Global Monkey Pox Detection Using machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "000e54b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from category_encoders import OneHotEncoder, OrdinalEncoder\n",
    "from sklearn.model_selection import train_test_split,GridSearchCV, cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import(ConfusionMatrixDisplay,\n",
    "                           classification_report,\n",
    "                           confusion_matrix) \n",
    "from imblearn.over_sampling import RandomOverSampler \n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8279b635",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate wrangle function\n",
    "def wrangle(filepath):\n",
    "    #read csv file and set index to ID\n",
    "    df = pd.read_csv(filepath, low_memory=False)\n",
    "    #drop unwanted  URL columns\n",
    "    df.drop(columns=[\"Source\",\"Source_II\",\"Source_III\",\"Source_IV\",\"Source_V\",\"Source_VI\",\"Source_VII\"], inplace=True)\n",
    "    #drop insignificant columns with extremely high null values and high autocorrelation\n",
    "    df.drop(columns=[\"Genomics_Metadata\",\"Country_ISO3\",\"City\",\"Date_onset\",\"Hospitalised (Y/N/NA)\",\n",
    "                     \"Date_hospitalisation\",\"Isolated (Y/N/NA)\",\"Date_isolation\",\n",
    "                     \"Outcome\",\"Contact_comment\",\"Contact_ID\",\"Contact_location\",\n",
    "                     \"Date_death\",\"Travel_history_entry\",\"Travel_history_start\",\"ID\", \"Location\", \"Travel_history_location\"], inplace=True)\n",
    "    #drop rows contontaining https://www.publichealthontario.ca/-/media/Documents/M/2022/monkeypox-episummary.pdf?sc_lang=en\"\n",
    "    df.drop(df.index[df[\"Status\"]==\"https://www.publichealthontario.ca/-/media/Documents/M/2022/monkeypox-episummary.pdf?sc_lang=en\"], inplace=True)\n",
    "    #drop rows with extreme null values ie rows containing status == omit_error\n",
    "    df.drop(df.index[df[\"Status\"]==\"omit_error\"], inplace=True)\n",
    "    #drop rows containing discarded cases of monkeypox\n",
    "    df.drop(df.index[df[\"Status\"]==\"discarded\"], inplace=True)\n",
    "    df.drop(df.index[df[\"Gender\"]==\"2022-06-03\"], inplace=True)\n",
    "    #fill null values according to the mode of suspected  and confirmed cases\n",
    "    df[\"Status\"] = df[\"Status\"].fillna(\"confirmed\")\n",
    "    df.loc[df[\"Status\"]==\"confirmed\"] = df.loc[df[\"Status\"]==\"confirmed\"].fillna({\"Symptoms\":df.Symptoms.mode()[0],\n",
    "                                                                                 \"Gender\":df.Gender.mode()[0],\n",
    "                                                                                 \"Age\":df.Age.mode()[0],\n",
    "                                                                                 \"Confirmation_method\":df.Confirmation_method.mode()[0]})\n",
    "    df.loc[df[\"Status\"]==\"suspected\"] = df.loc[df[\"Status\"]==\"suspected\"].fillna({\"Symptoms\":\"rash, fever\",\n",
    "                                                                                 \"Age\":\"0-39\",\n",
    "                                                                                 \"Gender\":\"Female\",\n",
    "                                                                                 \"Confirmation_method\":df.Confirmation_method.mode()[0]})\n",
    "    #fill null values for all other columns\n",
    "    df[\"Travel_history (Y/N/NA)\"] = df[\"Travel_history (Y/N/NA)\"].fillna(\"Y\")\n",
    "    df[\"Country\"] = df[\"Country\"].fillna(df[\"Country\"].mode()[0])\n",
    "    df[\"Travel_history_country\"] = df[\"Travel_history_country\"].fillna(df[\"Travel_history_country\"].mode()[0])\n",
    "    df[\"Date_last_modified\"] = df[\"Date_last_modified\"].fillna(df[\"Date_last_modified\"].mode()[0])\n",
    "    df[\"Date_confirmation\"] = df[\"Date_confirmation\"].fillna(df[\"Date_confirmation\"].mode()[0])\n",
    "    df[\"Date_entry\"] = df[\"Date_entry\"].fillna(df[\"Date_entry\"].mode()[0])\n",
    "    #convert time series object to datetime\n",
    "    #df[\"Date_last_modified\"] = pd.to_datetime(df[\"Date_last_modified\"], format=\"%Y-%m-%d\")\n",
    "    #df[\"Date_confirmation\"] = pd.to_datetime(df[\"Date_confirmation\"], format=\"%Y-%m-%d\")\n",
    "    #df[\"Date_entry\"] = pd.to_datetime(df[\"Date_entry\"], format=\"%Y-%m-%d\")\n",
    "\n",
    "\n",
    "   #drop duplicate rows\n",
    "    df.drop_duplicates(keep=\"first\",inplace=True)\n",
    "\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1cf78561",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = wrangle(\"Monkeypox_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "471f8231",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Status</th>\n",
