2_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "<h1 style=\"color:Black; font-size:250%; text-align:center; border-radius: 10px; padding-top: 10px; padding-bottom: 10px\">HOTEL BOOKING CANCELLATIONS PREDICTION</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a continuation of the previous notebook of Data Cleaning and EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "<h1 style=\"color:White; background-color:#212121; font-size:250%; text-align:center; border-radius: 10px; padding-top: 10px; padding-bottom: 10px\">1. Library and Data</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "import category_encoders as ce\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from imblearn.pipeline import Pipeline\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler, NearMiss\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, StratifiedKFold, GridSearchCV, RandomizedSearchCV\n",
    "\n",
    "from sklearn.feature_selection import SelectPercentile, RFE\n",
    "\n",
    "import statsmodels.api as sm #stats summary, simple model\n",
    "from statsmodels.api import OLS #Ordinary LS\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor #multicol\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression, Lasso, Ridge\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score, recall_score, precision_score\n",
    "from sklearn.metrics import plot_roc_curve, plot_precision_recall_curve\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, f1_score,\\\n",
    "plot_confusion_matrix, precision_recall_curve, plot_precision_recall_curve, confusion_matrix, roc_curve, plot_roc_curve\n",
    "\n",
    "import pickle\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.options.display.max_columns = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hotel</th>\n",
       "      <th>is_canceled</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>agent</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_guests</th>\n",
       "      <th>countries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>342</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
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       "      <td>Transient</td>\n",
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       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>737</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>C</td>\n",
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       "      <td>Transient</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.00</td>\n",
       "      <td>0</td>\n",
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       "      <td>GBR</td>\n",
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       "      <th>118721</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>August</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
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       "      <td>BB</td>\n",
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       "      <td>394.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>96.14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>BEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118722</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>August</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>225.43</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>FRA</td>\n",
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       "    <tr>\n",
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       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>August</td>\n",
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       "      <td>157.71</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DEU</td>\n",
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       "      <th>118724</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>109</td>\n",
       "      <td>August</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>104.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>GBR</td>\n",
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       "    <tr>\n",
       "      <th>118725</th>\n",
       "      <td>City Hotel</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>August</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>HB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>151.20</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>DEU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118726 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               hotel  is_canceled  lead_time arrival_date_month  \\\n",
       "0       Resort Hotel            0        342               July   \n",
       "1       Resort Hotel            0        737               July   \n",
       "2       Resort Hotel            0          7               July   \n",
       "3       Resort Hotel            0         13               July   \n",
       "4       Resort Hotel            0         14               July   \n",
       "...              ...          ...        ...                ...   \n",
       "118721    City Hotel            0         23             August   \n",
       "118722    City Hotel            0        102             August   \n",
       "118723    City Hotel            0         34             August   \n",
       "118724    City Hotel            0        109             August   \n",
       "118725    City Hotel            0        205             August   \n",
       "\n",
       "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
       "0                               1                        0   \n",
       "1                               1                        0   \n",
       "2                               1                        0   \n",
       "3                               1                        0   \n",
       "4                               1                        0   \n",
       "...                           ...                      ...   \n",
       "118721                         30                        2   \n",
       "118722                         31                        2   \n",
       "118723                         31                        2   \n",
       "118724                         31                        2   \n",
       "118725                         29                        2   \n",
       "\n",
       "        stays_in_week_nights meal distribution_channel  is_repeated_guest  \\\n",
       "0                          0   BB               Direct                  0   \n",
       "1                          0   BB               Direct                  0   \n",
       "2                          1   BB               Direct                  0   \n",
       "3                          1   BB            Corporate                  0   \n",
       "4                          2   BB                TA/TO                  0   \n",
       "...                      ...  ...                  ...                ...   \n",
       "118721                     5   BB                TA/TO                  0   \n",
       "118722                     5   BB                TA/TO                  0   \n",
       "118723                     5   BB                TA/TO                  0   \n",
       "118724                     5   BB                TA/TO                  0   \n",
       "118725                     7   HB                TA/TO                  0   \n",
       "\n",
       "        previous_cancellations  previous_bookings_not_canceled  \\\n",
       "0                            0                               0   \n",
       "1                            0                               0   \n",
       "2                            0                               0   \n",
       "3                            0                               0   \n",
       "4                            0                               0   \n",
       "...                        ...                             ...   \n",
       "118721                       0                               0   \n",
       "118722                       0                               0   \n",
       "118723                       0                               0   \n",
       "118724                       0                               0   \n",
       "118725                       0                               0   \n",
       "\n",
       "       assigned_room_type  booking_changes  agent  days_in_waiting_list  \\\n",
       "0                       C                3    NaN                     0   \n",
       "1                       C                4    NaN                     0   \n",
       "2                       C                0    NaN                     0   \n",
       "3                       A                0  304.0                     0   \n",
       "4                       A                0  240.0                     0   \n",
       "...                   ...              ...    ...                   ...   \n",
       "118721                  A                0  394.0                     0   \n",
       "118722                  E                0    9.0                     0   \n",
       "118723                  D                0    9.0                     0   \n",
       "118724                  A                0   89.0                     0   \n",
       "118725                  A                0    9.0                     0   \n",
       "\n",
       "       customer_type     adr  required_car_parking_spaces  \\\n",
       "0          Transient    0.00                            0   \n",
       "1          Transient    0.00                            0   \n",
       "2          Transient   75.00                            0   \n",
       "3          Transient   75.00                            0   \n",
       "4          Transient   98.00                            0   \n",
       "...              ...     ...                          ...   \n",
       "118721     Transient   96.14                            0   \n",
       "118722     Transient  225.43                            0   \n",
       "118723     Transient  157.71                            0   \n",
       "118724     Transient  104.40                            0   \n",
       "118725     Transient  151.20                            0   \n",
       "\n",
       "        total_of_special_requests  total_guests countries  \n",
       "0                               0           2.0       PRT  \n",
       "1                               0           2.0       PRT  \n",
       "2                               0           1.0       GBR  \n",
       "3                               0           1.0       GBR  \n",
       "4                               1           2.0       GBR  \n",
       "...                           ...           ...       ...  \n",
       "118721                          0           2.0       BEL  \n",
       "118722                          2           3.0       FRA  \n",
       "118723                          4           2.0       DEU  \n",
       "118724                          0           2.0       GBR  \n",
       "118725                          2           2.0       DEU  \n",
       "\n",
       "[118726 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_modeling = pd.read_csv('df_for_modeling.csv')\n",
    "df_modeling = df_modeling.drop(columns=['Unnamed: 0'])\n",
    "df_modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>d_type</th>\n",
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       "      <th>n_uniques</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hotel</td>\n",
       "      <td>object</td>\n",
       "      <td>[Resort Hotel, City Hotel]</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>is_canceled</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lead_time</td>\n",
       "      <td>int64</td>\n",
       "      <td>[342, 737, 7, 13, 14]</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arrival_date_month</td>\n",
       "      <td>object</td>\n",
       "      <td>[July, August, September, October, November]</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arrival_date_day_of_month</td>\n",
       "      <td>int64</td>\n",
       "      <td>[1, 2, 3, 4, 5]</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>stays_in_weekend_nights</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 2, 4, 3]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>stays_in_week_nights</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 2, 3, 4]</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>meal</td>\n",
       "      <td>object</td>\n",
       "      <td>[BB, FB, HB, SC, Undefined]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>distribution_channel</td>\n",
       "      <td>object</td>\n",
       "      <td>[Direct, Corporate, TA/TO, GDS]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>is_repeated_guest</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1]</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>previous_cancellations</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 2, 3, 26]</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>previous_bookings_not_canceled</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 2, 3, 5]</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>assigned_room_type</td>\n",
       "      <td>object</td>\n",
       "      <td>[C, A, D, E, G]</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>booking_changes</td>\n",
       "      <td>int64</td>\n",
       "      <td>[3, 4, 0, 1, 2]</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>agent</td>\n",
       "      <td>float64</td>\n",
       "      <td>[nan, 304.0, 240.0, 303.0, 15.0]</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>days_in_waiting_list</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 50, 47, 65, 122]</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>customer_type</td>\n",
       "      <td>object</td>\n",
       "      <td>[Transient, Contract, Transient-Party, Group]</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>adr</td>\n",
       "      <td>float64</td>\n",
       "      <td>[0.0, 75.0, 98.0, 107.0, 103.0]</td>\n",
       "      <td>8856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>required_car_parking_spaces</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 2, 8, 3]</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>total_of_special_requests</td>\n",
       "      <td>int64</td>\n",
       "      <td>[0, 1, 3, 2, 4]</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>total_guests</td>\n",
       "      <td>float64</td>\n",
       "      <td>[2.0, 1.0, 3.0, 4.0, 5.0]</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>countries</td>\n",
       "      <td>object</td>\n",
       "      <td>[PRT, GBR, USA, ESP, IRL]</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Column   d_type  \\\n",
       "0                            hotel   object   \n",
       "1                      is_canceled    int64   \n",
       "2                        lead_time    int64   \n",
       "3               arrival_date_month   object   \n",
       "4        arrival_date_day_of_month    int64   \n",
       "5          stays_in_weekend_nights    int64   \n",
       "6             stays_in_week_nights    int64   \n",
       "7                             meal   object   \n",
       "8             distribution_channel   object   \n",
       "9                is_repeated_guest    int64   \n",
       "10          previous_cancellations    int64   \n",
       "11  previous_bookings_not_canceled    int64   \n",
       "12              assigned_room_type   object   \n",
       "13                 booking_changes    int64   \n",
       "14                           agent  float64   \n",
       "15            days_in_waiting_list    int64   \n",
       "16                   customer_type   object   \n",
       "17                             adr  float64   \n",
       "18     required_car_parking_spaces    int64   \n",
       "19       total_of_special_requests    int64   \n",
       "20                    total_guests  float64   \n",
       "21                       countries   object   \n",
       "\n",
       "                                    unique_sample  n_uniques  \n",
       "0                      [Resort Hotel, City Hotel]          2  \n",
       "1                                          [0, 1]          2  \n",
       "2                           [342, 737, 7, 13, 14]        479  \n",
       "3    [July, August, September, October, November]         12  \n",
       "4                                 [1, 2, 3, 4, 5]         31  \n",
       "5                                 [0, 1, 2, 4, 3]         15  \n",
       "6                                 [0, 1, 2, 3, 4]         31  \n",
       "7                     [BB, FB, HB, SC, Undefined]          5  \n",
       "8                 [Direct, Corporate, TA/TO, GDS]          4  \n",
       "9                                          [0, 1]          2  \n",
       "10                               [0, 1, 2, 3, 26]         15  \n",
       "11                                [0, 1, 2, 3, 5]         73  \n",
       "12                                [C, A, D, E, G]         11  \n",
       "13                                [3, 4, 0, 1, 2]         19  \n",
       "14               [nan, 304.0, 240.0, 303.0, 15.0]        332  \n",
       "15                           [0, 50, 47, 65, 122]        127  \n",
       "16  [Transient, Contract, Transient-Party, Group]          4  \n",
       "17                [0.0, 75.0, 98.0, 107.0, 103.0]       8856  \n",
       "18                                [0, 1, 2, 8, 3]          5  \n",
       "19                                [0, 1, 3, 2, 4]          6  \n",
       "20                      [2.0, 1.0, 3.0, 4.0, 5.0]         14  \n",
       "21                      [PRT, GBR, USA, ESP, IRL]         16  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def report(df_modeling):\n",
    "    col = []\n",
    "    d_type = []\n",
    "    uniques = []\n",
    "    n_uniques = []\n",
    "    \n",
    "    for i in df_modeling .columns:\n",
    "        col.append(i)\n",
    "        d_type.append(df_modeling [i].dtypes)\n",
    "        uniques.append(df_modeling [i].unique()[:5])\n",
    "        n_uniques.append(df_modeling [i].nunique())\n",
    "    \n",
    "    return pd.DataFrame({'Column': col, 'd_type': d_type, 'unique_sample': uniques, 'n_uniques': n_uniques})\n",
    "\n",
    "report(df_modeling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "<h1 style=\"color:White; background-color:#212121; font-size:250%; text-align:center; border-radius: 10px; padding-top: 10px; padding-bottom: 10px\">2. Modeling</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>hotel</th>\n",
       "      <th>lead_time</th>\n",
       "      <th>arrival_date_month</th>\n",
       "      <th>arrival_date_day_of_month</th>\n",
       "      <th>stays_in_weekend_nights</th>\n",
       "      <th>stays_in_week_nights</th>\n",
       "      <th>meal</th>\n",
       "      <th>distribution_channel</th>\n",
       "      <th>is_repeated_guest</th>\n",
       "      <th>previous_cancellations</th>\n",
       "      <th>previous_bookings_not_canceled</th>\n",
       "      <th>assigned_room_type</th>\n",
       "      <th>booking_changes</th>\n",
       "      <th>agent</th>\n",
       "      <th>days_in_waiting_list</th>\n",
       "      <th>customer_type</th>\n",
       "      <th>adr</th>\n",
       "      <th>required_car_parking_spaces</th>\n",
       "      <th>total_of_special_requests</th>\n",
       "      <th>total_guests</th>\n",
       "      <th>countries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>342</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>737</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>PRT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>7</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>Direct</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>13</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>BB</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Resort Hotel</td>\n",
       "      <td>14</td>\n",
       "      <td>July</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>BB</td>\n",
       "      <td>TA/TO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Transient</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>GBR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hotel  lead_time arrival_date_month  arrival_date_day_of_month  \\\n",
       "0  Resort Hotel        342               July                          1   \n",
       "1  Resort Hotel        737               July                          1   \n",
       "2  Resort Hotel          7               July                          1   \n",
       "3  Resort Hotel         13               July                          1   \n",
       "4  Resort Hotel         14               July                          1   \n",
       "\n",
       "   stays_in_weekend_nights  stays_in_week_nights meal distribution_channel  \\\n",
