{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Structure of the notebook \n", "\n", "### I. Data cleaning :\n", "\n", " #### 1. Checking data types.\n", " #### 2. Treatment of missing data.\n", " #### 3. Checking the existence of duplicates.\n", " #### 4. Checking the existence of constant and quasi-constant features.\n", " #### 5. Verification of the data range constraint for numerical variables.\n", " #### 6. Investigation of the existence of rare labels in categorical variables.\n", "\n", "### II. Exploratory data analysis\n", "\n", " #### 1. Exploring the distribution of numerical variables.\n", " #### 2. Exploring the distribution of the target variable.\n", "\n", "### III. Tuning and comparing the models.\n", "\n", " #### 1. Tuning the models.\n", " #### 2. Fitting the different pipelines.\n", " #### 3. Comparing the models.\n", " #### 4. Choosing the best model.\n", "\n", "### IV. Model interpretation using SHAP.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " " ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [], "source": [ "import re \n", "import time\n", "import pandas as pd \n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns \n", "import statsmodels.api as sm\n", "import pylab\n", "from feature_engine.selection import DropConstantFeatures, DropDuplicateFeatures, SmartCorrelatedSelection\n", "from feature_engine.encoding import RareLabelEncoder, OneHotEncoder, OrdinalEncoder, CountFrequencyEncoder, MeanEncoder, PRatioEncoder\n", "from feature_engine.wrappers import SklearnTransformerWrapper\n", "from feature_engine.outliers import OutlierTrimmer\n", "import scipy.stats as stats\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import cross_val_score, train_test_split\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n", "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n", "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, f1_score, precision_score, recall_score\n", "from xgboost import XGBClassifier\n", "import optuna\n", "import shap\n", "import pickle" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
02InternetRoundTrip262197SatAKLDELNew Zealand1005.520
11InternetRoundTrip112203SatAKLDELNew Zealand0005.520
22InternetRoundTrip2432217WedAKLDELIndia1105.520
31InternetRoundTrip96314SatAKLDELNew Zealand0015.520
42InternetRoundTrip682215WedAKLDELIndia1015.520
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" ], "text/plain": [ " num_passengers sales_channel trip_type purchase_lead length_of_stay \\\n", "0 2 Internet RoundTrip 262 19 \n", "1 1 Internet RoundTrip 112 20 \n", "2 2 Internet RoundTrip 243 22 \n", "3 1 Internet RoundTrip 96 31 \n", "4 2 Internet RoundTrip 68 22 \n", "\n", " flight_hour flight_day route booking_origin wants_extra_baggage \\\n", "0 7 Sat AKLDEL New Zealand 1 \n", "1 3 Sat AKLDEL New Zealand 0 \n", "2 17 Wed AKLDEL India 1 \n", "3 4 Sat AKLDEL New Zealand 0 \n", "4 15 Wed AKLDEL India 1 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \\\n", "0 0 0 5.52 \n", "1 0 0 5.52 \n", "2 1 0 5.52 \n", "3 0 1 5.52 \n", "4 0 1 5.52 \n", "\n", " booking_complete \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('customer_booking.csv',encoding = 'ISO-8859-1')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### I. Data cleaning :\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Checking data types" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 50000 entries, 0 to 49999\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 num_passengers 50000 non-null int64 \n", " 1 sales_channel 50000 non-null object \n", " 2 trip_type 50000 non-null object \n", " 3 purchase_lead 50000 non-null int64 \n", " 4 length_of_stay 50000 non-null int64 \n", " 5 flight_hour 50000 non-null int64 \n", " 6 flight_day 50000 non-null object \n", " 7 route 50000 non-null object \n", " 8 booking_origin 50000 non-null object \n", " 9 wants_extra_baggage 50000 non-null int64 \n", " 10 wants_preferred_seat 50000 non-null int64 \n", " 11 wants_in_flight_meals 50000 non-null int64 \n", " 12 flight_duration 50000 non-null float64\n", " 13 booking_complete 50000 non-null int64 \n", "dtypes: float64(1), int64(8), object(5)\n", "memory usage: 5.3+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerspurchase_leadlength_of_stayflight_hourwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
count50000.00000050000.00000050000.0000050000.0000050000.00000050000.00000050000.00000050000.00000050000.000000
mean1.59124084.94048023.044569.066340.6687800.2969600.4271407.2775610.149560
std1.02016590.45137833.887675.412660.4706570.4569230.4946681.4968630.356643
min1.0000000.0000000.000000.000000.0000000.0000000.0000004.6700000.000000
25%1.00000021.0000005.000005.000000.0000000.0000000.0000005.6200000.000000
50%1.00000051.00000017.000009.000001.0000000.0000000.0000007.5700000.000000
75%2.000000115.00000028.0000013.000001.0000001.0000001.0000008.8300000.000000
max9.000000867.000000778.0000023.000001.0000001.0000001.0000009.5000001.000000
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" ], "text/plain": [ " num_passengers purchase_lead length_of_stay flight_hour \\\n", "count 50000.000000 50000.000000 50000.00000 50000.00000 \n", "mean 1.591240 84.940480 23.04456 9.06634 \n", "std 1.020165 90.451378 33.88767 5.41266 \n", "min 1.000000 0.000000 0.00000 0.00000 \n", "25% 1.000000 21.000000 5.00000 5.00000 \n", "50% 1.000000 51.000000 17.00000 9.00000 \n", "75% 2.000000 115.000000 28.00000 13.00000 \n", "max 9.000000 867.000000 778.00000 23.00000 \n", "\n", " wants_extra_baggage wants_preferred_seat wants_in_flight_meals \\\n", "count 50000.000000 50000.000000 50000.000000 \n", "mean 0.668780 0.296960 0.427140 \n", "std 0.470657 0.456923 0.494668 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 1.000000 0.000000 0.000000 \n", "75% 1.000000 1.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " flight_duration booking_complete \n", "count 50000.000000 50000.000000 \n", "mean 7.277561 0.149560 \n", "std 1.496863 0.356643 \n", "min 4.670000 0.000000 \n", "25% 5.620000 0.000000 \n", "50% 7.570000 0.000000 \n", "75% 8.830000 0.000000 \n", "max 9.500000 1.000000 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Treatment of missing data." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# A function to verify if there is some missing values in other forms than \"NaN\".\n", "def miss_val(df, column, value):\n", " \"\"\" \n", " A function to get the indices of the rows with missing values\n", " \n", " Inputs:\n", " - The dataframe object.\n", " - The name of the column.\n", " - The value to verify.\n", " Outputs:\n", " - list containing the indices of the rows with missing values\n", " \"\"\"\n", " unique_vls = df[column].unique()\n", " null_rows = []\n", " for i in range(len(unique_vls)):\n", " if str(unique_vls[i]) == str(value):\n", " # returns the index of the missing column\n", " null_rows.append(df.loc[df[column]==unique_vls[i], df.columns].index)\n", " return null_rows" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Checking the following values.\n", "vals = ['', '.', '?', ' ', 'MISSING', 'missing', 'Missing', '!', 'NULL', 'Null','null']\n", "\n", "for column in df.columns :\n", " for j in vals:\n", " if len(miss_val(df, column, j))> 0:\n", " print(\"The used value for column '{0}' is '{1}'\".format(column, j))\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 3. Checking the existence of duplicates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Duplicated rows can be a source of data leakage, so it is a good practice to remove them." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
1965InternetRoundTrip2061713SunAKLKULMalaysia1008.830
3165InternetRoundTrip2061713SunAKLKULMalaysia1008.830
3845InternetRoundTrip2061713SunAKLKULMalaysia1008.830
4561InternetRoundTrip308515FriAKLKULMalaysia1008.831
5133InternetRoundTrip109204TueAKLKULSingapore1018.830
.............................................
498522InternetRoundTrip204614TuePENTPEMalaysia1004.670
499021MobileRoundTrip106611FriPENTPETaiwan1004.670
499341InternetRoundTrip263ThuPENTPEMalaysia0014.670
499441InternetRoundTrip263ThuPENTPEMalaysia0014.670
499611InternetRoundTrip3067ThuPENTPETaiwan0004.670
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719 rows × 14 columns

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" ], "text/plain": [ " num_passengers sales_channel trip_type purchase_lead length_of_stay \\\n", "196 5 Internet RoundTrip 206 17 \n", "316 5 Internet RoundTrip 206 17 \n", "384 5 Internet RoundTrip 206 17 \n", "456 1 Internet RoundTrip 30 85 \n", "513 3 Internet RoundTrip 109 20 \n", "... ... ... ... ... ... \n", "49852 2 Internet RoundTrip 204 6 \n", "49902 1 Mobile RoundTrip 106 6 \n", "49934 1 Internet RoundTrip 2 6 \n", "49944 1 Internet RoundTrip 2 6 \n", "49961 1 Internet RoundTrip 30 6 \n", "\n", " flight_hour flight_day route booking_origin wants_extra_baggage \\\n", "196 13 Sun AKLKUL Malaysia 1 \n", "316 13 Sun AKLKUL Malaysia 1 \n", "384 13 Sun AKLKUL Malaysia 1 \n", "456 15 Fri AKLKUL Malaysia 1 \n", "513 4 Tue AKLKUL Singapore 1 \n", "... ... ... ... ... ... \n", "49852 14 Tue PENTPE Malaysia 1 \n", "49902 11 Fri PENTPE Taiwan 1 \n", "49934 3 Thu PENTPE Malaysia 0 \n", "49944 3 Thu PENTPE Malaysia 0 \n", "49961 7 Thu PENTPE Taiwan 0 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \\\n", "196 0 0 8.83 \n", "316 0 0 8.83 \n", "384 0 0 8.83 \n", "456 0 0 8.83 \n", "513 0 1 8.83 \n", "... ... ... ... \n", "49852 0 0 4.67 \n", "49902 0 0 4.67 \n", "49934 0 1 4.67 \n", "49944 0 1 4.67 \n", "49961 0 0 4.67 \n", "\n", " booking_complete \n", "196 0 \n", "316 0 \n", "384 0 \n", "456 1 \n", "513 0 \n", "... ... \n", "49852 0 \n", "49902 0 \n", "49934 0 \n", "49944 0 \n", "49961 0 \n", "\n", "[719 rows x 14 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting the duplicated columns\n", "dup = df.duplicated()\n", "df[dup]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_durationbooking_complete
02InternetRoundTrip262197SatAKLDELNew Zealand1005.520
11InternetRoundTrip112203SatAKLDELNew Zealand0005.520
22InternetRoundTrip2432217WedAKLDELIndia1105.520
31InternetRoundTrip96314SatAKLDELNew Zealand0015.520
42InternetRoundTrip682215WedAKLDELIndia1015.520
.............................................
499952InternetRoundTrip2769SatPERPNHAustralia1015.620
499961InternetRoundTrip11164SunPERPNHAustralia0005.620
499971InternetRoundTrip24622SatPERPNHAustralia0015.620
499981InternetRoundTrip15611MonPERPNHAustralia1015.620
499991InternetRoundTrip19610ThuPERPNHAustralia0105.620
\n", "

49281 rows × 14 columns

\n", "
" ], "text/plain": [ " num_passengers sales_channel trip_type purchase_lead length_of_stay \\\n", "0 2 Internet RoundTrip 262 19 \n", "1 1 Internet RoundTrip 112 20 \n", "2 2 Internet RoundTrip 243 22 \n", "3 1 Internet RoundTrip 96 31 \n", "4 2 Internet RoundTrip 68 22 \n", "... ... ... ... ... ... \n", "49995 2 Internet RoundTrip 27 6 \n", "49996 1 Internet RoundTrip 111 6 \n", "49997 1 Internet RoundTrip 24 6 \n", "49998 1 Internet RoundTrip 15 6 \n", "49999 1 Internet RoundTrip 19 6 \n", "\n", " flight_hour flight_day route booking_origin wants_extra_baggage \\\n", "0 7 Sat AKLDEL New Zealand 1 \n", "1 3 Sat AKLDEL New Zealand 0 \n", "2 17 Wed AKLDEL India 1 \n", "3 4 Sat AKLDEL New Zealand 0 \n", "4 15 Wed AKLDEL India 1 \n", "... ... ... ... ... ... \n", "49995 9 Sat PERPNH Australia 1 \n", "49996 4 Sun PERPNH Australia 0 \n", "49997 22 Sat PERPNH Australia 0 \n", "49998 11 Mon PERPNH Australia 1 \n", "49999 10 Thu PERPNH Australia 0 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \\\n", "0 0 0 5.52 \n", "1 0 0 5.52 \n", "2 1 0 5.52 \n", "3 0 1 5.52 \n", "4 0 1 5.52 \n", "... ... ... ... \n", "49995 0 1 5.62 \n", "49996 0 0 5.62 \n", "49997 0 1 5.62 \n", "49998 0 1 5.62 \n", "49999 1 0 5.62 \n", "\n", " booking_complete \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "... ... \n", "49995 0 \n", "49996 0 \n", "49997 0 \n", "49998 0 \n", "49999 0 \n", "\n", "[49281 rows x 14 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.drop_duplicates()\n", "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4. Checking the existence of constant and quasi-constant features.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Constant and quasi-constant features contain almost no information that the model can use to discriminate between data points. So the best thing to do when we have this kind of feature is to drop them.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DropConstantFeatures(tol=0.99)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Checking the existence of constant and quasi-constant features in the clients dataset\n", "# Variables showing the same value in a percentage of observations greater than \n", "# \"tol\" will be considered as constant/quasi-constant and dropped.\n", "sel_2_clt = DropConstantFeatures(tol=0.99)\n", "sel_2_clt.fit(df)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sel_2_clt.features_to_drop_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there is no constant features." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 5. Verification of the data range constraint for numerical variables.\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Getting the target variable\n", "y = df.pop('booking_complete')\n", "\n", "# Getting categorical features \n", "cat_ftr = [col for col in df.columns if df[col].dtype == 'O']\n", "\n", "# Getting numerical features\n", "num_ftr= [col for col in df.columns if col not in cat_ftr]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "# Checking the number of features \n", "print((len(cat_ftr) + len(num_ftr)) == len(df.columns))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Investigating the existence of negative values.\n", "\n", "for col in num_ftr:\n", " if df[col].min() < 0:\n", " print(\"Feature {} has negative values\".format(col))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there is no negative values " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 6. Investigation of the existence of rare labels in categorical variables.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rare labels can cause the following issues :\n", " \n", " - The data points with rare categories are so few that the algorithm does not get any information from them, which turns them into a source of noise that can make the model overfit the data.