiIiIywEM0IiIiIiIiIiIiAzxEIyIiItLU+PHjkZiY6NbPmZeXB5PJhBs3brj18xIRERHpjodoRERERI+An3/+GSaTCfn5+Q7fP2nSJEyfPt3NVURERESPDh6iERERET0CRo4cCYvFApvN1uZ9lZWVOHz4MOLj4xWUERERET0aeIhGRERE9JA4ePAgfHx8sHnzZofvj4+PxzfffIO6uroWt2dmZsLPzw9Tp07F1q1b8dRTT8Hb2xsBAQF45ZVXUFNT0+7nTE5OxogRI1rc9sknnyAsLKzFbTabDYMHD0b37t0xaNAgZGRkdOprJCIiItIVD9GIiIiIHgJZWVmYO3cuNm/ejFdffdXhfebPn4979+4hOzvbfpuIIDMzEzExMejatSsaGhqQkpKCX375Bbt27cK5c+ewYMGCf9S2YcMGLF++HCtXrsTZs2exatUqvPfee/jqq6/+0cclIiIi0klX1QFERERE5FxGRgaSkpKwe/duvPDCC+3ez9fXF9HR0bDZbPaDsby8PJSXlyMuLg4A7P8EgP79++Ozzz7D6NGjUVtbix49enSqLyUlBenp6Zg1axYAIDw8HL/++ivWrVuHmJiYTn1MIiIiIt3wEI2IiIhIYzt27EB1dTXy8/MxevRow/vHx8fDarWirKwMAwcOxKZNmzB27FhERkYCAE6ePInk5GQUFRXh2rVraGpqAgBUVFRgyJAhHe67fPkyKisrER8fj0WLFtlvb2xshI+PT4c/HhEREZGu+Mc5iYiIiDQ2YsQI+Pn5wWazQUQM7x8VFYXQ0FBkZmbi1q1b2Llzp/0vFKirq4PVakWPHj2wdetWFBQUICcnBwDQ0NDg8ON16dKlzee9d++e/X83H8Jt2LABRUVF9rfTp0/j+PHjnfqaiYiIiHTE70QjIiIi0tiAAQOQnp6O8ePHw2w2Y82aNU7vbzKZEBsbi40bNyI4OBhdunTB3LlzAQC//fYbrly5grS0NPTr1w8AUFhY6PTj+fn54dKlSxARmEwmAEBRUZH9/X379sXjjz+O8vJyzJ8//x98pURERER643eiEREREWkuIiICR44cwY4dO5CYmGh4/9jYWFRVVSEpKQnz5s2Dl5cXACAkJAQeHh74/PPPUV5ejj179iAlJcXpxxo/fjwuX76M1atX448//sDatWtx4MCBFvdJTk5GamoqPv30U/z+++8oLi6GzWbDxx9/3OmvmYiIiEg3PEQjIiIieghERkbihx9+wLZt2/Dmm286vW9ISAiioqJw/fr1Fn+RgJ+fHzIzM5GdnY0hQ4YgLS0NH330kdOPNXjwYGRkZGDt2rWwWCw4ceIEli1b1uI+CxcuxMaNG5GZmYknn3wS48aNQ2ZmJsLDwzv/BRMRERFpxiSu/HANIiIiIiIiIiKi/2P8TjQiIiIiIiIiIiIDPEQjIiIiIiIiIiIywEM0IiIiIiIiIiIiAzxEIyIiIiIiIiIiMsBDNCIiIiIiIiIiIgM8RCMiIiIiIiIiIjLAQzQiIiIiIiIiIiIDPEQjIiIiIiIiIiIywEM0IiIiIiIiIiIiAzxEIyIiIiIiIiIiMsBDNCIiIiIiIiIiIgP/AYXzIgyPfIKvAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# classify test data with KNN classifier of sklearn (for each possible k value)\n",
    "\n",
    "# calculate accuracy, error, recall, precision, sensitivity, specifity, f1 measure, confusion matrix\n",
    "#           based on your defined functions (using k=7)\n",
    "\n",
    "# plot test errors for each k\n",
    "k_values=range(1,54)[::2]\n",
    "accuracies = []\n",
    "errors = []\n",
    "recalls = []\n",
    "precisions = []\n",
    "sensivities = []\n",
    "specifities = []\n",
    "f1s = []\n",
    "\n",
    "for k in k_values:\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors=k).fit(x_train_numerical, y_train)\n",
    "    predictions = knn_classifier.predict(x_test_numerical)\n",
    "    \n",
    "    accuracies.append(accuracy_score(y_test,predictions))\n",
    "    errors.append(1 - accuracy_score(y_test, predictions))\n",
    "    cm=confusion_matrix(y_test,predictions)\n",
    "    tp = cm[1, 1]\n",
    "    tn = cm[0, 0]\n",
    "    fp = cm[0, 1]\n",
    "    fn = cm[1, 0]\n",
    "    sensivities.append(tp/(tp+fn))\n",
    "    specifities.append(tn/(tn+fp))\n",
    "    recalls.append(recall_score(y_test,predictions,pos_label='Ordinary'))\n",
    "    precisions.append(precision_score(y_test,predictions,pos_label='Ordinary'))\n",
    "    f1s.append(f1_score(y_test,predictions,pos_label='Ordinary'))\n",
    "    \n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.plot(k_values,errors,marker='o')\n",
    "plt.title('Error for Different k Values')\n",
    "plt.xlabel('k Value')\n",
    "plt.ylabel('Error Rate')\n",
    "plt.ylim([0, 1])\n",
    "plt.xticks(list(k_values))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n8uidTPZOVJE"
   },
   "source": [
    "### Evaluation Part: Decision Boundries, ROC Curves, Performance Comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "WdVnFQqNOVJF",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show decision boundaries for each classifiers on same plot (show pairwise since we have more than 2 attiributes)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "X_train_pca = pca.fit_transform(x_train_numerical)\n",