       "      <th>Country</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Date_confirmation</th>\n",
       "      <th>Symptoms</th>\n",
       "      <th>Travel_history (Y/N/NA)</th>\n",
       "      <th>Travel_history_country</th>\n",
       "      <th>Confirmation_method</th>\n",
       "      <th>Date_entry</th>\n",
       "      <th>Date_last_modified</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>confirmed</td>\n",
       "      <td>England</td>\n",
       "      <td>20-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>2022-05-06</td>\n",
       "      <td>rash</td>\n",
       "      <td>Y</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>RT-PCR</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2022-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>confirmed</td>\n",
       "      <td>England</td>\n",
       "      <td>20-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>rash</td>\n",
       "      <td>N</td>\n",
       "      <td>Spain</td>\n",
       "      <td>RT-PCR</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2022-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>confirmed</td>\n",
       "      <td>England</td>\n",
       "      <td>20-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>vesicular rash</td>\n",
       "      <td>N</td>\n",
       "      <td>Spain</td>\n",
       "      <td>RT-PCR</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2022-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>confirmed</td>\n",
       "      <td>England</td>\n",
       "      <td>20-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>vesicular rash</td>\n",
       "      <td>N</td>\n",
       "      <td>Spain</td>\n",
       "      <td>RT-PCR</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2022-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>confirmed</td>\n",
       "      <td>England</td>\n",
       "      <td>20-69</td>\n",
       "      <td>Male</td>\n",
       "      <td>2022-05-15</td>\n",
       "      <td>vesicular rash</td>\n",
       "      <td>Y</td>\n",
       "      <td>England</td>\n",
       "      <td>RT-PCR</td>\n",
       "      <td>2022-05-18</td>\n",
       "      <td>2022-05-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Status  Country    Age Gender Date_confirmation        Symptoms  \\\n",
       "0  confirmed  England  20-69   Male        2022-05-06            rash   \n",
       "1  confirmed  England  20-69   Male        2022-05-12            rash   \n",
       "2  confirmed  England  20-69   Male        2022-05-13  vesicular rash   \n",
       "3  confirmed  England  20-69   Male        2022-05-15  vesicular rash   \n",
       "6  confirmed  England  20-69   Male        2022-05-15  vesicular rash   \n",
       "\n",
       "  Travel_history (Y/N/NA) Travel_history_country Confirmation_method  \\\n",
       "0                       Y                Nigeria              RT-PCR   \n",
       "1                       N                  Spain              RT-PCR   \n",
       "2                       N                  Spain              RT-PCR   \n",
       "3                       N                  Spain              RT-PCR   \n",
       "6                       Y                England              RT-PCR   \n",
       "\n",
       "   Date_entry Date_last_modified  \n",
       "0  2022-05-18         2022-05-18  \n",
       "1  2022-05-18         2022-05-18  \n",
       "2  2022-05-18         2022-05-18  \n",
       "3  2022-05-18         2022-05-18  \n",
       "6  2022-05-18         2022-05-18  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "75096a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "#get train and test data \n",
    "target = \"Status\"\n",
    "X = df.drop(columns=\"Status\")\n",
    "y = df[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "417055b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2004, 10)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a99d2bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2004,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "da48f1a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#perform train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "71502ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resample data due to imbalance\n",
    "Undersampler = RandomUnderSampler(random_state=42)\n",
    "X_train_under, y_train_under = Undersampler.fit_resample(X_train, y_train)\n",
    "Oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_over, y_train_over = Oversampler.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4cf6ff17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class balance for over sampled data: confirmed    0.5\n",
      "suspected    0.5\n",
      "Name: Status, dtype: float64\n",
      "class balance for undersampled data: confirmed    0.5\n",
      "suspected    0.5\n",