\n", " \n", " - Rare labels may appear only in the test set which will make it hard for the model to evaluate them. \n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For feature 'sales_channel' the percentage of datapoints with category 'Internet' is: \n", "89.12 %\n", "-------------------------------------------------------------------\n", "For feature 'sales_channel' the percentage of datapoints with category 'Mobile' is: \n", "10.88 %\n", "-------------------------------------------------------------------\n", "For feature 'trip_type' the percentage of datapoints with category 'RoundTrip' is: \n", "98.98 %\n", "-------------------------------------------------------------------\n", "For feature 'trip_type' the percentage of datapoints with category 'CircleTrip' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'trip_type' the percentage of datapoints with category 'OneWay' is: \n", "0.78 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Sat' is: \n", "11.61 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Wed' is: \n", "15.34 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Thu' is: \n", "14.86 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Mon' is: \n", "16.21 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Sun' is: \n", "13.07 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Tue' is: \n", "15.34 %\n", "-------------------------------------------------------------------\n", "For feature 'flight_day' the percentage of datapoints with category 'Fri' is: \n", "13.57 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLDEL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLHGH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLHND' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLICN' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLKIX' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLKUL' is: \n", "5.32 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLPEK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AKLTPE' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORICN' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORKIX' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORKTM' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORMEL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BBIMEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BBIOOL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BBIPER' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BBISYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOCTS' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOCTU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOHGH' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOICN' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOIKA' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOKIX' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOMEL' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOPEK' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOPER' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOPUS' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOPVG' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOSYD' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOTPE' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKICKG' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKICTS' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKICTU' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIHND' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIICN' is: \n", "0.43 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIKIX' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIMEL' is: \n", "0.36 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIOOL' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIPEK' is: \n", "0.19 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIPER' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIPUS' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIPVG' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKISYD' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIXIY' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BLRICN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BLRMEL' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BLRPER' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BLRSYD' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BOMMEL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BOMOOL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BOMPER' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BOMSYD' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTJJED' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTUICN' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTUPER' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTUSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTUWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNCKG' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNDEL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNHGH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNIKA' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNKTM' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNMEL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNOOL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNPER' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNSYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNTPE' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANDEL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANIKA' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANMEL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANMRU' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANPER' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CANSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUMEL' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUOOL' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUPER' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUSYD' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CCUTPE' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CEBMEL' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CEBOOL' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CEBPER' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CEBSYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKCKG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKCTS' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKCTU' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKDEL' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKHGH' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKHND' is: \n", "0.77 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKICN' is: \n", "0.82 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKIKA' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKJED' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKKIX' is: \n", "0.67 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKKTM' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKMEL' is: \n", "0.28 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKMRU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKOOL' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKPEK' is: \n", "0.26 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKPER' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKPUS' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKPVG' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKSYD' is: \n", "0.35 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKTPE' is: \n", "0.25 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CGKXIY' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGCOK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGDPS' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGJHB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGKCH' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGLOP' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGMAA' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGMEL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGPEN' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGPER' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGPNH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGSBW' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGSIN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGSUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGTGG' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGTRZ' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGTWU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBCTS' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBCTU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBHGH' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBHND' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBICN' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBKIX' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBMEL' is: \n", "0.93 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBOOL' is: \n", "0.26 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBPEK' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBPER' is: \n", "0.31 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBPVG' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBSYD' is: \n", "0.64 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CMBWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXHND' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXICN' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXKIX' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXMEL' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXOOL' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXPEK' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXPER' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXSYD' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXTPE' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKCTU' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKHGH' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKICN' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKKIX' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKMEL' is: \n", "0.96 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKOOL' is: \n", "0.33 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKPER' is: \n", "0.68 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKPUS' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKSYD' is: \n", "1.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKTPE' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CRKMEL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CRKOOL' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CRKSYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CSXPER' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSDMK' is: \n", "0.69 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSDPS' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSHKT' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSJHB' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSKBR' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSKCH' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSKNO' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSLGK' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSMEL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSOOL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSPEN' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSPER' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSSGN' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSSIN' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSSUB' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSSYD' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUDPS' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUHKT' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUIKA' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUJHB' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUKBV' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUKCH' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUKNO' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUMAA' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUMEL' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUPEN' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUPER' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSBW' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSIN' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSYD' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUTGG' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUTRZ' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUTWU' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CXRMEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACHGH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACHND' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACICN' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACKIX' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACMEL' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACOOL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACPER' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACSYD' is: \n", "0.37 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACTPE' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DADMEL' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DADOOL' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DADSYD' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELDMK' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELDPS' is: \n", "0.33 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELHKG' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELHKT' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELHND' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELJHB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELJOG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELKBV' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELKCH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELKIX' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELKNO' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELLGK' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELMEL' is: \n", "0.54 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELMFM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELMNL' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELMRU' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELOOL' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELPEN' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELPER' is: \n", "0.25 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELPNH' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSBW' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSGN' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSIN' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSUB' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSYD' is: \n", "0.42 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELSZX' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKHGH' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKHND' is: \n", "0.36 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKICN' is: \n", "0.55 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKIKA' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKKIX' is: \n", "1.48 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKKTM' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKMEL' is: \n", "0.7 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKMRU' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKOOL' is: \n", "1.32 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKPEK' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKPER' is: \n", "1.37 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKPUS' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKPVG' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKSYD' is: \n", "1.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DMKTPE' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSHGH' is: \n", "0.19 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSHND' is: \n", "0.63 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSICN' is: \n", "1.34 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSIKA' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSKIX' is: \n", "0.68 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSKTM' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSMEL' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSMRU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSOOL' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSPEK' is: \n", "0.51 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSPUS' is: \n", "0.46 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSPVG' is: \n", "0.79 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSSYD' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSTPE' is: \n", "0.34 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSXIY' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'GOIKUL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'GOIMEL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'GOIOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'GOIPER' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'GOISYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HANKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HANMEL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HANOOL' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HANPER' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HANSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYHGH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYKTM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYMEL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYPEK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYPER' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYTPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHHKT' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHJHB' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHJOG' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHKBR' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHKBV' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHKCH' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHKNO' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHLGK' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHLOP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHMAA' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHMEL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHOOL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHPEN' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHPER' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHSBW' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHSUB' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHSYD' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHTRZ' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGIKA' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGKTM' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGMEL' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGOOL' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGPER' is: \n", "0.36 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGSYD' is: \n", "0.27 