       "0                        0                     0   BB               Direct   \n",
       "1                        0                     0   BB               Direct   \n",
       "2                        0                     1   BB               Direct   \n",
       "3                        0                     1   BB            Corporate   \n",
       "4                        0                     2   BB                TA/TO   \n",
       "\n",
       "   is_repeated_guest  previous_cancellations  previous_bookings_not_canceled  \\\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",
       "  assigned_room_type  booking_changes  agent  days_in_waiting_list  \\\n",
       "0                  C                3    NaN                     0   \n",
       "1                  C                4    NaN                     0   \n",
       "2                  C                0    NaN                     0   \n",
       "3                  A                0  304.0                     0   \n",
       "4                  A                0  240.0                     0   \n",
       "\n",
       "  customer_type   adr  required_car_parking_spaces  total_of_special_requests  \\\n",
       "0     Transient   0.0                            0                          0   \n",
       "1     Transient   0.0                            0                          0   \n",
       "2     Transient  75.0                            0                          0   \n",
       "3     Transient  75.0                            0                          0   \n",
       "4     Transient  98.0                            0                          1   \n",
       "\n",
       "   total_guests countries  \n",
       "0           2.0       PRT  \n",
       "1           2.0       PRT  \n",
       "2           1.0       GBR  \n",
       "3           1.0       GBR  \n",
       "4           2.0       GBR  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_modeling.drop(columns = ['is_canceled'])\n",
    "y = df_modeling['is_canceled']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                                    y,\n",
    "                                                    stratify = y,\n",
    "                                                    test_size = 0.2,\n",
    "                                                    random_state = 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['is_canceled',\n",
       " 'lead_time',\n",
       " 'arrival_date_day_of_month',\n",
       " 'stays_in_weekend_nights',\n",
       " 'stays_in_week_nights',\n",
       " 'is_repeated_guest',\n",
       " 'previous_cancellations',\n",
       " 'previous_bookings_not_canceled',\n",
       " 'booking_changes',\n",
       " 'agent',\n",
       " 'days_in_waiting_list',\n",
       " 'adr',\n",
       " 'required_car_parking_spaces',\n",
       " 'total_of_special_requests',\n",
       " 'total_guests']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_modeling.select_dtypes(include=['int64','float64']).columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['hotel',\n",
       " 'arrival_date_month',\n",
       " 'meal',\n",
       " 'distribution_channel',\n",
       " 'assigned_room_type',\n",
       " 'customer_type',\n",
       " 'countries']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_modeling.select_dtypes(include=['object']).columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split numerical and categorical data\n",
    "num = ['lead_time','arrival_date_day_of_month','stays_in_weekend_nights', 'stays_in_week_nights','previous_cancellations','previous_bookings_not_canceled', 'booking_changes','days_in_waiting_list', 'adr', 'required_car_parking_spaces','total_of_special_requests','total_guests']\n",
    "cat = ['hotel','arrival_date_month','meal','distribution_channel','is_repeated_guest','assigned_room_type','agent','customer_type','countries']\n",
    "\n",
    "# Make pipeline for categorical features\n",
    "cat_scalerencoder_pipe = Pipeline([\n",
    "    ('imputer',SimpleImputer(strategy = 'constant',fill_value = 0)),\n",
    "    ('binary encoder',ce.BinaryEncoder())\n",
    "])\n",
    "\n",
    "# Make pipeline for numerical features\n",
    "num_scaler_pipe = Pipeline([\n",
    "    ('robust scaler', RobustScaler())\n",
    "])\n",
    "\n",
    "# Column Transformer\n",
    "transformer = ColumnTransformer([\n",
    "    ('cat pipe', cat_scalerencoder_pipe,cat),\n",
    "    ('num pipe', num_scaler_pipe,num)], \n",
    "    remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 2.3 Modeling with Default Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.3.1 Model assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model assignment\n",
    "logreg = LogisticRegression()\n",
    "dtc = DecisionTreeClassifier()\n",
    "rfc = RandomForestClassifier()\n",
    "gbc = GradientBoostingClassifier()\n",
    "xgbc = XGBClassifier()\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "models = [logreg, dtc, rfc, gbc, xgbc, knn]\n",
    "model_name = ['LogisticRegression', 'DecisionTreeClassifier', 'RandomForestClassifier', 'GradientBoostingClassifier', \n",
    "              'XGBClassifier','KNeighborsClassifier']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine all pipeline. Assign in to a list\n",
    "all_models = []\n",
    "for i in models:\n",
    "    modelpipe = Pipeline([\n",
    "        ('transformer', transformer),\n",
    "        ('resampling', smote),\n",
    "        ('clf',i)])\n",
    "    all_models.append(modelpipe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.3.2 Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am going to use __PRECISION SCORE__ as evaluation metrics because I want the FP as less as possible.\n",
    "\n",
    "1. FN is a condition when the __MODEL__ predict THE GUESTS ARE COMING __(0)__ but __ACTUALLY__ THE BOOKINGS ARE CANCELLED __(1)__\n",
    "    - High numbers of FN will cause __unoptimal revenue__ due to many rooms are reserved (and can not be booked by other potential guests) but the booking is cancelled\n",
    "\n",
    "\n",
    "2. FP is a condition when the __MODEL__ predict THE BOOKINGS ARE CANCELLED __(1)__ but __ACTUALLY__ THE GUESTS ARE COMING __(0)__\n",
    "    - High numbers of FP will cause __overbooking__.\n",
    "        -  overbooking can force the hotel to deny service to a customer; this can be a very bad experience for the guests and may result in online __complaints and generation of a negative impact in terms of social reputation__ \n",
    "        - the hotel also __lose future revenue__ from this overbooking, since the guests who experienced bad service have strong tendency to avoid that hotel. In other words, \n",
    "        - overbooking also cause the loss that occurs as a result of the hotel’s obligation to compensate the customer, including reallocation costs\n",
    "\n",
    "<img src=\"https://static.packt-cdn.com/products/9781838555078/graphics/C13314_06_05.jpg\" alt=\"Hotel Lobby\" style=\"width:690px;height:345px;\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   40.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   36.1s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   52.8s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  1.3min finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:   51.7s finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  1.8min finished\n"
     ]
    }
   ],
   "source": [
    "# Cross validation\n",
    "skfold = StratifiedKFold(n_splits=5, random_state=2021, shuffle=True)\n",
    "\n",
    "logit_score = cross_val_score(all_models[0], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "dtc_score = cross_val_score(all_models[1], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "rfc_score = cross_val_score(all_models[2], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "gbc_score = cross_val_score(all_models[3], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "xgbc_score = cross_val_score(all_models[4], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "knn_score = cross_val_score(all_models[5], X_train, y_train, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.670192</td>\n",
       "      <td>0.685931</td>\n",
       "      <td>0.673060</td>\n",
       "      <td>0.682467</td>\n",
       "      <td>0.676181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.770015</td>\n",
       "      <td>0.772604</td>\n",
       "      <td>0.777672</td>\n",
       "      <td>0.771148</td>\n",
       "      <td>0.768717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.865005</td>\n",
       "      <td>0.862391</td>\n",
       "      <td>0.868566</td>\n",
       "      <td>0.865482</td>\n",
       "      <td>0.857079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.759216</td>\n",
       "      <td>0.763364</td>\n",
       "      <td>0.767160</td>\n",
       "      <td>0.752446</td>\n",
       "      <td>0.755344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.828654</td>\n",
       "      <td>0.835068</td>\n",
       "      <td>0.828551</td>\n",
       "      <td>0.822034</td>\n",
       "      <td>0.823367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.704807</td>\n",
       "      <td>0.703202</td>\n",
       "      <td>0.705039</td>\n",
       "      <td>0.702842</td>\n",
       "      <td>0.697554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0         1         2         3         4\n",
       "LogisticRegression          0.670192  0.685931  0.673060  0.682467  0.676181\n",
       "DecisionTreeClassifier      0.770015  0.772604  0.777672  0.771148  0.768717\n",
       "RandomForestClassifier      0.865005  0.862391  0.868566  0.865482  0.857079\n",
       "GradientBoostingClassifier  0.759216  0.763364  0.767160  0.752446  0.755344\n",
       "XGBClassifier               0.828654  0.835068  0.828551  0.822034  0.823367\n",
       "KNeighborsClassifier        0.704807  0.703202  0.705039  0.702842  0.697554"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score\n",
    "crosval_results = [logit_score,dtc_score,rfc_score,gbc_score,xgbc_score,knn_score]\n",
    "pd.DataFrame(crosval_results, index=model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vd/oMs2xB1sosumJFcLTJh5cuCL6+r7SbG2w/3uqZYHt0swnB9u1pdwXbfeNvCLa7RF0RbLd0tw62E6xJwbbdaKO4tv4KODHmf15ZJNmWEmw3df7zC23r0H/O2XBxeMdg+4ro7sF2r7hrg+3rEwYE24NShgbbd6bfHWyPyPrnpKRjcx4Jth9t8ViwPaPVzGB7VptXg+057d8Ott/p+FGw/XHnL4PthZd/C9Qfdf768mVoNFoCBPi081foJT0BArzb8ROsSv0pA6+3n4db7wbgxTZziDBGokHDU62fp4k5AY0GprR4gmRrChqNlnE5k0m0JqFBw8is0TQxx6PVaBmefh8x5lgkjcytKbcRZYpG0kj1gZkxEkkjcXlUd1x6F5JGplXohdhUO5JGagjMzMhaGbc+FJ1WBQL1gRka6vxeSmtL8Pq9VFNFfvkBqr3V+AN+Np3awOXR3ajx17Dk6CLaey5G9ap8cGA+uSEt0Et65ux5mRaROfiqtMza+SxNzPHoJT0ztk3nsZZPo5f0PLp5QrA9dsODwfaItXcxveVTGGQjd60a+rvb96y+g2ktnsQoG3lw3b1MzJ2KUTYydsNDPJwzEYNkZPLm8TyQNRq9rOexrVO4J2MEesnAM9uf4Pa04eglAy/ueo5bkoeil/S8tmc2/RNvQifpeXvvG/SJ74dO0vNe3jtcHdsbVdLxaf5HdI2+ElWrsvDIAjpFdEHRqnx77BvaeTqgalVWHl9Bq9CLUDQyGwrXkeNqjqSR2Va0lQx7JrJWZk/JbhKtychaifzyA0SbYpE0EgVVxxpGmEmcrj2NU3WiFSPMhL+pbhlhv2n0zx/BbrdTWVlBYWEhLpeLzZs3Eh0dQ3x8AgsXLqBPHygtLeXQoYM/WW/FiqU0bZrDLbcM4euv/8Hbb7/B6NHj+fcLrMfGxvHxxx/g9/vx+/2MGHEXjz32dHC5JEk88MAYbrrpepo2zSEtLYPQ0DCefvoFZFlmwYLPSEpK5vDhQ2zb9j1JSSls3/79T7ah0WiC2+rSJZ0uXS6nqOg0n332MYWFhezevZOpU5+gpqaG3r270bnz5SxZ8g0TJkwhEAjQv3+fn4wMio1twpYtm0lOTuXkyROUlZVhtdp+3NpZfPXPL9KECRMmnOtO/BK/P0B1dd257sZZodcrf5t9EX4bUfvGS9T+ryUQCFDjrwHqA5uCqmNIGgl/IMDukp2oWhU/ftacWIVeMuDHz8LDCzApZvwBP+/m/R821YasanluyzM4VGf9EO6tj+LSuwgEAoxaPwKPIRxfwMcdK28l3BBJAD/XL7mGaFM0AeC6JVfRxJKAVqPlpmX9SLQmI2lkbvvuFlJsqahalXvW3EG6PQO9bGDU+vvJdGRhlI08sulhmoXkYFEsvLRrJk1DcrEpNj7N/5CmITnYdQ7WnVhN05AcnGoIhyryaRqSQ4jeTZ2/hqbOHFx6NzbFRpazKW69m1hzE9Idmbj0Lpo6c0i1p+PSu+gQfglJ1hRC9C56xvYi3pKIS+9iQNItxFmaYNc5uC7+BqJMMdhUGz1jexFhjMSiWOkSdQVhBg9mxUw7z8W49G6MsomW7lY4dE4MsoFMRzZW1YZO0pFgTcKsmFEllUhTFAbZgKyVcepCUCUVSSNhkI0NI2U0wS+Jf7bG9JnXaDRoNdrgv6qkotVokTQSJtmErJWRtTI21Y6iVVC0CiF6F6qkokoqHkM4OkmHTtIRZYpGLxvQS3riLE0wyEYMsoEkWzImxYRRNpJmT8esWDDJJrKcTbEoVsyKmVxXC6yqDYti4QJ3a2yqHatq5aKwdjh0DmyqnQ7hl+DUObGrdjpFdCZE78Khc3JZVFdcejdOnZNuMT1wG0IJ0bu4KrY3oYYwQvQurmnSF48hHLfeTd+EGwk3RhBqCKN/4k1EmCJx60Ppn3gTie54zNi4tkk/okzRhOhdXBlzNbHmWJx6F50jLyfOEo9T56S95xLiLYnYdQ4uCG1NojUZu85OU2cOybYUbKqNFFsaafZ0LIqVWHMc6Y4sLIoZjyGCTGc2FtkSXMekmNBpdeSENMcgG/D6veSENEcn6SmuLSI3pAWKVuZwxSFyXS2RNDK7SnbSwt0SDVrWn1pDS3crAgT49thiWrpb4w/4+fzQJ1zgbo3XX8c7+9+idehF1PpqeHX3S7QKvYhqbzVPb3+CC0PbUOmrZNqWSVwY2pZKbwWTN4+jdWgbKrzljN84mtbuiyjzljFmw0gucF9IWV0pD667l5bu1pTUlXDfmmE0d7WktLaE4auGkutqQUltMbevHEROSHOKa4sYvGIATUNyKao5zU3Lrifb2YzTNae4cem1ZDmbcqq6kBuWXEOmI5vTNacYtPxG0u2ZFNWe5o7vbiXVnkZxTRH3rR5Gki2VktpiRq0fQaI1mdLaEiZuHEO8JYGyujKmb51ErDmOcm85T297nBhTHOV15by48zmiTNFUeCuY88PLRBgj8Ute3tk9j1BDGF5/HcuPf4tD58QX8LGzZAdm2UwAOFVTiKpVg4HWufo7JZw9Z+Nvfnl5CWaz/Sz16Lc7duwoc+e+zpIli/j004/49NOPuPbafrz00kwWLPiUU6cKGT78PpKSktm0aQNvvvkaGzaso7i4iJ49e7Fhwzri4xNISEhkxozH+PLLz9mzZxc33TQYl8vFqlUrWL9+LRERkZSUFHPZZV05daqQWbOe4/PPP6F7956kpqYzb95b9OvXHwBZlklKSuaxx6Zw7bV9sVgszJgxnU8++ZCSkmK6d+9Jenomb731Op9//jGHDx/i9OlTXHNNX+bPn0fv3n2QZZnU1DTeemsOH3/8AV988QkdO3YmJSWNb775ildemcU333zNFVd0JyenOTt3bufpp59g8eKvycpqSqdOXfjyy89p06YdrVtfyHvvzeOjj95n4cIvufvu+4iOjv3JttavX4vdbicpKeUMr/i585/eayaT7hcfrwn8e8x3Hqmr81FcXHmuu/E/K60tpe+Sq7gtdTiXRHRiwNK+3JpyGx08l3Dr8gHclHwrbcM6MGzlYG5IHMiFoW25f81wrou/nlbuixi9fgS94/rQ3HUBEzaN5qrY3uSENGfqlkfoFt2DbGczZnz/GJdFdSXTkcXzO56hY0Rn0uwZvLJ7Fu3DLyHFlsabP7zGRWHtSLIm8+7+t7nA3ZomlgQ+yn+f5iEtiDXHseDQ52Q7mxFliuabo1+R7sgk3BDBd8eXkWRLIczgYd3JNcRbEnDp3Ww9vZloUwxOnZNdJTsJN0ZgU2wcKM/DpXdjViwcrTgS/OJ/uvoUZsWCTtJRXleOXtKjaBXq/LVIWhlJI53rcp11drvxb/E+Fn47Ufv/3o8hjaSpP8J/ovo4NsWOopX5oXQPEcZI9JKeDYXrSLKlYJJNLD66iJyQ5lhVGx/nv0/bsA44dE7e/OFVLovqhlvv5rntT9Er7lo8xnAmbx7PgMRbiDJFM3Lt3dyeNpxYcxOGrriZEVkPkWBN4qZl/RjTbAJJ1pT6dtMJJNlSgvcnW1MZuKwvY5pOIMWexoCl1zG66XhSbGnB+y+Iac7Ahf25J/MBkm0pjFk3ksGpd5BgSeSxrY9yfeIAYkyxvLRrJj1jexNhjOSd/W9xacRlhBnCWXDoUy4Ma0uIzsXK48vJdjbDptrYVrS1PjCRzRysyMdjCEcvGSitK8EsW4KjkMRR9XNHfOYbr8Ze+0AggB8/GjQEAn6qfNXoJT3+gJ+SuhJsipUAcKLqOC69GwhwqOIQkcYoAgTYV/YD8ZYEAgHYWbydVHsagUCAzac3ke1sBgRYc2IVLd2tAFhesJQ2Ye0AWHR0IR3DOwOw4PBnXBbZFYCP8z+gR0z9PCrv5r3NtU36AfDmD69xY+JAAF7e/SKDkocA8PzOZ7gtdTgaYMa2x7g74340aJi2dRIjs0aj0WiYuPFhHm42ETQaHl4/konNp2IyqQz5ZnBwpN59a4b9pP1kq+fQouXeNXfyxAXPImkl7l89nMdbPYNOq2Py5vGMaTYBnaTjhR3Pckf6XahalXn73qJfwo0oWpWFhxdweVQ3FK3CusI1XOC+EEUrs690b8NILJmimtOE6FzI2voTRsT/BX+8s/G5LyjIx+OJPfMDz7H8/AP88MNuLr30MkpKiunf/zref/8zVFU9J/1ZtWoFdruDtLQM1q1bw9y5c3j22Rf/lG1Lkhaf76932u9/eq+53ZZffLwYUfQn8Ad8BGQvGbZsXHo3hdUnyXY2w6V3c7A8n0xHNm59KDuLt5PhyCLM4GF94RrSG9pLCxaTas8g3BjOPw5/Tqo9A48xnPfz5pNiTyPcEMmbe18j2Z5KuDGSWTufJdmWQoQpiie+n0aSNYlIUxSPbHqYRGsikcYoRq0fQbwlkShTNA+su4cmlgSiTNHct3YYseYmRJmiuHv17cSYYokyRXHnqsFEmWKINEYx9LubiTBGEWWKYtDy+qNrkaYoBi7rR5ghnEhTFDd8ey1ufShRpij6Lrkal95NpCmKaxZfSYjORYQxgqu/6YpD5yTcGMGVX3fBrjqIMEbQbWEn7KqdcGMEvRZ1w6baCDOEc/2SXlgVG2EGDwOX9sWiWAkzhDF0xU1YFCuhhlCGr7oNi2LBpQ9l5Nq7MSlmQnQuHt7wAEbZRIjOxaTN4zDJJpw6J49tnYJBMuDUOXlm2xPoJQN21cGLO5/DIBuwKjbm7HkZvaTHqtr4v31vopf0mBULHxyYj17SYZLNfHHoE3SSDpNs4uujC1G1KgbJyLKCJZj0BjRemTUnV6FoFXSSni2nNiFrZHSSjp3F25G1CqpWIa9sH5JGRtEqHK08glYjIWklTtWcQkv9kdqyujI01B/BrfPXQsPRXOH881cZXfBjSBMgAIEABVXH0CIBAXaV7ETWKgCsObESVdKhQcvCIwswSHq0Gi3v7H8Ls2JG0ki8uPM5LIoVWaswbcsk7Do7slbhoXX34dSFoGgVbvvuZlz6UFStSt/FV+MxeNBJeq75pjtRpmiMson+S/sQa47DpJgZvGIg8dYErKqN4auGkmRNxqaz88C6e0ixpeLQORm/cRQZ9kxcBjfP73yGTEcWLr2bjw68R2bDyJjVJ74j29kMtz6UA+V5ZDubEWoIpdpXRbOQXEINYVgUa/1jDG6iTbFkOLMI0bnIdjYj3Z6BUxdC+/CLSbWn4VAd9Ii5mmRbCg6dkwFJt5BgTSTM5qJnRH3/zYqFnrG9iDJFY1JMdIm6gnBjBEbZSDvPxYQawjDIBlq6WxGid6GTdGQ6s7GrdlRJbRhJUx8C1Y+kMSIFR9Lo0Gq058VIGqHeX+UzL5x9jb32vzjCTFs/wkwKjjCz/XcjzCQ9etnQMMLMgEE2kmRLxiibMMom0uzpmBQzJsVMlrMpZsWCWbE0HDywYlWtDSPMbNhUGxeFtcOu2rE3jDBz6Jw4dE46RXTBqQvBqQvhsqiuhOhdhOhddI/piUvvxqV3c1Vsb9yGUNz6UK5p0pdQQxihhjD6JtyIxxBOvCuO3pHXE2b04NK5uCyqGzHmWGyqjRauC+pHxslmYs1xZDqz0Uk6jLKJnJDmoNFwsvoELVytqPPXsebkKlqHtqG8roz5efNoE9aeU9WFPLFtKh08HSmsPsmETWNoG9ae41UFjFh7F61DL+JY5VFuX3kLLdwXcLTyCDcuvZbmIS0pqDrG3atuJ8vRlMLqk0zaNI4UWxrFtUW8uGsmTSzxlNWV8cGBd4kwRlLlrWRpwWJceje1/hp2FG3DoljxBXycqDqOXtKDBgIBv/juyd9jRNF/S6fTMWfObObPn8fChQsYMOAWUlPTzll/fD4fTzwxlS+//JyNG9czfPi9uFzuP2XbWq3mZ6fU/RX81hFFIij6EyhahUuatMemqf+R1Dq0DR5jOIpWpa2nPRGm+vkVLg7vSJQpGlVS6RTZhWhzDKqkcllUV2LNcaiSjq7RPYizNEEn6egRezXxlgR0ko5ecdeSYE1CL+npE9+PJFsyeklPv4T+JNtS0Ut6+ifeTKo9HZ2kp3/SzaTY0zBIBq6Lv55UezoG2UDP2F6kOTIwyEauiOpOmj0Dk2zikvBLSXdkYlbMXBTalnRHFmbFTHNXSzIc2QvcQ38AACAASURBVJhlC5nObDKdWVgVG0m2FLIc2dhUGzHmOLKcTbGrDsIMHrKdzXA0DAFvGpKDQ+fEKBlpFpKDU+dEo9HSLCQXpy6EKl8lTZ05hOhcnKopDAZshyryyXRm49KHsrN4BxnOLNz6UDYUriXdnkWoIZSlBUtIt2cQavDw5eHPSbOnE2YI54MD75BqTyPMEM6bP7xGij0NjzGcWTufawjYInni+2kkWpOJNEbyyOaxJFqTiTBGMnr9CBKtSUQYo3hw3T3EWxIIN0Zy/5rhNLHEE26M5O7VtxNrjiPcGMmwVYOJs8bhUj3c9t3NRJmiCTdGcOuK/kSaIgk3RnDTsusJN4bjMUZw49I+hOk9hBvD6bekF259KOHGcK5d3AOXzo3HGE6vb7ri1IXgMXro8fVlOFQnHkM43b66FLvqwGPw0Oub7thUO2EGTzBgCzWEcfOy67GpNlx6N7d/dwtWxUqIzs1dq27DolgJ0YfwwNp7sCgWnLoQHt7wIGbZgkPnZNLmcZgVM3bVwePfT8Ekm7Apdp7d8SRG2YRNtfHSrpkYJCNWxcqcPS9jkAyYFQvz9s39LwM2HQbJwPLjS1G0KnpJz9qTq1GkfwnYtAo67T8DNuVfAzaN/LOATdJIaDVayr1laDVatGip89f9KT+q/5svEIFAgFp/bTCkOV5VgKbhfOfdJTuRtDIajYY1J1eiaBVkjcQ/Dn+BXtIja2Te2f8WBtmIolWZtfNZzLIFVasydcskrKoVvaTnwXX3YVPtGGQDQ1fcjFMXgkE2cN3iq3Dr3ZhVM70WdSPcEIFVZ+OGb68hyhSNXefg1uX9iTXH4dSHMGzVEOItiYToXYxYexeJ1mTc+lDGbniIVFs6YQYPM3c+TbojE4/Bw/sH3iGzIfBedXwFWc5swgwe8sr2k+1sSpjBQ5W3kqYhOYQZPFgUSzDIiTbFkOmoD9eznE1Jt2fiUJ2083QgzZEeDGlSbKnYVDv9k24myZaMWbHQN/4G4izxmBQzPWN7EW2OwSjXhzQRxkgMsoH2nosJM3jQS3pauls3/GBpCGl0DhStSqI1CYtiDYY0xoZA5l9DGmNDcPOv76fG/oOxMRO1b7xE7RsvvV6htsbXEIzJWFUrsrb+YGC4MaIhGDKSZEvBIBuxKFZyQppjViw4dA7aeTpgU+2E6F1cEd0dp85JmMHDNU364tK7iTBF0j/x5oawys0V0VcSZYrGoXNwgbs1CdYkTIqZeEsC6fZMdJIOS0NoBnC65hQt3K2o8Vez6dQGLgxrS3FtMR8deI92ng4cryrgqW2P0TGiM8eqjjBp8zg6hHfkcMUhHlx3L209HcgvP8CwVYO5MKwNeWX7GbisHxe4W3Oo/CB3r76dnJDmHK08yuTN40mzZ3Cy+gQv7ppJgiWR4trTvJ/3DtGmGMq8pXx7bDGh+jCqfFVsK9qKXXXgDdRRUFmAQTIAAfx/kSCqMQVFiqJw6aWXceWVV9Gjx9UkJSWf0/7Y7Q6uvPIqunXrQffuPf+0kAhEUHRe+LsERXB+fYH48WjPj0d+FK1Sf7qHRhOc90Gr0WJWLMEjQnadA33D6AGX3o1BNiBpJDyG8IYjRRKRpijMigVJK9VPQKpakbQy8dZEbKodWSuTbEvFoXMga2XSHZnBEQZZzqaE6F0oWoWckOa49aEoWpWW7laEGsJQJTUYsKlSQ8Bm/GfAFmmKQpV0wYBNJ+m4LKorMeY4dJKObj8J2HrRxJKAXtI3BGyJvxywyQb6J9aHavqGgC3ZlopBMtAnvh8ptjSMspEesb1ItaVjUkzBgM3cELC1iMxF8ekbArZMzIqlIWDLwqJYyXRmk+HIwqbYSLSlkOnIxqbaiTHH1gdsuvqA7cewzaE6aBrSDIcuBKNkrJ9rRBfSELDlEKL/acBW2BCwhehdHCw/QJajfnTFzuId9aPZDG42FK4jw1Efti07voR0RyZufRj/OPwFafZ0Qg1hfHDgXVJt9e03975Gqi2NMEMYL+6cSYotlTCDhye+n0ayLRWPMYJJm8eSZEsh3BjB6PUjG8K2CB5cdy/xlkTCjRENAVsC4caIhoCtCeHGCO5cOZgYcyzhxvCGgC0GjzGcW1cMIMoYTZjRw83LrifCGEmY0UP/pX3wGMIJM3rot6QXoYYwwgwe+izugUvvJswQRq9F3QjRuQg1hNUHbDonoYYwrvyqMw6dE7c+lN7fdMehc+LSu7nh297YVDtufejPAjabYsOpC+GeVXdgVaw4dPUBm1W14tA5GLfhISyKhRCTg3uWD8eiWDArVoasGIhNtWFVrPRZ3BOH6sSuc3LVossJ1YcRonfTb0kvwo0RuAyh3LL8BqJM0YQawrhj5a3EmeMIM3i4f81wEiwJeIzhjNnwAEm2FKJMUTy7YwZp9nQijJG8lzePDHsm4cYIVhxfSqYjm3BjBPvK9pLtbIrHEE6Vt6IhpAnDJJvJduYQaggjyhRdH8TqXGQ6m5LhyMKhc9IurANp9kzsOjs9Yq4m1Z6OVbEFPxcmxUTf+BuItyRglI30jO1NjDkOg2ygS9QVRJqi0Et62nsuDh49bulujUvvRpVUMp3ZOHROFK1CojUp+GW7PqSp/zvz46ibfw9pzqcvlOfT33vhzyVq33iJ2jdef0btfzwYIWllLIolGET9+L3YJJsaJvk3YFWtNAvJxaSYcOictPW0x6pacetDuSyqK3bVjscYTu8m1+HUhRBlimZA4i3B70tdY3oQaYzCoXPSyn0h8dYEzLKFRGsSafYMVK2KXbWTE9KcAH5Kakto6W5NpbeSrac30yasHUU1p/n04Id0CO/I0cojPLd9Bp2jLiO/PJ+pWybSMaIzeWX7GbNhJBeHd2Rf2Q/ctfo22nk6sLt0F7csv4ELQ9uwr2wv96y+g5buCzhYkc/kTePJdGRzrPIoL+6cSbItlZPVJ3k/7x1izXEU1xbz7bHFeAweKr0VbCvaikPnoNZXR0HVUQySkQABvP46tA2/ff4XjSkoEv6psQRFYo6iP0ljP3e9MWvstf9x3gIADRqqfVXIGgVJK1FSW4JBMqBqFU5Un8Ci1I+AOVRxEKcuBJNsYm/pHkINHsyymZ0l24k0RmFVrGw+vYlYcxwO1cHqk6tItCYRonOxrGAJqbZ03IZQFh35B5mO+pErXxz6lJyQ5kQYI/jwwHu0dLcmyhTFO/vf5sLQtsSYYnlz76u081xCnLkJL++eRceIS0mwJDJzx9N0ibyCJFsyT34/nW7RPUixpTF1y0R6xvYmzZ7BxI1j6N3kOjIdWYxeP5Lr4m/g4vi23LvkXnrHXUe2sxlPb3ucrtFXkmpP4/U9r9AhvCPxlkQ+zn+flu5WRJtiWXJsERn2+lE4m09vpIk5HofOSV7ZvvrXQTFTVHMai2INTq78d5zb66+ssX/mGzNR+8ZL1L7xErU/sx9/btb56yipLcauc1DlrSK/PI9EazJldaVsOb2J1qEXUVRTxLKCJXSL7kFh9Uk+OfghA5NupaDyKG/+8Br3ZT3I4YqDPLf9KR5t8TgHyvOYtGkss9u+wYHy/TyyaSyvtptLfnk+j2x6mFfbzeVgeT4T/639cts3Kag6xozvp/NU65kUVhfyyu4XeThnIkU1p/ngwHxuSx1GWV0p3xz9imua9KPSW8GGwrVcEt6ZGn8Nx30HSdCl4Qv4KKwurL+qo0aD1+9FL+n/qyDqrzJHkfBPjWWOIhEU/UnEfyKNl6h94yVq3ziJujdeovaNl6h94yVqf34IBALU+esorSvBrjqo9FZyqCKfBGsSpbUlfF+0hdbuNhTWnOS7gmV0i+lJQeVRFhz6jIHJt3KoPJ//2/cmI7JGsa/sB2btfI5pLWewu2Qn07ZM4pV2c9lVvIMpWybyRvt32F2ykylbJvJ6+3n8ULqbRzdP+Fk7r2wfT29/gpkXzuZwxSFe/+Flxuc+yvGqAj468B7D0u+l6nQJOkf9lA++gI9KbwVWxYYfPzW+aoyyiUAggDdQh6Ktnzg6QAAtWjEv4TnSWIIi+Y/ukCAIgiAIgiAIgiD8UTQaDaqk4pLq56qxqlYy1CwA9AY9nQxdADApJmIT4wCwqTZS7PUTMjt1TpqG5ADgNoTSOrQNAOHGCC4O7wRAmMFDrqslNsWKVbXxRpe5hGk86CQd43Im4da78Qf8DEm5A6fOSYW3gg6eSzArZhStgl11oGpVyuvKyS/Prw+A/F6qa4txqA6qfVWcqD6OWTZT6aukoOoYTcwJVPmqKKg6+rP2j4+PNcVR46+hsLqQSFMUtb5aimuLCDN4qPPXUVZX+vMgKuCjxl+DUTbhD/jxBryoPwZRgUBwihKh8RJBkSAIgiAIgiAIgiD8CkWr4NQ5gfqQKdYeTnFxJWEGD2EGDwBxlibEWZoAkGZPJ82eDkCuqwW5rhYAtAlrR5uwdgD45Co8tvpRHhatjEmuv4qsWWMh1qRD1soYNQYiDJHIWhk9etz6UCSthBJQsSjW+vmW0CJrZTRo8OOvvzIyUOuvpaS2GIfOSZW3sj6IUixUeCs5Xn2M0r2lTBg/Bk+0B51WT21dLRf36Mh1XW+gsq6SE1XHibM0odpbxamaU0SZoqnxVVNcW9wQRNXy9DNPMvD6W3CGhlDprcSm2vD564OobRu2UlBwjCu6d0cn6QgQOGMQtW/fXp566jEAduzYRlpaBhqNhuuvH8BFF7X93fXbv38fs2Y9S3V1NVVVVVx4YRtuuWUImzZt4JNPPmDixKm/+7kBTp0qZM6cVxgx4iGWLfuWl19+gR49erFp0wamTHn8f3ruc0EERYIgCIIgCIIgCIJwDv14sSEASSMhaQ0N96soav1oH52kQyfVT0BskA0Y5PrHmLQmTIoJAIvWgkWpP6XIptqwqbaGtr3+wkMaCYtiQS/p2aHZRm5ucx4Y+zAm2URpeTF333UHLZNbExMfg021BfulaJX6ICrgr7+CMBpq/bVcc+t1hFrCKKsr42T1cayKlUpvBcerC2h5QSvK68rIr8gjwZJEeV0Zx6sL6tu1ZZysPkETSwKV3gpO15wiyhRDWIyHh6aPJdwQQZ9re/LQlLFEWCKp89dxuuZU/ZXy/F5qfNWYFDP+gA+v34tO0hNouGqe9G+TlZeVlTFhwmgeffRxoqNj8Pl8jB37EJ988gExMXFnpX4hIS5GjHgIgJUrlzN06DDatm3Ptdf2PSvP/2cTQZEgCIIgCIIgCIIg/I1pNBpkTf3P//ogqv5iKFqNNhgs2S1Oel3Vh6XfLsa/2M+WLRvx+wNcd90NdOx4Kdu3b+OZZ54gEAjgdocyfvwknhnzBCNHjqa4uJjnn38KWVYwmy08NHYsC79cwIH8/QwcPIj5785j0aKFaLQacnNacMuQwXz5zheUnCzm1OlCjhUc5d67RpKem4XX7wXq52MqqysFIqn0VTLynrvxhERQXFrE7eOG8+nsj8g/lEeNr5ZhQ+8hITOR5eu+5cu3P8evCeAIczJx1BS++vZLErOSiYiMpKyujJLaYkaNGYdP42PD1rUEAlDlreK99+exbuUa6urqMJqMTJ3yJEeOHWbalEkosoIkSYwbPxFJqzJ+/Cj8fj8+n5cRI0ZjNBoZP340AwbczMqVK9i5czt2u53Ro0fy6acL2bdvL08//TiBQACbzcaoUePZs2cXs2Y9h6Io9OhxNZdf3u2cvDf+ExEUCYIgCIIgCIIgCMI5Zvvomv94f8nV7wNgWj4euXD7z5aXt52Iz52Bbud89Lvm/2y938LpdPLOO2+RnJzCrFmvUVNTw9ChN9OyZSsee+xRJk6cQlxcEz788D0OHDgQXG/FiqV06NCRfv36s2LFMmoratFoNGg1EscPHmfJ4kXMful1JElizJgH2LhmAybZRJVSxdMzXmDdutXMm/c2M1q3wapagfoQK8GShKSVcKgODJKRLp0vp227Dnz40XzsdgcjHxzFyaITPHDPvbz8+pu89dwbvDjrNfRWA7NffoF/fPkFRUVFRIRHoNFoCAT8+AI+TCYTpbUlVHqrAKioK6eg6BhPPfU8pd4SRo0cwa6d29m6awueJuGMuX8CKzes4FDhYapOVWEymZkwYTJ5eXlUVJRjNBoBaNu2A0uXLqFTpy5kZmYHX5/p0yczatQ4mjSJ5/PPP+btt9+gZctW1NbW8vLLb/zmOv3RRFAkCIIgCIIgCIIgCAIFBQV06XIFCxcuYNiwIQB4vV4KCo5RVHSauLj6OZh69br2J+v1738zb775GnfffTtudyjp6ZnBZfn5B8jIyEKW6+OHpk2bkZe3D4Dk5BQAQkM91NbW/GK/fjyVLCYmDkkrkZ+Xz9atm9ixYxsAPp+PqtJKik4XMW7cKABqamowySZiY5uwZ88utBotVtWGVbVx9OgRTpw4TqQxkvUaCNG78JjCeWTiw+j1BspPl+Hz+el15TXM/b83GHn/3RhNRgYPvY2mrXM4fPggDz10P7IsM3DgoDO+rvn5eTz55LSGvnqJjo5t2J/YX1vtnBFBkSAIgiAIgiAIgiCcY2caAVTRbuKvLq9J60NNWp/fvf3Kygo+++wjune/ipycFjz44Bj8fj+vv/4KkZGRuFwuDh06SHR0DG+99Xow7AD4+usv6dq1O8OG3cPcuXP49NMP8XjCAYiNjeOdd97C6/UiSRKbN2/i8su7sXfvHn7rxdW0Wm3wOUNDQxkw4BZqaqp5443XsNsdhIaGMm3aDMxmMytWLMVgMJKamsbcua9x9dXXEBkZhdfr5bnnnqJly1bB4Gvfvr0sX76Ul19+g+rqagYNuhGA1atW0TKnFbfdOoyvv/4H8+fNo0uXroSEuHjqqefZtm0rL730PKNHj//VfsfExPLww4/g8XjYunUzp04VNuzP+Xl1OREUCYIgCIIgCIIgCEIjtGHDeoYNG4IkSfh8PgYNGkr79pcwc+ZT3HHHrVRVVdK+/SUYjSZGjhzN1KmPoNVqCQkJoU+f63nvvXkApKZmMHnyBIxGI7Is88ADY9i8eSMACQmJdOx4KbffPohAIEB2dlPat7+YvXv3/O5+9+zZi+nTJzNs2BAqKsq5+upr0Wq13H33CEaOvJtAIIDRaGLs2ImYTGbGjJnI9OmT8fv9VFZW0qZNO66++ho2bdoAQFRUNAaDgUGD+qOqCiEhLgoLT5KRkcUjj4xFkiS0Wi333HM/bncY48aNZv78eWi1Wm6+efAZ+3v//aOYPHkcfr8fgIceGkth4cnfvf9/NE0gEAic6078kro6H8XFlee6G2eF3W782+yL8NuI2jdeovaNk6h74yVq33iJ2jdeovaN19mofUFBPh7P+XnqkfCfSZIWn89/rrvxm/2n95rbbfnFx2v/6A4JgiAIgiAIgiAIgiAIfw1nPPXM7/czYcIEdu/ejaqqTJ48mdjYfyZRr776Kl988QUajYbbbruNzp07EwgEaN++PXFxcQA0a9aM+++/n8WLF/P8888jyzK9e/emT5/ff/6kIAiCIAiCIAiCIAiCcHadMShatGgRtbW1vPvuu2zevJlp06Yxa9YsAEpLS5k7dy5fffUVVVVVXHXVVXTu3JmDBw+SkZHBiy++GHyeuro6pk6dyvvvv4/BYKBfv35ccskluN3uP27vBEEQBEEQBEEQBEEQhP/aGU8927BhA+3atQPqRwZt27YtuMxgMBAREUFVVRVVVVXBS9Zt376d48eP079/fwYPHsz+/fvZt28fMTEx2Gw2VFWlefPmrF+//g/aLUEQBEEQBEEQBEEQBOG3OuOIovLycsxmc/C2JEl4vV5kuX7V8PBwunXrhs/nY+jQoQC43W6GDBnCFVdcwfr16xk5ciSjRo3CYvnnZEkmk4ny8vJf3bYkabDbjb9rx843kqT92+yL8NuI2jdeovaNk6h74yVq33iJ2jdeovaN19mo/fHjGiRJTBv8V/NXrJlG89uylTMGRWazmYqKiuBtv98fDImWLVvGiRMn+OabbwAYNGgQubm5ZGZmIkkSAC1atOD48eM/e56KioqfBEf/ic8X+NtcRUBcEaHxErVvvETtGydR98ZL1L7xErVvvETtG6+zUftAIPCXvIJWY/ZXvepZIPDzbOXXrnp2xqAoNzeXJUuW0LVrVzZv3kxycnJwmc1mQ6/Xo6oqGo0Gi8VCaWkpM2fOxG63M3jwYHbt2kVERAQJCQnk5+dTXFyM0Whk/fr1DBo06H/YVUEQBEEQBEEQBEEQfo+NG9czbtwo4uKaoNFoqKioICIikvHjJ6Moyu96zvHjR9GzZ29yc1v8rvWPHTvKwIH9SE5OCd7XvHlLbr558O96vl9SUFDA3r17aNu2PQCffPIhX331JRqNBp/Py+DBd5Cb24JHH51Ap05daN36ov9pewsWfIbVaqVt2w5MmjSOQ4cO0rVrdzQaLT179jobu3RWnTEo6ty5M9999x19+/YlEAgwZcoU5syZQ0xMDJ06dWLlypX06dMHrVZLbm4ubdq0ISsri5EjR7J06VIkSWLq1KkoisJDDz3EoEGDCAQC9O7dm7CwsD9jHwVBEARBEARBEARB+DfNm7dg4sSpwdsTJoxhxYqlXHLJpeesT3FxTZg5c/Yfuo2NG9eRn3+Atm3bs2jRQtatW8Mzz8xClmWOHj3CsGFDeO21t8/a9rp2vTLYXrt2NZ999tVZe+4/whmDIq1WyyOPPPKT+xISEoLtu+66i7vuuusny202G7Nn/7ywHTt2pGPHjr+3r4IgCIIgCIIgCIIg/AHq6uo4daoQi8XKtGmTOHHiOCUlJbRufRGDB9/Oo49OQFEUCgqOcepUIaNHTyAlJZUPPpjP559/TEiIi6KiIgC8Xi9Tp07kyJEj+Hw++va9gU6dujBs2BASE5PJy9uHwWAgOzuHtWtXUV5ezowZM3+1f8899xRbt24G4P/Zu+/4pqo+juOfe29Wk450D0pbKJS9hyiIshUVF+JWBJkCLuABVKgKCG5liYoTB25xi4ig4nhkgwxpoWxoC91t5n3+SJs2tgXxQUH7e79ez+NN7snJufckJfnmnHP79LmAQYOuZcaMdPLz8ykoyOfhh5/k9ddfYcOGtXi9OldffT09e/bmvffe5rPPPkZVVVq3bsvIkWNYvPglysrKaNWqNR9++B5jx97pX2InIaEeL774GmFhdv9zFxcXMWvWdIqLi8jLO8Yll1zO5ZcPrFb3bbfdzsqVX7N48csYDAbi4xO49977efHF54iMjGTnzp0UFhYwadJddO/eg6ys3YwaNZZ33nmTZcu+QFEUevXqy1VXXVPt2EJDQ/+inq/un7cKkxBCCCGEEEII8S9z54+38fm+T07p9omsWfMLY8YM54YbrmLIkOvp3r0H9eol0qJFKx5/fC7z5z/PBx+84y8fFxfP44/P5corr2bp0vcoKiri7bffZOHCl5g163HcbhcAH374LmFhdp555gWeemo+zz23gLy8PACaN2/BU08twOl0YbFYePLJ+aSkNGD9+rUA7N69izFjhvv/l519hO+//5aDBw/w7LMvsWDBIpYt+5yMjJ2Ab1TUM8+8wJYtmzh4cD8LFrzA008/wyuvvEBhYSGffvoRt98+noULXyQhoR66rnPDDYPp0+cCunU7j5ycbBISEgPOS9WQCGDfvn307t2Xp56az8MPP8mSJb7RRr+v2+12s2zZF1x99XUsWLCITp3OClirefz4SYSGhjFr1uP++3btymT58mXMn/888+c/z7fffsOePbsDju3vDIngD4woEkIIIYQQQgghxL9PxdSz/Pw87rzzNuLjEwgNDWXr1i2sXfsLNpsNp9PlL9+4sW/toJiYWDZt2kBW1m4aNGiIyWQCoFmzFgDs3r2bjh07A2C12khJacD+/fsASEtrCkBISDApKQ3Kt0NxOh1AzVPPli37nDZt2qIoCgaDgRYtWrF7dyYASUnJAGRm7mT79m2MGTMc8I1qOnToIFOmTOWNNxbzzDNzaNGiVbVzEBcXz5EjhwgObuS/7+effyQ1tfJ2ZGQkb731OqtWrSAoyIbb7Qaose6xY+/k1Vdf4oMP3iU5OYXu3c8/bh9kZmZw+PAhbr99FACFhYXs27cv4Nj+bjKiSAghhBBCCCGEOM2e6DKPCxIvOqXbf1RYmJ377nuQ2bOns2TJ6wQHhzBt2nSuueYGHI4ydF0HfJdZryohoR67d2ficJTh8XjYsWM7ACkpKWzcuA6AkpJiMjIySEhIqLGOPyI5uYF/2pnb7Wbz5o0kJiaV16eWl0mhXbuOzJ37LE8//Qw9e/amXr16LF36AePHT2bu3Gf57bftbNq0AUVR0HXf1csuumgAL720yB/+7NmTxaxZD6KqlXHJG2+8SsuWrUlPn0HPnr3956OmupcufZ+hQ4czd+6z6LrOqlXfHPfYkpKSSUlpyJw5C5k791n697+Yhg0bBRzb301GFAkhhBBCCCGEEHVcgwYNGTjwan77bQd79uxm48b1WCwWEhPrk5OTXeNjwsPDufXWkYwcOQS7PZygoCAABgy4gtmzpzNq1FAcDgdDhgwjPDziT7eta9dzWbduDSNG3ILL5aJnz940adL0d2W6s27dGkaPvpXS0hK6d++B1WojNbURw4bdhN0eTnR0NM2bt8Rms/HKKy+QltaU3r37kZubw+jRt2I0GvF6Pdx33wMB7e3atTuPPvoQy5Z9TmhoKJqm4XQ6a6y7uLiYO+64jbCwMKxWK+ec04133llS67E1bpxGx46dGD16KE6ni2bNWhAdHf2nz9WpoOgVUdgZyOXykJdXcrqbcUrY7dZ/zbGIkyN9X3dJ39dN0u91l/R93SV9X3dJ39ddp6LvDx3KIi7u9EwtEn+Opql4PN7T3YyTVtNrLTo6pNbyMvVMCCGEEEIIIYQQQgASFAkhhBBCCCGEEEKIchIUCSGEEEIIIYQQQghAgiIhhBBCCCGEO+vBpgAAIABJREFUEEIIUU6CIiGEEEIIIYQQQggBSFAkhBBCCCGEEEIIIcpJUCSEEEIIIYQQQtRB+/fv4957JzJ8+GDGjRvJhAm3k5mZcdL1/PjjambMSAdgypQJJ/34Q4cO8d13qwCYMSOdm2++hjFjhjNixC3Mnj0dt9t90nXWJCNjJ+vXrwVg2rTJuFyuk64jMzODCRNuZ+zYEdx6600sWrQQXddZu/YXpk2b/H+3MTc3h0cfnQXAqlXfcOONg3j77Tf/1Hn9swx/2zMJIYQQQgghhBDijFBWVsakSXfxn//cS8uWrQH49dfNPP74bObOffZP1ztz5iMn/Zi1a/9LVtZuunXrDsCoUePo0uUcANLT7+Hbb7+hR4/ef7pNFb75ZjmRkZG0bdue++9/6KQfX1hYSHr6FGbMeIT69ZPweDzcd98kPvzwXZKSUv7v9gFERkYxfvwkAFav/pYRI8bQrVt3rrrqmlNS/x8hQZEQos7zenW8bi8et47BpKLrUHzUgTXMBMDR/cV43F5/GY/bW35bp14zO6qm8NsPR0hpH4VmUNi8fD9ej44lyIjH60U1qGhGBc2gktwmEpPVwN7NR0lIs2O0aBzJLCgvo6IZfOUqbluCjaiagtejo2kKiqqc5rMlhBBCCCH+Db7/fhUdOnTyh0QAzZu3ZM6chcyYkU5+fj4FBfnMnv04CxbM4ciRw+Tn59OlyzkMGzaK3bt38dBDD2CxBBEUZCEkJBSAAQP6sXTpF2Rk7OTJJx9B13XCwsKYPHkaO3Zs47XXXsFoNHDw4AF69uzDDTcMZvHilygrK6NVq9YBbfR4PJSUlBAbGwfAG28sZvnyL9E0jTZt2jF69DgKCwt58MH7KC4uxuPxMGzYKDp06MTChfNYu/YXvF4vffr0o0eP3nz22ccYDEbS0poydepkXnvtHR599CGMRiOHDh0kNzeHKVPSadKkKR9//AHvvvsWoaFhGAxGevXqg6qqtG/fifr1kwDQNI17770fo9HIpk0b/O1+990lrFy5ArfbTXBwMDNmPMLBgweYOfN+DAaD/3EGg5Fp0ybj9XrxeNyMHz8Fq9XKtGlTuOmmW1i9+ju2bt2C3W5nypQJxz2vCxbMwWg0MmDA5VxwwUX/12tDgiIhxGmn6zoet46qgq5DaaELr8tbHshUhDi+7chEG6qmsHfLMeIahaIZVTJ+zg4Ibzyuyu1m58VjtGj8/O4uWvdLxBRk4LvFvwWU93p0f1u6XtcIa5iJZQt+9W+vfGl7rW0PjjBjs5vZ+fMRohuEYLObOLqvGFVTKKAMl8NdeQwuL1FJwbgcHtZ9vIfgG82YrQZWv1n78N5uNzTGEmzkq2d+pdsNjQkKMfLNC9tRDUp5sOQLlyqCpdZ9EjHbDGz6aj9Nu8Vhshr47YfDvv3lIZRmVP23YxqGYjCpHN1XTHiCDc2gUHzMUb6/MrxSDSqqhFRCCCGEEH+ZvLEjA25bLrwYS/+L/fcHj7sLgKKnHw8o9/v77XOeoezTj7H0v/i4z3fgwAHq1avvvz1p0l0UFRWRm5tDTEwc55zTlauvvp6DBw/QokUrJk26D4fDwRVX9GfYsFE8//wCbr11BJ06dWHx4pfIytodUP/s2dOZPHkqDRo05OOPP+C1116mU6ezOHz4IC+99AYul4vLLruAm28eyg03DC4fUXQeK1euYMGCp1m8+CVycnIICQkmKSmFjIydfP31Mp555gU0TeOeeyby/fffsm7dGjp2PItBg64lO/sIo0ffypIlH/DFF58yd+6zREVF8+mnHxEdHcOFF15MZGQkzZu3DGhrXFw8Eyfew9Kl77N06XsMGzaaxYtf4aWXXsdoNDJunK8PcnKySUioF/BYq9UacNvr9ZKfn8+TT85HVVXuumsMW7du4bffdtCkSVPGjr2LDRvWUVhYwKFDh7DZgklPn86uXbsoLi7y11dxLnr16hsQ5tV2Xp1OJ8899/Jx+/yPkqBICIGu+8ISb5XRMh6XL0Sx2k0oqkLuniLC4oLQDCoHtuVVKacHjLZJaReFKUhj4xf7SDsnFrPNwH/f311tRE7FducrGmALN/PlvC2cc00qwREWvpy/pda2nnNNKsGRFtZ+lOXf/vWbA5Vhye+CE7fLiznYiMlqQFUVTEEaMamhgSN3qoQuYbFBmIIMnH11KuH1bBjNGj2GNg2oM+C5jL4A5cqpHfxt7H+n7w+53W4lL6+k2rnWvTBgUluMJg1d1+l7W4vKkKvKufe4vdjjrCgKtOqTSGi0BUVRSGkfVXkOXVXPqS/wcpV5yDtQgtvpxet1kflLNh63ju7V+b3uN6VhMKusenlH+bbG189trfHcK6rCuTc2xhRkYPUbO+l8ZQNMQQZ++WB3teCqIoxq3CUWo1klc00OKW0j0YwaB7YdKz/3SpUwync7NCoIVVMoK3ZhCTb6RlDpOoqqoCgSVAkhhBBCnCqxsbFs2/ar//asWb6gafjwwcTExJCUlAxAaGgoW7duYe3aX7DZbDidvnV9du3KpFkzX+DSqlXbakFRVtYuHnvMt9aOx+Omfn1ffQ0bNsJgMGAwGDCbLTW2rerUs+eff4a5c5+gc+ezadGiFQaDL8Zo06Ytu3ZlkJW1i759LwAgOjoGq9VGXt4x0tNnsHDhXHJzc/111aZx4yYAxMTEsmnTBvbt20uDBg2wWHztqwhq4uLi2bYt8LPygQP7OXLksP+2qqoYjUbS0+8hKCiII0eO4Ha7ufjiS3nttZe5++6x2GzBjBhxG126nMO+fXuYNOluDAYDN9889LjtPN55reivU0GCIiHOEF6P7wu/qvm+DJfkOzHbfOHGsQMlgUFClZE2samhmIIM7PjhMEktI3yjSZbvr3VETus+idjCzax4YRsdLkkmONLCF3M3Q/UMAYCzB6USFhvEqld20GVQQ+yxVn5+b1e1cqrm+9IfnRKC0RxE3qESnKVuLMFGAEwWDdVgLA8GKoMWq91MUIjRF4TEBGEJNtL5igaVIULVQMGgYg03YTCqDPhPW4wWDVVTuOqBjicMEbrflObf7nx5gxP2R/2WEf7t6JSQE5b/oxRFQdHAYjP677PHWY/zCJ9m3eP92+36J52w/IV3tPJvX3GfL8TyenT/66wiaLIEG9G90OPWpoTFBKF7dc6+OrX6a8fl27aFm0GHmIahmK1GQMdkNeBxe3E7PDiK3FWCQC/JrSNxlsCvKw4QnRKCqiqsWZpVa7vPG9wEVVNYsWgb5w1OQ9VUVizahqIQMBKqIuTrdFkKmlFlw+d7ad03Ec2osnXlwfKyla8bzaCgGlXqtwhH1VSOZBYQ0zAUVVPIO1hSre6KxxtMmoykEkIIIcTfwj7nmT90/x8pd6LRROAbsbJ48Uts3ryJli19nx337dtLdvYRzGYziuK79tWnn35McHAIEyfew759e1m69H10XScpKYXNmzfSpcs5bNtW/YfepKRk7r33AeLi4ti4cT25uTkA1PSxXVEUdN1bYztjYmI5ePAAyckpvPnmYtxuN5qmsX79Oi644CLy8vLYsGE9aWlNyc4+QmFhAcHBIaxYsZz09Jnous6NNw6id+9+qKqKt4YfT3//XSIxsT5ZWbtxOMowGk1s3bqF5OQUunU7l5dfXsTllw+kXr1E3G43c+Y8QadOZ5GS4vuOsXPnb6xa9Q3PPfcyZWVlDB16AwDffbeSNm3aMWTIcJYt+5zXXnuZfv36ExkZxRNPzGPz5o0sXDiPKVOmHbffajuvp/IzqwRFQpTz/8HQoazYVT5aQ682EsYeF4TBrLFvyzGiU0IwBRnI+PlI4NSnKqNC2vVJQjfq/PhWJs3PTyAkysLKl7ZX+yJe8XfxrIENiahn47OnNvm3VyzaVmu7zxrYkIhEG9u/O4Q9NogISzBHMgqqBS0Gk4bZ6ts2WjQSmtixhBix2Aw0Py/B/0Va+90X8Yh6NkxWAz2HNSMkyoLBpNL/rtaBI3JqWDvnwtsrg4oeQ5ue8PxXDUJS2kWdsLwlWC7aeLJUTUHVNAym6vuikyvDsKohWW06X1EZtlUN4Wpz1f0dAd/77JKJbXyvfX+YWf7+cvneX16vTsfLUgiNCcLr0WnZq16N76+KNaW8Hh230wP4RlPl7CkKfN+6Kj90RCTY0L06P76dyflDmqDrsPLF2qcWnj/E9+vSt6/soPvNTVBUhZ/eyQwYNVX1vdCyVz1wwNplWTQ+OxZFVdizIbfm4MqgElk/GFVVKDpaRnCkBVVVcJa5j/veEkIIIYQ4FaxWK7NnP8Ezz8zhmWdy8XjcaJqBiROn8PXXX/nLdejQifT0KWzcuB6LxUJiYn1ycrK5++5JTJs2mTfeeBW73Y7JZA6o/+67JzN9+lS8Xt9nsUmT7iMnJ7vGtqSmNuKVV14gLc33vaFi6pmmaXg8HiZPnkq9eon07NmbUaOGous6rVu3oXv382nXrj0PPfQA33yzHIfDwcSJ92AymQgNDWXw4OsICQmhU6cuxMbG0aRJM+bPf8of6tTGbrdz/fU3M3r0MEJDQ3E4HBgMBmy2YO65535mz56O1+ulpKSErl3P5fLLB7Ju3RrAFzIFBQUxdOiNmExGIiOjyMnJpkWLVjzwwH1omoaqqowdexdxcXFMnTqFt956A1VVueWWYSfst5M5r3+Wout6LeMITj+Xy1Nt2sY/VU1TUEQg3avj8QSOfvG6vFhCjKgGlaN7i3xBhVnj4Pa8yi+Xbm/5F07fdv0W4VjDzWz4bC8NO0YTHGnm53d3VR+RUxHkXJRERKKNT5/YROcrGhCVHMynT2yqtZ2dr2hAVEoInz6+MWBbUaj2pVEzKHS5PBWz3cBP72TS9Nx47HFW1n+2J2C6TdVAJz7NjjXMxIGtx4hMDsFiM5C7t6hKnYEjcgwm33/FmUfe92eGiqmVHrcXg9EXLJXkO7GGmfB6dPIOlfgDJe/vgqv6rSLwuLxk/pJNaucYPE4v2749WD24Kg+Wz7qyAWajkS+f38K5NzTGVeZh1Ss7am1bz2FNcTu9rHp5Bz1vbYrb5duuqvJvhELX6xuje2HtR1l0vqIBuq6z6av9v5sOWfk3KLVTNAAHtuWR2CIcUMjJKqw+hbJ82xpqQlF9o880gyoh1UmS93zdJX1fd0nf112nou8PHcoiLu7UTRcSp47b7ea11172TwW77bZh5Ytkd8TjqXnk05msptdadHTtsyZkRJHwq/gypSiAolBa4MRkMaAafFMzfv+lqGI7KjmEoFAjv60+THwTO7ZwM5u+2lfriJxm3eOJqGdj+bNbad03kcikYD57clPAgsJVdbwshdjUUL55cTsdL0shrlEoP76dWWNZzaAQFhNEUJiJnKxCEpraCYmy4HZ6UA0qJqtWGeSUf1Gy2s2YrAZa9EwgLM6K2Wakw4DkamunVHyZskVYMJo1Lh7fGlOQAc2gMjC9Q61rqFT8I3L+LZWjarpclXrC/khuWzmqJjY17ITlhRA1UxTF/74HUDUIjQ7y749pEHrCOtr0q1zo8ayBDY9b1m63ctnkdoDv7+rA9A6Ba3O5Kv+OhkYH4XF56XpdI0Kig/C4PHQYkBww3a9qKG62GXGWuLHaTagGBWepF2epO2AB96ojruo1s+MscbPukz1EJNpwlrj5YUnti6f3Gt4MZ6mbb1/9jV7Dm+FyeFj95s4ap4GqBoUOA1Lwur1s+/YQLXvXw+vWyVyTXWNwpRlU4tPC0HXIO1RCRKINgJI8Z83BlSYBuBBCCCFOD4PBQFlZGUOGXI/BYKR585a0adPudDfrbyMjiv4mfzRx1nUddHwf/Kv8ql01aAmJsmC2Gdm35SgR9WwEhZrI+O+Ral8qKrYbtI/CHmflh7cyaNwllsj6wXzzwrZqiwp73F7QocOAZBKa2PnokQ0B27XpMCCZhKZ2PnrYV75es3C+mLu5coqFMfDDf+OzfW1Y/9keUtpFER5vY+uqA9UXIi7fjkj0HWPu3iJCooIwWzWKjjqqhTiqdmYudiu/NNVd0vd105nU777F03VcDi9Gs4rHrVOS5/D/za+6cLrH7SU+zY6rzM2+LcdIbhuFs9TNrjXZtf571OGSZMoKXaz5KItzrmlESb6DH9/ODFhcvao+o5pTku/k+9d30ntkc8oKXXz32m81tl1Rodfw5jhL3fz3/V2ce2MazlI3G77YW2NwpRlVmp0Xj8fpZc+mo6R2isbj0Tn0W37l2lNVfiSoWLwewFHiW0/Ntz6C/qf/PTmT+l78vaTv6y7p+7pLRhTVTZqm1okRRRIU/Q2cpW7WLs2irMRdY5DTslc94tPsfPTIetr1TyaxRThLZ6+vtb52FydRv2UES2etp91FSdRv5dsG31WJKqc++f7bomc94tPC+O6132hyThwxDUP57we7AhZsDZj61DiMkOgg9mzMJSopGFu4mSOZBYELvVb5sG20GNAMCujINIUayAeIukv6vm6SfvepaTpxUIgRt9NLYU4pYbFW3E4PuXuKKoOr34VXjc6KoazQxc6fj9CiRwIl+S62rjwQMGqqanB13uAmFOaUsfqNnfS9rQVFuWWsfrP2EVR9R7eg6KivTN/RLSg65mD1GztBocYfLs65thGOYjdbVuyn46UpOEvc7PjhsP+HC2uwCZfb4//3NbltJB6Xl5ysIuLTwvB6dPKPlAZOUa4SXBnMsnj6P5W87+su6fu6S4KiukmCojPAvyUocpV5+PaVHVBtDRvfYqhJrSOJrB/M1pUHSGhixx5nLR+6H3iZ76pTn8w2AyV5Dsw2IwaTisflRdVU/xWzxJlDPkDUXdL3dZP0++lX8dHG69FxlXn8U/N+fxXIqKRgnCVujuwqoF6zcMqKXOz79ViN69l53F7aXlifolwHm7/eT+crG1KYXcq6T/YE1u3yUvHJqt/YluQfLuHHtzLpN7YlBYdL+eGt2oOrfmNaUpBdyk/vZtJnVAuKjzpY+0lWrSN0212URFmRm11rs2l+fgLOEjf7fj1W5YegwCv5RaeE4PXoFB9z+K5yqOs4yzwBnzckqPpz5H1fd0nf110SFNVNdSUoOuEaRV6vl/T0dLZv347JZGL69OkkJ1c+waJFi/jkk09QFIWRI0fSp08fCgsLmTBhAkVFRbhcLiZNmkS7du348ssvefjhh4mP913daOzYsXTu3PnPHus/htGiccXEDif8Q9Kqd6J/u3GX2BPWGxxh8W8bTNqfb6AQQgjxL1IxbUwzKGgnuEKi0azRINy36LcpyEDz84KOW94aZqZnQ9+6VsHhZuLT7P59druVY8eKfaOp3DqaUcUaZuLCO1phDTNhthk4f0iTGtf787i9BIUa0b06aWfHYrYZcDs8vnCn6sUb3Lp/XSpdh5J8J/u2HCPtnDjyDpWy5esDtbb9wttbcXR/MT+9kxmwXZWqKf6Rxr2GN6Mgu4xNy/bR7frGFB1zsO3bg5VXxzSqAcFVWtc4nMVuDmXkk9I2CleZh9x9RTWv+WdUsdlN6Lov0DMYZfF0IYQQ4kxxwqDoq6++wul0smTJEtavX8+sWbNYsGABAAUFBbz66qt8+eWXlJaWctlll9GnTx9efPFFunTpwuDBg8nMzOTuu+/m/fffZ8uWLUyYMIF+/fr95QcmhBBCCPF3UxQFRVNQy3+/Uc0aRrPvhmZQsTQwHvfxpiAD9ngrABabkcj6wcctb7ObSWweDkBIpIX6LSPweirXlKq6FqE1zIRmUul2fWOCQo1EKDY6XJpSOcrKFTjqymjWMJhUgiPMqAYVr9uLq8yDw+2qvs6hy0vDTjEc3V/M+k/3ktA0nJzdhfz83q5a297/zlbk7Cni53d30f/OVuTuLeaXD3cHTMdTqyyI3mVQKkW5Zez86QjtL06m+JiDrI25lSOuDEr5tHjfdmKLCFxlbvKPlBGdHIzH5aWkwOUPuf4J6xwKIYQQp8MJg6I1a9Zw7rnnAtC2bVs2b97s3xcUFERCQgKlpaWUlpb6/4EdPHgwJpMJAI/Hg9lsBmDLli1s3bqVl19+mdatWzN+/HgMBrnwmhBCCCHEqaBqCqqmYTDVvN9qNGEN9e0MidQIibTUXLBcTAOj/8qAQSH2gBFUNdYfaiS+iR2jRSOhmZ1+Y1tWX5+xPJAy24yEx9to3S8Rs9VISKSZ1M7RvlFWv58u6PKiqgrOUg8FR0rRdZ2iow6y1uf6w6pqbW8YyuGMAn75YDcX3d3av12bi+5uTU5WERu+2EvvEc05tr+YX1ceqL5GY3nQ1LpvfYqPOdi/9Rid+zck/3Ap2bsLA66UWnUKYHiCDY/bi7PUgzXU5L+AiWqQkEoIcXr8978/MnfuUzz77IuYzRZycrK5664xPPbYHDZsWMd7770NgKqqNG7chNGjx2E0Ghk48BJiY+NQFIXS0lL697+EK68cBEBmZgYLFjxNWVkZpaWlnH12V4YMGc66dWv48MN3uf/+h/6vNufm5vDii88zfvwkVq36hueem8+AAVewbt0aZs585P8+J8LnhClNUVERwcGVv2Zpmobb7fYHPPHx8Vx00UV4PB5GjBgBQGio7wNFdnY2EyZMYMqUKQB07dqV3r17k5iYyLRp03jzzTe54YYbTvlBCSGEEEKIv5+qqZitvul+JosBk+X4HzXDYoP8V5+LSAwmIvH4I6gSm4f7R1Alt4kkuU0k4FuXyuupuJqfL1wKCjGR0NROz2HNsAQbiU0N5ZxrGwUEV1XXoTJZDFjDTMSnhWEwqWgmFUuI0R9cucpcAVf/a9krkbyDJWxddZAOfZLJ2VPI2o+zam37JRPacGB7HmuWZnHxhDYcLN8GAsKliul8PYY2JXdvEb/9cJizr04l71AJu9bkBFy5r2qIldo5htICJ8f2F1OveTjOUjeFOWVVpgcGrnNlsmigICGVEHVYp05dOOusn5gz50nuuGM8U6dOZuzYO9m5cwcfffQBs2c/QUhICLquM2fO43z22ccMGHA5AI8/Phez2YzL5eL66wfSs2dvDAYj6elTmDHjEerXT8Lj8XDffZP48MN3SUpKOSVtjoyMYvz4SQCsXv0tI0aMoVu37lx11TWnpH7hc8KgKDg4mOLiYv9tr9frD4lWrVrFkSNHWL58OQBDhw6lffv2tG7dmu3bt3PXXXcxceJE/zpEV155pT9E6tWrF1988cVxn1vTFOx26587sjOMpqn/mmMRJ0f6vu6Svq+bpN/rLun7M1S98v9GQb2U8OMWjY4LoXFb3zqRMfGhNO0Yf9zy8fXDaNcrCU1TaXt+Es27JOBxe3G7ykdOucq33V5i4kMJspgICbUQExeCSdHQdBW3u0rZKtsRkTbKjrpQVRV7uJXCgw7yD5UGlnd50b2+0VRtzq/Pke0F/PTuLm7sEMfhbQV8//bOWtt+w4yz2bU+mx/ey+C6B7qwb+tRfvlkd+UFVIwqhirb59/QlNx9hWSszeasSxuSd7iEvb8e9ZUvL+cvb1Kp3zQCR6mbomNlRCUG43Z6cZa5q9St/SsuwiLv+7rrVPT94cMKmnb8tfT+aqNGjWHEiKFMnnw3nTufRZcu53DnnWMYO/YO7PYwf7k77hgfECxrmoqmqZSUODGbzYSFhfHll5/TsWNnUlJS/GWmTXsQo9HIpk0bUBTf8b799pusXPk1brcbmy2YWbMe4+DBAzz44DQMBgOapjF1qu9x9933H7xeHbfbzcSJU7DZbNx33yRuvnkoq1d/x9atW4iICGfSpPF88skydu78jSeeeARd1wkLC+Oee6axY8d25s17GqPRwKWXXsGFF14EvkGdACgK/gtR+Ler7Pdt+G7pun7a++zPUJSTy1ZOGBS1b9+eFStW0L9/f9avX09aWpp/X1hYGBaLBZPJhKIohISEUFBQwM6dO7n99tt58sknadq0KeA7oQMGDODNN98kLi6OH374gRYtWhz3uT0e/V9zFQG5IkLdJX1fd0nf103S73WX9H3dZbdbKSwu891QABNoJhUNlYpZgIXFZWCCmCahFJU4MIRopHSOqrXOEocTe5KV7oPTKHW6iGoUTL9xLauV83p0vB4vZS4XMWmhXHhHK0pdLiIaBNPj1qYBV+6ruhZVqcOJJdxIWtc4Ssoc6AaIahDiHzHlceu4XV4cZW68bi/5BSUc2lvArg3ZNDkvjt1bc1n/2Z5a23/ZlHbsXp/L+k/3cOnkdmRt8G1Xpaj4R0ZdeHsrDu7IY/u3h+gxrClHMgrI+G92ldFWFVfz891u0SOBguwysrMKSe0UQ0meg7yDJb76jNWnCwZHWvznymjWTtlIKnnf/3V0r46u+14nuhe8nsqrSlZM3fR9odcxWQ2+kX8OD5ZgI26HL5j0f9nX9cov/7pOSKQFR6mbskIXYTFBOErclBY4/fXpFXXj246sH0xpgZPiow6iUkIozXeil+kUFjmq1Y0O8U3tFOWWkX+4lHrNwynMKSPvYEm1ug2RHooLHJjL27/ype2omuIr4w2cUlu/ZQT1W0Ww+o2dNOwYTWKLCFa9XF7e63vuqsFG/ZYR1G8ZAegEhZpwO7y4ytwEhZlwOzw4yzz+uvv2vIg5Cx5l9K13kZ9TyoED+4m0x5GfXcKu/dt5ZsFcXE430VExTJ5wP16vzpjRI0FR2Lcvi/PO74Gr1Mu+3QeIi4unpNCJs8Tl7y8HTgqPluEsc5N7oJC8vDxmTX8KZ6mX9IcmsO6XdWz7dTsp9RsxbPAYtmzdwL7MQxzJPoxRC2L6rJns2L6Tg1k5pDS24PHoNG/QgXatOtG9ay/iw1PxenRy9hUy/YH7uee+aSTEJPHhB+/zyisv0aZlB0qKSnn6kRcAyN5TFHBuw+OtlBW7KC1wBWzXxGY3YbYdf73BM5GuV89W/q+rnvXp04fvv/+ea665Bl3XmTlzJi+++CJJSUn06tWL1atXM2jQIFRVpX379nTt2pXRo0fjdDqZMWMG4BuVtGAhQ7iNAAAgAElEQVTBAqZPn86YMWOwWCykpqYyaNCg//NwhRBCCCGEqLsq1qUC35V2jRbfdlCIkaCQ43+Ziawf7F8wPTY1lNjU0OOWb9ghmoYdfFcJTDs7lsZnxeDx6FXCpcp1pQxmjXrN7ARHmDGaVWIbhtLxspSA6X5Vr/5nMKqYggwER1lQNdU3gsDpwVOiByyaXrGIevPzE8jeXciGz/fSsEM0B3fks+HzvbW2/fJ72pO5JpsNn+/l8nvas2ttNpu+2h8w3a/qulLdb0rjSGYBWetz6XxlA3L3FrF/a15AcKUaFKxWE6WlLhp3ieHYgRKO7CqkSddYju4v5nBGoS8YgIAQQdd1WvSsR+6eIg7syKNV70Rysoo4sO1YQBBSNXxof3EyRzIL2Lv5KB0GpHA4o4A9G3MDgo2KsroOZ1+dysHteexam8M51zbiwLY8MtdkB5Sp2qbzBjdh35aj/PbTEXoMacrezUf57cfD/gDi94FLn9EtyFqfw/bvDtF3TEt2r8th26qDgQEOlaHIxePbkPnLEbZ8fYBLJrYl479H2PzV/hpDGXS44r72/PbjETYt2xewXZvfl9/584nL71qT4y+/a23OCcvv2XjUX37PpqMnLL9/a56//IFteTWW73C9nZI8J+YgAy6HB49bLw9+9GprrzlLPZQW+KbAouN7f1SU139XXgG3w4Oj2Bd2BIWY8Li9OErdBIWa8Lh1XKW+oOhI9iHefu91brlhFLMevZ+Z6U8RExPLgQP7SYxNoWWL1jz+yHx+27aTec8+5n/+B6c+htnkm3r2wMP/Ydnyz4mOjmXvgQygcoTO4SMHyM7JRilvmKZpGAwGZsyaisloITv7MB6vhwv7Xcxb773GtBkTsFpt3HLTCM7qfDaHDu9n8pTxqIrGNVfd7JsyCxiDNN/70axiCtJQFDAHGdi7P4vHH5+NroPL6SI5JRnNoJKUlIwlxEi1iFjx/c9o1iC0clsJqyFMVjjhlOp/ixMepaqqPPDAAwH3paam+rfHjRvHuHHjAvZXXBXt97p160a3bt3+TDuFEEIIIYQQZxBFVTCoCgZjzdMwbHYzNrvvojZV16OqTUITOwlNfAumJ7WKJKlV5HHLN+4SQ4MOURhMKg3aRxGfFhZw5b6qV/LTjCrRKSG0ubA+mlElPMFGo7NiqoRPgcGVqpUvnp5ThqIoFGSXsWfTUX95Ar/Dk9opmuzdhWxato/GXWLIySpiy9f7q5ws3/dRFAVFgebnJ3DsUAkZP2fTsmc98o+Usnt9LopSWcZX3PdA3atTnOfkSGYhulentNDF0X3F/v2/f5yug9vppazIN6rD6/bidnh8ZSraoyioann9gFqxdhVgKF+jq2LklaIQ8FwKEBRqIiLRhoJvlEVMw9DydgSWBV+7wmKspLSLQlEgPM5Ko84x1drufz5VITolmJa96qGoCjENQmjdNzGw7irnU9UU4hqHYbT4pjTGp4VhthkC2x1QXvUHmaqmktginNBoS63nUzWoJLWOILK+DdWgktwmkgYtoygqcpQ/R2B5zaDSoEMUCU3C0AyqbwRQ8/BqdeeXHCQsJggUMNuM9B7RDEVVKsO2KllFxWafkc39N/qMal75OjkBS7ARS7AxYNvlcvHI1Pu54867Ofvsbuw5mMH7n73GwIFX8/yL83jwwdkoqoLZZmTn3i0YTBphsUGomkJodFD5RauCiIiIBNVL7wt6MXLkYi6/4irq1UvE7XbzwuML6NTpLFJSGmA0qxzO28u3367kuedepqysjKFDb8Bg0lj360906tKJkbeNZtmyz3n/4zfp168/CUlx3HjLPDZv3sjChfOYMmUaiqpgs5sxmDQsNiM2uxlFVbDazSQnJ3PvvQ8QFxfHxo3ryc3NQTOqGM2a/2IONTGYNAwmrdr272maisfjPeH5/qdTdF3XT1zs9HC5PP+aoZwyLLXukr6vu6Tv6ybp97pL+r7ukr6vW3Rd9434cOmEhQWRn1+Kwayie3W8XtAMSuV6J+X/J4uG//ucivf9oUNZxMUln6IWnbwnnngYg8HI2LF3AlBcXMTQoTcyceI9FBTk8+67b5XfX0zjxmnccstw4uLi/Fc9U1UVr9dLdHQMU6ZMw2QysW3bVubPfwqv10tJSQldu54bcNWzyZOnMXHiHRQXF2MyGTEaTVx88aW0aNGKBx64D03TUFWVsWPvIi4ujqlTp1BWVoqqqtxyyzDq109i2rQpPPvsS8yYkU6vXn3p0uUcBgzox9KlX7Bt21bmzn0Cr9cX5kyadB85Odmn5Ipr8M8Nimp6rR1v6pkERX8T+QBRd0nf113S93WT9HvdJX1fd0nf113S93XXvyEoEievrgRF/7zluoUQQgghhBBCCCHEX0KCIiGEEEIIIYQQQggBSFAkhBBCCCGEEEIIIcpJUCSEEEIIIYQQQgghAAmKhBBCCCGEEEIIIUQ5CYqEEEIIIYQQQgghBCBBkRBCCCGEEEIIUeesXfsL06ZN9t9eseIrbrxxEOPGjWTKlAkBZQcM6Hfcun5fvqqDBw8wfPjgavfPmJHOjz+uPrlGH4fD4WDu3CcZPfpWbrttGOPHj+Pw4UMADBx4CQ6H4/9+jieeeIRDhw5RWFjIiBG3cOedt/Hqqy/x66+b/++6zyQSFAkhhBBCCCGEEHXYV199wauvvsRTTy0gNjaOjRvX8/nnn/zhx8+c+chf2Lo/5umnHyMmJob5859n3rznuOSSy5k6dfKJH3gS7rxzAnFxcWRmZhAVFcUTT8zjxhsH07x5y1P6PKeb4XQ3QAghhBBCCCGEEKfH559/wrvvLuHJJ+cTGhoKwMiRY1i06Fnat+9ITEysv2xRURGzZj1Afn4+AHfcMYHU1EYMGNCPpUu/4NdfN/P44w9jtVoJDw/HZDIzZMhw8vKOMXny3eTk5NCoUWP+8597AXj//bd5441X8Xg8TJp0H4mJ9XnjjcUsX/4lmqbRpk07Ro8ex6JFC9m8eSOlpaVMmnQfCxY8TXFxMQ5HGaNGjaNVqzZ8991Kxo+vDIbOO68Hbdu2CzjWzMydzJnzBF6vTlFRIXfcMZ5WrdowY0Y6+/fvw+l0cu21N9CrV18WLpzH2rW/4PV66dOnH4MGXcfo0cO4/fbxPPHEw+TkZLNo0UIOHTpIr1596dixM488MpN9+/bi9XoZNmwU7dt35MYbB1G/fjJGo5H775/5V3fnKSFBkRBCCCGEEEIIcZp98eQDte5L7XIejbqcF1Cm3x1T2fnjSjJ+XFljmX53TD3hc27YsJ7s7GwKCgrweDz++6Oiohk2bCSzZj3I44/P9d//yisv0KFDZy6/fCB79+5h5sz7WbBgkX//o48+xL33PkDDhqksXDiPnJxsAEpKipk8eRrBwcFcffXlHDt2FICWLdtw442D+eGH75g//2mGDh3B118v45lnXkDTNO65ZyLff/8tAMnJDbjjjvFkZmZw9GguTz45n2PHjrF3bxb5+flERESiKErA8YWF2QNu79qVyZgxd5Ka2ogvv/ycTz/9iNTURqxd+wvPP/8qiqLw888/+vrji0+ZO/dZoqKi+fTTj/x1GI1Gxo27iw8/fJehQ0cwY0Y6AB999AFhYXYmT55Kfn4et902nMWL36K0tJTBg4eSltb0hP1xppCgSAghhBBCCCGEqIMiI33Tpz7++AMefPA+Hn30af++vn0vZNWqFbz//jv++zIzd7J27S8sX/4lAIWFhQH15eTk0LBhKgBt2rTzl4uPr+cfrRQeHk5ZWRmAf8RPy5ZtmDfvabKydtOiRSsMBkN5HW3ZtSsDgKSkZAAaNkzliisGkZ5+D263m4EDr8Fut1NUVISu6wFh0Zdffk7Pnr39t6OiYnjppecxm82UlJRgs9mwWm3ceedEHn54BiUlxfTteyEA6ekzWLhwLrm5uXTpcs4Jz2VGxk42blznX6/I43GTn59X3vaUEz7+TCJBkRBCCCGEEEIIcZr9kRFAvy/TqHwU0cnWUyExMRGz2cyVV17NTz/9yCuvvBCwf/z4KQwffjMlJcUAJCen0Ldvc/r2vYBjx47y0UcfBJSPiYll165MGjRoyJYtm/z3/36kT4WtW7fQqlUbNmxYR8OGqSQnp/Dmm4txu91omsb69eu44IKL2LlzB6rqqyMjYyclJcU88shT5OTkMGrUEN5+eymdO3fhnXeWcNVV1wC+xbnffvt1+va9wP98Tz31CFOnTiclpQGLFi3k4MED5OTksH37Vh566FEcDgdXXnkRffpcwIoVy0lPn4mu69x44yB69z7+gt7JySnExMRw001DcDjKePnlFwgJCT3u8Z+pJCgSQgghhBBCCCHquMmTpzJkyPWoqkqvXn0BsNvtjB17J5MnjwfgppuGMGvWgyxd+h4lJcUMGTI8oI677/4PDz30AEFBVoxGA9HRMcd9zi1bNjFu3EgURWHy5KnExcXTs2dvRo0aiq7rtG7dhu7dz2fnzh3+xyQm1ufFF5/l888/wWAwMnToCADGjr2LOXMeZ+TIISgKhISEMn36wwHP17fvhUyadDcRERFER8eQn59HZGQkR4/mcsst1xEUZOWaa27AZDIRGhrK4MHXERISQqdOXYiNjTvusVx66RXMnj2dMWOGU1xcxOWXX4Wq/jOvH6bouq6f7kbUxuXykJdXcrqbcUrY7dZ/zbGIkyN9X3dJ39dN0u91l/R93SV9X3dJ39ddp6LvDx3KIi4u+RS16Mzw7rtv0bNnH8LDw3n22fkYjUZuuWXY6W7WKaNpKh6P93Q346TV9FqLjg6ptbyMKBJCCCGEEEIIIcT/LSIigrvuuo2gICvBwcHcc0/66W6S+BMkKBJCCCGEEEIIIcT/rUeP3vTo0fvEBcUZ7Z85YU4IIYQQQgghhBBCnHISFAkhhBBCCCGEEKfBGbxksPiX+DOvMQmKhBBCCCGEEEKIv5nBYKK4uEDCIvGX0XWd4uICDAbTST1O1igSQgghhBBCCCH+ZuHh0Rw7lk1RUd7pbor4gxRF+ccFewaDifDw6JN7zF/UFiGEEEIIIYQQQtRC0wxERcWf7maIk2C3W8nLKzndzfjLnTAo8nq9pKens337dkwmE9OnTyc5Odm/f9GiRXzyyScoisLIkSPp06cPZWVlTJgwgdzcXGw2G7NnzyYiIoKvv/6aefPmYTAYuPLKKxk0aNBfenBCCCGEEEIIIYQQ4o874RpFX331FU6nkyVLlnD33Xcza9Ys/76CggJeffVV3nzzTV544QVmzpwJwBtvvEFaWhqvv/46l112GfPnz8flcvHQQw/xwgsv8Oqrr7JkyRKys7P/uiMTQgghhBBCCCGEECflhEHRmjVrOPfccwFo27Ytmzdv9u8LCgoiISGB0tJSSktLURSl2mO6d+/ODz/8QEZGBklJSYSFhWEymejQoQO//PLLX3FMQgghhBBCCCGEEOJPOOHUs6KiIoKDg/23NU3D7XZjMPgeGh8fz0UXXYTH42HEiBH+x4SEhABgs9koLCwMuK/i/qKiolN6MEIIIYQQQgghhBDizzthUBQcHExxcbH/ttfr9YdEq1at4siRIyxfvhyAoUOH0r59+4DHFBcXExoaWq2e4uLigOCoJpqmYLdbT/6ozkCapv5rjkWcHOn7ukv6vm6Sfq+7pO/rLun7ukv6vu6Svq+b6kq/nzAoat++PStWrKB///6sX7+etLQ0/76wsDAsFgsmkwlFUQgJCaGgoID27duzcuVKWrduzapVq+jQoQOpqalkZWWRl5eH1Wrll19+YejQocd9bo9H/9esKF5XVkcX1Unf113S93WT9HvdJX1fd0nf113S93WX9H3d9G/q9+jo2gfunDAo6tOnD99//z3XXHMNuq4zc+ZMXnzxRZKSkujVqxerV69m0KBBqKpK+/bt6dq1Kx06dOA///kP1157LUajkcceewyj0cikSZMYOnQouq5z5ZVXEhsbe0oPVAghhBBCCCGEEEL8eYqu6/rpbkRtXC7Pvyat+zclj+LkSN/XXdL3dZP0e90lfV93Sd/XXdL3dZf0fd30b+r3440oOuFVz4QQQgghhBBCCCFE3SBBkRBCCCGEEEIIIYQAJCgSQgghhBBCCCGEEOUkKBJCCCGEEEIIIYQQgARFQgghhBBCCCGEEKKcBEVCCCGEEEIIIYQQApCgSAghhBBCCCGEEEKUk6BICCGEEEIIIYQQQgASFAkhhBBCCCGEEEKIchIUCSGEEEIIIYQQQghAgiIhhBBCCCGEEEIIUU6CIiGEEEIIIYQQQggBSFAkhBBCCCGEEEIIIcpJUCSEEEIIIYQQQgghAAmKhBBCCCGEEEIIIUQ5CYqEEEIIIYQQQgghBCBBkRBCCCGEEEIIIYQoJ0GREEIIIYQQQgghhAAkKBJCCCGEEEIIIYQQ5SQoEkIIIYQQQgghhBCABEVCCCGEEEIIIYQQopwERUIIIYQQQgghhBACAMPpboAQQgghxD+G7gXF9zubUpKD4nGAx4nidYHThFqi4w2tD6oBPA5QDKBqp7nRQgghhBB/nARFQgghhDizeD0ozgLwuFA8ThSvEzwuUDU84Y0AMO77HsVdWhnSeJwoHidlTa4EgwVTxqcYjm4vr8MBXheKx0VZ82txx7TGtHs5li2L/XUrHid4XThS+1Pa/ja0YzsJ+/CagLrxuvCEN+bYtcsBiHj9fFRHXkDTI4GcIRvQgyIJXTYWc8an6KoBXTODZkY3mCk6bxbOlF6YMj4haOMLoFnQy/ehmXEmnYcj7XKUkmyCNr/ie6yhvIxmRrdG4UzuCYDh0BpQ1MAyBgu6JdwfaAkhhBBCnIwTBkVer5f09HS2b9+OyWRi+vTpJCcnA7B161ZmzpzpL7t+/XrmzZvHt99+y7Zt2wDIzs4mNDSUt956i+nTp7N27VpsNhsA8+fPJyQk5K84LiGEEELUxOsGtwPFWx5+eFwoXie60YbXFguuUoxH1lUJT5woHhe6wYKz4QUAWDa/guIo8IU45QGK4nFQ3GUSuimYoA3PYzz4c7U6ijvfjav+uZi3v4ftp0fK768MasqaX0vReTPQ8jKIeKNntaa7wxtz7LoVAIR+ORq1NLdaGUeDvugGC+adH2HZ+RG6ooFm9AUpqhFn0vkQ0xrFVYxadABUY2W4ooagm3yfS3RTMM6k83zhjmoEzYSumXznqFxR16kougddM4FqwhpspqSg0F9HWeNLcUc2R3GXgcfhC6zcDrzW6PIaFFBUFGchiicHxV2G4nHgCUkEQCs+jO2/T1Q/D5HN/UFR2Ec3oDoLq5XJGboZ3WIn9PPhmLJW+I+R8v8WnfsArsSumDI/w7J1ie/+8rBK1yy4ErvhbNgPpSQHy473y+83+x/vtUTgTujsa2fu1srzWBFYGcygmkBR/sirUgghhBBnkBMGRV999RVOp5MlS5awfv16Zs2axYIFCwBo1qwZr776KgCfffYZMTExdO/ene7duwPgcrm47rrrePDBBwHYsmULzz//PBEREX/V8QghhBCnj677QhhnYcBIFt0SgW4KRinJQcvfHTDCBY8Db0gi7ti2KGV5WLa/G7BP8brwmkIo7TAGANu3U1FLc6sENE7wOCm44Fn0oAhs3z+IadeX1UbCFPV4FEfjS7D8+iYhKydVa3pp8+so6vEwWtEB7B8MqrbfHZbiD4qs/30KreSw75BVA6gmdM1ISYex6KZg1MIDaMcyygMWI7pmQjcE+Ue4eG2xuBLOQtfKAxjVBJoRV2x7335rDEXd0v3hjl4e9OjmMH978i962ff8mqm8jvLnsvg+YxT2fprCPnNrnfblaDwAR+MBtXal1xZHUc/HjtvdjmaB5ynIbsWRV+K/7Uy9CGdq7Y93pvbHmdq/1v3u6JZkj97jex24y4MmjyOgTEH/RSiuUvCUBZTRjb4f5RwpffGE1EfxlPlem+WBVcV+xVWCWnTIf7+vTJkvKGvYD61wH8Hf31+tba6YNuRd9QkA4e9ciuIuqVYme9h2MNkI+fI2jPt/BIM5ILAqOude3PEdMe1ahnnn0vKQyRdU6QYLroSzcNXvjlKai3nXl1VGXVnKw6pwPFHNAVAL9vkDwYr6JaQSQggh/pwTBkVr1qzh3HPPBaBt27Zs3ry5WpmSkhLmzJnD4sWLA+5fvHgxXbt2pUmTJni9XrKyspg6dSo5OTkMHDiQgQMHnqLDEEIIUSfo3vKRGS7/KBavNQZUA2rBPtTSnIDwRPG6cEe1wBtSD+1YBsb9q8u/SLvKgxQH7qjmOFMvQinNxfbjrPKApmK6kxPd8j/27jvOqTr7//jrtvRkMjMMMJRBhia9iEqRJiAoKFbEsqKuZXVdd+29I3Z3bevqqrhWVFxFVCwoCmJFRYoiSBFU+tRM+r3398fNZCYO4Jffrq5OzvPx8CG59+bm3nxmJsk7555PEbVj/gZAwZwTUGs3ZStk6qtlKqfMwwqUEpx3LsaqF2jxo8OuGXMXiW5H4V7/JsH5FzU5rdjexxJp1Q8lUUXgvWtyT1l1YYY7ZoMiY+sXKLGKbIVLfZCCbQFgBdqQbtknNzxR3ZgFTjVwqvUAIkOubFQB4oQ1ZkFHAMxAG6omPZOpHjEy+8h8+M6oPOFdJyDSXDu9vKnugKup280wptoNJdVu6K6H2RMm1ve03ewB0q367XY9mrH79b8Vigq6F1v3Yu9kdartkN3ePbH30SR2t77bUSS6HbXL9emS3mw/7ctGYZUTODUO4GrG3pO5DDATRKUzgZbudo6xdF9sw5cTZCmN9qFGt2Bs/iyzPJ4JrOLEBpxNqv1wtOr1O/29SbXqT9XRcwAoemqEc79GbM3N9tOWg+4l+NZ56Js/a1RV5QRS0f0vci5F/PZtXOvn5VZeaW5Srfch3WY/lFgFxvfvZy7v82QvE7TcIayCvQBQ4pXYmsf5vZC+VEIIIX7DfjIoikQiBAKB7G1N00in0+h6w11nzZrF+PHjcyqFkskkM2fOZNasWYATJp144omccsopmKbJSSedRK9evdh77713+diaphAO+/6/TuzXRtPUZnMuYs/I2Oev3+TYW6bzAc9MOf/2Zf6uV6yBZDQbwNT/Z3cYCoYPZeOHsGN1NmSp/8/qPA5a90b5fjHK0pnZkCZ7/7IhWPufDbWb0Wad2OT+tr8l5qlOPxj9vv4oVd82OeTUH5dAuAxt4V2oS59usj498V7s9l1Qvl2G/u5lTU+5z/GY+xwDagX6hvnOhzzNBXqm0sXjy46jVlQGvlB2m/pqllBRIfh8KH2OwmrX2wlXMlU26G687QfhDfug13jSrTtm7p+5HEpzoXsLCQd9EOpG6rxvQM8cg2pkqyLC9Qf8+zebBAYKkK21GfGnnQ5tNjYJD4TOA3e6jcMHJWN3sz6zjWjiN/k7/3/yE20CBhyx08Xu+n8MO3un67PvLoeegTX0jNyVto2BTVhRIbg/qXOWQjpTFVV/KZ/uyT7f5sF3ZAKqTJCV2TZc7PRqUku7oygpZ112m2qCfh077EP9egPq2ley91WstLPfoRdg9RiJUvMF+ut/aHIOVrv9MafORdNUih/ph2KbzuGrBhge0D2k/7QcNAP11fNQtiwD3euEaLoHdDfm0AugZQ+Ude+grF+QWe5pqMAq7Q+lfSFWibLp8+z97cw2uIIQaJk5oLTTSF38Yprv7734KTL2+Slfxv0nX0kCgQB1dQ3fC1qWlRMSAcyZM4e77747Z9kHH3zAvvvum+1B5PV6Oemkk/B6vQAMGjSIlStX7jYoMk2bqqqmpcy/ReGwr9mci9gzMvb5q8nY2zbYphOUABhesNJoNRtyKlwUK4WNmu3/4Vo/DyVR1aifjLNdvMcJ2J4w7tVz0Dd/kqmyaej5Eu95Aqn2wzA2vIt/8V2NKnGcapjEXmOoG3YdauQHip4Y5lTiZKpSAMxAKRVTPwGg6MmjnOP8kR0nvodVsBeBTx7D+9XMJuujSgFxTyfcP6wh8NXsnF4vqAaJUBeiVVGUWIqQHsR2Nb6MyIXla0Fd5jn09pyKkqprqHDRDGcfSTd2VRR975NQ249rVGXj7MsMtsOuikKbg1BO/ixTJVMfxOhOEFMVBYpg6uKdD2b9OA69aefrk0AyCq1GEu52yM5/56uiQCEUDWq6zmz0GHic/ZHO/Cd+K+Tv/c+pCDSc/9yNFtc/3x0O3/ndquPO/3ucAT12seuqKHQ72fmvnpV2Auv6vw/eLmhT3mxSFWW7AqSqooQLvCSGXt2kqkoxE0RqkqCk8CtBdD3ohF2JymywFamoIO2K4lvzIb5P/u5UMzZSt+/5RL1dML5bTHj2sU0OP9lmENVHzALbpuTv7Zs0T7c1N5UnLARFwb/wGvTKb3Kap9uam1j/MzHD5Rjff+A0SG/UPB3dTbq4B2ZRF5REDVrlN5mQqlGDdcMHRvP/4LQz8nufv2Ts81NzGveSkl1/EfSTQdGAAQOYP38+hxxyCEuWLKFr164562tra0kmk5SWluYsf//997O9igDWr1/PeeedxwsvvIBlWXz22WccccTOv4ESQvwG2ZZTgaKoTsm9mURJ1WWXK7YJtuU0jPUWg5lCq1oDtuWss0wnRFFU0q36A6Bv+sRp0mpbzpt220SxLJIdRmK7guibP0WvWA12Oudxkm2HYLbogbbja9xr5zr7tS3nG2LbJF3cnUS3o1ASNfg/uMlZnz1GE9sIEBnpBAKBdy9Hrf0exU6DZWW3rR17N1awLb7F9+Ba80ruetskut+FsN/xuL55meD8ixouhcrUgsS7TKL2oPtQY9spenJ4k6fT9LWk4pTPAPC/dx169bom2yQ7jsf0hNE3fez0tdEyFSyqE8QoiWpnQ0XLfLvtw2rUmLd+9ijbFSTW55RseFK/D8sdyj5WZPg050NTowDGVg0sf2sA6gZfRnTf85qENCjO5ReJLoeS6HLorn98vORSCVEAACAASURBVMVUH/rkbn/EYv3O2O36dElvKOm96w0Mn/NhRgghfoqq51bmGD7M4u673l5RiPX9/W53WTe4aW+wxqIDzyU68NxMZWcSxYyjpOPZv1vpkt5UHvlCo7Aqs95T6OzAtqjb78LcnlbpuPPlQqYyUbHTKKkISmxHJtCKZ754mOKc5sYF+D+9p+mx738x0aIu6Fu/IPzScU3WJ9sdQPWkmWClafHP7k36Sdm6l6pjXgbA/8F0tOpvm4ZVvU/GCrV3Xtu3r8gJu9DcpAs7Y4XKIFmHVrcpJ8iq31b6UgkhxH/HTwZFY8eOZdGiRUyZMgXbtpk+fTozZsygrKyM0aNHs27dOtq2bdvkfuvWrePwwxu+3enUqROHHnookydPxjAMJk2aRJcuXf67ZyPEf0N9YNEoOMAysd0hUHWUeCVKMgJW2qm+sE2w0lj+1tjeIpRYBVrVmmz4oVToGLVRbF8J6ZJekI47FSLZcCSzD9Ug0e1IANwrZ6HGK7LHUr9trPdUbG8xrjWvYmz+NHMMZjYkSXSdRKrtEPTNnzpTLv8ohEm1Hkhsn3NQ4pWE5p6ec/zYFrYrRPXhzwBOLxatan02oKkPWqqOehEzXI5/4dV4VzzZ8FxlApDaEdOJ9zoJ99q5hN74Y5OnN975UGrH3Y8Sr6Ro5pimT7+3BTtOXQJAcP5F6JXfNNmm4rj5mEVBPCufw7viiSbra0dMx2zRA71iJf6PbwfAVlQntFBUEp0nOj05zCTuta86syKpWma9huUtzu5LjW5FjW7LBmC2ooOqOtVBgOUOYgVKG613HqN+H1bBXsS7H9skYDGLumbuX0DNmLuy4U59tYtteLPHUH3o45mDabSN5gLN6RlTN/wG6obfsKufaFLtD6C6/QG7XG+7gtQNuXKX64HsDEu73Ie3eKf9U4QQQuwhVQPVi23k9qWy3SHSpfvu9n7Rff+y211Hht+42/XR/S8iOvDPOY3NlXQCKxNGpVv0dBrJZyunnKDJ8mUufbMtYr1OalR1lek51ahaVY1sRqtY3RBkmc42ic4TsULtca+di+/zfzQ99sGXERvwR1ybPqLg5ZOarE+WjXC+cEjHKXp8aMOle/VhkhGg+jDnCwnfx3egRjY1NE/PbBPfezJWoBR96xdOQ/76sCsTWFmhMmfmw3QMNVHj3C9hQirpvDdQdOlPJYRoFhTbtn+17+1TKbPZlHX94iVq9dUd4DT0tC2nuiAn/LAAGyvU3tmsai2kYjnhiGKbpEp6g+FDq1iFVvtdQ5BimyiWSbqkF2a4HLVmA67v3stWVSiZ8MEs2Itkx7GQiuJb8kA2HHEqMExs3WkmCeD7+E7Uuk0ojcIabJO6oVdhBdrgWf6YE7I0OgYsi1i/00iWH4yxcQGBRdOywUb9MSY7jCQy/EaUWAVFT4/KCWgU28RyF1Bx8qcAFD41Cr1ydZOntOK4tzGLuhJ493K8yx9rsr52+I3Ee0/FvXoOoTfOarI+3mkiteP/gRLdTosZTZuwWp4idvx+6e6PYcpbmMXd8C+8Bu+XTznNZLMBiEZk8KUkuh+L69u38S+8plH4oWIrGqmy4dQNvhwlXkVo7u+ddZl92IqG7QlTO+YuAPyLbnACElXLhCzOm5/owD9j+Vs5YdWWzzPVKpnHUTWSZaNIt+yDVrUW17dvNwphnOM0Q2VOE9tMYJY9/vqQRXdnm7Pq25Y7/XIyx1kf9pjhvUBzo8QqnAaqmePPnovhdb5ZtEzAzjwHv/y3jM2pNFX838m45y8Z+/wlY/9flI6hJOsaQiYzgZKOYQVKsQJtUOs2Y/zwUU6Q5cwe2ZZE1yMgHSOw8JqcIEtJJ0BRs0FR6JVT0Lcta3SZYBzFtqg85lXSLfvgf+86fF/8s8mhRYZcRaz/mbjWz6PglZObrE/sNYaaCY+iJCMUP9w7+x7HzrwPsl0hKk76wDmGl6eiV6xyljd6v1Yz7n7Moq54v3gI9+qXct7/oGrEep5AstME9C1L8H12r7P/zH1RNdIlvZ3JANJxAotucO7b+P2g7iW63/kAeL58CjW6A1tteC+JohLvMgnb1wJ982foFasa3m9m3quZLXpghstRotswti3LPL6WCcs0LE8RZlEXsEy0HSuzy51jVUE1sELtAKcJPLbV8FzV70dz73SyhF8T+b3PT81p3P+jS8/Ef05JRtBeOJdgIplT3WEbPmrH3Q9A8K3z0KrXZys7nJAmTc34BzHD5fg+vhPP17NyLr/BTlM3+DLiPY7HteZVQm+c01B9kpHodAg14x9EiVfS4pG+TY7N8hSy4/fLAAi9ehp65aom21RMmYdZvDfe5f/Cu+xfTdbXDrseM1yOvnUpwfkXN1mfKD+YZMexKOk4/o+dqYYbv6BY3hbZoMj4fpHzPNS/mGRe2JR0PPNc1qJGt2df8LIvJjghgK17MYPtsi9I9S+cZkG5s15zkyg/BFS10TFo2EZDw/ZYn1NR41UNL5qZF2jLVwJAvOuRpFr2a/Sip2OrKukWvQBIttmfqkOfzO47EPJTW5fKlobbnjAVU+Y1CkcyIYzW8OtYddTshuepUQhT/4JZN+w66oY1na64XrLDgbutALE9YaqPeH6X6wHqhl612/U/Na2zGS4nFi7f9Q50D8nOE3f7GOmSXrtdb3uLdl/FIt/qCSGEEL8tu5nlD8DytybRZdJu7x8ZdetuH6JmwozcBbadaQTuvG+IDjyXeK/fZSqeEtkKKzPszA6ZLupG7YibUcw4XrdCLBpHsZwvRwFsVXMul67/Yjbz/txWXdmHTJf0xvaEG32Bmvl8oDlNuGzNg+0KZNannd5VadPpNQgoqTq0qnU5X4xiW87l5YBipXB/81LTFgCGryEoWvYvjO0rmjw/qTb7k/a1wL16Nr6lDzdZHxl6DbF+5RibP6NgbtNLLhN7HUTNhEdQUnUUPTuuyXrLCLDjjJUAFMyestNjqJz8GumSXvjfuw7v0oezIVb954O6QZcS7z0VY8M7BN++8EfvqzVS7Q9wKuiSdYRnT24SRNlGkJoJjwDgX3iN82V49otH57Hq9r/YqXJbPQfj+0WNgq7M55Peh0KoH1rlN5lQr1Fop2iY4Y4kOx4EZgLPV881/fyhubPvpY3vP0BJRZsEe6mSPuDyo9Z+jxrb0fDZp74K3tcC213gVLnFq5p8QWtrLqc5vm3jfHn66w7fxK+HVBT9ApRENcX/PgzTVnICENtd4FzPDQTevgCt9vtGf4Cc5D5ywLXOH6iVzznVOjnrNRKdJ5JqOxhtx1d4Vr2Q88cJVSNd2JlkpwmQjuNd8WTONxa2qoPuyb7YGhsXOD1lfhTSpFoNcP5A1WzIDWnU+j9QJU4IkoqhxiszfwT17B8p5w+UN9PIt/4bg/y5hrw5pc5iz8jY5ycZ9/wlY5+/ZOzz12967OvbC1gNQZXTiiCYabdQ5bRbaNQGAdvE8rV02i3Eq3bSb9LC9hQ6X/aZSVzfvpUJqhr6OKJoTvUX4F49ByW2PaeVArZJvPux2L4SXOvfQt/8ae4VEbZJsnyc025h23I8y2Y4+7caziFd0pvYgLMhWUfo9T/kPr5lgeHJ9kYMvn6W0+rAbuhFqdgW1RMexSzqgu+j2xraLTQK3qwDr6Wiywm41s6lYO7pTZ7eRMdx1BzyMEq8ihYPN/3y03KF2HH6lwAUzhyLvuOrJttUTH4ds6Qn/oVX41v6SJP1kQOuJdb3tD06hsZhleUJZycuKXjxWLTKb5pUgNUc8ghmURe8n92Pe/Xs3CBK0Yj1OYVk54nomxbjW3xXkysa0q36E+t/JqRiBBZe2VC9lvnMaBt+ooOcYgPP0hmose1NriaI732MU+X2w8foFSubBH/plv0wCzuj1m1Gz1zx0DjYs7wtMFv0ACuNvvWL3KsZMlVuZmEnANS6zc7PyI8/0xoBUDXCBV6qqmM//fv1G7C7iiIJin4hv+kXEfEfkbHPXzL2+UnGPX/J2OcvGfv8JWOfv3LG/kdBlWKb2Kjg8oNtoUa35kx64gRXdjac0CpW7XQSmHTLvtiuANqOlc7ss42CLGyTdElvJyCp2YBr44Im9zdDHUiWj4NUDN/n9+ccH5aJrbmIDroEAN8nf0ON/NBwfJkrXeoGXYYVaodnxZO41r/ZJPiL9T6ZZKcJGN8twv/B9JzHxzZJtRvqtABJVFP49OimwaIrQMXUj53n9JlxO60wqzj2DcwWPQgsuBLvskebrI8ccB2xvr/HteYVCl47s8n6RPl4ag5+yLnK5uGmE59Y7gJ2nOY8buHMMeg7Vu7yGMI+m6po8yh6kKDoV0BeRPKXjH3+krHPTzLu+cNOpyGdBt25dDhk2NSYGooqpf35Rn7v85eMff6Ssf8ZZYO3zKzCugdUzalwS0VzgigsE9tTiO0JoySqUWu+y6mQc9aHnYlkzKRzlc6Pgj1UjWT5wQC41s51rpL5Ud/deLejsL1FhIM6VbXp//ET9N8hPYqEEEII8T9h27bzZiudxjbTYNuo/gBWdRV2NIri8aIWFpJeuwZSyWz4ohYVo7UvI/nJR9ixGHr3HigeL8lFCzL7MiGdxrXfIJRAkPicF8A08R73O9Krvibx7ttOiGOmsdMmgXPPJ7XsC+KvvITaooTAuecTffRhUp8vdh7TNFFblRK67kYid91B8sP3cY8dh//UM6g8cTJWRQWYJnY6jWfSEQTOPZ+KSeOxa2oI3nAzWvsy1p18PKgqSjCIEgwR+MuFaOWdiD5wH0qoAN9pZ2Jt3Yr5zWqUUAi1oAAlGEJt1RpFk55qQgghBOBcDqapgJGz2HYFwBXYZQ8z212AWVKw6/1qrp+cybc+MNrdPqB5BEW7I0GREEII8Sth2zakUmCa4HY7wUltxAlY0mnsdBqtbTvs2lrMTT+g6Dp6126kv1mNtX1bNmRRPB5cg4aQ/Gwx1ncb0co6YPQbQOyFWdiRSDa00bt1xz1sBHUP/h27thb3hENRi1sQvf9e5zEzYYz3uBNRW7ai9sbrwDQJ3XonqY8/IvrYI9njIp0m/PDjJBctoO7ev6G1bUf4oceoveZykvPfyp6j1qEjhU88Q+T2m0m+8zaew48icMElVJ97FnZ1VXa7+uW111+NXVVJ8Nob0co7Ebkxt5G/eu2NaJ06E/2nM522Z9KRmOvXkZgz26n00XUUTcNOJrFqqjHXrnGeY8DOBFOKroPbgxpwJjbQWpeid+uO1qoUANeQA7ATCdA05znv7UwO4TvpVOx0Cq1jOWphIS0uupi6rTuwa6qxqqtRQgXYNTWkli7BrqnBf/pZJN9/j+j99+ScQ9FrbxOf+wrRfz2C0bsPoem3EX38UcyNG5xAKVSA1q4d7gPHkl7zTSbUaoVaEMa2bZQ86vsnhBBCiJ+fBEVCCCF+c2zbzoYYdjqNomkoXi/m9m2QTKKEwyi6gfnt+ky1iBNkaGUdUDxeUp9/im2mce0/BGvrFlLLlzYEHqaJZ8JhmBs3kPzgPRR/AO/RxxJ/fS7pr7/KPq5SUID/jLOJzXqG1JLPMfYZiPeIo9l80YUktjSENq4DhuM76RSq/ng61o4d+P90HnpZB6rOPj1T8WJim2mCl1+D1rkLVSdOBqDoxVdJLHyXujtuyTn3ohdeJfGes1xrX0bhU7OIPv4oybffzG6jtS/DNWgI8dkvkHz7TTyHHYHRbwDRRx/GrtjhbKQoeCYdiXvYCJLvzseqrsbYdz/UUAGpFcuc8ETTQNex4/FMc0sFxeNxGp8GAmjty0DTG7bVNLT2ZbjHT0Bt0QIA96gx6OWdndBG01DDYcAJglyDhqLttRcAwSuuwbYsp7JG11FbtgKg4I67AVBL26B4PBTO/Hcm/MmEQH4/aBrFby9yghxVxXPY4XgOO7zJz41n7Hg8Y8dnb/tPP2unP1/eY4/H2+i2/+xzd7ldY+GTToKdXIZQ9OzshmOYdIQTPNVUY9XUYFdXo/j8aB32wj1iJGqx87yZ328k9eknWDXVEI+j9+2H+8Cx1N19J6nPFuP93cn4Tj+LHeNGonh9qKEQSkEBnomT8IyfQN0/7kMJBHCPHYfi9ZL+ZjVqqKChksnt2ek5CSGEEEJIUCSEEM2cbVlOBYVloXi9WJEIdizqhBTpNKgqWtt2mD98j1VZgVoQRmvXntTnn2HHotmgRW1VitGjJ4l5r2NFIrgGDEQpLCT+0otOEJO5LMc9cjRqixZE/+VUm/jO/CPp1V+TeHl2Njyx02mCV1xDevkyok8/jlpUTOj6m6j7x70kFy1seMyWrQjf+yCR224iMe8N3OMOJnD+xVQeORFr+7bsObonTiJ4yRVUnzYVa8d2Alddj7F3d6pOPTHnuQhcdT1G9x7UXHoBAIXPzyH1+WdEbpues51r+EjSK78i+tADqG3b4T36WFKfLSb5ztugO0GG1rY9ANaWLZjfrkMrL3ee73gC2zJRDAO8XhSvEznoHTtht2rtfFgPBHCPGJUNTxRdRyvrgFpYiO/0s5zlXi9G3/74L7jECWJ0J5BRAgFcg4ag3XKnE5IAvt+fgXfylExgo6O4nemVA+ddhH3On1F8PgCKZv47G+g0vtSp8Mnncs6/6JkXdvqzFL7nHw3P0f6Dce0/uMk2ap9+GH36NYzNqNE73Zdrn31zbw8eutPt9K7dcm5rbdvtdDt+I32BVH8A1R9osty17/649t0/ezt46VXZf9uJBHYyAYD/7D9hbtns/PyZJt4jj3ECp5pq7JoasGzsRILYc09DMonRrz92XR01F/455/EK7nsQUIjceQtquJDQX+8l+f57pJctbQiTQgW4h43AqqoCM40SKnB+roUQQgjRrEkz61+INDvLXzL2vz1O75MUaDooCnZVJXY6jVrcAjsWw9q6BdKpbOih790Dq6qK9DerUAwD136DSC1dgnvHFqK1TtCieLx4DplI4r0FmKtXoXXqjHv4SKL/etj5EJYJT4xeffBMOJTIHbdgVezAc9RktLIORKZdm612wUzjO+V0tPJO1Jz/J+x0moL7HiT1+afU3XVnpieLc2yFM18g9cmHRG65EbVNW4qeeYHaG64h8cbc7Pn+eLl7wqEEL72KiqMmYm3dmt3OfcihBC9rWB644hqMPv2oPPaIhidPVQlcdjVGn75U/f4k0HXCDz9GevlS6h68PxtkoOsU3Hon6a9XEpv5JGrLlgSvvI7Ys0+TWvpFJhTRUIuK8Z99LvHXXiW9aiVGz964R48lNvNJ7EQ8W1mideqMa9/9Scx7AzuVxOjbHzVcSHLxR9nKEzQNvWM5SiBIet0aJ5zp0NH5EF5TnamMcUIgJRhy+uqAE6rswaU98jufv35tY2/Hnd8ROxYlvXoVdiZQsmpq8Bw8AXPrVmKPPeL8Dbntb9Q98HdiM59wAmRA8fspfm0+kbvvJP7cTIwhB1Bwy53UXHoB5tat2Somo08/vEdNJjHvdexUCqNXH9Q2bTG/2+gEo8Gg87vfjP3axl78cmTs85eMfX5qTuMuzayFEL+I7OVAZhpQUNxurMoK7GTS+aBguDC/XZ8TZGht26MEAiQ//QRSKVz7D8basZ3UF583zCiUSuGeOAlr0w8kFryD4vPhO+5E51Kg5Usz26VQAkECf76A2HMzSX78EcY+A/FNOYGaKy/B2roFO5VyLgUaOhz/H/5I1ZmnYm76gcC556P37EXlCcc4j5fJzwOXXY3Rr382CCl89kVSn31K5OYbcs678XK1dSlFz80mPmc21a+9kt1GbV2K55CJJN+dT+K1V3CPPwT38JEk3nwDa8c2JzzJXD4FYG7c4Dx3dXUoKE5/FD1TKaL7wTBQXC60zl1QtExY0rqNU72h6w2VJT4v+t7d8Z1xNmqB09zPfchEjL79soFNfVWKd8oJuMeOy17yE7zxVrDshmqWUAiAggdmoKCgBANguCh+492GyphGVR3Fcxv1pTlwLO4Dxzb5mXENOQDXkAOyt72Tj8M7+bgm23nGHwLjD2nYbsoJO/0ZdI85KPf28FE73c7Yu0f234phQKBphcdvpUJFiF1RPM7lZUowhGvAwCbr1eIWGDfdnr3tP/NsfGec5VQS1tRgR503wu7RB6G1L0MtaQmA1r4Dtm07odM3W1FDzt+WuocfxPpuI/4LL8Xt82UvowRQAgH8f74A19Bh1F57JWoohO+Pf8aurSW1+GOUUIETPIUK0Dt3cSrfVFVmkBNCCCH+B6Si6BfSnJJHsWf+r2Of03PFMlF9fqzaGicocLtRC4tIf7se4jEnGEmlUArC6B3LSS7+GDsSQe/aDTVcSGL+vOw2pNMY++6P2qo1sZlPZhrTnoD5/fck5r7shCeZ/i3+P51Het1aYjOfQg0GCV59PdHHHiG58N2GJrnhQsL3/IPIPX8l8cZruEeMInDhpVQefzTmxg3Z83GPO5jglddRMXkS1qZNBC69EmPgflQefVjOeQcuuQJj3/2zywtn/pvUsi+aNKxtvFwtbUPRsy8S+ettJN560wkyDAO1ZUvC9/2T6Ix/kly0EGPIAfhPPYPaaddgVVU5gYCmYwzYB++Rx1D3j3uxI7W4x01A26sjsSf/ldP7xDV4KFqbtsRff9WpFBo1GrumhvTKr5ygJhOgGL37YEdjmFs2obg96B3LsSorCLoUaqKpzHYGajDoPI+wx1Uq4rdD/t7nr3wfe6u2Fru2JlNBZJB4792cKib36LGorVpTe8UlWLU1FNzzD1IfLCJya+6ln4XPzc6G71p5JwoffYroY4+QWrY022dJK22Dd/JxpL5cgV1bg9auvdPoPRp1Lvn8hf++5vvY5zMZ+/wlY5+fmtO4766iSIKiX0hz+oH6NWrS2NbrhVQKq6oSbButtA3mli1YlTty+rIYffqRXr0K8/vvUEtKMHr2JjF/HlZ1dcMMQ+3LcA8dRuzZp7EqKnANH4nWvozoA/flNMl1TzgMvXNXam+8FtJpApddhbluLYkZD5JOJCHlzDJUcOc9pL9eSeSOW1ALws7sP7fdRPylhp4gaosSil54hdqbp5F45SXcY8cRvPoGKo47Cuu7jdntssunHIn1/XcELrocY/AQKo+cmPP8BC66DGPwUGe5rlP42EzS36wicudt2bADXafgznsw168j+siDqMUtCN18B7Fnnyb5yUfZChU1HCZw4aXEX3uF9PJl6D164TlkItGZT2JHItn96eWdcA05wLkUIR7H6NMPtVUrkh+831Dxoutoe3VELQiTXrMaRTfQyjpgp1PYNTUoutEwa1GmxwqK8psJWOT3Pj/JuOcvGfs9Z5smdqQWu6YGK9NnydhnX+f1c+G7KG43vt+dTN0jD5J6/71sPya1VWsK//U0NVdeSvLdhtnzdhw6LhNWOX2W3AdPwHfCVCJ33QGqimfCYaglLUl98mGmiqkApaAAtaj4P+q/JGOfv2Ts85eMfX5qTuMul56J3cqGLCmn54ricoGuZ/qwpFFbl2LHY5jffwepdHZaZaNnb6zaGtJfrgBVxT18JKmlS7JT99rpNIphOH0LFr5LesUytI6d8Iw72ClP37YlE8aYGD164p18HLW33Ii16Qc8x0zB6NmbmgvPzVSyOLMC+U49Hdc++1J50nFgpgk/MIP06lXUXntFzjmFn3yO9NdfEbn+apSiYopnzyX22CM5YYxSVETx7NeIv/g88ZdewHXgWIzrehN96AHMDd9mt3MdOBb30GHEZ/8b84fv0dq2RSstJfHufOdNpa6BpuMaOhwUsKsqQTecxsEuN1phETZKQzhiGKitWuMeeWD2Uh5j/8EoRcUN/VEyjU49B0/A6N0n27w1cP7FzmVc9bMHZWbHCd10G5gWasuWKD4/hc/Nzg1ZPB5nVqAFH2VDFq19Ge5RY5r8PGilbXKayu76UqAJMH5C9rZvl5cCjcu9PfLAnW6XcymQywU+/063E0II0XwomoZSEIaCMFqj5Xq3vdG77Z297T/1DDj1jOxt2zSd5ef8Ge/k41Ayl9b6TjrFuWw3U8WkFjiz7KU++gBz+zZcA/fDTiSovSb3fUPwptvRWrWm+sJzUUMFhB+YQerzT0kseCdntjj3wROxqiqxq6tRCwuzr8NCCCFEcyJB0S/ATqeJL19GqrIuG7Jo7cucRqvvvwdmGmPQUOyaapIffZANWUil8EychFWxg8Sbr4Hhwv/7M4i/+RqpxZ80qp7xELz0KmKzniG58F30vv3wn3oGNVddirlhQ7YfjGu/QQTOv5iqs07DXLcG/9l/xnXAMComHZxzvP7zL8Y9YhSVk52phcOPzcRct6bJm6rGy5VwYabfyuvEX3w+u41SEMZ71GRSiz8mPudF3CNH4xl3MKnPF2P98EM2ZFGLipznKhp1erGYaefSnMJi1PrwRNdRC4vA43H6sBgGit+P1rEc78mnZUIW53IgtaAAo2dvApdeheJzer54Dj8KY8gBDf1WMrMCeU86Fc8RRzf0X7nrfufgjcx2hss53yeezalkKZ7z+k7HO/zPf2X/rbUupWTUAU1SZ61FCUb3hmDEPXwk7uEjm+zL6Nsfo2//7O3GM+I0pnfslLv/1qU73U4IIYRoDupn7dNal+a85nmPmbLT7QufmgVkvhxLJgn/6+lMmORUMeldu0EyiWvocGf2OI8Hc8tmUp98hFVTDQln1jn3IYeSePkloo88iN67L+G//5PqS86n8ptVEAihhEIYPXvj/8Mfib/yElZVFUb/fTB69CT1xedOpVMmeFJcrp/5WRJCCCH+/8ilZ78Aq6qKikNzG6z6/3wB7rHjqJjoLA8/+hTmxg3UXnVpznbhGU9ifv8dtdddiVpYRNHzc6h78O8kXnu1YarkgjDh+x8iOvNJkgvewRiwD/7T/kDkr7dhbdua3U7v0Qvv0ccSfewRrKoq3CMPRO+6N9EnH0MxMo1vdR1jn4Fo7TuQmPeG06dlyAHYiTjmid5HIAAAIABJREFU1ysbKlQMA71LN+x0GmvrFhTDuWTIqq2BZNLZzjCcJruZQCZfNafyRLFnZOzzk4x7/pKxb77sRByrpgatpCXmxg2k13yD4vXi2n8wsVnPoG5YS2J7BVZNNfpe5QQuvJSqs04jvXwpvjP/iPeYKewYMyxnn74zzsZ73IlUnXEKaiiE/88XgKaReO2VTJhUkJ1VTnEZ2NEoSjDU7GeQ+62R3/v8JWOfn5rTuEuPov8xO53GteIz6uJmtgGu1qYdSmEh5vp1Tp+W0jZgW9iRSEMYoxvgcv1m+rGInWtOf0zEnpGxz08y7vlLxj5/7WzsbduGWAwUBQyD1JLPspfE2TXVGP32Qe/chdrrr8KqriZ42VWkN26g9oqLnZYA9ft+5AnMzZuovfwiFJ+f4tfnE338UZLvvu2ESaEC1BYtCJzzF1JLv3Auky/rgNGjJ+aWLSgeN0ogmK3CEv9d8nufv2Ts81NzGnfpUfQ/pug6/hEjSe3kB0rv1Dl3W7fnlzosIYQQQgjxM1EUBeonYgBcA/fb6Xahm+/I/lsr64Br/vvYdXXZS+O09mUofj/+v1yYDZDUggKUwiLsmhrMTT+grHMuY4vPfZnEy7NxjT4I49pp1Jz3R2dGUkVBCQRxjx1H4LyLiNx+E3Y0hueww9G7dSf+6hynD1MmeNLatUMJBMG2UVT1Z3yWhBBC/BpJUCSEEEIIIcSvhKIoKIEABAJobdoCoLVpi/eoydltPIcdgeewI5rc13/On/Ed/zunOh3wnX4W1vZt2SomvUtXAMzNmzE3fItr2Ais7duo+9vtOfsJXH41Rt/+VB53FEowRPiRxzHXrSX27+dQQ6HMjHEhPEcdix2pxdy4AbWoGL1TZ+xUyqmMl4p4IYT4zZKgSAghhBBCiGZA9QcgM3Mq4Ez+sRMFt9+V/bdtWRS99Hq2sbdVU43euSuKy8B74lRnhrdAkHQshrVtG+aab7BrarBjUTyHH0Vy0ULq/nY72l4dKXz8GWpvuJrkwnedCqVgAXqXLgSvmUZs9r+xvtuIMWAgrsFDSS5aCB6PUx0VLEANh/O+r6UQ4j9TP2EBlgVutzP5U13EmSnTtMBMo7ZshR2LOb18FQW9Yznm999h7dgBloltmk7f3r79Sa9ehbnpe9SSVhjde5BY8A6J7l2gpO3/+lR/dhIUCSGEEEIIkacUVUUpLEQtLGyyzn/6Wdl/u0ceiHvkgdnbdjIJhoH7wDHonbs0bDdqNFqbdti1TuikhAoASH/xOYkF74Cq4ho8lJprLs/OJgfgOfZ4Auf8hcoTjgHdIHDBxagtSog+/mhOFZMxaAhqIOhcchcKoRa3kOolkVdsy3KCEE1zZsuOxQAbNRjCqq3Brqtz1qfToCho7cswN2/CqqxEDQbR2rUntWI5dizqXM5qWU5FYLe9SX74PnakFn3vHqjFLUjMe915PNME08QYMBC1tJT4rGfBsvAccRTW1q0k3nrDOZbMdr6TT8P84TviL72I4vcTOPd8Yi8+T2rxx9nt1GCI4FXXEX3qcZKLFuAauB++U06n5rILMTf9kD02Y/BQAuf8haqzT8f8/jv8Z5+Lsc9Aqk6YjG2Z2WMLXHgpxv6DqTzqUMCZ7TL15Qoi067Jef4Kn36e1IrlRKZdg1rSkqJ/v0z08UdJvPJSdhu1RQlFL7xC7N/PkXh5Nu6x4zCuvoG6++/BOOpI1KNP+CWH/H9CgiIhhBBCCCHEHlFcTl8kpbAItbAou9w9agzuUWOabB+8+gaCgG2a2LZN+IEZ2Somu7oarWM5tm2j9+6LXVON4vNhVVaQ+mARVk01pFIAhO76O5aqUv2nPwBQPP99os/NJD7rmUZ9lkIEr72R9NIvSH39FXr7MlxDh5FetxYUJRs8yQxyv162bTthR31IYVnOz0S0DqIxcBmooQLMLZuxE4lsWKD4/WilbUiv+ho7FkVr2x4lGCS15LOcIEPv2g21qJjEO2+BaeEaMQq7soLkxx8621lO9Yln4iSs7dtJzH8LxePBd+JUEm+/SWrZUlK6SiIWR/H68f/xXOIvzya5+BOMXr3xHn0skdtvxtyyBcy0E3j0H4Bv6u+pufoyrE0/4D3xZIw+fan+01nZShZME98ZZ+PaZyCVU48D0yL84AzSq76mdto1znlm5qIqfPI5Ul9/ReT6q1GLW1D04qvU3X8viTkvZp/H+uXRx2aQmPMi7jEHEbxmGrXTrsH6bmN2O9fogwhdO43IXXdgfbcR/4WX4ho6jMit03PGxX/hpbgKCoj+837nfgeOwfx2PbHnZoKqOk3zNQ3vMVOwduwgtfgj1OIWAFg7dmBu+NYJuFQtex4oivO7mGm4rxYXO8s1Z5lW2gYAo29/9I7laK1bo3i8uCcciqLpoKnOdp27ogaC+M44GzTNmTWyew/851/sHJfqbKeECzH69iN4/U0oXi8A3snH4T5wjLNe1ZxqJMA39VS8Rx6DEgoBUPDXeykobUHtr3Y6sP8emfXsF9KcuqOLPSNjn79k7POTjHv+krHPXzL2Py/btiEex6qpRg2HsaNRZxa5SATPoYeTeG8ByQXvNMwqF49R+MgT1N1/D7GnHsfYbxAFd9xN1Zmnkv5yeXa/xpADKLjlTmpvnoa1dQuew47ANWwE0RkPZcIkJ3jSOnV2LlepqW4yg9x/a+ztVCoTZKSdMEDTUP0BrO3bsVNJlGAIxePB3PhtJhSxsE3T+dAcDJFa9oVT8dGzN1ZdhPRXX2ZDFtsycQ0+ALu6itSnn4Bh4Dl4IsnFH5Ne9XVmuzS4PfimnEBiwTukly9FK++MZ/whRGc8hLV9W0PI0r0H3iOPcUKFLZvxTDoSvXsPaq+9MieM8R53Akaf/lSffw6YJqGbbsdct5bIXXdkwx/bNAnf+wDpNd9Qe9P1qOEwRc/OJnL7TcRnv5B9fpSiIopnv5Zd7jpwLKHrbqTyhGOc8CHjx8v9F1yCe9gIKg4/JOf59p9/Me7hI7PLw48/g/nNamqvuzJnu8bL66tMInffSWLuyyi6jq2oqEVFFD76FHUP/YPEW2/iGnIAgT+dR80VF2Nt2ZwNPIyB++I/9Qxqb74Ba8cOvEceg96rD5FbbwRNdxrHaxrugyegd9ub6AN/B03D97uTsSp2kHhrHmgNYYznsCOxqqtIffg+is+H59DDSS1d4jSwz4QxiseDe/hI0t+swtqyBbWkJXrXbqRWLIdUMntsajiM1qYt5sYN2JkKI8Xnw9q+PfN4mZDF4wXDcILbTPiSbxV9zenv/e5mPZOg6BfSnH6gxJ6Rsc9fMvb5ScY9f8nY5y8Z+18n27axo3WQSqOGw6SWfoG1dUu2kklt2QrPIROJ3H4z6W9W4TlyMq5Bg6mYeFBDtQPg/9N5uA+eSMUhTs+n8MOPY0Vqif7jPnS3QTqRAsskeP1NmBs3UHf3nSiBIOEHHqHugb+TmPuyE55YJkowRNHMfxP5623EZ/8b1/CRhK6/icoTj8X8dl32MV2jRmeWT8b8dr0TbIwYRcWkg3PO0X/+xbhHHkjFYeOdY3tsJua6NdRec0XOduHHnsZct5baa65ACRdSPOd1InfcQvzF57PbKAVhil9+g8jfbif+8mzcIw4keNV1mUt+NmaqNzQnCDnvIqovOR9r82Z8J52Csd/+1Fz0l4aAQtPwHH0sRt/+RKZfB5qG/0/nY23fSvyFWU5FieZs5z3599iVlcRffxXVH8B3ymkkFy10QqxMSKF4fXiPPIbUks9Ir1+H1q49roH7kXhvAXYsmgkzdNSWTi+Z1NIl2MkkWlkH1KJi0iu/zAlj1JISFH8gE+RoqC1KGi7jqg9FNA0Ml1PxspMwRH7v81NzGvf/KCiyLItrr72Wr7/+GpfLxbRp0+jQoQMAX331FdOnN5SjLVmyhPvuu48+ffowbtw4unZ1ZlYYM2YMU6dO5dlnn2XmzJnous5ZZ53FqFGjdnvgEhSJ5kDGPn/J2OcnGff8JWOfv2TsmxfbspwGuNVOhZJaUoIaCBJ/5SWsmmq8R07GXL+W6OOPoquQtgBNI3D+xVg7thN79mnUQJDARZcRf+M1Up9/mq0CUfx+/GecTWLhu6RXfonesRPuMQcRf+lFrNqa7HZa+zJcg4aQeOdt7GgUvWcvtNI2JBctyAljtI7lqC1KSC9bCpqG3rUbdiqJtWlTQ8WHpqG2LgXLch5D11ELi7ATcafBb+YxUVUnSBH/J/J7n5+a07j/R0HRG2+8wdtvv83NN9/MkiVLeOCBB7j//vubbDd37lzmzZvHHXfcwfvvv89bb73FVVddlV2/bds2Tj31VJ5//nkSiQTHH388zz//PK7M9c07I0GRaA5k7POXjH1+knHPXzL2+UvGPn/J2OcvGfv81JzGfXdB0U9Gxp9++inDhg0DoF+/fixfvrzJNtFolHvuuYcrrnBKHJcvX86KFSs48cQTOffcc9m6dStLly6lf//+uFwugsEgZWVlrFy58v/3nIQQQgghhBBCCCHEf9lPtvqPRCIEAoHsbU3TSKfT6I1mCZg1axbjx4+nqMiZ8aC8vJxevXoxZMgQXnrpJaZNm8bo0aMJBhsSK7/fTyQS2e1ja5pCOOzb45P6NdI0tdmci9gzMvb5S8Y+P8m45y8Z+/wlY5+/ZOzzl4x9fsqXcf/JoCgQCFBXV5e9bVlWTkgEMGfOHO6+++7s7UGDBuHNTDU3duxY7r77biZNmpSzn7q6upzgaGdM0242ZV3NqURN7BkZ+/wlY5+fZNzzl4x9/pKxz18y9vlLxj4/Nadx/48uPRswYAALFiwAnGbV9Q2q69XW1pJMJiktLc0uu/LKK3n99dcB+OCDD+jZsyd9+vTh008/JZFIUFtby5o1a5rsSwghhBBCCCGEEEL87/xkRdHYsWNZtGgRU6ZMwbZtpk+fzowZMygrK2P06NGsW7eOtm3b5tznggsu4PLLL+fpp5/G6/Uybdo0SkpK+N3vfsfxxx+Pbducd955uN3un+3EhBBCCCGEEEIIIcSe+clZz/6XZNYz0RzI2OcvGfv8JOOev2Ts85eMff6Ssc9fMvb5qTmN+3906ZkQQgghhBBCCCGEyA8SFAkhhBBCCCGEEEIIQIIiIYQQQgghhBBCCJEhQZEQQgghhBBCCCGEACQoEkIIIYQQQgghhBAZEhQJIYQQQgghhBBCCECCIiGEEEIIIYQQQgiRIUGREEIIIYQQQgghhAAkKBJCCCGEEEIIIYQQGRIUCSGEEEIIIYQQQghAgiIhhBBCCCGEEEIIkSFBkRBCCCGEEEIIIYQAJCgSQgghhBBCCCGEEBkSFAkhhBBCCCGEEEIIQIIiIYQQQgghhBBCCJEhQZEQQgghhBBCCCGEACQoEkIIIYQQQgghhBAZEhQJIYQQQgghhBBCCECCIiGEEEIIIYQQQgiRIUGREEIIIYQQQgghhAAkKBJCCCGEEEIIIYQQGRIUCSGEEEIIIYQQQghAgiIhhBBCCCGEEEIIkaH/1AaWZXHttdfy9ddf43K5mDZtGh06dADgq6++Yvr06dltlyxZwn333Ufnzp25/PLLMU0T27a5/vrrKS8vZ8aMGcyaNYuioiIArrvuOsrLy3+mUxNCCCGEEEIIIYQQe+Ing6J58+aRTCZ55plnWLJkCTfffDP3338/AN27d+fxxx8HYO7cubRs2ZLhw4dzySWXcOKJJzJmzBgWLlzInXfeyb333suKFSu45ZZb6NWr1897VkIIIYQQQgghhBBij/1kUPTpp58ybNgwAPr168fy5cubbBONRrnnnnt44oknALjkkksIBoMAmKaJ2+0GYMWKFTz44INs27aNkSNHcuaZZ/7XTkQIIYQQQgghhBBC/Gd+MiiKRCIEAoHsbU3TSKfT6HrDXWfNmsX48eOzl5TV/3/t2rXccsst3HfffQBMmDCB448/nkAgwDnnnMP8+fMZNWrULh9b0xTCYd//35n9ymia2mzORewZGfv8JWOfn2Tc85eMff6Ssc9fMvb5S8Y+P+XLuP9kUBQIBKirq8vetiwrJyQCmDNnDnfffXfOsg8//JDrrruOW2+9lfLycmzbZurUqdlKoxEjRvDll1/uNigyTZuqqugendCvVTjsazbnIvaMjH3+krHPTzLu+UvGPn/J2OcvGfv8JWOfn5rTuJeUBHe57idnPRswYAALFiwAnGbVXbt2zVlfW1tLMpmktLQ0u+zDDz/kxhtv5KGHHqJ3796AU5k0ceJE6urqsG2bjz76SHoVCSGEEEIIIYQQQvyK/GRF0dixY1m0aBFTpkzBtm2mT5/OjBkzKCsrY/To0axbt462bdvm3Gf69OmkUikuvfRSADp27Mj111/Peeedx0knnYTL5WLw4MGMGDHi5zkrIYQQQgghhBBCCLHHFNu27f/1QexKKmU2m7Ku5lSiJvaMjH3+krHPTzLu+UvGPn/J2OcvGfv8JWOfn5rTuP9Hl54JIYQQQgghhBBCiPwgQZEQQgghhBBCCCGEACQoEkIIIYQQQgghhBAZEhQJIYQQQgghhBBCCECCIiGEEEIIIYQQQgiRIUGREEIIIYQQQgghhAAkKBJCCCGEEEIIIYQQGRIUCSGEEEIIIYQQQghAgiIhhBBCCCGEEEIIkSFBkRBCCCGEEEIIIYQAJCgSQgghhBBCCCGEEBkSFAkhhBBCCCGEEEIIQIIiIYQQQgghhBBCCJEhQZEQQgghhBBCCCGEACQoEkIIIYQQQgghhBAZEhQJIYQQQgghhBBCCECCIiGEEEIIIYQQQgiRIUGREEIIIYQQQgghhAAkKBJCCCGEEEIIIYQQGfr/+gDygZlK8sWb84nH0yiKAoqCoqgoioKiKBTv1ZlgcUs2fPExKCqaYVDWZyCV339LZMc2FEWloLQd/sIiNq/6MnP/zD7Uhv0FikvwBEJUfLceRVVRVY3CtmVEqytJRutQFAVPqADD46Ouwtlvw74UFNW5bbi9aLpOKh5z1qsqhtuDZaaxbXK3F0II0azYto1t2ygAioKZSjrLLAvbtrAtG2wb27bQ3R40w0W0qgLnBQICRSXEaqpI1EWwLQtvQSEur5fK7zdk9mH/aH8WoZaleAIhNq9egW3bqKpGm+592LFxHZHtW7Fti6L2HfEVFPHtko/AsrBsyzkOy84eV6su3QkUl7D6vbewbRvNcLH3iIP4bsXnVGxYh2VZtOvVn4JWbVj2xuzs8ZA5jvrj2mufIRS2LWPxC0+AbWO4Pex79FRWfzCfTSuXY9sWXQaPorhDOe8/8UD2voaukjZtFFVFUVS6DD2Qko5d+OiZR1BUFZfPz35HT2XtJ4vYumYliqqy14DBFLXrwBevPp+5n/P6mv1PUWnbsx+hlqWsem8eiqJieDx0GXIgW775iurNP6CoKi3LuxIoLuHbJZ802Y+qOP8vKG2HNxRm29qvQVHQDBcle3UmsmMb8UgNiqriLyzG5fVTu21z5n2Gmn1/oGb+bXi8aLpBKh7LrNfQXS4sy0IBeX8ghBBC/MZJUPQLSCUSvPuvB3e5ft+jp+IZOJSFj94LgMsXoOzWgaxa9DZfL3gju03HgUOZd99Nu91Px4FDefW2K7P7mXLrP1n2+otN9vPCtX/5yf08e+kZOfv55PnHs/vZ75iT2WufITx72ZnZ4IhG4Zeiquxz+PF06D+IF68/D0VRcfsDTLrqDj5/+VnWfrQQRVXoe/BRtOuzD3Nvvzo3RGsUgPUccyhte/Rl3n03o6gKLq+P0WddwlfvvMbGpYtRFIVuI8bRuksPFj56z48CsIZ9dR40gpbl3fjomUecQMzjZf/Jp7Bu8SI2r/4KRVHYa8BgijuU8/mcZ3Z+XopKu94DKGxbxop5c1AUFc3loufoifywcpkT0ikKrbv2JNy6Lavfn4/P5yYWTzU5rxaZgPDbJR+jKIoTEPbdl4rvviWyYyuKohBuU4YvXMTmVct3eV7B4pZ4ggVUbFznvJHXdIradSBaVUEiExB6Q2EMry+7352FhPVv/JOxaM4yM50G2wJFRc18WAAnMBTip1iWhW2a2LaFqmkoikoqHsOyrGzY0BAeWLi8fnSXm8iOrZmwQiHUqpS6yh0k6mqxLQtf5oPsjg1rs/fnR+FDQet2eIMFfP/VF5AJHtr1HsD2b9dQs3Uztm3RokMn/IUtWLd4UUNokdkHmf+37taLYEkrVr77OmSChx4HHsLGZZ+yff032JZF+z4DCZe244tXZ2HbNi5DJR5PYdv/r707D5OiutcH/tbae0/PzrAMMOyrbKKIgIrggsoV2QRHo/FKvElMcPkZc6NiJAhx+ZnF3QTjdhXBGDWaEJDrAgoRBWVRUBRQUBGYfbqnq6vuH1Vd3dXLjCg40P1+noenu6urD9Vzpqar3/qeUzqgG9ANHVUjTkZRl25Y98wjMAwdisuDE2f+ENveWIk9W9+DYejoPXo8Srr3xOuP/NEOP2AFIPGfT//TJqG8Zz+svHeh+X95fZj40//GxpeW4tP1b0LXdRx39lR07DsIf7/9v53vJ+n9DT9/NjoPGIq/zvs5DEOHyxfA1Pn34K2n/oRtb6wAAJww4zJUHnc8nvnllVn7N77Oszf9FADgDgQx/bYHsPGlZWnt/P23/91mOyvvXeRoZ/sbK7Ft9UrHOqsfvbfVdtz+gBnwWO30HTcRn73/jr097kAQvsJibFn5YuLvu+i8La6sQkF5R3y+eYN5ksUfBAA07v8a+3ftgCCK1u+jYZ3QMf+WRmURWjRmB1ctzU3QNc38XTF0qF4/AODg559i14Z1MAwdxV26I1jWAdveWJHYH+IBlsXtD8ATDGH9X58AAHiCIfQ66TTs+PdqbE/6+XTxjMAbf/ljqz+fLoNHYPnv51s/iwJMv+1+vL/8b852Bo/A3+Zf22Z/xX834u2se/rPaf317M1XJQIn67Mrfn/Y5AvRqf8QvHTHjfbJrLOuvgXv/eNZ7Hx3HQRRxKCJk1Heqz9WPXhH4iRbSpDWd+xElHTviTeffAiCIMDlC+DEmT/ER2+9ii+3b4UgiqgaeTKKOnczP9/tbXG212XQcBR06IStq162PwP7jp2IvR9uQs3ezyCIIjr06gd/cRk+Wb8GoiACVlv+gBvNzRoEUURhp67wFhTii22bAUGArKoo79kPdfu+QLiuFoJontxz+fxmu/GfS1IwJ4giXL4AJEVFpKEOgiBClCSoXh9iUfPvS3wZgzkiIjrcGBR9D1xeHy6/91HU1jRaZy4TB/2GbsDl80N2uTH5xjsdB4YDJ5yHnqNOgaHr1hk+D868+hbry0PSWVSrzWB5BRS3B6fNuc48gJAkAEDPUaegvGdfGLqB4i7dobhcGF19pb0NqW2V9+gDWXVhxAXVgG5AlBUAQJdBw+EtKDS/YHXrCUlRMPiM8xNfPuyzsub9gg6dIcoyug0/yTzzrLgAAAVlFejQewAMwzzTLAgiirp0S/xsUt6XopqvU9wewNAhWdtj6LpV5WTYX0TD9XUZ3xcMA5HGBuixGL76ZJvZrscLADi4Zzc+e3+9ecBeWYVQx87YsfZ1xzYkvmAZ8BQUIlBajo0vLTP71+fHgPHnYPd7bzuCNF9hMdY980jW34uR034A9/BR9kG9y+dH5XHHY/uaV1ICuVH2l6ds7XQbPgov3XGj3c6MRQ/h/eV/S2vnuVvmttnOkl/McbTz9rJH8eHr/7LWuRTdhp2IJTfMcQRNyWHaiCnVqBwyEs/9+moIogC3L2AGhM8/jY/XvQZBEDFk0lR0HjTc/nKQKSQcOGEyOvUbbIajggiX14fx/3U9tqx6CbvfWw9BENB33Bno0HsAXlv8+wwBmNlWz1GnoKyqD9566k8Q4gHhjMuw499v4IvtWyAIIroPPwnFXavwzt/+J/HFIeV9dRk8AqGOXbB5xYsQRAGy6jIDwq3vWQGhiA59BqCgvJP5hUsQ4HEraGqKOL6gd+w7CMHyjtiy8kW74mHQxMnYtfHf2PfJNhi6jsrjRqKwUyXeef4p++9E6u901cgxKOnaA2/9z8Nm8OD24KTZc/Dh6//CZ5vehWHo6DtmIkp79MarD9+dVskRr6AYMOE8VPQZiH/+7tfWfuHDWVfPw4a/P4Md/34Dhq5jyKRp6DRgCF5YcH1KsGPYgcqIKdXoMng4lv7qxzAMA25fANMXPoB1zyzGttfNL+gjp1+KrkNPwDM3/Cj776G1znO/vhoA4PYHMX3hA3h/+XNp7fzjrpvbbOd/H7zT0c5Hb/1vWjtvPpk9yB85/VJ4CkJ49/mn7Hb6n3Y29mzZiG2rV0IQBPiKShAoKce21a9AsKouDAh2sCoIAsqr+iDUsQu++vhD84usPwAACNfXou6rvRBEAS3hZsAAWhobzN89MfG7LEoiIMgQRfOLoTsQhCCYFSoA4CkoRKhTpRVqBCDKMjr06u+sYLV+tyEICJZWQFZd6D1mgrVfuAEAnQYMgTtQAEE0wxLZ5cawyRfabSD+ZdbavvKefaG4vRg1+wozOFdUAObnTofe/c2/7527QfX4cOqca5P2zUSFirk9HeDy+XHWNb82f4ayeXgy6Mzz0WfsRAiiAE+wEIrbg/NvvtuqdnW+NyRVxM68/U+J92v148jplyb2bQAX/e7xrP0eN+03zlBqyDnTMOScaY5l596w0L4fCnlRU9OU1s758+52PB7+H7Mx/D9mO5bNuusRx+Pkz1UIIgQAF97xZ3P/g2G1MwvHnTUFuq7D5fVBUlT7WCIROCX2WX9RCVSPDxN/diMM3QxvAaDv2InoPHAoDMNAYcdKqB4fxvzgp879Pam98p59IbvcGDGlGoau2/3eedAweApCMAwDxZVVVkXXGUlVX84gzFdUAkESUdajDwzd/DsGAC5fAP7iUhi6Dtk6BpAU1f67FdM0e1ug69BaItBjMdR9uQeGrkP1mftX3Zd7sHflBmE6AAAgAElEQVTbJsAw0KFXPxSUd8Qnb69xBMy6rtt/E32hYngLCrHhxSUAzECu79iJ+HT9m9i+5hUAwIkzL4fq8eHNJ7L/3Thx5g/ReeAwrLxvkd3OtAX3YfO/Xkhqx1znxYU3tNnO0l/92NHOumcecWxP54FDsfTGnyTt584A7Pgp1ejY/zi8uOiX5smjgkJMum4+3n1xCXa+uxaCIOC4s6eiQ6/+WHHvwpTgNBFc9T/1LJR27403Hr0XgijC7Q/gpNlzsG31SnyxbQtEUUTPUaegqHM3vP3cE1aQZv0NSwrAKoecgFCHTti84kVAME8A9j/tbHy+ZSMO7tkFURBR0XcQ/CVl2LHu9cxBmiCipGsPeAoKsWfrRvOEm+pCx76DULP3MzTX1UAQRATLK+DyBXBg9ydZ35cnGIKsutBUe8AO3zzBEKKRMPSYBkEQIasqBFGCHos5jxOIiHKUYCQnE0eZaDSW8YDrWJTt4JGOXckH8aIkI6ZpZnClm2GWKEmINDUgGHCjtrYp7cu16rUqJ6xhFRAEFJR3TFROGAa8oSK4vD7s3/VJ1mAvWFYBTzCEvR++D+gGBElEp/5DnEM2OneDr7AYO99dmwgLUtoqq+qDQGkHbF/zCgwrkOszZgL2bH0P+3d/AhgGKvoOQkH84C45QEsautHluONR1Kkr3nn+KbMCQ3VhxPmzzWBm22YYuoGqkSejuLIKa5/+szOQS3pfvUefhrKqPnjtkT/A0A0objdOuXyuGRRt/DcMw0C/U89Gh979sfKehVnf16AzzkfHfoPx8p03IV6xcs71C/Du80/jo7WvAoaBoefOQOdBw/Dcr69xvC/7vq7j+KmXoHLISDvkcPkDmLHwQax9+s+Zg7QsUteJBxj/XvoXO2gww7bj8bdbr3GGX0ln5I+bNA2dBwzFy3fdDEEQoHr9OOvqeXh/+d+w6921gChi4OnnoqLPQLzywO2J4CulcqLPyRNQ1rMv1jx+v33AftLsOfjorVfxxYebAEFA1cgxKO3WE2//9QlHsGd+yTfbqRxihlublv/NPKh2uTBwwnn4fMsGHNhtDoet6DsIBeUdrYAlvZIDgoDS7r0RKC5NqrRTE5V2B8zKjcKOlfAUFOLL7alDcRMH7/6SMrh8fhzcs9s+8A9VdEZzXQ2izc2AaFaJyC43mmsPJsLKlC8SkqJClCToWtR+r/Ev19nw733+Yt8f+wzDgB6L2UGU4nIjGgnbQzAVlxuiJKOp9oAjAPP7VdTVNFmf3YVQ3F4c+OxTGLoOQRRR2q0nar74HE0H9yNe9ej2B7Fn60ZnoJcUpJVW9YY3VIQd6163A7leJ51qBiqf7zRPPvQ/DoGScmxZ+XfzBJqROBEQb6fbsFEoqOiE9c89aQZpbg+On3oJtr2x0g7Seo0ej6LOXbH6sfsTAVpKOwNOPwel3Xth5b2LYOg6XD4/Jl71K7z7wtP49J23YBg6hp4zA+W9+uHvi35pfyanvq+R03+ATv2Ow9IbfwIY5gm4ab+5F2uefBAfrVkFADjxwsvRqf8QLLvxJ1n7KnUdu50nHsRHbx5aO50HDE0Eclm2J3kdAHYQL8kSREnB8VMvRsf+x+Efd82DJMvwFBRhwk9uwOaVL2LP1vcgyQr6jjsDxV2r8PayxyHJMkRZhigr9n1JVtCp/3Hwl5Tj0/VrIMoKVLcHXQaPQM3e3Wiuq4UoywiWVcDl9aF+35dprxcl8z6DrCOPf/PzUy71e2lpIOtzbQZFuq5j3rx5+PDDD6GqKubPn4+uXbsCALZu3YoFCxbY627YsAH33HMPBg4ciGuvvRbhcBhlZWW47bbb4PF4sGTJEjz11FOQZRlXXnklTj311FY3nEER5QL2fW5JrgoUJQl6TDO/VBiGeXAmimhpboJh6AiFfKirjzgqppIP3ngQl5u4z+cv9n3+Yt9/O/FqNVGS0k64CZKESEOdY3hycuDkCRRAUl2o/eIzO5Ar6twNdV/tRXNdDQzdQLCsA1SvH19u35JWIadblWTFXXvAEwzh03feNKvYFBVVI8dg74ebcHDPLkA3UNF3IHxFpfYw5OQgTZUFNDU2o9vwkxAsq8C6Zx6BrmlQPB6MvuhHeP+fz2H3++9A16IYfNYUlHTtgZfuvAm6pkHXNMS0qP2+AWDU7CtQ0Wcgnr3pKgCAt7AYU2/9I1Y/fj8+fuvVpHUG2cN+Mxk1+wp07DsYLy78BURZgb+4FGddfQs2vrQUn23eAFGSMWjieSjpZg7bTA6aJCvAEmUZ3YaeYJ5IXP0KRFmGy+dH1fEn4+tPP0JjzQFIkozCzl3teVLNdqzX223JdsiaS7jf56dc6vfvFBQtX74cr7zyChYuXIgNGzbggQcewH333Ze23ssvv4wVK1bgzjvvxPz589G/f39MmTIFDz74IFRVxaRJk3DZZZdh2bJliEQimDVrFpYtWwZVVbP+3wyKKBew7/MX+z4/sd/zF/s+f7Hv89fh6ntd16FrmjnUGAKa62oQ06Lm9BJlFajb9wWaaw8iFo0iVNEZiseLz95fbwZs8cDJCttiWhRdBo+Ar7AEG//+DGKaBtXjwYgp1djyykvY+8H7iGlRDDj9HBR2rMTK+xaltBOFHoshpkUx6sL/RFmPPvb8pv7iUky55fd449F7sWPd6wCA0Rf9CGU9+7Y6B+pJF/0I5T374blbfg5RVhAs64Bzb1iEt599DLvffweiJGHYeTNR3LUHXl/8e6tSKlEhJVnBVc8TxyFYXoEtK/4OUZHh9gfRZ8wE7P1wExr274Moyyir6gNPsABfbt/qeG1ygOUOBCEpKmItEWuZdMgn8Ljf56dc6vfWgqI2Y93169djzJgxAIAhQ4Zg06ZNaes0NTXhD3/4Ax5//HH7NXPmmMMqxo4di7vuugtdunTB0KFDoaoqVFVFZWUlPvjgAwwePPhbvSkiIiIiIqJcIIoixKQT6L7CYsfzwdIOCJZ2cCzrPmJ0m+2eMOMyx+P+p52N/qed7Vh27g3Z58IEzHlBL7zjz4hpUbvyadh5MzFg/CTENA3+4lLIqgvj/+t6O3DStahdJRbToijp2gOK24OBEyfbwRUABEo7oLiyCnpMg+LxWu0LiIbDiGj11usTQVhFn4Fw+QPYtOJ5GLqOQGkH9BkzAdvXrMKn69cAAE6+5Mco6drDnicsk5Mv/i+UdOtpz4kYKCnH+fPuxptPPoRdG9dBlBWMnPYDFFdW4ZX7f2uFTYo51NAKsVxuF3qMPh0FZR2x4aVnIMkKPAWFGHj6udj93tuo++oLiLKMTv2Og6cghN3vvwMpKbhKDrECxWWQXS401daY6ygqVI8XhmGwAp3aRZtBUUNDA/x+v/1YkiRomgZZTrx06dKlOPPMM1FUVGS/JhAw0ymfz4f6+nrHsvjyhoaGVv9vSRIQCnkP7R0dpSRJzJn3QoeGfZ+/2Pf5if2ev9j3+Yt9n7/yp+/9jkeZ3nNJ2UltttKh+lLH4xPOnQxgsmPZjHkL0ZafPvpX6HoMuhaDrKo444ofo6X5MsQ0DZ5gASRZwrR5v0UsGrVDpvj9mKahY5/+cHl9OPnCH1jBlRehkBfdBg6C2+uCrmko71wBf8iHooqOZlgVtdoJN6FFi6JR09BPjMGt6Ni1YR1i0SgKyspx8tQZeHPjW9i+djUAoPjH18DvV1u9KuWZP74GJV2r7OGEoQ4dcfEd9+Eff7wD29ethiTLmDDn5yjtVoVnF/wKkqxAUhRIkmzeWoHT8edNRaiiI9548hFIsoxAcQlOnDobH6z+Xxz4fDckWUHPkSfBV1iED9e8mmjHCq3ibRZ36QrV48XBvZ9DUhQoLhcCxaXWhTcMSIpZmZWPIVa+7PNtBkV+vx+NjY32Y13XHSERALzwwgv4/e9/n/Yat9uNxsZGBIPBtHYaGxsdwVEmsZiRM2VduVSiRoeGfZ+/2Pf5if2ev9j3+Yt9n7/Y90eBJg2ACMg+QAaaWwC0xOAp6dLqyyIxoGr0GfbjmpomVAw+ERWDT7SXaQBOvjTzkLrkvp9+2wOOdk6Y9SOMmPZDxDQNissFAwIm33hnotIqqUoqFtPg69AdUagYNesKxLQoFLcHNTVN6NB/KFzBYsRiGiRfIZqaYyjvOcCe28psR4OmRaFHIqivb4Yu12DvR9ugW9VefU9vwrZ1a7Hz3bdg6DrcheUIljfj1b9kv3rj2Mt+hlBFJzz/m/8HACjo0AmTf3UHXv3T3dj57lprnasQ6liJl++4EaKUqLRKHuY37LyZCJSWY+1TfzYrp0rKMGJKNba9sQIHP98FUVbQ44Sx8BYUYvuaV5KGGjrnuSrt3guKx4v9u3ZAkmQoHq99kZKYpkGSZageH0RJggGzQu9IyaV9/jsNPRs2bBhWrVqFs88+Gxs2bEDv3r0dz9fX16OlpQUVFRWO17z66quYMmUKXnvtNQwfPhyDBw/G3XffjUgkgpaWFnz88cdpbREREREREREdy0RJhijJUJKWFZR3bPN1vU5yXuyp27BR6DZslGPZ6OoftdnO+Tf/f8fjsZf+FLj0p+Yk7gBgGJh22/3OwCppnquC8k4QZRnjLp8LXYtCVl0AgB4jx6Kka0/oMQ2FHSuhuD3oOerUtKGG8cnaRVmGrmkIN9abV5C17Pv0I3z2/nrosRjKe/UDALz7wtNZ388p/3k1/MVlWP67WwEAoY5dcN4vf4u1T/8Zuzb+GwAw7vK5CJaW44XbfmFepdYOrhIh1gnTL4W/uBRvPHofJFlCsLwTRl14OTaveBH7d39iXZ1wIjzBELasehmSJENUZCuwMkOwqkGDoBZWZN3WXNFmUDRhwgSsXr0aM2fOhGEYWLBgARYvXozKykqMHz8en3zyCTp16uR4zZVXXonrr78eS5YsQWFhIe688054vV5UV1dj1qxZMAwDc+fOhcvlOmJvjIiIiIiIiIhMyZU2nkBBm+t3HTLS8bjzoGHoPGiYY9nxF1S32c6k6+Y7Ho++yBl2GYaBi373WGKIX8xZdeUrKoEgCJh41a8Q06KQFHM+r77jzkCnAUMR06Io6twVsurCcZOm2pVWuh2CmW2pXh8AQHG77TALABr2f4X9u3YgpkXRbfgoCKKIba//yzEvV5yMavQcm/tBUZtXPWtPvOoZ5QL2ff5i3+cn9nv+Yt/nL/Z9/mLf5y/2fX6IX5HQrJqKorg0hKZIe2/V4fGdhp4REREREREREeUb+4qE1lUJVY8XTZHcDwiP3CxPRERERERERER0TGFQREREREREREREABgUERERERERERGRhUEREREREREREREBYFBEREREREREREQWBkVERERERERERASAQREREREREREREVkYFBEREREREREREQAGRUREREREREREZGFQREREREREREREABgUERERERERERGRRW7vDSAiOhYYhoHGlhhqmqOobY6ipllDTXPU/lcbdi5riGjwuWR4FQkBl4SAS0bALSPgkuF3mbcBlwy/W0Yw6X7AJcMlM8MnIiIiIqL2waCIiPJOPPRJDndq44FPxhDIfBzTjYztSQJQ4FEQ8igo8CjoVuRFwCUhJgg4UB9BQ0TDl/UR1EdiqA9H0RLL3E6cKgkIuBU7YLKDJbczYDLvS9a65n2/S4YiMWgiIiIiIqJvh0ERER3TDMNAc1R3BDvJ4U5t8rKkAEhrI/Qp8CgIuWVUFnoQSgqBQh7ZfhzyKChwK/C7JAiCkNZWKORFTU1T2vKIpqM+oqEhrKE+Yv1Lut+Qsqw2rOHz2jDqwxrqIlrWwCrOLYuOUCngTq5iktJDqOQAyiVDEtPfCxERERER5QcGRUR01DAMA2FNTwp2MlT3NGuoCTsDoGiWCh1RAILuRLjTOeTGgIoACtwZAh8rBPK7ZIgZQp/DySWLcMkqSnzqIb82/jOKh0jxUKkunBwwxRLLIxq+bmjBJ/ub7GVt5EzwqVKGKqZMy5whVNAtw6tKR/znR0RERERERw6DIiI6YsLRWFolT2rFT3IoVBvWENH0jG0JAILuRLjTMehGv3J/StCjoCBpnYD7yIc+3zdBEOBRJHgUCWUB1yG/3jAMNEVjjgqmeLBUl1LlFA+W9taFsd1eFmt9+wArODKDpaDbOXQuOVRyzNXkkhBwm3M6ZarOIiIiOpIMw0A0Zn5GhqMxNEVjaG4xb5tadDRHY/a/ppbErcetwIjpUGURqiRAlUS4ZBGqJEKVU+5bt6p9K5jPW89JosDPQCI6KjAoIqJvJByNOcKd2gzDuVJDoLZCn3i40yHgQt8yf0qFj+wIgAIcEnVYCIIAnyrDp8ro8C1eH9MNNLXEUBeJoiEcS4RNVrDkrGwy7++uabbumwfcrZEEOIbDpYVN9nA5CUGXYgdM8edcssiDbCKiHBfTjfTQJhpDsxXoJIc88XXCUd3x2Pl687m2hnYnc8ki3NZnTjgaQ0tMb7Nity2igETQJItQJGe45JKFpPvJgVNKECWLcEkp68bXy7huIrjiZygRAQyKiPJSRNMdQ7cyzemTGgCFs4Q+QKLSp8AtoyzgQu+k0Ce5wie50oehz7FJEgUzmHHLQMGhv17TDTQkhUp25VKG+ZoaImYQtWN/k70sW/gYp0hC+pXlHGGT1GoIpfKKc0REh41hGIhoulWV03poEw9sMgU9zfZj8/m2PguSSQLgUSV4rWpcr2reFnlV67HoWO5RrHXjr7Gejy/3qhLcigTZOo5Jno9Q0w20aDpaNB2RmPM2+X40lvS8ZqAly7pRex3dXqemWTPXiVnL48/F9KxD8Q+FKglmQPWNqqK+aQVV5tAq9XWKJNo/VyJqXwyKiI5xLZpuXb0rc8CTaVLn5mj2A6yAS7areUr8KnqU+hByJyp8Uqt+gm6FH+r0jcmiYP/+fBstmo6GlvRgyaxiimWcGHxvXdiexynbJOZxLllMq1xKDpVSQyjnROASZF5xjoiOUdGY7qzOieqJsCYltGnKULkTdlT3mK9tjsZwKNGFR0kPbfyqjFK/C14lEdgkQpxEAORRRPt19q0iQfkeq2RkUYCsmv9/e9ANIxEc2cGT4Qyhkm4jGQKneHAVTVtu3ja2aDjQlNyO4Wjju0ZVkoA2Q6W0oXspIVX8VpEzV2Q5l4mOYOz7/H0hOpoxKCI6ikRjVqVPWEuv+Emb1NkMgRpbsg/l8bska+JmBUVeFVXFXsdl3EPWBM7xdQrcMr/o0lFNlUUUySqKvN9uIvCIprcaKqUGUAeboth9sNlcPxxFWydrPYqIoEeBX5XSK5vcMoJJ9+NXnouHTT6VlXZE1LbUYVfx6py2Qhtn0JNexdNWkJ5MlQQ7kHEnVeB0CCppYY8z0BEzVvd4FAluRcy5eQW/b6IgwK2YfdIeDMOAphuOCqiIlhIqpYRWmcKolkxVVknrNlgVxnaYldTuoQwfzMZZFSWkhFOJaim/RwEMIy2MSqvIyjDMzyWJUFIqsuKhFY8F6GjAoIjoCNFiOmrDGvZFYtj1VX1iSFfaJM6Jx62FPj5VssOdQq+C7sVeO+AJJc3nkzyps8LQh8gmJB1Al/gP/fWGYaA5qieCpQzD5eojGloM4Ou6MBoiGvY1tGBH/IpzYa3NM60+VUqvVEq68lzArdgBU+pV6HjFOaKjS/Kwq2Zr/pzM1TnOKp3Mc+8k5tE5lGFXooCMQ6riw64yVeGkBT2pr1dEnlSijARBgGINXWsvmm6kVUOlh1ZGxiGBmaqtMg0PrA9r2B/TEasNo7lFSwvDvitZFFIqqDLPTZUaRimtzGPV6pxWnFSdMmBQRPQNaLqBunByhY+z4qc2QwDU2tWhvIpkVvJYoU6XQo8z8HEnD/Ey12PoQ9S+BEGA1xpSUN7KFeeS56tIphvmRODOeZhS5mqyKp0awuZV6PbUhVH/lfl8a0EyYH4h9GcIkOJXoMs4XC7pvkfhROCUv6IxPW1i49TqnMScOumBTtQA6ppaENZ0R7XOoXxldMvpoY1PlVHsU9Oqc8z76dU58eqe+HqcnJjyjSwKkEVznzjSMn3e69bV85LnmcpcNfUtq61iBsKajrqwlhZwxdc5XJOqZw6V0sOo1iuokiqyWpvHipOqH3XaDIp0Xce8efPw4YcfQlVVzJ8/H127drWff/XVV3HPPfcAAPr374+bb74ZDz30EF5//XUAQF1dHb7++musXr0aixcvxtKlS1FUVAQAuOWWW1BVVXUk3hdRVjE79Emfu6emWbPn+6lNWlYf0bK251HEpImbFXQqcDsu2d6p2AfF0B3rcMJcovwjCoId5FQED/31MWsi8ESoZAZL8VAptcqpIaJh54Eme93W5iYDrInKU4Ilx2Tf7kwhVGL99hrqQPklphsIa0kTG7c6pCo99IkvS70i1qEMu1IkITEvjhXUBDwKygOujFU48QmT7ZBHTp9jh8OuiHKDKAhwyWaFTqAd/n/DMBDTjbTgKVsFVWIIX/ZJ1VOrrVqbVD2i6Yf09zQbRWp7onQlqRKq9QqqzHNTtTb/Fedf/QZB0YoVK9DS0oKnn34aGzZswMKFC3HfffcBABoaGnD77bfj0UcfRVFRER566CEcPHgQV1xxBa644goAwJw5c3DttdcCADZv3oxFixZh4MCBR/AtUT6J6QbqwxpqwhkCnwwBUG1zFHWtDP9wy2LS/D0yOlqhT4E7sSw5BCpwt/3lKFt1ARHRoZBEwR5e+m1oMR0NkVjWUMlZ2aShPhzDVw2NZhj1Da44p0pCxlDJeWU5KX1icOs+qyZzS3zY1be9bHm2K2Ad6rCr9KFTovn5HnRlnGMnecLkbHPsZBp2xc96IjoaCIIAWRIgSziqJ1WPxHREv8Gk6tmGBDamDPmLxAzHOt9Va5OqV4Q8uHFCL/hduT04q813t379eowZMwYAMGTIEGzatMl+7t1330Xv3r2xaNEi7N69G9OmTbOrhQBg+fLlCAaD9us3b96MBx98EPv27cMpp5yCOXPmHO73Q8cw3bBCn5SwpzZlyFdytU9roY9LFh2XZq8I+jOGPclX9OIZcSLKVbIkIuQVEfJ+u6ApMRG4c+hc6hxN9eGYPSfTntqw/dw3ueKcM1QyrySXrbIpOWzyu2Se/fsOtFji8uVZQxst+xWwki9bnqjkiR3S8If4sCs7tLGqcIp9SpbLlospAU/KhMmKBJfM4ZRERN+3o31SdTOkMtoc5mdWW6UPCfTkyZyQbQZFDQ0N8PsTs35KkgRN0yDLMg4ePIi1a9fiueeeg9frxezZszFkyBB0794dAPDAAw/grrvusl87adIkzJo1C36/Hz/5yU+watUqnHrqqVn/b0kSEAp5v8v7O2pIkpgz7+Wb0HUD9RENB5tacLCxBQeaoqhpasHBpqi5LOnxgcYWHGxqQW1zNOtBpSqLKPQqKPSaVzvqUuRFyKuay3zmbciroshap9CrwtNOKXqqfOt7SmDf56dc7vfyb/k6wzAQjuqoC5sBf11z1L5fH46irllzPFcfNqtAd9eG7XXbCh18qjnZd4FHRsCtIOiWEXQrCFhXdgx6FASsZUG3bK8bdCvwu2SIhyFoOtJ9r+uGFehoaG6JobHFmjunRUNjJB7qmPfjy5ta0u83WvebrfvRQ5h8VZEEeFXZnq/Lp8rwqjIK/WZQ47OfM299rnjoI8Pnkhyvja/nUaRj/io/ubzfU+vY9/mLfZ+fJElELPbdq5aOdm0GRX6/H42NjfZjXdchy+bLQqEQBg0ahNLSUgDAiBEjsHXrVnTv3h0fffQRgsGgPZ+RYRi45JJLEAiYozXHjRuHLVu2tBoUxWJGzpTxHsslyYZhoCESs67Ylb26pzZpWV0rl5FWJMFR0VNV5EFBp2DSMjlpImfzn/uQzgoaiDRFEDlKftzHct/Td8O+z0/s9+xcAEpVEaWqCyjIPiF4KsMwA5L6sIYGa8LvunDyXE2J4XPxZZ/Z8zOZFU6tRSECAJ9VvZQ8B1OmScDN4XLOdb2KBEEQ7L6PD7vKetny6DeZYyd9wuTwIZTTC0DafDleRYRPkVDq86QNqXIr5hw6ya9JVPckJlk+rEMEYzq05hbUNx++JtsL9/v8xb7PX+z7/JRL/V5amn0mrTaDomHDhmHVqlU4++yzsWHDBvTu3dt+buDAgdi2bRsOHDiAYDCIjRs3Yvr06QCANWvWYOzYsfa6DQ0NOOecc/DSSy/B6/Vi7dq1uOCCC77L+6JvwTAMNLbE0oIdZwiUtMy6mlcsy6lcWRQcAU9Vidcxf09q4FPgSRxQExERfROCIMCnyvCp324+AN0w0GgFTPaQOWsS8IaUoXTxsOmzmrC97JtccS7gkuFSJDRGtEMeduWSxYxDp4p9SuuXLU+ZUNkTnzCZw66IiIjoO2jziGvChAlYvXo1Zs6cCcMwsGDBAixevBiVlZUYP348rrnmGlx++eUAgDPPPNMOkj755BOMHj3abicQCGDu3Lm4+OKLoaoqRo0ahXHjxh2ht5Uf4mdY26ruSYRA5uNsoY9khT7xgKd7kTetwid+Off4Oj6VoQ8RER3dREEwK4Lc3y5o0qwrziXPyZQ+AXgMgiRCNAx7QuRM1Tmpc+zkwrArIiIiyi2CYRjf/fp1R0g0GsuZsq62StQMw0BzVE8a1uUMd2pTLtceXyfb5KCSAPvqOKGUcCe1wid+n6HPkZFL5Yl0aNj3+Yn9nr/Y9/mLfZ+/2Pf5i32fn3Kp37/T0DP67r6sj+C1nTXYs7/RWe0TdlYAtWSZ1EcUgAJ3PNyR0TnkxoCKQFLgIyc9b4U+rvyYjZ2IiIiIiIiIDh8GRd+DRSu24/UdBwCYk0smX6K9Y9CN/uUBe1lBSsVPyGNOnMnQh4iIiIiIiIiONAZF34PfnNMPYUGEENUQcMmci4CIiIiIiIiIjkoMir4HHkVCRQ6NZSQiIiIiIiKi3CS29wYQEREREREREdHRgUEREREREREREREBYDr8zjkAAAZ4SURBVFBEREREREREREQWBkVERERERERERASAQREREREREREREVkYFBEREREREREREQAGRUREREREREREZGFQREREREREREREABgUERERERERERGRhUEREREREREREREBAATDMIz23ggiIiIiIiIiImp/rCgiIiIiIiIiIiIADIqIiIiIiIiIiMjCoIiIiIiIiIiIiAAwKCIiIiIiIiIiIguDIiIiIiIiIiIiAsCgiIiIiIiIiIiILAyKDiNd13HTTTdhxowZqK6uxs6dOx3PL1myBFOmTMH06dOxatWqdtpKOhLa6vv58+djypQpqK6uRnV1Nerr69tpS+lI2bhxI6qrq9OWv/LKK7jgggswY8YMLFmypB22jI60bH2/ePFiTJo0yd7vd+zY0Q5bR0dCNBrFddddh1mzZmHq1KlYuXKl43nu97mprX7nPp+7YrEYbrjhBsycOROzZ8/Grl27HM9zn89dbfU99/vct3//fowbNw4ff/yxY3mu7/dye29ALlmxYgVaWlrw9NNPY8OGDVi4cCHuu+8+AMC+ffvw2GOPYdmyZYhEIpg1axZGjx4NVVXbeavpcGit7wFg8+bNePjhh1FUVNSOW0lHykMPPYTnn38eHo/HsTwajeK2227D0qVL4fF4cOGFF+LUU09FaWlpO20pHW7Z+h4w9/tFixZh4MCB7bBldCQ9//zzCIVCuP3223Hw4EGcf/75GD9+PADu97mstX4HuM/nsvgJ3qeeegpr167FbbfdZh/ncZ/Pba31PcD9PtdFo1HcdNNNcLvdactzfb9nRdFhtH79eowZMwYAMGTIEGzatMl+7r333sPQoUOhqioCgQAqKyvxwQcftNem0mHWWt/ruo6dO3fipptuwsyZM7F06dL22kw6QiorK/GHP/whbfnHH3+MyspKFBQUQFVVDB8+HG+//XY7bCEdKdn6HjAPHh988EFceOGFeOCBB77nLaMj6cwzz8TPfvYz+7EkSfZ97ve5q7V+B7jP57LTTz8dt956KwBgz549KCkpsZ/jPp/bWut7gPt9rlu0aBFmzpyJsrIyx/J82O8ZFB1GDQ0N8Pv99mNJkqBpmv1cIBCwn/P5fGhoaPjet5GOjNb6vqmpCRdddBFuv/12PPzww3jyyScZEuaYM844A7KcXqDJ/T73Zet7AJg0aRLmzZuHv/zlL1i/fj2HHOcQn88Hv9+PhoYGXHXVVfj5z39uP8f9Pne11u8A9/lcJ8syrr/+etx6660444wz7OXc53Nftr4HuN/nsmeffRZFRUV2MUCyfNjvGRQdRn6/H42NjfZjXdftLxCpzzU2Njp+uejY1lrfezweXHzxxfB4PPD7/TjxxBMZFOUJ7vf5yzAMXHLJJSgqKoKqqhg3bhy2bNnS3ptFh9HevXtx8cUXY/LkyTj33HPt5dzvc1u2fuc+nx8WLVqEf/7zn7jxxhvR1NQEgPt8vsjU99zvc9uyZcuwZs0aVFdXY+vWrbj++uuxb98+APmx3zMoOoyGDRuG1157DQCwYcMG9O7d235u8ODBWL9+PSKRCOrr6/Hxxx87nqdjW2t9/+mnn2LWrFmIxWKIRqN45513MGDAgPbaVPoe9ejRAzt37kRNTQ1aWlrw9ttvY+jQoe29WfQ9aGhowDnnnIPGxkYYhoG1a9dy/oIc8vXXX+Oyyy7Dddddh6lTpzqe436fu1rrd+7zue25556zhxV5PB4IgmAPPeQ+n9ta63vu97ntiSeewOOPP47HHnsM/fr1w6JFi+w5iPJhv+dk1ofRhAkTsHr1asycOROGYWDBggVYvHgxKisrMX78eFRXV2PWrFkwDANz586Fy+Vq702mw6Stvj/33HMxffp0KIqCyZMno1evXu29yXQEvfDCC2hqasKMGTPwi1/8Aj/84Q9hGAYuuOAClJeXt/fm0RGU3Pdz587FxRdfDFVVMWrUKIwbN669N48Ok/vvvx91dXW49957ce+99wIApk2bhubmZu73Oaytfuc+n7smTpyIG264AbNnz4amafjlL3+J5cuX87M+D7TV99zv80s+HeMLhmEY7b0RRERERERERETU/jj0jIiIiIiIiIiIADAoIiIiIiIiIiIiC4MiIiIiIiIiIiICwKCIiIiIiIiIiIgsDIqIiIiIiIiIiAgAgyIiIiIiIiIiIrIwKCIiIiIiIiIiIgAMioiIiIiIiIiIyPJ/QfAsMfYp1NUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cross validation score\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.lineplot(data=pd.DataFrame(crosval_results, index=model_name).T)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>score_mean</th>\n",
       "      <th>score_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.677566</td>\n",
       "      <td>0.005841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.772031</td>\n",
       "      <td>0.003098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.863705</td>\n",
       "      <td>0.003850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.759506</td>\n",
       "      <td>0.005305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.827535</td>\n",
       "      <td>0.004619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.702689</td>\n",
       "      <td>0.002708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            score_mean  score_std\n",
       "LogisticRegression            0.677566   0.005841\n",
       "DecisionTreeClassifier        0.772031   0.003098\n",
       "RandomForestClassifier        0.863705   0.003850\n",
       "GradientBoostingClassifier    0.759506   0.005305\n",
       "XGBClassifier                 0.827535   0.004619\n",
       "KNeighborsClassifier          0.702689   0.002708"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score mean and std\n",
    "crosval_results_mean = [logit_score.mean(),dtc_score.mean(),rfc_score.mean(),gbc_score.mean(),xgbc_score.mean(),knn_score.mean()]\n",
    "crosval_results_std = [logit_score.std(),dtc_score.std(),rfc_score.std(),gbc_score.std(),xgbc_score.std(),knn_score.std()]\n",
    "pd.DataFrame({'score_mean':crosval_results_mean, 'score_std':crosval_results_std}, index=model_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be said that all the models are stable. Now let's check the training and test precision accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.3.3 Fit & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:47:08] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[17:53:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:00:54] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:07:56] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[18:14:55] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.3.0/src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "# Fit and predict\n",
    "tr_score = []\n",
    "ts_score = []\n",
    "default_models = []\n",
    "\n",
    "skf = StratifiedKFold(random_state=2021, shuffle=True)\n",
    "\n",
    "for train_index, test_index in skf.split(X_train, y_train):\n",
    "    for i in all_models:\n",
    "        each_models = i.fit(X_train, y_train)\n",
    "        default_models.append(each_models)\n",
    "        tr_score.append(precision_score(y_train, each_models.predict(X_train)))\n",
    "        ts_score.append(precision_score(y_test, each_models.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>train_score_logreg</th>\n",
       "      <th>test_score_logreg</th>\n",
       "      <th>train_score_dtc</th>\n",
       "      <th>test_score_dtc</th>\n",
       "      <th>train_score_rfc</th>\n",
       "      <th>test_score_rfc</th>\n",
       "      <th>train_score_gbc</th>\n",
       "      <th>test_score_gbc</th>\n",
       "      <th>train_score_xgbc</th>\n",
       "      <th>test_score_xgbc</th>\n",
       "      <th>train_score_knn</th>\n",
       "      <th>test_score_knn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.680941</td>\n",
       "      <td>0.680704</td>\n",
       "      <td>0.994756</td>\n",
       "      <td>0.781548</td>\n",
       "      <td>0.994029</td>\n",
       "      <td>0.864629</td>\n",
       "      <td>0.751175</td>\n",
       "      <td>0.746854</td>\n",
       "      <td>0.848787</td>\n",
       "      <td>0.824425</td>\n",
       "      <td>0.796191</td>\n",
       "      <td>0.706753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.682109</td>\n",
       "      <td>0.682428</td>\n",
       "      <td>0.995009</td>\n",
       "      <td>0.778750</td>\n",
       "      <td>0.993946</td>\n",
       "      <td>0.865239</td>\n",
       "      <td>0.753830</td>\n",
       "      <td>0.748424</td>\n",
       "      <td>0.845334</td>\n",
       "      <td>0.822763</td>\n",
       "      <td>0.794940</td>\n",
       "      <td>0.704145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.681178</td>\n",
       "      <td>0.680221</td>\n",
       "      <td>0.995544</td>\n",
       "      <td>0.773250</td>\n",
       "      <td>0.994057</td>\n",
       "      <td>0.863006</td>\n",
       "      <td>0.752576</td>\n",
       "      <td>0.746361</td>\n",
       "      <td>0.847697</td>\n",
       "      <td>0.822319</td>\n",
       "      <td>0.797499</td>\n",
       "      <td>0.708254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.681724</td>\n",
       "      <td>0.680933</td>\n",
       "      <td>0.995375</td>\n",
       "      <td>0.777501</td>\n",
       "      <td>0.994281</td>\n",
       "      <td>0.864276</td>\n",
       "      <td>0.756382</td>\n",
       "      <td>0.750430</td>\n",
       "      <td>0.845162</td>\n",
       "      <td>0.822274</td>\n",
       "      <td>0.796152</td>\n",
       "      <td>0.704188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.680773</td>\n",
       "      <td>0.680897</td>\n",
       "      <td>0.996023</td>\n",
       "      <td>0.771899</td>\n",
       "      <td>0.994085</td>\n",
       "      <td>0.864591</td>\n",
       "      <td>0.754473</td>\n",
       "      <td>0.750642</td>\n",
       "      <td>0.846798</td>\n",
       "      <td>0.823340</td>\n",
       "      <td>0.796535</td>\n",
       "      <td>0.705431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_score_logreg  test_score_logreg  train_score_dtc  test_score_dtc  \\\n",
       "0            0.680941           0.680704         0.994756        0.781548   \n",
       "1            0.682109           0.682428         0.995009        0.778750   \n",
       "2            0.681178           0.680221         0.995544        0.773250   \n",
       "3            0.681724           0.680933         0.995375        0.777501   \n",
       "4            0.680773           0.680897         0.996023        0.771899   \n",
       "\n",
       "   train_score_rfc  test_score_rfc  train_score_gbc  test_score_gbc  \\\n",
       "0         0.994029        0.864629         0.751175        0.746854   \n",
       "1         0.993946        0.865239         0.753830        0.748424   \n",
       "2         0.994057        0.863006         0.752576        0.746361   \n",
       "3         0.994281        0.864276         0.756382        0.750430   \n",
       "4         0.994085        0.864591         0.754473        0.750642   \n",
       "\n",
       "   train_score_xgbc  test_score_xgbc  train_score_knn  test_score_knn  \n",
       "0          0.848787         0.824425         0.796191        0.706753  \n",
       "1          0.845334         0.822763         0.794940        0.704145  \n",
       "2          0.847697         0.822319         0.797499        0.708254  \n",
       "3          0.845162         0.822274         0.796152        0.704188  \n",
       "4          0.846798         0.823340         0.796535        0.705431  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of train and test score\n",
    "train_score_logreg = tr_score[0::6]\n",
    "train_score_dtc = tr_score[1::6]\n",
    "train_score_rfc = tr_score[2::6]\n",
    "train_score_gbc = tr_score[3::6]\n",
    "train_score_xgbc = tr_score[4::6]\n",
    "train_score_knn = tr_score[5::6]\n",
    "\n",
    "test_score_logreg = ts_score[0::6]\n",
    "test_score_dtc = ts_score[1::6]\n",
    "test_score_rfc = ts_score[2::6]\n",
    "test_score_gbc = ts_score[3::6]\n",
    "test_score_xgbc = ts_score[4::6]\n",
    "test_score_knn = ts_score[5::6]\n",
    "\n",
    "train_vs_test = pd.DataFrame({'train_score_logreg':train_score_logreg,\n",
    "              'test_score_logreg':test_score_logreg,\n",
    "              'train_score_dtc':train_score_dtc,\n",
    "              'test_score_dtc':test_score_dtc,\n",
    "              'train_score_rfc':train_score_rfc,\n",
    "              'test_score_rfc':test_score_rfc,\n",
    "              'train_score_gbc':train_score_gbc,\n",
    "              'test_score_gbc':test_score_gbc,\n",
    "              'train_score_xgbc':train_score_xgbc,\n",
    "              'test_score_xgbc':test_score_xgbc,\n",
    "              'train_score_knn':train_score_knn,\n",
    "              'test_score_knn':test_score_knn\n",
    "             })\n",
    "train_vs_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the score of each models\n",
    "plt.figure(figsize=(30,15))\n",
    "\n",
    "plt.subplot(321)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_logreg','test_score_logreg']])\n",
    "\n",
    "plt.subplot(322)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_dtc','test_score_dtc']])\n",
    "\n",
    "plt.subplot(323)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_rfc','test_score_rfc']])\n",
    "\n",
    "plt.subplot(324)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_gbc','test_score_gbc']])\n",
    "\n",
    "plt.subplot(325)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_xgbc','test_score_xgbc']])\n",
    "\n",
    "plt.subplot(326)\n",
    "plt.ylim(0.65, 1)\n",
    "sns.lineplot(data=train_vs_test[['train_score_knn','test_score_knn']])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average score of each models\n",
    "avg_train_score_logreg = sum(train_score_logreg)/len(train_score_logreg)\n",
    "avg_test_score_logreg = sum(test_score_logreg)/len(test_score_logreg)\n",
    "avg_train_score_dtc = sum(train_score_dtc)/len(train_score_dtc)\n",
    "avg_test_score_dtc = sum(test_score_dtc)/len(test_score_dtc)\n",
    "avg_train_score_rfc = sum(train_score_rfc)/len(train_score_rfc)\n",
    "avg_test_score_rfc = sum(test_score_rfc)/len(test_score_rfc)\n",
    "avg_train_score_gbc = sum(train_score_gbc)/len(train_score_gbc)\n",
    "avg_test_score_gbc = sum(test_score_gbc)/len(test_score_gbc)\n",
    "avg_train_score_xgbc = sum(train_score_xgbc)/len(train_score_xgbc)\n",
    "avg_test_score_xgbc = sum(test_score_xgbc)/len(test_score_xgbc)\n",
    "avg_train_score_knn = sum(train_score_knn)/len(train_score_knn)\n",
    "avg_test_score_knn = sum(test_score_knn)/len(test_score_knn)\n",
    "\n",
    "trainScore = [avg_train_score_logreg,avg_train_score_dtc, avg_train_score_rfc, avg_train_score_gbc, avg_train_score_xgbc, avg_train_score_knn]\n",
    "testScore = [avg_test_score_logreg, avg_test_score_dtc, avg_test_score_rfc, avg_test_score_gbc, avg_test_score_xgbc, avg_test_score_knn]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>trainScore</th>\n",
       "      <th>testScore</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.681345</td>\n",
       "      <td>0.681037</td>\n",
       "      <td>0.000308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTreeClassifier</th>\n",
       "      <td>0.995342</td>\n",
       "      <td>0.776590</td>\n",
       "      <td>0.218752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.994080</td>\n",
       "      <td>0.864348</td>\n",
       "      <td>0.129732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GradientBoostingClassifier</th>\n",
       "      <td>0.753687</td>\n",
       "      <td>0.748542</td>\n",
       "      <td>0.005145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.846756</td>\n",
       "      <td>0.823024</td>\n",
       "      <td>0.023732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNeighborsClassifier</th>\n",
       "      <td>0.796263</td>\n",
       "      <td>0.705754</td>\n",
       "      <td>0.090509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            trainScore  testScore  difference\n",
       "LogisticRegression            0.681345   0.681037    0.000308\n",
       "DecisionTreeClassifier        0.995342   0.776590    0.218752\n",
       "RandomForestClassifier        0.994080   0.864348    0.129732\n",
       "GradientBoostingClassifier    0.753687   0.748542    0.005145\n",
       "XGBClassifier                 0.846756   0.823024    0.023732\n",
       "KNeighborsClassifier          0.796263   0.705754    0.090509"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of average score\n",
    "avg_train_vs_test = pd.DataFrame({'trainScore':trainScore,\n",
    "                                  'testScore':testScore},\n",
    "                                 index = model_name\n",
    "                            )\n",
    "\n",
    "avg_train_vs_test['difference'] = avg_train_vs_test['trainScore']-avg_train_vs_test['testScore']\n",
    "avg_train_vs_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the table and graphic above, we can see that: \n",
    "- the highest precision occured in Random Forest Classifier (87.9%) followed by Extra Gradient Boosting Classifier (84.1%)\n",
    "- the highest precision occured in Decision Tree Classifier (99.5%) followed by Random Forest Classifier (99.4%)\n",
    "- DTC model has the highest difference between train and test score. Although the train score of DTC model is the highest, it can't predict the test data well. It can be said that Decision Tree Classifier has a tendency to overfit and not to generalize well for future bookings\n",
    "\n",
    "Now, I will choose 2 models to be treated with another strategy, which is Feature Selection using Recursive Feature Elemination (RFE). Considering the train and test score as well as the overfit indication, I decide to choose __Random Forest Classifier and Extra Gradient Boosting Classifier__."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 2.4 Modeling with Default Parameters + RFE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: is_canceled, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_ = df_modeling.drop(columns='is_canceled')\n",
    "y_ = df_modeling['is_canceled']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_, X_test_, y_train_, y_test_ = train_test_split(\n",
    "    X_,\n",
    "    y_,\n",
    "    stratify = y_,\n",
    "    test_size = 0.2, \n",
    "    random_state = 2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.4.1 RFE assignment in Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectPercentile, RFE\n",
    "# Combine all pipeline. Assign in to a list\n",
    "all_models_rfe = []\n",
    "for i in models:\n",
    "    modelpipe_rfe = Pipeline([\n",
    "        ('transformer', transformer),\n",
    "        ('resampling', smote),\n",
    "        ('rfe', RFE(rfc)),\n",
    "        ('clf',i)])\n",
    "    all_models_rfe.append(modelpipe_rfe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.4.2 Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 23.4min finished\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed: 25.0min finished\n"
     ]
    }
   ],
   "source": [
    "skfold = StratifiedKFold(n_splits=5, random_state=2021, shuffle=True)\n",
    "\n",
    "# logit_score_rfe = cross_val_score(all_models_rfe[0], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "# dtc_score_rfe = cross_val_score(all_models_rfe[1], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "rfc_score_rfe = cross_val_score(all_models_rfe[2], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "# gbc_score_rfe = cross_val_score(all_models_rfe[3], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "xgbc_score_rfe = cross_val_score(all_models_rfe[4], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)\n",
    "# knn_score_rfe = cross_val_score(all_models_rfe[5], X_train_, y_train_, scoring='precision', cv=skfold, n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.850409</td>\n",
       "      <td>0.858806</td>\n",
       "      <td>0.850386</td>\n",
       "      <td>0.852211</td>\n",
       "      <td>0.849101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.818967</td>\n",
       "      <td>0.830593</td>\n",
       "      <td>0.821280</td>\n",
       "      <td>0.815729</td>\n",
       "      <td>0.813426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               0         1         2         3         4\n",
       "RandomForestClassifier  0.850409  0.858806  0.850386  0.852211  0.849101\n",
       "XGBClassifier           0.818967  0.830593  0.821280  0.815729  0.813426"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score\n",
    "crosval_results_rfe = [rfc_score_rfe, xgbc_score_rfe]\n",
    "pd.DataFrame(crosval_results_rfe, index=[model_name[2], model_name[4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot cross validation score\n",
    "sns.set_style('darkgrid')\n",
    "plt.figure(figsize=(20,5))\n",
    "sns.lineplot(data=pd.DataFrame(crosval_results_rfe, index=[model_name[2], model_name[4]]).T)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>score_mean_rfe</th>\n",
       "      <th>score_std_rfe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.852183</td>\n",
       "      <td>0.003457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.819999</td>\n",
       "      <td>0.005939</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        score_mean_rfe  score_std_rfe\n",
       "RandomForestClassifier        0.852183       0.003457\n",
       "XGBClassifier                 0.819999       0.005939"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross validation score mean and std\n",
    "crosval_results_mean_rfe = [rfc_score_rfe.mean(),xgbc_score_rfe.mean()]\n",
    "crosval_results_std_rfe = [rfc_score_rfe.std(),xgbc_score_rfe.std()]\n",
    "pd.DataFrame({'score_mean_rfe':crosval_results_mean_rfe, 'score_std_rfe':crosval_results_std_rfe}, index=[model_name[2], model_name[4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### 2.4.3 Fit & Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:35:45] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[13:58:51] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[14:22:05] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[14:45:23] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n",
      "[15:08:44] WARNING: ../src/learner.cc:1061: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    }
   ],
   "source": [
    "# Fit and predict\n",
    "tr_score_rfe = []\n",
    "ts_score_rfe = []\n",
    "default_models_rfe = []\n",
    "\n",
    "skf = StratifiedKFold(random_state=2020, shuffle=True)\n",
    "\n",
    "for train_index_rfe, test_index_rfe in skf.split(X_train_, y_train_):\n",
    "    for i in [all_models_rfe[2], all_models_rfe[4]]:\n",
    "        each_models_rfe = i.fit(X_train_, y_train_)\n",
    "        default_models_rfe.append(each_models_rfe)\n",
    "        tr_score_rfe.append(precision_score(y_train_, each_models_rfe.predict(X_train_)))\n",
    "        ts_score_rfe.append(precision_score(y_test_, each_models_rfe.predict(X_test_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>train_score_rfc_rfe</th>\n",
       "      <th>test_score_rfc_rfe</th>\n",
       "      <th>train_score_xgbc_rfe</th>\n",
       "      <th>test_score_xgbc_rfe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.993295</td>\n",
       "      <td>0.849709</td>\n",
       "      <td>0.838644</td>\n",
       "      <td>0.813616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.993462</td>\n",
       "      <td>0.850816</td>\n",
       "      <td>0.841650</td>\n",
       "      <td>0.817827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.993267</td>\n",
       "      <td>0.851071</td>\n",
       "      <td>0.840611</td>\n",
       "      <td>0.817294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.993686</td>\n",
       "      <td>0.853213</td>\n",
       "      <td>0.839714</td>\n",
       "      <td>0.811256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.994107</td>\n",
       "      <td>0.851662</td>\n",
       "      <td>0.842131</td>\n",
       "      <td>0.820608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train_score_rfc_rfe  test_score_rfc_rfe  train_score_xgbc_rfe  \\\n",
       "0             0.993295            0.849709              0.838644   \n",
       "1             0.993462            0.850816              0.841650   \n",
       "2             0.993267            0.851071              0.840611   \n",
       "3             0.993686            0.853213              0.839714   \n",
       "4             0.994107            0.851662              0.842131   \n",
       "\n",
       "   test_score_xgbc_rfe  \n",
       "0             0.813616  \n",
       "1             0.817827  \n",
       "2             0.817294  \n",
       "3             0.811256  \n",
       "4             0.820608  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of train and test score\n",
    "train_score_rfc_rfe = tr_score_rfe[0::2]\n",
    "train_score_xgbc_rfe = tr_score_rfe[1::2]\n",
    "\n",
    "test_score_rfc_rfe = ts_score_rfe[0::2]\n",
    "test_score_xgbc_rfe = ts_score_rfe[1::2]\n",
    "\n",
    "train_vs_test_rfe = pd.DataFrame({'train_score_rfc_rfe':train_score_rfc_rfe,\n",
    "              'test_score_rfc_rfe':test_score_rfc_rfe,\n",
    "              'train_score_xgbc_rfe':train_score_xgbc_rfe,\n",
    "              'test_score_xgbc_rfe':test_score_xgbc_rfe,\n",
    "             })\n",
    "train_vs_test_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the score of each models\n",
    "plt.figure(figsize=(30,5))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.ylim(0.75, 1)\n",
    "sns.lineplot(data=train_vs_test_rfe[['train_score_rfc_rfe','test_score_rfc_rfe']])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.ylim(0.75, 1)\n",
    "sns.lineplot(data=train_vs_test_rfe[['train_score_xgbc_rfe','test_score_xgbc_rfe']])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Average score of each models\n",
    "avg_train_score_rfc_rfe = sum(train_score_rfc_rfe)/len(train_score_rfc_rfe)\n",
    "avg_test_score_rfc_rfe = sum(test_score_rfc_rfe)/len(test_score_rfc_rfe)\n",
    "avg_train_score_xgbc_rfe = sum(train_score_xgbc_rfe)/len(train_score_xgbc_rfe)\n",
    "avg_test_score_xgbc_rfe = sum(test_score_xgbc_rfe)/len(test_score_xgbc_rfe)\n",
    "\n",
    "trainScore_rfe = [avg_train_score_rfc_rfe, avg_train_score_xgbc_rfe]\n",
    "testScore_rfe = [avg_test_score_rfc_rfe, avg_test_score_xgbc_rfe]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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>trainScore_rfe</th>\n",
       "      <th>testScore_rfe</th>\n",
       "      <th>difference_rfe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RFC RFE</th>\n",
       "      <td>0.993564</td>\n",
       "      <td>0.851294</td>\n",
       "      <td>0.142269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost RFE</th>\n",
       "      <td>0.840550</td>\n",
       "      <td>0.816120</td>\n",
       "      <td>0.024430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             trainScore_rfe  testScore_rfe  difference_rfe\n",
       "RFC RFE            0.993564       0.851294        0.142269\n",
       "XGBoost RFE        0.840550       0.816120        0.024430"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of average score\n",
    "avg_train_vs_test_rfe = pd.DataFrame({'trainScore_rfe':trainScore_rfe,\n",
    "                                  'testScore_rfe':testScore_rfe},\n",
    "                                 index = ['RFC RFE','XGBoost RFE']\n",
    "                            )\n",
    "\n",
    "avg_train_vs_test_rfe['difference_rfe'] = avg_train_vs_test_rfe['trainScore_rfe']-avg_train_vs_test_rfe['testScore_rfe']\n",
    "avg_train_vs_test_rfe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "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>Without RFE</th>\n",
       "      <th>WITH RFE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.864348</td>\n",
       "      <td>0.851294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBClassifier</th>\n",
       "      <td>0.823024</td>\n",
       "      <td>0.816120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Without RFE  WITH RFE\n",
       "RandomForestClassifier     0.864348  0.851294\n",
       "XGBClassifier              0.823024  0.816120"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testScore2BestModel = []\n",
    "testScore2BestModel.append(avg_train_vs_test['testScore'].iloc[2])\n",
    "testScore2BestModel.append(avg_train_vs_test['testScore'].iloc[4])\n",
    "testScore2BestModel\n",
    "\n",
    "# Test score comparison with and without RFE\n",
    "with_vs_withoutRFE = pd.DataFrame({'Without RFE':testScore2BestModel,\n",
    "                                  'WITH RFE':testScore_rfe},\n",
    "                                 index = [model_name[2], model_name[4]]\n",
    "                            )\n",
    "with_vs_withoutRFE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test score is slighty drop from the non-RFE model and RFC model still has the best score. So, I will choose RFC to be tuned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 2.6 Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperparam_rfc = {'clf__n_estimators': [100, 200, 300, 400],\n",
    "                  \"clf__min_samples_split\":  [2, 5, 10],\n",
    "                  \"clf__min_samples_leaf\": [1, 2, 5]\n",
    "                    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init signature:\n",
    "# RandomForestClassifier(\n",
    "#     n_estimators=100,\n",
    "#     *,\n",
    "#     criterion='gini',\n",
    "#     max_depth=None,\n",
    "#     min_samples_split=2,\n",
    "#     min_samples_leaf=1,\n",
    "#     min_weight_fraction_leaf=0.0,\n",
    "#     max_features='auto',\n",
    "#     max_leaf_nodes=None,\n",
    "#     min_impurity_decrease=0.0,\n",
    "#     min_impurity_split=None,\n",
    "#     bootstrap=True,\n",
    "#     oob_score=False,\n",
    "#     n_jobs=None,\n",
    "#     random_state=None,\n",
    "#     verbose=0,\n",
    "#     warm_start=False,\n",
    "#     class_weight=None,\n",
    "#     ccp_alpha=0.0,\n",
    "#     max_samples=None,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=None, shuffle=False),\n",
       "             estimator=Pipeline(steps=[('transformer',\n",
       "                                        ColumnTransformer(remainder='passthrough',\n",
       "                                                          transformers=[('cat '\n",
       "                                                                         'pipe',\n",
       "                                                                         Pipeline(steps=[('imputer',\n",
       "                                                                                          SimpleImputer(fill_value=0,\n",
       "                                                                                                        strategy='constant')),\n",
       "                                                                                         ('binary '\n",
       "                                                                                          'encoder',\n",
       "                                                                                          BinaryEncoder())]),\n",
       "                                                                         ['hotel',\n",
       "                                                                          'arrival_date_month',\n",
       "                                                                          'meal',\n",
       "                                                                          'distribution...\n",
       "                                                                          'previous_bookings_not_canceled',\n",
       "                                                                          'booking_changes',\n",
       "                                                                          'days_in_waiting_list',\n",
       "                                                                          'adr',\n",
       "                                                                          'required_car_parking_spaces',\n",
       "                                                                          'total_of_special_requests',\n",
       "                                                                          'total_guests'])])),\n",
       "                                       ('resampling', SMOTE()),\n",
       "                                       ('clf', RandomForestClassifier())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'clf__min_samples_leaf': [1, 2, 5],\n",
       "                         'clf__min_samples_split': [2, 5, 10],\n",
       "                         'clf__n_estimators': [100, 200, 300, 400]},\n",
       "             scoring='precision', verbose=1)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skf = StratifiedKFold(n_splits=5)\n",
    "\n",
    "rfc_grid = GridSearchCV(all_models[2], param_grid=hyperparam_rfc, scoring='precision', cv=skf, verbose=1, n_jobs=-1)\n",
    "rfc_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'clf__min_samples_leaf': 1,\n",
       " 'clf__min_samples_split': 2,\n",
       " 'clf__n_estimators': 200}"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of   5 | elapsed:  9.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.85315605, 0.85855166, 0.84826884, 0.85365495, 0.8466068 ])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_tuned = rfc_grid.best_estimator_\n",
    "rfc_tuned_score = cross_val_score(rfc_tuned, X_train, y_train, scoring='precision', cv=skf, n_jobs=-1, verbose=1)\n",
    "rfc_tuned_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8520476603612546"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_tuned_score.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8519078641228478"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rfc_tuned = rfc_tuned.predict(X_test)\n",
    "precision_score(y_test, y_pred_rfc_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_clf__min_samples_leaf</th>\n",
       "      <th>param_clf__min_samples_split</th>\n",
       "      <th>param_clf__n_estimators</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102.255559</td>\n",
       "      <td>1.319300</td>\n",
       "      <td>2.936177</td>\n",
       "      <td>0.244833</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>{'clf__min_samples_leaf': 1, 'clf__min_samples...</td>\n",
       "      <td>0.868985</td>\n",
       "      <td>0.865734</td>\n",
       "      <td>0.866796</td>\n",
       "      <td>0.859611</td>\n",
       "      <td>0.856436</td>\n",
       "      <td>0.863512</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>158.125736</td>\n",
       "      <td>4.323462</td>\n",
       "      <td>5.228753</td>\n",
       "      <td>0.258270</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>400</td>\n",
       "      <td>{'clf__min_samples_leaf': 1, 'clf__min_samples...</td>\n",
       "      <td>0.868049</td>\n",
       "      <td>0.867482</td>\n",
       "      <td>0.867245</td>\n",
       "      <td>0.856580</td>\n",
       "      <td>0.857205</td>\n",
       "      <td>0.863312</td>\n",
       "      <td>0.005252</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>162.018987</td>\n",
       "      <td>6.742612</td>\n",
       "      <td>5.374843</td>\n",
       "      <td>0.297485</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>{'clf__min_samples_leaf': 1, 'clf__min_samples...</td>\n",
       "      <td>0.868160</td>\n",
       "      <td>0.866597</td>\n",
       "      <td>0.869167</td>\n",
       "      <td>0.855360</td>\n",
       "      <td>0.856475</td>\n",
       "      <td>0.863152</td>\n",
       "      <td>0.005974</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72.749593</td>\n",
       "      <td>2.716784</td>\n",
       "      <td>1.559536</td>\n",
       "      <td>0.093361</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>{'clf__min_samples_leaf': 1, 'clf__min_samples...</td>\n",
       "      <td>0.868239</td>\n",
       "      <td>0.867676</td>\n",
       "      <td>0.866208</td>\n",
       "      <td>0.855907</td>\n",
       "      <td>0.857373</td>\n",
       "      <td>0.863081</td>\n",
       "      <td>0.005321</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126.783093</td>\n",
       "      <td>5.564243</td>\n",
       "      <td>3.994754</td>\n",
       "      <td>0.102347</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>{'clf__min_samples_leaf': 1, 'clf__min_samples...</td>\n",
       "      <td>0.866050</td>\n",
       "      <td>0.868122</td>\n",
       "      <td>0.866388</td>\n",
       "      <td>0.855409</td>\n",
       "      <td>0.856023</td>\n",
       "      <td>0.862399</td>\n",
       "      <td>0.005505</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "1     102.255559      1.319300         2.936177        0.244833   \n",
       "3     158.125736      4.323462         5.228753        0.258270   \n",
       "7     162.018987      6.742612         5.374843        0.297485   \n",
       "0      72.749593      2.716784         1.559536        0.093361   \n",
       "2     126.783093      5.564243         3.994754        0.102347   \n",
       "\n",
       "  param_clf__min_samples_leaf param_clf__min_samples_split  \\\n",
       "1                           1                            2   \n",
       "3                           1                            2   \n",
       "7                           1                            5   \n",
       "0                           1                            2   \n",
       "2                           1                            2   \n",
       "\n",
       "  param_clf__n_estimators                                             params  \\\n",
       "1                     200  {'clf__min_samples_leaf': 1, 'clf__min_samples...   \n",
       "3                     400  {'clf__min_samples_leaf': 1, 'clf__min_samples...   \n",
       "7                     400  {'clf__min_samples_leaf': 1, 'clf__min_samples...   \n",
       "0                     100  {'clf__min_samples_leaf': 1, 'clf__min_samples...   \n",
       "2                     300  {'clf__min_samples_leaf': 1, 'clf__min_samples...   \n",
       "\n",
       "   split0_test_score  split1_test_score  split2_test_score  split3_test_score  \\\n",
       "1           0.868985           0.865734           0.866796           0.859611   \n",
       "3           0.868049           0.867482           0.867245           0.856580   \n",
       "7           0.868160           0.866597           0.869167           0.855360   \n",
       "0           0.868239           0.867676           0.866208           0.855907   \n",
       "2           0.866050           0.868122           0.866388           0.855409   \n",
       "\n",
       "   split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "1           0.856436         0.863512        0.004711                1  \n",
       "3           0.857205         0.863312        0.005252                2  \n",
       "7           0.856475         0.863152        0.005974                3  \n",
       "0           0.857373         0.863081        0.005321                4  \n",
       "2           0.856023         0.862399        0.005505                5  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(rfc_grid.cv_results_).sort_values('rank_test_score').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score Before Tuning: 0.8643481168795232\n",
      "Score After Tuning: 0.8519078641228478\n"
     ]
    }
   ],
   "source": [
    "print(f'Score Before Tuning: {testScore[2]}')\n",
    "print(f'Score After Tuning: {precision_score(y_test, y_pred_rfc_tuned)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> __The tuning is failed to make the model better. So, I will use the RFC model with default parameter__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 2.7 Threshold Adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curve(default_models[2], X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.\n",
    "\n",
    "The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.\n",
    "\n",
    "\n",
    "1. FN is a condition when the __MODEL__ predict THE GUESTS ARE COMING __(0)__ but __ACTUALLY__ THE BOOKINGS ARE CANCELLED __(1)__\n",
    "    - High numbers of FN will cause __unoptimal revenue__ due to many rooms are reserved (and can not be booked by other potential guests) but the booking is cancelled\n",
    "\n",
    "\n",
    "2. FP is a condition when the __MODEL__ predict THE BOOKINGS ARE CANCELLED __(1)__ but __ACTUALLY__ THE GUESTS ARE COMING __(0)__\n",
    "    - High numbers of FP will cause __overbooking__.\n",
    "        -  overbooking can force the hotel to deny service to a customer; this can be a very bad experience for the guests and may result in online __complaints and generation of a negative impact in terms of social reputation__ \n",
    "        - the hotel also __lose future revenue__ from this overbooking, since the guests who experienced bad service have strong tendency to avoid that hotel.\n",
    "        - overbooking also cause the loss that occurs as a result of the hotel’s obligation to compensate the customer, including reallocation costs\n",
    "\n",
    "\n",
    "<img src=\"https://static.packt-cdn.com/products/9781838555078/graphics/C13314_06_05.jpg\" alt=\"Hotel Lobby\" style=\"width:690px;height:345px;\">\n",
    "\n",
    "As I write before, I will focus more to precision score because I want the FP as less as possible. We know that there will be a trade-off in adjusting thresholds. Hence, I will choose a thresholds that will improve precision score without reducing recall score too much."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 4.00000000e-04, 1.00000000e-03, ...,\n",
       "       9.93333333e-01, 9.96250000e-01, 1.00000000e+00])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_scores = default_models[2].predict_proba(X_test)[:, 1]\n",
    "p, r, thresholds = precision_recall_curve(y_test, y_scores)\n",
    "thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_precision_recall_vs_threshold(p, r, thresholds): \n",
    "    fig = plt.figure(figsize= (8,5))\n",
    "    plt.plot(thresholds, p[:-1], \"b--\", label=\"Precision\")\n",
    "    plt.plot(thresholds, r[:-1], \"g-\", label=\"Recall\")\n",
    "    plt.legend()\n",
    "\n",
    "plot_precision_recall_vs_threshold(p, r, thresholds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1196\n",
      "1196\n",
      "1195\n"
     ]
    }
   ],
   "source": [
    "print(len(p))\n",
    "print(len(r))\n",
    "print(len(thresholds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html), __the last precision and the recall value should be ignored since the last precision and recall values are always 1. and 0. respectively and do not have a corresponding threshold__. This ensures that the graph starts on the y axis. That is why the dimension of threshold different with precision and recall. I will remove the last value of precision and recall array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1195\n",
      "1195\n",
      "1195\n"
     ]
    }
   ],
   "source": [
    "thresholds_ = thresholds.tolist()\n",
    "p_ = p.tolist()\n",
    "r_ = r.tolist()\n",
    "del p_[1195]\n",
    "del r_[1195]\n",
    "\n",
    "print(len(p_))\n",
    "print(len(r_))\n",
    "print(len(thresholds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0.860297</td>\n",
       "      <td>0.834277</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     precision    recall  threshold\n",
       "828   0.860297  0.834277        0.5"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df = pd.DataFrame({'precision': p_, 'recall': r_, 'threshold': thresholds_})\n",
    "# pr_df\n",
    "# \n",
    "pr_df[pr_df['threshold']==0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.900092</td>\n",
       "      <td>0.776620</td>\n",
       "      <td>0.590833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0.900210</td>\n",
       "      <td>0.776620</td>\n",
       "      <td>0.591905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>0.900197</td>\n",
       "      <td>0.776507</td>\n",
       "      <td>0.593333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>0.900315</td>\n",
       "      <td>0.776507</td>\n",
       "      <td>0.594167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>0.900302</td>\n",
       "      <td>0.776393</td>\n",
       "      <td>0.595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>0.900420</td>\n",
       "      <td>0.776393</td>\n",
       "      <td>0.596667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>0.900526</td>\n",
       "      <td>0.776280</td>\n",
       "      <td>0.598333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>0.900644</td>\n",
       "      <td>0.776280</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>0.903941</td>\n",
       "      <td>0.771749</td>\n",
       "      <td>0.602500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>0.903928</td>\n",
       "      <td>0.771636</td>\n",
       "      <td>0.604118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>0.904048</td>\n",
       "      <td>0.771636</td>\n",
       "      <td>0.605000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>0.904155</td>\n",
       "      <td>0.771522</td>\n",
       "      <td>0.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>0.907129</td>\n",
       "      <td>0.766765</td>\n",
       "      <td>0.611526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>0.907105</td>\n",
       "      <td>0.766538</td>\n",
       "      <td>0.613333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>0.907226</td>\n",
       "      <td>0.766538</td>\n",
       "      <td>0.613750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>0.907348</td>\n",
       "      <td>0.766538</td>\n",
       "      <td>0.614667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>0.907335</td>\n",
       "      <td>0.766425</td>\n",
       "      <td>0.615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>0.907323</td>\n",
       "      <td>0.766312</td>\n",
       "      <td>0.615000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>0.907311</td>\n",
       "      <td>0.766198</td>\n",
       "      <td>0.616000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>0.907298</td>\n",
       "      <td>0.766085</td>\n",
       "      <td>0.618616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     precision    recall  threshold\n",
       "916   0.900092  0.776620   0.590833\n",
       "917   0.900210  0.776620   0.591905\n",
       "918   0.900197  0.776507   0.593333\n",
       "919   0.900315  0.776507   0.594167\n",
       "920   0.900302  0.776393   0.595000\n",
       "921   0.900420  0.776393   0.596667\n",
       "922   0.900526  0.776280   0.598333\n",
       "923   0.900644  0.776280   0.600000\n",
       "924   0.903941  0.771749   0.602500\n",
       "925   0.903928  0.771636   0.604118\n",
       "926   0.904048  0.771636   0.605000\n",
       "927   0.904155  0.771522   0.610000\n",
       "928   0.907129  0.766765   0.611526\n",
       "929   0.907105  0.766538   0.613333\n",
       "930   0.907226  0.766538   0.613750\n",
       "931   0.907348  0.766538   0.614667\n",
       "932   0.907335  0.766425   0.615000\n",
       "933   0.907323  0.766312   0.615000\n",
       "934   0.907311  0.766198   0.616000\n",
       "935   0.907298  0.766085   0.618616"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df[pr_df['precision']>0.90].sort_values('recall', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the 0.590833 threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "precision    0.900092\n",
       "recall       0.776620\n",
       "threshold    0.590833\n",
       "Name: 916, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best threshold -> 0.590833\n",
    "pr_df.iloc[916]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from numpy import argmax\n",
    "\n",
    "# # convert to f score\n",
    "# fscore = (2 * p * r) / (p + r)\n",
    "# # locate the index of the largest f score\n",
    "# ix = argmax(fscore)\n",
    "# print('Best Threshold=%f, F-Score=%.3f' % (thresholds[ix], fscore[ix]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Precision and recall score for the best fscore\n",
    "# pr_df[pr_df['threshold']>=0.47]\n",
    "# pr_df.iloc[791]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>0.860297</td>\n",
       "      <td>0.834277</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.900092</td>\n",
       "      <td>0.776620</td>\n",
       "      <td>0.590833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     precision    recall  threshold\n",
       "828   0.860297  0.834277   0.500000\n",
       "916   0.900092  0.776620   0.590833"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparison between best threshold with default threshold (0.5)\n",
    "pr_df.iloc[[828, 916]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision score improvement: 3.9795000000000025\n",
      "Recall score reduction: 5.765700000000007\n"
     ]
    }
   ],
   "source": [
    "print(f'Precision score improvement: {(0.900092-0.860297)*100}')\n",
    "print(f'Recall score reduction: {(0.834277-0.776620)*100}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The threshold of 0.590833 increase precision score as much as almost 4% while losing recall score 5.7%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEFAULT THRESHOLD\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.92      0.91     14918\n",
      "           1       0.86      0.83      0.85      8828\n",
      "\n",
      "    accuracy                           0.89     23746\n",
      "   macro avg       0.88      0.88      0.88     23746\n",
      "weighted avg       0.89      0.89      0.89     23746\n",
      "\n",
      "\n",
      "NEW THRESHOLD\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.95      0.91     14918\n",
      "           1       0.90      0.78      0.83      8828\n",
      "\n",
      "    accuracy                           0.88     23746\n",
      "   macro avg       0.89      0.86      0.87     23746\n",
      "weighted avg       0.89      0.88      0.88     23746\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification report comparison\n",
    "new_thr = 0.590833\n",
    "y_pred = default_models[2].predict(X_test) # threshold 0.5\n",
    "y_pred_newthr = [1 if i > new_thr else 0 for i in default_models[2].predict_proba(X_test)[:,1]] # threshold 0.590833\n",
    "\n",
    "print('DEFAULT THRESHOLD')\n",
    "print(classification_report(y_test, y_pred))\n",
    "print('')\n",
    "print('NEW THRESHOLD')\n",
    "print(classification_report(y_test, y_pred_newthr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('bmh')\n",
    "plot_confusion_matrix(default_models[2], X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('bmh')\n",
    "plot_confusion_matrix(default_models[2], X_train, y_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "<h1 style=\"color:White; background-color:#212121; font-size:250%; text-align:center; border-radius: 10px; padding-top: 10px; padding-bottom: 10px\">3. Model Deployment</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 3.1 Pickle Save and Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickle save\n",
    "RFC_best = default_models[2]\n",
    "RFC_best.fit(X,y)\n",
    "filenamenew = 'finalmodelRFCfit.sav'\n",
    "pickle.dump(default_models[2], open(filenamenew, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickle load\n",
    "file = 'finalmodelRFCfit.sav'\n",
    "model_loaded = pickle.load((open(file, 'rb')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('cat pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(fill_value=0,\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('binary '\n",
       "                                                                   'encoder',\n",
       "                                                                   BinaryEncoder())]),\n",
       "                                                  ['hotel',\n",
       "                                                   'arrival_date_month', 'meal',\n",
       "                                                   'distribution_channel',\n",
       "                                                   'is_repeated_guest',\n",
       "                                                   'assigned_room_type',\n",
       "                                                   'agent', 'customer_type',\n",
       "                                                   'countries'])...\n",
       "                                                                   'scaler',\n",
       "                                                                   RobustScaler())]),\n",
       "                                                  ['lead_time',\n",
       "                                                   'arrival_date_day_of_month',\n",
       "                                                   'stays_in_weekend_nights',\n",
       "                                                   'stays_in_week_nights',\n",
       "                                                   'previous_cancellations',\n",
       "                                                   'previous_bookings_not_canceled',\n",
       "                                                   'booking_changes',\n",
       "                                                   'days_in_waiting_list',\n",
       "                                                   'adr',\n",
       "                                                   'required_car_parking_spaces',\n",
       "                                                   'total_of_special_requests',\n",
       "                                                   'total_guests'])])),\n",
       "                ('resampling', SMOTE()), ('clf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_loaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_data = pd.DataFrame({\n",
    "    'hotel': ['Resort Hotel'],\n",
    "    'lead_time': [91],\n",
    "    'arrival_date_month':['July'],\n",
    "    'arrival_date_day_of_month':[10],\n",
    "    'stays_in_weekend_nights':[0],\n",
    "    'stays_in_week_nights':[2],\n",
    "    'meal': ['BB'],\n",
    "    'distribution_channel':['TA/TO'],\n",
    "    'is_repeated_guest': [0],\n",
    "    'previous_cancellations': [0],\n",
    "    'previous_bookings_not_canceled': [0],\n",
    "    'assigned_room_type': ['A'],\n",
    "    'booking_changes': [0],\n",
    "    'agent': [240],\n",
    "    'days_in_waiting_list': [0],\n",
    "    'customer_type': ['Transient'],\n",
    "    'adr': [73.8],\n",
    "    'required_car_parking_spaces': [0],\n",
    "    'total_of_special_requests': [1],\n",
    "    'total_guests': [2],\n",
    "    'countries': ['PRT'],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_loaded.predict(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Guest is likely to COME\n",
      "Probability to cancel: [0.09]\n"
     ]
    }
   ],
   "source": [
    "proba = model_loaded.predict_proba(sample_data)[:,1]\n",
    "\n",
    "if proba >= 0.590833:\n",
    "    print('The Booking is likely to be CANCELLED')\n",
    "else:\n",
    "    print('The Guest is likely to COME')\n",
    "    \n",
    "print(f'Probability to cancel: {model_loaded.predict_proba(sample_data)[:,1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Without FIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickle save\n",
    "filenamenew = 'finalmodel.sav'\n",
    "pickle.dump(default_models[2], open(filenamenew, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickle load\n",
    "filename = 'finalmodel.sav'\n",
    "loaded_model = pickle.load((open(filename, 'rb')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('transformer',\n",
       "                 ColumnTransformer(remainder='passthrough',\n",
       "                                   transformers=[('cat pipe',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(fill_value=0,\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('binary '\n",
       "                                                                   'encoder',\n",
       "                                                                   BinaryEncoder())]),\n",
       "                                                  ['hotel',\n",
       "                                                   'arrival_date_month', 'meal',\n",
       "                                                   'distribution_channel',\n",
       "                                                   'is_repeated_guest',\n",
       "                                                   'assigned_room_type',\n",
       "                                                   'agent', 'customer_type',\n",
       "                                                   'countries'])...\n",
       "                                                                   'scaler',\n",
       "                                                                   RobustScaler())]),\n",
       "                                                  ['lead_time',\n",
       "                                                   'arrival_date_day_of_month',\n",
       "                                                   'stays_in_weekend_nights',\n",
       "                                                   'stays_in_week_nights',\n",
       "                                                   'previous_cancellations',\n",
       "                                                   'previous_bookings_not_canceled',\n",
       "                                                   'booking_changes',\n",
       "                                                   'days_in_waiting_list',\n",
       "                                                   'adr',\n",
       "                                                   'required_car_parking_spaces',\n",
       "                                                   'total_of_special_requests',\n",
       "                                                   'total_guests'])])),\n",
       "                ('resampling', SMOTE()), ('clf', RandomForestClassifier())])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ## 3.2 Predict New Observation Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hotel Type:  Resort Hotel\n",
      "Lead Time:  91\n",
      "Arrival Month:  July\n",
      "Arrival Date:  10\n",
      "Nights Spent in Weekends:  0\n",
      "Nights Spent in Weekdays:  2\n",
      "Meal:  BB\n",
      "Booking Channel:  TA/TO\n",
      "Repeated Guest?:  0\n",
      "Number of Previous Cancellation:  0\n",
      "Number of Bookings Not Cancelled:  0\n",
      "Room Type:  A\n",
      "Number of Booking Changes:  0\n",
      "Agent Code:  240\n",
      "Days in Waiting List:  0\n",
      "Customer Type:  Transient\n",
      "Price:  73.8\n",
      "Required Car Parking Spaces:  0\n",
      "Number of Special Requests:  1\n",
      "Number of Guests:  2\n",
      "Country of Origin:  PRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Guest is likely to COME\n",
      "Probability to cancel: [0.11]\n"
     ]
    }
   ],
   "source": [
    "# front-end\n",
    "hotel_ = str(input('Hotel Type: '))\n",
    "lead_time_ = float(input('Lead Time: '))\n",
    "arrival_date_month_ = str(input('Arrival Month: ')) \n",
    "arrival_date_day_of_month_ = int(input('Arrival Date: '))\n",
    "stays_in_weekend_nights_ = int(input('Nights Spent in Weekends: '))\n",
    "stays_in_week_nights_ = int(input('Nights Spent in Weekdays: '))\n",
    "meal_ = str(input('Meal: '))\n",
    "distribution_channel_ = str(input('Booking Channel: '))\n",
    "is_repeated_guest_ = int(input('Repeated Guest?: '))\n",
    "previous_cancellations_ = int(input('Number of Previous Cancellation: '))\n",
    "previous_bookings_not_canceled_ = int(input('Number of Bookings Not Cancelled: '))\n",
    "assigned_room_type_ = str(input('Room Type: '))\n",
    "booking_changes_ = int(input('Number of Booking Changes: '))\n",
    "agent_ = int(input('Agent Code: '))\n",
    "days_in_waiting_list_ = int(input('Days in Waiting List: '))\n",
    "customer_type_ = str(input('Customer Type: '))\n",
    "adr_ = float(input('Price: '))\n",
    "required_car_parking_spaces_ = int(input('Required Car Parking Spaces: '))\n",
    "total_of_special_requests_ = int(input('Number of Special Requests: '))\n",
    "total_guests_ = int(input('Number of Guests: '))\n",
    "countries_ = str(input('Country of Origin: '))\n",
    "\n",
    "\n",
    "#back-end\n",
    "df = pd.DataFrame([[hotel_, lead_time_, arrival_date_month_, arrival_date_day_of_month_, stays_in_weekend_nights_, stays_in_week_nights_,\n",
    "                   meal_, distribution_channel_, is_repeated_guest_, previous_cancellations_, previous_bookings_not_canceled_,\n",
    "                   assigned_room_type_, booking_changes_, agent_, days_in_waiting_list_, customer_type_, adr_,\n",
    "                    required_car_parking_spaces_, total_of_special_requests_, total_guests_, countries_]], \n",
    "                  columns=X_train.columns, index=[0])\n",
    "class_label = loaded_model.predict(df)\n",
    "proba = loaded_model.predict_proba(df)\n",
    "\n",
    "if proba[:,1] >= 0.590833:\n",
    "    print('The Booking is likely to be CANCELLED')\n",
    "else:\n",
    "    print('The Guest is likely to COME')\n",
    "    \n",
    "print(f'Probability to cancel: {loaded_model.predict_proba(sample_data)[:,1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             Resort Hotel\n",
       "is_canceled                                  0\n",
       "lead_time                                   91\n",
       "arrival_date_month                        July\n",
       "arrival_date_day_of_month                   10\n",
       "stays_in_weekend_nights                      0\n",
       "stays_in_week_nights                         2\n",
       "meal                                        BB\n",
       "distribution_channel                     TA/TO\n",
       "is_repeated_guest                            0\n",
       "previous_cancellations                       0\n",
       "previous_bookings_not_canceled               0\n",
       "assigned_room_type                           A\n",
       "booking_changes                              0\n",
       "agent                                      240\n",
       "days_in_waiting_list                         0\n",
       "customer_type                        Transient\n",
       "adr                                       73.8\n",
       "required_car_parking_spaces                  0\n",
       "total_of_special_requests                    1\n",
       "total_guests                                 2\n",
       "countries                                  PRT\n",
       "Name: 255, dtype: object"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_modeling.iloc[255]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:21:11.905083Z",
     "start_time": "2020-06-15T06:21:11.883964Z"
    }
   },
   "outputs": [],
   "source": [
    "sample_data = pd.DataFrame({\n",
    "    'hotel': ['Resort Hotel'],\n",
    "    'lead_time': [91],\n",
    "    'arrival_date_month':['July'],\n",
    "    'arrival_date_day_of_month':[10],\n",
    "    'stays_in_weekend_nights':[0],\n",
    "    'stays_in_week_nights':[2],\n",
    "    'meal': ['BB'],\n",
    "    'distribution_channel':['TA/TO'],\n",
    "    'is_repeated_guest': [0],\n",
    "    'previous_cancellations': [0],\n",
    "    'previous_bookings_not_canceled': [0],\n",
    "    'assigned_room_type': ['A'],\n",
    "    'booking_changes': [0],\n",
    "    'agent': [240],\n",
    "    'days_in_waiting_list': [0],\n",
    "    'customer_type': ['Transient'],\n",
    "    'adr': [73.8],\n",
    "    'required_car_parking_spaces': [0],\n",
    "    'total_of_special_requests': [1],\n",
    "    'total_guests': [2],\n",
    "    'countries': ['PRT'],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Guest is likely to COME\n",
      "Probability to cancel: [0.11]\n"
     ]
    }
   ],
   "source": [
    "proba = loaded_model.predict_proba(sample_data)[:,1]\n",
    "\n",
    "if proba >= 0.590833:\n",
    "    print('The Booking is likely to be CANCELLED')\n",
    "else:\n",
    "    print('The Guest is likely to COME')\n",
    "    \n",
    "print(f'Probability to cancel: {loaded_model.predict_proba(sample_data)[:,1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotel                             Resort Hotel\n",
       "is_canceled                                  1\n",
       "lead_time                                   68\n",
       "arrival_date_month                      August\n",
       "arrival_date_day_of_month                    9\n",
       "stays_in_weekend_nights                      2\n",
       "stays_in_week_nights                         5\n",
       "meal                                        BB\n",
       "distribution_channel                     TA/TO\n",
       "is_repeated_guest                            0\n",
       "previous_cancellations                       0\n",
       "previous_bookings_not_canceled               0\n",
       "assigned_room_type                           G\n",
       "booking_changes                              0\n",
       "agent                                      240\n",
       "days_in_waiting_list                         0\n",
       "customer_type                        Transient\n",
       "adr                                        214\n",
       "required_car_parking_spaces                  0\n",
       "total_of_special_requests                    0\n",
       "total_guests                                 4\n",
       "countries                                  PRT\n",
       "Name: 1000, dtype: object"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_modeling.iloc[1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:21:11.905083Z",
     "start_time": "2020-06-15T06:21:11.883964Z"
    }
   },
   "outputs": [],
   "source": [
    "sample_data = pd.DataFrame({\n",
    "    'hotel': ['Resort Hotel'],\n",
    "    'lead_time': [68],\n",
    "    'arrival_date_month':['August'],\n",
    "    'arrival_date_day_of_month':[9],\n",
    "    'stays_in_weekend_nights':[2],\n",
    "    'stays_in_week_nights':[5],\n",
    "    'meal': ['BB'],\n",
    "    'distribution_channel':['TA/TO'],\n",
    "    'is_repeated_guest': [0],\n",
    "    'previous_cancellations': [0],\n",
    "    'previous_bookings_not_canceled': [0],\n",
    "    'assigned_room_type': ['G'],\n",
    "    'booking_changes': [0],\n",
    "    'agent': [240],\n",
    "    'days_in_waiting_list': [0],\n",
    "    'customer_type': ['Transient'],\n",
    "    'adr': [214],\n",
    "    'required_car_parking_spaces': [0],\n",
    "    'total_of_special_requests': [0],\n",
    "    'total_guests': [4],\n",
    "    'countries': ['PRT'],\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Booking is likely to be CANCELLED\n",
      "Probability to cancel: [0.87]\n"
     ]
    }
   ],
   "source": [
    "proba = loaded_model.predict_proba(sample_data)[:,1]\n",
    "\n",
    "if proba >= 0.590833:\n",
    "    print('The Booking is likely to be CANCELLED')\n",
    "else:\n",
    "    print('The Guest is likely to COME')\n",
    "    \n",
    "print(f'Probability to cancel: {loaded_model.predict_proba(sample_data)[:,1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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