      "Name: Status, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#check class balance between oversampled and undersampled data\n",
    "print(\"class balance for over sampled data:\",y_train_over.value_counts(normalize=True))\n",
    "print(\"class balance for undersampled data:\",y_train_under.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9fc49f82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "baseline score: 0.978601997146933\n"
     ]
    }
   ],
   "source": [
    "#build baseline model\n",
    "acc_baseline = y_train.value_counts(normalize=True).max()\n",
    "print(\"baseline score:\", acc_baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d748a4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;ordinalencoder&#x27;,\n",
       "                 OrdinalEncoder(cols=[&#x27;Country&#x27;, &#x27;Age&#x27;, &#x27;Gender&#x27;,\n",
       "                                      &#x27;Date_confirmation&#x27;, &#x27;Symptoms&#x27;,\n",
       "                                      &#x27;Travel_history (Y/N/NA)&#x27;,\n",
       "                                      &#x27;Travel_history_country&#x27;,\n",
       "                                      &#x27;Confirmation_method&#x27;, &#x27;Date_entry&#x27;,\n",
       "                                      &#x27;Date_last_modified&#x27;],\n",
       "                                mapping=[{&#x27;col&#x27;: &#x27;Country&#x27;,\n",
       "                                          &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                                          &#x27;mapping&#x27;: Italy                                1\n",
       "Slovenia                             2\n",
       "Honduras                             3\n",
       "Spain                                4\n",
       "Portugal                             5\n",
       "Ireland                              6\n",
       "Czech Republic                       7\n",
       "United States...\n",
       "2022-07-07    18\n",
       "2022-08-23    19\n",
       "2022-06-21    20\n",
       "2022-06-29    21\n",
       "2022-07-19    22\n",
       "2022-08-24    23\n",
       "2022-07-14    24\n",
       "2022-05-18    25\n",
       "2022-08-02    26\n",
       "2022-07-27    27\n",
       "2022-08-16    28\n",
       "2022-07-28    29\n",
       "2022-06-11    30\n",
       "2022-07-29    31\n",
       "2022-07-18    32\n",
       "2022-07-20    33\n",
       "2022-06-04    34\n",
       "2022-05-29    35\n",
       "2022-08-19    36\n",
       "2022-05-26    37\n",
       "2022-07-21    38\n",
       "2022-06-03    39\n",
       "NaN           -2\n",
       "dtype: int64}])),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;ordinalencoder&#x27;,\n",
       "                 OrdinalEncoder(cols=[&#x27;Country&#x27;, &#x27;Age&#x27;, &#x27;Gender&#x27;,\n",
       "                                      &#x27;Date_confirmation&#x27;, &#x27;Symptoms&#x27;,\n",
       "                                      &#x27;Travel_history (Y/N/NA)&#x27;,\n",
       "                                      &#x27;Travel_history_country&#x27;,\n",
       "                                      &#x27;Confirmation_method&#x27;, &#x27;Date_entry&#x27;,\n",
       "                                      &#x27;Date_last_modified&#x27;],\n",
       "                                mapping=[{&#x27;col&#x27;: &#x27;Country&#x27;,\n",
       "                                          &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                                          &#x27;mapping&#x27;: Italy                                1\n",
       "Slovenia                             2\n",
       "Honduras                             3\n",
       "Spain                                4\n",
       "Portugal                             5\n",
       "Ireland                              6\n",
       "Czech Republic                       7\n",
       "United States...\n",
       "2022-07-07    18\n",
       "2022-08-23    19\n",
       "2022-06-21    20\n",
       "2022-06-29    21\n",
       "2022-07-19    22\n",
       "2022-08-24    23\n",
       "2022-07-14    24\n",
       "2022-05-18    25\n",
       "2022-08-02    26\n",
       "2022-07-27    27\n",
       "2022-08-16    28\n",
       "2022-07-28    29\n",
       "2022-06-11    30\n",
       "2022-07-29    31\n",
       "2022-07-18    32\n",
       "2022-07-20    33\n",
       "2022-06-04    34\n",
       "2022-05-29    35\n",
       "2022-08-19    36\n",
       "2022-05-26    37\n",
       "2022-07-21    38\n",
       "2022-06-03    39\n",
       "NaN           -2\n",
       "dtype: int64}])),\n",
       "                (&#x27;decisiontreeclassifier&#x27;,\n",
       "                 DecisionTreeClassifier(random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(cols=[&#x27;Country&#x27;, &#x27;Age&#x27;, &#x27;Gender&#x27;, &#x27;Date_confirmation&#x27;,\n",
       "                     &#x27;Symptoms&#x27;, &#x27;Travel_history (Y/N/NA)&#x27;,\n",
       "                     &#x27;Travel_history_country&#x27;, &#x27;Confirmation_method&#x27;,\n",
       "                     &#x27;Date_entry&#x27;, &#x27;Date_last_modified&#x27;],\n",
       "               mapping=[{&#x27;col&#x27;: &#x27;Country&#x27;, &#x27;data_type&#x27;: dtype(&#x27;O&#x27;),\n",
       "                         &#x27;mapping&#x27;: Italy                                1\n",
       "Slovenia                             2\n",
       "Honduras                             3\n",
       "Spain                                4\n",
       "Portugal                             5\n",
       "Ireland                              6\n",
       "Czech Republic                       7\n",
       "United States                        8\n",
       "Chile                                9\n",
       "Netherlands                         10\n",
       "Israel                              11\n",
       "Puerto...\n",
       "2022-06-10    12\n",
       "2022-08-12    13\n",
       "2022-06-06    14\n",
       "2022-08-01    15\n",
       "2022-08-03    16\n",
       "2022-05-27    17\n",
       "2022-07-07    18\n",
       "2022-08-23    19\n",
       "2022-06-21    20\n",
       "2022-06-29    21\n",
       "2022-07-19    22\n",
       "2022-08-24    23\n",
       "2022-07-14    24\n",
       "2022-05-18    25\n",
       "2022-08-02    26\n",
       "2022-07-27    27\n",
       "2022-08-16    28\n",
       "2022-07-28    29\n",
       "2022-06-11    30\n",
       "2022-07-29    31\n",
       "2022-07-18    32\n",
       "2022-07-20    33\n",
       "2022-06-04    34\n",
       "2022-05-29    35\n",
       "2022-08-19    36\n",
       "2022-05-26    37\n",
       "2022-07-21    38\n",
       "2022-06-03    39\n",
       "NaN           -2\n",
       "dtype: int64}])</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('ordinalencoder',\n",
       "                 OrdinalEncoder(cols=['Country', 'Age', 'Gender',\n",
       "                                      'Date_confirmation', 'Symptoms',\n",
       "                                      'Travel_history (Y/N/NA)',\n",
       "                                      'Travel_history_country',\n",
       "                                      'Confirmation_method', 'Date_entry',\n",
       "                                      'Date_last_modified'],\n",
       "                                mapping=[{'col': 'Country',\n",
       "                                          'data_type': dtype('O'),\n",
       "                                          'mapping': Italy                                1\n",
       "Slovenia                             2\n",
       "Honduras                             3\n",
       "Spain                                4\n",
       "Portugal                             5\n",
       "Ireland                              6\n",
       "Czech Republic                       7\n",
       "United States...\n",
       "2022-07-07    18\n",
       "2022-08-23    19\n",
       "2022-06-21    20\n",
       "2022-06-29    21\n",
       "2022-07-19    22\n",
       "2022-08-24    23\n",
       "2022-07-14    24\n",
       "2022-05-18    25\n",
       "2022-08-02    26\n",
       "2022-07-27    27\n",
       "2022-08-16    28\n",
       "2022-07-28    29\n",
       "2022-06-11    30\n",
       "2022-07-29    31\n",
       "2022-07-18    32\n",
       "2022-07-20    33\n",
       "2022-06-04    34\n",
       "2022-05-29    35\n",
       "2022-08-19    36\n",
       "2022-05-26    37\n",
       "2022-07-21    38\n",
       "2022-06-03    39\n",
       "NaN           -2\n",
       "dtype: int64}])),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(random_state=42))])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model into training data to get best performing data for model building\n",
    "#X_train, y_train\n",
    "model_reg = make_pipeline(OrdinalEncoder(),DecisionTreeClassifier(random_state=42))\n",
    "model_reg.fit(X_train,y_train)\n",
    "#X_train_over, y_train_over\n",
    "model_over = make_pipeline(OrdinalEncoder(),DecisionTreeClassifier(random_state=42))\n",
    "model_over.fit(X_train_over,y_train_over)\n",
    "#X_train_under, y_train_under\n",
    "model_under = make_pipeline(OrdinalEncoder(),DecisionTreeClassifier(random_state=42))\n",
    "model_under.fit(X_train_under,y_train_under)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c2c078e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score: 1.0\n",
      "test score: 0.9983388704318937\n",
      "train score: 1.0\n",
      "test score: 0.9983388704318937\n",
      "train score: 1.0\n",
      "test score: 1.0\n"
     ]
    }
   ],
   "source": [
    "#get best performing data\n",
    "for model in [model_reg,model_over,model_under]:\n",
    "    #get train score\n",
    "    train_score = model.score(X_train,y_train)\n",
    "    #get test score\n",
    "    test_score = model.score(X_test, y_test)\n",
    "    \n",
    "    print(\"train score:\",train_score)\n",
    "    print(\"test score:\", test_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8edd3566",
   "metadata": {},
   "source": [
    "To avoid errors and bias we get the best performing model from the resampled data, i choose the model from the oversampled data\n",
    "that conforms to real life scenarios bcs there's no perfect model in real life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "32961a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get tree depth\n",
    "model_over.named_steps[\"decisiontreeclassifier\"].get_depth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e5648e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Communicate model by getting feature importances\n",
    "features = model_over.named_steps['decisiontreeclassifier'].feature_importances_\n",
    "Feature_Importance = pd.Series(features, index=X_train_over.columns).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "71fdef37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                    0.000000\n",
       "Age                        0.000000\n",
       "Symptoms                   0.000000\n",
       "Travel_history (Y/N/NA)    0.000000\n",
       "Travel_history_country     0.000000\n",
       "Confirmation_method        0.000000\n",
       "Date_entry                 0.000000\n",
       "Date_confirmation          0.021835\n",
       "Date_last_modified         0.053331\n",
       "Gender                     0.924835\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature_Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "890c2911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABAQAAAF1CAYAAAB/ITgsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAzqUlEQVR4nO3deZRdVZ3+//cjICJiaCUqDhClQZwgQAENAo0KTtgKiqLSKDjg3A6tNj/bVpBWUforTk0joqCIE4i2I6AyCSpQgZCAiq2AIogMKgJChPD5/XF24aWsqlSSSlWS836tVevee84+e3/uzV0sznP2PjdVhSRJkiRJ6pd7zXQBkiRJkiRp+hkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSQIgyZVJbktyy8DfQ6egz92mqsZJjHdwks9N13gTSbJ/knNmug5JksZjICBJkgb9U1Xdb+DvmpksJsmaMzn+slpV65Yk9YuBgCRJmlCSWUk+leS3Sa5O8p9J1mj7NklyepIbk9yQ5IQk67d9xwMbAd9osw3enmTXJL8Z1f/dswjaFf6TknwuyZ+A/ScafxK1V5LXJvm/JDcnObTV/KMkf0ry5ST3bm13TfKbJO9o7+XKJPuO+hw+m+T6JL9K8s4k92r79k9ybpIjkvwe+BJwFLBDe+9/bO32SHJRG/uqJAcP9D+n1fvSJL9uNfz7wP41Wm2/bO9lXpJHtH2bJ/lukt8nuSzJC5bqH1mS1EsGApIkaUk+A9wJ/D2wFfBU4BVtX4D3Aw8FHgM8AjgYoKr2A37NX2cdfHCS4z0HOAlYHzhhCeNPxtOBbYB/AN4OHA3s22p9PPCigbYPATYAHga8FDg6yaPbvo8Bs4BHAf8IvAQ4YODY7YHLgQcB/wy8GvhRe+/rtza3tuPWB/YAXpNkz1H17gQ8GngK8K4kj2nb39JqfSZwf+BlwJ+TrAt8F/h8G/tFwJFJHjf5j0iS1EcGApIkadDXkvyx/X0tyYOBZwBvqqpbq+o64AjghQBV9Yuq+m5VLaqq64EP0Z0sL48fVdXXquouuhPfccefpA9U1Z+q6lLgEuC0qrq8qm4CvkMXMgz6j/Z+zgK+BbygzUjYB/j/qurmqroS+H/AfgPHXVNVH6uqO6vqtrEKqaozq2phVd1VVQuAL/C3n9chVXVbVV0MXAxs2ba/AnhnVV1WnYur6kbgWcCVVXVsG/tC4CvA3kvxGUmSesj1bZIkadCeVfW9kRdJtgPWAn6bZGTzvYCr2v4HAR8FdgbWa/v+sJw1XDXwfOOJxp+k3w08v22M1w8ZeP2Hqrp14PWv6GY/bADcu70e3PewceoeU5LtgcPoZibcG1gbOHFUs2sHnv8ZuF97/gjgl2N0uzGw/ciyhGZN4Pgl1SNJ6jdnCEiSpIlcBSwCNqiq9dvf/atqZDr6+4ECtqiq+9NNlc/A8TWqv1uB+468aFfeZ49qM3jMksafan/XpuCP2Ai4BrgBuIPu5Htw39Xj1D3Wa+im9X8deERVzaK7z0DGaDeWq4BNxtl+1sDns35bpvCaSfYrSeopAwFJkjSuqvotcBrw/5LcP8m92k35Rqa5rwfcAvwxycOAt43q4nd0a+5H/By4T7u53lrAO+muki/r+CvCIUnunWRnuun4J1bVYuDLwHuTrJdkY7o1/RP9xOHvgIeP3LSwWQ/4fVXd3mZfvHgp6joGODTJpulskeSBwDeBzZLsl2St9rftwL0HJEkak4GAJElakpfQTW//Cd1ygJOADdu+Q4CtgZvo1tufPOrY9wPvbPckeGtbt/9aupPbq+lmDPyGiU00/lS7to1xDd0NDV9dVT9r+95AV+/lwDl0V/s/PUFfpwOXAtcmuaFtey3wniQ3A++iCxkm60Ot/WnAn4BPAetU1c10N1p8Yav7WuADTBC0SJIEkKqxZrNJkiT1S5Jdgc9V1cNnuBRJkqaFMwQkSZIkSeohAwFJkiRJknrIJQOSJEmSJPWQMwQkSZIkSeohAwFJkiRJknpozZkuQDNvgw02qDlz5sx0GZIkSZKkFWDevHk3VNXs0dsNBMScOXMYHh6e6TIkSZIkSStAkl+Ntd0lA5IkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk95M8OioVX38Scg74102VIkiRJ0irjysP2mOkSlpszBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEVpAkD07y+SSXJ5mX5EdJ9pqCfndN8s2pqFGSJEmS1F8GAitAkgBfA86uqkdV1TbAC4GHz0At/pKEJEmSJOlvGAisGE8G/lJVR41sqKpfVdXHkqyR5PAkFyRZkORVcPeV/zOTnJTkZ0lOaMECSZ7etp0DPHekzyTrJvl06+uiJM9p2/dPcmKSbwCnTes7lyRJkiStErx6vGI8DrhwnH0vB26qqm2TrA2cm2TkpH2rduw1wLnAE5MMA5+kCxl+AXxpoK9/B06vqpclWR84P8n32r4dgC2q6vdjFZHkQOBAgDXuP3vZ3qUkSZIkaZVlIDANkvw3sBPwF+BXwBZJ9m67ZwGbtn3nV9Vv2jHzgTnALcAVVfV/bfvnaCfywFOBZyd5a3t9H2Cj9vy744UBAFV1NHA0wNobblrL/y4lSZIkSasSA4EV41LgeSMvqup1STYAhoFfA2+oqlMHD0iyK7BoYNNi/vrvM94Je4DnVdVlo/raHrh1OeqXJEmSJK3mvIfAinE6cJ8krxnYdt/2eCrwmiRrASTZLMm6E/T1M+CRSTZpr180sO9U4A0D9xrYakqqlyRJkiSt9gwEVoCqKmBP4B+TXJHkfOAzwL8BxwA/AS5McgnwCSaYqVFVt9MtEfhWu6ngrwZ2HwqsBSxofR26At6OJEmSJGk1lO7cVX229oab1oYv/fBMlyFJkiRJq4wrD9tjpkuYtCTzqmpo9HZnCEiSJEmS1EMGApIkSZIk9ZC/MiCe8LBZDK9C010kSZIkScvPGQKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg8ZCEiSJEmS1EMGApIkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg+tOdMFaOYtvPom5hz0rZkuY7lcedgeM12CJEmSJK1SnCEgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg9NayCQZHGS+UkuTXJxkrckmbCGJHOSvHgZx5uT5JJlPPYdy3LcskpyZZIN2vMfDmw/vH1ehyd5dZKXLGW/t0x1rZIkSZKkVd90/+zgbVU1FyDJg4DPA7OAd09wzBzgxa3tdHoH8L5pHhOAqtpx4OWrgNlVtWgmapEkSZIkrZ5mbMlAVV0HHAi8Pp05SX6Q5ML2N3JSfBiwc5tZ8OYka7Sr5RckWZDkVZMZb7z+k2yY5OzW/yVJdk5yGLBO23bCBP39LMkx7bgTkuyW5Nwk/5dku9buAUm+1mr9cZIt2vYHJjktyUVJPgFkoO9b2uPXgXWB85Lsk+TgJG9t+zZJckqSee19bd62PzLJj9rnc+jS/rtIkiRJkvphRu8hUFWXtxoeBFwH7F5VWwP7AB9tzQ4CflBVc6vqCODlwE1VtS2wLfDKJI+cxHDj9f9i4NQ2c2FLYH5VHUSbzVBV+07Q598DHwG2ADZvfe0EvJVuhgHAIcBFVbVF2/bZtv3dwDlVtRXwdWCjMT6fZw/U8aVRu48G3lBV27TxjmzbPwL8T/t8rh2v8CQHJhlOMrz4zzdN8BYlSZIkSauj6V4yMJaRK+NrAR9PMhdYDGw2TvunAlsk2bu9ngVsClyxhHHG6/8C4NNJ1gK+VlXzl6L2K6pqIUCSS4HvV1UlWUi31AG6gOB5AFV1epsZMAvYBXhu2/6tJH+Y7KBJ7gfsCJyY3D2xYO32+MSR8YDjgQ+M1UdVHU0XKrD2hpvWZMeWJEmSJK0eZjQQSPIoupPz6+iumP+O7ir9vYDbxzuM7sr4qUs53JvH6r+qzk6yC7AHcHySw6vqs+N3cw+D6/rvGnh9F3/9bMPfqlGPS+tewB9H7scwQf+SJEmSJI1pxpYMJJkNHAV8vKqK7kr/b6vqLmA/YI3W9GZgvYFDTwVe067ok2SzJOtOYsgx+0+yMXBdVX0S+BSwdWt/x8gYy+lsYN821q7ADVX1p1HbnwH83WQ7bMdfkeT57fgk2bLtPhd4YXs+0XIHSZIkSVKPTXcgMHKjvkuB7wGn0a2xh24N/EuT/JhuOv+tbfsC4M72M4VvBo4BfgJc2H5S8BNMbqbDeP3vCsxPchHdVPuPtO1HAwvGu6ngUjgYGEqygO4GiS9t2w8BdklyId0yiF8vZb/7Ai9PcjFwKfCctv2NwOuSXEAXgkiSJEmS9DfSXZxXn6294aa14Us/PNNlLJcrD9tjpkuQJEmSpJVSknlVNTR6+4z+yoAkSZIkSZoZK8OvDCy3JE+gu6P+oEVVtf0U9P1A4Ptj7HpKVd24vP1LkiRJkjQTXDIghoaGanh4eKbLkCRJkiStAC4ZkCRJkiRJdzMQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqoTVnugDNvIVX38Scg741Zf1dedgeU9aXJEmSJGnFcIaAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST00o4FAksVJ5ie5NMnFSd6SZMKaksxJ8uLpqnGCOr6QZEGSNyd5T5LdVsAY6yd57cDrhyY5aarHkSRJkiT1z0z/7OBtVTUXIMmDgM8Ds4B3T3DMHODFre2MSPIQYMeq2ngJ7daoqsXLMdT6wGuBIwGq6hpg7+XoT5IkSZIkYCVaMlBV1wEHAq9PZ06SHyS5sP3t2JoeBuzcZha8OckaSQ5PckG7Yv+qicZJ8vYkC9uMhMPatrlJftyO/2qSv2vbz0zygSTnJ/l5kp1bN6cBD2o17JzkuCR7t2OuTPKuJOcAz2+v35fkR0mGk2yd5NQkv0zy6nbM/ZJ8v73PhUmeM/BeN2njHN4+k0vaMfdJcmxrf1GSJ7Xt+yc5OckpSf4vyQfH+RwObPUML/7zTcv0byZJkiRJWnXN9AyBe6iqy9uSgQcB1wG7V9XtSTYFvgAMAQcBb62qZ0F3YgvcVFXbJlkbODfJaVV1xej+kzwD2BPYvqr+nOQBbddngTdU1VlJ3kM3Q+FNbd+aVbVdkme27bsBzwa+OTC74eWjhrq9qnZq+w4DrqqqHZIcARwHPBG4D3ApcBRwO7BXVf0pyQbAj5N8vb3Xxw+MM2dgjNe1z+wJSTYHTkuyWds3F9gKWARcluRjVXXVqM/6aOBogLU33LRGf1aSJEmSpNXbShUINGmPawEfTzIXWAxsNk77pwJbjFyhp1tysCnwN4EA3cn8sVX1Z4Cq+n2SWcD6VXVWa/MZ4MSBY05uj/PolitMxpdGvf56e1wI3K+qbgZuTnJ7kvWBW4H3JdkFuAt4GPDgJYyxE/Cx9j5+luRX/PUz+n5V3QSQ5CfAxsBVY/YiSZIkSeqllSoQSPIoupP/6+iuxv8O2JJuacPt4x1Gd3X/1MkMASzt1fBF7XExk/+8bh2nj7sGno+8XhPYF5gNbFNVdyS5km4GwUQywb7BMZambkmSJElST6w09xBIMptu+vzHq6rorvT/tqruAvYD1mhNbwbWGzj0VOA1SdZq/WyWZN1xhjkNeFmS+7a2D2hX0v8wcH+A/YCzxjl+RZkFXNfCgCfRXdGHv32vg86mCxJoSwU2Ai5b0YVKkiRJklYPM33leJ0k8+mWB9wJHA98qO07EvhKkucDZ/DXq+4LgDuTXEy3Hv8jdFP5L0wS4Hq6+wT8jao6pS1BGE7yF+DbwDuAlwJHtaDgcuCAqXyTk3AC8I0kw8B84Get3huTnNtuJPgd4L8HjjmSruaFdJ/d/lW1qPsIJEmSJEmaWLqL8eqztTfctDZ86YenrL8rD9tjyvqSJEmSJC2fJPOqamj09pVmyYAkSZIkSZo+M71kYIVI8gS65QeDFlXV9jNRz8ruCQ+bxbBX9SVJkiSpV1bLQKCqFgJzZ7oOSZIkSZJWVi4ZkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEBiQZHGS+UkuTXJxkrckmfAzSjInyYunscb9kzx0usaTJEmSJK2eDATu6baqmltVjwN2B54JvHsJx8wBpi0QAPYHxgwEkqwxjXVIkiRJklZhBgLjqKrrgAOB16czJ8kPklzY/nZsTQ8Ddm4zC96cZI0khye5IMmCJK+aaJwkbxtoe0jbNifJT5N8ss1WOC3JOkn2BoaAE9p46yS5Msm7kpwDHJTkwoG+N00yb4V8QJIkSZKkVZqBwASq6nK6z+hBwHXA7lW1NbAP8NHW7CDgB21mwRHAy4GbqmpbYFvglUkeOVb/SZ4KbApsB8wFtkmyS9u9KfDfbbbCH4HnVdVJwDCwbxvvttb29qraqareC9yUZG7bfgBw3DhjH5hkOMnw9ddfv7QfjSRJkiRpFbfmTBewCkh7XAv4eDvZXgxsNk77pwJbtKv5ALPoTu6vGKftU4GL2uv7tba/Bq6oqvlt+zy6pQnj+dLA82OAA5K8hS642G6sA6rqaOBogKGhoZqgb0mSJEnSashAYAJJHkV38n8d3b0EfgdsSTdr4PbxDgPeUFWnTmYI4P1V9YlR484BFg1sWgysM0E/tw48/0qr9XRgXlXdOIk6JEmSJEk945KBcSSZDRwFfLyqiu5K/2+r6i5gP2DkBn43A+sNHHoq8Joka7V+Nkuy7jjDnAq8LMn9WtuHJXnQEkobPd49VNXtrd//AY5dQl+SJEmSpJ5yhsA9rZNkPt3ygDuB44EPtX1HAl9J8nzgDP56VX4BcGeSi+nW63+Ebnr/hUkCXA/sOdZgVXVakscAP+qacgvwz3QzAsZzHHBUktuAHcZpcwLwXOC0id6sJEmSJKm/0l381uokyVuBWVX1H5NpPzQ0VMPDwyu4KkmSJEnSTEgyr6qGRm93hsBqJslXgU2AJ890LZIkSZKklZeBwDRI8gS65QeDFlXV9lM9VlXtNdV9SpIkSZJWPwYC06CqFgJzZ7oOSZIkSZJG+CsDkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg8ZCEiSJEmS1EMGApIkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg8ZCEiSJEmS1EMGApIkSZIk9dByBQJJHpLki0l+meQnSb6dZLNl6GfnJJcmmZ/kYUlOWp66JhhnzySPHXj9niS7rYixlkWS/ZM8dOD1lUk2WI7+lut4SZIkSdLqa5kDgSQBvgqcWVWbVNVjgXcAD16G7vYF/quq5lbV1VW19xjjrbmstQ7YE7g7EKiqd1XV96ag36myP/DQJTWSJEmSJGl5Lc8MgScBd1TVUSMbqmo+cE6Sw5NckmRhkn0Akuya5MwkJyX5WZIT0nkF8ALgXW3bnCSXtGP2T3Jikm8Ap7XXX0vyjSRXJHl9krckuSjJj5M8oB33yiQXJLk4yVeS3DfJjsCzgcPbTIRNkhyXZO92zFNaPwuTfDrJ2m37lUkOSXJh27f5eB9IkoOTfCbJae245yb5YDvulCRrtXbbJDkrybwkpybZsNUxBJzQ6lundfuG0WMneUD7HBa0971F2/7ANvZFST4BZIJaD0wynGT4+uuvX9p/e0mSJEnSKm55AoHHA/PG2P5cYC6wJbAb3Qn4hm3fVsCb6K7SPwp4YlUdA3wdeFtV7TtGfzsAL62qJw+M+2JgO+C9wJ+raivgR8BLWpuTq2rbqtoS+Cnw8qr64cA4c6vqlyMDJLkPcBywT1U9AVgTeM1ADTdU1dbA/wBvXcLnsgmwB/Ac4HPAGa3P24A9WijwMWDvqtoG+DTw3qo6CRgG9m313TbB2IcAF1XVFnSzMj7btr8bOKd9Hl8HNhqvyKo6uqqGqmpo9uzZS3hLkiRJkqTVzYq4qeBOwBeqanFV/Q44C9i27Tu/qn5TVXcB84E5k+jvu1X1+4HXZ1TVzVV1PXAT8I22feFAf49P8oMkC+mWIzxuCWM8Griiqn7eXn8G2GVg/8ntcd4kav5OVd3R6lkDOGVUfY+mCzW+m2Q+8E7g4RP0N9bYOwHHA1TV6cADk8xqNX+ubf8W8Icl1CpJkiRJ6qnlWZd/KfA3a/2ZYJo6sGjg+eJJjn/rBH3cNfD6roH+jgP2rKqLk+wP7LqEMSaqeXDMydS8CKCq7kpyR1XVqPoCXFpVOyyhn4nGHqveGvUoSZIkSdK4lmeGwOnA2kleObIhybZ0V6X3SbJGktl0V63PX74yl9p6wG/b9PzBZQg3t32j/QyYk+Tv2+v96GY2rAiXAbOT7ACQZK0kIzMYxqtvtLNp7yvJrnTLCv40avszgL+b0solSZIkSauNZQ4E2pXvvYDd288OXgocDHweWABcTBcavL2qrp2CWpfGfwDnAd+lO9kf8UXgbe2me5uMbKyq24EDgBPbMoO7gKNYAarqL3QzKz6Q5GK6pRM7tt3HAUeNuqngWA4GhpIsAA4DXtq2HwLskuRC4KnAr6f8DUiSJEmSVgv564x29dXQ0FANDw/PdBmSJEmSpBUgybyqGhq9fUXcVFCSJEmSJK3kluemgr2V5ADgjaM2n1tVr5uJeiRJkiRJWloGAsugqo4Fjp3pOiRJkiRJWlYuGZAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQemlQgkOSBSea3v2uTXD3w+t5TWVCSXZN8c4L9Byd56zj7friEvt+xvPXNtCRvSnLfma5DkiRJkrRqm1QgUFU3VtXcqpoLHAUcMfK6qv6SZM0VWuUkVdWOS2iyVIFAOivbLIo3AWMGAknWmN5SJEmSJEmrqmU+2U1yXJIPJTkD+ECS7ZL8MMlF7fHRrd15SR43cNyZSbZJsm6STye5oB3znKUY/rGtn8uT/MtA37e0xw2TnN1mMFySZOckhwHrtG0ntHZvafsvSfKmtm1Okp8mORK4EPiPJEcMjPHKJB+a4HN5SZIFSS5OcnzbtnGS77ft30+y0cBnuPcY9e/a3t9JSX6W5IQWTvwL8FDgjPa5k+SWJO9Jch7wziRfHehv9yQnL8XnKkmSJEnqieW9sr8ZsFtVLU5yf2CXqrozyW7A+4DnAV8EXgC8O8mGwEOral6S9wGnV9XLkqwPnJ/ke5Mcd3PgScB6wGVJ/qeq7hjY/2Lg1Kp6b7tqft+q+kGS17dZDiTZBjgA2B4IcF6Ss4A/AI8GDqiq1yZZF1iQ5O1tjAOAV41VVAs+/h14YlXdkOQBbdfHgc9W1WeSvAz4KLDnEt7jVsDjgGuAc1ufH03yFuBJVXVDa7cucElVvStJgJ8mmV1V17dajx2n1gOBAwE22mijJZQiSZIkSVrdLO90+BOranF7Pgs4McklwBF0J7MAXwae356/ADixPX8qcFCS+cCZwH2AyZ6ZfquqFrWT4uuAB4/afwFwQJKDgSdU1c1j9LET8NWqurWqbgFOBnZu+35VVT8GqKpbgdOBZyXZHFirqhaOU9eTgZNGTtar6vdt+w7A59vz49vYS3J+Vf2mqu4C5gNzxmm3GPhKG69a///cQpYdgO+MdVBVHV1VQ1U1NHv27EmUI0mSJElanSxvIHDrwPNDgTOq6vHAP9Gd4FNVVwM3JtkC2IduxgB0V+WfN3Avgo2q6qeTHHfRwPPFjJrpUFVnA7sAVwPHJ3nJGH1kku8L4Bhgfya44j7QZ02w/+4S2+OdtH+DdnV/8AaNE77HAbcPhDK0+v4ZeBFdYHPnJOqRJEmSJPXMVN4wbxbdCTh0J8+Dvgi8HZg1cHX9VOAN7USYJFtNVSFJNgauq6pPAp8Ctm677kiyVnt+NrBnkvu2ZQF7AT8Yq7+qOg94BN1ShC9MMPT3gRckeWCrY2TJwA+BF7bn+wLntOdXAtu0588BRmqbyM10SyXGVFXX0C0zeCdw3CT6kyRJkiT10FQGAh8E3p/kXGD03e5Pojsh/vLAtkPpToAXtGUGh05hLbsC85NcRHcfg4+07Ue38U6oqgvpTpjPB84Djqmqiybo88vAuVX1h/EaVNWlwHuBs5JcDIzcfPBf6JYwLAD2A97Ytn8S+Mck59Pdy2D0zISxHA18Z+SmguM4Abiqqn4yif4kSZIkST2Ubtm5liTJN+l+bvH7M13LkiT5OHBRVX1qMu2HhoZqeHh4BVclSZIkSZoJSeZV1dDo7VM5Q2C1lGT9JD8HbltFwoB5wBbA52a6FkmSJEnSymt5f3ZwhUlyAH+dWj/i3Kp63XTWUVV/pPt5xbu1ewSMFQ48papunI66xlNV2yy5lSRJkiSp71baQKCqjmXiO/rPmHbSP3em65AkSZIkaVm5ZECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQemjAQSPLAJPPb37VJrh54fe+pLCTJrkm+OcH+g5O8dZx9P1xC3+9Y3vom6DtJTk8yK8k5SZ4xsO8FSU4ZeP2JJE9Mclz7LNdu2zdIcuWofk9J8rAkZyYZHtg+lOTMUW0/0vq718C2ZyU5ZOrfsSRJkiRpdTBhIFBVN1bV3KqaCxwFHDHyuqr+kmTNaalyCapqxyU0WapAoJ3kT3b2xDOBi6vqJuDVwIeS3CfJusB7gdcNtN0e+HF7vhh42TjjrwM8oKqubpseNBg0jGp7L2Av4Cpgl4Fd3wKeneS+k3wfkiRJkqQeWeolA+3q9oeSnAF8IMl2SX6Y5KL2+OjW7rwkjxs47swk2yRZN8mnk1zQjnnOUgz/2NbP5Un+ZaDvW9rjhknObjMYLkmyc5LDgHXathNau7e0/ZckeVPbNifJT5McCVwI/EeSIwbGeGWSD41R077A/wJU1SXAN4B/A94NfLaqftmOfwzw86pa3I77MPDmcUKVXYEzB14fDrxznM/kScAlwP8ALxrZWFXV+njWOMdJkiRJknpsWe8hsBmwW1X9K/AzYJeq2gp4F/C+1uaLwAugO1EHHlpV84B/B06vqm3pTmYPb1fTJ2Nz4GnAdsC7k6w1av+LgVPbjIYtgflVdRBwW5vVsG+SbYAD6K7W/wPwyiRbteMfTXcSvxXwX3RX2EfGOAA4doyangjMG3h9SKvjGcAHB7Y/Azhl4PWvgXOA/cboc3TbHwGLkjxpjLYvAr4AfBV41qjPZBjYeYxjSHJgkuEkw9dff/1YTSRJkiRJq7FlDQROHLjSPQs4McklwBHAyKyALwPPb89fAJzYnj8VOCjJfLor2PcBNprkuN+qqkVVdQNwHfDgUfsvAA5IcjDwhKq6eYw+dgK+WlW3VtUtwMn89aT5V1X1Y4CquhU4ne4ke3NgrapaOEZ/Dxgcpx33JeD4qlo00O5p3PMkH7rw5G387b/DE+nCgkH/yahZAu0+Ds8EvlZVfwLOo/t8R1wHPHSMmqmqo6tqqKqGZs+ePVYTSZIkSdJqbFkDgVsHnh8KnFFVjwf+ie4En7b+/cYkWwD70M0YAAjwvIF7EWxUVT+d5LiDJ9iLgXtMt6+qs+nW0V8NHJ/kJWP0kUm+L4BjgP0Zf3YAwJ1j3G/grvbXDdit41+/qq4ZVe8vgPm0mRSt7aOAq6rqL6Pank732f7DwOan0wUyC9tNCXdiYNlAa3/bOHVLkiRJknpsKn52cBbdCTh0J8+Dvgi8HZg1cHX9VOANSQIwMF1/uSXZGLiuqj4JfArYuu26Y2Aq/dnAnknu25Yq7AX8YKz+quo84BF0SwC+MM6wlwGPWkJpTwLOGGffe4HBX08YvVxgdNu3D7x+EfCKqppTVXOARwJPHbiR4GZ09xeQJEmSJOkepiIQ+CDw/iTnAmuM2ncS8EK65QMjDgXWAha0ZQaHTkENI3YF5ie5CHge8JG2/eg23glVdSFwHHA+3RT7Y6rqogn6/DJwblX9YZz932rjTmTck/yqupTuJoYjnj5B228D18Pdsw6e1sYf2X8r3VKDf2qbnjS4X5IkSZKkEeluRq/xJPkm3c8tfn+c/RvS3Yhw9wn6uBDYvqruWMJYa9OFD0PLU3Pr68HA56vqKUtqOzQ0VMPDw8s7pCRJkiRpJZRk3ljnmVMxQ2C1lGT9JD+n+4WCMcMAgKr6LfDJJPefoM3WSwoDWrtFUxEGNBsB/zpFfUmSJEmSVjNrLrnJ9EpyAPDGUZvPrarXTWcdVfVHujX4k2n75SW3ml5VdcFM1yBJkiRJWnmtdIFAVR3L+Hf0lyRJkiRJU8AlA5IkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg8ZCEiSJEmS1EMGApIkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgITSPLvSS5NsiDJ/CTbr6Bx1k/y2hXRtyRJkiRJY1lzpgtYWSXZAXgWsHVVLUqyAXDvFTTc+sBrgSNXUP+SJEmSJN2DMwTGtyFwQ1UtAqiqG4DHJPnqSIMkuyc5uT2/JckHksxL8r0k2yU5M8nlSZ7d2uyf5H+TnJLksiTvbl0dBmzSZiEcns7hSS5JsjDJPu34XZOcleTLSX6e5LAk+yY5v7XbpLV7fjv24iRnT99HJkmSJElaVRgIjO804BHtxPvIJP8InE4XCsxubQ4Ajm3P1wXOrKptgJuB/wR2B/YC3jPQ73bAvsBc4PlJhoCDgF9W1dyqehvw3LZ/S2A34PAkG7bjtwTeCDwB2A/YrKq2A44B3tDavAt4WlVtCTx7rDeX5MAkw0mGr7/++mX6gCRJkiRJqy4DgXFU1S3ANsCBwPXAl4CXAscD/5xkfWAH4DvtkL8Ap7TnC4GzquqO9nzOQNffraobq+o24GRgpzGG3wn4QlUtrqrfAWcB27Z9F1TVb9vMhV/SBRcjY46Mcy5wXJJXAmuM8/6OrqqhqhqaPXv2WE0kSZIkSasx7yEwgapaDJwJnJlkIV0g8CrgG8DtwIlVdWdrfkdVVXt+FzCy1OCuJIOfc3FPo18DZIKyFg08v2vg9V20f8+qenW7AeIewPwkc6vqxgn6lCRJkiT1jDMExpHk0Uk2Hdg0F/hVVV0DXAO8EzhuGbrePckDkqwD7El3Nf9mYL2BNmcD+yRZoy1P2AU4fylq36SqzquqdwE3AI9YhjolSZIkSasxZwiM737Ax9rSgDuBX9AtHwA4AZhdVT9Zhn7PoVt28PfA56tqGCDJuUkuoVuC8Ha65QgX080geHtVXZtk80mOcXgLMwJ8v/UjSZIkSdLd8tdZ7pqsJB8HLqqqTy3lcfsDQ1X1+hVS2DIaGhqq4eHhmS5DkiRJkrQCJJlXVUOjtztDYCklmQfcCvzrTNciSZIkSdKyMhBYSu1nBZf12ONYtvsOSJIkSZI0pbypoCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kICBJkiRJUg8ZCEiSJEmS1EMGApIkSZIk9ZCBgCRJkiRJPWQgIEmSJElSDxkISJIkSZLUQwYCkiRJkiT1kIGAJEmSJEk9ZCAgSZIkSVIPGQhIkiRJktRDBgKSJEmSJPWQgYAkSZIkST1kILCSS7JXkkqy+UzXIkmSJElafRgIrPxeBJwDvHCmC5EkSZIkrT4MBFZiSe4HPBF4OS0QSHKvJEcmuTTJN5N8O8nebd82Sc5KMi/JqUk2nMHyJUmSJEkrMQOBlduewClV9XPg90m2Bp4LzAGeALwC2AEgyVrAx4C9q2ob4NPAe2egZkmSJEnSKmDNmS5AE3oR8OH2/Ivt9VrAiVV1F3BtkjPa/kcDjwe+mwRgDeC343Wc5EDgQICNNtpoRdQuSZIkSVqJGQispJI8EHgy8PgkRXeCX8BXxzsEuLSqdphM/1V1NHA0wNDQUC1/xZIkSZKkVYlLBlZeewOfraqNq2pOVT0CuAK4AXheu5fAg4FdW/vLgNlJ7l5CkORxM1G4JEmSJGnlZyCw8noRfzsb4CvAQ4HfAJcAnwDOA26qqr/QhQgfSHIxMB/YcdqqlSRJkiStUlwysJKqql3H2PZR6H59oKpuacsKzgcWtv3zgV2msUxJkiRJ0irKQGDV9M0k6wP3Bg6tqmtnuB5JkiRJ0irGQGAVNNbsAUmSJEmSlob3EJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknrIQECSJEmSpB4yEJAkSZIkqYcMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohA4EpluQhSb6Y5JdJfpLk20k2m8L+d02y41T1J0mSJEnqJwOBKZQkwFeBM6tqk6p6LPAO4MFTOMyuwJiBQJI1p3AcSZIkSdJqzEBgaj0JuKOqjhrZUFXzgXOSHJ7kkiQLk+wDd1/t/+ZI2yQfT7J/e35lkkOSXNiO2TzJHODVwJuTzE+yc5LjknwoyRnA4Un+L8ns1se9kvwiyQbT9QFIkiRJklYNXlGeWo8H5o2x/bnAXGBLYAPggiRnT6K/G6pq6ySvBd5aVa9IchRwS1X9F0CSlwObAbtV1eIkfwT2BT4M7AZcXFU3jO44yYHAgQAbbbTRUr1JSZIkSdKqzxkC02Mn4AtVtbiqfgecBWw7ieNObo/zgDkTtDuxqha3558GXtKevww4dqwDquroqhqqqqHZs2dPohRJkiRJ0urEQGBqXQpsM8b2jNP+Tu75b3CfUfsXtcfFTDyb49aRJ1V1FfC7JE8Gtge+M1HBkiRJkqR+MhCYWqcDayd55ciGJNsCfwD2SbJGW9+/C3A+8CvgsUnWTjILeMokxrgZWG8JbY4BPgd8eWDmgCRJkiRJdzMQmEJVVcBewO7tZwcvBQ4GPg8sAC6mCw3eXlXXtqv5X277TgAumsQw3wD2Grmp4Dhtvg7cj3GWC0iSJEmSlO4cVquTJEPAEVU1XmBwD0NDQzU8PLyCq5IkSZIkzYQk86pqaPR2f2VgNZPkIOA1dL80IEmSJEnSmFwysJqpqsOqauOqOmema5EkSZIkrbwMBCRJkiRJ6iEDAUmSJEmSeshAQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeqhVNVM16AZluRm4LKZrkNaQTYAbpjpIqQVwO+2Vmd+v7U68/utmbBxVc0evXHNmahEK53LqmpopouQVoQkw36/tTryu63Vmd9vrc78fmtl4pIBSZIkSZJ6yEBAkiRJkqQeMhAQwNEzXYC0Avn91urK77ZWZ36/tTrz+62VhjcVlCRJkiSph5whIEmSJElSDxkI9ESSpye5LMkvkhw0xv4k+WjbvyDJ1jNRp7QsJvH93rd9rxck+WGSLWeiTmlZLOn7PdBu2ySLk+w9nfVJy2My3+8kuyaZn+TSJGdNd43SsprE/5/MSvKNJBe37/cBM1Gn+s0lAz2QZA3g58DuwG+AC4AXVdVPBto8E3gD8Exge+AjVbX9DJQrLZVJfr93BH5aVX9I8gzgYL/fWhVM5vs90O67wO3Ap6vqpOmuVVpak/zv9/rAD4GnV9Wvkzyoqq6biXqlpTHJ7/c7gFlV9W9JZgOXAQ+pqr/MRM3qJ2cI9MN2wC+q6vL2H5gvAs8Z1eY5wGer82Ng/SQbTneh0jJY4ve7qn5YVX9oL38MPHyaa5SW1WT++w1doPsVwBMlrUom8/1+MXByVf0awDBAq5DJfL8LWC9JgPsBvwfunN4y1XcGAv3wMOCqgde/aduWto20Mlra7+7Lge+s0IqkqbPE73eShwF7AUdNY13SVJjMf783A/4uyZlJ5iV5ybRVJy2fyXy/Pw48BrgGWAi8sarump7ypM6aM12ApkXG2DZ6rchk2kgro0l/d5M8iS4Q2GmFViRNncl8vz8M/FtVLe4uMkmrjMl8v9cEtgGeAqwD/CjJj6vq5yu6OGk5Teb7/TRgPvBkYBPgu0l+UFV/WsG1SXczEOiH3wCPGHj9cLokcmnbSCujSX13k2wBHAM8o6punKbapOU1me/3EPDFFgZsADwzyZ1V9bVpqVBadpP9/5MbqupW4NYkZwNb0q3NllZmk/l+HwAcVt1N3X6R5Apgc+D86SlRcslAX1wAbJrkkUnuDbwQ+PqoNl8HXtJ+beAfgJuq6rfTXai0DJb4/U6yEXAysJ9XlbSKWeL3u6oeWVVzqmoOcBLwWsMArSIm8/8n/wvsnGTNJPelu/HxT6e5TmlZTOb7/Wu62S8keTDwaODyaa1SvecMgR6oqjuTvB44FViD7g7UlyZ5ddt/FPBtul8Y+AXwZ7rEUlrpTfL7/S7ggcCR7SrqnVU1NFM1S5M1ye+3tEqazPe7qn6a5BRgAXAXcExVXTJzVUuTM8n/fh8KHJdkId0Sg3+rqhtmrGj1kj87KEmSJElSD7lkQJIkSZKkHjIQkCRJkiSphwwEJEmSJEnqIQMBSZIkSZJ6yEBAkiRJkqQeMhCQJEmSJKmHDAQkSZIkSeohAwFJkiRJknro/wcrzqDPUdY0CwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualize feature importance\n",
    "fig,ax = plt.subplots(figsize=(16,6))\n",
    "Feature_Importance.plot.barh(title=\"Feature Importance\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "55b5e871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot confusion matrix\n",
    "ConfusionMatrixDisplay.from_estimator(model_over, X_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d95d1e17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "   confirmed       1.00      1.00      1.00       593\n",
      "   suspected       0.90      1.00      0.95         9\n",
      "\n",
      "    accuracy                           1.00       602\n",
      "   macro avg       0.95      1.00      0.97       602\n",
      "weighted avg       1.00      1.00      1.00       602\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#classification report\n",
    "print(classification_report(y_test, model_over.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ce7a57",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}