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTHND' is: \n", "0.23 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTICN' is: \n", "0.75 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTKIX' is: \n", "0.46 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTKTM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTMEL' is: \n", "0.46 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTOOL' is: \n", "0.55 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTPEK' is: \n", "0.36 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTPER' is: \n", "0.66 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTPUS' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTPVG' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTSYD' is: \n", "0.69 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTTPE' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTXIY' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDIKA' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDJOG' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDKBR' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDKBV' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDKCH' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDKNO' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDLGK' is: \n", "0.31 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDLOP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDMAA' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDMEL' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDMLE' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDPEN' is: \n", "0.89 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDPER' is: \n", "0.52 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDPNH' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDREP' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDRGN' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDSBW' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDSGN' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDSIN' is: \n", "0.58 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDSUB' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HNDTRZ' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDMEL' is: \n", "0.31 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDOOL' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDPER' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDSYD' is: \n", "0.53 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNIKA' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNJED' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNJHB' is: \n", "0.32 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNKBR' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNKBV' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNKCH' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNKNO' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNKTM' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNLGK' is: \n", "0.26 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNMAA' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNMEL' is: \n", "0.59 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNMLE' is: \n", "0.36 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNMYY' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNOOL' is: \n", "0.42 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNPEN' is: \n", "0.6 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNPER' is: \n", "0.67 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNREP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNRGN' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSBW' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSDK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSIN' is: \n", "1.61 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSUB' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNSYD' is: \n", "1.39 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNTRZ' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNVTZ' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAKCH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAKIX' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKALOP' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAMEL' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAMFM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAMNL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAOOL' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAPEK' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAPEN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAPER' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAPUS' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKAPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKASGN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKASIN' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKASUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKASYD' is: \n", "0.19 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKATPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDJOG' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDKNO' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDMEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDMNL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDPDG' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDPEN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDSUB' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBKIX' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBKTM' is: \n", "0.83 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBMEL' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBPEK' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBPUS' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBPVG' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBSYD' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBTPE' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBXIY' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGKIX' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGKTM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGMEL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGPER' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGPVG' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGSYD' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JOGTPE' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRKIX' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRMEL' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBROOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRPEK' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRPER' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRSYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRTPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVMEL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVOOL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVPEK' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVPER' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVPVG' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVSYD' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHKIX' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHKTM' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHMEL' is: \n", "0.46 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHOOL' is: \n", "0.18 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHPEK' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHPER' is: \n", "0.5 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHPUS' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHPVG' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHSYD' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHTPE' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHXIY' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KHHMEL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KHHOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KHHPER' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KHHSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXKNO' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXKTM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXLGK' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXLOP' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXMAA' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXMEL' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXMLE' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXMYY' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXOOL' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXPEN' is: \n", "0.43 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXPER' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXPNH' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXREP' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXRGN' is: \n", "0.13 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXSBW' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXSGN' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXSIN' is: \n", "0.53 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXSUB' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXTGG' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXTRZ' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KLOMEL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KLOOOL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOKTM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOMEL' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOOOL' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOPEK' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOPER' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOPUS' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOPVG' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOSYD' is: \n", "0.23 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOTPE' is: \n", "0.41 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KOSMEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KOSOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KOSPEK' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KOSSYD' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMMEL' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMMFM' is: \n", "0.55 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMMYY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMPEN' is: \n", "0.63 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMPER' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMREP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMSGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMSIN' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMSUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMTGG' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMTPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMURT' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KWLPER' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LBUPER' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKMEL' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKOOL' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKPER' is: \n", "0.19 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKPUS' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKPVG' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKTPE' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPOOL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPPEK' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPPVG' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPTPE' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LPQMEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LPQOOL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LPQPER' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LPQTPE' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAMEL' is: \n", "0.27 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAOOL' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAPER' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAASYD' is: \n", "0.31 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAATPE' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MAAWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELMFM' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELMLE' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELMNL' is: \n", "0.47 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELMRU' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELMYY' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELPEK' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELPEN' is: \n", "1.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELPNH' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELPUS' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELPVG' is: \n", "0.23 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELREP' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELRGN' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSBW' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSGN' is: \n", "1.69 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSIN' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSUB' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSWA' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELSZX' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELTGG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELTPE' is: \n", "1.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELTRZ' is: \n", "0.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELTWU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELURT' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELUTP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELVTE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELVTZ' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELXIY' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MFMOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MFMPER' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MFMSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLEPEK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLEPER' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLESYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MNLMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MNLOOL' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MNLPER' is: \n", "0.25 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MNLSYD' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUOOL' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUPEK' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUPEN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUPER' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUPVG' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUSGN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUSIN' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUSUB' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUSYD' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUSZX' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MYYOOL' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MYYPER' is: \n", "0.25 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MYYPUS' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MYYSYD' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MYYXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'NRTSYD' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLPEK' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLPEN' is: \n", "0.37 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLPNH' is: \n", "0.3 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLPUS' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLPVG' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLREP' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLRGN' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSBW' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSDK' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSGN' is: \n", "0.6 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSIN' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLSZX' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLTGG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLTPE' is: \n", "0.3 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLTRZ' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLTWU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLURT' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLUTP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLVTE' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'OOLXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKPEN' is: \n", "0.32 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKPER' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKREP' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKRGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKSBW' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKSIN' is: \n", "0.2 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKSUB' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKSYD' is: \n", "0.21 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKTGG' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKTRZ' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKTWU' is: \n", "0.23 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENPER' is: \n", "0.88 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENPUS' is: \n", "0.22 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENPVG' is: \n", "0.48 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENSYD' is: \n", "0.74 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENTPE' is: \n", "1.85 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PENXIY' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERPNH' is: \n", "0.44 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERPUS' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERPVG' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERREP' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERRGN' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSBW' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSDK' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSGN' is: \n", "0.72 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSIN' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSWA' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSZX' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERTGG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERTPE' is: \n", "0.63 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERTRZ' is: \n", "0.14 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERTWU' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERUTP' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERVTE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERVTZ' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERXIY' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PNHSYD' is: \n", "0.29 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PNHTPE' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PNKTPE' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSRGN' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSSBW' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSSGN' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSSIN' is: \n", "0.35 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSSUB' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSSYD' is: \n", "0.19 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PUSTRZ' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGREP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGRGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGSIN' is: \n", "0.17 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGSUB' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGSYD' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGTGG' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGTWU' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGURT' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'REPSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'REPTPE' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'RGNSYD' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'RGNTPE' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SBWSYD' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SBWTPE' is: \n", "0.23 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SBWXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SDKSYD' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SGNSYD' is: \n", "1.24 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SGNXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SINSYD' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SINTPE' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SINWUH' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SINXIY' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SRGTPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SUBSYD' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SUBTPE' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SUBXIY' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDSZX' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDTPE' is: \n", "0.4 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDTRZ' is: \n", "0.28 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDTWU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDVTE' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDVTZ' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SYDXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TGGTPE' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TGGXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TPETRZ' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TPEVTE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TRZWUH' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TRZXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TWUXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHSGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNTGG' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JHBOOL' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVTPE' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXTWU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LBUTPE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PVGSGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SBWWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELREP' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DPSWUH' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKGJED' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVKIX' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVPUS' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXLPQ' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKPEK' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LOPPER' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PEKSGN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'PERSUB' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TPETWU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BDOWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BKIDEL' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGSGN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUKBR' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTULGK' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUREP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACPEK' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELRGN' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHTGG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTWUH' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNVTE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRPUS' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KCHWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KLOSYD' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KNOWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLETPE' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SDKTPE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'SUBWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TWUWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORPUS' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BTUCKG' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'BWNWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGKNO' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGLGK' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXDEL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXPUS' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSJOG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTSSBW' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUDMK' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTULOP' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELKBR' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DELURT' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HDYKIX' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHSIN' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHTWU' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HYDMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'IKASZX' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBVXIY' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXLBU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'LGKWUH' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MELNRT' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLEOOL' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUTPE' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'TPEURT' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'URTXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'AORPER' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGHKT' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CKGMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CNXXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'COKCTS' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CSXMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CSXSYD' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUMLE' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSGN' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUSRG' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'CTUURT' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'DACPUS' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HGHMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTIKA' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'HKTJED' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'ICNMRU' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'JEDMFM' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KBRWUH' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KIXMRU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'KTMTWU' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MLEPVG' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'route' the percentage of datapoints with category 'MRUXIY' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'New Zealand' is: \n", "2.15 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'India' is: \n", "2.55 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'United Kingdom' is: \n", "0.35 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'China' is: \n", "6.66 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'South Korea' is: \n", "9.14 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Japan' is: \n", "7.75 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Malaysia' is: \n", "14.32 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Singapore' is: \n", "2.1 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Switzerland' is: \n", "0.04 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Germany' is: \n", "0.11 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Indonesia' is: \n", "4.7 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Czech Republic' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Vietnam' is: \n", "0.78 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Thailand' is: \n", "4.04 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Spain' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Romania' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Ireland' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Italy' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Slovakia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'United Arab Emirates' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Tonga' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Réunion' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category '(not set)' is: \n", "0.16 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Saudi Arabia' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Netherlands' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Qatar' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Hong Kong' is: \n", "0.6 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Philippines' is: \n", "0.54 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Sri Lanka' is: \n", "0.15 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'France' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Croatia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'United States' is: \n", "0.92 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Laos' is: \n", "0.05 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Hungary' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Portugal' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Cyprus' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Australia' is: \n", "35.9 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Cambodia' is: \n", "0.27 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Poland' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Belgium' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Oman' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Bangladesh' is: \n", "0.07 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Kazakhstan' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Brazil' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Turkey' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Kenya' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Taiwan' is: \n", "4.14 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Brunei' is: \n", "0.33 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Chile' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Bulgaria' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Ukraine' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Denmark' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Colombia' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Iran' is: \n", "0.03 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Bahrain' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Solomon Islands' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Slovenia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Mauritius' is: \n", "0.09 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Nepal' is: \n", "0.08 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Russia' is: \n", "0.06 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Kuwait' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Mexico' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Sweden' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Austria' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Lebanon' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Jordan' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Greece' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Mongolia' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Canada' is: \n", "0.12 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Tanzania' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Peru' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Timor-Leste' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Argentina' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'New Caledonia' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Macau' is: \n", "0.61 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Myanmar (Burma)' is: \n", "0.1 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Norway' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Panama' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Bhutan' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Norfolk Island' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Finland' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Nicaragua' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Maldives' is: \n", "0.02 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Egypt' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Israel' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Tunisia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'South Africa' is: \n", "0.01 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Papua New Guinea' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Paraguay' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Estonia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Seychelles' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Afghanistan' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Guam' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Czechia' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Malta' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Vanuatu' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Belarus' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Pakistan' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Iraq' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Ghana' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Gibraltar' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Guatemala' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Algeria' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n", "For feature 'booking_origin' the percentage of datapoints with category 'Svalbard & Jan Mayen' is: \n", "0.0 %\n", "-------------------------------------------------------------------\n" ] } ], "source": [ "# The percentage of rows with a given category\n", "for col in cat_ftr:\n", " if col != 'id':\n", " for cat in df[col].unique():\n", " perct = round((len(df[df[col] == cat])/len(df)) * 100, 2)\n", " print(\"For feature '{}' the percentage of datapoints with category '{}' is: \\n{} %\".format(col,cat,perct))\n", " print(\"-------------------------------------------------------------------\")\n", " " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "RareLabelEncoder(n_categories=1, replace_with='other', tol=0.0003,\n", " variables=['sales_channel', 'trip_type', 'flight_day', 'route',\n", " 'booking_origin'])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Grouping rare categories in a new category called \"other\"\n", "enc_1 = RareLabelEncoder(variables=cat_ftr ,n_categories=1, tol=0.0003, replace_with='other')\n", "enc_1.fit(df)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channeltrip_typepurchase_leadlength_of_stayflight_hourflight_dayroutebooking_originwants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_duration