    "X_test_pca = pca.transform(x_test_numerical)\n",
    "\n",
    "y_train_binary = y_train.map({'Ordinary': 0, 'Heavy': 1}).values.astype(int)\n",
    "\n",
    "\n",
    "clf_dt = DecisionTreeClassifier(random_state=44)\n",
    "clf_dt.fit(X_train_pca, y_train_binary)\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=7)\n",
    "clf_knn.fit(X_train_pca, y_train_binary)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(16, 8))\n",
    "custom_handles = [\n",
    "    plt.Line2D([0], [0], marker='s', color='w', markerfacecolor='blue', markersize=8, label='Ordinary'),\n",
    "    plt.Line2D([0], [0], marker='^', color='w', markerfacecolor='orange', markersize=8, label='Heavy')\n",
    "]\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plot_decision_regions(X=X_train_pca, y=y_train_binary, clf=clf_dt, legend=2)\n",
    "plt.title('Decision Boundaries with PCA (Decision Tree Classifier)')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend(handles=custom_handles, loc='upper right',frameon=False)\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plot_decision_regions(X=X_train_pca, y=y_train_binary, clf=clf_knn, legend=2)\n",
    "plt.title('Decision Boundaries with PCA (KNN Tree Classifier)')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.legend(handles=custom_handles, loc='upper right',frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "qYewxuUKOVJF",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw ROC curves for each classifier\n",
    "\n",
    "knn_classifier_7 = KNeighborsClassifier(n_neighbors=7).fit(x_train_numerical, y_train)\n",
    "knn_classifier_17 = KNeighborsClassifier(n_neighbors=17).fit(x_train_numerical, y_train)\n",
    "dt_classifier=DecisionTreeClassifier(random_state=44).fit(x_train_numerical, y_train)\n",
    "\n",
    "# Compute ROC curve and area under the curve\n",
    "dt_fpr, dt_tpr, dt_thresholds = roc_curve(y_test, dt_classifier.predict_proba(x_test_numerical)[:, 1],pos_label=\"Ordinary\")\n",
    "dt_roc_auc = auc(dt_fpr, dt_tpr)\n",
    "\n",
    "knn_fpr_7, knn_tpr_7, knn_thresholds_7 = roc_curve(y_test, knn_classifier_7.predict_proba(x_test_numerical)[:, 1],pos_label=\"Ordinary\")\n",
    "knn_roc_auc_7 = auc(knn_fpr_7, knn_tpr_7)\n",
    "\n",
    "knn_fpr_17, knn_tpr_17, knn_thresholds_17 = roc_curve(y_test, knn_classifier_17.predict_proba(x_test_numerical)[:, 1],pos_label=\"Ordinary\")\n",
    "knn_roc_auc_17 = auc(knn_fpr_17, knn_tpr_17)\n",
    "\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(dt_fpr, dt_tpr, color='orange', lw=2, label='ROC curve (area = {:.2f})'.format(dt_roc_auc))\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - Decision Tree Classifier')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(knn_fpr_7, knn_tpr_7, color='orange', lw=2, label='ROC curve for k=7 (area = {:.2f})'.format(knn_roc_auc_7))\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - KNN-7 Classifier')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(knn_fpr_17, knn_tpr_17, color='green', lw=2, label='ROC curve for k=17 (area = {:.2f})'.format(knn_roc_auc_17))\n",
    "plt.plot([0, 1], [0, 1], color='blue', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve - KNN-17 Classifier')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "1T3Dp5Q-OVJF",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix for k-NN(7):\n",
      "[[ 3  4]\n",
      " [ 2 10]]\n",
      "\n",
      "Confusion Matrix for k-NN(17):\n",
      "[[ 3  4]\n",
      " [ 0 12]]\n",
      "\n",
      "Confusion Matrix for Decision Tree:\n",
      "[[ 3  4]\n",
      " [ 1 11]]\n",
      "\n",
      "Classification Metrics:\n",
      "        Metric   k-NN(7)  k-NN(17)  Decision Tree\n",
      "0     Accuracy  0.684211  0.789474       0.736842\n",
      "1        Error  0.315789  0.210526       0.263158\n",
      "2       Recall  0.684211  0.789474       0.736842\n",
      "3    Precision  0.672180  0.842105       0.739474\n",
      "4  Sensitivity  0.833333  0.833333       0.916667\n",
      "5    Specifity  0.428571  0.428571       0.428571\n",
      "6   F1 Measure  0.670040  0.762406       0.715577\n"
     ]
    }
   ],
   "source": [
    "# show classification results based on all measures (acc,err,recall, specifity, etc.) inside a table.\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=7).fit(x_train_numerical, y_train)\n",
    "y_pred_knn = knn_classifier.predict(x_test_numerical)\n",
    "knn_classifier_17 = KNeighborsClassifier(n_neighbors=17).fit(x_train_numerical, y_train)\n",
    "y_pred_knn_17 = knn_classifier_17.predict(x_test_numerical)\n",
    "dt_classifier=DecisionTreeClassifier(random_state=44).fit(x_train_numerical, y_train)\n",
    "y_pred_dt = dt_classifier.predict(x_test_numerical)\n",
    "\n",
    "conf_matrix_knn = confusion_matrix(y_test, y_pred_knn)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "error_knn = 1-accuracy_knn\n",
    "recall_knn = recall_score(y_test, y_pred_knn, average='weighted')\n",
    "precision_knn = precision_score(y_test, y_pred_knn, average='weighted')\n",
    "sensitivity_knn = conf_matrix_knn[1,1]/(conf_matrix_knn[1,1]+conf_matrix_knn[1,0])\n",
    "specifity_knn = conf_matrix_knn[0,0]/(conf_matrix_knn[0,0]+conf_matrix_knn[0,1])\n",
    "f1_knn = f1_score(y_test, y_pred_knn, average='weighted')\n",
    "\n",
    "conf_matrix_knn_17 = confusion_matrix(y_test, y_pred_knn_17)\n",
    "accuracy_knn_17 = accuracy_score(y_test, y_pred_knn_17)\n",
    "error_knn_17 = 1-accuracy_knn_17\n",
    "recall_knn_17 = recall_score(y_test, y_pred_knn_17, average='weighted')\n",
    "precision_knn_17 = precision_score(y_test, y_pred_knn_17, average='weighted')\n",
    "sensitivity_knn_17 = conf_matrix_knn[1,1]/(conf_matrix_knn[1,1]+conf_matrix_knn[1,0])\n",
    "specifity_knn_17 = conf_matrix_knn[0,0]/(conf_matrix_knn[0,0]+conf_matrix_knn[0,1])\n",
    "f1_knn_17 = f1_score(y_test, y_pred_knn_17, average='weighted')\n",
    "\n",
    "conf_matrix_dt = confusion_matrix(y_test, y_pred_dt)\n",
    "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
    "error_dt = 1-accuracy_dt\n",
    "recall_dt = recall_score(y_test, y_pred_dt, average='weighted')\n",
    "precision_dt = precision_score(y_test, y_pred_dt, average='weighted')\n",
    "sensitivity_dt = conf_matrix_dt[1,1]/(conf_matrix_dt[1,1]+conf_matrix_dt[1,0])\n",
    "specifity_dt = conf_matrix_dt[0,0]/(conf_matrix_dt[0,0]+conf_matrix_dt[0,1])\n",
    "f1_dt = f1_score(y_test, y_pred_dt, average='weighted')\n",
    "\n",
    "data = {\n",
    "    'Metric': ['Accuracy','Error','Recall','Precision', 'Sensitivity','Specifity', 'F1 Measure'],\n",
    "    'k-NN(7)': [accuracy_knn, error_knn, recall_knn, precision_knn, sensitivity_knn, specifity_knn, f1_knn],\n",
    "    'k-NN(17)': [accuracy_knn_17, error_knn_17, recall_knn_17, precision_knn_17, sensitivity_knn_17, specifity_knn_17, f1_knn_17],\n",
    "    'Decision Tree': [accuracy_dt, error_dt, recall_dt, precision_dt, sensitivity_dt, specifity_dt, f1_dt]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(\"Confusion Matrix for k-NN(7):\")\n",
    "print(conf_matrix_knn)\n",
    "\n",
    "print(\"\\nConfusion Matrix for k-NN(17):\")\n",
    "print(conf_matrix_knn_17)\n",
    "\n",
    "print(\"\\nConfusion Matrix for Decision Tree:\")\n",
    "print(conf_matrix_dt)\n",
    "\n",
    "# Display the table\n",
    "print(\"\\nClassification Metrics:\")\n",
    "print(df)"
   ]
  }
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