02InternetRoundTrip262197SatAKLDELNew Zealand1005.52
11InternetRoundTrip112203SatAKLDELNew Zealand0005.52
22InternetRoundTrip2432217WedAKLDELIndia1105.52
31InternetRoundTrip96314SatAKLDELNew Zealand0015.52
42InternetRoundTrip682215WedAKLDELIndia1015.52
\n", "
" ], "text/plain": [ " num_passengers sales_channel trip_type purchase_lead length_of_stay \\\n", "0 2 Internet RoundTrip 262 19 \n", "1 1 Internet RoundTrip 112 20 \n", "2 2 Internet RoundTrip 243 22 \n", "3 1 Internet RoundTrip 96 31 \n", "4 2 Internet RoundTrip 68 22 \n", "\n", " flight_hour flight_day route booking_origin wants_extra_baggage \\\n", "0 7 Sat AKLDEL New Zealand 1 \n", "1 3 Sat AKLDEL New Zealand 0 \n", "2 17 Wed AKLDEL India 1 \n", "3 4 Sat AKLDEL New Zealand 0 \n", "4 15 Wed AKLDEL India 1 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \n", "0 0 0 5.52 \n", "1 0 0 5.52 \n", "2 1 0 5.52 \n", "3 0 1 5.52 \n", "4 0 1 5.52 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = enc_1.transform(df)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### II. Exploratory data analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Exploring the distribution of numerical variables." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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Rx3pgPcDIyAhjY2PTCf9xRvaBDYft3H3FWbKrGHfs2DGjNswFY5naMMVjLJMbplhg+OKRJEmSpNk2rYRSVT0GHJ5kP+CTwI/NZVC7iGMjsBFg7dq1NTo62nsb77v0Cs67aVrNnhV3njI65bKxsTFm0oa5YCxTG6Z4jGVywxQLDF88kiRJkjTbej3lraoeBK4GfgLYL8l4ZuZAYHt7vx04CKAtfwZw32D5hHWmKpckSZIkSdIQms5T3p7TRiaRZB/g5cCtdImlV7ZqpwFXtPeb22fa8s9VVbXyk9tT4A4BVgPXAdcDq9tT4/aim7h78yy0TZIkSZIkSXNgOvd+HQBc0uZRehJwWVV9KsktwKYk7wS+DFzY6l8IfCTJVuB+ugQRVXVzksuAW4CdwBntVjqSnAlcCawALqqqm2ethZIkSZIkSZpVu00oVdWNwIsnKb+d7gltE8u/B/zCFNs6Fzh3kvItwJZpxCtJkiRJkqQF1msOJUmSJEmSJMmEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRpziV5SpLrknwlyc1J3tbKD0lybZKtST6WZK9Wvnf7vLUtP3hgW2e38q8lOW6gfF0r25rkrHlvpCRJkrSMmFCSJM2HR4Gjq+pFwOHAuiRHAe8Gzq+qQ4EHgNNb/dOBB1r5+a0eSdYAJwMvANYBH0iyIskK4P3A8cAa4NWtriRJkqQ5YEJJkjTnqrOjfXxyexVwNHB5K78EOKm9P7F9pi0/Jkla+aaqerSq7gC2Ake019aqur2qvg9sanUlSZIkzQETSpKkedFGEt0A3AtcBXwdeLCqdrYq24BV7f0q4C6Atvwh4FmD5RPWmapckiRJ0hxYudABSJKWh6p6DDg8yX7AJ4EfW4g4kqwH1gOMjIwwNjbWexsj+8CGw3buvuIsmUmMc23Hjh1DGdd88hh4DMBjIElavkwoSZLmVVU9mORq4CeA/ZKsbKOQDgS2t2rbgYOAbUlWAs8A7hsoHze4zlTlE/e/EdgIsHbt2hodHe3dhvddegXn3TR/Xeidp4zO276ma2xsjJkcu6XEY+AxAI+BJGn58pY3SdKcS/KcNjKJJPsALwduBa4GXtmqnQZc0d5vbp9pyz9XVdXKT25PgTsEWA1cB1wPrG5PjduLbuLuzXPeMEmSJGmZcoSSJGk+HABc0p7G9iTgsqr6VJJbgE1J3gl8Gbiw1b8Q+EiSrcD9dAkiqurmJJcBtwA7gTParXQkORO4ElgBXFRVN89f8yRJkqTlxYSSJGnOVdWNwIsnKb+d7gltE8u/B/zCFNs6Fzh3kvItwJY9DlaSJEnSbnnLmyRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRp0UiyIsmXk3yqfT4kybVJtib5WJK9Wvne7fPWtvzggW2c3cq/luS4gfJ1rWxrkrPmvXGStIiYUJIkSZK0mLwRuHXg87uB86vqUOAB4PRWfjrwQCs/v9UjyRrgZOAFwDrgAy1JtQJ4P3A8sAZ4dasrSZqECSVJkiRJi0KSA4FXAL/XPgc4Gri8VbkEOKm9P7F9pi0/ptU/EdhUVY9W1R3AVuCI9tpaVbdX1feBTa2uJGkSKxc6AEmSJEmapt8Gfg14Wvv8LODBqtrZPm8DVrX3q4C7AKpqZ5KHWv1VwDUD2xxc564J5UdOFkSS9cB6gJGREcbGxno3ZGQf2HDYzt1XnCW7i3HHjh0zasdcMZ7dG7aYjGfXhi0e2POYTChJkiRJGnpJfha4t6q+mGR0IWOpqo3ARoC1a9fW6Gj/cN536RWcd9P8XY7decroLpePjY0xk3bMFePZvWGLyXh2bdjigT2PyYSSJEmSpMXgZcC/SnIC8BTg6cB7gf2SrGyjlA4Etrf624GDgG1JVgLPAO4bKB83uM5U5ZKkCZxDSZIkSdLQq6qzq+rAqjqYblLtz1XVKcDVwCtbtdOAK9r7ze0zbfnnqqpa+cntKXCHAKuB64DrgdXtqXF7tX1snoemSdKi5AglSZIkSYvZm4FNSd4JfBm4sJVfCHwkyVbgfroEEVV1c5LLgFuAncAZVfUYQJIzgSuBFcBFVXXzvLZEkhYRE0qSJEmSFpWqGgPG2vvb6Z7QNrHO94BfmGL9c4FzJynfAmyZxVAlacnyljdJkiRJkiT1YkJJkiRJkiRJvZhQkiRJkiRJUi8mlCRJkiRJktSLCSVJkiRJkiT1stuEUpKDklyd5JYkNyd5Yyt/a5LtSW5orxMG1jk7ydYkX0ty3ED5ula2NclZA+WHJLm2lX8syV6z3VBJkiRJkiTNjumMUNoJbKiqNcBRwBlJ1rRl51fV4e21BaAtOxl4AbAO+ECSFUlWAO8HjgfWAK8e2M6727YOBR4ATp+l9kmSJEmSJGmW7TahVFV3V9WX2vtHgFuBVbtY5URgU1U9WlV3AFuBI9pra1XdXlXfBzYBJyYJcDRweVv/EuCkGbZHkiRJkiRJc6zXHEpJDgZeDFzbis5McmOSi5Ls38pWAXcNrLatlU1V/izgwaraOaFckiRJkiRJQ2jldCsmeSrwceBNVfVwkg8C7wCq/Xse8Po5ifIHMawH1gOMjIwwNjbWexsj+8CGw3buvuIs2VWMO3bsmFEb5oKxTG2Y4jGWyQ1TLDB88UiSJEnSbJtWQinJk+mSSZdW1ScAquqegeUfAj7VPm4HDhpY/cBWxhTl9wH7JVnZRikN1n+cqtoIbARYu3ZtjY6OTif8x3nfpVdw3k3TzqPtsTtPGZ1y2djYGDNpw1wwlqkNUzzGMrlhigWGLx5JkiRJmm3TecpbgAuBW6vqPQPlBwxU+3ngq+39ZuDkJHsnOQRYDVwHXA+sbk9024tu4u7NVVXA1cAr2/qnAVfsWbMkSZIkSZI0V6YzVOdlwGuAm5Lc0MreQveUtsPpbnm7E/glgKq6OcllwC10T4g7o6oeA0hyJnAlsAK4qKpubtt7M7ApyTuBL9MlsCRJkiRJkjSEdptQqqrPA5lk0ZZdrHMucO4k5VsmW6+qbqd7CpwkSZIkSZKGXK+nvEmSJEmSJEkmlCRJkiRJktSLCSVJkiRJkiT1YkJJkiRJkiRJvZhQkiRJkiRJUi8mlCRJkiRJktSLCSVJkiRJkiT1YkJJkjTnkhyU5OoktyS5OckbW/lbk2xPckN7nTCwztlJtib5WpLjBsrXtbKtSc4aKD8kybWt/GNJ9prfVkqSJEnLhwklSdJ82AlsqKo1wFHAGUnWtGXnV9Xh7bUFoC07GXgBsA74QJIVSVYA7weOB9YArx7Yzrvbtg4FHgBOn6/GSZIkScuNCSVJ0pyrqrur6kvt/SPArcCqXaxyIrCpqh6tqjuArcAR7bW1qm6vqu8Dm4ATkwQ4Gri8rX8JcNKcNEaSJEkSKxc6AEnS8pLkYODFwLXAy4Azk5wKfIFuFNMDdMmmawZW28YPElB3TSg/EngW8GBV7Zyk/sT9rwfWA4yMjDA2Nta7DSP7wIbDdu6+4iyZSYxzbceOHUMZ13zyGHgMwGMgSVq+TChJkuZNkqcCHwfeVFUPJ/kg8A6g2r/nAa+fyxiqaiOwEWDt2rU1Ojraexvvu/QKzrtp/rrQO08Znbd9TdfY2BgzOXZLicfAYwAeA0nS8mVCSZI0L5I8mS6ZdGlVfQKgqu4ZWP4h4FPt43bgoIHVD2xlTFF+H7BfkpVtlNJgfUmSJEmzzDmUJElzrs1xdCFwa1W9Z6D8gIFqPw98tb3fDJycZO8khwCrgeuA64HV7Ylue9FN3L25qgq4GnhlW/804Iq5bJMkSZK0nDlCSZI0H14GvAa4KckNrewtdE9pO5zulrc7gV8CqKqbk1wG3EL3hLgzquoxgCRnAlcCK4CLqurmtr03A5uSvBP4Ml0CS5IkSdIcMKEkSZpzVfV5IJMs2rKLdc4Fzp2kfMtk61XV7XRPgZMkSZI0x7zlTZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvu00oJTkoydVJbklyc5I3tvJnJrkqyW3t3/1beZJckGRrkhuTvGRgW6e1+rclOW2g/KVJbmrrXJAkc9FYSZIkSYtTkqckuS7JV9p1ydta+SFJrm3XEh9Lslcr37t93tqWHzywrbNb+deSHDdQvq6VbU1y1rw3UpIWkemMUNoJbKiqNcBRwBlJ1gBnAZ+tqtXAZ9tngOOB1e21HvggdAko4BzgSOAI4JzxJFSr84aB9dbtedMkSZIkLSGPAkdX1YuAw4F1SY4C3g2cX1WHAg8Ap7f6pwMPtPLzWz3atczJwAvorjs+kGRFkhXA++muZ9YAr251JUmT2G1CqarurqovtfePALcCq4ATgUtatUuAk9r7E4EPV+caYL8kBwDHAVdV1f1V9QBwFV0ncADw9Kq6pqoK+PDAtiRJkiSJdn2xo318cnsVcDRweSufeF0yfr1yOXBMuxPiRGBTVT1aVXcAW+m+8D4C2FpVt1fV94FNra4kaRIr+1Ruw0RfDFwLjFTV3W3Rt4CR9n4VcNfAatta2a7Kt01SPtn+19ONemJkZISxsbE+4QMwsg9sOGxn7/Vmalcx7tixY0ZtmAvGMrVhisdYJjdMscDwxSNJ0lLRRhF9ETiUbjTR14EHq2r8D/zBa4l/uP6oqp1JHgKe1cqvGdjs4DoTr1eOnINmSNKSMO2EUpKnAh8H3lRVDw9Oc1RVlaTmIL7HqaqNwEaAtWvX1ujoaO9tvO/SKzjvpl55tD1y5ymjUy4bGxtjJm2YC8YytWGKx1gmN0yxwPDFI0nSUlFVjwGHJ9kP+CTwYwsRx1L7ohuG7wsx49m9YYvJeHZt2OKBPY9pWpmVJE+mSyZdWlWfaMX3JDmgqu5ut63d28q3AwcNrH5gK9sOjE4oH2vlB05SX5IkSZKeoKoeTHI18BN0U2ysbKOUBq8lxq9LtiVZCTwDuI+pr1fYRfnE/S+pL7ph+L4QM57dG7aYjGfXhi0e2POYpvOUtwAXArdW1XsGFm0Gxp/UdhpwxUD5qe1pb0cBD7Vb464Ejk2yf5uM+1jgyrbs4SRHtX2dOrAtSZIkSSLJc9rIJJLsA7ycbn7Xq4FXtmoTr0vGr1deCXyuzdm6GTi5PQXuELqHAl0HXA+sbk+N24tu4u7Nc94wSVqkppMSfxnwGuCmJDe0srcA7wIuS3I68A3gVW3ZFuAEusntvgu8DqCq7k/yDroTNcDbq+r+9v6XgYuBfYDPtJckSZIkjTsAuKTNo/Qk4LKq+lSSW4BNSd4JfJnuy3Davx9JshW4ny5BRFXdnOQy4Ba6J1qf0W6lI8mZdF+ErwAuqqqb5695krS47DahVFWfBzLF4mMmqV/AGVNs6yLgoknKvwC8cHexSJIkSVqequpGugcETSy/ne4JbRPLvwf8whTbOhc4d5LyLXRfkEuSdmO3t7xJkiRJkiRJg0woSZIkSZIkqRcTSpIkSZIkSerFhJIkac4lOSjJ1UluSXJzkje28mcmuSrJbe3f/Vt5klyQZGuSG5O8ZGBbp7X6tyU5baD8pUluautc0J4cKkmSJGkOmFCSJM2HncCGqloDHAWckWQNcBbw2apaDXy2fQY4nu4xzquB9cAHoUtAAecAR9JNwHrOeBKq1XnDwHrr5qFdkiRJ0rJkQkmSNOeq6u6q+lJ7/whwK7AKOBG4pFW7BDipvT8R+HB1rgH2S3IAcBxwVVXdX1UPAFcB69qyp1fVNe1pox8e2JYkSZKkWWZCSZI0r5IcTPfY52uBkaq6uy36FjDS3q8C7hpYbVsr21X5tknKJUmSJM2BlQsdgCRp+UjyVODjwJuq6uHBaY6qqpLUPMSwnu42OkZGRhgbG+u9jZF9YMNhO2c5sqnNJMa5tmPHjqGMaz55DDwG4DGQJC1fJpQkSfMiyZPpkkmXVtUnWvE9SQ6oqrvbbWv3tvLtwEEDqx/YyrYDoxPKx1r5gZPUf4Kq2ghsBFi7dm2Njo5OVm2X3nfpFZx30/x1oXeeMjpv+5qusbExZnLslhKPgccAPAaSpOXLW94kSXOuPXHtQuDWqnrPwKLNwPiT2k4DrhgoP7U97e0o4KF2a9yVwLFJ9m+TcR8LXNmWPZzkqLavUwe2JUmSJGmWOUJJkjQfXga8BrgpyQ2t7C3Au4DLkpwOfAN4VVu2BTgB2Ap8F3gdQFXdn+QdwPWt3tur6v72/peBi4F9gM+0lyRJkqQ5YEJJkjTnqurzQKZYfMwk9Qs4Y4ptXQRcNEn5F4AX7kGYkiRJkqbJW94kSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUiwklSZIkSZIk9WJCSZIkSZIkSb2YUJIkSZIkSVIvJpQkSZIkSZLUy24TSkkuSnJvkq8OlL01yfYkN7TXCQPLzk6yNcnXkhw3UL6ulW1NctZA+SFJrm3lH0uy12w2UJIkSZIkSbNrOiOULgbWTVJ+flUd3l5bAJKsAU4GXtDW+UCSFUlWAO8HjgfWAK9udQHe3bZ1KPAAcPqeNEiSJEmSJElza7cJpar6c+D+aW7vRGBTVT1aVXcAW4Ej2mtrVd1eVd8HNgEnJglwNHB5W/8S4KR+TZAkSZIkSdJ82pM5lM5McmO7JW7/VrYKuGugzrZWNlX5s4AHq2rnhHJJkiRJkiQNqZUzXO+DwDuAav+eB7x+toKaSpL1wHqAkZERxsbGem9jZB/YcNjO3VecJbuKcceOHTNqw1wwlqkNUzzGMrlhigWGLx5JkiRJmm0zSihV1T3j75N8CPhU+7gdOGig6oGtjCnK7wP2S7KyjVIarD/ZfjcCGwHWrl1bo6OjvWN/36VXcN5NM82j9XfnKaNTLhsbG2MmbZgLxjK1YYrHWCY3TLHA8MUjSZIkSbNtRre8JTlg4OPPA+NPgNsMnJxk7ySHAKuB64DrgdXtiW570U3cvbmqCrgaeGVb/zTgipnEJEmSJEmSpPmx26E6ST4KjALPTrINOAcYTXI43S1vdwK/BFBVNye5DLgF2AmcUVWPte2cCVwJrAAuqqqb2y7eDGxK8k7gy8CFs9U4SZIkSZIkzb7dJpSq6tWTFE+Z9Kmqc4FzJynfAmyZpPx2uqfASZIkSZIkaRHYk6e8SZIkSZIkaRkyoSRJmnNJLkpyb5KvDpS9Ncn2JDe01wkDy85OsjXJ15IcN1C+rpVtTXLWQPkhSa5t5R9r8/VJkiRJmiMmlCRJ8+FiYN0k5edX1eHttQUgyRq6hze8oK3zgSQrkqwA3g8cD6wBXt3qAry7betQ4AHg9DltjSRJkrTMmVCSJM25qvpz4P5pVj8R2FRVj1bVHcBWurn2jgC2VtXtVfV9YBNwYpIARwOXt/UvAU6azfglSZIkPd5uJ+WWJGkOnZnkVOALwIaqegBYBVwzUGdbKwO4a0L5kcCzgAerauck9Z8gyXpgPcDIyAhjY2O9gx7ZBzYctnP3FWfJTGKcazt27BjKuOaTx8BjAB4DSdLyZUJJkrRQPgi8A6j273nA6+d6p1W1EdgIsHbt2hodHe29jfddegXn3TR/Xeidp4zO276ma2xsjJkcu6XEY+AxAI+BJGn5MqEkSVoQVXXP+PskHwI+1T5uBw4aqHpgK2OK8vuA/ZKsbKOUButLkiRJmgPOoSRJWhBJDhj4+PPA+BPgNgMnJ9k7ySHAauA64HpgdXui2150E3dvrqoCrgZe2dY/DbhiPtogSZo/SQ5KcnWSW5LcnOSNrfyZSa5Kclv7d/9WniQXtCeA3pjkJQPbOq3Vvy3JaQPlL01yU1vngjZPnyRpEiaUJElzLslHgb8Cnp9kW5LTgd9sf7TfCPwM8J8Bqupm4DLgFuBPgDOq6rE2+uhM4ErgVuCyVhfgzcCvJNlKN6fShfPYPEnS/NhJN9/eGuAo4Iz2tM+zgM9W1Wrgs+0zdE8FXd1e6+lutSbJM4Fz6ObhOwI4ZzwJ1eq8YWC9yZ5QKknCW94kSfOgql49SfGUSZ+qOhc4d5LyLcCWScpvp7sokCQtUVV1N3B3e/9IklvpHsJwIjDaql0CjNF90XAi8OE2kvWaJPu10bGjwFVVdT9AkquAdUnGgKdX1TWt/MN0Tw39zDw0T5IWHUcoSZIkSVpUkhwMvBi4FhhpySaAbwEj7f0qnvh00FW7Kd82SbkkaRKOUJIkSZK0aCR5KvBx4E1V9fDgNEdVVUlqHmJYT3cbHSMjI4yNjfXexsg+sOGwnbMc2dR2F+OOHTtm1I65Yjy7N2wxGc+uDVs8sOcxmVCSJEmStCgkeTJdMunSqvpEK74nyQFVdXe7pe3eVj7VU0O384Nb5MbLx1r5gZPUf4Kq2ghsBFi7dm2Njo5OVm2X3nfpFZx30/xdjt15yugul4+NjTGTdswV49m9YYvJeHZt2OKBPY/JW94kSZIkDb32xLULgVur6j0DizbTPeETHv+kz83Aqe1pb0cBD7Vb464Ejk2yf5uM+1jgyrbs4SRHtX2dik8NlaQpOUJJkiRJ0mLwMuA1wE1JbmhlbwHeBVzWniD6DeBVbdkW4ARgK/Bd4HUAVXV/kncA17d6bx+foBv4ZeBiYB+6ybidkFuSpmBCSZIkSdLQq6rPA5li8TGT1C/gjCm2dRFw0STlXwBeuAdhStKy4S1vkiRJkiRJ6sURSnPs4LM+PeWyDYft5LW7WD4Td77rFbO6PUmSJEmSpIkcoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknrZbUIpyUVJ7k3y1YGyZya5Kslt7d/9W3mSXJBka5Ibk7xkYJ3TWv3bkpw2UP7SJDe1dS5IktlupCRJkiRJkmbPdEYoXQysm1B2FvDZqloNfLZ9BjgeWN1e64EPQpeAAs4BjgSOAM4ZT0K1Om8YWG/iviRJkiRJkjREdptQqqo/B+6fUHwicEl7fwlw0kD5h6tzDbBfkgOA44Crqur+qnoAuApY15Y9vaquqaoCPjywLUmSJEmSJA2hlTNcb6Sq7m7vvwWMtPergLsG6m1rZbsq3zZJ+aSSrKcb+cTIyAhjY2P9A98HNhy2s/d6c2EuYpnJMQHYsWPHjNedbcMUCwxXPMYyuWGKBYYvHkmSJEmabTNNKP2DqqokNRvBTGNfG4GNAGvXrq3R0dHe23jfpVdw3k173OxZseGwnbMey52njM5ovbGxMWZyPOfCMMUCwxWPsUxumGKB4YtHkiRJkmbbTJ/ydk+7XY32772tfDtw0EC9A1vZrsoPnKRckiRJkiRJQ2qmCaXNwPiT2k4DrhgoP7U97e0o4KF2a9yVwLFJ9m+TcR8LXNmWPZzkqPZ0t1MHtiVJWiJ8YqgkSZK0tOw2oZTko8BfAc9Psi3J6cC7gJcnuQ34l+0zwBbgdmAr8CHglwGq6n7gHcD17fX2Vkar83ttna8Dn5mdpkmShsjF+MRQSZIkacnY7QQ+VfXqKRYdM0ndAs6YYjsXARdNUv4F4IW7i0OStHhV1Z8nOXhC8YnAaHt/CTAGvJmBJ4YC1yQZf2LoKO2JoQBJxp8YOkZ7YmgrH39iqF9QSJIkSXNkpre8SZK0pxbkiaGSJEmS9txwPO5MkrSszecTQ5Osp7uVjpGREcbGxnpvY2Sf7kmd82UmMc61HTt2DGVc88lj4DEAj4EkafkyoSRJWij3JDmgqu7u8cTQ0QnlY/R8YmhVbQQ2Aqxdu7ZGR0enqjql9116BefdNH9d6J2njM7bvqZrbGyMmRy7pcRj4DEAj4EkafnyljdJ0kLxiaGSJEnSIuUIJUnSnGtPDB0Fnp1kG93T2t4FXNaeHvoN4FWt+hbgBLqnf34XeB10TwxNMv7EUHjiE0MvBvahm4zbCbklSZKkOWRCSZI053xiqCRJkrS0eMubJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5WLnQAkiRp1w4+69Pzur873/WKed2fJEmSFh9HKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmShl6Si5Lcm+SrA2XPTHJVktvav/u38iS5IMnWJDcmecnAOqe1+rclOW2g/KVJbmrrXJAk89tCSVpcTChJkiRJWgwuBtZNKDsL+GxVrQY+2z4DHA+sbq/1wAehS0AB5wBHAkcA54wnoVqdNwysN3FfkqQBJpQkSZIkDb2q+nPg/gnFJwKXtPeXACcNlH+4OtcA+yU5ADgOuKqq7q+qB4CrgHVt2dOr6pqqKuDDA9uSJE3ChJIkSZKkxWqkqu5u778FjLT3q4C7Bupta2W7Kt82SbkkaQorFzoASZIkSdpTVVVJaj72lWQ93a10jIyMMDY21nsbI/vAhsN2znJkU9tdjDt27JhRO+aK8ezesMVkPLs2bPHAnsdkQkmSJEnSYnVPkgOq6u5229q9rXw7cNBAvQNb2XZgdEL5WCs/cJL6k6qqjcBGgLVr19bo6OhUVaf0vkuv4Lyb5u9y7M5TRne5fGxsjJm0Y64Yz+4NW0zGs2vDFg/seUze8iZJkiRpsdoMjD+p7TTgioHyU9vT3o4CHmq3xl0JHJtk/zYZ97HAlW3Zw0mOak93O3VgW5KkSThCSZIkSdLQS/JRutFFz06yje5pbe8CLktyOvAN4FWt+hbgBGAr8F3gdQBVdX+SdwDXt3pvr6rxib5/me5JcvsAn2kvSdIUTChJkiRJGnpV9eopFh0zSd0CzphiOxcBF01S/gXghXsSoyQtJ97yJkmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqZY8SSknuTHJTkhuSfKGVPTPJVUlua//u38qT5IIkW5PcmOQlA9s5rdW/Lclpe9YkSZIkSZIkzaXZGKH0M1V1eFWtbZ/PAj5bVauBz7bPAMcDq9trPfBB6BJQwDnAkcARwDnjSShJkiRJkiQNn7m45e1E4JL2/hLgpIHyD1fnGmC/JAcAxwFXVdX9VfUAcBWwbg7ikiRJkiRJ0ixYuYfrF/CnSQr4n1W1ERipqrvb8m8BI+39KuCugXW3tbKpyp8gyXq60U2MjIwwNjbWO+CRfWDDYTt7rzcX5iKWmRwTgB07dsx43dk2TLHAcMVjLJMbplhg+OIZdknuBB4BHgN2VtXaNnr1Y8DBwJ3Aq6rqgSQB3gucAHwXeG1Vfalt5zTgv7XNvrOqLkGSJEnSnNjThNJPVtX2JP8IuCrJ/x1cWFXVkk2zoiWsNgKsXbu2RkdHe2/jfZdewXk37WmzZ8eGw3bOeix3njI6o/XGxsaYyfGcC8MUCwxXPMYyuWGKBYYvnkXiZ6rqbwY+j98+/a4kZ7XPb+bxt08fSXf79JEDt0+vpfuy44tJNreRr5IkSZJm2R7d8lZV29u/9wKfpJsD6Z52Kxvt33tb9e3AQQOrH9jKpiqXJC1f3j4tSZIkDbEZD49Jsi/wpKp6pL0/Fng7sBk4DXhX+/eKtspm4Mwkm+i+VX6oqu5OciXwGwMTcR8LnD3TuCRJi463Tw+Z6RwTb+30GIDHADwGkqTla0/utxoBPtlNZ8FK4A+r6k+SXA9cluR04BvAq1r9LXRzXmylm/fidQBVdX+SdwDXt3pvr6r79yAuSdLi4u3TQ2Y6t097a6fHADwG4DGQJC1fM/5ruKpuB140Sfl9wDGTlBdwxhTbugi4aKaxSJIWr8Hbp5M87vbpNpJ1urdPj04oH5vj0CVJkqRla4/mUJIkaU8k2TfJ08bf0932/FV+cPs0PPH26VPTOYp2+zRwJXBskv3bLdTHtjJJkiRJc2DpjteXJC0G3j4tSZIkLUImlCRJC8bbpyVJkqTFyYSSJEl6nIPP+vRu62w4bCevnUa96bjzXa+Yle1IkiRp/jiHkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReVi50AJpdB5/16Rmtt+Gwnbx2Buve+a5XzGh/kiRJkiRp8XKEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpl5ULHYAkSVreDj7r0/O+zzvf9Yp536ckSdJS4gglSZIkSZIk9eIIJe2RufhWecNhO3ntLrbrt8qSJEmSJC0sRyhJkiRJkiSpFxNKkiRJkiRJ6sVb3iRJkiRpidvdVBW7m3aiL6epkJY+RyhJkiRJkiSpFxNKkiRJkiRJ6sWEkiRJkiRJknpxDiVJkrTs7G4ukenoM9+Ic4lIkqSlxhFKkiRJkiRJ6sWEkiRJkiRJknoxoSRJkiRJkqReTChJkiRJkiSpFyfllnbjpu0PTXvS1dngxK2SJEmSpGE3NCOUkqxL8rUkW5OctdDxSJIWH/sSSdKesi+RpOkZihFKSVYA7wdeDmwDrk+yuapuWdjIpPm3q0dZ93lE9XQ5IkpLhX2JJGlP2ZfMnl39TTsdff/u9W9aaf4NywilI4CtVXV7VX0f2AScuMAxSZIWF/sSSdKesi+RpGkaihFKwCrgroHP24AjFygWaVmZ6bdHczFaaqaGKRaAi9ftu9AhLFf2JRpae/pNfV9+Uy/NmH3JIjXX59lh+HvTc7uGTapqoWMgySuBdVX179rn1wBHVtWZE+qtB9a3j88HvjaD3T0b+Js9CHc2GcvkhikWGK54jGVywxQLzDye51XVc2Y7mOViGfclC8Vj4DEAjwEM3zGwL9kDy7wvMZ5dG7Z4YPhiMp5dG7Z4YOqYptWXDMsIpe3AQQOfD2xlj1NVG4GNe7KjJF+oqrV7so3ZYiyTG6ZYYLjiMZbJDVMsMHzxLCPLsi9ZKB4DjwF4DMBjsAQt277EeHZt2OKB4YvJeHZt2OKBPY9pWOZQuh5YneSQJHsBJwObFzgmSdLiYl8iSdpT9iWSNE1DMUKpqnYmORO4ElgBXFRVNy9wWJKkRcS+RJK0p+xLJGn6hiKhBFBVW4At87CrPRqaOsuMZXLDFAsMVzzGMrlhigWGL55lY5n2JQvFY+AxAI8BeAyWnGXclxjPrg1bPDB8MRnPrg1bPLCnt+4Ow6TckiRJkiRJWjyGZQ4lSZIkSZIkLRLLJqGU5KIk9yb56hDEclCSq5PckuTmJG9cwFiekuS6JF9psbxtoWIZiGlFki8n+dQCx3FnkpuS3JDkCwscy35JLk/yf5PcmuQnFjCW57djMv56OMmbFjCe/9x+d7+a5KNJnrKAsbyxxXHzQh4TzZ0k65J8LcnWJGctdDxzZap+Kskzk1yV5Lb27/6tPEkuaMflxiQvWdgWzJ6JfVKbqPfa1taPtUl7SbJ3+7y1LT94QQOfJZP1P8vt92Cyfma5/R5odi1UXzLZ9dBC/n8etr4mU1wXLfT/92HqhzLJ9dEC/w4NVR+VKa6TFjim/5w57MOWTUIJuBhYt9BBNDuBDVW1BjgKOCPJmgWK5VHg6Kp6EXA4sC7JUQsUy7g3ArcucAzjfqaqDh+Cxzu+F/iTqvox4EUs4PGpqq+1Y3I48FLgu8AnFyKWJKuA/wSsraoX0k2eefICxfJC4A3AEXQ/o59NcuhCxKK5kWQF8H7geGAN8OoFPHfPtan6qbOAz1bVauCz7TN0x2R1e60HPjj/Ic+ZiX3Su4Hzq+pQ4AHg9FZ+OvBAKz+/1VsKJut/ls3vwS76meX2e6BZssB9ycU88XpoIf8/D1tfM9V10UL/fx+2fmji9dFC/g4NVR+1i+ukBYlpPvqwZZNQqqo/B+5f6DgAquruqvpSe/8I3S/+qgWKpapqR/v45PZasIm1khwIvAL4vYWKYdgkeQbwL4ALAarq+1X14IIG9QPHAF+vqm8sYAwrgX2SrAR+CPjmAsXxT4Frq+q7VbUT+DPgXy9QLJobRwBbq+r2qvo+sAk4cYFjmhO76KdOBC5p1S4BTmrvTwQ+3PqUa4D9khwwv1HPvol9UpIARwOXtyoTj8H4sbkcOKbVX7R20f8sq98DntjP3M0y+j3QrFuwvmSK66EF+/88bH3NLq6LFuz/+yLphxbk57UI+qjB66SFjGlO+7Blk1AaVm0Y2YuBaxcwhhVJbgDuBa6qqgWLBfht4NeAv1/AGMYV8KdJvphk/QLGcQjwbeD30w13/b0k+y5gPINOBj66UDuvqu3AbwF/TXdyfKiq/nSBwvkq8FNJnpXkh4ATgIMWKBbNjVXAXQOft7FAXwbMpwn91EhV3d0WfQsYae+X6rH5bR7fJz0LeLAljeHx7fyHY9CWP9TqL2ZT9T/L5vdgsn4G+CLL6/dAs2vY/p8Mxf/nYelrJl4XAV9nYf+//zbD1Q9Ndn20UD+vYe+jBq+TFiSm+ejDTCgtoCRPBT4OvKmqHl6oOKrqsTYs70DgiHbrzrxL8rPAvVX1xYXY/yR+sqpeQjcU8Ywk/2KB4lgJvAT4YFW9GPgOPxgmuWDavbb/CvijBYxhf7pM+iHAc4F9k/ziQsRSVbfSDQv9U+BPgBuAxxYiFmm27KqfqqpiAUe0zrUh7JMWwm77n2Xwe/CEfobhmUJBmlUL9f95mPqaiddFwI/N174nGtJ+aJfXR/P88xraPmpX10nzGdN89GEmlBZIkifTnTgvrapPLHQ8AG2I4NUs3B9KLwP+VZI76Yb/Hp3kDxYolvGMLlV1L929r0csUCjbgG0DI8cupzt5LrTjgS9V1T0LGMO/BO6oqm9X1d8BnwD++UIFU1UXVtVLq+pf0N2P/P8WKhbNie08ftTZga1sSZqin7pnfCh2+/feVr4Uj80T+iS6uRr2a8PG4fHt/Idj0JY/A7hvPgOeA1P1P8vp92CyfuZlLK/fA82uYft/sqD/n4e1rxm4LvoJFu7/+9D1Q1NcHy3Uz2uY+6iJ10kLFdOc92EmlBZAuw/xQuDWqnrPAsfynCT7tff7AC8H/u9CxFJVZ1fVgVV1MN0Qwc9V1YKMNkmyb5Knjb8HjqW7pWneVdW3gLuSPL8VHQPcshCxTPBqFvB2t+avgaOS/FD7f3UMCzhheZJ/1P79Ebr5k/5woWLRnLgeWJ3uyRh70Z2nNi9wTHNiF/3UZuC09v404IqB8lPTOYru9tO7WcSm6JNOobvAeGWrNvEYjB+bV7b6i3rkzi76n2Xze8Dk/cwtLKPfA826YetLFuz/87D1NVNcF93KAv1/H7Z+aBfXRwvy8xryPmriddJCxTT3fVhVLYsX3Q/0buDv6LKZpy9gLD9JN8ztRrrbYm4ATligWP4Z8OUWy1eBX1/on1WLaxT41ALu/0eBr7TXzcB/XeDjcTjwhfZz+l/A/gscz7502epnDMHvytvokqBfBT4C7L2AsfxFO0l/BThmoY+Nrzn5GZ9AN/Ls6wt9Xpjjdk7aT9HdR/9Z4DbgfwPPbPVD99SirwM30T1NZMHbMYvH4x/6pNY/XAdspRvKvncrf0r7vLUt/9GFjnuW2v6E/me5/R5M1s8st98DX7P7Wqi+hEmuhxby//Ow9TVMcV00DP/fh6EfYorrowX+HTqcIeujmOQ6aYGP0Zz2YWkrSpIkSZIkSdPiLW+SJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKGlRSPL8JDckeSTJ/Une2cp/KsnXprmN0STbZimeO5P8y9nYliRJk5nNfkuStDgleVmS25LsSHLSAuzfvkhTMqGkxeLXgKur6mnA5vHCqvqLqnr+bOwgycXjiSpJ0uKxkEn+JG9N8gcLsW9J0uwa0i+N3w78TlU9tar+10IHM5+8Pht+JpS0WDwPuHmhg5hvSVYudAySpJlLx7+3JElPMM2/9Wd8HTTZ9pOsmMm2pMn4B46GXpLPAT8D/E6SHcBeA8seNwQzyUuSfLndGvdHST42MaudZEOSe5PcneR1rWw9cArwa2046R9PI7TDk9yY5KG2n6cM7OMNSba22/M2J3luKz84SQ2e3JOMJfl37f1rk/xlkvOT3Ae8tf8Rk6ThkeR1g+fUNmz/jwY+35Xk8CTvbe8fTvLFJD81UOetSS5L8uF2fr85ydq27CPAjwB/3M7fv5bkKUn+IMl9SR5Mcn2Skd3E+YwkF7a+YXuSdyZZkWSvdsv1f2z1VrTz9K8nWQe8Bfg3bd9faXXGkpyb5C+B7wI/2o7DrS3+25P8Uo9j+JYkf9O+OT9loPwVrc97uB27t05Y79Qk32jH4f8b/OY9yT5JLknyQIvr1yb0p2cl+XqL95YkPz+wbEWS81pMdyQ5c7Bvm+pYTre9kjSZRdSfjCX570muazFckeSZbdn4tcDpSf4a+Fwrf307Fz+Q5Mokz2vlXwd+dCCmvXd1js0k1xLpRvl8MMmWJN8BfibJc5N8PMm323n8Pw3Ev09b54EktwA/Ps2fz5tbPI8k+VqSY1r5kwb6lPva8X/mwHp/lORb6a6p/jzJC1r5TK7PNM9MKGnoVdXRwF8AZ1bVU4HvT1YvyV7AJ4GLgWcCHwV+fkK1HwaeAawCTgfen2T/qtoIXAr8ZhtO+nPTCO1VwDrgEOCfAa9tcRwN/Pe2/ADgG8CmaTYX4EjgdmAEOLfHepI0jP4M+Kn2B+Vz6b4U+AmAJD8KPBW4EbgeOJzu/P2HwB9lIFEP/Cu6c+l+dLc+/w5AVb0G+Gvg59r5+zeB0+jO9QcBzwL+PfC3u4nzYmAncCjwYuBY4N9V1feBXwTenuSfAmcBK4Bzq+pPgN8APtb2/aKB7b0GWA88ja4fuBf4WeDpwOuA85O8ZPeHjx8Gnk3Xb50GbEwyfqv3d4BT2zF5BfAf0ubXSLIG+ADdH+MH8IO+b9w5wMF0Fyovb20c9HXgp9p6bwP+IMkBbdkbgOPpfl4vAU6asO7FTHIsp9FWSdqVxdKfQHdufj3d+XcncMGE5T8N/FPguCQn0n058a+B59Bd93y0xfSPJ8T0KLs/x052LfFv2/unAf8H+GPgK3T9wjHAm5Ic1+qeA/zj9jquHYNdav3SmcCPtylKjgPubIv/I10/8dPAc4EHgPcPrP4ZYDXwj4Av0V2TMcPrM80zE0paSo4CVgIXVNXfVdUngOsm1Pk74O1t+RZgBzDTOZguqKpvVtX9dCflw1v5KcBFVfWldtI/G/iJJAdPc7vfrKr3VdXOqppOhyVJQ6uqbgceoTtH/gvgSuCbSX6M7o/Lv6iqv6+qP6iq+9q57zxgbx5/fv58VW2pqseAjwAvYmp/R/eH/6FV9VhVfbGqHp6qcvu2+QTgTVX1naq6FzgfOLm14avAO4H/Bfwq8JoWx65cXFU3t/b8XVV9uqq+Xp0/A/6ULmEzHf9fVT3a1vs03RcWVNVYVd3Ujt+NdBcgP93WeSXwx1X1+ZYU+3WgBrb5KuA3quqBqtrGhIudqvqj1sf9fVV9DLgNOGJg3fdW1baqegB41/h6uzuWkjRTi6E/GfCRqvpqVX0H+P+AV00YqfnWdo78W7ok1X+vqluraifdFxWHj49SGjTNc+xk1xJXVNVfVtXfA4cBz6mqt1fV99tx/dDANl5F96XJ/VV1F09Mhk3mMbrjvCbJk6vqzqr6elv274H/2vqMR+nuwHjl+KjWqrqoqh4ZWPaiJM+Yxj41BJyfRUvJc4HtVTX4B/NdE+rc107U475L923GTHxrwnaeOxDHl8YXVNWONuR0FbB9GtudGLMkLXZ/BozSfZv6Z8CDdH/8/0T7TJJfpRs5+ly6xMfT6UbmjJt4zn1KkpUTzunjPkL3bfKmJPsBf0D3x+zfTRHf84AnA3cnGS97Eo8/H19C9+3ux6vqtt01eMK6JDme7lvff9K2/UPATdPYzgPtgmTcN2j9TZIj6ZI5L6T7pn5vYPz2j+cOxlBV3219EZMtnyTeU4FfoRvFBF1fOf7z2NW60zmWkjRTw96fjBs8532D7rz47CmWPw94b5LzBspCd+3wjQnbnc45drLz7cT9PTfJgwNlK+hGRsETz/ETY3iCqtqa5E10CaEXJLkS+JWq+mbb3yeT/P3AKo8BI0m+Rde3/gLd6KzxOs8GHtrdfrXwHKGkpeRuYFUGzq50HcB01e6rTMv4iROAJPvSfbOxne72BOguJMb98BzFIUnDYvwC4Kfa+z+juwD4aeDP0s1v8Wt034ruX1X70f0hmck2NonHnTfbiKC3VdUa4J/T3Wp26i7Wvwt4FHh2Ve3XXk+vqhcM1PkA8Cm62xN+cqp9T1aeZG/g48BvASOtfVuYXvv2b/3IuB+h62egu5VjM3BQVT0D+N2Bbd4NHDgQwz50fRGTLWegv2zfin+I7vaFZ7V4vzrVtnl8XzudYylJMzXs/cm4wfPij9CNdPqbKfZzF/BLA+fM/apqn6r6P5Nsdzrn2Mn6pYn7u2PC/p5WVSe05XdPEv9uVdUfVtVP0l0HFfDugf0dP2F/T6mq7XS34p0I/Eu6WwsPbuuM/7y8LhpyJpS0lPwVXbb7zCQr2/3IR+xmnUH30M0lsac+Crwu3aSAe9MNW722Df38Nl1i6RfTTWr6err7kyVpKfszuocr7NNur/oLujnongV8mW5Oh53At4GVSX6d7hvl6Xrc+TvJzyQ5rN1e8DDdH/J/P9XKVXU33S1o5yV5epuf4x8n+em2vdcAL6WbK+8/AZckGR/deg9wcHb9JLfx0UPfBna20UrH9mjf29JNDv5TdBcz46OQngbcX1XfS3IE3R/m4y4Hfi7JP29zDL6Vx19QXQacnWT/JKvokkfj9qX7I/7brf2voxsFNbjuG5Osat/Yv3l8we6OpSTtoaHuTwb8YpI1SX4IeDtw+S5ulf5duvPx+GTUz0jyC5NVnKVz7HXAI+km0d6nXZO8MMn45NuD/cOBdHMg7VKS5yc5ul37fI9unqnx4/S7wLn5wUTjz2nXadD9vB4F7qP7wv03Jmx6tq7PNEdMKGnJaHNE/Gu6Ia4P0k0w+im6k9R0XEh33++DSf7XHsTxv+nulf44XYb/H/P4+5rfAPwXuhPnC+gmxpOkJauq/h/dnHV/0T4/TDdh6F+2P7CvBP4E+H90Q+u/R79bpP478N/a+ftX6UZ+Xk73x/+tdBcgH9nNNk6lS/zcQjdh6OXAAUl+BPht4NSq2lFVfwh8gW7OCvhBcue+JF9iElX1CF0i6rK27X9LN7JoOr7V1vkm3eSk/76q/m9b9st0k4U/QjdH0mUD+7yZ7iJgE11ftINuYvDxPvHtwDbgDuB/t/Y+2ta9BTiP7ouae+jm2/jLgZg+RHdBcyPdBdwWugu48YulSY/lNNsrSVNaJP0Jrc7FdOfwp9D1AVO16ZN0o3k2JXmYbkTo8bvY9h6dY9tx+lm6uajuoBs59Xt0I4SgexDDN9qyP2V67d2b7hbsv6Fr8z+im0cW4L10fd6ftv7qGrqJwwE+3Pa1vbXnmgnbnZXrM82dPH66GWlpSXIt8LtV9fsLHYskSQuljah6EFhdVXdMsvw/ACdXVe+RRG3E1e9W1RMmkJWk5SbJGPAHVfV7Cx2LNNccoaQlJclPJ/nhdsvbacA/o/uWQpKkZSXJzyX5oTYH02/RTQJ+Z1t2QJKXtdslng9sAD45ze3uk+SE1teuoptsfFrrSpKkpcOEkpaa5wNfofsWdgPwynavcS9JfiTJjile05qYTpI0XHZxXv+pBYzpLVPE9JlZ2PyJdLfKfRNYTTcCaXxo+l7A/6R7BPfngCvoJh6fVth0t0Q8QHfL2610t9xJ0rIwjP3JXPP6SJPxljdJkiRJkiT14gglSZIkSZIk9bJyoQOYqWc/+9l18MEH917vO9/5Dvvuu+/sBzTEbPPysRzbvZTa/MUvfvFvquo5Cx3HcmJfMrml3j5Y+m20fYvfTNtoXzL/7Esmt9TbB0u/jbZv8ZvrvmTRJpQOPvhgvvCFL/Reb2xsjNHR0dkPaIjZ5uVjObZ7KbU5yTcWOoblxr5kcku9fbD022j7Fr+ZttG+ZP7Zl0xuqbcPln4bbd/iN9d9ibe8SZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6MaEkSZIkSZKkXkwoSZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6MaEkSZIkaegleUqS65J8JcnNSd7Wyi9OckeSG9rr8FaeJBck2ZrkxiQvGdjWaUlua6/TBspfmuSmts4FSTLvDZWkRWLlQgcgSZIkSdPwKHB0Ve1I8mTg80k+05b9l6q6fEL944HV7XUk8EHgyCTPBM4B1gIFfDHJ5qp6oNV5A3AtsAVYB3wGSdITOEJJkiRJ0tCrzo728cntVbtY5UTgw229a4D9khwAHAdcVVX3tyTSVcC6tuzpVXVNVRXwYeCkuWqPJC12jlCSJEmStCgkWQF8ETgUeH9VXZvkPwDnJvl14LPAWVX1KLAKuGtg9W2tbFfl2yYpnyyO9cB6gJGREcbGxnq3ZceOHTNab7FY6u2Dpd9G27f4zXUbTShJkiRJWhSq6jHg8CT7AZ9M8kLgbOBbwF7ARuDNwNvnOI6NbV+sXbu2RkdHe29jbGyMmay3WCz19sHSb6PtW/zmuo3e8iZJkiRpUamqB4GrgXVVdXe7re1R4PeBI1q17cBBA6sd2Mp2VX7gJOWSpEk4Qkl75OCzPj3v+7zzXa+Y931KkqTlayH+3rl43b7zvs9hl+Q5wN9V1YNJ9gFeDrw7yQFVdXd7IttJwFfbKpuBM5NsopuU+6FW70rgN5Ls3+odC5xdVfcneTjJUXSTcp8KvG+u2nPT9od47Tz+bvk3tKTZZkJJkiRJ0mJwAHBJm0fpScBlVfWpJJ9ryaYANwD/vtXfApwAbAW+C7wOoCWO3gFc3+q9varub+9/GbgY2Ifu6W4+4U2SpmBCSZIkSdLQq6obgRdPUn70FPULOGOKZRcBF01S/gXghXsWqSQtD86hJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJGnOJXlKkuuSfCXJzUne1soPSXJtkq1JPpZkr1a+d/u8tS0/eGBbZ7fyryU5bqB8XSvbmuSseW+kJEmStIyYUJIkzYdHgaOr6kXA4cC6JEcB7wbOr6pDgQeA01v904EHWvn5rR5J1gAnAy8A1gEfSLIiyQrg/cDxwBrg1a2uJEmSpDlgQkmSNOeqs6N9fHJ7FXA0cHkrvwQ4qb0/sX2mLT8mSVr5pqp6tKruALYCR7TX1qq6vaq+D2xqdSVJkiTNgZULHYAkaXloo4i+CBxKN5ro68CDVbWzVdkGrGrvVwF3AVTVziQPAc9q5dcMbHZwnbsmlB85RRzrgfUAIyMjjI2N9W7Ljh07ZrTeYrHU2wdLv422b3ZtOGzn7ivNsqX+M5QkLX4mlCRJ86KqHgMOT7If8EngxxYojo3ARoC1a9fW6Oho722MjY0xk/UWi6XePlj6bbR9s+u1Z3163vY17uJ1+y7pn6EkafHzljdJ0ryqqgeBq4GfAPZLMv7lxoHA9vZ+O3AQQFv+DOC+wfIJ60xVLkmSJGkOmFCSJM25JM9pI5NIsg/wcuBWusTSK1u104Ar2vvN7TNt+eeqqlr5ye0pcIcAq4HrgOuB1e2pcXvRTdy9ec4bJkmSJC1T3vImSZoPBwCXtHmUngRcVlWfSnILsCnJO4EvAxe2+hcCH0myFbifLkFEVd2c5DLgFmAncEa7lY4kZwJXAiuAi6rq5vlrniRJkrS8mFCSJM25qroRePEk5bfTPaFtYvn3gF+YYlvnAudOUr4F2LLHwUqSJEnaLW95kyRJkiRJUi8mlCRJkiRJktSLCSVJkiRJkiT1YkJJkiRJkiRJvZhQkiRJkjT0kjwlyXVJvpLk5iRva+WHJLk2ydYkH0uyVyvfu33e2pYfPLCts1v515IcN1C+rpVtTXLWvDdSkhaRPUooJfnP7WT+1SQfbSf5WTuhS5IkSVLzKHB0Vb0IOBxYl+Qo4N3A+VV1KPAAcHqrfzrwQCs/v9UjyRrgZOAFwDrgA0lWJFkBvB84HlgDvLrVlSRNYsYJpSSrgP8ErK2qFwIr6E7Ms3JCn2lckiRJkpae6uxoH5/cXgUcDVzeyi8BTmrvT2yfacuPSZJWvqmqHq2qO4CtwBHttbWqbq+q7wObWl1J0iT29Ja3lcA+SVYCPwTczeyd0CVJkiTpH7SRRDcA9wJXAV8HHqyqna3KNmBVe78KuAugLX8IeNZg+YR1piqXJE1i5UxXrKrtSX4L+Gvgb4E/Bb7INE/oSQZP6NcMbNoTtyRJkqQnqKrHgMOT7Ad8EvixhYgjyXpgPcDIyAhjY2O9tzGyD2w4bOfuK86SmcS4J3bs2DHv+5xvS72Ntm/xm+s2zjihlGR/utFFhwAPAn9Ed8vanJmNE/dy+KWZaC7bPJ+d4LjptGU5/pxhebZ7ObZZkqTlrqoeTHI18BPAfklWti+1DwS2t2rbgYOAbe2OimcA9w2UjxtcZ6ryifvfCGwEWLt2bY2OjvZuw/suvYLzbprx5Vhvd54yOm/7gu5v9pkcl8VkqbfR9i1+c93GPTmD/Uvgjqr6NkCSTwAvY3ZP6I8zGyfu5fBLM9Fctvm1Z316Tra7K9PpDJfjzxmWZ7uXY5slSVqOkjwH+LuWTNoHeDndvKxXA6+km/PoNOCKtsrm9vmv2vLPVVUl2Qz8YZL3AM8FVgPXAQFWJzmE7nrkZODfzlf7JGmx2ZM5lP4aOCrJD7W5kI4BbuEHJ3SY/IQOAyf0Vn5yewrcIfzghC5JkiRJ4w4Ark5yI3A9cFVVfQp4M/ArSbbSTalxYat/IfCsVv4rwFkAVXUzcBndtcufAGdU1WPtC/EzgSuBW4HLWl1J0iT2ZA6la5NcDnwJ2Al8mW700KeBTUne2coGT+gfaSf0++ky/lTVzUnGT+g7aSf0mcYlSZIkaempqhuBF09SfjuTPNSnqr4H/MIU2zoXOHeS8i3Alj0OVpKWgT26abeqzgHOmVA8ayd0SZIkSZIkDZ89ueVNkiRJkiRJy5AJJUmSJEmSJPViQkmSJEmSJEm9mFCSJEmSJElSLyaUJEmSJEmS1IsJJUmSJEmSJPViQkmSJEmSJEm9mFCSJEmSJElSLyaUJEmSJEmS1IsJJUmSJEmSJPViQkmSJEmSJEm9mFCSJEmSJElSLyaUJEmSJEmS1IsJJUmSJEmSJPViQkmSJEmSJEm9mFCSJEmSJElSLyaUJEmSJEmS1IsJJUmSJEmSJPViQkmSJEmSJEm9mFCSJEmSJElSLyaUJElzLslBSa5OckuSm5O8sZW/Ncn2JDe01wkD65ydZGuSryU5bqB8XSvbmuSsgfJDklzbyj+WZK/5baUkSZK0fJhQkiTNh53AhqpaAxwFnJFkTVt2flUd3l5bANqyk4EXAOuADyRZkWQF8H7geGAN8OqB7by7betQ4AHg9PlqnCRJkrTcmFCSJM25qrq7qr7U3j8C3Aqs2sUqJwKbqurRqroD2Aoc0V5bq+r2qvo+sAk4MUmAo4HL2/qXACfNSWMkSZIkmVCSJM2vJAcDLwaubUVnJrkxyUVJ9m9lq4C7Blbb1sqmKn8W8GBV7ZxQLkmSJGkOrFzoACRJy0eSpwIfB95UVQ8n+SDwDqDav+cBr5/jGNYD6wFGRkYYGxvrvY0dO3bMaL3FYqm3D5Z+G23f7Npw2M7dV5plS/1nKEla/EwoSZLmRZIn0yWTLq2qTwBU1T0Dyz8EfKp93A4cNLD6ga2MKcrvA/ZLsrKNUhqs/zhVtRHYCLB27doaHR3t3ZaxsTFmst5isdTbB0u/jbZvdr32rE/P277GXbxu3yX9M5QkLX7e8iZJmnNtjqMLgVur6j0D5QcMVPt54Kvt/Wbg5CR7JzkEWA1cB1wPrG5PdNuLbuLuzVVVwNXAK9v6pwFXzGWbJEmSpOXMEUqSpPnwMuA1wE1Jbmhlb6F7StvhdLe83Qn8EkBV3ZzkMuAWuifEnVFVjwEkORO4ElgBXFRVN7ftvRnYlOSdwJfpEliSJEmS5oAJJUnSnKuqzwOZZNGWXaxzLnDuJOVbJluvqm6newqcJEmSpDnmLW+SJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEkaekkOSnJ1kluS3Jzkja38rUm2J7mhvU4YWOfsJFuTfC3JcQPl61rZ1iRnDZQfkuTaVv6x9kRRSdIkTChJkiRJWgx2Ahuqag1wFHBGkjVt2flVdXh7bQFoy04GXgCsAz6QZEWSFcD7geOBNXRPHB3fzrvbtg4FHgBOn6/GSdJiY0JJkiRJ0tCrqrur6kvt/SPArcCqXaxyIrCpqh6tqjuArXRPAz0C2FpVt1fV94FNwIlJAhwNXN7WvwQ4aU4aI0lLwMqFDkCSJEmS+khyMPBi4FrgZcCZSU4FvkA3iukBumTTNQOrbeMHCai7JpQfCTwLeLCqdk5Sf+L+1wPrAUZGRhgbG+vdhpF9YMNhO3dfcZbMJMY9sWPHjnnf53xb6m20fYvfXLfRhJIkSZKkRSPJU4GPA2+qqoeTfBB4B1Dt3/OA189lDFW1EdgIsHbt2hodHe29jfddegXn3TR/l2N3njI6b/uCLoE1k+OymCz1Ntq+xW+u22hCSZIkSdKikOTJdMmkS6vqEwBVdc/A8g8Bn2oftwMHDax+YCtjivL7gP2SrGyjlAbrS5ImcA4lSZIkSUOvzXF0IXBrVb1noPyAgWo/D3y1vd8MnJxk7ySHAKuB64DrgdXtiW570U3cvbmqCrgaeGVb/zTgirlskyQtZo5QkiRJkrQYvAx4DXBTkhta2VvontJ2ON0tb3cCvwRQVTcnuQy4he4JcWdU1WMASc4ErgRWABdV1c1te28GNiV5J/BlugSWJGkSJpQkSZIkDb2q+jyQSRZt2cU65wLnTlK+ZbL1qup2uqfASZJ2w4SSNGQOPuvTM153w2E7eW3P9e981ytmvD9JkiRJ0vLkHEqSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqZeVCBzDfbtr+EK8969Pztr873/WKeduXJEmSJEnSfFh2CSWpr4PnMQEpSZIkSdJisEcJpST7Ab8HvBAo4PXA14CPAQcDdwKvqqoHkgR4L3AC8F3gtVX1pbad04D/1jb7zqq6ZE/i0tI2nQTPhsN2zutINEmSJEmSlpM9nUPpvcCfVNWPAS8CbgXOAj5bVauBz7bPAMcDq9trPfBBgCTPBM4BjgSOAM5Jsv8exiVJkiRJkqQ5MuOEUpJnAP8CuBCgqr5fVQ8CJwLjI4wuAU5q708EPlyda4D9khwAHAdcVVX3V9UDwFXAupnGJUmSJEmSpLm1JyOUDgG+Dfx+ki8n+b0k+wIjVXV3q/MtYKS9XwXcNbD+tlY2VbkkSZIkSZKG0J7MobQSeAnwH6vq2iTv5Qe3twFQVZWk9iTAQUnW090ux8jICGNjY723MbJPN7/OfJlJjLNtx44dcxbHfB7LPub75zwsZtLuYfgd3RNz+fstSZIkSZrcniSUtgHbqura9vlyuoTSPUkOqKq72y1t97bl24GDBtY/sJVtB0YnlI9NtsOq2ghsBFi7dm2Njo5OVm2X3nfpFZx30/w93O7OU0bnbV9TGRsbYybHajqGdeLrDYftnNef87CYSbuH4Xd0T8zl77ckSZIkaXIzvuWtqr4F3JXk+a3oGOAWYDNwWis7Dbiivd8MnJrOUcBD7da4K4Fjk+zfJuM+tpVJkiRJkiRpCO3pEI7/CFyaZC/gduB1dEmqy5KcDnwDeFWruwU4AdgKfLfVparuT/IO4PpW7+1Vdf8exiVJkiRJkqQ5skcJpaq6AVg7yaJjJqlbwBlTbOci4KI9iUWSJEmSJEnzY/lNMrPEHTzJnEYbDts5tHMdSZIkSZKkxWfGcyhJkiRJkiRpeTKhJEmac0kOSnJ1kluS3Jzkja38mUmuSnJb+3f/Vp4kFyTZmuTGJC8Z2NZprf5tSU4bKH9pkpvaOhckyfy3VJIkSVoeTChJkubDTmBDVa0BjgLOSLIGOAv4bFWtBj7bPgMcD6xur/XAB6FLQAHnAEcCRwDnjCehWp03DKy3bh7aJUmSJC1LJpQkSXOuqu6uqi+1948AtwKrgBOBS1q1S4CT2vsTgQ9X5xpgvyQHAMcBV1XV/VX1AHAVsK4te3pVXdMeAvHhgW1JkiRJmmUmlCRJ8yrJwcCLgWuBkaq6uy36FjDS3q8C7hpYbVsr21X5tknKJUmSJM0Bn/ImSZo3SZ4KfBx4U1U9PDjNUVVVkpqHGNbT3UbHyMgIY2NjvbexY8eOGa23WCz19sHSb6Ptm10bDts5b/sat9R/hpKkxc+EkiRpXiR5Ml0y6dKq+kQrvifJAVV1d7tt7d5Wvh04aGD1A1vZdmB0QvlYKz9wkvpPUFUbgY0Aa9eurdHR0cmq7dLY2BgzWW+xWOrtg6XfRts3u1571qfnbV/jLl6375L+GUqSFj9veZMkzbn2xLULgVur6j0DizYD409qOw24YqD81Pa0t6OAh9qtcVcCxybZv03GfSxwZVv2cJKj2r5OHdiWJEmSpFnmCCVJ0nx4GfAa4KYkN7SytwDvAi5LcjrwDeBVbdkW4ARgK/Bd4HUAVXV/kncA17d6b6+q+9v7XwYuBvYBPtNekiRJkuaACSVJ0pyrqs8DmWLxMZPUL+CMKbZ1EXDRJOVfAF64B2FKkiRJmiZveZMkSZI09JIclOTqJLckuTnJG1v5M5NcleS29u/+rTxJLkiyNcmNSV4ysK3TWv3bkpw2UP7SJDe1dS7I4NMjJEmPY0JJkiRJ0mKwE9hQVWuAo4AzkqwBzgI+W1Wrgc+2zwDHA6vbaz3wQegSUMA5wJHAEcA540moVucNA+utm4d2SdKi5C1vc+zgBXgqiCRJkrTUtAcw3N3eP5LkVmAVcCI/eALoJXRP/3xzK/9wu436miT7tSeKjgJXjc/Bl+QqYF2SMeDpVXVNK/8wcBLOySdJkzKhJEmSJGlRSXIw8GLgWmCkJZsAvgWMtPergLsGVtvWynZVvm2S8sn2v55u1BMjIyOMjY31bsPIPrDhsJ2915upmcS4J3bs2DHv+5xvS72Ntm/xm+s2mlCSJEmStGgkeSrwceBNVfXw4DRHVVVJaq5jqKqNwEaAtWvX1ujoaO9tvO/SKzjvpvm7HLvzlNF52xd0CayZHJfFZKm30fYtfnPdRudQkiRJkrQoJHkyXTLp0qr6RCu+p93KRvv33la+HThoYPUDW9muyg+cpFySNAkTSpIkSZKGXnvi2oXArVX1noFFm4HxJ7WdBlwxUH5qe9rbUcBD7da4K4Fjk+zfJuM+FriyLXs4yVFtX6cObEuSNIG3vEmSJElaDF4GvAa4KckNrewtwLuAy5KcDnwDeFVbtgU4AdgKfBd4HUBV3Z/kHcD1rd7bxyfoBn4ZuBjYh24ybifklqQpmFCSJEmSNPSq6vNAplh8zCT1Czhjim1dBFw0SfkXgBfuQZiStGx4y5skSZIkSZJ6MaEkSZIkSZKkXkwoSZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6MaEkSZIkSZKkXkwoSZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6MaEkSZIkSZKkXkwoSZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6MaEkSZIkSZKkXkwoSZIkSZIkqRcTSpIkSZIkSerFhJIkSZIkSZJ6WbnQAUiStNjctP0hXnvWp+dtf3e+6xXzti9JkiRpOhyhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmSJEmSpF5MKEmSJEmSJKkXE0qSJEmSJEnqxYSSJEmSJEmSejGhJEmac0kuSnJvkq8OlL01yfYkN7TXCQPLzk6yNcnXkhw3UL6ulW1NctZA+SFJrm3lH0uy1/y1TpIkSVp+TChJkubDxcC6ScrPr6rD22sLQJI1wMnAC9o6H0iyIskK4P3A8cAa4NWtLsC727YOBR4ATp/T1kiSJEnLnAklSdKcq6o/B+6fZvUTgU1V9WhV3QFsBY5or61VdXtVfR/YBJyYJMDRwOVt/UuAk2YzfkmSJEmPt3KhA5AkLWtnJjkV+AKwoaoeAFYB1wzU2dbKAO6aUH4k8CzgwaraOUn9J0iyHlgPMDIywtjYWO+gR/aBDYft3H3FWTKTGPfEjh075n2f822pt9H2za75/P8+bqn/DCVJi58JJUnSQvkg8A6g2r/nAa+f651W1UZgI8DatWtrdHS09zbed+kVnHfT/HWhd54yOm/7gi6BNZPjspgs9Tbavtn12rM+PW/7Gnfxun2X9M9QkrT4mVCSJC2Iqrpn/H2SDwGfah+3AwcNVD2wlTFF+X3AfklWtlFKg/UlSZIkzQHnUJIkLYgkBwx8/Hlg/Alwm4GTk+yd5BBgNXAdcD2wuj3RbS+6ibs3V1UBVwOvbOufBlwxH22QJEmSlqs9Tii1J+98Ocmn2udJH93cLgw+1sqvTXLwwDYmfTy0JGlpSPJR4K+A5yfZluR04DeT3JTkRuBngP8MUFU3A5cBtwB/ApxRVY+10UdnAlcCtwKXtboAbwZ+JclWujmVLpzH5kmS5kGSi5Lcm+SrA2VvTbI9yQ3tdcLAskmvMZKsa2Vbk5w1UD7pdYwkaXKzccvbG+n+sH96+zz+6OZNSX6X7tHNH2z/PlBVhyY5udX7NxMeD/1c4H8n+SdV9dgsxCZJGgJV9epJiqdM+lTVucC5k5RvAbZMUn473VPgJElL18XA7wAfnlB+flX91mDBVNcYbfH7gZfTPcTh+iSbq+oWpr6OkSRNYo9GKCU5EHgF8Hvt864e3Xxi+0xbfkyrP9XjoSVJkiQJgKr6c+D+aVaf6hrjCGBrVd1eVd8HNgEn7uY6RpI0iT0dofTbwK8BT2ufd/Xo5lW0xz1X1c4kD7X6u3o8tCRJkiTtyplJTgW+AGyoqgfY9TXGXRPKj2TX1zFPkGQ9sB5gZGSEsbGx3kGP7AMbDtu5+4qzZCYx7okdO3bM+z7n21Jvo+1b/Oa6jTNOKCX5WeDeqvpiktFZi2jX+1x0J+5hYJuXj5m0e7GfRJdDRyBJkqb0QeAdQLV/zwNeP9c7raqNwEaAtWvX1ujoaO9tvO/SKzjvpvl76Padp4zO276g+xtzJsdlMVnqbbR9i99ct3FPzmAvA/5Vm/juKXRzKL2XqR/dPP4Y6G1JVgLPoHvU864eD/04i/HEPQw2HLbTNi8TM2n3fP9xMduWQ0cgSZImV1X3jL9P8iHgU+3jrq4xJiu/j6mvYyRJk5jxFXdVnQ2cDdBGKP1qVZ2S5I/oHt28icc/unlz+/xXbfnnqqqSbAb+MMl76CbMG388tCRJkiRNKckBVXV3+/jzwPgT4Ka6xgiwOskhdAmjk4F/265Lrmby6xhJ2mMHn/Xped/nxev2ndPtz8UQjjcDm5K8E/gyP3iKz4XAR9ojne+nO3lTVTcnGX889E7a46HnIC5JkiRJi1SSjwKjwLPz/7d39+GanXV96L8/CWgMYAI57sYkGFqjNZIKcRrS+tJBNCTBY7D1pHDQTGiO8WhotU2PxrZXQ0HPCbbY01gaDTImoUBAlGbaBEJMGVGPoQkvEgJixhjIjCFREgIjVRz6O388a+zDsGdmr/3yPPvl87mu59rrudfb755n9l4z373udVftTXJVku1V9exMhrw9kOSHkyP/H6OqXp7ktiRPSLKzu+8dTnG4/8cAsIhVCZS6e3eS3cPyolM3d/efJfnfDrP/otNDAwAAJEl3v2SR5sOGPof7P0Z335rk1kXaF/1/DACL+7J5FwAAAADAxiJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAFhzVbWzqh6pqg9PtT2tqm6vqvuGrycM7VVV11TVnqr6UFWdNbXPjmH7+6pqx1T7t1TVPcM+11RVzbaHAACwtQiUAJiF65Ocd0jblUnu6O7Tk9wxvE+S85OcPrwuS3JtMgmgklyV5LlJzk5y1cEQatjmh6b2O/RcAADAKhIoAbDmuvs9SR49pPnCJDcMyzckedFU+409cWeS46vqpCQvSHJ7dz/a3Y8luT3JecO6p3b3nd3dSW6cOhYAALAGBEoAzMtCdz80LH8yycKwfHKSB6e22zu0Hal97yLtAADAGjlm3gUAQHd3VfUszlVVl2UylC4LCwvZvXv36GMsHJtcceaBVa7s8JZT40rs379/5uectc3eR/1bXbP8fj9os3+GAGx8AiUA5uXhqjqpux8ahq09MrTvS3Lq1HanDG37kmw/pH330H7KItsvqruvS3Jdkmzbtq23b99+uE0P6+ffeHNec8/sLqEPvHT7zM6VTAKs5fy5bCSbvY/6t7ouufKWmZ3roOvPO25Tf4bLUVU7k3xPkke6+1lD29OSvCXJaUkeSHJRdz82TM7w75JckORzSS7p7vcP++xI8i+Gw/50d98wtH9LJs/8OzbJrUl+bBhKDcAiDHkDYF52JTk4U9uOJDdPtV88zPZ2TpLHh6FxtyU5t6pOGB7GfW6S24Z1n6mqc4b/QFw8dSwANo/rY4IHgHVDoATAmquqNyf5nSTfUFV7q+rSJFcn+e6qui/Jdw3vk8lvhe9PsifJ65L8aJJ096NJXpXkruH1yqEtwza/NOzzB0neMYt+ATA7JngAWF8MeQNgzXX3Sw6z6vmLbNtJLj/McXYm2blI+91JnrWSGgHYkOYywYPn8R3dVngO2Gbvo/6trs34PD6BEgAAsOHNcoIHz+M7us3+LLdk8/dR/1bXZnwenyFvAADARvXwMFwtIyZ4OFz7kid4AECgBAAAbFwmeACYE0PeAACAdW+Y4GF7khOram8ms7VdneStw2QPH09y0bD5rUkuyGSyhs8leVkymeChqg5O8JB86QQP1yc5NpPJHUzwAHAEAiUAAGDdM8EDwPpiyBsAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARll2oFRVp1bVu6vqI1V1b1X92ND+tKq6varuG76eMLRXVV1TVXuq6kNVddbUsXYM299XVTtW3i0AAAAA1spK7lA6kOSK7j4jyTlJLq+qM5JcmeSO7j49yR3D+yQ5P8npw+uyJNcmkwAqyVVJnpvk7CRXHQyhAAAAAFh/lh0odfdD3f3+YfmzST6a5OQkFya5YdjshiQvGpYvTHJjT9yZ5PiqOinJC5Lc3t2PdvdjSW5Pct5y6wIAAABgbR2zGgepqtOSPCfJe5MsdPdDw6pPJlkYlk9O8uDUbnuHtsO1L3aeyzK5uykLCwvZvXv36FoXjk2uOPPA6P02Mn3eOpbT7+V8H60n+/fv3/B9AAAA2GhWHChV1ZOT/GqSH+/uz1TVX67r7q6qXuk5po53XZLrkmTbtm29ffv20cf4+TfenNfcsyo52oZxxZkH9HmLWE6/H3jp9rUpZkZ2796d5fwsAAAAYPlW9D/uqnpiJmHSG7v714bmh6vqpO5+aBjS9sjQvi/JqVO7nzK07Uuy/ZD23SupC+Cg0668ZebnfODqF878nAAAALO0klneKsnrk3y0u39uatWuJAdnatuR5Oap9ouH2d7OSfL4MDTutiTnVtUJw8O4zx3aAAAAAFiHVnKH0rcm+cEk91TVB4e2f5bk6iRvrapLk3w8yUXDuluTXJBkT5LPJXlZknT3o1X1qiR3Ddu9srsfXUFdAAAAAKyhZQdK3f1bSeowq5+/yPad5PLDHGtnkp3LrQUAAACA2Vn2kDcAAAAAtiaBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAHNVVQ9U1T1V9cGquntoe1pV3V5V9w1fTxjaq6quqao9VfWhqjpr6jg7hu3vq6od8+oPAABsBQIlANaD53X3s7t72/D+yiR3dPfpSe4Y3ifJ+UlOH16XJbk2mQRQSa5K8twkZye56mAIBQAArD6BEgDr0YVJbhiWb0jyoqn2G3viziTHV9VJSV6Q5PbufrS7H0tye5LzZlwzAHPibleA2RMoATBvneRdVfW+qrpsaFvo7oeG5U8mWRiWT07y4NS+e4e2w7UDsHW42xVgho6ZdwEAbHnf1t37quqrk9xeVb83vbK7u6p6tU42hFaXJcnCwkJ27949+hgLxyZXnHlgtUo6quXUuBL79++f+TlnbbP3Uf9W1yy/3w/a7J/hjFyYZPuwfEOS3Ul+MlN3uya5s6oO3u26PcPdrklSVQfvdn3zbMsG2BgESgDMVXfvG74+UlVvz+S3wg9X1Und/dDwj/xHhs33JTl1avdThrZ9+Z//aTjYvvsw57suyXVJsm3btt6+fftimx3Rz7/x5rzmntldQh946faZnSuZBFjL+XPZSDZ7H/VvdV1y5S0zO9dB15933Kb+DNfAwbtdO8kvDj/r3e0KsIYESgDMTVUdl+TLuvuzw/K5SV6ZZFeSHUmuHr7ePOyyK8nLq+qmTIYkPD6ETrcl+b+nhiacm+SnZtgVAObL3a5H4W7X1bfZ+6h/q2sz3u0qUAJgnhaSvL2qksk16U3d/c6quivJW6vq0iQfT3LRsP2tSS5IsifJ55K8LEm6+9GqelWSu4btXnlwyAIAm5+7XY/O3a6rb7P3Uf9W12a821WgBFvcaTP+wfbA1S+c6flY37r7/iTfvEj7p5I8f5H2TnL5YY61M8nO1a4RgPXN3a4A8yFQAgAANjJ3uwLMgUAJAADYsNztCjAfXzbvAgAAAADYWARKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYJRj5l0AsLWcduUtq3q8K848kEtW+ZgAAAAcmTuUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUdZNoFRV51XVx6pqT1VdOe96ANh4XEsAWCnXEoClWReBUlU9Iclrk5yf5IwkL6mqM+ZbFQAbiWsJACvlWgKwdOsiUEpydpI93X1/d38+yU1JLpxzTQBsLK4lAKyUawnAEh0z7wIGJyd5cOr93iTPPXSjqrosyWXD2/1V9bFlnOvEJH+yjP02rH+kz1vGVuz3euxzvXrZu37tKpaxFW3aa8kK/k4t17r7vloDm72P+rfBPe/Vy+6ja8nKuJasnk3/fZrN30f92+DW+lqyXgKlJenu65Jct5JjVNXd3b1tlUraEPR569iK/d6KfWZlXEuObrP3L9n8fdS/jW8r9HEjcy05us3ev2Tz91H/Nr617uN6GfK2L8mpU+9PGdoAYKlcSwBYKdcSgCVaL4HSXUlOr6pnVtWTkrw4ya451wTAxuJaAsBKuZYALNG6GPLW3Qeq6uVJbkvyhCQ7u/veNTrdim5N3aD0eevYiv3ein1mEa4lq2qz9y/Z/H3Uv41vK/Rx3XEtWVWbvX/J5u+j/m18a9rH6u61PD4AAAAAm8x6GfIGAAAAwAYhUAIAAABglC0TKFXVeVX1saraU1VXzrueWamqB6rqnqr6YFXdPe961kJV7ayqR6rqw1NtT6uq26vqvuHrCfOscbUdps+vqKp9w2f9waq6YJ41rraqOrWq3l1VH6mqe6vqx4b2Tf1ZM19Hu3ZU1ZdX1VuG9e+tqtPmUOayLaF//2T4nvtQVd1RVV87jzqXa6nX/qr6e1XVVbXhpg5eSh+r6qKpn51vmnWNK7GEv6PPGK4NHxj+nm6oa99i1/ND1ldVXTP0/0NVddasa2TlXEtcS9Y71xLXkmXr7k3/yuSBen+Q5K8meVKS301yxrzrmlHfH0hy4rzrWOM+fkeSs5J8eKrtZ5NcOSxfmeTV865zBn1+RZJ/Ou/a1rDPJyU5a1h+SpLfT3LGZv+sveb3Wsq1I8mPJvmFYfnFSd4y77pXuX/PS/KVw/KPbLb+Dds9Jcl7ktyZZNu8616Dz/D0JB9IcsLw/qvnXfcq9++6JD8yLJ+R5IF51z2yj19yPT9k/QVJ3pGkkpyT5L3zrtlr9GfsWuJasq5friWuJSt5bZU7lM5Osqe77+/uzye5KcmFc66JVdLd70ny6CHNFya5YVi+IcmLZlnTWjtMnze17n6ou98/LH82yUeTnJxN/lkzV0u5dkz//XtbkudXVc2wxpU4av+6+93d/bnh7Z1JTplxjSux1Gv/q5K8OsmfzbK4VbKUPv5Qktd292NJ0t2PzLjGlVhK/zrJU4flr0ryRzOsb8WWcD2/MMmNPXFnkuOr6qTZVMcqcS1xLVnvXEtcS5ZtqwRKJyd5cOr93qFtK+gk76qq91XVZfMuZoYWuvuhYfmTSRbmWcwMvXy4jXHnZh76NdwK/pwk783W/axZe0u5dvzlNt19IMnjSZ4+k+pWbuy18dJMfru1URy1f8Mt36d29y2zLGwVLeUz/PokX19Vv11Vd1bVeTOrbuWW0r9XJPmBqtqb5NYk/3A2pc3MVv437GbhWvLFXEvWH9cS15Jl2yqB0lb2bd19VpLzk1xeVd8x74JmrSf3+fW865iBa5P8tSTPTvJQktfMtZo1UlVPTvKrSX68uz8zvW4LfdYwU1X1A0m2JfnX865ltVTVlyX5uSRXzLuWNXZMJkMVtid5SZLXVdXx8yxolb0kyfXdfUomt/S/YfhsgXXGtWRDcy1hUVvlD2lfklOn3p8ytG163b1v+PpIkrdncsvfVvDwwdv4hq8b6bbMZenuh7v7C939P5K8Lpvws66qJ2YSJr2xu39taN5ynzUzs5Rrx19uU1XHZHKb9KdmUt3KLenaWFXfleSfJ/ne7v7zGdW2Go7Wv6ckeVaS3VX1QCbPFNi1wR6mupTPcG+SXd39F939h5k8f+70GdW3Ukvp36VJ3pok3f07Sb4iyYkzqW42tuy/YTcR15K4lqxzriWuJcu2VQKlu5KcXlXPrKonZfKwu11zrmnNVdVxVfWUg8tJzk2y6JPfN6FdSXYMyzuS3DzHWmbikHGw35dN9lkPzxJ4fZKPdvfPTa3acp81M7OUa8f037/vT/JfhzvlNoKj9q+qnpPkFzP5D8BGC2uP2L/ufry7T+zu07r7tEye6/G93b2RZkRdyt/R/5TJb5RTVSdmMmzh/hnWuBJL6d8nkjw/SarqGzP5T8Afz7TKtbUrycXDDD3nJHl8apg3G4NriWvJeuda4lqybMesxkHWu+4+UFUvT3JbJk9539nd9865rFlYSPL24Zl+xyR5U3e/c74lrb6qenMmP+BOHMa9XpXk6iRvrapLk3w8yUXzq3D1HabP26vq2ZkM+XogyQ/Pq7418q1JfjDJPVX1waHtn2WTf9bMz+GuHVX1yiR3d/euTELON1TVnkwehvji+VU8zhL796+TPDnJrwzXkk909/fOregRlti/DW2JfbwtyblV9ZEkX0jyf3X3hrjzYYn9uyKToRf/OJPr3yUb6D/ih7uePzFJuvsXMnmWxwVJ9iT5XJKXzadSlsu1xLVkvXMtcS1Z0bk30J8TAAAAAOvAVhnyBgAAAMAqESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikCJDa+q3lFVO1Z4jIWqek9VfbaqXlNVr6iq/zise0ZV7a+qJyzhOKdVVVfVMSupZ5Y2Ys0ASVJV31BVHxx+dj9aVT89tH97VX1sicfYXlV716i+6w/WNAtV9dKqeteszgcAbG0CJeaqqh6oqu9ayTG6+/zuvmGFpVyW5E+SPLW7rzjk+J/o7id39xdWeI5MB1UArNhPJHl3dz8lya6Djd39m939DatxglmHQku12C8DuvuN3X3uPOsCALYOgRJMfG2Sj3R3z7sQAJbsa5PcO+8i1sJS7ooFAJgngRKHVVUvq6r/PPX+vqr6lan3D1bVs6vq3w3Ln6mq91XVt09t84qqemtV3TgMSbi3qrYN696Q5BlJ/vMwpOwnquorquo/VtWnqurTVXVXVS0cpc7dVfV/DMuXVNVvVdW/qarHquoPq+r8o+x/fZIdSX5iqOO7Dln/Rb8FrqpnTg2P+/Wqeu0idx29tKo+UVV/UlX/fNjvvCT/LMnfH87zu0vo109X1f83bP+fq+rpVfXG4c/6rqo6bWr7v15Vtw/DPj5WVRdNrXthVX1g2O/BqnrFEc57SVXdP/TvD6vqpUeqE2Aequq/Jnlekn9fVfuTPGlq3RcNY6uqs4afgZ+tql+pqrccetdRVV1RVY9U1UNV9bKh7bIkL83/vD785xxBVT2nqt4/nOctSb5iat0lVfVbh2zfVfV1w/L1VXVtVd1aVX+a5HlH+dn9nuHrp4fa/tah56iqvz1cKx4fvv7tqXW7q+pVVfXbQ73vqqoTj9Q/AIBpAiWO5DeSfHtVfVlVfU0m/1j/W0lSVX81yZOTfCjJXUmeneRpSd6U5Feq6iumjvO9SW5KcnwmQxL+fZJ09w8m+USS/3UYUvazmQQ7X5Xk1CRPT/J/JvnvI+t+bpKPJTkxyc8meX1V1eE27u5Lkrwxyc8Odfz6UY7/piT/bajvFUl+cJFtvi3JNyR5fpJ/WVXf2N3vTPJ/J3nLcJ5vXkJfXjwc/+Qkfy3J7yT55Uz+rD+a5Kokqarjktw+1PbVw37/oarOGI7zp0kuzuQzeGGSH6mqFx16suE41yQ5fxhC8reTfHAJdQLMVHd/Z5LfTPLy7n5yks8vtl1VPSnJ25Ncn8nPzjcn+b5DNvsrmVx7Tk5yaZLXVtUJ3X1dvvj68L8erp7hPP8pyRuG8/xKkr83slv/e5KfSfKUJL+VI//s/o7h6/FDbb9zSD1PS3JLJj/Tn57k55LcUlVPP+R8L8vkuvGkJP90ZL0AwBYmUOKwuvv+JJ/NJCz6jiS3JfmjqvrrSf5Okt/s7v/R3f+xuz/V3Qe6+zVJvjyTMOWg3+ruW4dnEL0hyZGClL/I5B++X9fdX+ju93X3Z0aW/vHuft1wvhuSnJTkiHc5LVVVPSPJ30zyL7v78939W5l6bseUf9Xd/727fzfJ7+bIfT6SX+7uP+jux5O8I8kfdPevd/eBTP6z8pxhu+9J8kB3//LwOXwgya8m+d+SpLt3d/c9w+f1oUz+Q/V3DnPO/5HkWVV1bHc/1N2bcjgJsGWck+SYJNd09190969l8kuBaX+R5JXD+luT7M8XX8eWep4nJvl/h+O8LZNfuIxxc3f/9vCz+s9G/uw+1AuT3NfdbxiuC29O8ntJpkOxX+7u3+/u/57krZlc7wEAlkSgxNH8RpLtmQRKv5Fkdyb/mP07w/tU1T+tqo8Ot9R/OpPf8k7fNv/JqeXPJfmKOvyMYm/IJLi6qar+qKp+tqqeOLLmvzxfd39uWHzyyGMcztckeXTquEny4JFqyKTPyz3/w1PL/32R9weP+7VJnluTYYKfHj6Hl2byW/dU1XOr6t1V9cdV9Xgmd359ydCG7v7TJH9/WP9QVd0yBIgAG9XXJNl3yDPyDv25/akhqD9oOT+3FzvPx0ce44vqWurP7iPUc+j5P57JXVgHrda1CgDYggRKHM3BQOnbh+XfyFSgVJPnJf1EkouSnNDdxyd5PMlhh5gd4osegj38VvdfdfcZmQy3+p5MbvdfLx5K8rSq+sqptlNH7L9WD/1+MMlvdPfxU68nd/ePDOvflMmdVKd291cl+YUc5jPq7tu6+7szubPr95K8bo1qBpiFh5KcfMjQ57X4ub3YeZ4xtfynSf7y2lFVf2UJ5zrSz+6j1fVHmfyyYdozkuw7yn4AAEsiUOJofiOTh54e2917M3lexXmZDEv7QCbPeTiQ5I+THFNV/zLJU0cc/+Ekf/Xgm6p6XlWdWZPZbT6TyTCE/7EaHVkN3f3xJHcneUVVPamq/la+ePjA0Tyc5LSqWu3vvf+S5Our6ger6onD629W1TcO65+SyZ1Vf1ZVZ2fy3IwvUVULVXXh8CylP89k2Me6+fMHWIbfSfKFJC+vqmOq6sIkZ4/Y/4uuU0c5z4Ek/2j4Gfx3DznP7yb5pppMZvEVmTyD72iO9LP7jzP5+Xy42m7N5Lrwvw/9/vtJzsjkegEAsGICJY6ou38/k1DhN4f3n0lyf5LfHp5RdFuSdyb5/Uxupf+zLD4E7HD+nyT/Yhim9U8zGaL1tkzCpI9mEmi9YXV6s2pemsnDyT+V5KeTvCWT8GUpDs6S96mqev9qFdTdn01ybiYP4/6jTIYxvDqT51klyY8meWVVfTbJv8zkWRmL+bIk/2Q4xqOZ3In2I4fZFmDd6+7PJ/m7mTxs+9NJfiCTUGWpP7dfn+SM4Tr1n5Zwnksy+fn595P82tT630/yyiS/nuS+TB66fTSH/dk9DL3+mSS/PdR2ziH1fCqTu3yvyOR69RNJvqe7/2QJ5wUAOKr64qH+wFjD1NC/191XzbsWAI6uqt6b5Be6+5fnXQsAwEblDiUYaRhK9teq6suq6rwkF2YyVTQA61BV/Z2q+ivD0K8dSf5GJnfXAgCwTIebaQvWlaraf5hV53f3b87qGIO/kskwhqcn2ZvkR7r7AyP2X4uaADi8b8hkuNhxmQzb/v7ufmjsQarqGUk+cpjVZ3T3J5ZfIgDAxmLIGwAAAACjGPIGAAAAwCgCJQAAAABG2bDPUDrxxBP7tNNOG73fn/7pn+a4445b/YLWic3ev2Tz91H/Nr7l9vF973vfn3T3/7IGJXEYy72WrKat8D2RbJ1+Jlunr/q5PrmWADArGzZQOu2003L33XeP3m/37t3Zvn376he0Tmz2/iWbv4/6t/Ett49V9fHVr4YjWe61ZDVthe+JZOv0M9k6fdXP9cm1BIBZMeQNAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMcM+8CZu2efY/nkitvmdn5Hrj6hTM7FwDAVnDaDP8td8WZB7J9ZmcDgI3DHUoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBsOaq6tSqendVfaSq7q2qHxvan1ZVt1fVfcPXE4b2qqprqmpPVX2oqs6aOtaOYfv7qmrHVPu3VNU9wz7XVFXNvqcAALA1CJQAmIUDSa7o7jOSnJPk8qo6I8mVSe7o7tOT3DG8T5Lzk5w+vC5Lcm0yCaCSXJXkuUnOTnLVwRBq2OaHpvY7bwb9AgCALUmgBMCa6+6Huvv9w/Jnk3w0yclJLkxyw7DZDUleNCxfmOTGnrgzyfFVdVKSFyS5vbsf7e7Hktye5Lxh3VO7+87u7iQ3Th0LAABYZQIlAGaqqk5L8pwk702y0N0PDas+mWRhWD45yYNTu+0d2o7UvneRdgAAYA0cM+8CANg6qurJSX41yY9392emH3PU3V1VPYMaLstkGF0WFhaye/futT7lEe3fv3/uNczCVulnsnX6Os9+XnHmgZmda+HYbInPEwDGEigBMBNV9cRMwqQ3dvevDc0PV9VJ3f3QMGztkaF9X5JTp3Y/ZWjbl2T7Ie27h/ZTFtn+S3T3dUmuS5Jt27b19u3bF9tsZnbv3p151zALW6Wfydbp6zz7ecmVt8zsXFeceSAXbYHPEwDGMuQNgDU3zLj2+iQf7e6fm1q1K8nBmdp2JLl5qv3iYba3c5I8PgyNuy3JuVV1wvAw7nOT3Das+0xVnTOc6+KpYwEAAKvsqIHSEaZ6fkVV7auqDw6vC6b2+alh2uaPVdULptrPG9r2VNWVU+3PrKr3Du1vqaonrXZHAZirb03yg0m+85DrxtVJvruq7kvyXcP7JLk1yf1J9iR5XZIfTZLufjTJq5LcNbxeObRl2OaXhn3+IMk7ZtExAADYipYy5O3gVM/vr6qnJHlfVd0+rPu33f1vpjcepoF+cZJvSvI1SX69qr5+WP3aJN+dycNS76qqXd39kSSvHo51U1X9QpJLM0wRDcDG192/laQOs/r5i2zfSS4/zLF2Jtm5SPvdSZ61gjIBAIAlOuodSkeY6vlwLkxyU3f/eXf/YSa/KT57eO3p7vu7+/NJbkpy4TA04TuTvG3Yf3raaAAAAADWmVEP5T5kqudvTfLyqro4yd2Z3MX0WCZh051Tu01P3XzoVM/PTfL0JJ/u7gOLbH/o+Vc8M8/CsbOdGWTWs4JshZllNnsf9W/j2wp9BAAAtrYlB0qLTPV8bSbPsejh62uS/IM1qXKwGjPz/Pwbb85r7pnd5HYPvHT7zM6VbI2ZZTZ7H/Vv49sKfQQAALa2JSUri0313N0PT61/XZL/Mrw93FTPOUz7p5IcX1XHDHcpHXaqZwAAAADmbymzvC061XNVnTS12fcl+fCwvCvJi6vqy6vqmUlOT/LfMpmN5/RhRrcnZfLg7l3Dg1ffneT7h/2np40GAAAAYJ1Zyh1KB6d6vqeqPji0/bMkL6mqZ2cy5O2BJD+cJN19b1W9NclHMpkh7vLu/kKSVNXLk9yW5AlJdnb3vcPxfjLJTVX100k+kEmABQAAAMA6dNRA6QhTPd96hH1+JsnPLNJ+62L7dff9mcwCBwAAAMA6d9QhbwAAAAAwTaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEY5Zt4FAKyl0668ZebnvP6842Z+TgAAgFlyhxIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFGOGihV1alV9e6q+khV3VtVPza0P62qbq+q+4avJwztVVXXVNWeqvpQVZ01dawdw/b3VdWOqfZvqap7hn2uqapai84CAAAAsHJLuUPpQJIruvuMJOckubyqzkhyZZI7uvv0JHcM75Pk/CSnD6/LklybTAKoJFcleW6Ss5NcdTCEGrb5oan9zlt51wAAAABYC0cNlLr7oe5+/7D82SQfTXJykguT3DBsdkOSFw3LFya5sSfuTHJ8VZ2U5AVJbu/uR7v7sSS3JzlvWPfU7r6zuzvJjVPHAgAAAGCdOWbMxlV1WpLnJHlvkoXufmhY9ckkC8PyyUkenNpt79B2pPa9i7Qvdv7LMrnrKQsLC9m9e/eY8pMkC8cmV5x5YPR+y7WcGldi//79Mz/nrG32Purf6prl9/tBm/0zBAAAWHKgVFVPTvKrSX68uz8z/Zij7u6q6jWo74t093VJrkuSbdu29fbt20cf4+ffeHNec8+oHG1FHnjp9pmdK5kEWMv5c9lINnsf9W91XXLlLTM710HXn3fcpv4MAQAAljTLW1U9MZMw6Y3d/WtD88PDcLUMXx8Z2vclOXVq91OGtiO1n7JIOwAAAADr0FJmeaskr0/y0e7+ualVu5IcnKltR5Kbp9ovHmZ7OyfJ48PQuNuSnFtVJwwP4z43yW3Dus9U1TnDuS6eOhYAAAAA68xS7lD61iQ/mOQ7q+qDw+uCJFcn+e6qui/Jdw3vk+TWJPcn2ZPkdUl+NEm6+9Ekr0py1/B65dCWYZtfGvb5gyTvWIW+AbBOVNXOqnqkqj481faKqtp3yLXl4Lqfqqo9VfWxqnrBVPt5Q9ueqrpyqv2ZVfXeof0tVfWk2fUOAAC2nqM+TKi7fytJHWb18xfZvpNcfphj7Uyyc5H2u5M862i1ALBhXZ/k32cyk+e0f9vd/2a6oarOSPLiJN+U5GuS/HpVff2w+rVJvjuTCRzuqqpd3f2RJK8ejnVTVf1CkkuTXLtWnQEAgK1uSc9QAoCV6O73JHn0qBtOXJjkpu7+8+7+w0zuXj17eO3p7vu7+/NJbkpy4TBc+juTvG3Y/4YkL1rN+gEAgC8mUAJgnl5eVR8ahsSdMLSdnOTBqW32Dm2Ha396kk9394FD2gEAgDVy1CFvALBGrs3k2Xo9fH1Nkn+w1ietqsuSXJYkCwsL2b1791qf8oj2798/9xpmYav0M9k6fZ1nP68488DRN1olC8dmS3yeADCWQAmAuejuhw8uV9XrkvyX4e2+JKdObXrK0JbDtH8qyfFVdcxwl9L09oud97ok1yXJtm3bevv27SvryArt3r07865hFrZKP5Ot09d59vOSK2+Z2bmuOPNALtoCnycAjGXIGwBzUVUnTb39viQHZ4DbleTFVfXlVfXMJKcn+W+ZzBB6+jCj25MyeXD3rmEyiHcn+f5h/x1Jbp5FHwAAYKtyhxIAa66q3pxke5ITq2pvkquSbK+qZ2cy5O2BJD+cJN19b1W9NclHkhxIcnl3f2E4zsuT3JbkCUl2dve9wyl+MslNVfXTST6Q5PWz6RkAAGxNAiUA1lx3v2SR5sOGPt39M0l+ZpH2W5Pcukj7/ZnMAgcAAMyAIW8AAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjHDVQqqqdVfVIVX14qu0VVbWvqj44vC6YWvdTVbWnqj5WVS+Yaj9vaNtTVVdOtT+zqt47tL+lqp60mh0EAAAAYHUt5Q6l65Oct0j7v+3uZw+vW5Okqs5I8uIk3zTs8x+q6glV9YQkr01yfpIzkrxk2DZJXj0c6+uSPJbk0pV0CAAAAIC1ddRAqbvfk+TRJR7vwiQ3dfefd/cfJtmT5Ozhtae77+/uzye5KcmFVVVJvjPJ24b9b0jyonFdAAAAAGCWjlnBvi+vqouT3J3kiu5+LMnJSe6c2mbv0JYkDx7S/twkT0/y6e4+sMj2X6KqLktyWZIsLCxk9+7do4teODa54swDR99wlSynxpXYv3//zM85a5u9j/q3umb5/X7QZv8MAQAAlhsoXZvkVUl6+PqaJP9gtYo6nO6+Lsl1SbJt27bevn376GP8/BtvzmvuWUmONs4DL90+s3MlkwBrOX8uG8lm76P+ra5LrrxlZuc66PrzjtvUnyEAAMCykpXufvjgclW9Lsl/Gd7uS3Lq1KanDG05TPunkhxfVccMdylNbw8AAADAOrSUh3J/iao6aert9yU5OAPcriQvrqovr6pnJjk9yX9LcleS04cZ3Z6UyYO7d3V3J3l3ku8f9t+R5Obl1AQAAADAbBz1DqWqenOS7UlOrKq9Sa5Ksr2qnp3JkLcHkvxwknT3vVX11iQfSXIgyeXd/YXhOC9PcluSJyTZ2d33Dqf4ySQ3VdVPJ/lAktevVucAAAAAWH1HDZS6+yWLNB829Onun0nyM4u035rk1kXa789kFjgAAAAANoBlDXkDAAAAYOsSKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAGuuqnZW1SNV9eGptqdV1e1Vdd/w9YShvarqmqraU1UfqqqzpvbZMWx/X1XtmGr/lqq6Z9jnmqqq2fYQAAC2FoESALNwfZLzDmm7Mskd3X16kjuG90lyfpLTh9dlSa5NJgFUkquSPDfJ2UmuOhhCDdv80NR+h54LAABYRQIlANZcd78nyaOHNF+Y5IZh+YYkL5pqv7En7kxyfFWdlOQFSW7v7ke7+7Ektyc5b1j31O6+s7s7yY1TxwIAANbAMfMuAIAta6G7HxqWP5lkYVg+OcmDU9vtHdqO1L53kfZFVdVlmdz5lIWFhezevXv5PVgF+/fvn3sNs7BV+plsnb7Os59XnHlgZudaODZb4vMEgLEESgDMXXd3VfWMznVdkuuSZNu2bb19+/ZZnPawdu/enXnXMAtbpZ/J1unrPPt5yZW3zOxcV5x5IBdtgc8TAMYy5A2AeXl4GK6W4esjQ/u+JKdObXfK0Hak9lMWaQcAANaIQAmAedmV5OBMbTuS3DzVfvEw29s5SR4fhsbdluTcqjpheBj3uUluG9Z9pqrOGWZ3u3jqWAAAwBow5A2ANVdVb06yPcmJVbU3k9nark7y1qq6NMnHk1w0bH5rkguS7EnyuSQvS5LufrSqXpXkrmG7V3b3wQd9/2gmM8kdm+QdwwsAAFgjAiUA1lx3v+Qwq56/yLad5PLDHGdnkp2LtN+d5FkrqREAAFg6Q94AAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwylEDparaWVWPVNWHp9qeVlW3V9V9w9cThvaqqmuqak9VfaiqzpraZ8ew/X1VtWOq/Vuq6p5hn2uqqla7kwAAAACsnmOWsM31Sf59khun2q5Mckd3X11VVw7vfzLJ+UlOH17PTXJtkudW1dOSXJVkW5JO8r6q2tXdjw3b/FCS9ya5Ncl5Sd6x8q4BACzutCtvWbNjX3HmgVxyyPEfuPqFa3Y+AIB5OOodSt39niSPHtJ8YZIbhuUbkrxoqv3GnrgzyfFVdVKSFyS5vbsfHUKk25OcN6x7anff2d2dSWj1ogAAAACwbi3lDqXFLHT3Q8PyJ5MsDMsnJ3lwaru9Q9uR2vcu0r6oqrosyWVJsrCwkN27d48v/NjJbw5nZTk1rsT+/ftnfs5Z2+x91L/VNcvv94M2+2cIAACw3EDpL3V3V1WvRjFLONd1Sa5Lkm3btvX27dtHH+Pn33hzXnPPiru9ZA+8dPvMzpVMAqzl/LlsJJu9j/q3ug4ddjIL15933Kb+DAEAAJabrDxcVSd190PDsLVHhvZ9SU6d2u6UoW1fku2HtO8e2k9ZZHsAYLCWz/tZjOf9AABwNEd9htJh7EpycKa2HUlunmq/eJjt7Zwkjw9D425Lcm5VnTDMCHduktuGdZ+pqnOG2d0unjoWAAAAAOvQUe9Qqqo3Z3J30YlVtTeT2dquTvLWqro0yceTXDRsfmuSC5LsSfK5JC9Lku5+tKpeleSuYbtXdvfBB33/aCYzyR2byexuZngDAAAAWMeOGih190sOs+r5i2zbSS4/zHF2Jtm5SPvdSZ51tDoAAAAAWB+WO+QNAAAAgC1KoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABjlmHkXAAAbzWlX3rJqx7rizAO5ZBWPBwAAs+AOJQAAAABGESgBMFdV9UBV3VNVH6yqu4e2p1XV7VV13/D1hKG9quqaqtpTVR+qqrOmjrNj2P6+qtoxr/4AAMBWIFACYD14Xnc/u7u3De+vTHJHd5+e5I7hfZKcn+T04XVZkmuTSQCV5Kokz01ydpKrDoZQAADA6hMoAbAeXZjkhmH5hiQvmmq/sSfuTHJ8VZ2U5AVJbu/uR7v7sSS3JzlvxjUDAMCW4aHcAMxbJ3lXVXWSX+zu65IsdPdDw/pPJlkYlk9O8uDUvnuHtsO1f4mquiyTu5uysLCQ3bt3jy74ijMPjN7ncBaOXd3jrYbl/Jkczf79+9fkuMu1ln/mi32m66nvq2Wen+ksv2cWjt2cnx8ArJRACYB5+7bu3ldVX53k9qr6vemV3d1D2LQqhsDquiTZtm1bb9++ffQxVnNWtivOPJDX3LO+LscPvHT7qh9z9+7dWc6f9VpZy5n1FvtM1+LPdN7m+ZnOcmbEK848kIvW0d9dAFgvDHkDYK66e9/w9ZEkb8/kGUgPD0PZMnx9ZNh8X5JTp3Y/ZWg7XDsAALAGBEoAzE1VHVdVTzm4nOTcJB9OsivJwZnadiS5eVjeleTiYba3c5I8PgyNuy3JuVV1wvAw7nOHNgAAYA2sr3vsAdhqFpK8vaqSyTXpTd39zqq6K8lbq+rSJB9PctGw/a1JLkiyJ8nnkrwsSbr70ap6VZK7hu1e2d2Pzq4bAACwtQiUAJib7r4/yTcv0v6pJM9fpL2TXH6YY+1MsnO1awQAAL6UIW8AAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEZZUaBUVQ9U1T1V9cGquntoe1pV3V5V9w1fTxjaq6quqao9VfWhqjpr6jg7hu3vq6odK+sSAAAAAGtpNe5Qel53P7u7tw3vr0xyR3efnuSO4X2SnJ/k9OF1WZJrk0kAleSqJM9NcnaSqw6GUAAAAACsP2sx5O3CJDcMyzckedFU+409cWeS46vqpCQvSHJ7dz/a3Y8luT3JeWtQFwAAAACr4JgV7t9J3lVVneQXu/u6JAvd/dCw/pNJFoblk5M8OLXv3qHtcO1foqouy+TupiwsLGT37t2jC144NrnizAOj91uu5dS4Evv375/5OWdts/dR/1bXLL/fD9rsnyEAAMBKA6Vv6+59VfXVSW6vqt+bXtndPYRNq2IIrK5Lkm3btvX27dtHH+Pn33hzXnPPSru9dA+8dPvMzpVMAqzl/LlsJJu9j/q3ui658paZneug6887blN/hgAAACsa8tbd+4avjyR5eybPQHp4GMqW4esjw+b7kpw6tfspQ9vh2gEAAABYh5YdKFXVcVX1lIPLSc5N8uEku5IcnKltR5Kbh+VdSS4eZns7J8njw9C425KcW1UnDA/jPndoAwAAAGAdWsnYr4Ukb6+qg8d5U3e/s6ruSvLWqro0yceTXDRsf2uSC5LsSfK5JC9Lku5+tKpeleSuYbtXdvejK6gLAAAAgDW07ECpu+9P8s2LtH8qyfMXae8klx/mWDuT7FxuLQAAAADMzoqeoQQAAADA1iNQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwyjHzLgAA2NpOu/KWeZcAAMBIAiUAgE1mFiHdFWceyCXDeR64+oVrfj4AYH0x5A0AAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMIpACQAAAIBRBEoAAAAAjCJQAgAAAGAUgRIAAAAAowiUAAAAABhFoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACjCJQAAAAAGEWgBAAAAMAoAiUAAAAARhEoAQAAADCKQAkAAACAUQRKAAAAAIwiUAIAAABgFIESAAAAAKMIlAAAAAAYRaAEAAAAwCgCJQAAAABGESgBAAAAMMox8y7goKo6L8m/S/KEJL/U3VfPuSQANhjXEpiP0668Zd4lAAAzti4Cpap6QpLXJvnuJHuT3FVVu7r7I/OtDICNwrWE9UzgAgBsNutlyNvZSfZ09/3d/fkkNyW5cM41AbCxuJYAAMCMrIs7lJKcnOTBqfd7kzx3TrUAsDG5lgBrYh53mD1w9Qtnfk4AGGO9BEpLUlWXJblseLu/qj62jMOcmORPVq+qI6tXz+pMf2mm/ZuTzd5H/dvgnvfqZffxa1e7Fr7UKl1LVs0/WoffE2t07Vp3/Vwr6/EzXQv6ubZW8H3oWgLATKyXQGlfklOn3p8ytH2R7r4uyXUrOVFV3d3d21ZyjPVss/cv2fx91L+Nbyv0cZ2a2bVkNW2Vvy9bpZ/J1umrfgLA1rZenqF0V5LTq+qZVfWkJC9OsmvONQGwsbiWAADAjKyLO5S6+0BVvTzJbZlM9byzu++dc1kAbCCuJQAAMDvrIlBKku6+NcmtMzjVuhnmsEY2e/+Szd9H/dv4tkIf16UZXktW01b5+7JV+plsnb7qJwBsYdXd864BAAAAgA1kvTxDCQAAAIANYtMGSlV1XlV9rKr2VNWVi6z/8qp6y7D+vVV12hzKXLYl9O+fVNVHqupDVXVHVW2oKWSP1r+p7f5eVXVVbbjZV5bSx6q6aPgc762qN826xpVYwt/RZ1TVu6vqA8Pf0wvmUedyVdXOqnqkqj58mPVVVdcM/f9QVZ016xpZ/6rqgaq6p6o+WFV3z7uetVJVx1fV26rq96rqo1X1t+Zd02qrqm8YPseDr89U1Y/Pu661UlX/eLg2fbiq3lxVXzHvmtZCVf3Y0Md7N/PnCQDLsSkDpap6QpLXJjk/yRlJXlJVZxyy2aVJHuvur0vyb5O8erZVLt8S+/eBJNu6+28keVuSn51tlcu3xP6lqp6S5MeSvHe2Fa7cUvpYVacn+akk39rd35Tkx2dd53It8TP8F0ne2t3PyWQ2rv8w2ypX7Pok5x1h/flJTh9elyW5dgY1sTE9r7ufvcmnJf93Sd7Z3X89yTcn+eic61l13f2x4XN8dpJvSfK5JG+fb1Vro6pOTvKPMvl3xrMyeQj+i+db1eqrqmcl+aEkZ2fy9/Z7qurr5lsVAKwfmzJQyuTCv6e77+/uzye5KcmFh2xzYZIbhuW3JXl+VdUMa1yJo/avu9/d3Z8b3t6Z5JQZ17gSS/n8kuRVmQSBfzbL4lbJUvr4Q0le292PJUl3PzLjGldiKf3rJE8dlr8qyR/NsL4V6+73JHn0CJtcmOTGnrgzyfFVddJsqoP1o6q+Ksl3JHl9knT357v703Mtau09P8kfdPfH513IGjomybFVdUySr8wG+xm+RN+Y5L3d/bnuPpDkN5L83TnXBADrxmYNlE5O8uDU+71D26LbDP9IeDzJ02dS3cotpX/TLk3yjjWtaHUdtX/D8KFTu/uWWRa2ipbyGX59kq+vqt+uqjur6kh3w6w3S+nfK5L8QFXtzWRWrn84m9JmZuz3KVtTJ3lXVb2vqi6bdzFr5JlJ/jjJLw9DXH+pqo6bd1Fr7MVJ3jzvItZKd+9L8m+SfCLJQ0ke7+53zbeqNfHhJN9eVU+vqq9MckGSU+dcEwCsG5s1UGJQVT+QZFuSfz3vWlZLVX1Zkp9LcsW8a1ljx2QyXGp7kpckeV1VHT/PglbZS5Jc392nZPKP9DcMny1sJd/W3WdlMkTy8qr6jnkXtAaOSXJWkmuHIa5/muSwz8bb6KrqSUm+N8mvzLuWtVJVJ2RyF+Yzk3xNkuOGf29sKt390UzuhH5Xkncm+WCSL8yzJgBYTzbrf9725Yt/g3TK0LboNsPt2l+V5FMzqW7lltK/VNV3JfnnSb63u/98RrWthqP17ylJnpVkd1U9kOScJLs22IO5l/IZ7k2yq7v/orv/MMnvZxIwbQRL6d+lSd6aJN39O0m+IsmJM6luNpb0fcrWNtzpcXBI69szGS662exNsre7Dz7v7m2ZBEyb1flJ3t/dD8+7kDX0XUn+sLv/uLv/IsmvJfnbc65pTXT367v7W7r7O5I8lsm1GADI5g2U7kpyelU9c/hN4YuT7Dpkm11JdgzL35/kv3Z3z7DGlThq/6rqOUl+MZMwaSM9eyc5Sv+6+/HuPrG7T+vu0zJ5RtT3dvdGmiFpKX9H/1Mmdyelqk7MZAjc/TOscSWW0r9PZPKckVTVN2YSKP3xTKtcW7uSXDzM9nZOJkNCHpp3UawfVXXcMLlAhiFg52YyxGZT6e5PJnmwqr5haHp+ko/MsaS19pJs4uFug08kOaeqvnJ4/uTzswkftJ4kVfXVw9dnZPL8pA014yoArKVj5l3AWujuA1X18iS3ZTLzyM7uvreqXpnk7u7elcnDQd9QVXsyebDuhpmdZIn9+9dJnpzkV4ZnjX+iu793bkWPsMT+bWhL7ONtSc6tqo9kcov9/9XdG+IuuiX274pMhvH940yeI3PJBgp1U1VvziTwO3F4DtRVSZ6YJN39C5k8F+qCJHsyme3pZfOplHVsIcnbh5/RxyR5U3e/c74lrZl/mOSNQ8B8fzbp98MQDH53kh+edy1rqbvfW1VvS/L+JAcymVn2uvlWtWZ+taqenuQvkly+BR4oDwBLVhvo/28AAAAArAObdcgbAAAAAGtEoAQAAADAKAIlAAAAAEYRKAEAAAAwikAJAAAAgFEESgAAAACMIlACAAAAYBSBEgAAAACj/P9kMIy4f4XVHgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[num_ftr].hist(figsize=(20,20))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2. Exploring the distribution of the target variable" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(data=pd.DataFrame(y), x='booking_complete')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can clearly see that our target variable is unbalanced, which implies that we should use the \"f-1\" score as our evaluation metric. This will allow us to get a clear picture of our model's performance.\n", "Using \" accuracy\" as a metric will lead to choosing the wrong model. The reason for this can be explained by the example of a dummy model that always predicts the most common class, this model will be considered a good model in an evaluation process where we use \"accuracy\" as a metric. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### III. Tuning and comparing the models." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34496 34496\n" ] } ], "source": [ "# Splitting the data\n", "train_X, test_X, train_y, test_y = train_test_split(df, y, test_size=0.30)\n", "print(len(train_X), (len(train_y)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 1. Tuning the models." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32m[I 2022-11-20 12:55:41,598]\u001b[0m A new study created in memory with name: no-name-b024e4e5-3d4a-46bc-a419-23f9eb7cee37\u001b[0m\n", "Progress bar is experimental (supported from v1.2.0). The interface can change in the future.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "499e37d648a74aef9e14435bf6986f8a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/300 [00:00\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
f-1 scoresprediction time
models
Decision Tree0.2903970.061006
Random Forest0.2400240.181001
Logistic Regression0.1918480.055008
\n", "" ], "text/plain": [ " f-1 scores prediction time\n", "models \n", "Decision Tree 0.290397 0.061006\n", "Random Forest 0.240024 0.181001\n", "Logistic Regression 0.191848 0.055008" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creating the dataframe \n", "df_dict = {'f-1 scores':scores_list,\n", " 'prediction time': pred_time_list,\n", " 'models':model_names}\n", "results = pd.DataFrame(df_dict).set_index('models')\n", "results.sort_values(by='f-1 scores', ascending = False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision tree classifier is the classifier with the highest F-1 score." ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The classification report for the 'Random Forest' classifier\n", "----------------------------------------------\n", " precision recall f1-score support\n", "\n", " 0 0.87 0.95 0.91 12571\n", " 1 0.37 0.18 0.24 2214\n", "\n", " accuracy 0.83 14785\n", " macro avg 0.62 0.56 0.57 14785\n", "weighted avg 0.79 0.83 0.81 14785\n", "\n", "The classification report for the 'Logistic Regression' classifier\n", "----------------------------------------------\n", " precision recall f1-score support\n", "\n", " 0 0.86 0.97 0.91 12571\n", " 1 0.43 0.12 0.19 2214\n", "\n", " accuracy 0.84 14785\n", " macro avg 0.65 0.55 0.55 14785\n", "weighted avg 0.80 0.84 0.81 14785\n", "\n", "The classification report for the 'Decision Tree' classifier\n", "----------------------------------------------\n", " precision recall f1-score support\n", "\n", " 0 0.87 0.88 0.88 12571\n", " 1 0.30 0.28 0.29 2214\n", "\n", " accuracy 0.79 14785\n", " macro avg 0.59 0.58 0.58 14785\n", "weighted avg 0.79 0.79 0.79 14785\n", "\n" ] } ], "source": [ "# Printing the classification report and the confusion matrix for each model\n", "\n", "for name, pipe in pipes.items():\n", " print(\"The classification report for the '{}' classifier\".format(name))\n", " print(\"----------------------------------------------\")\n", " print(classification_report(test_y, pipe.predict(test_X)))\n", "\n", " \n", " \n" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Visualizing the confusion matrix for the random forest classifier\n", "cfm = confusion_matrix(test_y, RF_pipe.predict(test_X))\n", "ConfusionMatrixDisplay(cfm).plot()\n" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Visualizing the confusion matrix for the decision tree classifier\n", "\n", "cfm=confusion_matrix(test_y, tree_pipe.predict(test_X))\n", "ConfusionMatrixDisplay(cfm).plot()" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Visualizing the confusion matrix for the logistic regression classifier\n", "\n", "cfm=confusion_matrix(test_y, log_pipe.predict(test_X))\n", "ConfusionMatrixDisplay(cfm).plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 4. Choosing the best model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The decision tree classifier is the one with the highest capability to distinguish the minority class (1) in the test set. It correctly identified 28.4% of the positive examples. \n", "On the other hand the Random Forest classifier correctly predicted 17.9% of the positive examples and the Logistic Regression classifier made an exact prediction for 12.3% of the positive examples.\n", "By assuming that \"false negatives\" (incorrectly identifying clients that will make a booking as not making a booking) are very expensive for the company we choose the Decision Tree classifier as the best model." ] }, { "cell_type": "code", "execution_count": 136, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(criterion='entropy', max_depth=24)" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Getting the classifier\n", "clf = tree_pipe[-1]\n", "clf" ] }, { "cell_type": "code", "execution_count": 137, "metadata": {}, "outputs": [], "source": [ "# Saving the classifier \n", "pickle.dump(clf, open('classifier_BA.sav', 'wb'))\n" ] }, { "cell_type": "code", "execution_count": 141, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['num_passengers', 'sales_channel', 'purchase_lead', 'length_of_stay',\n", " 'flight_hour', 'flight_day', 'route', 'wants_extra_baggage',\n", " 'wants_preferred_seat', 'wants_in_flight_meals', 'flight_duration'],\n", " dtype='object')\n" ] } ], "source": [ "# Getting the names of the selected features\n", "\n", "# The selector object\n", "selector = tree_pipe[1]\n", "\n", "# The indices of the features\n", "indx = selector.get_support(indices=True)\n", "\n", "select_ftr = df.columns[indx]\n", "print(select_ftr)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channelpurchase_leadlength_of_stayflight_hourflight_dayroutewants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_duration
02Internet262197SatAKLDEL1005.52
11Internet112203SatAKLDEL0005.52
22Internet2432217WedAKLDEL1105.52
31Internet96314SatAKLDEL0015.52
42Internet682215WedAKLDEL1015.52
....................................
499952Internet2769SatPERPNH1015.62
499961Internet11164SunPERPNH0005.62
499971Internet24622SatPERPNH0015.62
499981Internet15611MonPERPNH1015.62
499991Internet19610ThuPERPNH0105.62
\n", "

49281 rows × 11 columns

\n", "
" ], "text/plain": [ " num_passengers sales_channel purchase_lead length_of_stay \\\n", "0 2 Internet 262 19 \n", "1 1 Internet 112 20 \n", "2 2 Internet 243 22 \n", "3 1 Internet 96 31 \n", "4 2 Internet 68 22 \n", "... ... ... ... ... \n", "49995 2 Internet 27 6 \n", "49996 1 Internet 111 6 \n", "49997 1 Internet 24 6 \n", "49998 1 Internet 15 6 \n", "49999 1 Internet 19 6 \n", "\n", " flight_hour flight_day route wants_extra_baggage \\\n", "0 7 Sat AKLDEL 1 \n", "1 3 Sat AKLDEL 0 \n", "2 17 Wed AKLDEL 1 \n", "3 4 Sat AKLDEL 0 \n", "4 15 Wed AKLDEL 1 \n", "... ... ... ... ... \n", "49995 9 Sat PERPNH 1 \n", "49996 4 Sun PERPNH 0 \n", "49997 22 Sat PERPNH 0 \n", "49998 11 Mon PERPNH 1 \n", "49999 10 Thu PERPNH 0 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \n", "0 0 0 5.52 \n", "1 0 0 5.52 \n", "2 1 0 5.52 \n", "3 0 1 5.52 \n", "4 0 1 5.52 \n", "... ... ... ... \n", "49995 0 1 5.62 \n", "49996 0 0 5.62 \n", "49997 0 1 5.62 \n", "49998 0 1 5.62 \n", "49999 1 0 5.62 \n", "\n", "[49281 rows x 11 columns]" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The new dataframe with the selected features \n", "final_df = df[select_ftr]\n", "final_df" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11\n" ] } ], "source": [ "# # Getting the names of the features after feature selection \n", "# Getting numerical features \n", "num_ftr = [col for col in final_df.columns if final_df[col].dtype in ['int64', 'float64']]\n", "\n", "# Getting categorical features \n", "cat_ftr = [col for col in final_df.columns if col not in num_ftr]\n", "\n", "print(len(num_ftr)+len(cat_ftr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### V. Model interpretation using SHAP\n", "SHAP is a technique used to interpret machine learning models. It explains a prediction by calculating what are known as Shapley values, which were developed in Game Theory. These values represent the contributions of the different explanatory variables to the prediction in question." ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channelpurchase_leadlength_of_stayflight_hourflight_dayroutewants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_duration
01.0Internet2.2446810.086957-0.250SatAKLDEL0.00.00.0-0.638629
10.0Internet0.6489360.130435-0.750SatAKLDEL-1.00.00.0-0.638629
21.0Internet2.0425530.2173911.000WedAKLDEL0.01.00.0-0.638629
30.0Internet0.4787230.608696-0.625SatAKLDEL-1.00.01.0-0.638629
41.0Internet0.1808510.2173910.750WedAKLDEL0.00.01.0-0.638629
....................................
499951.0Internet-0.255319-0.4782610.000SatPERPNH0.00.01.0-0.607477
499960.0Internet0.638298-0.478261-0.625SunPERPNH-1.00.00.0-0.607477
499970.0Internet-0.287234-0.4782611.625SatPERPNH-1.00.01.0-0.607477
499980.0Internet-0.382979-0.4782610.250MonPERPNH0.00.01.0-0.607477
499990.0Internet-0.340426-0.4782610.125ThuPERPNH-1.01.00.0-0.607477
\n", "

49281 rows × 11 columns

\n", "
" ], "text/plain": [ " num_passengers sales_channel purchase_lead length_of_stay \\\n", "0 1.0 Internet 2.244681 0.086957 \n", "1 0.0 Internet 0.648936 0.130435 \n", "2 1.0 Internet 2.042553 0.217391 \n", "3 0.0 Internet 0.478723 0.608696 \n", "4 1.0 Internet 0.180851 0.217391 \n", "... ... ... ... ... \n", "49995 1.0 Internet -0.255319 -0.478261 \n", "49996 0.0 Internet 0.638298 -0.478261 \n", "49997 0.0 Internet -0.287234 -0.478261 \n", "49998 0.0 Internet -0.382979 -0.478261 \n", "49999 0.0 Internet -0.340426 -0.478261 \n", "\n", " flight_hour flight_day route wants_extra_baggage \\\n", "0 -0.250 Sat AKLDEL 0.0 \n", "1 -0.750 Sat AKLDEL -1.0 \n", "2 1.000 Wed AKLDEL 0.0 \n", "3 -0.625 Sat AKLDEL -1.0 \n", "4 0.750 Wed AKLDEL 0.0 \n", "... ... ... ... ... \n", "49995 0.000 Sat PERPNH 0.0 \n", "49996 -0.625 Sun PERPNH -1.0 \n", "49997 1.625 Sat PERPNH -1.0 \n", "49998 0.250 Mon PERPNH 0.0 \n", "49999 0.125 Thu PERPNH -1.0 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \n", "0 0.0 0.0 -0.638629 \n", "1 0.0 0.0 -0.638629 \n", "2 1.0 0.0 -0.638629 \n", "3 0.0 1.0 -0.638629 \n", "4 0.0 1.0 -0.638629 \n", "... ... ... ... \n", "49995 0.0 1.0 -0.607477 \n", "49996 0.0 0.0 -0.607477 \n", "49997 0.0 1.0 -0.607477 \n", "49998 0.0 1.0 -0.607477 \n", "49999 1.0 0.0 -0.607477 \n", "\n", "[49281 rows x 11 columns]" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Scaling numerical features \n", "scaler = SklearnTransformerWrapper(RobustScaler())\n", "final_df = scaler.fit_transform(final_df)\n", "final_df" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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num_passengerssales_channelpurchase_leadlength_of_stayflight_hourflight_dayroutewants_extra_baggagewants_preferred_seatwants_in_flight_mealsflight_duration
01.012.2446810.086957-0.250300.00.00.0-0.638629
10.010.6489360.130435-0.75030-1.00.00.0-0.638629
21.012.0425530.2173911.000600.01.00.0-0.638629
30.010.4787230.608696-0.62530-1.00.01.0-0.638629
41.010.1808510.2173910.750600.00.01.0-0.638629
....................................
499951.01-0.255319-0.4782610.00031220.00.01.0-0.607477
499960.010.638298-0.478261-0.6250122-1.00.00.0-0.607477
499970.01-0.287234-0.4782611.6253122-1.00.01.0-0.607477
499980.01-0.382979-0.4782610.25041220.00.01.0-0.607477
499990.01-0.340426-0.4782610.1255122-1.01.00.0-0.607477
\n", "

49281 rows × 11 columns

\n", "
" ], "text/plain": [ " num_passengers sales_channel purchase_lead length_of_stay \\\n", "0 1.0 1 2.244681 0.086957 \n", "1 0.0 1 0.648936 0.130435 \n", "2 1.0 1 2.042553 0.217391 \n", "3 0.0 1 0.478723 0.608696 \n", "4 1.0 1 0.180851 0.217391 \n", "... ... ... ... ... \n", "49995 1.0 1 -0.255319 -0.478261 \n", "49996 0.0 1 0.638298 -0.478261 \n", "49997 0.0 1 -0.287234 -0.478261 \n", "49998 0.0 1 -0.382979 -0.478261 \n", "49999 0.0 1 -0.340426 -0.478261 \n", "\n", " flight_hour flight_day route wants_extra_baggage \\\n", "0 -0.250 3 0 0.0 \n", "1 -0.750 3 0 -1.0 \n", "2 1.000 6 0 0.0 \n", "3 -0.625 3 0 -1.0 \n", "4 0.750 6 0 0.0 \n", "... ... ... ... ... \n", "49995 0.000 3 122 0.0 \n", "49996 -0.625 0 122 -1.0 \n", "49997 1.625 3 122 -1.0 \n", "49998 0.250 4 122 0.0 \n", "49999 0.125 5 122 -1.0 \n", "\n", " wants_preferred_seat wants_in_flight_meals flight_duration \n", "0 0.0 0.0 -0.638629 \n", "1 0.0 0.0 -0.638629 \n", "2 1.0 0.0 -0.638629 \n", "3 0.0 1.0 -0.638629 \n", "4 0.0 1.0 -0.638629 \n", "... ... ... ... \n", "49995 0.0 1.0 -0.607477 \n", "49996 0.0 0.0 -0.607477 \n", "49997 0.0 1.0 -0.607477 \n", "49998 0.0 1.0 -0.607477 \n", "49999 1.0 0.0 -0.607477 \n", "\n", "[49281 rows x 11 columns]" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Encoding categorical features \n", "enc = OrdinalEncoder()\n", "final_df = enc.fit_transform(final_df, y)\n", "final_df" ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# The code to get shap values \n", "explainer = shap.TreeExplainer(clf)\n", "shap_values = explainer.shap_values(final_df)\n", "\n", "# Plotting feature importance \n", "shap.summary_plot(shap_values[0], final_df, max_display=26, plot_type='bar')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The three most predictive features are :\n", "- Purchase lead.\n", "- Length of stay.\n", "- Flight day." ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Investigating the global effect of each feature on the model output(pushing it down or up).\n", "shap.summary_plot(shap_values[0], final_df, max_display=26)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this visual we can conclude that:\n", "\n", "- A small purchase lead (the number of days between travel date and booking date) increases the chances of the client completing the booking.\n", "- A longer period of stay influences negatively the chances of the client completing the booking.\n", "- When the number of passengers is high, the client is less likely to make a booking.\n", "- Asking for extra baggage and a flight meal decreases the chances of the client completing the booking.\n", "- Making a booking on the weekend (according to the encoding of the feature flight day) increases its chances to be completed.\n", "\n", "\n", "\n", "\n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }