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Created August 19, 2024 01:35
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Obligatorio Ingold - Verges - Diploma AI - 2024-08-18
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obligatorio-ingold-verges-diploma-ai-2024-08-18.ipynb
{
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"metadata": {
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/matismasters/b858830785d5deb1ddd9d424b402bf5a/obligatorio-ingold-verges-diploma-ai-2024-08-18.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## Panorama General\n",
"\n",
"Este documento es parte de la evaluación doimicialiaria obligatorio del diploma de en Inteligencia Artificial del Instituto CTC de Colonia. En este jupyter realizamos la evaluacion y procesamiento de los datos, que conlleva al entrenamiento de un modelo de clasificación que predice la probabilidad de abandono de clientes de un banco."
],
"metadata": {
"id": "XYOt3xNWcRcP"
}
},
{
"cell_type": "code",
"source": [
"# Instrucciones para utilizar este Notebook\n",
"# 1. Al correr este bloque de codigo se generan algunas carpetas en el GDrive\n",
"# de quien lo corra. La raiz de esta estructura de carpetas es:\n",
"# MyDrive/diploma_ia_ctc/\n",
"# 2. Dentro de esta carpeta crear la siguiente estructura de carpetas\n",
"# MyDrive/diploma_ia_ctc/datasets/bank/\n",
"# 3. Agregar el archivo de datos de entrenamiento en esta carpeta para que\n",
"# quede MyDrive/diploma_ia_ctc/datasets/bank/data.csv\n",
"# 4. Agregar el archivo de test de kaggl en esta carpeta tambien\n",
"# MyDrive/diploma_ia_ctc/datasets/bank/test_kaggl.csv\n",
"# 5. Con esos archivos se deberian poder correr todos los bloques de codigo\n",
"# de este notebook.\n",
"\n",
"# Instrucciones para correr con otro set de datos de test\n",
"# 1. Cambiar el contenido del archivo `test_kaggl.csv` por el archivo\n",
"# de prueba que se quiera utilizar.\n",
"# 2. Correr el último bloque de código\n",
"# 3. Revisar los resultados, y buscar por un archivo con el nombre descrito\n",
"# en la última línea del bloque de código.\n",
"#\n",
"# Nota: En caso de no querer realizar el entrenamiento, utilizar la función\n",
"# `load_model` al final de este bloque de código para cargar el modelo\n",
"# enviado en la entrega.\n",
"\n",
"# IMPORTS Y CHEQUEO DEL SISTEMA\n",
"# Requerimos Python ≥3.7\n",
"import sys\n",
"assert sys.version_info >= (3, 7)\n",
"\n",
"# Requerimos Scikit-Learn ≥ 1.0.1\n",
"import sklearn\n",
"assert sklearn.__version__ >= \"1.0.1\"\n",
"\n",
"# Imports comunes\n",
"import numpy as np\n",
"import datetime\n",
"import os\n",
"\n",
"# Para graficar\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"mpl.rc('axes', labelsize=14)\n",
"mpl.rc('xtick', labelsize=12)\n",
"mpl.rc('ytick', labelsize=12)\n",
"\n",
"# Para procesar y entrenar\n",
"import pandas as pd\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.ensemble import StackingClassifier\n",
"from sklearn.metrics import precision_recall_curve\n",
"from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
"from sklearn.metrics import precision_recall_curve, f1_score, classification_report\n",
"from sklearn.model_selection import train_test_split, cross_val_predict, StratifiedKFold\n",
"from imblearn.pipeline import Pipeline as ImbPipeline\n",
"from imblearn.over_sampling import SMOTE\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.linear_model import LogisticRegression\n",
"from collections import Counter\n",
"import joblib\n",
"\n",
"# Instalamos category_encoders para usar target encoder y demas\n",
"!pip install category_encoders\n",
"\n",
"from category_encoders import TargetEncoder\n",
"# CONEXIÓN A GOOGLE DRIVE Y SCRIPTS PARA GUARDAR IMAGEN\n",
"# Conectamos con google drive\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"\n",
"# Dónde salvar las imágenes\n",
"DRIVE_ROOT_DIR = \"/content/drive/MyDrive/diploma_ia_ctc/\"\n",
"os.makedirs(DRIVE_ROOT_DIR, exist_ok=True)\n",
"os.chdir(DRIVE_ROOT_DIR)\n",
"PROJECT_ROOT_DIR = '.' # Relativo a dónde nos cambiamos de dirección\n",
"CHAPTER_ID = \"obligatorio-ingold-verges-diploma-ai-ctc\"\n",
"IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID)\n",
"os.makedirs(IMAGES_PATH, exist_ok=True)\n",
"\n",
"# Dónde guardar el archivo CSV que luego submiteamos a Kaggl\n",
"os.chdir(DRIVE_ROOT_DIR)\n",
"CSV_PATH = os.path.join(PROJECT_ROOT_DIR, \"submissions\", CHAPTER_ID)\n",
"os.makedirs(CSV_PATH, exist_ok=True)\n",
"\n",
"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
" print(\"Saving figure\", fig_id)\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)\n",
"\n",
"def save_csv(df, file_name):\n",
" # Obtener la fecha y hora actuales\n",
" timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
" # Crear el nombre del archivo con la fecha y hora\n",
" file_name_with_timestamp = f\"{file_name}_{timestamp}\"\n",
" path = os.path.join(CSV_PATH, file_name_with_timestamp + \".csv\")\n",
" print(\"Saving CSV file\", file_name_with_timestamp)\n",
" df.to_csv(path, index=False)\n",
"\n",
"def load_csv(path):\n",
" csv_path = os.path.join(path)\n",
" return pd.read_csv(csv_path)\n",
"\n",
"def load_bank_csv():\n",
" return load_csv('datasets/bank/data.csv')\n",
"\n",
"def load_kaggle_csv():\n",
" return load_csv('datasets/bank/test_kaggl.csv')\n",
"\n",
"def check_class_balance(y_train, y_test):\n",
" train_counter = Counter(y_train)\n",
" test_counter = Counter(y_test)\n",
"\n",
" train_ratio = train_counter[1] / train_counter[0] if train_counter[0] > 0 else 0\n",
" test_ratio = test_counter[1] / test_counter[0] if test_counter[0] > 0 else 0\n",
"\n",
" print(\"Balance de clases en el conjunto de entrenamiento:\")\n",
" print(f\"Clase 0: {train_counter[0]} ({train_counter[0] / len(y_train):.2%})\")\n",
" print(f\"Clase 1: {train_counter[1]} ({train_counter[1] / len(y_train):.2%})\")\n",
" print(f\"Ratio (Clase 1 / Clase 0): {train_ratio:.4f}\\n\")\n",
"\n",
" print(\"Balance de clases en el conjunto de prueba:\")\n",
" print(f\"Clase 0: {test_counter[0]} ({test_counter[0] / len(y_test):.2%})\")\n",
" print(f\"Clase 1: {test_counter[1]} ({test_counter[1] / len(y_test):.2%})\")\n",
" print(f\"Ratio (Clase 1 / Clase 0): {test_ratio:.4f}\")\n",
"\n",
"# Guardar modelo en Google Drive\n",
"def save_model(model, file_name):\n",
" # Obtener la fecha y hora actuales\n",
" timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
" # Crear el nombre del archivo con la fecha y hora\n",
" file_name_with_timestamp = f\"{file_name}_{timestamp}\"\n",
" path = os.path.join(GOOGLE_DRIVE_PATH, file_name_with_timestamp + \".pkl\")\n",
" print(\"Saving model file\", file_name_with_timestamp)\n",
" joblib.dump(model, path)\n",
"\n",
"# Para cargar el modelo desde un archivo de drive.\n",
"def load_model(file_path):\n",
" print(\"Loading model from\", file_path)\n",
" model = joblib.load(file_path)\n",
" return model\n",
"\n",
"df = load_bank_csv()"
],
"metadata": {
"id": "83rgyCgdagDe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ed11b093-42c6-4dab-d370-7f488f67cbe2"
},
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: category_encoders in /usr/local/lib/python3.10/dist-packages (2.6.3)\n",
"Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.26.4)\n",
"Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.3.2)\n",
"Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (1.13.1)\n",
"Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.14.2)\n",
"Requirement already satisfied: pandas>=1.0.5 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (2.1.4)\n",
"Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category_encoders) (0.5.6)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2024.1)\n",
"Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.5->category_encoders) (2024.1)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.1->category_encoders) (1.16.0)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (1.4.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.20.0->category_encoders) (3.5.0)\n",
"Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.9.0->category_encoders) (24.1)\n",
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Descripción de los datos\n",
"\n",
"Contamos con un conjunto de datos, provenientes de un banco, que contiene información de clientes que abandonaron el banco y de los que no. Los datos se encuentran en un archivo CSV en GDrive en el path `datasets/bank/data.csv`\n"
],
"metadata": {
"id": "sHQ1ETASefoD"
}
},
{
"cell_type": "code",
"source": [
"# Cargamos los datos del CSV\n",
"RAW_DATA = load_bank_csv()\n",
"\n",
"# Revisamos las columnas y sus datos para tener una idea general de los tipos de\n",
"# datos y los posibles valores\n",
"RAW_DATA.head()"
],
"metadata": {
"id": "jfx5ucshfrfU",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"outputId": "7e664c46-c422-4f41-c50d-11f270e20f2a"
},
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" customer_id credit_score country gender age tenure balance \\\n",
"0 15634602 619 France Female 42 2 0.00 \n",
"1 15647311 608 Spain Female 41 1 83807.86 \n",
"2 15619304 502 France Female 42 8 159660.80 \n",
"3 15701354 699 France Female 39 1 0.00 \n",
"4 15737888 850 Spain Female 43 2 125510.82 \n",
"\n",
" products_number credit_card active_member estimated_salary churn \n",
"0 1 1 1 101348.88 1 \n",
"1 1 0 1 112542.58 0 \n",
"2 3 1 0 113931.57 1 \n",
"3 2 0 0 93826.63 0 \n",
"4 1 1 1 79084.10 0 "
],
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" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-9737e2ad-179d-4cb3-9c22-7b4e266d4032 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "RAW_DATA",
"summary": "{\n \"name\": \"RAW_DATA\",\n \"rows\": 8000,\n \"fields\": [\n {\n \"column\": \"customer_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 71579,\n \"min\": 15565701,\n \"max\": 15815690,\n \"num_unique_values\": 8000,\n \"samples\": [\n 15770225,\n 15703205,\n 15800229\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 96,\n \"min\": 350,\n \"max\": 850,\n \"num_unique_values\": 455,\n \"samples\": [\n 688,\n 465,\n 558\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"country\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"France\",\n \"Spain\",\n \"Germany\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10,\n \"min\": 18,\n \"max\": 92,\n \"num_unique_values\": 69,\n \"samples\": [\n 61,\n 42\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tenure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 10,\n \"num_unique_values\": 11,\n \"samples\": [\n 6,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62391.19258427413,\n \"min\": 0.0,\n \"max\": 250898.09,\n \"num_unique_values\": 5120,\n \"samples\": [\n 137453.43,\n 146311.58\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"products_number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 4,\n \"num_unique_values\": 4,\n \"samples\": [\n 3,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_card\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"active_member\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"estimated_salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 57524.002767849975,\n \"min\": 11.58,\n \"max\": 199992.48,\n \"num_unique_values\": 7999,\n \"samples\": [\n 114149.8,\n 140746.13\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 43
}
]
},
{
"cell_type": "code",
"source": [
"# Chequeamos las columnas\n",
"RAW_DATA.columns.values"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kpSOGI8pjYsK",
"outputId": "4c0d1100-9fa7-4227-c6cb-2eff85ec6a12"
},
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['customer_id', 'credit_score', 'country', 'gender', 'age',\n",
" 'tenure', 'balance', 'products_number', 'credit_card',\n",
" 'active_member', 'estimated_salary', 'churn'], dtype=object)"
]
},
"metadata": {},
"execution_count": 44
}
]
},
{
"cell_type": "markdown",
"source": [
"Interpretamos las columnas y su función:\n",
"\n",
"- `customer_id` variable de identificación unica del cliente\n",
"- `credit_score` el record crediticio del cliente\n",
"- `country` pais\n",
"- `gender` genero\n",
"- `age` edad\n",
"- `tenure` permanencia del cliente con el banco en años\n",
"- `balance` balance de la cuenta\n",
"- `products_number` cantidad de productos que tiene el cliente\n",
"- `credit_card` si tiene tarjeta de credito\n",
"- `active_member` si es miembro activo\n",
"- `estimated_salary` salario del cliente\n",
"- `churn` si abandono el banco"
],
"metadata": {
"id": "alDrtUkJjnLo"
}
},
{
"cell_type": "code",
"source": [
"# Chequeamos los tipos de datos de cada columna para ver si tenemos que hacer\n",
"# alguna transformación\n",
"RAW_DATA.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EnDyZS22kF3k",
"outputId": "1b3d89d2-27ac-4b27-f8ce-7c56385857c8"
},
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8000 entries, 0 to 7999\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 customer_id 8000 non-null int64 \n",
" 1 credit_score 8000 non-null int64 \n",
" 2 country 8000 non-null object \n",
" 3 gender 8000 non-null object \n",
" 4 age 8000 non-null int64 \n",
" 5 tenure 8000 non-null int64 \n",
" 6 balance 8000 non-null float64\n",
" 7 products_number 8000 non-null int64 \n",
" 8 credit_card 8000 non-null int64 \n",
" 9 active_member 8000 non-null int64 \n",
" 10 estimated_salary 8000 non-null float64\n",
" 11 churn 8000 non-null int64 \n",
"dtypes: float64(2), int64(8), object(2)\n",
"memory usage: 750.1+ KB\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"RAW_DATA.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"id": "ssSTq5CPllpI",
"outputId": "72d1fb28-8b4e-43f1-b9dd-8fb4a0937290"
},
"execution_count": 46,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" customer_id credit_score age tenure balance \\\n",
"count 8.000000e+03 8000.000000 8000.000000 8000.000000 8000.000000 \n",
"mean 1.569069e+07 650.179625 38.937875 5.012750 76800.037193 \n",
"std 7.157970e+04 96.844314 10.511224 2.884376 62391.192584 \n",
"min 1.556570e+07 350.000000 18.000000 0.000000 0.000000 \n",
"25% 1.562869e+07 583.000000 32.000000 3.000000 0.000000 \n",
"50% 1.569035e+07 651.000000 37.000000 5.000000 97658.060000 \n",
"75% 1.575234e+07 717.000000 44.000000 7.000000 127827.332500 \n",
"max 1.581569e+07 850.000000 92.000000 10.000000 250898.090000 \n",
"\n",
" products_number credit_card active_member estimated_salary \\\n",
"count 8000.000000 8000.000000 8000.000000 8000.000000 \n",
"mean 1.528000 0.701625 0.512625 100198.588701 \n",
"std 0.583102 0.457574 0.499872 57524.002768 \n",
"min 1.000000 0.000000 0.000000 11.580000 \n",
"25% 1.000000 0.000000 0.000000 51271.410000 \n",
"50% 1.000000 1.000000 1.000000 100272.165000 \n",
"75% 2.000000 1.000000 1.000000 149372.387500 \n",
"max 4.000000 1.000000 1.000000 199992.480000 \n",
"\n",
" churn \n",
"count 8000.000000 \n",
"mean 0.205875 \n",
"std 0.404365 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
],
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>customer_id</th>\n",
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" <th>count</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.569069e+07</td>\n",
" <td>650.179625</td>\n",
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" <td>5.012750</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>11.580000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
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" <td>583.000000</td>\n",
" <td>32.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>51271.410000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <td>97658.060000</td>\n",
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" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
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" <td>7.000000</td>\n",
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" <td>1.000000</td>\n",
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" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-a2428b78-dff2-47e5-84bd-85857e78a98f button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"RAW_DATA\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"customer_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7245310.289440879,\n \"min\": 8000.0,\n \"max\": 15815690.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 15690688.752125,\n 15690352.0,\n 8000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2641.9559761243445,\n \"min\": 96.84431430141338,\n \"max\": 8000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 650.179625,\n 651.0,\n 8000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2814.771416305745,\n \"min\": 10.511223820880781,\n \"max\": 8000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 38.937875,\n 37.0,\n 8000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tenure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2826.767131669616,\n \"min\": 0.0,\n \"max\": 8000.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 5.01275,\n 5.0,\n 8000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 84579.63625538436,\n \"min\": 0.0,\n \"max\": 250898.09,\n \"num_unique_values\": 7,\n \"samples\": [\n 8000.0,\n 76800.0371925,\n 127827.33249999999\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"products_number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2827.8661323579463,\n \"min\": 0.5831024790400335,\n \"max\": 8000.0,\n \"num_unique_values\": 6,\n \"samples\": [\n 8000.0,\n 1.528,\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_card\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2828.2170845714795,\n \"min\": 0.0,\n \"max\": 8000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.701625,\n 1.0,\n 0.4575735253119254\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"active_member\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2828.2244936781203,\n \"min\": 0.0,\n \"max\": 8000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.512625,\n 1.0,\n 0.49987182692708276\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"estimated_salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 68508.17273715373,\n \"min\": 11.58,\n \"max\": 199992.48,\n \"num_unique_values\": 8,\n \"samples\": [\n 100198.58870125,\n 100272.16500000001,\n 8000.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2828.3458164524327,\n \"min\": 0.0,\n \"max\": 8000.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 0.205875,\n 1.0,\n 0.4043648392731557\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 46
}
]
},
{
"cell_type": "markdown",
"source": [
"Anotaciones:\n",
"\n",
"- Son 8000 casos.\n",
"- Prácticamente la mitad de los clientes son active member\n",
"- El 20% solamente abandonó el banco\n",
"- Edad mínima 18, máxima 92.\n",
"- Promedio de edad 39\n",
"- Credit score promedio es 650\n",
"- Mínimo de credit score es 350, y el Máximo es 850. Tal vez tendriamos que redimensionar esta columna\n",
"- El 70% de los usuarios usa tarjeta de crédito\n",
"- El número de productos, tenure, edad, y si tienen tarjeta de credito son valores muy pequeños por lo que sería necesario escalarlos\n",
"- Sera posible que la distribución del salario estimado sea tan pareja entre los cuartiles?\n",
"- No hay nulos en los datos!\n"
],
"metadata": {
"id": "vBw80oGdmLn5"
}
},
{
"cell_type": "code",
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"RAW_DATA.hist(bins=50, figsize=(20,15))\n",
"save_fig(\"attribute_histogram_plots\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "JaS1OFzBmOf3",
"outputId": "98272ff1-4c6f-48e5-e451-333bb983b02a"
},
"execution_count": 47,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure attribute_histogram_plots\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 2000x1500 with 12 Axes>"
],
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},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# Hacemos una grafica para ver active_members vs churn\n",
"# nos aseguramos que se ven los valores especificos de cada barra\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Calculamos las proporciones manualmente\n",
"grouped_data = RAW_DATA.groupby('active_member')['churn'].value_counts(normalize=True).unstack()\n",
"\n",
"# Creamos el gráfico de barras\n",
"ax = grouped_data.plot(kind='bar')\n",
"\n",
"# Añadimos las etiquetas de los valores encima de cada barra\n",
"for container in ax.containers:\n",
" ax.bar_label(container, labels=[f'{v:.2f}' for v in container.datavalues], fmt='%.2f')\n",
"\n",
"# Establecemos las etiquetas para el eje X\n",
"plt.xticks(ticks=[0, 1], labels=['No', 'Yes'], rotation=0)\n",
"plt.xlabel(\"Active member\")\n",
"plt.title(\"Churn by active member\")\n",
"plt.ylabel(\"Proportion\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 481
},
"id": "HitA8g09wH4G",
"outputId": "c134a67e-5fc9-43d2-d08b-0af0c57b8d49"
},
"execution_count": 48,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Anotaciones:\n",
"- Credit score 850 parece ser el máximo por eso vemos un pico al final.\n",
"- Hay mucha gente con balance 0 y rompe la distribución de los datos.\n",
"- La población descrita en los datos tiene un buen promedio de salario estimado y balance \\$100,000\n",
"- Los salarios estimados estan muy bien proporcionados, seguramente es parte de la selección de datos.\n",
"- Tenemos que agregar categorias a la edad para asegurarnos la distribucion proporcional al momento de separar los conjuntos de test y train"
],
"metadata": {
"id": "DDqZQNL8qQP-"
}
},
{
"cell_type": "code",
"source": [
"RAW_DATA['balance'].value_counts()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 489
},
"id": "9iApQc4NrhFI",
"outputId": "7c4a2461-4077-4059-ab68-ea9426b711bb"
},
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"balance\n",
"0.00 2880\n",
"130170.82 2\n",
"115350.63 1\n",
"179305.09 1\n",
"110942.90 1\n",
" ... \n",
"157780.93 1\n",
"107247.69 1\n",
"82506.72 1\n",
"129177.01 1\n",
"142662.68 1\n",
"Name: count, Length: 5120, dtype: int64"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>balance</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.00</th>\n",
" <td>2880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130170.82</th>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115350.63</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179305.09</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110942.90</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157780.93</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107247.69</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82506.72</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129177.01</th>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142662.68</th>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5120 rows × 1 columns</p>\n",
"</div><br><label><b>dtype:</b> int64</label>"
]
},
"metadata": {},
"execution_count": 49
}
]
},
{
"cell_type": "markdown",
"source": [
"Anotaciones:\n",
"- Efectivamente tenemos 2880 casos con balance \\$0"
],
"metadata": {
"id": "tUh81wAEt5N9"
}
},
{
"cell_type": "code",
"source": [
"from matplotlib import pyplot as plt\n",
"RAW_DATA.plot(kind='scatter', x='credit_score', y='age', s=32, alpha=.8)\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"\n",
"save_fig(\"age_vs_credit_score\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 505
},
"id": "uaiylW36t_gA",
"outputId": "cc6bbf3c-6347-456b-f939-6c79a87e0c42"
},
"execution_count": 50,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure age_vs_credit_score\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Anotaciones:\n",
"- Si, 197 personas parecen tener 850 de credit score. Vamos a revisar si realizamos alguna transformación en los datos para tener esto en cuenta."
],
"metadata": {
"id": "C46ho3l2uLJ7"
}
},
{
"cell_type": "code",
"source": [
"RAW_DATA[\"age_cat\"] = pd.cut(RAW_DATA[\"age\"],\n",
" bins=[17, 25, 35, 45, 55, np.inf],\n",
" labels=[1, 2, 3, 4, 5])"
],
"metadata": {
"id": "3jRRV7jxzBD5"
},
"execution_count": 51,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Anotaciones:\n",
"- Con `age_cat` tenemos una mejor distribución de los datos."
],
"metadata": {
"id": "gOC65tDI33-w"
}
},
{
"cell_type": "code",
"source": [
"RAW_DATA.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "mv_pKYB6NIxF",
"outputId": "a0ee5980-eb10-4168-a970-baa474954286"
},
"execution_count": 52,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" customer_id credit_score country gender age tenure balance \\\n",
"0 15634602 619 France Female 42 2 0.00 \n",
"1 15647311 608 Spain Female 41 1 83807.86 \n",
"2 15619304 502 France Female 42 8 159660.80 \n",
"3 15701354 699 France Female 39 1 0.00 \n",
"4 15737888 850 Spain Female 43 2 125510.82 \n",
"\n",
" products_number credit_card active_member estimated_salary churn \\\n",
"0 1 1 1 101348.88 1 \n",
"1 1 0 1 112542.58 0 \n",
"2 3 1 0 113931.57 1 \n",
"3 2 0 0 93826.63 0 \n",
"4 1 1 1 79084.10 0 \n",
"\n",
" age_cat \n",
"0 3 \n",
"1 3 \n",
"2 3 \n",
"3 3 \n",
"4 3 "
],
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" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b0d93ec7-1bcc-4a1f-8a47-a6ed0f31a312')\"\n",
" title=\"Suggest charts\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-b0d93ec7-1bcc-4a1f-8a47-a6ed0f31a312 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "RAW_DATA",
"summary": "{\n \"name\": \"RAW_DATA\",\n \"rows\": 8000,\n \"fields\": [\n {\n \"column\": \"customer_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 71579,\n \"min\": 15565701,\n \"max\": 15815690,\n \"num_unique_values\": 8000,\n \"samples\": [\n 15770225,\n 15703205,\n 15800229\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 96,\n \"min\": 350,\n \"max\": 850,\n \"num_unique_values\": 455,\n \"samples\": [\n 688,\n 465,\n 558\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"country\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"France\",\n \"Spain\",\n \"Germany\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"Male\",\n \"Female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 10,\n \"min\": 18,\n \"max\": 92,\n \"num_unique_values\": 69,\n \"samples\": [\n 61,\n 42\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tenure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 10,\n \"num_unique_values\": 11,\n \"samples\": [\n 6,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"balance\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 62391.19258427413,\n \"min\": 0.0,\n \"max\": 250898.09,\n \"num_unique_values\": 5120,\n \"samples\": [\n 137453.43,\n 146311.58\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"products_number\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 4,\n \"num_unique_values\": 4,\n \"samples\": [\n 3,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"credit_card\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"active_member\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"estimated_salary\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 57524.002767849975,\n \"min\": 11.58,\n \"max\": 199992.48,\n \"num_unique_values\": 7999,\n \"samples\": [\n 114149.8,\n 140746.13\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"churn\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"age_cat\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n 4,\n 5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"source": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"RAW_DATA.groupby('age_cat').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"plt.yticks([0, 1, 2, 3, 4], ['18-25', '26-35', '36-45', '46-55', '56>'])\n",
"\n",
"save_fig(\"age_cat_frequency\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 562
},
"id": "tDsCFjjuP7Lb",
"outputId": "6ab03be5-8aad-4e21-dc9f-01c6775239fb"
},
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-53-2aff61b0a913>:3: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" RAW_DATA.groupby('age_cat').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure age_cat_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"RAW_DATA.plot(kind='scatter', x='credit_score', y='age', s=32, alpha=.8)\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"\n",
"save_fig(\"age_vs_credit_score\")"
],
"cell_type": "code",
"execution_count": 54,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure age_vs_credit_score\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 505
},
"id": "zH5YC5oEPZms",
"outputId": "39a5f2ee-6945-4c8e-8384-30a42364340b"
}
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"RAW_DATA['credit_score'].plot(kind='hist', figsize=(8, 4), title='credit_score')\n",
"plt.gca().spines[['top', 'right']].set_visible(False)\n",
"\n",
"\n",
"save_fig(\"credit_score_frequency\")"
],
"cell_type": "code",
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure credit_score_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x400 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"id": "PyTqTzcSOlA7",
"outputId": "d9f76edb-2503-4c39-db2d-9425ab372a4d"
}
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"RAW_DATA.groupby('gender').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"\n",
"save_fig(\"gender_frequency\")"
],
"cell_type": "code",
"execution_count": 56,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure gender_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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AHeQdduvWrYtOnTptc5v169dHRUVFvocAAKAO8g67Hj16xIIFC7a5zeuvvx69e/fO9xAAANRB3mE3bty4+K//+q94+umna1z/61//Ol5++eU4/vjj8z0EAAB1kPe9Yj/66KMYNGhQrFy5Ms4444xYsWJF/O53v4tbbrklXnrppZg1a1b07Nkz5s+fX+M9ZNk+94oFAOoi77CLiHjvvffitNNOi5deeqnauiFDhsSsWbNizz33rM/4mjVhBwDURb3C7ktvvPFGvPzyy7FmzZooLi6OIUOGVLv8CXUn7ACAumiQsOPvQ9gBAHWR971izz777O1u06JFiyguLo5+/frFuHHjonv37vkeDgCA7cj7jF2LFi0il8tFRERNu8jlclWWFxQUxNVXXx0/+MEP8hxq8+OMHQBQF3lf7mTRokUxbty46NSpU5SWlsbcuXPj3Xffjblz50ZpaWl07tw5jjvuuHjllVfizjvvjG7dusXkyZPjgQceaMjxAwDw/+V9xm7atGnxs5/9LN58883o3LlztfUrVqyIgQMHxmWXXRZXXHFFLFu2LPbZZ58YOHBgzJ07t94Dbw6csQMA6iLvM3Z33313TJgwocaoi4jo0qVLjB8/Pu66666IiOjevXuMGzcu3nzzzXwPCQDANuQddkuXLo3CwsJtblNUVBRLly6tfNyzZ8/YsGFDvocEAGAb8g677t27x6OPPrrVUNuwYUM8+uijVb4Ju2rVqth1113zPSQAANuQd9idc845sWjRohg6dGg89thjsXr16oiIWL16dTz22GMxdOjQeO+996pcFuX555+P/fffv/6jBgCgmryvY3fFFVfEO++8E/fdd1+ccMIJEfGXS6Bs2bIlIv5yCZRTTjklrrzyyoiIWLlyZYwdOzZGjx7dAMMGAOBv1fvOE88880zce++98dZbb0V5eXkUFxfH/vvvH6eeemocddRRDTXOZsm3YgGAunBLsSZM2AEAdZH3Z+wAAGhahB0AQCKEHQBAIoQdAEAihB0AQCKEHQBAIoQdAEAihB0AQCKEHQBAIoQdAEAihB0AQCKEHQBAIoQdAEAihB0AQCKEHQBAIoQdAEAiChp7AGxf//smR4tWhY09DACagaVnTWvsIVAPztgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtgBACRC2AEAJELYAQAkQtjV0+LFiyOXy8WMGTMaeygAQDPX5MJuxowZkcvlavy58sorG3t4AABNVkFjD2BrpkyZEr169aqybL/99muk0QAANH1NNuzGjBkTgwcPbuxhAADsMJrcW7G18eSTT8awYcOiTZs2scsuu8TYsWPj7bffrrLNmWeeGW3bto0lS5bEuHHjom3bttG9e/eYPn16REQsWLAgRowYEW3atIk99tgjZs6cWeX5a9asicsvvzwGDBgQbdu2jeLi4hgzZky8+eabtRrju+++GyeeeGK0b98+ioqKYvDgwfHYY481zC8AAKAGTTbsysrK4uOPP67yExFx7733xtixY6Nt27Zx/fXXx6RJk+L3v/99DB06NBYvXlxlHxUVFTFmzJjo0aNH3HDDDbHnnnvGhRdeGDNmzIjRo0fH4MGD4/rrr49ddtklTj/99Hj//fcrn/vee+/Fo48+GuPGjYubbropJk6cGAsWLIjhw4fHhx9+uM2xv/3223HIIYfEO++8E1deeWX85Cc/iTZt2sTxxx8fjzzySIP/rgAAIprwW7EjR46stmzdunVx8cUXx7nnnht33nln5fIzzjgj+vXrF6WlpVWWb9iwIf75n/85rrrqqoiIOOWUU6Jbt25x9tlnx6xZs+Kkk06KiIijjz46+vfvH7/85S/jmmuuiYiIAQMGxMKFC6NFi7+272mnnRb9+/ePu+++OyZNmrTVsV9yySXRs2fPmDdvXhQWFkZExPnnnx9Dhw6N73//+3HCCSfU+LyNGzfGxo0bKx+Xl5dv79cEAFCpyYbd9OnTo2/fvlWWzZkzJ9auXRvf/va3K8/gRUS0bNkyhgwZEs8++2y1/Zx77rmV/92uXbvo169f/OlPf4oJEyZULu/Xr1+0a9cu3nvvvcplXwZZxF/O/K1duzbatm0b/fr1i9dff32r416zZk0888wzMWXKlFi3bl2sW7euct2oUaNi8uTJsWzZsujevXu151533XVx7bXXbnXfAADb0mTD7uCDD6725YkbbrghIiJGjBhR43OKi4urPC4qKoqOHTtWWVZSUhK777575HK5ass/+eSTysdbtmyJm2++OW677bZ4//33o6KionLdbrvtttVx/+lPf4osy2LSpElbPau3atWqGsPuqquuissuu6zycXl5efTo0WOrxwIA+KomG3Y12bJlS0T85XN2Xbp0qba+oKDqH6dly5Y17mdry7Msq/zv0tLSmDRpUpx99tkxderUaN++fbRo0SIuvfTSynFsa4yXX355jBo1qsZt+vTpU+PywsLCKmcKAQDqYocKu969e0dERKdOnWr8DF5Deuihh+LrX/963H333VWWr127Njp06LDV5+21114REbHTTjv93ccIAPBVTfZbsTUZNWpUFBcXR2lpaXzxxRfV1n/00UcNdqyWLVtWOYMXEfHggw/GsmXLtvm8Tp06xZFHHhl33HFHLF++/O86RgCAr9qhztgVFxfH7bffHqeddloMGjQoTj755OjYsWMsWbIknnjiiTj88MPj1ltvbZBjjRs3LqZMmRJnnXVWHHbYYbFgwYK4//77K8/Ibcv06dNj6NChMWDAgDjvvPNir732ipUrV8ZLL70US5curfW18AAA6mKHCruIv16yZNq0aXHjjTfGxo0bo3v37jFs2LA466yzGuw4//7v/x6fffZZzJw5Mx544IEYNGhQPPHEE7W6X+0+++wTr776alx77bUxY8aMWL16dXTq1CkOOOCAuPrqqxtsjAAAX5XL/vb9RpqM8vLyKCkpia7TL40WrXypAoC/v6VnTWvsIVAPO9Rn7AAA2DphBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQCGEHAJAIYQcAkAhhBwCQiFyWZVljD4KalZeXR0lJSZSVlUVxcXFjDwcAaOKcsQMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEiEsAMASISwAwBIhLADAEhEQWMPgK3LsiwiIsrLyxt5JABAY9tll10il8ttcxth14StXr06IiJ69OjRyCMBABpbWVlZFBcXb3MbYdeEtW/fPiIilixZEiUlJY08GvJRXl4ePXr0iA8++GC7fxlpuszjjs8c7vjM4V/O2G2PsGvCWrT4y0cgS0pKmu3/xKkoLi42hwkwjzs+c7jjM4fb5ssTAACJEHYAAIkQdk1YYWFhTJ48OQoLCxt7KOTJHKbBPO74zOGOzxzWTi778poaAADs0JyxAwBIhLADAEiEsAMASISwAwBIhLBrgjZu3Bjf//73o1u3btGqVasYMmRIzJkzp7GH1ax8+umnMXny5Bg9enS0b98+crlczJgxo8Zt33nnnRg9enS0bds22rdvH6eddlp89NFH1bbbsmVL3HDDDdGrV68oKiqKf/qnf4pZs2bVa59s3bx58+LCCy+MfffdN9q0aRM9e/aMCRMmxMKFC6ttaw6bprfffjvGjx8fe+21V7Ru3To6dOgQRxxxRPz2t7+ttq053DH86Ec/ilwuF/vtt1+1dS+++GIMHTo0WrduHV26dImLL744Pv3002rb1eU1srb7TEpGk3PyySdnBQUF2eWXX57dcccd2aGHHpoVFBRkzz//fGMPrdl4//33s4jIevbsmR155JFZRGT33HNPte0++OCDrEOHDlnv3r2zm2++OfvRj36U7brrrtn++++fbdy4scq2V155ZRYR2XnnnZfdeeed2dixY7OIyGbNmpX3Ptm6b33rW1mXLl2yiy66KLvrrruyqVOnZp07d87atGmTLViwoHI7c9h0PfHEE9moUaOya665Jrvzzjuzn/3sZ9mwYcOyiMjuuOOOyu3M4Y7hgw8+yFq3bp21adMm23fffausmz9/flZUVJQdcMAB2e233579x3/8R1ZYWJiNHj262n5q+xpZl32mRNg1Ma+88koWEdmNN95YuWz9+vVZ7969s0MPPbQRR9a8bNiwIVu+fHmWZVk2b968rYbdd7/73axVq1bZn//858plc+bMqfbCs3Tp0mynnXbKLrjggsplW7ZsyYYNG5btvvvu2ebNm+u8T7bthRdeqPYCvHDhwqywsDA79dRTK5eZwx3L5s2bs/333z/r169f5TJzuGM46aSTshEjRmTDhw+vFnZjxozJunbtmpWVlVUuu+uuu7KIyGbPnl25rC6vkbXdZ2qEXRMzceLErGXLllX+R8yyLCstLc0iIluyZEkjjaz52lbYderUKRs/fny15X379s2OOuqoysfTp0/PIiJ7++23q2w3c+bMLCKq/EuztvskP4MGDcoGDRpU+dgc7njGjRuXde7cufKxOWz65s6dm7Vs2TJ76623qoVdWVlZVlBQkE2cOLHKczZu3Ji1bds2O+eccyqX1fY1si77TI3P2DUx8+fPj759+1a7wfHBBx8cERFvvPFGI4yKmixbtixWrVoVgwcPrrbu4IMPjvnz51c+nj9/frRp0yb23nvvatt9ub6u+6TusiyLlStXRocOHSLCHO4oPvvss/j4449j0aJF8dOf/jSefPLJOOqooyLCHO4IKioq4qKLLopzzz03BgwYUG39ggULYvPmzdV+3zvvvHMMHDiw2hzW5jWyLvtMjbBrYpYvXx5du3attvzLZR9++OE/ekhsxfLlyyMitjpfa9asiY0bN1Zu27lz58jlctW2i/jrvNZln9Td/fffH8uWLYuTTjopIszhjuJ73/tedOzYMfr06ROXX355nHDCCXHrrbdGhDncEfziF7+IP//5zzF16tQa12/v9/3V173avkbWZZ+pEXZNzPr162u8D15RUVHlepqGL+eiNvNV23mtyz6pm3fffTcuuOCCOPTQQ+OMM86ICHO4o7j00ktjzpw58ctf/jLGjBkTFRUVsWnTpogwh03d6tWr4+qrr45JkyZFx44da9xme7/vr/6uG2oOU54/YdfEtGrVqsZ/CW7YsKFyPU3Dl3NRm/mq7bzWZZ/U3ooVK2Ls2LFRUlISDz30ULRs2TIizOGOon///jFy5Mg4/fTT4/HHH49PP/00jj322MiyzBw2cT/4wQ+iffv2cdFFF211m+39vr/6u26oOUx5/oRdE9O1a9fKU8hf9eWybt26/aOHxFZ8eYp/a/PVvn37yn8tdu3aNVasWBFZllXbLuKv81qXfVI7ZWVlMWbMmFi7dm089dRTVf4OmcMd04knnhjz5s2LhQsXmsMm7I9//GPceeedcfHFF8eHH34YixcvjsWLF8eGDRviiy++iMWLF8eaNWu2+/v+27+ztXmNrMs+UyPsmpiBAwfGwoULo7y8vMryV155pXI9TUP37t2jY8eO8eqrr1Zb97//+79V5mrgwIHx+eefxzvvvFNlu7+d17rsk+3bsGFDHHvssbFw4cJ4/PHHY5999qmy3hzumL58G62srMwcNmHLli2LLVu2xMUXXxy9evWq/HnllVdi4cKF0atXr5gyZUrst99+UVBQUO33vWnTpnjjjTeqzWFtXiPrss/kNOp3cqnm5ZdfrnaNng0bNmR9+vTJhgwZ0ogja762dbmTf/3Xf81atWpV5TI0Tz/9dBYR2e2331657IMPPtjq9bO6d+9e5fpZtd0n27Z58+bsuOOOywoKCrInnnhiq9uZw6Zr5cqV1ZZt2rQpGzRoUNaqVats3bp1WZaZw6bqo48+yh555JFqP/vuu2/Ws2fP7JFHHsneeuutLMuybPTo0VnXrl2z8vLyyuf/53/+ZxYR2ZNPPlm5rC6vkbXdZ2qEXRM0fvz4yuvv3HHHHdlhhx2WFRQUZHPnzm3soTUrt9xySzZ16tTsu9/9bhYR2Te/+c1s6tSp2dSpU7O1a9dmWZZlS5YsyXbbbbesd+/e2c9//vOstLQ023XXXbMBAwZkGzZsqLK/iRMnZhGR/cu//Et21113VV7x/v7776+yXV32ydZdcsklWURkxx57bHbvvfdW+/mSOWy6jj/++GzEiBHZNddcU3n3kP79+2cRkf3kJz+p3M4c7lhqukDxa6+9lhUWFla5S0RRUVF2zDHHVHt+bV8j67LPlAi7Jmj9+vXZ5ZdfnnXp0iUrLCzMDjrooOypp55q7GE1O3vssUcWETX+vP/++5Xb/d///V92zDHHZK1bt87atWuXnXrqqdmKFSuq7a+ioiIrLS3N9thjj2znnXfO9t133+y+++6r8di13SdbN3z48K3O39++WWEOm6ZZs2ZlI0eOzDp37pwVFBRku+66azZy5MjsN7/5TbVtzeGOo6awy7Ise/7557PDDjssKyoqyjp27JhdcMEFVc62fakur5G13WdKcln2N58iBQBgh+TLEwAAiRB2AACJEHYAAIkQdgAAiRB2AACJEHYAAIkQdgAAiRB2AACJEHYAAIkQdgAAiRB2AACJEHYAAIkQdgAAifh/vhzN84jEvdcAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 505
},
"id": "MrySxGFmOOYJ",
"outputId": "82dc861f-933e-42e1-e485-8a1d18ab907d"
}
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"RAW_DATA.groupby('country').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"\n",
"save_fig(\"country_frequency\")"
],
"cell_type": "code",
"execution_count": 57,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure country_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 505
},
"id": "mPz_fY8ZN9a_",
"outputId": "723ad9d0-ebc7-4131-f8b1-d28499e27a2d"
}
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"RAW_DATA['tenure'].plot(kind='hist', bins=20, title='tenure')\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"\n",
"\n",
"save_fig(\"tenure_frequency\")"
],
"cell_type": "code",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure tenure_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
},
"id": "yo1Kd9frN1EA",
"outputId": "bcb1094c-9c72-4cd1-a4bb-269bf1946bd9"
}
},
{
"source": [
"from matplotlib import pyplot as plt\n",
"RAW_DATA['credit_score'].plot(kind='hist', bins=20, title='credit_score')\n",
"plt.gca().spines[['top', 'right',]].set_visible(False)\n",
"save_fig(\"credit_score_frequency\")"
],
"cell_type": "code",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving figure credit_score_frequency\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 504
},
"id": "DJ6hRudoNaJg",
"outputId": "7f817c27-fa2c-4699-c38c-4f07e1ec90b5"
}
},
{
"cell_type": "markdown",
"source": [
"# En vista del estudio de las columnas y sus valores procedemos a realizar las transformaciones que creemos convenientes\n",
"\n",
"- age_cat: Creamos categorías para las edades de forma de lograr una distribución mas uniforme\n",
"- credit_score_cat: Creamos categorías para el credit_score también para lograr una distribución mas uniforme. Utilizando la escala FICO.\n",
"- tenure: Probamos sacar la columna tenure, ya que no parece tener ninguna correlación. Experimentaremos con esto al momento de probar entrenar modelos.\n"
],
"metadata": {
"id": "rdCqaxHzu9iC"
}
},
{
"cell_type": "code",
"source": [
"# Creamos categorias para Edad\n",
"RAW_DATA[\"age_cat\"] = pd.cut(RAW_DATA[\"age\"],\n",
" bins=[17, 25, 35, 45, 55, np.inf],\n",
" labels=[1, 2, 3, 4, 5])\n",
"\n",
"RAW_DATA[\"credit_score_cat\"] = pd.cut(RAW_DATA[\"age\"],\n",
" bins=[349, 400, 450, 550, 600, 650, 700, 750, 800, np.inf],\n",
" labels=[1, 2, 3, 4, 5, 6, 7, 8, 9])"
],
"metadata": {
"id": "GhP8z6sqmBv2"
},
"execution_count": 58,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Realizamos una prueba para asegurarnos que age_cat queda bien distribuido\n",
"# entre train y test\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"train_set, test_set = train_test_split(RAW_DATA, test_size=0.2, random_state=42)\n",
"\n",
"print(\"Churn value count Test: \")\n",
"value_counts_test = test_set['churn'].value_counts()\n",
"print(value_counts_test)\n",
"print(\"Churn value count Train: \")\n",
"value_counts_train = train_set['churn'].value_counts()\n",
"print(train_set['churn'].value_counts())\n",
"print(\"----------\")\n",
"print(\"Porcentaje churn train: \" + (value_counts_train[1] / value_counts_train[0]).astype(str))\n",
"print(\"Porcentaje churn test: \" + (value_counts_test[1] / value_counts_test[0]).astype(str))\n",
"print(\"------=====\")\n",
"print(test_set[\"age_cat\"].value_counts() / len(test_set))\n",
"print(\"\")\n",
"print(RAW_DATA[\"age_cat\"].value_counts() / len(RAW_DATA))\n",
"\n",
"churn_ratio_train = train_set.groupby(\"age_cat\")[\"churn\"].value_counts(normalize=True).unstack()\n",
"churn_ratio_test = test_set.groupby(\"age_cat\")[\"churn\"].value_counts(normalize=True).unstack()\n",
"\n",
"# ahora combinamos los datos de train y test para poder visualizarlos juntos\n",
"\n",
"churn_ratio = pd.concat([churn_ratio_train, churn_ratio_test], axis=1)\n",
"churn_ratio.columns = [\"train_no_churn\", \"train_churn\", \"test_no_churn\", \"test_churn\"]\n",
"\n",
"import matplotlib.pyplot as plt\n",
"churn_ratio.plot(kind=\"bar\", figsize=(10, 6))\n",
"plt.title(\"Churn ratio by age category\")\n",
"plt.ylabel(\"Ratio\")\n",
"plt.show()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "2QmdmioDvC_F",
"outputId": "93038fd6-ac4c-4d5f-9901-5ddf90fd171c"
},
"execution_count": 59,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Churn value count Test: \n",
"churn\n",
"0 1265\n",
"1 335\n",
"Name: count, dtype: int64\n",
"Churn value count Train: \n",
"churn\n",
"0 5088\n",
"1 1312\n",
"Name: count, dtype: int64\n",
"----------\n",
"Porcentaje churn train: 0.2578616352201258\n",
"Porcentaje churn test: 0.2648221343873518\n",
"------=====\n",
"age_cat\n",
"3 0.380000\n",
"2 0.344375\n",
"4 0.130000\n",
"5 0.082500\n",
"1 0.063125\n",
"Name: count, dtype: float64\n",
"\n",
"age_cat\n",
"3 0.372500\n",
"2 0.353875\n",
"4 0.131125\n",
"5 0.080375\n",
"1 0.062125\n",
"Name: count, dtype: float64\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"<ipython-input-59-dbcbc79c022b>:22: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" churn_ratio_train = train_set.groupby(\"age_cat\")[\"churn\"].value_counts(normalize=True).unstack()\n",
"<ipython-input-59-dbcbc79c022b>:23: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
" churn_ratio_test = test_set.groupby(\"age_cat\")[\"churn\"].value_counts(normalize=True).unstack()\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"- La distribución del ratio de valores de las categorías de age_cat entre train y test es similar"
],
"metadata": {
"id": "hFSJdRXvL4r_"
}
},
{
"cell_type": "markdown",
"source": [
"# Correlacion de datos"
],
"metadata": {
"id": "d7SeRa8OVHFY"
}
},
{
"cell_type": "code",
"source": [
"corr_matrix = RAW_DATA.corr(numeric_only=True)\n",
"corr_matrix[\"churn\"].sort_values(ascending=False)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 397
},
"id": "8pGoj6qcVFWe",
"outputId": "f923dbf4-b035-493a-ad1e-50e8a2544e38"
},
"execution_count": 60,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"churn 1.000000\n",
"age 0.291932\n",
"balance 0.115943\n",
"estimated_salary 0.015666\n",
"customer_id -0.003100\n",
"credit_card -0.005795\n",
"tenure -0.011790\n",
"credit_score -0.026729\n",
"products_number -0.048045\n",
"active_member -0.164081\n",
"Name: churn, dtype: float64"
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>churn</th>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>0.291932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>balance</th>\n",
" <td>0.115943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>estimated_salary</th>\n",
" <td>0.015666</td>\n",
" </tr>\n",
" <tr>\n",
" <th>customer_id</th>\n",
" <td>-0.003100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>credit_card</th>\n",
" <td>-0.005795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tenure</th>\n",
" <td>-0.011790</td>\n",
" </tr>\n",
" <tr>\n",
" <th>credit_score</th>\n",
" <td>-0.026729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>products_number</th>\n",
" <td>-0.048045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>active_member</th>\n",
" <td>-0.164081</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div><br><label><b>dtype:</b> float64</label>"
]
},
"metadata": {},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.title(\"Matriz de correlación\")\n",
"plt.imshow(corr_matrix)\n",
"plt.colorbar()\n",
"print(corr_matrix.columns)\n",
"_ = plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns, rotation=45, ha='right', rotation_mode=\"anchor\")\n",
"_ = plt.yticks(range(len(corr_matrix.columns)), corr_matrix.columns)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 627
},
"id": "AmBJivPfWd2c",
"outputId": "3221bee2-b860-4c47-cfc1-f891ed92f247"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Index(['customer_id', 'credit_score', 'age', 'tenure', 'balance',\n",
" 'products_number', 'credit_card', 'active_member', 'estimated_salary',\n",
" 'churn'],\n",
" dtype='object')\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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A4O45Z1hb6c9MhLZjBuo6BE162CFqlKaP68HrX0xKuf78bmcwSVa9v1IhUxnr3y+5MNK/mGQq/fodT09Pxl+HQ6X384KQmpqKR0+UuPN3eVgXz/3/p4SXKrjUvovo6GhYW1tL5QXR2wcA5ubmmc7jS05OlvYXNCZ+pFcyututrYzy9J85v5mYmuk6BE369/kDIz1MsqCHDyeSmerP73YGk3Q9TPxM9O+XnIlfzhXGdB3r4vnzWWFtba2W+BWUjDuC35VR5ujoWOAxMPEjIiIig6QUKijzkPcqReH+wVOzZk2cOHECKpVKbf7jmTNnYGFhgcqVKxd4DPr3ZycRERFRDqgg8rwVlIcPH+LatWtIS0uTyrp3747Hjx9jy5YtUllsbCw2bdqEjh07FtgQ89vY40dEREQGSQUV8tJnl9vWixYtQlxcHB48eAAA2LFjB+7fvw8AGD16NGxsbBAUFIRVq1bhzp07cHZ2BvAm8atfvz4GDRqEq1evolSpUli8eDGUSiWmTZuWhyvJOSZ+RERERFqYO3cu7t69K32/ZcsWqRevX79+sLGxybSdsbExdu/ejfHjx+OHH35AUlIS6tati5UrV8LNza1QYpcJoYczn6nISkhIgI2NDV7cqKBXN3c0DQzUdQia9G+OuX7e1auHb3FKM/353c5gksSbO3KCN3e8X3paMk7vn4r4+PgCu2Ei47Mi+lrZPN/V6+QeU6Cx6hv2+BEREZFByus8vYKc46ev9O/PTiIiIiIqEOzxIyIiIoOkgoCSPX5aYeJHREREBolDvdrjUC/lysCBA6Xb07MTFRUFmUyGlStXFnhMRERElD0mfvkoMTERISEhOHr0qK5DISIi+uAphcjzVtRwqDcfJSYmSgsw+vj46DaYAvbzzz9DpdK/5R+IiKjoUP3/lpf2RQ0TP5KoVCqkpqbCzMzsvXVNTU0LISIiIiLKT0ViqDcmJgYBAQFwdHSEXC6Hi4sLRowYgdTUVISEhEAm01yQc+XKlZDJZIiKipLKzp49C19fX5QqVQrm5uZwcXHB4MGDAbyZy6ZQKAAA06ZNg0wmg0wmQ0hIiNT+8OHD8Pb2hqWlJWxtbfHJJ5/g33//VTtvRjw3btyQVv9WKBQIDg6GEALR0dH45JNPYG1tjTJlyuC7777TiD0lJQVTp05FxYoVIZfL4eTkhAkTJiAlJUWtnkwmw6hRo7B27VpUrVoVcrkce/fuzdFrmtkcv7i4OAwcOBA2NjawtbXFgAEDEBcXl6PjERERaUv5/3f15mUraj74Hr8HDx6gXr16iIuLQ2BgINzd3RETE4PIyEgkJibm+DhPnjxB69atoVAoMGnSJNja2iIqKkp6RItCocCSJUswYsQIdOnSBV27dgUAVK9eHQBw8OBBtG3bFhUqVEBISAiSkpKwcOFCNGrUCOfOndNIonr27IkqVapg1qxZ2LVrF2bMmIESJUrgp59+QvPmzTF79mysXbsW48aNQ926ddGkSRMAb3rtOnXqhJMnTyIwMBBVqlTBP//8g/nz5+PGjRvYtm2b2nkOHz6MX3/9FaNGjUKpUqVydMNGZoQQ+OSTT3Dy5EkMHz4cVapUwdatWzFgwIBs26WkpKglpAkJCbk6PxERFT1K8WbLS/ui5oNP/IKCgvDo0SOcOXMGderUkcqnT58ObZ5Wd+rUKbx48QL79+9XO86MGTMAAJaWlujevTtGjBiB6tWro1+/fmrtx48fjxIlSuD06dMoUaIEAKBz5874+OOPMXXqVKxatUqtfr169fDTTz8BAAIDA+Hs7Iwvv/wSoaGhmDhxIgCgd+/ecHR0xIoVK6TEb926dTh48CCOHTuGxo0bS8fz9PTE8OHDcerUKTRs2FAqv379Ov755x94eHjk+LXIzPbt23H8+HHMmTMH48ePBwCMGDECzZo1y7ZdaGhooT2YmoiIPiyc46e9D3qoV6VSYdu2bejYsaNaspYhsyHerNja2gIAdu7cibS0NK3iePjwIS5cuICBAwdKSR/wpjewVatW2L17t0abIUOGSF8bGxujTp06EEIgICBALSY3Nzfcvn1bKtu0aROqVKkCd3d3xMbGSlvz5s0BAEeOHFE7T9OmTfOc9AHA7t27YWJighEjRqjFPXr06GzbBQUFIT4+Xtqio6PzHAsRERFl7oNO/J4+fYqEhAR4enrm+VhNmzZFt27dMG3aNJQqVQqffPIJwsPDNebNZebu3bsAADc3N419VapUQWxsLF6/fq1W/tFHH6l9b2NjAzMzM5QqVUqj/MWLF9L3N2/exJUrV6BQKNS2ypUrA3gzZP02FxeX98afE3fv3oWDgwOsrKzUyjO75rfJ5XJYW1urbURERDmhggzKPGwq5LwD6EPxwQ/1vk9WvX5KpVKjXmRkJP744w/s2LED+/btw+DBg/Hdd9/hjz/+0Eh48srY2DhHZQDUhqxVKhWqVauGefPmZVrXyclJ7Xtzc/M8RElERKQ7KvFmy0v7ouaDTvwUCgWsra1x+fLlLOvY2dkBeHNHasZwLvC/Xrp31a9fH/Xr18e3336LdevWoW/fvtiwYQOGDBmSZRJZvnx5AG/m073r2rVrKFWqFCwtLXN6WdlydXXFxYsX0aJFC62GsvOqfPnyOHToEF69eqWWBGd2zURERKQbH/RQr5GRETp37owdO3bg7NmzGvuFEHB1dQUAHD9+XCp//fq1xs0WL1680LgZpGbNmgAgDfdaWFgAgMYSJg4ODqhZsyZWrVqltu/y5cvYv38/2rVrl6vry4yfnx9iYmLw888/a+xLSkrSGFLOL+3atUN6ejqWLFkilSmVSixcuLBAzkdERJSXYd6Mraj5oHv8AGDmzJnYv38/mjZtKi1v8vDhQ2zatAknT55E69at8dFHHyEgIADjx4+HsbExVqxYAYVCgXv37knHWbVqFRYvXowuXbrA1dUVL1++xM8//wxra2spcTM3N4eHhwc2btyIypUro0SJEvD09ISnpyfCwsLQtm1bNGjQAAEBAdJyLjY2Nmpr/eVV//798euvv2L48OE4cuQIGjVqBKVSiWvXruHXX3/Fvn37Mr3RJa86duyIRo0aYdKkSYiKioKHhwe2bNmC+Pj4fD8XERERgDwnb0z8PkBly5bFmTNnEBwcjLVr1yIhIQFly5ZF27ZtYWFhAVNTU2zduhUjR45EcHAwypQpg7Fjx8LOzg6DBg2SjtO0aVP8+eef2LBhAx4/fgwbGxvUq1cPa9euVbtB4pdffsHo0aPx+eefIzU1FVOnToWnpydatmyJvXv3YurUqZgyZQpMTU3RtGlTzJ49O99usADe9HJu27YN8+fPx+rVq7F161ZYWFigQoUK+Oyzz6SbPPKbkZERtm/fjrFjx2LNmjWQyWTo1KkTvvvuO3z88ccFck4iIiLSjkxos5gdUQFLSEh4c6fyjQqwLq4/MxGaBgbqOgRNeviHqlGaHr6d6OFbnNJMf363M5gk6d+KZioT/fslF0b6F5NMz+5QSE9Lxun9UxEfH19gKzVkfFacvOwIqzx8Vrx6qUJjzwcFGqu++eB7/IiIiOjDxKFe7THxIzXPnz9HampqlvuNjY2lZxITERGRYWHiR2q6du2KY8eOZbm/fPnyiIqKKryAiIiIsqCEEZR5WKBE+f4qHxwmfqTmu+++U3sSyLu44DMREekLIWRQidwP14o8tDVUTPxITe3atXUdAhERUY5wjp/29O/WMiIiIiIqEOzxIyIiIoOkFEZQijzM8dOvlXAKBRM/IiIiMkgqyKDKw+ClCkUv8+NQLxEREVERwR4/0kttxwyEiamZrsOQHFu2TNchaPAZOlTXIWjIw4hLgRHG+heUUbr+9TLo4xMp9PEJJ8bJ+veEE6NU/YrJKL3w4uHNHdpj4kdEREQGKe9z/PTvj7CCpn9/ThERERFRgWCPHxERERmkNzd35H64Ni9tDRUTPyIiIjJIqjw+so139RIRERHRB4s9fkRERGSQeHOH9pj4ERERkUFSwYgLOGuJiR8REREZJKWQQSnysI5fHtoaKs7xIyIiIioi2ONHREREBkmZx7t6lRzqJSIiIjIMKmEEVR5u7lAVwZs7ONRLREREVEQw8StgAwcOhLOzs1qZTCZDSEiITuIhIiL6UGQM9eZlK2qK3hXroVOnTiEkJARxcXG6DoWIiMhgqPC/O3tzs6l0fQE6wMRPB5KSkvD1119L3586dQrTpk1j4kdERKTnUlJSMHHiRDg6OsLc3BxeXl44cOBAjtoePHgQzZo1Q6lSpWBra4t69eohIiKigCNWx8QvC69fvy6wY5uZmcHE5MO5r6YgXysiIqKsZCzgnJdNWwMHDsS8efPQt29fLFiwAMbGxmjXrh1OnjyZbbvt27ejdevWSE1NRUhICL799luYm5vD398f8+fPz+1LoLUik/jFxMQgICAAjo6OkMvlcHFxwYgRI5CamoqVK1dCJpPh2LFjGDlyJOzt7VGuXDmp7Z49e+Dt7Q1LS0sUL14c7du3x5UrVzTOsW3bNnh6esLMzAyenp7YunVrprG8PccvJCQE48ePBwC4uLhAJpNBJpMhKioqR9f16NEjDBo0COXKlYNcLoeDgwM++eQTjfZ79uxB06ZNUbx4cVhbW6Nu3bpYt26dWp1Nmzahdu3aMDc3R6lSpdCvXz/ExMSo1Rk4cCCsrKxw69YttGvXDsWLF0ffvn0BACqVCt9//z2qVq0KMzMzlC5dGsOGDcOLFy9ydC1ERETayHhkW142bfz555/YsGEDQkNDERYWhsDAQBw+fBjly5fHhAkTsm27aNEiODg44PDhwxg1ahQ+/fRTHDp0CK6urli5cmUeXgXtfDjdTtl48OAB6tWrh7i4OAQGBsLd3R0xMTGIjIxEYmKiVG/kyJFQKBSYMmWK1IsVERGBAQMGwNfXF7Nnz0ZiYiKWLFmCxo0b4/z589KNG/v370e3bt3g4eGB0NBQPHv2TErIstO1a1fcuHED69evx/z581GqVCkAgEKhyNG1devWDVeuXMHo0aPh7OyMJ0+e4MCBA7h3754U28qVKzF48GBUrVoVQUFBsLW1xfnz57F371706dNHqjNo0CDUrVsXoaGhePz4MRYsWIDff/8d58+fh62trXTO9PR0+Pr6onHjxpg7dy4sLCwAAMOGDZOOM2bMGNy5cweLFi3C+fPn8fvvv8PU1FQj/pSUFKSkpEjfJyQk5Oi6iYiICltkZCSMjY0RGBgolZmZmSEgIABfffUVoqOj4eTklGnbhIQE2NnZQS6XS2UmJibS535hKRKJX1BQEB49eoQzZ86gTp06Uvn06dMh3lrDp0SJEjh06BCMjY0BAK9evcKYMWMwZMgQLFu2TKo3YMAAuLm5YebMmVL5xIkTUbp0aZw8eRI2NjYAgKZNm6J169YoX758lrFVr14dtWrVwvr169G5c2eNO4CzExcXh1OnTiEsLAzjxo1Tu94M8fHxGDNmDOrVq4ejR4/CzMxM2pdx7WlpaZg4cSI8PT1x/PhxqU7jxo3RoUMHzJ8/H9OmTZPapaSkoEePHggNDZXKTp48iV9++QVr166VkkkAaNasGdq0aYNNmzaplWcIDQ1VOzYREVFOqSCDCrl/7Jq2bc+fP4/KlSvD2tparbxevXoAgAsXLmSZ+Pn4+GD27NkIDg7GgAEDIJPJsG7dOpw9exa//vpr7i4gFz74oV6VSoVt27ahY8eOaklfBpnsfz/0oUOHSkkfABw4cABxcXHo3bs3YmNjpc3Y2BheXl44cuQIAODhw4e4cOECBgwYICV9ANCqVSt4eHgU2LWZm5ujWLFiOHr0aJbDqQcOHMDLly8xadIktaQP+N+1nz17Fk+ePMHIkSPV6rRv3x7u7u7YtWuXxnFHjBih9v2mTZtgY2ODVq1aqb1WtWvXhpWVlfRavSsoKAjx8fHSFh0drdVrQERERVd+DfUmJCSobW+PRL3t4cOHcHBw0CjPKHvw4EGWsQYHB8PPzw/ffvstKlWqhIoVK2LWrFnYvHkzunbtmg+vRs588D1+T58+RUJCAjw9Pd9b18XFRe37mzdvAgCaN2+eaf2MjP/u3bsAgEqVKmnUcXNzw7lz57SKOafkcjlmz56NL7/8EqVLl0b9+vXRoUMH+Pv7o0yZMgCAW7duAUC2158Rv5ubm8Y+d3d3jQmrJiYmGkPYN2/eRHx8POzt7TM9x5MnT7K8hre7vYmIiHIq749se9P23V66qVOnZrreblJSUqafWRmdJklJSVmeSy6Xo3LlyujevTu6du0KpVKJZcuWoV+/fjhw4ADq16+f6+vQxgef+GnD3Nxc7XuV6s0KPxEREVIi9TZ9uDN37Nix6NixI7Zt24Z9+/YhODgYoaGhOHz4MD7++OMCOadcLoeRkfp/NJVKBXt7e6xduzbTNjmds0hERFTYoqOj1YZvs+qQMDc3z7Q3MDk5WdqflVGjRuGPP/7AuXPnpM9QPz8/VK1aFZ999hnOnDmTl0vIMd1nLgVMoVDA2toaly9f1rqtq6srAMDe3h4tW7bMsl7GHL6MHsK3Xb9+/b3neXu4OTdcXV3x5Zdf4ssvv8TNmzdRs2ZNfPfdd1izZo10DZcvX0bFihUzbZ8R//Xr1zV6N69fv57tHMW3Yzh48CAaNWqU7S8+ERFRflEJGVQiD3P8/r+ttbW1xry9zDg4OGisdgG8GQIGAEdHx0zbpaamYvny5ZgwYYJax4mpqSnatm2LRYsWITU1FcWKFcvNZWjlg5/jZ2RkhM6dO2PHjh04e/asxn6RzQOafX19YW1tjZkzZyItLU1j/9OnTwG8+UWoWbMmVq1ahfj4eGn/gQMHcPXq1ffGaGlpCQBaL+CcmJgo/ZWRwdXVFcWLF5f+ImndujWKFy+O0NBQjboZ116nTh3Y29tj6dKlan/J7NmzB//++y/at2//3lj8/PygVCrxzTffaOxLT0/n4tRERJTvVHl8XJu26/jVrFkTN27c0FiBIqO3rmbNmpm2e/bsGdLT06FUKjX2paWlQaVSZbqvIHzwPX4AMHPmTOzfvx9NmzZFYGAgqlSpgocPH2LTpk3ZLrhobW2NJUuWoH///qhVqxZ69eoFhUKBe/fuYdeuXWjUqBEWLVoE4M3dqe3bt0fjxo0xePBgPH/+HAsXLkTVqlXx6tWrbOOrXbs2AGDy5Mno1asXTE1N0bFjRykhzMqNGzfQokUL+Pn5wcPDAyYmJti6dSseP36MXr16Sdcwf/58DBkyBHXr1kWfPn1gZ2eHixcvIjExEatWrYKpqSlmz56NQYMGoWnTpujdu7e0nIuzszM+//zz977GTZs2xbBhwxAaGooLFy6gdevWMDU1xc2bN7Fp0yYsWLAA3bt3f+9xiIiI9FX37t0xd+5cLFu2TFpNIyUlBeHh4fDy8pLmCt67dw+JiYlwd3cH8Gbk0NbWFlu3bsX06dOlnr1Xr15hx44dcHd3L7TRsiKR+JUtWxZnzpxBcHAw1q5di4SEBJQtWxZt27aV1qDLSp8+feDo6IhZs2YhLCwMKSkpKFu2LLy9vTFo0CCpXsaSJV9//TWCgoLg6uqK8PBw/Pbbbzh69Gi256hbty6++eYbLF26FHv37oVKpcKdO3fem/g5OTmhd+/eOHToECIiImBiYgJ3d3f8+uuv6Natm1QvICAA9vb2mDVrFr755huYmprC3d1dLaEbOHAgLCwsMGvWLEycOBGWlpbo0qULZs+erbaGX3aWLl2K2rVr46effsJXX30FExMTODs7o1+/fmjUqFGOjkFERJRTKmEElZaLML/bXhteXl7o0aMHgoKC8OTJE1SsWBGrVq1CVFQUli9fLtXz9/fHsWPHpJE1Y2NjjBs3Dl9//TXq168Pf39/KJVKLF++HPfv38eaNWtyfQ3akonsxjqJCllCQgJsbGxQv810mJiavb9BITn21jqO+sJn6FBdh6BJD99NhHHe5tAWBJlK/14oWeGMMmkl3UL/ZiMZJ6t0HYIGo1T9iik9PRm/H5mG+Pj4HM2by42Mz4pv/mwOM6vc92Elv0pHcL3DWsWanJyM4OBgrFmzBi9evED16tXxzTffwNfXV6rj4+OjlvhlWLduHRYsWIAbN24gJSUF1atXx/jx49U6awpakejxIyIiIsoPZmZmCAsLQ1hYWJZ1shrp69OnT6YPMyhMTPz0VHx8fLbrAQHIdIkZIiKioqKwh3o/BEz89NRnn32GVatWZVuHo/RERFSUKQEo8/DINj2c4VDgmPjpqQkTJqBfv366DoOIiIg+IEz89JSHh0eBPueXiIjI0HGoV3tM/IiIiMggKYURlHlI3vLS1lAx8SMiIiKDJCCDKg9z/EQe2hqqopfqEhERERVR7PEjIiIig8ShXu0x8SP9JPv/TU/o41Myjv78s65D0OAzRP9eJ5WJHv0i/b9iCfq3iISymP59AKZa6l9M5kn69ZQMAIjxKabrENSoklXAkUI6l5BBJXL/fzwvbQ2V/v2vIiIiIqICwR4/IiIiMkhKGEGZhz6svLQ1VEz8iIiIyCBxqFd7RS/VJSIiIiqi2ONHREREBkkFI6jy0IeVl7aGiokfERERGSSlkEGZh+HavLQ1VEUv1SUiIiIqotjjR0RERAaJN3doj4kfERERGSQhjKDKw9M3BJ/cQURERGQYlJBBmYfHPOWlraEqeqkuERERURHFHj8iIiIySCqRt3l6KpGPwRgIJn5ERERkkFR5nOOXl7aGquhdMREREVERxR4/IiIiMkgqyKDKww0aeWlrqJj4ERERkUHikzu0x6HeIuru3bsYOXIk3NzcYG5ujpIlS6JHjx6IiorSqHvp0iU0bdoU5ubmKFeuHGbMmIHw8HDIZDKN+nv27IG3tzcsLS1RvHhxtG/fHleuXCmciyIiIqJssceviPrrr79w6tQp9OrVC+XKlUNUVBSWLFkCHx8fXL16FRYWFgCAmJgYNGvWDDKZDEFBQbC0tMQvv/wCuVyuccyIiAgMGDAAvr6+mD17NhITE7FkyRI0btwY58+fh7Ozs0ablJQUpKSkSN8nJCQU2DUTEdGHhTd3aI+JXxHVvn17dO/eXa2sY8eOaNCgATZv3oz+/fsDAGbPno0XL17g3LlzqFmzJgBg0KBBqFSpklrbV69eYcyYMRgyZAiWLVsmlQ8YMABubm6YOXOmWnmG0NBQTJs2LZ+vjoiIigIV8vjItiI4x6/opboEADA3N5e+TktLw7Nnz1CxYkXY2tri3Llz0r69e/eiQYMGUtIHACVKlEDfvn3VjnfgwAHExcWhd+/eiI2NlTZjY2N4eXnhyJEjmcYRFBSE+Ph4aYuOjs7fCyUiIiIJe/yKqKSkJISGhiI8PBwxMTEQ4n+rWMbHx0tf3717Fw0aNNBoX7FiRbXvb968CQBo3rx5pueztrbOtFwul2c6bExERPQ+Io939Yoi2OPHxK+IGj16NMLDwzF27Fg0aNAANjY2kMlk6NWrF1QqldbHy2gTERGBMmXKaOw3MeGvGhER5S+VyONQbxG8q5efxkVUZGQkBgwYgO+++04qS05ORlxcnFq98uXL47///tNo/26Zq6srAMDe3h4tW7bM/4CJiIjewZs7tFf0rpgAAMbGxmrDuwCwcOFCKJVKtTJfX1+cPn0aFy5ckMqeP3+OtWvXatSztrbGzJkzkZaWpnG+p0+f5l/wRERElCvs8SuiOnTogIiICNjY2MDDwwOnT5/GwYMHUbJkSbV6EyZMwJo1a9CqVSuMHj1aWs7lo48+wvPnzyGTvekmt7a2xpIlS9C/f3/UqlULvXr1gkKhwL1797Br1y40atQIixYt0sWlEhHRB4pDvdpj4ldELViwAMbGxli7di2Sk5PRqFEjHDx4EL6+vmr1nJyccOTIEYwZMwYzZ86EQqHAp59+CktLS4wZMwZmZmZS3T59+sDR0RGzZs1CWFgYUlJSULZsWXh7e2PQoEGFfYlERPSB4yPbtMfEr4iytbXFihUrNMoze3JHzZo1cfz4cbWysWPHwszMDKVKlVIr9/HxgY+PT36GSkRERPmEiR+9V1JSktq6f8+ePUNERAQaN24MY2NjHUZGRERFGYd6tcfEj96rQYMG8PHxQZUqVfD48WMsX74cCQkJCA4O1nVoRERUhDHx0x4TP3qvdu3aITIyEsuWLYNMJkOtWrWwfPlyNGnSRNehERERkRaY+NF7zZw5EzNnztR1GERERGrY46c9Jn5ERERkkJj4aY8LOBMREREVEezxIyIiIoMkkLe1+MT7q3xw2ONHREREBiljqDcvm7ZSUlIwceJEODo6wtzcHF5eXjhw4ECO22/cuBENGjSApaUlbG1t0bBhQxw+fFjrOHKLiR8REREZJF0kfgMHDsS8efPQt29f6SlY7dq1w8mTJ9/bNiQkBL1794aTkxPmzZuHGTNmoHr16oiJicnN5ecKh3pJLxmlCRjpUSe80MM/kXyGDNV1CBqO/vKzrkPQ0GzQEF2HoCHdQv8WPleZ6N8kd8uHqboOQYPSTP9+do4n03Udgpr0tHTc1nUQBeTPP//Ehg0bEBYWhnHjxgEA/P394enpiQkTJuDUqVNZtv3jjz8wffp0fPfdd/j8888LK2QNevhxRkRERPR+hd3jFxkZCWNjYwQGBkplZmZmCAgIwOnTpxEdHZ1l2++//x5lypTBZ599BiEEXr16levrzgsmfkRERGSQ8ivxS0hIUNtSUlIyPd/58+dRuXJlWFtbq5XXq1cPAHDhwoUsYz106BDq1q2LH374AQqFAsWLF4eDgwMWLVqUPy9GDjHxIyIioiLNyckJNjY20hYaGpppvYcPH8LBwUGjPKPswYMHmbZ78eIFYmNj8fvvvyM4OBiTJk3Cxo0bUbNmTYwePRo//fRT/l3Me3COHxERERkkIWQQeViEOaNtdHS0Wi+eXC7PtH5SUlKm+8zMzKT9mckY1n327Bk2bNiAnj17AgC6d++OatWqYcaMGRg2bFiur0Mb7PEjIiIig6SCLM8bAFhbW6ttWSV+5ubmmQ4DJycnS/uzagcApqam6N69u1RuZGSEnj174v79+7h3716eXoucYuJHRERElAMODg54+PChRnlGmaOjY6btSpQoATMzM5QsWRLGxup3htvb2wN4MxxcGJj4ERERkUEq7Lt6a9asiRs3biAhIUGt/MyZM9L+zBgZGaFmzZp4+vQpUlPVlynKmBeoUCi0iiW3mPgRERGRQcqY45eXTRvdu3eHUqnEsmXLpLKUlBSEh4fDy8sLTk5OAIB79+7h2rVram179uwJpVKJVatWSWXJyclYu3YtPDw8suwtzG+8uYOIiIgoB7y8vNCjRw8EBQXhyZMnqFixIlatWoWoqCgsX75cqufv749jx45BiP89iGDYsGH45Zdf8Omnn+LGjRv46KOPEBERgbt372LHjh2Fdg1M/IiIiMgg5faxa2+319bq1asRHByMiIgIvHjxAtWrV8fOnTvRpEmTbNuZm5vj8OHDmDBhAlasWIHXr1+jZs2a2LVrF3x9fXN7CVpj4kdEREQGKb+Wc9GGmZkZwsLCEBYWlmWdo0ePZlpub2+PlStXan3O/MTEj4iIiAySyGOPX16SRkPFmzuIiIiIiggmfnrs1KlTCAkJQVxcnK5DISIi0jsCgBB52HR9ATrAxE+PnTp1CtOmTWPiR0RElIn8enJHUcLEj/IsMTFR1yEQERFRDjDx01MhISEYP348AMDFxQUymQwymQxRUVEAgDVr1qB27dowNzdHiRIl0KtXL0RHR6sdw8fHB56enrh69SqaNWsGCwsLlC1bFnPmzFGrt3LlSrVjZzh69ChkMpna3UkZx/z777/RpEkTWFhY4KuvvgLwZhHLqVOnomLFipDL5XBycsKECRMyfa4hERFRXhX2As4fAt7Vq6e6du2KGzduYP369Zg/fz5KlSoF4M0jXb799lsEBwfDz88PQ4YMwdOnT7Fw4UI0adIE58+fh62trXScFy9eoE2bNujatSv8/PwQGRmJiRMnolq1amjbtm2uYnv27Bnatm2LXr16oV+/fihdujRUKhU6deqEkydPIjAwEFWqVME///yD+fPn48aNG9i2bVumx0pJSVFLDN99DA4REVFWVEIGWSGv42fomPjpqerVq6NWrVpYv349OnfuDGdnZwDA3bt3MXXqVMyYMUPqaQPeJIoff/wxFi9erFb+4MEDrF69Gv379wcABAQEoHz58li+fHmuE79Hjx5h6dKlGDZsmFS2Zs0aHDx4EMeOHUPjxo2lck9PTwwfPhynTp1Cw4YNNY4VGhqKadOm5SoOIiIi0g6Heg3Mli1boFKp4Ofnh9jYWGkrU6YMKlWqhCNHjqjVt7KyQr9+/aTvixUrhnr16uH27du5jkEul2PQoEFqZZs2bUKVKlXg7u6uFlfz5s0BQCOuDEFBQYiPj5e2d4eriYiIspKnO3r/fytq2ONnYG7evAkhBCpVqpTpflNTU7Xvy5UrB5lMvSvbzs4Oly5dynUMZcuWRbFixTTi+vfff6FQKDJt8+TJk0zL5XI55HJ5rmMhIqKiSxdP7jB0TPwMjEqlgkwmw549e2BsbKyx38rKSu37zOoAUHtw9LuJYQalUplpubm5eaZxVatWDfPmzcu0jZOTU6blREREVHiY+OmxzBIyV1dXCCHg4uKCypUr58t57OzsAEBjvcC7d+/m+Biurq64ePEiWrRokWUiSURElJ/Y46c9zvHTY5aWlgDUE7KuXbvC2NgY06ZNU+u1A9704j179kzr87i6ugIAjh8/LpUplUosW7Ysx8fw8/NDTEwMfv75Z419SUlJeP36tdZxERERZUf1/8/qzctW1LDHT4/Vrl0bADB58mT06tULpqam6NixI2bMmIGgoCBERUWhc+fOKF68OO7cuYOtW7ciMDAQ48aN0+o8VatWRf369REUFITnz5+jRIkS2LBhA9LT03N8jP79++PXX3/F8OHDceTIETRq1AhKpRLXrl3Dr7/+in379qFOnTpaxUVERJSdvN6gwZs7SK/UrVsX33zzDZYuXYq9e/dCpVLhzp07mDRpEipXroz58+dLS6E4OTmhdevW6NSpU67OtXbtWgwbNgyzZs2Cra0tAgIC0KxZM7Rq1SpH7Y2MjLBt2zbMnz8fq1evxtatW2FhYYEKFSrgs88+y7dhaSIiIso9mXh3vJBIhxISEmBjY4OGLafBxNRM1+FIBCdF5MjRXzSH+nWt2aAhug5Bg6qY/v1CqUz0b8jL9GXORx0Ki9Is8xvmdMkoXb8+xtPTknHq4FTEx8fD2tq6QM6R8VlRac0kGFvk/rNCmZiMm/1mFWis+oY9fkRERGSQeHOH9vTvz04iIiIiKhDs8SMiIiKDJP5/y0v7ooaJHxERERkkDvVqj0O9REREREUEe/yIiIjIMHGsV2tM/IiIiMgw5XGoFxzqJSIiIqIPFXv8iIiIyCDxkW3aY+JHeiqP/5vzmTDWv85xfXzSgj4+JeNI+C+6DkGDr2NNXYegIdVX/56lrTLVv/93Qg8/NV/Z61dQytTCi4d39WpPv35biIiIiHJKyPI2T68IJn769+cUERERERUI9vgRERGRQeIcP+0x8SMiIiLDxHX8tMahXiIiIqIigj1+REREZJB4V6/2mPgRERGR4SqCw7V5waFeIiIioiKCPX5ERERkkDjUqz0mfkRERGSYeFev1jjUS0RERFREsMePiIiIDJTs/7e8tC9a2ONXyEJCQiCTyRAbG5svx/Px8YGPj0++HIuIiMigiHzYihj2+BEREZFh4hw/rbHHj4iIiKiIYI8fERERGSYhe7PlpX0Rwx4/HYmNjYWfnx+sra1RsmRJfPbZZ0hOTpb2h4eHo3nz5rC3t4dcLoeHhweWLFny3uOmpqZiypQpqF27NmxsbGBpaQlvb28cOXJErV5UVBRkMhnmzp2LZcuWwdXVFXK5HHXr1sVff/2lcdxr167Bz88PCoUC5ubmcHNzw+TJk9XqxMTEYPDgwShdujTkcjmqVq2KFStW5PIVIiIiyp4Qed+KGiZ+OuLn54fk5GSEhoaiXbt2+OGHHxAYGCjtX7JkCcqXL4+vvvoK3333HZycnDBy5Ej8+OOP2R43ISEBv/zyC3x8fDB79myEhITg6dOn8PX1xYULFzTqr1u3DmFhYRg2bBhmzJiBqKgodO3aFWlpaVKdS5cuwcvLC4cPH8bQoUOxYMECdO7cGTt27JDqPH78GPXr18fBgwcxatQoLFiwABUrVkRAQAC+//77LONNSUlBQkKC2kZERKSvUlJSMHHiRDg6OsLc3BxeXl44cOCA1sdp1aoVZDIZRo0aVQBRZo1DvTri4uKC3377DQDw6aefwtraGosXL8a4ceNQvXp1HDt2DObm5lL9UaNGoU2bNpg3bx4+/fTTLI9rZ2eHqKgoFCtWTCobOnQo3N3dsXDhQixfvlyt/r1793Dz5k3Y2dkBANzc3PDJJ59g37596NChAwBg9OjREELg3Llz+Oijj6S2s2bNkr6ePHkylEol/vnnH5QsWRIAMHz4cPTu3RshISEYNmyY2vVkCA0NxbRp03L8uhEREUl0cHPHwIEDERkZibFjx6JSpUpYuXIl2rVrhyNHjqBx48Y5OsaWLVtw+vRp7U+eD9jjpyPvJm+jR48GAOzevRsA1JKk+Ph4xMbGomnTprh9+zbi4+OzPK6xsbGU9KlUKjx//hzp6emoU6cOzp07p1G/Z8+eUtIHAN7e3gCA27dvAwCePn2K48ePY/DgwWpJHwDIZG/mRgghsHnzZnTs2BFCCMTGxkqbr68v4uPjMz03AAQFBSE+Pl7aoqOjs7w2IiIiNRlz/PKyaeHPP//Ehg0bEBoairCwMAQGBuLw4cMoX748JkyYkKNjJCcn48svv8TEiRNzc8V5xsRPRypVqqT2vaurK4yMjBAVFQUA+P3339GyZUtYWlrC1tYWCoUCX331FQBkm/gBwKpVq1C9enWYmZmhZMmSUCgU2LVrV6bt3k3mMpLAFy9eAPhfAujp6Znl+Z4+fYq4uDgsW7YMCoVCbRs0aBAA4MmTJ5m2lcvlsLa2VtuIiIj0UWRkJIyNjdWmZpmZmSEgIACnT5/OUefFnDlzoFKpMG7cuIIMNUsc6tUTGb1nAHDr1i20aNEC7u7umDdvHpycnFCsWDHs3r0b8+fPh0qlyvI4a9aswcCBA9G5c2eMHz8e9vb2MDY2RmhoKG7duqVR39jYONPjCC1mvGbE069fPwwYMCDTOtWrV8/x8YiIiHJCJt5seWkPQGN+uVwuh1wu16h//vx5VK5cWaOTol69egCACxcuwMnJKcvz3bt3D7NmzcKKFSsynf5UGJj46cjNmzfh4uIiff/ff/9BpVLB2dkZO3bsQEpKCrZv367WI/funbmZiYyMRIUKFbBlyxa1ZHLq1Km5irNChQoAgMuXL2dZR6FQoHjx4lAqlWjZsmWuzkNERKS1fJrj926yNnXqVISEhGhUf/jwIRwcHDTKM8oePHiQ7em+/PJLfPzxx+jVq1fu4s0HHOrVkXfvzl24cCEAoG3btlIv3Nu9bvHx8QgPD3/vcTNre+bMmVxPIlUoFGjSpAlWrFiBe/fuqe3LOIexsTG6deuGzZs3Z5ogPn36NFfnJiIiKgzR0dFq882DgoIyrZeUlJRpT6CZmZm0PytHjhzB5s2bs13pojCwx09H7ty5g06dOqFNmzY4ffo01qxZgz59+qBGjRowMzNDsWLF0LFjRwwbNgyvXr3Czz//DHt7ezx8+DDb43bo0AFbtmxBly5d0L59e9y5cwdLly6Fh4cHXr16latYf/jhBzRu3Bi1atVCYGAgXFxcEBUVhV27dklLxMyaNQtHjhyBl5cXhg4dCg8PDzx//hznzp3DwYMH8fz581ydm4iIKEv5tIBzTueYm5ubIyUlRaM8Yx3erIZv09PTMWbMGPTv3x9169bNfbz5gImfjmzcuBFTpkzBpEmTYGJiglGjRiEsLAzAmyVVIiMj8fXXX2PcuHEoU6YMRowYAYVCgcGDB2d73IEDB+LRo0f46aefsG/fPnh4eGDNmjXYtGkTjh49mqtYa9SogT/++APBwcFYsmQJkpOTUb58efj5+Ul1SpcujT///BPTp0/Hli1bsHjxYpQsWRJVq1bF7Nmzc3VeIiKibBXyci4ODg6IiYnRKM/olHF0dMy03erVq3H9+nX89NNP0k2cGV6+fImoqCjY29vDwsJCu4ByQSa0mcVPVMASEhJgY2ODhi1DYGJiputwJKpi+jcrQmWif48aMklU6joEDUfCf9F1CBp8HWvqOgQNqb51dB2CBmGkf7/jKrn+xZRUIvOb9HRFmZqMixGTER8fX2ArNWR8Vjh99w2MzHP/WaFKSkb0l8E5jnX8+PGYP38+nj9/rlZ/5syZmDx5Mu7du5fpzR0hISHvXbN269at6Ny5s9bXoC32+BERERHlQPfu3aVHnWYsx5KSkoLw8HB4eXlJSd+9e/eQmJgId3d3AECvXr1Qs2ZNjeN16dIF7dq1w9ChQ+Hl5VUo18DEj4iIiAxTIQ/1enl5oUePHggKCsKTJ09QsWJFrFq1ClFRUWpPxvL398exY8ekmyDd3d2lJPBdLi4uhdLTl4GJHxERERmmfLq5QxurV69GcHAwIiIi8OLFC1SvXh07d+5EkyZNch9HIWLiR0RERJRDZmZmCAsLk27IzExOb6bUxW0WTPyIiIjIIOXXkzuKEiZ+REREZJgKeY7fh0D/1qggIiIiogLBxI+IiIioiOBQLxERERkkGfI4xy/fIjEcTPxILynlRpCZ6k+HtFG6/k0EKZagf0/JSLfQrycIAPr5lIx9Dy7oOgQNPkN0+/zQTOnhp7IsXdcRaLK5rfnsWF1KT9eveEgdEz8iIiIyTDpYx8/QMfEjIiIiw8S7erXGxI+IiIgMExM/renPJCoiIiIiKlDs8SMiIiKDxCd3aI+JHxERERkmDvVqjUO9REREREUEe/yIiIjIMLHHT2tM/IiIiMggcY6f9jjUS0RERFREsMePiIiIDBOf3KE1Jn5ERERkmDjHT2sGN9S7cuVKyGQyREVF6TqUD0bGa3r27Fldh0JERJRjGXP88rIVNQaX+BW0Bw8eICQkBBcuXNB1KERERET5ionfOx48eIBp06Yx8SMiItJ3Ih+2IqbQEj+VSoXk5OTCOh3podevX+s6BCIi+pDkdZiXid/7hYSEQCaT4dq1a/Dz84O1tTVKliyJzz77TC2xk8lkGDVqFNauXYuqVatCLpdj7969AIDz58+jbdu2sLa2hpWVFVq0aIE//vhD41xXrlxB8+bNYW5ujnLlymHGjBlQqVQa9WQyGUJCQjTKnZ2dMXDgQLWyuLg4fP7553B2doZcLke5cuXg7++P2NhYHD16FHXr1gUADBo0CDKZDDKZDCtXrgQA3Lx5E926dUOZMmVgZmaGcuXKoVevXoiPj8/x6zdw4EBYWVkhJiYGnTt3hpWVFRQKBcaNGwelUinVO3r0KGQyGY4eParWPioqSi2mt4957949dOjQAVZWVihbtix+/PFHAMA///yD5s2bw9LSEuXLl8e6desyjS0xMRHDhg1DyZIlYW1tDX9/f7x48UKj3p49e+Dt7Q1LS0sUL14c7du3x5UrVzK9zlu3bqFdu3YoXrw4+vbtm+PXiYiIiPJfru/q9fPzg7OzM0JDQ/HHH3/ghx9+wIsXL7B69WqpzuHDh/Hrr79i1KhRKFWqFJydnXHlyhV4e3vD2toaEyZMgKmpKX766Sf4+Pjg2LFj8PLyAgA8evQIzZo1Q3p6OiZNmgRLS0ssW7YM5ubmub7YV69ewdvbG//++y8GDx6MWrVqITY2Ftu3b8f9+/dRpUoVTJ8+HVOmTEFgYCC8vb0BAA0bNkRqaip8fX2RkpKC0aNHo0yZMoiJicHOnTsRFxcHGxubHMehVCrh6+sLLy8vzJ07FwcPHsR3330HV1dXjBgxIlfXplQq0bZtWzRp0gRz5szB2rVrMWrUKFhaWmLy5Mno27cvunbtiqVLl8Lf3x8NGjSAi4uL2jFGjRoFW1tbhISE4Pr161iyZAnu3r0rJaEAEBERgQEDBsDX1xezZ89GYmIilixZgsaNG+P8+fNwdnaWjpeeng5fX180btwYc+fOhYWFhUbcKSkpSElJkb5PSEjI1fUTEVERxLt6tZbrxM/FxQW//fYbAODTTz+FtbU1Fi9ejHHjxqF69eoAgOvXr+Off/6Bh4eH1K5Lly5IS0vDyZMnUaFCBQCAv78/3NzcMGHCBBw7dgwAMHv2bDx9+hRnzpxBvXr1AAADBgxApUqVchsywsLCcPnyZWzZsgVdunSRyr/++msIISCTydC2bVtMmTIFDRo0QL9+/aQ6Fy5cwJ07d7Bp0yZ0795dKp8yZYrWcSQnJ6Nnz54IDg4GAAwfPhy1atXC8uXLc534JScno1+/fggKCgIA9OnTB46Ojhg8eDDWr1+Pnj17AgBatWoFd3d3rFq1SqOXtFixYjh06BBMTU0BAOXLl8eECROwY8cOdOrUCa9evcKYMWMwZMgQLFu2TGo3YMAAuLm5YebMmWrlKSkp6NGjB0JDQ7OMOzQ0FNOmTcvVNRMRURHHxE9ruZ7j9+mnn6p9P3r0aADA7t27pbKmTZuqJX1KpRL79+9H586dpaQPABwcHNCnTx+cPHlS6vHZvXs36tevLyV9AKBQKPI0XLh582bUqFFDLenLkNGjlZWMHr19+/YhMTEx1zFkGD58uNr33t7euH37dp6OOWTIEOlrW1tbuLm5wdLSEn5+flK5m5sbbG1tMz1XYGCglPQBwIgRI2BiYiL9TA8cOIC4uDj07t0bsbGx0mZsbAwvLy8cOXJE45jvS2SDgoIQHx8vbdHR0VpfNxEREeVMrhO/d3veXF1dYWRkpLa+3rtDiU+fPkViYiLc3Nw0jlelShWoVCrpg//u3buZ9u5l1janbt26BU9Pz1y1dXFxwRdffIFffvkFpUqVgq+vL3788Uet5vdlMDMzg0KhUCuzs7PLdD5dXo5pY2ODcuXKaSS1NjY2mZ7r3dfbysoKDg4O0s/05s2bAIDmzZtDoVCobfv378eTJ0/U2puYmKBcuXLZxi2Xy2Ftba22ERER5QTX8dNevj25I7Mes7zMx8sPb98skR++++47DBw4EL/99hv279+PMWPGSHMc35fgvM3Y2Pi9dbLqgczqmrI6ZlblQmj/255xY01ERATKlCmjsd/ERP3XSS6Xw8iIKwYRERHpi1wnfjdv3lTr0fvvv/+gUqnUJve/S6FQwMLCAtevX9fYd+3aNRgZGcHJyQnAm/llGT1Mb8usrZ2dHeLi4tTKUlNT8fDhQ7UyV1dXXL58ObvLeu+Qb7Vq1VCtWjV8/fXXOHXqFBo1aoSlS5dixowZ2bbTlp2dHQBoXNfdu3fz9Txvu3nzJpo1ayZ9/+rVKzx8+BDt2rUD8Ob1AwB7e3u0bNmywOIgIiKigpHr7piMpUIyLFy4EADQtm3bLNsYGxujdevW+O2339SGhB8/fox169ahcePG0lBfu3bt8Mcff+DPP/+U6j19+hRr167VOK6rqyuOHz+uVrZs2TKN3rFu3brh4sWL2Lp1q8YxMnrALC0tAWgmXAkJCUhPT1crq1atGoyMjNTuSs0v5cuXh7GxscZ1LV68ON/PlWHZsmVIS0uTvl+yZAnS09Oln6mvry+sra0xc+ZMtXoZnj59WmCxERERaeACzlrLdY/fnTt30KlTJ7Rp0wanT5/GmjVr0KdPH9SoUSPbdjNmzMCBAwfQuHFjjBw5EiYmJvjpp5+QkpKCOXPmSPUmTJiAiIgItGnTBp999pm0nEv58uVx6dIltWMOGTIEw4cPR7du3dCqVStcvHgR+/btQ6lSpdTqjR8/HpGRkejRowcGDx6M2rVr4/nz59i+fTuWLl2KGjVqwNXVFba2tli6dCmKFy8OS0tLeHl54eLFixg1ahR69OiBypUrIz09HRERETA2Nka3bt1y+zJmycbGBj169MDChQshk8ng6uqKnTt3asyjy0+pqalo0aIF/Pz8cP36dSxevBiNGzdGp06dAADW1tZYsmQJ+vfvj1q1aqFXr15QKBS4d+8edu3ahUaNGmHRokUFFh8REdHb8jpPj3P8tLBx40ZMmTIFkyZNgomJCUaNGoWwsLD3tqtatSpOnDiBoKAghIaGQqVSwcvLC2vWrJHW8APe3Ol75MgRjB49GrNmzULJkiUxfPhwODo6IiAgQO2YQ4cOxZ07d7B8+XLs3bsX3t7eOHDgAFq0aKFWz8rKCidOnMDUqVOxdetWrFq1Cvb29mjRooU0R8/U1BSrVq1CUFAQhg8fjvT0dISHh6Np06bw9fXFjh07EBMTAwsLC9SoUQN79uxB/fr1c/syZmvhwoVIS0vD0qVLIZfL4efnh7CwsFzfoPI+ixYtwtq1azFlyhSkpaWhd+/e+OGHH9SGvzOWiZk1axbCwsKQkpKCsmXLwtvbG4MGDSqQuIiIiLJUBJO3vJAJLWf5h4SEYNq0aXj69KlGjxpRXiUkJMDGxgZe7afDxNRM1+FIjNL1753FOFnzKTa6lm7x/huXCpvZjj/fX6mQ7XtwQdchaPAZMlTXIWjKfsq1Toj3zAPXBdNX6e+vVIjS05Nx4sR0xMfHF9hKDRmfFRUnzYSxPPefFcqUZPw366sCjVXf5NtdvURERESFigs4a42JXz6Jj49HUlJStnUyWwKFiIiIcodz/LTHxC+ffPbZZ1i1alW2dXKzdh4RERFRftE68QsJCdF4xiu9uQv57Wf7EhERUQHjUK/W2OOXTzw8PNSeS0xEREQFi0O92uPztIiIiIiKCPb4ERERkWHiUK/WmPgRERGRYWLipzUO9RIREREVEezxI71kkqyCSbr+PJlCGOnfav3KYvr3d5vKRP9ep1TfOroOQYPPkLq6DkHD0V9+1nUIGnwC9O9pIkZ6uCyXvj0xJz2t8OLhzR3a079PDiIiIqKcEPmwaSklJQUTJ06Eo6MjzM3N4eXlhQMHDry33ZYtW9CzZ09UqFABFhYWcHNzw5dffom4uDjtg8gDJn5ERERkmHSQ+A0cOBDz5s1D3759sWDBAhgbG6Ndu3Y4efJktu0CAwPx77//ol+/fvjhhx/Qpk0bLFq0CA0aNHjvk7/yE4d6iYiIiHLgzz//xIYNGxAWFoZx48YBAPz9/eHp6YkJEybg1KlTWbaNjIyEj4+PWlnt2rUxYMAArF27FkOGDCnI0CXs8SMiIiKDlDHHLy+bNiIjI2FsbIzAwECpzMzMDAEBATh9+jSio6OzbPtu0gcAXbp0AQD8+++/2gWSB+zxIyIiIsOUT8u5JCQkqBXL5XLI5XKN6ufPn0flypVhbW2tVl6vXj0AwIULF+Dk5JTj0z969AgAUKpUKW2izhP2+BEREVGR5uTkBBsbG2kLDQ3NtN7Dhw/h4OCgUZ5R9uDBA63OO3v2bBgbG6N79+7aB51L7PEjIiIig5Rfy7lER0er9eJl1tsHAElJSZnuMzMzk/bn1Lp167B8+XJMmDABlSpV0iLqvGHiR0RERIYpn4Z6ra2tNYZvM2Nubo6UlBSN8uTkZGl/Tpw4cQIBAQHw9fXFt99+m/N48wGHeomIiIhywMHBAQ8fPtQozyhzdHR87zEuXryITp06wdPTE5GRkTAxKdw+OCZ+REREZJgKeR2/mjVr4saNGxo3g5w5c0ban51bt26hTZs2sLe3x+7du2FlZaVdAPmAiR8REREZJFk+bNro3r07lEolli1bJpWlpKQgPDwcXl5e0h299+7dw7Vr19TaPnr0CK1bt4aRkRH27dsHhUKh7eXmC87xIyIiIsoBLy8v9OjRA0FBQXjy5AkqVqyIVatWISoqCsuXL5fq+fv749ixYxBvPdu5TZs2uH37NiZMmICTJ0+qPemjdOnSaNWqVaFcAxM/HRs4cCCOHj2KqKgoqUwmk2Hq1KkICQnRWVz5JWPByqNHj+o0DiIi+gDl080d2li9ejWCg4MRERGBFy9eoHr16ti5cyeaNGmSbbuLFy8CAObMmaOxr2nTpkz86H9OnTqF/fv3Y+zYsbC1tdV1OERERHohv5Zz0YaZmRnCwsIQFhaWZZ3MOjve7v3TJSZ+eigpKUntLp9Tp05h2rRpGDhwIBM/IiKiDDro8TN0vLkjl16/fl1gxzYzMyv027tzqyBfByIiIspfTPz+X0xMDAICAuDo6Ai5XA4XFxeMGDECqampWLlyJWQyGY4dO4aRI0fC3t4e5cqVk9ru2bMH3t7esLS0RPHixdG+fXtcuXJF4xzbtm2Dp6cnzMzM4Onpia1bt2Yai0wmk+b3hYSEYPz48QAAFxcXyGQyyGQytTmB73Pt2jX4+flBoVDA3Nwcbm5umDx5srT/7t27GDlyJNzc3GBubo6SJUuiR48eGud43+uwbNkyuLq6wtzcHPXq1cOJEydyHCMREVGuFNJSLh8Kw+hWKmAPHjxAvXr1EBcXh8DAQLi7uyMmJgaRkZFITEyU6o0cORIKhQJTpkyReroiIiIwYMAA+Pr6Yvbs2UhMTMSSJUvQuHFjnD9/Hs7OzgCA/fv3o1u3bvDw8EBoaCiePXuGQYMGqSVOmenatStu3LiB9evXY/78+dKDnHN6G/ilS5fg7e0NU1NTBAYGwtnZGbdu3cKOHTuk1cL/+usvnDp1Cr169UK5cuUQFRWFJUuWwMfHB1evXoWFhYXaMTN7HZYvX45hw4ahYcOGGDt2LG7fvo1OnTqhRIkS2T6wOiUlRW0V9HfXRiIiIsqKLub4GTomfgCCgoLw6NEjnDlzBnXq1JHKp0+frjYZs0SJEjh06BCMjY0BAK9evcKYMWMwZMgQtTV9BgwYADc3N8ycOVMqnzhxIkqXLo2TJ0/CxsYGwJu7eFq3bo3y5ctnGVv16tVRq1YtrF+/Hp07d5YSyZwaPXo0hBA4d+4cPvroI6l81qxZ0tft27fXeEB0x44d0aBBA2zevBn9+/dX2/fu65CWloavvvoKNWvWxJEjR1CsWDEAgIeHBwIDA7NN/EJDQzFt2jStromIiIhyp8gP9apUKmzbtg0dO3ZUS/oyyGT/W95x6NChUrIDAAcOHEBcXBx69+6N2NhYaTM2NoaXlxeOHDkC4M2jXC5cuIABAwZISR8AtGrVCh4eHgV2bU+fPsXx48cxePBgtaTv3et6+9mCaWlpePbsGSpWrAhbW1ucO3dO47jvvg5nz57FkydPMHz4cCnpA94sVfP29WYmKCgI8fHx0hYdHa31dRIRURFVyE/u+BAU+R6/p0+fIiEhAZ6enu+t6+Liovb9zZs3AQDNmzfPtH7GA5/v3r0LAKhUqZJGHTc3t0yTq/xw+/ZtAHjvtSUlJSE0NBTh4eGIiYlR6+WMj4/XqP/u65DV9ZmamqJChQrZnlsul0Mul2dbh4iIKDMc6tVekU/8tPF2zxjwprcQeDPPr0yZMhr1DeXO3NGjRyM8PBxjx45FgwYNYGNjA5lMhl69eknX+LZ3XwciIiIyDIaRmRQghUIBa2trXL58Weu2rq6uAAB7e3u0bNkyy3oZc/gyegjfdv369fee5+1hWW1k9La979oiIyMxYMAAfPfdd1JZcnIy4uLicnSet6/v7d7PtLQ03LlzBzVq1NAyciIiohzgOn5aK/Jz/IyMjNC5c2fs2LEDZ8+e1dif3Urbvr6+sLa2xsyZM5GWlqax/+nTpwAABwcH1KxZE6tWrVIbOj1w4ACuXr363hgtLS0BIMeJWAaFQoEmTZpgxYoVuHfvntq+t6/L2NhY4zoXLlwIpVKZo/PUqVMHCoUCS5cuRWpqqlS+cuVKrWMmIiLKqYyh3rxsRU2R7/EDgJkzZ2L//v1o2rQpAgMDUaVKFTx8+BCbNm1Se4jyu6ytrbFkyRL0798ftWrVQq9evaBQKHDv3j3s2rULjRo1wqJFiwC8uXu1ffv2aNy4MQYPHoznz59j4cKFqFq1Kl69epVtfLVr1wYATJ48Gb169YKpqSk6duwoJYTZ+eGHH9C4cWPUqlULgYGBcHFxQVRUFHbt2oULFy4AADp06ICIiAjY2NjAw8MDp0+fxsGDB1GyZMkcvX6mpqaYMWMGhg0bhubNm6Nnz564c+cOwsPD3zvHj4iIiAoPEz8AZcuWxZkzZxAcHIy1a9ciISEBZcuWRdu2bTXWsHtXnz594OjoiFmzZiEsLAwpKSkoW7YsvL29MWjQIKlemzZtsGnTJnz99dcICgqCq6srwsPD8dtvv2X6TL+31a1bF9988w2WLl2KvXv3QqVS4c6dOzlK/GrUqIE//vgDwcHBWLJkCZKTk1G+fHn4+flJdRYsWABjY2OsXbsWycnJaNSoEQ4ePAhfX9/3Hj9DYGAglEolwsLCMH78eFSrVg3bt29HcHBwjo9BRESkFQ71ak0m9OWpwUR4s4CzjY0NGrUIgYmJma7DkQij3M2zLEhC/0KC0kz/Zo+YJOZsykJhEsb698M7+svPug5Bg0/AUF2HoEEfhwaFnv23S09Lxun9UxEfHy+tbpHfMj4rqg+cCeNiuf+sUKYm49LKrwo0Vn3DHj8iIiIySFzORXtM/AxUfHw8kpKSsq2T2RIzREREVHQx8TNQn332GVatWpVtHY7iExHRB41z/LTGxM9ATZgwAf369dN1GERERDojEwKyPHRy5KWtoWLiZ6A8PDwK9Dm/RERE9OFh4kdERESGiUO9WmPiR0RERAaJd/VqT89W/yEiIiKigsIePyIiIjJMHOrVGhM/0ksqYxlUJvrzdAN9fCJFqqX+xWT5MFXXIWhQmerf6wT9+dWW6ONTMo4u17+niTQbrH+vk8WNWF2HoCZdmVJo5+JQr/b08B2RiIiIiAoCe/yIiIjIMHGoV2tM/IiIiMggcahXe0z8iIiIyDCxx09rnONHREREVESwx4+IiIgMVlEcrs0LJn5ERERkmIR4s+WlfRHDoV4iIiKiIoI9fkRERGSQeFev9pj4ERERkWHiXb1a41AvERERURHBHj8iIiIySDLVmy0v7YuaD7bHb+DAgXB2dtZ1GAbD2dkZHTp00HUYREREOSfyYStiDDrxe/DgAUJCQnDhwgVdh0JERESk9wx6qPfBgweYNm0anJ2dUbNmTbV9P//8M1SqItiHS0REVETwrl7tGXTilx1TU1Ndh0DvSE9Ph0qlQrFixXQdChERfQi4gLPWCn2o9+7duxg5ciTc3Nxgbm6OkiVLokePHoiKitKoGxcXh88//xzOzs6Qy+UoV64c/P39ERsbi6NHj6Ju3boAgEGDBkEmk0Emk2HlypUA1Of4paWloUSJEhg0aJDGORISEmBmZoZx48ZJZSkpKZg6dSoqVqwIuVwOJycnTJgwASkpKVpd68CBA2FlZYV79+6hQ4cOsLKyQtmyZfHjjz8CAP755x80b94clpaWKF++PNatW5fpazB27Fg4OTlBLpejYsWKmD17tlpvZlRUFGQyGebOnYsff/wRFSpUgIWFBVq3bo3o6GgIIfDNN9+gXLlyMDc3xyeffILnz59nGvP+/ftRs2ZNmJmZwcPDA1u2bMlzTN9//z1cXV0hl8tx9epVrV5DIiKirGT0+OVlK2oKvcfvr7/+wqlTp9CrVy+UK1cOUVFRWLJkCXx8fHD16lVYWFgAAF69egVvb2/8+++/GDx4MGrVqoXY2Fhs374d9+/fR5UqVTB9+nRMmTIFgYGB8Pb2BgA0bNhQ45ympqbo0qULtmzZgp9++kmtx2nbtm1ISUlBr169AAAqlQqdOnXCyZMnERgYiCpVquCff/7B/PnzcePGDWzbtk2r61UqlWjbti2aNGmCOXPmYO3atRg1ahQsLS0xefJk9O3bF127dsXSpUvh7++PBg0awMXFBQCQmJiIpk2bIiYmBsOGDcNHH32EU6dOISgoCA8fPsT333+vdq61a9ciNTUVo0ePxvPnzzFnzhz4+fmhefPmOHr0KCZOnIj//vsPCxcuxLhx47BixQq19jdv3kTPnj0xfPhwDBgwAOHh4ejRowf27t2LVq1a5Sqm8PBwJCcnIzAwEHK5HCVKlFDbn5KSopZQJyQkaPX6EhERUc4VeuLXvn17dO/eXa2sY8eOaNCgATZv3oz+/fsDAMLCwnD58mVs2bIFXbp0kep+/fXXEEJAJpOhbdu2mDJlCho0aIB+/fple96ePXtixYoV2L9/v9rdqxs3bkSFChVQp04dAMC6detw8OBBHDt2DI0bN5bqeXp6Yvjw4Th16lSmyWVWkpOT0a9fPwQFBQEA+vTpA0dHRwwePBjr169Hz549AQCtWrWCu7s7Vq1ahZCQEADAvHnzcOvWLZw/fx6VKlUCAAwbNgyOjo4ICwvDl19+CScnJ+lcMTExuHnzJmxsbAC8STpDQ0ORlJSEs2fPwsTkzY/76dOnWLt2LZYsWQK5XC61v3HjBjZv3oyuXbsCAAICAuDu7o6JEydKiZ+2Md2/fx///fcfFApFpq9PaGgopk2bluPXk4iISMIFnLVW6EO95ubm0tdpaWl49uwZKlasCFtbW5w7d07at3nzZtSoUUMt6csgk8m0Pm/z5s1RqlQpbNy4USp78eIFDhw4ICVfALBp0yZUqVIF7u7uiI2NlbbmzZsDAI4cOaL1uYcMGSJ9bWtrCzc3N1haWsLPz08qd3Nzg62tLW7fvq0Wi7e3N+zs7NRiadmyJZRKJY4fP652nh49ekhJHwB4eXkBAPr16yclfRnlqampiImJUWvv6Oio9npbW1vD398f58+fx6NHj3IVU7du3bJM+gAgKCgI8fHx0hYdHZ31C0lERPQWDvVqr9ATv6SkJEyZMkWaH1aqVCkoFArExcUhPj5eqnfr1i14enrm23lNTEzQrVs3/Pbbb9LQ4pYtW5CWlqaW+N28eRNXrlyBQqFQ2ypXrgwAePLkiVbnNTMz00h8bGxsUK5cOY0E1sbGBi9evFCLZe/evRqxtGzZMtNYPvroI43jAVDrgXu7/O1zAUDFihU1Ysq47ow5mNrGlDFsnRW5XA5ra2u1jYiISF+lpKRg4sSJcHR0hLm5Oby8vHDgwIEctY2JiYGfnx9sbW1hbW2NTz75RK3DpzAU+lDv6NGjER4ejrFjx6JBgwawsbGBTCZDr169Cnz5lV69euGnn37Cnj170LlzZ/z6669wd3dHjRo1pDoqlQrVqlXDvHnzMj3Gu0nU+xgbG2tVLt66w0ilUqFVq1aYMGFCpnUzkrL8OFdOaRvT2z28RERE+UoHd/UOHDgQkZGRGDt2LCpVqoSVK1eiXbt2OHLkiNoUsXe9evUKzZo1Q3x8PL766iuYmppi/vz5aNq0KS5cuICSJUvm/jq0UOiJX2RkJAYMGIDvvvtOKktOTkZcXJxaPVdXV1y+fDnbY2k75NukSRM4ODhg48aNaNy4MQ4fPozJkydrnPfixYto0aJFroaU85OrqytevXol9aYVtP/++0+aP5nhxo0bACDdIV3YMREREWWlsNfx+/PPP7FhwwaEhYVJq4H4+/vD09MTEyZMwKlTp7Jsu3jxYty8eRN//vmntCpJ27Zt4enpie+++w4zZ87M9XVoo9CHeo2NjTV6mhYuXAilUqlW1q1bN1y8eBFbt27VOEZGe0tLSwDQSBqzYmRkhO7du2PHjh2IiIhAenq62jAvAPj5+SEmJgY///yzRvukpCS8fv06R+fKD35+fjh9+jT27dunsS8uLg7p6en5er4HDx6ovd4JCQlYvXo1atasiTJlyugkJiIiIn0RGRkJY2NjBAYGSmVmZmYICAjA6dOns52nHhkZibp160pJHwC4u7ujRYsW+PXXXws07rcVeo9fhw4dEBERARsbG3h4eOD06dM4ePCgRhfn+PHjERkZiR49emDw4MGoXbs2nj9/ju3bt2Pp0qWoUaMGXF1dYWtri6VLl6J48eKwtLSEl5dXtvPKevbsiYULF2Lq1KmoVq0aqlSpora/f//++PXXXzF8+HAcOXIEjRo1glKpxLVr1/Drr79i37590h3ABW38+PHYvn07OnTogIEDB6J27dp4/fo1/vnnH0RGRiIqKgqlSpXKt/NVrlwZAQEB+Ouvv1C6dGmsWLECjx8/Rnh4uM5iIiIiylI+3dX77lJicrlcbdWLDOfPn0flypU15qPXq1cPAHDhwoVMp4SpVCpcunQJgwcP1thXr1497N+/Hy9fvkTx4sVzeyU5VuiJ34IFC2BsbIy1a9ciOTkZjRo1wsGDB+Hr66tWz8rKCidOnMDUqVOxdetWrFq1Cvb29mjRogXKlSsH4M36fKtWrUJQUBCGDx+O9PR0hIeHZ5v4NWzYEE5OToiOjtbo7QPe9Apu27YN8+fPx+rVq7F161ZYWFigQoUK+OyzzzTmsBUkCwsLHDt2DDNnzsSmTZuwevVqWFtbo3Llypg2bZraHbz5oVKlSli4cCHGjx+P69evw8XFBRs3blT72RR2TERERFnJr6Hed5O1qVOnSkurve3hw4dwcHDQKM8oe/DgQabnef78OVJSUt7b1s3NTZvwc0UmcjPDn6iAJCQkwMbGBg1aT4OJqZmuw5EozQp9VsR7pVrqX0yWD1N1HYIGlan+vU6FP8kmB/Tw0eZHl2tOudG1ZoOH6joEDeb/xeo6BDXpyhQcuv0D4uPjC2ylhozPioa+0/P0WZGeloxT+6YgOjpaLdasevxcXV3h5uaG3bt3q5Xfvn0brq6umD9/PsaOHavRLjo6Gh999BFmz56tcXPkihUrEBAQgPPnz6NmzZq5vpac+mCf1UtEREQfOJV4s+WlPZDj5cTMzc0zfXxrcnKytD+rdgBy1Ta/MfHLhfj4eCQlJWVbJ+NmCCIiIioghfzkDgcHB42HHwBvhoCBNw9CyEyJEiUgl8uletq0zW9M/HLhs88+w6pVq7KtwxF0IiKigiVDHuf4aVm/Zs2aOHLkCBISEtR6CM+cOSPtz4yRkRGqVauGs2fPauw7c+YMKlSoUCg3dgBM/HJlwoQJ7302MBEREX1Yunfvjrlz52LZsmXSOn4pKSkIDw+Hl5eXdJPIvXv3kJiYCHd3d7W2kyZNwtmzZ6XVQa5fv47Dhw9LxyoMTPxywcPDAx4eHroOg4iIqGgr5Cd3eHl5oUePHggKCsKTJ09QsWJFrFq1ClFRUVi+fLlUz9/fH8eOHVMb/Rs5ciR+/vlntG/fHuPGjYOpqSnmzZuH0qVL48svv8z9NWiJiR8REREZpMJ+cgcArF69GsHBwYiIiMCLFy9QvXp17Ny5E02aNMm2XfHixXH06FF8/vnnmDFjBlQqFXx8fDB//nwoFIpcXoH2mPgRERER5ZCZmRnCwsIQFhaWZZ2jR49mWl6uXDls2rSpgCLLGSZ+REREZJgK+a7eDwETPyIiIjJIMiEgy8Mcv7y0NVRM/EgvCSMZhJG2N9oXHONk/XusgXmS/sWkNDPWdQgahB6+y8nSdR2BJiM9/ADUx6dkHFmhf08TaTIiUNchqElPSwZu6zoKyooeviUSERER5YAKeXvcoP79/VzgmPgRERGRQeJQr/b08VHhRERERFQA2ONHREREhol39WqNiR8REREZpkJ+cseHgIkfERERGSRdPLnD0HGOHxEREVERwR4/IiIiMkwc6tUaEz8iIiIySDLVmy0v7YsaDvUSERERFRHs8SMiIiLDxKFerTHxIyIiIsPEdfy0pvdDvc7Ozhg4cKCuw8hXR48ehUwmw9GjR3Vy/pUrV0ImkyEqKkon5yciIiLd0IvE79SpUwgJCUFcXJyuQ1Ezc+ZMbNu2TddhEBERUSYyntWbl62o0ZvEb9q0aZkmftevX8fPP/9c+EGBiR8REZFey5jjl5etiNGLxC87crkcpqamug6DspGYmKjrEIiIiCgH8pz4xcTEYPDgwShdujTkcjmqVq2KFStWqNVZuHAhqlatCgsLC9jZ2aFOnTpYt24dACAkJATjx48HALi4uEAmk6nNP3t3jl/G/LSTJ09izJgxUCgUsLW1xbBhw5Camoq4uDj4+/vDzs4OdnZ2mDBhAsQ7Gf3cuXPRsGFDlCxZEubm5qhduzYiIyPV6shkMrx+/RqrVq2SYno7jpxcNwDcv38fnTt3hqWlJezt7fH5558jJSVF69c5LS0N06ZNQ6VKlWBmZoaSJUuicePGOHDggFTn0qVLGDhwICpUqAAzMzOUKVMGgwcPxrNnz957/N9++w3t27eHo6Mj5HI5XF1d8c0330CpVKrV8/HxgaenJ/7++280adIEFhYW+OqrrzBgwACUKlUKaWlpGsdu3bo13NzctL5mIiKibAkAqjxsRa/DL2939T5+/Bj169eHTCbDqFGjoFAosGfPHgQEBCAhIQFjx47Fzz//jDFjxqB79+747LPPkJycjEuXLuHMmTPo06cPunbtihs3bmD9+vWYP38+SpUqBQBQKBTZnnv06NEoU6YMpk2bhj/++APLli2Dra0tTp06hY8++ggzZ87E7t27ERYWBk9PT/j7+0ttFyxYgE6dOqFv375ITU3Fhg0b0KNHD+zcuRPt27cHAERERGDIkCGoV68eAgMDAQCurq45vm4ASEpKQosWLXDv3j2MGTMGjo6OiIiIwOHDh7V+rUNCQhAaGirFlJCQgLNnz+LcuXNo1aoVAODAgQO4ffs2Bg0ahDJlyuDKlStYtmwZrly5gj/++AMymSzL469cuRJWVlb44osvYGVlhcOHD2PKlClISEhAWFiYWt1nz56hbdu26NWrF/r164fSpUvD0tISq1evxr59+9ChQwep7qNHj3D48GFMnTo10/OmpKSoJcIJCQlavzZERFQ05XWeXlGc4ycT73aHaWHIkCHYvXs3/vnnH5QsWVIq7927N/bs2YOHDx+id+/e+O+//3D58uUsjzN37lyMHz8ed+7cgbOzs9o+Z2dn+Pj4YOXKlQDeJCiDBg2Cr68v9uzZIyUzDRs2xB9//IFhw4ZhyZIlAAClUglnZ2e4urqq3UGblJQEc3Nz6fu0tDTUqlUL9vb2OHTokFRuZWWF7t27S+fW5rrNzc2xYMECjB07Fr/++it69OgB4M2waI0aNfDff//hyJEj8PHxee/rDAA1a9ZEuXLlsHPnzizrvHtdALBhwwb07t0bx48fh7e3t9pr+PbrnVnb4cOHIyIiAs+fP4dcLgfwpsfv2LFjWLp0KYYNGybVValUKF++PBo1aoQNGzZI5fPnz8eXX36JW7duwcXFRSPmkJAQTJs2TaO8fpvpMDE1e8+rUnhkKj18c9DHkIyz/uNCV4QeLlolS9d1BJqM0vXwF0oPHVmhmznn2WkyIlDXIahJT0vGmV1TEB8fD2tr6wI5R0JCAmxsbNC85iSYGMtzfZx0ZQoOX5hVoLHqm1wP9QohsHnzZnTs2BFCCMTGxkqbr68v4uPjce7cOdja2uL+/fv466+/8jNuBAQEqPVgeXl5QQiBgIAAqczY2Bh16tTB7du31dq+neC8ePEC8fHx8Pb2xrlz59573pxeNwDs3r0bDg4O6N69u9TewsJC6kHUhq2tLa5cuYKbN29mWeft60pOTkZsbCzq168PAO+9trfbvnz5ErGxsfD29kZiYiKuXbumVlcul2PQoEFqZUZGRujbty+2b9+Oly9fSuVr165Fw4YNM036ACAoKAjx8fHSFh0dnW2cRERElHu5TvyePn2KuLg4LFu2DAqFQm3LSAqePHmCiRMnwsrKCvXq1UOlSpXw6aef4vfff89z4B999JHa9zY2NgAAJycnjfIXL16ole3cuRP169eHmZkZSpQoAYVCgSVLliA+Pv69583pdQPA3bt3UbFiRY0h1tzMd5s+fTri4uJQuXJlVKtWDePHj8elS5fU6jx//hyfffYZSpcuDXNzcygUCinhet+1XblyBV26dIGNjQ2sra2hUCjQr1+/TNuWLVsWxYoV0ziGv78/kpKSsHXrVgBv7sj++++/0b9//yzPK5fLYW1trbYRERHlCO/q1VquB0FUqjdPNu7Xrx8GDBiQaZ3q1avD3t4e169fx86dO7F3715s3rwZixcvxpQpUzId4sspY2PjHJe/PZp94sQJdOrUCU2aNMHixYvh4OAAU1NThIeHSzecZCen153fmjRpglu3buG3337D/v378csvv2D+/PlYunQphgwZAgDw8/PDqVOnMH78eNSsWRNWVlZQqVRo06aNFHdm4uLi0LRpU1hbW2P69OlwdXWFmZkZzp07h4kTJ2q0fXdIOIOHhwdq166NNWvWwN/fH2vWrEGxYsXg5+eXfy8EERFRBhWAvMwwyfqj8YOV68RPoVCgePHiUCqVaNmyZbZ1LS0t0bNnT/Ts2ROpqano2rUrvv32WwQFBcHMzCzbmw7y2+bNm2FmZoZ9+/ZJ89YAIDw8XKNuZnFpc93ly5fH5cuXIYRQO9b169dzFXuJEiUwaNAgDBo0CK9evUKTJk0QEhKCIUOG4MWLFzh06BCmTZuGKVOmSG2yGxrOcPToUTx79gxbtmxBkyZNpPI7d+5oHaO/vz+++OILPHz4EOvWrUP79u1hZ2en9XGIiIgo/+V6qNfY2BjdunXD5s2bM71x4+nTpwCgsZRIsWLF4OHhASGEtPSHpaUlABTKkzuMjY0hk8nUlimJiorKdKFmS0tLjZhyet0A0K5dOzx48EBtqZjExEQsW7ZM67jffR2trKxQsWJF6Y7YjJ7Od+/V+f7779977MzapqamYvHixVrH2bt3b8hkMnz22We4ffu2NFxMRESU3/jkDu3l6X63WbNm4ciRI/Dy8sLQoUPh4eGB58+f49y5czh48CCeP3+O1q1bo0yZMmjUqBFKly6Nf//9F4sWLUL79u1RvHhxAEDt2rUBAJMnT0avXr1gamqKjh07Sglhfmrfvj3mzZuHNm3aoE+fPnjy5Al+/PFHVKxYUWPOXO3atXHw4EHMmzcPjo6OcHFxgZeXV46uGwCGDh2KRYsWwd/fH3///TccHBwQEREBCwsLreP28PCAj48PateujRIlSuDs2bOIjIzEqFGjAADW1tZo0qQJ5syZg7S0NJQtWxb79+/PUa9dw4YNYWdnhwEDBmDMmDGQyWSIiIjQSCJzQqFQoE2bNti0aRNsbW2l5XGIiIjyXV7n6THx007p0qXx559/Yvr06diyZQsWL16MkiVLomrVqpg9ezYAYNiwYVi7di3mzZuHV69eoVy5chgzZgy+/vpr6Th169bFN998g6VLl2Lv3r1QqVS4c+dOgSR+zZs3x/LlyzFr1iyMHTsWLi4umD17NqKiojQSv3nz5iEwMBBff/01kpKSMGDAAHh5eeXouoE3d/AeOnQIo0ePxsKFC2FhYYG+ffuibdu2aNOmjVZxjxkzBtu3b8f+/fuRkpKC8uXLY8aMGdLi1wCwbt06jB49Gj/++COEEGjdujX27NkDR0fHbI9dsmRJ7Ny5E19++SW+/vpr2NnZoV+/fmjRogV8fX21ihN4M9y7c+dO+Pn5qQ2nExERkW7laR0/osz89ttv6Ny5s9ragTmVsTYT1/HLAX0Miev45QjX8TNcXMfv/QpzHb8WHuPyvI7foatzi9Q6fnr4lkiG7ueff0aFChXQuHFjXYdCREQfMg71ao2Jn44lJSW9d429EiVKZLpunr7ZsGEDLl26hF27dmHBggWFerc2ERERvR8TPx3buHGjxlMw3qXNo910qXfv3rCyskJAQABGjhyp63CIiOhDx3X8tMbET8d8fX1x4MCBbOvUqFGjkKLJG04XJSKiwpTXJVm4nAsVOgcHBzg4OOg6DCIiIsPDOX5ay/UCzkRERERkWNjjR0RERIZJJQBZHnrt9HGprgLGxI+IiIgME4d6tcahXiIiIqIigj1+pJdkKqFXT8swStW/e/5jfPRvbUfHk/r3SIpX9vr3NmdzO0XXIWhItzDWdQgaLG7E6joEDfr2lAwAOL5kma5DUJPwUgW7XYV1tjz2+OnjI5AKmP69IxIRERHlBId6tcahXiIiIqIigokfERERGSaVyPtWSOLi4hAYGAiFQgFLS0s0a9YM586de287lUqFlStXolOnTnBycoKlpSU8PT0xY8YMJCcnax0Hh3qJiIjIMAnVmy0v7QuBSqVC+/btcfHiRYwfPx6lSpXC4sWL4ePjg7///huVKlXKsm1iYiIGDRqE+vXrY/jw4bC3t8fp06cxdepUHDp0CIcPH4ZMlvPn1jHxIyIiIipAkZGROHXqFDZt2oTu3bsDAPz8/FC5cmVMnToV69aty7JtsWLF8Pvvv6Nhw4ZS2dChQ+Hs7Cwlfy1btsxxLBzqJSIiIsOUcXNHXrZCEBkZidKlS6Nr165SmUKhgJ+fH3777TekpGR9p3+xYsXUkr4MXbp0AQD8+++/WsXCxI+IiIgMk4HM8Tt//jxq1aoFIyP1tKtevXpITEzEjRs3tD7mo0ePAAClSpXSqh0TPyIiIjJM+dTjl5CQoLZl1wOXGw8fPoSDg4NGeUbZgwcPtD7mnDlzYG1tjbZt22rVjokfERERFWlOTk6wsbGRttDQ0CzrqlQqJCcn52gT/59YJiUlQS6XaxzLzMxM2q+NmTNn4uDBg5g1axZsbW21asubO4iIiMgwCeRxAec3/0RHR8Pa2loqzixJy3D8+HE0a9YsR4f/999/4e7uDnNz80x7ETOWYzE3N89xyBs3bsTXX3+NgIAAjBgxIsftMjDxIyIiIsOUT0/usLa2Vkv8suPu7o7w8PAc1c0YynVwcMDDhw819meUOTo65uh4Bw4cgL+/P9q3b4+lS5fmqM27mPh9oFauXIlBgwbhr7/+Qp06dXQdDhER0QehTJkyGDhwoFZtatasiRMnTkClUqnd4HHmzBlYWFigcuXK7z3GmTNn0KVLF9SpUwe//vorTExyl8Jxjh8REREZJpUq71sh6N69Ox4/fowtW7ZIZbGxsdi0aRM6duyoNrR869Yt3Lp1S639v//+i/bt28PZ2Rk7d+7Uamj4XezxIyIiIsOUT0O9Ba179+6oX78+Bg0ahKtXr0pP7lAqlZg2bZpa3RYtWgAAoqKiAAAvX76Er68vXrx4gfHjx2PXrl1q9V1dXdGgQYMcx8LEj/Lk9evXsLS01HUYREREesvY2Bi7d+/G+PHj8cMPPyApKQl169bFypUr4ebmlm3bZ8+eITo6GgAwadIkjf0DBgzQKvHjUK8Bi4mJQUBAABwdHSGXy+Hi4oIRI0YgNTVVqpOSkoIvvvhCeih0ly5d8PTpU7XjyGQyhISEaBzf2dlZbR7DypUrIZPJcOzYMYwcORL29vYoV64cAMDHxweenp64evUqmjVrBgsLC5QtWxZz5swpkGsnIiIylCd3AICdnR1++eUXxMbG4vXr1zh69Gimc/CjoqKk3j7gzWexECLLbeXKlVrFwR4/A/XgwQPUq1cPcXFxCAwMhLu7O2JiYhAZGYnExESp3ujRo2FnZ4epU6ciKioK33//PUaNGoWNGzfm+twjR46EQqHAlClT8Pr1a6n8xYsXaNOmDbp27Qo/Pz9ERkZi4sSJqFatmtYLTBIREb2XSkBakyXX7YsWJn4GKigoCI8ePcKZM2fU/mKYPn26tGAkAJQsWRL79++HTCYD8GbhyR9++AHx8fGwsbHJ1blLlCiBQ4cOwdjYWK38wYMHWL16Nfr37w8ACAgIQPny5bF8+fIsE7+UlBS1tY0SEhJyFRMRERG9H4d6DZBKpcK2bdvQsWPHTLuJM5I8AAgMDFT73tvbG0qlEnfv3s31+YcOHaqR9AGAlZUV+vXrJ31frFgx1KtXD7dv387yWKGhoWqrpTs5OeU6LiIiKlqEUOV5K2qY+Bmgp0+fIiEhAZ6enu+t+9FHH6l9b2dnB+DNsGxuubi4ZFperlw5tSQz43zZnSsoKAjx8fHSljGBlYiI6L2EeDNcm9utEOf46QsO9X7gMuuZA6A2HJwVpVKZaXlW6wfl5lxyuTzbR+MQERFlSeRxjl8RTPzY42eAFAoFrK2tcfny5Xw5np2dHeLi4tTKUlNTM328DBERERkuJn4GyMjICJ07d8aOHTtw9uxZjf056c17m6urK44fP65WtmzZsix7/IiIiPSCgTy5Q59wqNdAzZw5E/v370fTpk0RGBiIKlWq4OHDh9i0aRNOnjyp1bGGDBmC4cOHo1u3bmjVqhUuXryIffv2oVSpUgUUPRERUT7gUK/WmPgZqLJly+LMmTMIDg7G2rVrkZCQgLJly6Jt27awsLDQ6lhDhw7FnTt3sHz5cuzduxfe3t44cOCA9NgYIiIi+jDIhLbjgkQFKCEhATY2NmjQehpMTM10HY7EKFX/hgNifIrpOgQNjifTdR2ChleO+vf3rc3tlPdXKmTpFpnfnKVLFjdidR2ChldVFboOQcPxJct0HYKahJcq2FW+jfj4eFhbWxfMOf7/s6K5RS+YyHL/XpguUnE4cUOBxqpv9O8dkYiIiCgnONSrNd7cQURERFREsMePiIiIDJNKADL2+GmDiR8REREZJiEA5GEOdhFM/DjUS0RERFREsMePiIiIDJJQCYg8DPUWxYVNmPgRERGRYRIq5G2oV/+W6ipoTPyIiIjIILHHT3uc40dERERURLDHj/RKxl9f6enJOo5EnVG6/g0HqJL1L6b0NP17cocyVf/e5tLT9fDJHWn69+SOdKU+vk769d4EvHlShj5JePUmnsLoTUsXKXkark1HWj5GYxj4yDbSK/fv34eTk5OuwyAiojyKjo5GuXLlCuTYycnJcHFxwaNHj/J8rDJlyuDOnTswM9Ofx4QWJCZ+pFdUKhUePHiA4sWLQyaT5elYCQkJcHJyQnR0tN48g5Ex5QxjyhnGlDOMKWfyKyYhBF6+fAlHR0cYGRXcjLLk5GSkpqbm+TjFihUrMkkfwKFe0jNGRkb5/heitbW13ryxZmBMOcOYcoYx5Qxjypn8iMnGxiafosmamZlZkUrY8gtv7iAiIiIqIpj4ERERERURTPzogyWXyzF16lTI5XJdhyJhTDnDmHKGMeUMY8oZfYyJ8h9v7iAiIiIqItjjR0RERFREMPEjIiIiKiKY+BEREREVEUz8iIiIiIoIJn5ERERERQQTPyIiIqIigokffZC2bduGo0ePgqsVERWO9evX44svvtB1GFnSx/cClUolfa2P8dGHiYkffXCeP38Of39/jBo1CqdOneIbKn3Q3v791tXvelJSElavXo2IiAgEBwfrJIbsKJVKyGQyAEBcXJxug/l/KpUKRkZvPoJTUlIgk8n4XkWFgokffXBKlCiBo0eP4tWrVxgzZgx+//13vXxDffuvffqfzF4Xffz56YP09HTIZDKkp6fj8ePHUnJT2MzNzfHLL7+gefPmWLlyJb766iudxJEVY2NjAEDjxo0xadIkvUj+MpK+/v37w9/fHyqVSmc/P4D/x4oSJn70QapVqxa2bt2KR48eYdSoUThx4oTOEy2lUqn2vT690e7fvx+//PILtm/fjkuXLuksDqVSCSMjIzx58gQnTpzApk2bcP36dY3XrrBl9rPSh5+fiYkJAMDPzw9ffvklXrx4oZM4VCoVypYti++//x716tXDmjVrMGnSJJ3E8rb09HTp6/Xr1yMmJga1a9eGmZmZDqP6n7S0NDx69Aj//PMPXr58CUA3v1cZPaKxsbH4448/cP/+fbx+/RoA/0D9IAmiD0xaWpr09fXr10XJkiVFy5YtxZEjR4RSqdRJTOnp6dLXoaGhonfv3qJx48ZiwYIF4p9//tFJTBk++eQTYWNjI0xMTIRMJhOVKlUSYWFhhR5Hxs/mypUrwtPTU9ja2gpTU1Nhbm4uPv/8c/HXX38VekxC/O9n9/z5c/HPP/+Iv/76S9y7d08nsWR4+3f8hx9+EPb29mLdunXi1atXOo/n9u3bonnz5sLV1VUEBwfrJJ537d69WwwbNkx06tRJxMfH6zocIYQQKpVKCCHExYsXhampqZg5c6ZO47hy5Ypwd3cXFhYWonTp0qJfv37i2rVrQgihs/dNKhhM/OiD8naCFRQUJD799FNRsWJFIZPJRNOmTcXx48elN7rC8vb52rVrJxQKhahVq5Zo2bKlMDU1FXXq1BGbN28u1Jgy9O3bVzg4OIhly5aJ+/fvi7///lvUqFFDyGQycfDgwUKP5/bt28LBwUE0b95crF69Wpw9e1ZMnz5dyGQyMWjQIPH06dNCjSfj9+nKlSuiTp06QqFQCHNzc1GiRAmxePFi8eTJk0KN5127d+8WixcvFkOGDBGpqak6ieHtpMDf31/07NlTWFpaCktLS2FsbCymTJmik7gyzJ07V8hkMlGxYkXx7bffCiF0k8i8/d6UcX6VSiXi4uLEJ598ImrUqCFu3rxZqDFlvDc9fvxYODs7i4YNG4o5c+aIoUOHinLlyomPP/5YXLhwQS1mMnxM/MhgZbxpZZbIderUSZQtW1aMHDlShIeHi4CAAFGyZElRu3ZtnSR/QrxJRMuUKSPWr18vYmNjhRBCBAcHC5lMJubMmSOSk5MLNZ5r166JSpUqiVmzZkm9IH/99ZcwNzcXw4cPL9RerYwPleDgYFGtWjVx6tQpad8333wjTExMxJo1awr9NRJCiP/++08oFArh7e0tfvzxR7FhwwYxePBgIZPJRGhoqHj9+nWhxyTEm55jmUwmLCwsxFdffSWEUE8uCluvXr2Evb29WLBggdi/f7/YtWuX8PDwEKVKlZLi04XExEQxaNAgIZPJhIeHh7h161ahx/D2+01cXJzG/s2bNwuZTCbWr18vhCicJCvjHHFxceL+/fuiVatWav/vfvnlF1G1alXh5ubG5O8Dw8SPDNadO3cyLd+xY4ewsLAQ33//vfShnJSUJI4dOyYcHBxEnTp1xLFjxwr1TSwxMVE0adJE+Pv7S8Nxhw4dEtbW1sLf319ERUUJIQr3g/vgwYNCJpNJQ6gHDx4U5ubmom/fviImJkaq9++//xZYDO8m4O3atRMtW7aUvh83bpwwMTERy5YtEwkJCUKINz/LwujdUqlUQqVSiVGjRokaNWqIP//8U9o3ZswYYWRkJFavXq2znrYzZ86Ibt26CXNzc9GpUyepXBcfzpcuXRL29vZi7NixaolwdHS0aNeunbCzsxNTp04t8DiyuvbXr19LyXpQUJB49OhRgceS4e3/0yNGjBBmZmbi+++/F+fOnVOr17ZtW+Hm5laosUVHR4vy5cuLGjVqiLp166oN2QshREREBJO/DxATPzJIe/fuFcbGxmLFihUa+9auXStkMpnYv3+/EEI9uTh06JCwsbERzZs3F0ePHi2wN7GMN9CMN/3Xr18LJycnMXfuXCGEEPv375eSrIcPH6rFV1hv/CdOnBC2trbi/Pnz4siRI8Lc3Fz069dPPHjwQKoTEREhunTpohZjfsl4ba5duyYiIyOFEEK0b99etGvXTgghxKRJk4SpqalYtmyZSExMlNp169ZN7NmzJ9/jyUrDhg1F3759pe8nTJggTExMxC+//CIloxnXUlC/T1kd9+zZs6JTp05CJpOpJVYF/eH87h8of/31l5DJZGLp0qVCiDf/5zL+D9y/f1+4uroKExOTAh32fXee4b///qv2x2Fqaqro2bOnMDExEd988414/PhxgcWS4e2fw5IlS8S0adPE0KFDhbm5uShbtqwICgoSV65cEUIIsXHjRlG8eHGxdu1aIUTB/RGYcdxnz56JFy9eiDp16gh7e3tRt25dqU5KSor09erVq0XVqlWFp6enOHv2bIHERIWLiR8ZpP379wtPT08xf/58jX2//fabkMlkYuPGjUII9Q+EhIQE0aFDByGTyUSjRo3EoUOHCjTOfv36iblz5wqVSiXq1Kkj/Pz8xIULF4SFhYXo06ePWpK1c+dOUaZMGfH7778XWDw3btyQvk5LSxPOzs6idu3awtLSUvj7+6v19N2+fVv4+vqKrl27imfPnhVIPDdv3hSlSpUSgwYNEo8ePRJLly4VCoVCtG3bVpiYmIjly5erTcbfsWOHcHR0FCtXriyQeN7+IyHj63r16okBAwYIIYSYOHGiRjKqVCpF69atC+zmk7d/f589eyauX78ukpKSpLKzZ8+Kjh07CplMJs1hy4irILx93AEDBojbt2+Le/fuieLFi4u+ffuqDWVm9IauXLlS2NnZifLly4sxY8bke0xvJ0mDBw8WlSpVEhYWFqJkyZJi+vTpUq91amqq6NWrl5T8FeQczXfn9n788cfSH6N79+4VY8aMEZaWlqJChQpi2LBhIiYmRlSpUkX4+voWWEwZ/vnnH2FhYSGOHz8ubt26JVq2bClkMpkYN26cVOft5G/NmjXC0dFR1KtXTyQnJ+tkqgzlHyZ+ZLDu3r0rfX3ixAnp60ePHonatWsLZ2dncf/+fSGE+pvY119/LRo2bCgsLCxEREREvsb09gfQ559/LhwcHMSqVatEamqqWLJkibCwsBBGRkaif//+4tmzZ9IbaHR0tBgzZoyoWbOm1AOQ37755hu1oV0h3vToOTk5CXt7e+kOPiHeDKMHBweLkiVLSvOO8kvGa6RSqcTGjRtFw4YNpZiuX78uatWqJWQymRg4cKBauz///FO0bNlS1KpVSy1Bze+4Xr58KV6+fCmVffrpp8LDw0P07dtXSkYz9gvxpqemXLlyBXKDztu/T0OGDBGVK1cWMplMVK9eXXzxxRfS/r///lvq+Xs7+cvvXqO3P/BHjhwp5HK51Os+ePBgYW1tLbZv365x3oULF4oyZcqIJk2aiF9++SVfY3rbJ598IhQKhZg4caJYsmSJGD9+vJDJZKJ3797SdIrk5GTRq1cvYW5uLkJCQgqkh/3t63/+/Llo0KCBWg9xhv/++0+MGDFCODk5CVtbW+lGtPx+XxLifwn7q1evRIcOHYSPj4/Yu3evEOJNr3vLli2FjY2NWq/s2++bGzdu1Mn8SMp/TPzI4Lz712bGDRIbNmyQyhYsWCBsbW1Fw4YNpeRPCCFiYmJEu3btxJw5cwp0Uv7Dhw9FcHCwCAoKknpn/vvvP9G+fXthaWkpRo8eLdW9efOmmDx5srCwsBBLliwpkHiOHDkipkyZIkxNTUWlSpWkROvx48dixowZwsrKStSrV09Mnz5dhIWFibZt2wq5XC5mz54tHSM//8q/efOm6Nixo2jZsqUICAhQ23f48GHh7Ows7OzsxJdffikOHTokZs6cKRo2bChKliwpLl++nG9xZMj4oL5+/bro0qWL8Pb2lhKCS5cuCSsrK6lH5O0er7Nnz4rmzZuLRo0aFegQfadOnUTp0qXFqFGjREREhOjRo4cwNjYWzZo1U1sWpFOnTsLExESEhITkewxv//yfPHkimjZtKr777jupN/jGjRvCy8tLlClTRmzZskUqj4mJEaNGjRLjxo0r0P9z69evF2XLlhVr1qyRemOPHDkiZDKZGDNmjFqvdXJysujWrZuQyWTizJkzBRbToEGDRNeuXUXFihWlP1Qzfn8y/k1NTRXPnz8XkydPFl5eXsLIyEj07t1bCJG//+eEEOLevXvi4sWLolmzZmL16tVqx7969ep7kz/6MDDxI4O3d+9e0aJFC1GmTBlpfowQb+6itbW1FQ4ODuKHH34QixcvFsOGDROWlpZqSWJ+D4kNHz5cyGQyUapUKekv94zE4tKlS9JQs7u7u2jSpImoUqWKsLKyErNmzZKOkZ9v+O3btxeenp6ievXqom7dukImk4kyZcqIP/74QwghRGxsrNi0aZOoUaOGsLGxEebm5qJZs2ZqPTP5/RrNnDlTGoqbPHmyEOLNTRsZ133y5EnRvXt3YWtrK2QymVAoFKJFixYFkvRlnPPq1avC3t5eNG/eXGP9ud27dwszMzNRrVo18f3334v79++LRYsWiWbNmokSJUoUWC+tEG8SmtKlS4tVq1ZJw97bt28XxsbGYvDgwWrDlRcuXBDt2rUTMplM/PnnnwUyJNetWzfh4+MjatasqXbjj1KpFMePHxcNGzYU5ubmolWrVuKLL76Qhu1//PFHqW5BxDV9+nRRvnx5aT7q/7V33mFRXO/bfwbYBZbemxQBFRVLgooVFbtEEVHsUcFeEcUosSvW2MFELEENon5VEhsau4gdbNh7A8ReEETgfv/w3fObATSJ7Cri+VyX14Wzs7NnZ2fOueep4rjVojLUs7KysGPHDpWPQ8mlS5fg5OQEW1tbuLu7s6QusTWw4Hm4fv06wsPDoampqfIwlNevX6NKlSrQ0NCAo6Mjrly5wsYjvgeaNm0KY2NjTJ48WaWfzyk5cOHHKRUcOHAA3t7esLS0xB9//MG2R0VFoVGjRhAEgYmx2bNnq3Usv/76Kzw8PCRFWd+9e8fE0927d7F27Vq0a9cOXl5eGDZsGP766y/2flWKrBEjRsDAwADr169nsVcxMTGoWrUqLC0tmfgD3i9CN27cwO3btyVxdaoYT1EuxwkTJkBXVxdmZmYsCP/du3dsEXr27Bnu3LmD+Ph4XL9+Hc+ePSv2OD7EkydP4OnpCS8vL4krXDzuvXv3wsXFBZqamhAEAQYGBvD09FR7Ae6xY8eibNmyzL28e/du6OnpSQRNQSvkli1b1DKW58+fIyQkBEZGRszKXjAuUhm2ULZsWZiamsLd3R0LFy5Uy3jEDB06FBUqVAAAJCYmsuQpcRxteHg4SyQSo654yEOHDqFWrVoQBAFTp0794OeJ/3/79m24ubmx2ozFGZv4t8nKysK6devg6ekJQRCwYMECiTVPLP5atmzJyhVxSh9c+HG+asQT2/79+4sUf2/evEFCQgKOHz+Os2fPsu2qmOzFny8WCdHR0XB1dYVCoWBlQMSi5kOoegFq3LgxateuLQm4z8/Px969e+Hu7g5bW9uPZuqp0jJz9epVrF+/XuLumzx5MnR0dODp6clisAqWlPgcXLhwAfr6+pg3bx7bJv7uyt/lwYMHOHXqFGJiYnD27NnPUlA6NDQUHh4eAN6LT4VCUUjQrFixAkuWLCn0XnUImgcPHjCLbYcOHT7o4n7w4AHu378vicdU9T0nPubGjRthY2ODkJAQKBQK9OjRQ2Lpu3DhAipWrIjhw4ervATPx77XkSNH4OHhAYVCgYiIiH/1nlatWqFWrVrFuv+U783IyGAZzJmZmdi8eTPc3NwkIR8F33Pu3Dm0a9dOEvfLKT1w4cf56vmQ+BO7fQuiDitWwcVk1apVcHBwgJmZGavZVVTZD3VlyL19+xaVKlVCkyZN2DZxmZmlS5dCEAQ4Ozsz8afOEhJBQUEQBEESgwWAJZF4eXkx8fe5a4Vt3boVgiCwWmVi8SnusqBOihKaALBkyRJoaWlh8eLF0NfXL5QNnpycDDc3N0yfPl2lovlj18L9+/cxceJEaGpqol+/fhIL8YfeV9zzl5eXJ/l+b968QU5ODvu827dvs64zXl5eePr0Kdv3wYMHGD16NBwdHVlCg6oQj+nYsWM4dOhQobjBhIQE1KxZExYWFhKXd8HrPD8/H5cvX0bNmjXRpEkTSRLRp5Ceno5y5cohMDCQCfTMzEz8+eefcHJyQuXKlXHu3DnJe5Rj4rF9pRcu/DilgqLEn62t7UfFX3EQL25TpkyBr68v3N3d8dNPP0lqzEVHR8PZ2RkWFhaFxJ86UU7eo0aNgkKhkLiSxQK1UqVKMDMzg5WVFcvYU5founfvHrp27QqFQoHVq1dLxN+4ceNgZmaGBg0aFAqC/xwkJSWx4r5Fub8AICQkBPfu3VPL5xcUbOJyLdnZ2ahXrx4EQUDLli1Z1xfgvaAJCwuDo6Mjdu/erZbxxMXFISIiArGxsZLv/+DBA0yYMAGampoYMGCAWnrgbtiwAb///nuh7SEhIfD29kbt2rURGBjIBPv58+dhYmICKysrTJo0CRkZGdixYweGDh0KmUwmseiqAvG93LVrV1hbW0NTUxMKhQLDhg2TWIQPHTqEmjVrwtzc/KNJXPHx8ZKHkOLSoUMHWFtbY9iwYSz+8c2bN/jzzz/h6OhYpPjjlG648OOUaP6LlUC874EDB1C/fn3IZDJcuXJFpdYa8bF8fHxgZmaG6tWrw8vLC9ra2jA3N5csMNHR0ShbtixsbW2RlJSksnH8G/bs2QMTExPUq1cPhw8flryWnJwMFxcXREVFoVq1avj+++8LlZv4VIqqhwe8FwsBAQEfFH/W1taoWrWq2trFfUhM5uTkoEaNGkxAFRTnCQkJcHFxUVkpkg0bNrAQAPH5CQ0NRdOmTVGtWjVMnDiRtdDasWMHqlatCisrK6xduxZ37tzB4cOHMWTIEMhkMsydO1cl4wKkYqZ9+/YwNDSEXC6HIAioW7cu1q5dy14Xi7/BgwerTPzl5+cjNTUVhoaGsLe3l3zmDz/8AD09PdSrVw+1a9eGnp4eFAoFKytz9uxZeHp6QkdHB1paWpDJZHBxcZHck6p+qPD394elpSUmTpyIDRs2YObMmdDS0kKfPn2YFRt4L/5q164NQ0PDj4pQVYQQiB/wevfuDTMzsw+Kv+rVqxfqJMIpvXDhxymxiBcgcc2+jyFeRHfv3l1kILeqCA8Ph6GhIdauXcvcSvHx8ahXrx40NTUlAe1r1qyBnZ0dtLS0kJGRoRa34bp16zBnzhwsXboUN2/eZNujoqIgCAI8PT1ZUevLly9j4sSJ8PDwwOXLlzFz5kxoampi165dxR6H8rulpaVJavYpEYu/NWvWSGL+QkJC4OLiIlksVYVyLLdu3UJERASmT5+O69evM3fa2bNnYWtri6pVq+J///sfs/wlJCSgZcuWKFeunEoE6b1792BlZQUHBwfJYtuuXTvo6+ujRo0azMLn5OSEqKgoAO+v5wYNGkAQBGhpaUEul6NMmTIfjEssLp07d4a1tTWWLFmCixcvIiUlBVZWVqhevTqWLVvG9nvw4AEmTZoEQRDQo0cPlcbP/f3333ByckK5cuUQExODe/fuwcPDAzExMeyhYdeuXWjRogV0dXVZbO+DBw9w+PBhLFy4ENu3b5dYzz5V9Injg8UsXLgQLi4uWLNmDbuWlJ2FBEFAx44dJfPXgQMHUL58eSxdurTQsYoTVqC8vsX9rD8k/pRu3zdv3uCvv/6CgYEB6tSpw9273whc+HFKPN7e3ujfv7+kHt/HKGrSVPUTfm5uLvz9/fHdd98xt5zyc48cOYKaNWuiTJkyOHjwIHvP8uXLi2wxpwp8fX1haGgIhUIBQRAKLc7Lly+HjY0NNDQ04OLiAltbW2hoaLBivw8fPoQgCFi8eLFKxvPs2TPY2Nigffv2RYq/+/fvo127djAyMkJsbKwklkmd3RQuXLgAS0tLyGQyCIIAExMTzJ49myUg7NmzBw4ODpDJZKhSpQrq1asHOzs7WFpaqswdlpOTgy1btrAA+6SkJCQmJqJcuXISIXzkyBG4urrC3NwcK1asYO/fvHkzli5diri4OJUnKylZt24dnJ2dER0dzX6b8+fPQy6Xw8jICC4uLhIX7P379xEaGqqy7F1ln2TgfUJLmTJl4Obmhr59+8Le3r6Qy/3s2bNo0qQJDA0NP5qQ8KnC+OjRo4Uyc4H3JVIGDBiALl26sASKxMREloCzYMECyGQy9OrVS9I+Th0FyIH3D3Tt27eXFLQXi7nevXtDJpNh+PDhkpi/HTt24Nq1a2oZE6fkwYUfp8Qhji/avHkz7O3tsWrVKolb8HNTcMHIzs6Gt7c3qlatyraJLZRxcXHQ1NTEggULijyeKhfpLl26wMbGBlFRUbh69Sru3bsHBwcHVKpUSdLS7vDhw5g7dy5atmyJAQMGYPXq1ey16OhoyGQybN68WSVjev78OaZPnw49PT307NmzkPjLzc3F9u3bIQgCbG1tsWLFCklcmypRnuusrCwEBASgVatWiI+Px/nz59G7d28IgoCQkBC2GGdkZGDw4MFo2rQpGjRogJCQEJUvijk5Odi+fTtcXV1RtWpVLFy4EHXr1mUiS2mpOXPmDBwdHVGhQoWPxnypOpRh1apVqFOnDrNwnjx5EgqFAkFBQTh06BCMjIxQtmxZiSBV1qlT5XiUx9mzZw/KlCkDOzs71KhRg20XW7Ti4uIgCALGjRun0jEA7z0OgwYNgkwmk9TbBN63iFT+NteuXYOZmRk6dOiAtLQ0pKens3IunTt3ZuJPOTZVW/53797NYkGPHj3KtovPU6NGjaCjo4Pg4OB//TDNKV1w4ccpscTGxqJ///7w8fEpVuyZOBj+UxALOqX4zM/PR2BgILS1tXHgwAH2uli0Ojk54YcffgCgvkSFjRs3wtnZGb///jsTDWfOnIG2tjZMTU1RpkwZSQmJosZy7NgxNGnSBGXLlv3khaCoBezJkydYsGAB5HK5RPyJRWCtWrXg4uICa2trSckZVXPv3j3s2LEDNWvWLGR1DQkJYeJPbJVRjlFdKMVfhQoVIAgCXF1dWchAfn4++5327NkDQRCwaNEitYyjqGSj69evs3CBW7duwcbGBu3atWPnZ/bs2dDT00O1atVUZiX+EGLLn52dHQRBwC+//MJeF1u07O3t0bFjR7WM4969exg+fDgEQSgk/oD353HEiBGoVKmSpLbjoEGDmPhTV31F4P/ua2VySNOmTSXiTzl3rVq1CoaGhjAwMMBPP/0kKeDM+Tbgwo9TIpk1axbrbqGsIP8p2bBTpkyBu7u7pF3Tf0H8mdOnT8ewYcPYZHrjxg3Y2NigUaNGuHbtmkRQXbp0Cba2thg7duwnfe6/Zf369ahfvz6LITp16hR0dXURFBSE06dPw8bGBjY2NkUuzrm5uRg1ahQaNGhQLFem8hw9f/4ct27dklh+MjIyJOJPbLWNi4tDjRo1kJKSwtxk6uDNmzdwdnaGubk5KleuzASmOBZKLP7+bTypKsjJycHWrVtRu3ZtaGpqYv369ZKSP3l5eXj16pXaHiLEDyrLli0rMtt08eLFcHJykpQomTp1KpydnWFjY4Po6GiVjQf4+H1+6NAh2NnZwcrKqlC2740bN2Bvb4++ffuqLXP+Y+IvLy8PTZs2RZ06ddhvdPPmTdSuXRtLlixRuXu3qBAKJdu2bWPiT1ykHXhfO7Nbt24IDg7mdfq+Ubjw45RYOnfuzCwhysX4vzyZKlsfzZ49+5Pqm4kX2LZt28LOzg6BgYGs7Elubi5Wr14NIyMjeHl5saf5a9euYfz48dDT00NcXNx//tz/wosXL1gixK1bt2BrayuxzCxZsgR6enqoVKlSoY4lr169go+PD/z9/SWtt/4LynN08eJF1qe1bt26mDJlCjvnSvGnUCjQpk0b7Nq1C3FxcWjVqhUaNGig1o4cSnbv3g17e3tmLRL3SVUSEhICDQ0N9OvXTy0xWB8SI1lZWdi6dSvc3NxQtmxZJCQkSMZ148YNlClTBv369VPpeMTXd7t27VC2bFl8//33hb77zz//DBMTE3bd379/H926dcPixYtV7p4Xn6MVK1bgjz/+KFR3TxnzZ2pqiqlTp+Lx48c4cuQI69ktLt6uDj4m/oYMGQIjIyMkJCQgMTERU6ZMgaWlpaTUzqcK90OHDklCJYD3c82QIUMwfPjwQgW8xeJv+/btAN677L29vVlsL+fbhAs/zhfnYxNht27dIAgCBg0axIKR/434Cw8Ph4aGBubOnVvsLMOBAwfCwsICsbGxTKQox/zy5UusXbuWiQpLS0vY2tpCW1v7s7c7ioqKgoODAysBArx3ybm6usLZ2Vni4lSOPzs7u9hFYu/cuQNbW1tUq1YNAwcORK1atWBpaYmAgAB27p88eYL169fDxsaGZaVaWlp+MFNSlYj7/1paWqJy5cqS7GXx9dG/f38YGBio3AJZMP5z9erVuHfvHgthyMrKwrZt21CuXDmWyZuamoozZ84wQSMuaaJKevTowfpcF1VGJCoqCpqamhg2bBhWrVqFUaNGQU9PD6tWrWL7qNpV6OPjA0EQoKGhwe5/sSV57969sLW1hSAIKF++PBwdHeHp6YlZs2apdBzi7yV+eLx7926R4u/8+fOsRaRCoYBcLlfJPDB16lR2DSjv3cuXL8PU1BRmZmawtLSEIAjw9/fHnTt3JG5fKysrmJmZoWrVqqyg/MWLF4s9Js7XCxd+nC+KeDK9fv06zp49i/v370vEoJ+fHwRBQGhoKFuQP7bQqFL0PXjwABUrVkSvXr1YjbKiPvvhw4f4+eef0bt3b4wdO1ZtvXc/xtSpU2FgYIALFy4AeG+Z6dGjxwctnsVZrMXC8dy5c2jUqBFr/5SVlYUxY8bA2toabdq0kcRgPXz4ECtWrEBMTEyheDpVII4jLKo0xYEDB2BiYoLvv/8ef//9N9suvk7U6XZu06YN6xttYmKCkSNHsgxVpfirVKkSBEGAvb09PDw8ULlyZbX1lz5z5gwsLS0xfvx4lk1c1HUxfPhwGBoaQktLS+39rpcvX45y5cph+fLl2LFjB4YMGQK5XM4SJpQcOnQITk5OKFOmDMLCwiSWY3V05imYXHbnzh0m/sTi7vHjx4iMjERERISkmHtxxnTixAm0bt0a+vr6WL16Nd6+fYu+ffuiWbNmOH78OG7fvo2oqCiYmZmhSZMmuHr1Kvu8EydOICQkBC1atECHDh246ONw4cf5cogn1l69esHJyQkymQympqYICwuTxBS1b9/+X4k/pXtXFaIPeN+zUhAEVrj3Q4WJPzSez9l94ty5c9DU1ERQUBB+/fVXBAcHQ6FQSGKhVGmZuXnzJqpVqwYvLy8Wf6bk1atXmDBhAqysrAqJP3Uhdn/1798fdevWRY8ePQoVXRaLP7ELTnm9qCvQffHixXB1dcWiRYtw6NAhdOzYEXK5HJ07d2aJFFlZWdiyZQvq168PQRCwadMm5mIFVH89/fnnn5IuEeJ7UpxgAryvqbd3715JzJg6BNbixYvRvn17Zol++PAh5s+fD11dXfj7+0t6A//9999MDInHrcoxjR8/Hi1btoS7uzvmzZsnqZF5+/ZtJv6mT5/+weOp4jydOXMGrVu3hkKhQFxcHLp06SIp3J2dnY3t27fD3NyciT/luVA++Kkrc57zdcGFH+eL065dO1haWmLcuHGIjo5mri0fHx9J8HH79u2hoaGBESNGSJ78lSxevBgaGhr45ZdfPkn0FTU5p6SkQC6XY8SIER+ME9y+fbvEvfol2bdvH0xMTCCTyWBlZSXJflQ1W7ZsgY2NDSwtLdG1a1cA0n6qYvHn5+encvFXVG/bixcvwsLCAo6OjmjYsCHKly8POzs7DBw4UPJepfjz9PRk8U+qpqCgCQsLQ79+/STX0aBBg6Cnp4eAgAAmKJQdFczNzREbG8v2VYcg3b9//z+6kcU14cSoWvTt27cPe/fuxYgRIwp1tXj69CkWLFgAHR2dQuJP1V1exN9L2ZmnTp06aN26NQRBQNu2bbF//362j1L8aWlpYdq0aSodCyD93c+cOYOmTZtCU1MTZmZm2Lp1q2SfvLw8bNu2jYm/69evq3w8nK8fLvw4X5T//e9/her0JScnswzLx48fSxZKpausqMVoyZIlWLp06SeJPvECdOXKFUk8UevWreHg4MBcJOKJ+OrVq/Dx8fnkBBJ1cPfuXRw5cgSnTp1i29Rledy0aRNcXFwgCIKkS4ryXLx+/RqTJk2CTCZj4lCViL/XgwcPUK1aNTRv3pwJ8czMTHh4eEAQBAQGBkree/DgQQiCgEaNGkm6h6gC8fV05MgRnDt3DsHBwZgzZw4Aac27wYMHQ6FQICAggLm/37x5oxZXeEEuXLgAPT09+Pn5ST5PeY2fP38ejo6O2LRpk8o/W3wf+fn5QSaTQVtbm3WZKSjolOLPwMAAbdu2RWpqquQ4qr7Ghw0bBkdHR6xdu5a5kadPnw5BENC6dWscOnSI7Xvnzh0MHjwYgiCwkAdVobyWsrKy8O7dO1y6dAkBAQEQBAHDhg0r5ILOy8vD9u3bYWNjAw8PD4mFksMBuPDjfGGmT58OW1tbFue0d+9e6OrqomvXrpKJX7xQbtu27YPH+5TJX7xI9+3bF+7u7li5ciWzUCkzQitWrIizZ88yd8nt27cxadIkmJubq7U+V3FRhaXoY+dVKf7s7e0lWcxK8ffy5UtMnz4dV65cKfY4gPfJNuKAemUdsoiICLi4uEjct+PGjYOWlha8vb2hp6dXKDP28OHDai1p4efnx7qpCIIAX19f9pq4nMzgwYNhaGgIPz+/QlYadYcL/PLLLxAEASNHjpR8dmpqKiZOnAgnJyeJyFEF4ntuzpw5sLe3x8yZM/HLL7+gbdu2rFCysq6hkmfPnmHu3LkQBOGj80BxSUlJgYeHB8aMGcNE3969e2FoaAgvLy9oamrC29tbUsPz5s2bkv+rAuV5unLlCnx9fdGhQwe8evUKp06dgq+vL3R0dLBmzZpC93heXh7i4uLg4uLyWcsTcb4OuPDjfBGUE1VwcDDc3NwAvHcp6erqolu3buxpHnjfCzM2NrbQAqiKBVE8YbZu3Rr29vYYNWqUJK4qOzsb0dHRcHBwYNmqwcHBqF+/PrS0tNQa6F4SEPe4nTNnDgIDAzFz5kzs2bOH7bN+/XpUqFABDg4OEvGn6ri5mzdvolKlSnB1dUVkZKTktX379sHf35/9Pzw8HFpaWli9ejXu3LnD+t/2799fJWMpCrGgGTVqFOzs7PDzzz/jt99+Y5bR8ePHs33E4q9///4QBEFtrueCKH+TzMxMjBgxAoIgoEmTJli8eDFWrVqFzp07Q0tLi1kp1cHJkyfRs2dPjBgxgl0rN27cQI8ePSCTyTBnzpxC4u/Jkyc4ffq02sYEvK/DOWjQIPawcvbsWejp6aFz587Izc3F8uXLoaGhgfbt22Pv3r2F3q/KuUkZvtC4cWMEBwez10+fPo1WrVpBV1cXMTExRYo/8QMzh6OECz/OZ6GoSQl438zc2NgYgwcPhr6+Prp37y6x9F28eBFubm4IDg5WafP3gowdOxZ2dnZYv359kV1C8vLycPLkSQQEBMDOzg5mZmZo2rSpJHHgcyZyfC6U3+nChQuwtbWFjY0NXFxcoK+vDxcXF4wcOZLtu3HjRib+xFnNqub06dNo3LgxnJ2dP9iVZOfOnTAwMMD06dOZxWb9+vWws7ODlpYWBgwYoLbxAe/dztOnT8ekSZOYK+78+fNo0qQJTExMMHHiRLavWPyJY8c+Jzk5OYiIiIC1tTWzTlaoUEFS+FvV13f//v2hUChQvXp17NixA8D/zRNpaWkS8fehWo+qjjMUu/yVHX8ePXqEWrVqoUWLFuyB8OrVq6w0kYeHh9p67z5+/Bi1atVCgwYNJKEbSv5J/HE4RcGFH0ftiGPfCrZPu3XrFry9vVlcj3gRvH//Pn766SfY29uzhUEd5OXlwdvbG61bt2YT/6NHj7Bnzx707dsXo0aNkrhy7927h/T0dIlALI2iT0l6ejoqVKiApk2b4uDBgwDeu7nNzMxgZ2eHEydOsH03btyIypUrQ19fXy2WK+XCdvr0aTRs2BDOzs4ScaJ8OJg3bx7s7Owkltvhw4ejdu3aCAsLU5nbuSgGDRoEQRBgbW2NdevWAfi/e+DChQto0qQJjI2NJeKvYLZlca+nT33/3bt3ceHCBZw7d07Svk8d1/eZM2dQsWJFCIKAoKCgQudAKf4UCgVmzpz5yd13PoZ4bpo6dSomTJgguZ6B925WKysrSd/txMRENG/eHCtXrsTChQtVPi4lZ86cgb6+vuQz8vPzJQJPKf4MDQ0lvZM5nA/BhR9HrYgXjAEDBsDDwwNt2rTBkiVLWLmGhIQEuLi4sDIuN2/exNatWzFgwADIZLJCGX6qJD8/H8+fP4ebmxvatGmDe/fuISUlBS1btoSlpSVMTEygUCjg4OAgcW0WPEZpRPm94uPjUaZMGZZBCLxvhSeXyyU9gpX88ccfqFmzJq5evarWcX1I/AHvW/7JZDLWhi4pKQnNmjXDL7/8ovJ2XgV//2XLlqFWrVqQy+Usq/rdu3cS62mTJk1gbm6OcePGqWwcyuOLxcy/zaT+2DWsiuv7Q8e4ePEiKlWqBEtLS6xfv75QglRaWhq6dOkCQRCQlJRU7HGIEc9Nvr6+sLKyQlBQUKF+1YcOHYKGhgZzeWdkZCA0NBTff/+95PyqYx5Yt24dBEFgiWVir4f470uXLsHT0xM2Njas3iiH8yG48ON8Frp27Qp9fX14eXnB3t4eCoUC3bt3Zy6chIQE5rLQ0dGBXC5HuXLlMH/+fHYMdVrVoqKiWOcNpftG2dbo9OnTkom/tKJcuJQLitJFGRkZCWNjYxZrFRoaCplMhqioKBZDlJmZicTERHasotzl6hjrh8Tf33//DVdXVzg6OsLPzw/u7u4wMzP75NZ0/zQOAJKuFzExMShfvjy0tbVZhnFubq6k7IyXlxcEQSjUS/VTePfuHZYtWyYJPahZsyZCQkK++IOJWMy9fPkSjx49kmxLSUmBk5MTnJ2dsXHjxkLi78GDB5JOK6qme/fusLa2RmxsbJGdSwCgRYsW0NXVha+vL1q0aAG5XC6Zm9TFmTNnoKGhgQkTJrBtBX/P6dOn48WLF7h8+TJP5OD8K7jw46gF8eSUkZEBLy8vrFmzBm/fvkV+fj569eoFY2NjtGnThom/Bw8eICkpCZGRkdi5cyfOnz/PjqEu0SceZ1xcHEaNGoXffvuNZRkD7wsCW1lZYdGiRWoZQ0kgMzMTERERLFYpOTkZ5cuXx7Vr1xATEwMdHR2kp6djwoQJ0NLSQlRUlKSMRFBQEPr27as2wfex318s/sS/0dq1a9G+fXtUqlQJPj4+rKOJOvD390ffvn0l8amxsbFwdXWFiYkJjh49CkAq/s6dO6eyMilPnz5FWFgY60XcsmVLmJubY8OGDcUuM1Qc4Si2rgYHB6NmzZpwdHRE3bp1sXv3bhb6kZKSgrJly8LZ2RmbNm364JhVPQ8cP34cDg4OmDNnjuQhJjU1FfHx8Sy0AXgvEMuVK4datWrh119/ZdvVKawfPnyIypUrw9XVtVDPYuB9TKiFhYXa2vlxSidc+HFUTsFJOyUlBRUqVJAsillZWRg8eDCMjIzQtm1bPH/+/IPH+5wWi4JuwLt372LixIkwNzfHvn37Pts4PjcnTpyAi4sLatasyYpA165dGzdu3MDr16/h7OwMa2traGlpISYmRuJOOnToEKpVq4bRo0erpUOH8jdJS0tDXFwc1q5dW6hsRnJyMhN/4lis58+f48WLF8XuR/xPhIaGQkNDA6GhoRKry9q1a1G+fPlC4q/gdfYpgiY+Pl5yTd66dQu9e/eGhoYGDA0NsX379mL9HqqMg/Tx8YG5uTm6d++OESNGoH79+tDR0cGUKVPYva8Uf+XLl8eGDRvUmsylJDExETKZjHW3uXnzJoYMGcJ6b2tqakqysF+8eCG59j9HbO/Ro0ehp6eHGjVqYP369Wz74cOH0bp1a1SsWFHlRaw5pRsu/DgqpWCroy5dumDMmDGoW7cua7WmTODIzs7GoEGDYGRkhPbt27MJtaQkSvz9998YNmwYdHR0Sr2b9+XLl1i3bh2MjY2hra0Nb29vXLt2DcB74T1//nw4ODigbNmyyMjIYO9LSEhAy5Yt4eLiopZCseK4OFdXVxgaGkIQBBgYGGDQoEGSfcXir2DM3+dg2rRprB6eWPzFxsaifPnysLCwwOHDhwEU72EmPz8faWlpEASh0KLfvXt3lpWrDFX4FCZOnAhBEFTS+WHhwoWws7PDunXr2L2/ZcsWCIKAcePGSazEFy5cgJ2dHUxNTT9L14lz586hYsWKqFq1KgIDA2FpaYkKFSqgT58+iI+PR5s2bWBlZVVkRu3nfCDduXMnjIyMoFAoULduXTRu3Bhly5aFhYUFi2PlcP4tXPhx1IKfnx+0tLTg4OAADQ0NCIKAKVOmsNeVlojs7GwMHToUurq68Pb2VvlT/qcWdD548CDkcnkh92FJEaXq4OrVq0xYVa1aVdIW6/nz55g4cSKMjIxgZ2eHH3/8EW3btkW5cuXUvvhcv34d9vb2aNy4MWJjY5GcnIzJkyezmnzibNDk5GSWNRsVFaXysfxTYsjUqVOLFH/r16+Hk5MTBEFAWlqaSkTDjh07CvWH3bhxI1atWoVu3bpBEARJoet/S3h4OLS1tTFjxgyV3I+BgYGoVasWi5/bt28fFAoFevToUWRM2tmzZ7Fy5cpif+6/Zd26dWjYsCFsbW3Rr18/SV2+BQsWQKFQqDw29FO4cuUKBg8ejFq1aqFWrVoYPHiw2hKoOKUbLvw4KkG8IKakpKBSpUpYs2YNcnJykJiYiEaNGkEQBInlTCn+srKy0LNnTyxZskQlYylOdqOSN2/eYPv27SpvSF+SSUtLw6xZszBlyhSYmZmhdu3akvpkL1++RHx8PIubq1GjhtoXn8zMTPTs2RMNGzaUJI8MHz6cWba6desmEX8nTpzADz/8wCyW6mDs2LE4e/Zska8pxd+oUaMkbdBWrVqF6OjoYn3uhzJbx44dKzk/ly9fRo8ePYoUf5mZmR8UsOHh4dDQ0MDcuXOLLfry8vKQn5+P+vXro2PHjgD+T/QVLNIeGRlZpLtSnfecWHy/ePGiUGLH/fv3MXjwYHz33Xclpu2ZeMxfOmmH8/XChR9HpQwYMABz586Fj4+PJK7q1KlTrMm5ssQF8H+CTDzBF2dCU1d247c0yWZmZmLFihUwNTWFp6cn0tLSJK/n5ubi+fPnyM7OVnt/4vT0dNSqVQtTp05l28LCwqClpYWFCxcykTVgwABJsom4HqSqSUlJgaamJurUqcPKbBRk4MCBkMvlmDBhgqSWoJJPETT79++HhoYGwsLC2LZ3797h3LlzEAQBDRs2lLRWS0lJwY8//igRf0+fPsXUqVPRp0+fQr9dcUXfh4q0T5o0CS4uLli8eDH09PTQrVs3ScmUpKQkGBoaqixL9r+c2w/d1xcuXMCYMWOgra39RcIGPgQXfhxVwIUfR2UcPXoUJiYmEAQB9erVw+vXryWWhVOnTqFVq1YQBEFSm09Vog9QT3ajckzf0kT76tUrLF++HGZmZvD09GRxfZcvX0ZgYKBKSpAURVHneO/evUyIREVFQSaTISIiAnl5ebh9+zZMTU1ZH1x1JJcUJC8vD/v374eDgwNq164tEX/Ka3nbtm3Q1dVlolQVySUXL15Ex44dYWFhISn+DLy3pMnlcnh5eRUSfz179mTtxbp27QpBEDBmzBjJ+6dOnQotLa1PFn3ie6vg++Pj42Fubg5BEODj4yOxzt6/fx8jRoxA+fLlWfxjcVi5ciUWL15cLOE/c+ZMeHh4wNLSUuKh+Jbuf07phgs/jsrIy8vDunXr4OnpCT09PdZ+qqD4a9OmDQRBkFhxisPXlN34NfH69WssX74c5ubm+O677zBlyhQ0a9YMgiAgJSVFpZ+Vn5/PrpPHjx8jJSUFR44ckVjxnj59Cm9vb/j5+bFEIQBo0KAB2rVrBwcHB0kZHlXwIZdoTk4O9u3bBzs7Oyb+xMJg165daNmyJUaOHKnSAuSXL19G165dYWxsLMk2Bd4LZC0trULi79KlSwgLC4O5uTlcXV0lWc/A++QTbW1tzJ49+5NEn/gcTZw4Ed26dcOMGTMkDwcLFiyAIAioW7cutm3bBgA4duwYQkNDIZfLVVIqKSsrC506dYIgCFi2bNkni7+oqCh06dJF0nO6tId5cL4tuPDjfBIfWhCV4q98+fKwsbFhtfjE+588eRL169cvtmvna8tu/Bp5/fo11q5di4oVK8LAwABubm4qS+Tw9fVl4l+cvVulShUYGhpCLpejUqVKWLp0KVJTU5GVlQVjY2MMGzaMHePAgQNwd3fHzp07VV6yRXzN/vbbb5g3bx42bdrEslDfvn3LxJ+npycr1Pz48WMMGzYMvr6+Ku0SorSqXblyBb169YKFhQUmT54s2edD4i83Nxf379+XxGMqz/mBAwewadOmYsf0+fn5QVNTk/WwLVeunKTe3dy5c2FtbQ2ZTAYHBwcYGxvDxsYGs2fPZvsU16qWnp6OoKAgyOVyLF269JMf+JQ1/QAu+jilDy78OP8ZsVsnKSkJW7ZsQXJyMguAzs3Nxfr16+Hi4vJB8Se22BSXryW78WslLy8PT58+RWJiosp+t2fPnqFJkyYQBIFZoFJTU+Hi4oI6depg6tSpmDFjBurWrQtBENCnTx/cvXsXvXr1gpmZGfbu3YvY2Fj88MMPqFChgkqvp4L4+PhAJpNBS0uLdZ9Rur6zs7Nx6NAhuLi4wNLSEr6+vmjZsiUEQVBZshIgvXcSEhIwdOhQuLm5QRAEzJo1S7KvWPwlJCQUebyCAqu4guvUqVP47rvvsG7dOmRkZCA5ORlly5aFra2tZHyHDh1CVFQUgoKCEBkZKSmQrAqBlZ+fj4yMDPTs2bPIQuMcDocLP85/RDw5d+7cmcVXCYKA8uXLs6b0eXl5EvGndA0WjLP71AXna8pu5BRNeno6AgICIAgCIiIicOPGDVSrVo2FCCgJCgpiJVL+97//oUGDBhAEATo6OrC3t/9gdu2nIr5GFy9eDHt7eyxfvhzXrl3DtGnTULZsWTg5OUnEZlpaGvz9/VG5cmW4u7tLXJeqjA1r3bo1XFxc4Onpie7du0OhUEBLS6tIy5+Ojg7q1KlT6HyqgoL3zY4dO1C5cmVJIfbLly+jatWqhax6RaEK0Se+T2/dugVfX18YGhoiOjr6k8WfOpOEOJwvBRd+nE+iS5cusLKyQnh4OBITEzF37ly4u7tLLB1Ky1+FChVgZmaGM2fOqOSzS3p2I+ffk56ejg4dOkAQBDRr1gwNGzZkr4nPfdeuXWFqaork5GS8fv0amzZtwtatWyXZoapm7969WLlypSRj+O3bt1i/fj0cHR3h6OhYyNL4+vVr1oYMUK2bcNasWZDJZJLOKSdPnoSPjw8MDQ0ldTIBYPfu3RAEAf/73/9UNgZAKvqioqIwa9YsREZGolu3bgDe34vK3+7y5cuoUqUKbGxsJLGOqnSBA9LzPGjQIDRs2BDVq1eHIAgwNDT8pJi/6dOnY86cOZ8lYYjD+Zxw4cf5z5w+fRrm5uaYOHEiMjMz2fbExESWtavsQaq0/JmZmWHZsmUq+fySnN3I+e+kp6eja9eu0NbWhq2tLS5cuFCoFuOVK1egUCgwcODAzzKm8PBwCIIAY2NjFiMqLj20YcMGJv6Ubl/lWNWVBR4YGAgHBwcWf6YUTykpKSzppqDbV9XJLmKUSVrif8pWevn5+ex8XL58GdWrV4epqWmx4m3/DZ06dYKlpSXCw8Nx5swZREREoFmzZtDW1kZUVNS/Fn8zZsyAIAiYO3cuj/HjlDq48OP8IwWfzrdt2wZBEFj5BfET8aFDh+Dq6opatWqxBTEvL0/lxXRLYnYj59NJTU1lLnnx76kUT2/evIGjoyO6dOnyWcZz5MgR+Pn5QUdHB507d2bbldeFUvwpQxnEXU7URf/+/WFsbIxnz56xMShJTEyEhoYGtLW1MW7cOLZdef5UIV7ElvFt27bByckJK1aswL179zBv3jwYGhrC1dWVxRWKxd+lS5dgb2+vlm4qSi5evAhjY2OMHj2aCbz8/Hxcv34dXbp0gba2NpYtW/aPbl+lxX/+/Pl8HuCUSrjw4xRJbm6uJLMNAIvTO3HiBARBwOTJk9nCUrBHr4aGRpHtmFS5AJXU7EbOp5GWlsZi/mbMmCF5oDhy5AgsLCwwZMgQ5Ofnf5aaaqdOnYKPjw8bjxKx+Fu3bh2MjIywYsUKlX3uhxIv5s2bB0EQEBkZyWrh5ebmste9vLxQvXp1aGpq4ujRoyobT0Hmz5+PlStXokePHhILWlRUFBwcHODm5ibpSay8X5WCVV0cPXoUgiCw30I8titXrqBOnTowMjLC6tWrPyj+eJgH51uACz9OIXJzc7Fs2TLMmDGDtTFq3rw5+vbti5cvX+L58+coX7483N3dJbW6lBP8smXLIAgCLly4oJaxKSmJ2Y2c4pGeno6OHTtCEAQEBAQgIiICs2fPRuPGjWFiYvLZayomJyezjjPi60ss/lTZsq6gdb3gw5eHhwfKlCmDP//8UyJerl27Bnd3dyxYsAB79uxR2XgKEh0dDUEQoKuriwEDBgCApCBzVFQU7O3t4ebmxpKslK3b1F0I/cmTJ7CwsEBgYCDbJj6fkyZNgiAIkMvlWLhwYaGH0BkzZnDRx/km4MKPUyTKGKcRI0bAy8sLJiYm+Ouvv9hic+DAAejr66NZs2aSLNq0tDT06tULrq6uao0vKinZjRzVk56eji5dukAmk0Eul6NPnz7o0aOHWh4k/g1JSUnM8leU+FNSXGu2WKSEhobC29sb9vb2CAwMxMaNGwH8Xx9sGxsbzJgxA6mpqTh//jzGjRsHW1tbXLp0SWXjKYqMjAyMHDkS5ubmqFy5MrPKiq1ry5Ytg7OzM5ydnT9LRrGS58+fo2fPntDR0ZHEEyt/pzVr1sDLywsdOnTAwoULJe+dOXMm6yjERR+ntMOFH+eDBAcHQyaTQU9PTzKRKp/g//jjDxgYGMDZ2RlDhw5lFe+VfVTVRUnJbuSoj9TUVAQGBkpcd18SpfjT0tIqVDNS1bRu3Rrm5ubw8fFBUFAQKlSoIOkZe/nyZXh5ebGECoVCUcgdrQ6UQvLx48cYNWoUBEFAq1at2Oti8ffbb7/BwMAAq1atUukYxHGGe/fuRUxMDDZs2MDmgaSkJLi4uMDJyQm//fYb2/fhw4cIDAxE586dCxX6fv78OcaMGcNj+jjfDFz4cQqhnOB79+7NFpfBgwfjwYMHhfY9duwYPDw8oKenBw0NDVSoUEEi+tTh1ilp2Y0c9fDgwQP07NlT5e3hPpXk5GQ0b94cgiDg1KlTarm2Z8+eDUtLS2zYsIHVxFO2OxswYIAki37t2rWYNm0aZs6cydqgAertNKH8zk+ePMGoUaMgk8nwww8/sNfF4k/Vv5v4e/n5+cHS0hIaGhrQ1dWFvb09e7A7cOAA3NzcIJPJ4O/vj9DQUNZVZPny5YW+CwC8ePGCZ+9yvhm48OMwCrpQUlJScODAAQwYMACCIGDYsGG4ffs2e105UWZmZuLWrVs4d+6cpK6auibSL53dyPl8qLPe26dw4sQJ/PXXXyoajVR85OXloXPnzmjWrBkLqdi3bx/09PTQu3dvliz1MavU57i+xeJv5MiRhcSfOOZPHWPq1q0bbGxssHjxYqSkpODQoUNwc3ODqakpizlOTk7G8OHD4ejoCLlcjnLlyqn9gZTD+Vrgwo8DQOpCWb58OavHpWTw4MFM/N26dYttf/HiBVJTUwsdTxUTa0nPbuR8XYhFpLLUUHFQVUyf+DheXl6sEPK+ffugUCjQrVs3yT22Zs0aSVztl6Cg5U+hUKBFixZq/9zz58/DwcEBc+fOZe7dvXv3QqFQ4Mcff5RY9nNycpCdnY379+9LCm3zhz/Otw4XfhzJgtihQwdYWVnhu+++w8uXLyWvDRkyhIm/+/fv482bN5gzZw46deqkcldqSc9u5HxdiK+noUOHok6dOpIyP1+SevXqoU+fPgCAgIAAeHh4YPv27VAoFOjataskxOLgwYNwdXVFbGzslxouQyz+goODIQiCSq2hQGGRtnXrVmhoaLC+4Hv27GHiWHyeChZqVndGMYfzNcGFH4fh7+8PW1tbrF69WuKyFS+aSstf/fr14e/vD0EQMG3aNJWO42vIbuR8PYh/fx8fHzg4OKB3797FKioutnr/V8TW9Xnz5sHZ2Zm5IU+cOAFjY2MIgoCuXbsiLS2N7ZuamoqRI0fCzc0NJ06c+OTPVyVKIfXo0aNiC2lxTdCC96zSYnfixAloa2vj5MmTOHr0KBPHYovoggULULt2bZ6oweF8AC78OACAdevWwdTUFNHR0cy6plygbt26JZmIJ0yYgLJly6JSpUpqjZspqdmNnK+TIUOGoEyZMli/fj1zExbFP13HEyZMgCAIRRYo/y+cPXsWP/30E3788UeWtPH8+XNMnToVRkZG8PX1ZSVszp07hzFjxkAul7PrX1UU98Go4PuLc7ycnBysXr0aS5cuZdvatWuHESNGIDMzE1evXoWTkxOaNGkCAwMD9OjRQyL6rl69Cn9/f/j6+uLp06efPA4OpzTDhR8HwPsSKcbGxmwxS01NxezZs+Hh4QFtbW0EBAQUqtenzriZkp7dyPm6ePbsGapUqYLAwED2YPPs2TMkJydj/PjxiI6OxunTp//xOOHh4dDW1sb06dP/s0VJLCiVxYRtbW0xe/ZsyX737t3D1KlTYWxszDJWLSwsYGpqKslWV8WDltj6mJiYiB07dmDPnj2Srimfch99alLOu3fvWA3HWbNmoUmTJjAxMcHWrVvZMZXzgJOTk8TKeP/+fYwfPx4WFhYlwhXO4ZRUuPD7BhFP9kpiY2OZ2/a3335DuXLlUKZMGTRu3BijR4+GtrY2+vfvX+TxVJ3I8bVkN3JKDh+L4crMzER6ejp0dXXx008/AQBu3LiBgIAAWFtbQ1NTE4IgoEmTJix2rCiK085LLITy8/Nx7tw51p6uTZs2ePz4caExX7p0CaGhoejbty/mzp2LvXv3stdVcX2LjxEQEAAbGxtoaGhAU1MTLVu2xObNm//zMQ8ePFjscQGAp6cnFAoFDAwMiqy/OXHiRAiCgAYNGiAyMhK//vorAgICmGBUwmP6OJzCcOH3DTN//nwWBP348WMMHDgQ2tra0NLSgq+vr+SpuWfPnrC3t2fWN1XyNWc3ckoOb9++xZ9//onjx4+zbb1792ZxoUOHDoUgCKhevToEQYC7uzvGjBmDly9fYs2aNR9NTlCV6JsyZQoGDRqE58+fIzk5Gf7+/pDL5Vi2bJnk+v+YYFH1Q01AQAAsLS0xf/587N27F3///Te0tbULtWT8J6ZNmwZBEFif3k9BeW7r1q0LQRCgp6eHyZMns+8sfmiNjIxEpUqVIAgCNDQ04OnpKXER84c/DqdouPD7RomPj4cgCOjSpQubTN+8eYOkpCQkJiZKJtjbt2+jWbNmaNeuncQFpGq+1uxGTskgNTUV3t7eqFKlCo4ePYpmzZpBV1cXu3btQn5+PjIzMxEeHo4ffvgB4eHhkhZwSUlJ0NXVLdLKNXXqVGhpaX2S6BOLjx9++AFOTk7w8vJiVuvTp0+jdevW0NXVRUxMjETwfQ5r1cGDB+Ho6IjffvuNucCTk5Mhk8kwcOBApKen/6vjhIeHQy6XY86cOSpJqtiyZQtiYmJQo0YNGBgYYNq0aax0k/j4GRkZuHXrFm7duiWxmnLRx+F8GC78vlGePXuGyMhI6OnpoVOnTh+crG/duoVJkybByMhIre2XvvbsRs6XJz8/H0ePHoWNjQ1MTU1hamqKuLi4QiJAXP4HeB8bFhYWBjs7O5w6dUry2h9//AGZTIbZs2cXS9AMHjwYlpaWiI2NLZRYcvr0abRq1apI8aduYmJioK+vjytXrgB4b13X1dVF165dJZn9H0uGKY41FPi4SMvJyUHNmjWZ+BP/dm/evJEkcPCSLRzOv4MLv28AsatJPMk+f/4cERER0NHRQefOnQtZ89asWYOmTZvCwMAAM2fOZNtVPbGWlOxGTumgatWqEAQBrq6u2L17N9teVMLBkSNHEBoaCrlcjnnz5hV6fdeuXdiwYUOxRF9GRgZcXV3Rp08fSVKS+D5Sij9DQ0OsXr1aLeKlqNjelStXQl9fH2/fvsWpU6egq6tbKKRi/vz5GD16dJECbfr06cUSfeIxnTx5Ejt37sSZM2ckv9WrV69Qq1YtGBgYIDw8HMD7On2LFi1CUFDQR0Uph8MpDBd+3xDKJ3jxZCsWf126dGHi78aNG+jZsycaNWoksfSpwoVSErMbOV8vYpHw8uVLTJs2DZMnT4a1tTW+//577N+/n1234lpxW7ZsgaGhIcqUKSMRfR/qGPOpnD59GoIgsLAE8f0nHntKSgoaN24MQRCYBU4dhISEICEhAQBw6dIllC1bFs2bN4dCoUDPnj0lZWquXbsGb29vdO7cGa9evZIcZ8qUKdDU1Pxk0Sf+7p06dYKFhQUEQYCNjQ18fX0lrd9ev34NT09PGBoaws/PDwMHDoQgCJgzZ85//lwO51uHC79SjHjBGj58OARBYIWNC4q/+fPnQxAE9OvXj4m/Bw8eSJ78VSH6SmJ2I+frRXwdb9u2DZcuXUJWVhby8/ORkJAAa2trfPfddzhw4IDk2svJyUFycjKio6Oxb98+tl0d19OjR49gaGiIgQMHsm0FxWR8fDyePXuGlJQU/Pnnnyofg5Lz589LBFNWVhb69esHDQ0NVKxYUdKB5969ewgLC4O1tTVLkFGydetWmJiYfLILXPz9O3bsCGtra0ycOBEHDx7ElClTYGRkhNq1axcSf23btoWtrS2sra0xd+7cIo/H4XA+Dhd+pZSCAis+Ph7u7u4wMzMrUvzdvXsXFStWZAkfBSdzVUysJT27kfN1UdBiZGVlhY4dO0osU/v27YONjQ2+//571n/69evXWLlyJdasWSMRFuoSDy9evED9+vVhZ2eH7du3s+3K6/fGjRsoV64cpk+fLnmfqq9v5fcLDAyEpaUlzp07x8anTDBp1qwZdu3ahWXLlqFHjx7Q0tIq0qp248YN7Ny5s9iJHBEREXBzc0N0dDSrGBAfHw89PT0IgoBq1apJfqOcnBzcunVL0nWFzwMczn+DC79SiHhBnDx5MoYMGYLnz59j586dqFKlCkxMTHDx4kUAkMT1DRkyBPXr14cgCJJCyKqgpGc3cr5eOnToAGtrayxfvhy3b98u9Pr+/fthY2OD6tWrY+bMmQgLC4MgCFiyZMlnG2NycjL09PRQq1YtScmYu3fvYuLEibCysip2n9uPuajFf8fFxcHAwACzZs1ic8WLFy8QHBwMFxcXaGpqQi6X4/vvv0dkZCR7n6oFVk5ODnr06IEGDRrg0aNHAIDDhw9DoVCgc+fOmD17NgRBQO3atVnZqYJj4HMDh/Pf4cKvlPEhgaV04ezYsYOJv5SUFLbvnTt3UK9ePURGRharh+k/UVKzGzlfJ3FxcbCwsMCiRYtYxmdRAiUxMRGOjo4QBKFQstLnIj4+Hvr6+jAzM0PHjh3x008/oXHjxixruLgo76fc3NwiE1nEFv727dvDwcFBcg++e/cODx8+xJ49e3Du3DlJrJ+6rI+3b9/GyZMnAQDXr1+Hubk5OnbsiCdPniAzM5PFPFauXLlQNjaHw/k0uPArpXxMYO3YsQPVq1eHoaEh1q1bhx07drBWR+Liq6qe7EtKdiOn9DBr1izo6uqy8AUxBa+dV69eYefOnUxoAJ/fTXj+/Hm0a9cO9vb2UCgUaNSoEVauXFns8WzevBl2dnY4f/68ZPvQoUMxfvz4Qsfdt28fTE1NMXr06H88tqrDPIri3bt3CAsLg4uLC5KSkphIHT58OOrUqQOFQoHVq1cXexwcDocLv1LJhwSWePJPTExE27ZtIQgCBEGApqYmZsyYodZxlbTsRs7XRVGxn2PHjoW+vj4yMjIAFC0w0tLSioxF+1KxYVlZWXj79i0yMjIkVqzijCcyMhJGRkaIiopi265fvw4HBwfo6+ujYsWKWLhwIW7cuAHgvQhu1aoVKlasyHpuq+shS/ybrF+/Hn/99ReSk5PZtry8POTn56Ndu3Zwd3dn2+/evQtvb2/MmTNHrV4IDudbgwu/UsjHBJb47/z8fGzbtg2xsbFqz2wESlZ2I+frQnxNiov2/vnnn5DL5awHLyDt7LB79240b95c4rb80hQVe6cK0XXmzBn29927dwG8fwjctWsXK9dibW2NiIgIPHr0CJcuXYKuru5nc3u3adOGPWja2NhgxIgR7LW8vDxMnToVGhoaWL9+PU6fPo1x48bB2NiYZ/FzOCqGC79SyL8RWNu2bZMsoErUObGWlOxGztdLz5490aVLF9a+7+HDh/D09IS5ubnE2gUA6enpGDt2LMqVK4ezZ89+ieF+FgpaOQcOHAgLCwtJSzoAWLt2LTp37gxNTU1UrlwZwcHB8PX1Rbly5ViGr6pRzju//PILnJ2dMW/ePOzYsQMtWrSAQqFAQEAA2/fo0aNo2bIlBEGAtrY2tLW1v0gsJodT2uHCrxTybwRWhQoVMHny5M8+ts+R3cgpvXTp0gVmZmYYNGgQs+Jdu3YN1tbWMDExweDBg3Hx4kVs2bIFgwYNgkwmw4IFC77wqFVLwYe4gg9GS5YsgZubG6pWrVpk7OOuXbvg6+vLCiYLgoBjx46pdIwFxejkyZMxdOhQ5tq+f/8+hg4dCk1NTXTo0IHtd+XKFWzcuBGRkZHYs2cP284f/jgc1cGFXynl3wisLVu2fJGxqTu7kVP6EAuJwYMHw9TUFAMHDmTlW65fvw5vb29W/00QBNjZ2ZXaIr/Z2dlISEjArVu32LZx48ax5I6VK1eiQoUKcHd3x+XLlwG8P4fKc/Do0SOcPHkSLVq0UPk9J/6tEhIScOHCBfzwww/45ZdfJK8/fPiQib/27dt/8Hhc9HE4qoULv1JMSRZY6spu5JQOiuorK47dGzhwIBN/SsvfkydPcPz4cURGRmLr1q1ISkpi+5e26+ns2bOoXr06goKCcP/+fTRp0gQGBgZITExk+6xYsaKQ+CvqvCpRdTtGf39/6OnpsTaLPXv2ZPsoP+vhw4cYNmwYtLW14evrW+zP53A4/wwXfqWckiyw1JHdyPk62b17d5F12saPHy8J7i8o/nR1dTFo0CBJq7GClCZLn5KsrCzMmTMHgiDAwcEBlpaW2LJlSyFht2LFCri5ucHd3Z25fZUWN1UXSRdb+sLCwmBnZ4eff/4ZYWFhqFixIjQ0NNjcU1D8DRo0CIIgSEJTOByOeuDC7xugpAosdWU3cr4uLl++DEEQ4OPjw9pzKfs4C4KARo0aSepLisWfn58fBEHAkCFDWCbrt3QNWVtbQ0NDA82aNWPdeACpZU8p/qpVqybZp7h8qJtGRkYGgoKC8PPPP7N9Dh48yBI3Vq1aBUAq/tLS0lhLPQ6Ho1648PsG4AKLU5J58eIFfv31VxgbG8PPz0/Sm3Xv3r2Qy+Xw8vJCQkIC267c59ixYzAzM4OdnR1+/PFHvH79+rOP/3MiFlk3b95Ex44d0alTJwiCgL59+0rq3YnF3++//46yZcuiTJkyePr0abHv/9jYWAQFBSEtLU2yvU+fPtDX10fVqlWxc+dOyWvHjx//qPgr6jtyOBzVo0WcUo8gCIX+Fm/jcL4khoaG1LVrV9LQ0KCQkBDq3LkzxcTEkJ6eHnl7e1N8fDy1aNGCfv75Z5o+fTrVq1ePdHR0iIgoPT2dHB0dycnJiWrXrk16enpf+Nuoj9zcXNLSej9lJycnU9myZWnFihWkqalJNWrUoNGjR1N+fj6NHTuWXFxc2L5ERL169aLc3FySyWRkYmJS7LFs3ryZNm7cSJ06dSJra2siIsrJyaEmTZpQUlISnTlzho4dO0aNGzcmuVxORES1atWiyZMnExFRnz59KD8/n3r16lVoLtLQ0Cj2+Dgczkf40sqTw+F82ygtU69fv0ZMTAz09PTQvXt3vHr1iu2zd+9eaGhooGHDhiwOLD09HSEhIejdu7fkeKXRmi2On+vVqxfs7e0xZswY5vbOzs7GjBkzIAgCgoKCWIeOFy9eYMOGDZJEF0A152jr1q3s72fPngEAMjMzERcXh0qVKsHV1VXSoUPJiRMn0KRJEwiCUGS5GQ6Ho14EAPjS4pPD4Xyb5OXlkaamJhERRUVF0enTp2n//v109epV+vHHHykyMpJZ8fbv30+dOnUiuVxO1atXp5ycHNq3bx/Nnz+fhg4dSkREAEq1NdvX15eSkpJo+PDh5OfnR66uruy1d+/e0dy5cyksLIx69epFPj4+dPXqVfr5558pNjaWOnXqpJIxiC2PREQ9evQgTU1NmjVrFllZWdGbN29o9+7dNHz4cNLX16fY2FiqUqWK5BhHjhyhjIwMateunUrGxOFw/gNfWHhyOBwOWrduDUdHR7Rv3x6zZs3Cd999B0EQ4O/vL+k3ferUKfTo0QPu7u6oVq0aFi9e/AVH/XlZunQpLCwssGLFCnZOCsbD5eTkYO7cuZLuF1OnTi3W5xa0DhbMHG7Xrh3Mzc0RHBzMYv7evHmDP//8E46OjnB3d/9oZxAe08fhfF648ONwOF+UVatWQUtLCytXrmTu3Xv37mHOnDnQ1dVFhw4dJEkbmZmZyMrKQmpqKtv2LYiHgQMHokyZMqxdnZiC4iwhIQG//fYbdu3axbYV5xxlZ2fj6tWrkm3iFnlBQUEwMzPDsGHDihR/1apVK9Vt8zicrwkeRcvhcL4oqampJAgCNWjQgPT19Sk/P5/KlClDQUFBNGHCBNq0aRMNHTqUMjMziYhIR0eHdHR0yMbGhojeu3dLc0IA/n80zo0bN8jExIRsbW2JiCg/P5/to3Rvv3r1igBQ/fr1qX///tS8eXO2b3HO0aFDh6hTp04UExNDRESNGzemYcOG0fnz54mIaPny5dS2bVuKiYmhGTNmUHp6Ounq6lLz5s1p0aJFlJGRQT4+PvTkyZNPHgOHw1ENpXe25HA4XwWGhoaUm5tLr1+/JqL/EzomJibUuXNnsre3p+joaOrVqxdlZWUVEjClOaaP6P/OR+PGjeny5cu0efNmInqf/ZqXl8f2++uvv2jcuHGUnZ1d6BjFFcY2NjZka2tLPXr0oCpVqtC1a9do7dq1VL58eTa+lStXkq+vbyHx16xZM1q0aBFNmTKFzMzMijUODodTfLjw43A4nwUUyCNT/t/d3Z2MjY1p2rRp9OjRI9LU1KTc3FwiInJycqIWLVpQnTp1aNOmTbR79+7PPu7PiVjIKVGKtvr165ORkRHNnj2bEhISiIhYYszDhw9p165ddPjwYcrIyFD5uNzd3WnmzJlkaGhIFy9eJH9/f/Lz8yNtbW0iIvZ7rVixgom/WbNmUVpaGunq6pKfnx/17t2biApfBxwO5/PC6/hxOBy1I87ezc7OpszMTNLT0yMdHR3y8vIiPz8/+v3338nJyYlGjx5NlpaWRER07949SklJIT8/P1qzZg05Ozt/ya+hVsTZshs2bKB79+6RgYEBNWvWjMqWLUv169enGTNmUL9+/Sg4OJiCg4OpQ4cOdObMGdq8eTNFR0fTvHnzyNHRUWVjgihL+tatW+Tq6koAKCIigjw8POjHH38kQRBIS0uLjV9ZW3D58uX06tUrWrRoEenq6rJjlnYLLYdT4vlCsYUcDucbQVyDbsSIEahTpw5MTEzg5eWFhQsXstfatGkDQRDQvHlz7N+/H/Hx8Rg1ahRMTEwk/XpLYyKH+By1a9cOhoaGUCgUEAQB1atXx/Lly9nrK1euhL29PTQ0NGBmZgY9PT2Ymppi1qxZbB9V9959/fo18vLy8ODBAxw7dgy+vr6Feu8W/NyAgAD8+uuvxR4Hh8NRLbyOH4fD+Sy0adOGjh07RnXr1iVra2vat28f3bhxgwIDA2n58uVERDRkyBCKi4ujtLQ0IiLS1tamSZMm0U8//fQlh/7Z6Nq1Kx04cIAmT55MjRo1Il1dXapXrx7p6+tT3759KTg4mIiITp06RZcvX6aEhASqUqUKVa5cmRo3bkxExU/kIJJaaMeOHUtpaWnUtWtXliySlJREkydPpu3bt9OyZcsoMDCQiIieP39OSUlJ1KRJE8nxUMrrK3I4XxVfWHhyOJxvgKVLl8LAwABr1qzBy5cvAQAXL15EaGgotLS00K9fP7bvpUuXsHnzZsTFxSExMZFtL42WPjEbN26Es7Mzfv/9d1bW5syZM9DW1oapqSnKlCmDiIiIjx5DFedIfIy2bdvCxsYGQ4YMwe3btyX7nTp1Cm3atGGWv5SUFMycOROCIGDPnj1sv9LYSYXD+ZrhMX4cDkflFLQ6Xbp0iXR1dalFixZkYGBAREQVK1ak4OBgys3NpQULFpCnpycFBgaSm5sbubm5ffR4pZG8vDyytbUlb29v0tfXp6SkJGrQoAF1796dhgwZQq1bt6bw8HACQEOGDCGiwudFFedIeYygoCA6efIkLViwgJo1a1aox6+HhweNHz+eZDIZBQUFkbW1NT158oQmTZoksfhxSx+HU7Lgrl4Oh6NSxG7CpKQkql69OoWEhNCKFSsoLS2NDAwMJPucPXuWmjZtSs2bN6eYmJhv1i348uVLevbsGTk6OtLt27epXr16VKtWLZo/fz45OTnRr7/+SqGhoeTo6Ei9evWi0NBQtY3lypUr1KZNG+rYsSOFhYWRnp4evXr1itLS0ujYsWOkq6tLHTt2JCKi27dv065du+jcuXPUsGFDCggIIKJvQ6xzOF8j3OLH4XBUilLQNWrUiIiIli5dSpUrV6Y3b95QREQEjRw5kuRyOcsCrVatGlWtWpUuXLhA2dnZpKOj8wVH/+UwNDQkQ0NDIiLavXs3aWlp0ejRo8nJyYmIiF6/fk02NjaUnZ1N5ubmah/PgwcPSE9Pj/T09Oj69es0ffp02rlzJ6WnpxMR0Y4dO1gmdv/+/SVinos+Dqfkwu9MDoejEpS13IiIjh07Rk+fPqVevXpRhQoVKCgoiKpXr04LFy6kDRs20Nu3b1npktu3b9Pjx4+pSpUqrC7ct87Dhw/p2bNnZGRkRETvRdj58+epX79+dOXKFVYTTxUUVTvQysqKGjRoQPPnz6fWrVtTzZo16fjx49SxY0c6deoUhYSEUExMDO3atYu9Ryn6iFTjcuZwOOqBW/w4HI5KUAq5OXPm0K1bt0hDQ4Patm1LRO9FwebNm6lZs2Y0YsQIunjxIgUHB9Pdu3fpr7/+osuXL1NoaOg36eItCl9fX5o0aRLNmzePatSoQVeuXKFNmzaRt7c3O8+qcImLawfGxsbStWvXSFNTk9q2bUuTJ0+mqKgoOnnyJAUGBlLHjh2pdu3aRPQ+ZlNTU5PFa3I4nK8HHuPH4XBUxt9//00tW7YkW1tbatCgAcXGxhLR/wmM+/fvU8eOHen48eNE9L5ci1wup7CwsG+mZMu/Zf/+/eTv70+vX78mU1NTCg0NpZEjR6rs+GJ3bNu2benw4cMkCAJpamrS48ePaerUqdSlSxeysbGRFGBOTU2l+fPn07Zt22jjxo1UuXJllY2Jw+GoHy78OBzOJ1NULNfSpUtp4MCBREQUHx9PLVq0IKL/E385OTm0e/duSklJIQsLC3J2dmbxgDw2TMq9e/fo/v37JJfLycPDg4hUf4769+9P27Zto/DwcGrTpg3l5ubSoEGDKC4ujuLi4sjHx4dZBU+fPk2xsbG0cOFCmj17Ng0fPlxl4+BwOJ8HLvw4HM4nIXYTPn78mN6+fUu2trYkCAKtXbuWunfvTvXq1aNffvmFPD09iYjo3bt3JJPJijweF33/jKoznh89ekReXl7Url07GjNmDBkZGdH+/fupbdu25O/vT1OmTCEHBwciIlq0aBEtXbqUnj59SqNHj6YRI0aoZUwcDke98Bg/Dofzn8nPz2eir1+/fnTo0CF6+fIlOTk5UUhICHXt2pU0NDSoa9euNGHCBJo2bRrVrFmTZDLZB4UCF33/jKoF1oMHD+jKlSvk6+tLRkZGtHv3bvL19SV/f3+aMWMG2djYEBFRWloa1a1bl+7evUtNmzalli1bEhEX6xzO1wgXfhwO5z8BgC32Xbp0oZ07d1Lz5s1JoVDQ8ePHKSAggPr160eRkZGUn59P3bt3JyKi8PBwqlGjBrcOlSAcHR3JysqKTp8+TdnZ2eTr60vt27enOXPmkLW1NRERrVmzhoYOHUo3btygGTNmMIstF30cztcJF34cDudfI17sX7x4QVeuXKEZM2ZQnz59SEtLi96+fUsDBw6kqKgo0tLSooiICHrz5g3169ePcnNzadq0aVSnTp0v/C04SjQ1Nal8+fI0e/Zsevz4MbVv354WLlxIpqamRER08+ZN2rNnD3333XeUnZ1NZmZm7L1c9HE4Xydc+HE4nH+NcrH39fUlQ0ND0tbWpjZt2pCWlhbl5uaStrY2rVy5kgBQVFQUtWvXjvr06UNZWVk0fPhwysjI+MLfgCPG0NCQFi5cSI0aNaK8vDxq1aoVE3d37tyh6Oho2rJlCy1cuJDs7Oy+8Gg5HI4q4MkdHA7nP/H8+XMKCAighIQEkslktH37dqpfvz4JgsASPh4/fkzVq1en6tWr07Zt24iIKCUlhdzd3b/w6DlF8ffff5O/vz8ZGRmRt7c3lSlTho4dO0ZHjx6lSZMmsVI7PJGDw/n64bZ6DofznzA2NqZVq1ZR586dKTMzk7Zs2cLEgLJ7g7m5OZUrV45SU1Pp7du3RESs3lt+fv6XGTjngzRv3pyOHDlCNWvWpMOHD9Ovv/5Kenp6tGzZMib68vPzuejjcEoB3NXL4XD+MzY2NjRjxgzKysqiuXPnkrm5Of30009MGKSnp9Pr16/JxMSE8vPzJZYiHhtWMqlSpQqtW7eOZDIZvXz5kvT19VnmNk/k4HBKD1z4cTicT8La2poWLlxI+fn5NHbsWLp9+za1aNGCDAwMaNu2bZSUlERRUVGSrg+cko1cLidBEMjY2JiUUUDiLG4Oh/P1w2P8OBxOsXj48CEFBwfTxo0biYioYcOG9O7dO/L396dhw4YREY8N43A4nJICt/hxOJxiYWVlRfPnzye5XE5r166lhg0b0vjx49nr3E3I4XA4JQcu/DgcTrGxtrammTNnUlZWFk2cOJH09PQoJCSEiz4Oh8MpYXDhx+FwVIKNjQ0tXryYiIhGjRpFMpmMhg4d+oVHxeFwOBwxXPhxOByVYWVlRYsXLyZNTU0aPnw4yWQyGjBgwJceFofD4XD+P1z4cTgclWJlZUXz5s0jHR0d8vLy+tLD4XA4HI4IntXL4XDUQl5eHivozOFwOJySARd+HA6Hw+FwON8IPN2Ow+FwOBwO5xuBCz8Oh8PhcDicbwQu/DgcDofD4XC+Ebjw43A4HA6Hw/lG4MKPw+FwOBwO5xuBCz8Oh8PhcDicbwQu/DgcDofD4XC+Ef4fsewkbSOjSooAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Notas:\n",
" - La correlacion entre age y churn parece ser reelevante"
],
"metadata": {
"id": "ghF2-QavXTYp"
}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "Erup4BIUZ68m"
}
},
{
"cell_type": "markdown",
"source": [
"# Analizamos las correlaciones\n",
"\n",
"Utilizamos los valores como vienen, y tenemos este resultado:\n",
"\n",
"**churn: 1.000000**\n",
"\n",
"Esto es un autovalor, lo cual significa que churn está perfectamente correlacionado consigo mismo, lo cual siempre será 1.\n",
"\n",
"**age: 0.291932**\n",
"\n",
"Hay una correlación positiva moderada entre la edad del cliente y la probabilidad de abandono. Esto sugiere que a mayor edad, mayor es la probabilidad de que el cliente abandone el banco.\n",
"\n",
"**balance: 0.115943**\n",
"\n",
"Hay una correlación positiva débil entre el balance de la cuenta y la probabilidad de abandono. Esto sugiere que los clientes con balances más altos tienen una ligera tendencia a abandonar el banco.\n",
"\n",
"**balance_estimated_salary_ratio: 0.024168**\n",
"\n",
"Correlación muy débilmente positiva entre la proporción balance/salario estimado y la probabilidad de abandono. Esto indica que no hay una relación fuerte entre estas variables.\n",
"\n",
"**estimated_salary: 0.015666**\n",
"\n",
"Correlación muy débilmente positiva entre el salario estimado del cliente y la probabilidad de abandono. De nuevo, indica poca o ninguna relación.\n",
"\n",
"**credit_card: -0.005795**\n",
"\n",
"Correlación muy débilmente negativa entre tener una tarjeta de crédito y la probabilidad de abandono. Esto indica que tener una tarjeta de crédito tiene un efecto prácticamente nulo en la probabilidad de abandonar el banco.\n",
"\n",
"**tenure: -0.011790**\n",
"\n",
"Correlación muy débilmente negativa entre la permanencia del cliente en el banco y la probabilidad de abandono. Esto sugiere que cuanto más tiempo lleva un cliente con el banco, muy ligeramente menos probable es que abandone.\n",
"\n",
"**credit_score: -0.026729**\n",
"\n",
"Correlación débilmente negativa entre el puntaje crediticio y la probabilidad de abandono. Esto indica que un puntaje crediticio más alto está ligeramente asociado con una menor probabilidad de abandono.\n",
"\n",
"**products_number: -0.048045**\n",
"\n",
"Correlación débilmente negativa entre la cantidad de productos que tiene el cliente y la probabilidad de abandono. Esto sugiere que tener más productos con el banco está ligeramente asociado con una menor probabilidad de abandono.\n",
"\n",
"**age_tenure: -0.123400**\n",
"\n",
"Correlación negativa entre la combinación de la edad y la permanencia del cliente en el banco y la probabilidad de abandono. Esto indica que una combinación de mayor edad y mayor permanencia está asociada con una menor probabilidad de abandono.\n",
"\n",
"**active_member: -0.164081**\n",
"\n",
"Correlación negativa entre ser un miembro activo y la probabilidad de abandono. Esto sugiere que los miembros activos tienen menos probabilidades de abandonar el banco."
],
"metadata": {
"id": "oqNP_-4IYqBe"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "7fhDJGigKVVh"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Definir variables\n",
"X = df.drop('churn', axis=1)\n",
"y = df['churn']\n",
"\n",
"# Columnas numéricas y categóricas\n",
"num_cols = ['credit_score', 'age', 'tenure', 'balance', 'products_number', 'estimated_salary']\n",
"cat_cols = ['country', 'gender', 'credit_card', 'active_member']\n",
"\n",
"# Preprocesamiento para columnas numéricas\n",
"num_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"# Preprocesamiento para columnas categóricas\n",
"cat_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"# Combinación de transformadores\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', num_transformer, num_cols),\n",
" ('cat', cat_transformer, cat_cols)\n",
" ]\n",
")"
],
"metadata": {
"id": "1Lfo9rd3QZ97"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Separamos en train y split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
],
"metadata": {
"id": "r6mHNbb-QvWc"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"# Aplicar preprocesador a los datos de entrenamiento\n",
"X_train_processed = preprocessor.fit_transform(X_train)\n",
"\n",
"# Inicializar PCA sin especificar n_components para obtener todos los componentes\n",
"pca = PCA()\n",
"pca.fit(X_train_processed)\n",
"\n",
"# Calcular la varianza explicada acumulada\n",
"explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
"\n",
"# Graficar la varianza explicada acumulada\n",
"plt.figure(figsize=(8, 5))\n",
"plt.plot(np.arange(1, len(explained_variance) + 1), explained_variance, marker='o')\n",
"plt.xlabel('Número de componentes')\n",
"plt.ylabel('Varianza explicada acumulada')\n",
"plt.title('Varianza explicada por el PCA')\n",
"plt.grid()\n",
"plt.show()\n",
"\n",
"# Decidir cuántos componentes utilizar (por ejemplo, donde se alcanza el 95% de la varianza explicada)\n",
"n_components = np.argmax(explained_variance >= 0.95) + 1\n",
"print(f'Número óptimo de componentes que explican al menos el 95% de la varianza: {n_components}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 514
},
"id": "B8-zC5ccQgkt",
"outputId": "2477592a-7d31-4d59-f607-8335b4369ce0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x500 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Número óptimo de componentes que explican al menos el 95% de la varianza: 10\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## PCA\n",
"\n",
"Para decidir cuantos cuantos componentes vamos a pedir al utilizar PCA, creamos un PCA sin numero de componentes para poder luego calcular la varianza acumulada y graficarla.\n",
"\n",
"Vemos que el número de componentes no se reduce tanto en relación a la cantidad de variables. Sin embargo hay algunos casos en los que utilizar PCA puede ser recomendado ya que elimina ruido y ayuda a manejar la colinealidad(cuando dos o más variables estan fuertemente correlacionadas). Sin embargo, Random Forest, o Decision Tree no se ven beneficiados por esto, por lo que si decidimos ir por esos algoritmos concluímos que el PCA no será necesario."
],
"metadata": {
"id": "UQbkQxRSS0mp"
}
},
{
"cell_type": "code",
"source": [
"# Intento (1) con PCA[10] + GradientBoosting(800)\n",
"# =============================================================================\n",
"\n",
"# Definir variables\n",
"X = df.drop('churn', axis=1)\n",
"y = df['churn']\n",
"\n",
"# Columnas numéricas y categóricas\n",
"num_cols = ['credit_score', 'age', 'tenure', 'balance', 'products_number', 'estimated_salary']\n",
"cat_cols = ['country', 'gender', 'credit_card', 'active_member']\n",
"\n",
"# Preprocesamiento para columnas numéricas\n",
"num_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"# Preprocesamiento para columnas categóricas\n",
"cat_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"# Combinación de transformadores\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', num_transformer, num_cols),\n",
" ('cat', cat_transformer, cat_cols)\n",
" ]\n",
")\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Pipeline que incluye preprocesamiento y modelo\n",
"model = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('pca', PCA(n_components=10)),\n",
" ('classifier', GradientBoostingClassifier(n_estimators=800, random_state=42))\n",
"])\n",
"\n",
"# Entrenar el modelo\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Hacer predicciones\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Evaluar el modelo\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"# Curva de Precisión y Recall\n",
"y_proba = model.predict_proba(X_test)\n",
"precision, recall, thresholds = precision_recall_curve(y_test, y_proba[:,1])\n",
"\n",
"plt.figure(figsize=(10, 6))\n",
"plt.plot(recall, precision, marker='o', label='Precision-Recall Curve')\n",
"plt.xlabel('Recall')\n",
"plt.ylabel('Precision')\n",
"plt.title('Precision-Recall Curve')\n",
"plt.legend()\n",
"plt.show()"
],
"metadata": {
"id": "wfEGXbM_Q0wR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 729
},
"outputId": "975fd302-5adb-4301-ce1b-4898cc8c20fa"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.87 0.96 0.91 1265\n",
" 1 0.74 0.47 0.57 335\n",
"\n",
" accuracy 0.85 1600\n",
" macro avg 0.80 0.71 0.74 1600\n",
"weighted avg 0.84 0.85 0.84 1600\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1000x600 with 1 Axes>"
],
"image/png": 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r188wm83Gn3/+aRiGYZw6dcoYOnSo0ahRI8Pf39+oXbu2cf311xtvvfWWkZuba78uPz/fGDt2rNG0aVPD39/fqFOnjnH77bcbv/32W6GfZYsWLYzAwECjQYMGxpgxY4xPPvnEkGQkJyfbzytvK3abw4cPG0899ZTRpEkTIzAw0AgODjauvfZa47XXXjMyMzPt5xUUFBjPP/+8Ubt2bSM4ONjo0qWL8eeff5bYir20z9ySJUsMSYbJZDIOHjxY7Dl79+41+vTpY0RHRxt+fn7GpZdeavzjH/8w5s2b59D7AgBvZjKMUtZ1AAAAAAAcwjNXAAAAAOAChCsAAAAAcAHCFQAAAAC4AOEKAAAAAFyAcAUAAAAALkC4AgAAAAAXYBPhElitVh0+fFihoaEymUyeLgcAAACAhxiGoVOnTik2NlY+PiXPTxGuSnD48GHVq1fP02UAAAAA8BIHDx5U3bp1S3ydcFWC0NBQSed+gGFhYR6uBgAAAICnZGVlqV69evaMUBLCVQlsSwHDwsIIVwAAAAAu+rgQDS0AAAAAwAUIVwAAAADgAoQrAAAAAHABnrkCAACo4gzDUH5+vgoKCjxdCuCVzGazfH19nd6CiXAFAABQheXm5io1NVVnz571dCmAVwsODlZMTIz8/f3LPQbhCgAAoIqyWq1KTk6W2WxWbGys/P39nf6TeaCqMQxDubm5Onr0qJKTk9W4ceNSNwouDeEKAACgisrNzZXValW9evUUHBzs6XIArxUUFCQ/Pz/t379fubm5CgwMLNc4NLQAAACo4sr7p/BAdeKKf0/4Nw0AAAAAXIBwBQAAAAAuQLgCAADARRVYDa3de1xfbz6ktXuPq8BqeLoktzCZTPrf//7n8nMrux9//FEmk0kZGRmSpGnTpikiIsKjNXkjwhUAAABKtXhbqm4cs1w9P/pFg+dsVs+PftGNY5Zr8bZUt92zX79+MplMMplM8vf3V6NGjfTKK68oPz/fbfeUpNTUVN1+++0uP9cZDRo0sP8sgoOD1bx5c3388cduv68rrFixQl27dlWtWrUUHBysZs2a6ZlnntGhQ4c8XZpbeF24On36tIYPH67ExERFRkbKZDJp2rRpDl+fkZGhAQMGqE6dOqpRo4Y6duyojRs3uq9gAACAKmzxtlQ9NmujUjNzCh1Py8zRY7M2ujVgJSYmKjU1VXv27NEzzzyjESNGaOzYscWem5ub65J7RkdHKyAgwOXnOuuVV15Ramqqtm3bpt69e+uRRx7Rd999VyH3Lq/JkyfrtttuU3R0tL788ktt375dH3zwgTIzM/X222+Xe1xX/bN2B69rxX7s2DG98sorql+/vq6++mr9+OOPDl9rtVp1xx13aMuWLXr22WdVu3Ztvf/++7r55pv122+/qXHjxu4r3E0KrIbWJ59QWlaOTpy2KCLYXxlncy/618ga/ooKC5QMKf20pUzXunOs0saMrOGv6PAgtYmLlNmHPTgAAHAHwzCUnVfg0LkFVkPDF/yh4hYAGpJMkkYs2K4bGtW+6P+7g/zMZd5jKyAgQNHR0ZKkxx57TPPnz9eCBQs0dOhQ9evXTxkZGWrdurXee+89BQQEKDk5WQcPHtQzzzyjH374QT4+Prrppps0YcIENWjQwD7uJ598orffflt//vmnIiMjde+992rixImSzi31mz9/vv75z38qNzdXTz/9tL788kudPHlSl1xyiR599FENHTq0yLmStHXrVg0ePFhr165VcHCw7r33Xo0bN04hISGSZK/5xhtv1Ntvv63c3Fzdf//9euedd+Tn51fqzyI0NNT+s3j++ef15ptvasmSJfaZs4yMDA0ZMkRff/21LBaLrrvuOo0fP15XX321fYyFCxfqlVde0datWxUSEqKbbrpJ8+fPlyTNnDlTEyZM0K5du1SjRg3dcssteueddxQVFVWmf2Y2f/31l5588kk9+eSTGj9+vP14gwYN1L59e/vywhEjRuh///ufNm/ebD/nnXfe0TvvvKOUlJRCP7fz/1n37NlTy5Yt07p16wrd9+qrr9a9996rl19+WZL08ccf6+2331ZycrIaNGigJ598Uo8//ni53pMjvC5cxcTEKDU1VdHR0fr111/VunVrh6+dN2+efv75Z82dO1dJSUmSpO7du6tJkyYaPny4Pv30U3eV7RaLt6Vq5MLtRf6kqKqLCQ/U8G7NlBgf4+lSAACocrLzCtTs5e9dMpYhKS0rR81H/HDRc7e/0kXB/s796hkUFKTjx4/bv1+2bJnCwsK0ZMkSSVJeXp66dOmidu3aafXq1fL19dWoUaOUmJio33//Xf7+/po0aZKefvppvfHGG7r99tuVmZmpNWvWFHu/d999VwsWLNAXX3yh+vXr6+DBgzp48GCx5545c8Z+7w0bNig9PV0PP/ywBg0aVGgV1ooVKxQTE6MVK1bozz//VI8ePdSyZUs98sgjDv0MrFar5s+fr5MnT8rf399+/L777lNQUJC+++47hYeHa/Lkybr11lu1e/duRUZG6ttvv9Xdd9+t//znP5oxY4Zyc3O1aNEi+/V5eXl69dVXdcUVVyg9PV1PP/20+vXrV+icspg7d65yc3P13HPPFft6WZ/XuvCftSSNHj1ae/fuVcOGDSVJf/zxh37//Xd9+eWXkqTZs2fr5Zdf1sSJE3XNNddo06ZNeuSRR1SjRg317du3XO/rYrwuXJ3/JxRlNW/ePF1yySW655577Mfq1Kmj7t27a9asWbJYLBU2dess2xR81XxUtHSp/3+ZwaTerQhYAABAhmFo2bJl+v777/XEE0/Yj9eoUUMff/yxPWTMmjVLVqtVH3/8sX2WbOrUqYqIiNCPP/6ozp07a9SoUXrmmWc0ePBg+zgl/WH+gQMH1LhxY914440ymUy67LLLSqzx008/VU5OjmbMmKEaNWpIkiZOnKhu3bppzJgxuuSSSyRJNWvW1MSJE2U2m9W0aVPdcccdWrZs2UXD1fPPP6+XXnpJFotF+fn5ioyM1MMPPyxJ+umnn7R+/Xqlp6fbf9d966239L///U/z5s3TgAED9Nprr+n+++/XyJEj7WOeP6v1f//3f/a/v/zyy/Xuu++qdevWOn36tH3mrSz27NmjsLAwxcS45ne5C/9ZS+fq//TTTzVs2DBJ58JUQkKCGjVqJEkaPny43n77bXs2iIuL0/bt2zV58uTqE66csWnTJrVq1arIBmBt2rTRhx9+qN27d6t58+Yeqs5xBVZDIxdur5bBysaQNHLhdnVqFs0SQQAAXCjIz6ztr3Rx6Nz1ySfUb+qGi5437aHWahMXedH7ltU333yjkJAQ5eXlyWq16oEHHtCIESPsrzdv3rzQL9tbtmzRn3/+qdDQ0ELj5OTkaO/evUpPT9fhw4d16623OnT/fv36qVOnTrriiiuUmJiof/zjH+rcuXOx5+7YsUNXX321PVhJ0g033CCr1apdu3bZw9VVV10ls/nvn0VMTIy2bt0qSXr99df1+uuv21/bvn276tevL0l69tln1a9fP6WmpurZZ5/V448/bg8RW7Zs0enTp1WrVq1CNWVnZ2vv3r2SpM2bN5ca4H777TeNGDFCW7Zs0cmTJ2W1WiWdC5jNmjVz6Od1PsMwyrwMtDQX/rOWpF69eumTTz7RsGHDZBiGPvvsMz399NOSzs0k7t27V/379y/0vvPz8xUeHu6yui5UpcJVamqq2rdvX+S4LTEfPny4xHBlsVhksVjs32dlZbmnSAesTz5R7ZYCFic1M0frk0+oXcNaFz8ZAAA4xGQyObw876bGdRQTHqi0zJxi/9DXJCk6PFA3Na7jlj8M7dixoyZNmiR/f3/FxsbK17dw3ecHGelcY7Rrr71Ws2fPLjJWnTp1ivwB/MW0atVKycnJ+u6777R06VJ1795dt912m+bNm1f2N/P/XfhslclksgeZRx99VN27d7e/Fhsba//72rVrq1GjRmrUqJHmzp2r5s2b67rrrlOzZs10+vRpxcTEFNurwLb8LigoqMSabEsau3TpotmzZ6tOnTo6cOCAunTpUu7mEU2aNFFmZqZSU1NLnb3y8fGRYRT+dOXl5RU578J/1pLUs2dPPf/889q4caOys7N18OBB9ejRQ9K5z4IkffTRR0pISCh03fnh1tW8rlugM7Kzs4td9hcYGGh/vSSjR49WeHi4/atevXpuq/Ni0k8RrGz4WQAA4DlmH5OGdzs3a3FhdLJ9P7xbM7etMqlRo4YaNWqk+vXrFwlWxWnVqpX27NmjqKgoexCxfYWHhys0NFQNGjTQsmXLHK4hLCxMPXr00EcffaTPP/9cX375pU6cOFHkvCuvvFJbtmzRmTNn7MfWrFkjHx8fXXHFFQ7dKzIyslDNJb3nevXqqUePHvbGGq1atVJaWpp8fX2LvO/atWtLklq0aFHi+965c6eOHz+uN954QzfddJOaNm2q9PR0h2ouSVJSkvz9/fXmm28W+7qtoUWdOnWUlpZWKGCd39yiNHXr1lWHDh00e/ZszZ49W506dbI34LjkkksUGxurffv2FfmZxMXFOfXeSlOlwlVQUFCh2SebnJwc++slGTp0qDIzM+1fJT2sWBGiQgM9dm9vw88CAADPSoyP0aTerRQdXvj/ydHhgV73fHSvXr1Uu3Zt3XXXXVq9erWSk5P1448/6sknn9Rff/0l6Vx3urffflvvvvuu9uzZo40bN+q///1vseONGzdOn332mXbu3Kndu3dr7ty5io6OLrYZQ69evRQYGKi+fftq27ZtWrFihZ544gk9+OCD9iWBrjR48GAtXLhQv/76q2677Ta1a9dO//znP/XDDz8oJSVFP//8s/7zn//o119/lXTu+aPPPvtMw4cP144dO7R161aNGTNGklS/fn35+/vrv//9r/bt26cFCxbo1Vdfdaq+evXqafz48ZowYYL69++vlStXav/+/VqzZo0GDhxoH//mm2/W0aNH9eabb2rv3r167733ytRivlevXpozZ47mzp2rXr16FXpt5MiRGj16tN59913t3r1bW7du1dSpUzVu3Din3ltpqlS4snUavJDt2PlTqxcKCAhQWFhYoS9PaRMXqZjwwCJ/QlTdxIQHXnT9NgAAcL/E+Bj99Pwt+uyRtppwf0t99khb/fT8LV4VrCQpODhYq1atUv369XXPPffoyiuvVP/+/ZWTk2P/3a5v375655139P777+uqq67SP/7xD+3Zs6fY8UJDQ/Xmm2/quuuuU+vWrZWSkqJFixYVu7wwODhY33//vU6cOKHWrVsrKSlJt956q73Fu6s1a9ZMnTt31ssvvyyTyaRFixapffv2euihh9SkSRPdf//92r9/vz3Y3XzzzZo7d64WLFigli1b6pZbbtH69eslnZs9mjZtmubOnatmzZrpjTfe0FtvveV0jY8//rh++OEHHTp0SHfffbeaNm2qhx9+WGFhYRoyZIikczN+77//vt577z1dffXVWr9+vf01RyQlJen48eM6e/asvSW+zcMPP6yPP/5YU6dOVfPmzdWhQwdNmzbNrTNXJuPCRY5exNaKferUqerXr99Fz7/vvvu0evVqHT58uNCHfsCAAZo9e7ZOnDjhcLfArKwshYeHKzMz0yNBqzp3C5TOLTXwtj8NAwCgssnJyVFycrLi4uLsj0kAKF5p/744mg0q7cxVamqqdu7cWeiBt6SkJB05ckRfffWV/dixY8c0d+5cdevWrdK0YZf+noKPCa9+/yGM8cJlBgAAAMDFeGW3wIkTJyojI0OHDx+WdG43ads62SeeeELh4eEaOnSopk+fbt9tWToXrtq2bauHHnpI27dvV+3atfX++++roKCgUE//yiIxPkadmkVrffIJpWXl6MRpiyKC/ZVxNveif42s4a+osEDJkNJPW8p0rTvHKm7MhVsPa876g7oyOkQvd4tXm7hI2q8DAACg0vHKcPXWW29p//799u+/+uor+2xU7969S+xNbzabtWjRIj377LN69913lZ2drdatW2vatGkOd2nxNmYfU5VvRX4oM1tz1h9UdHhQlX+vAAAAqLq8MlylpKRc9Jxp06Zp2rRpRY7XrFlTH3/8sT7++GPXFwYAAAAAJai0z1wBAADAMV7cvwzwGq7494RwBQAAUEX5+flJks6ePevhSgDvZ/v3xPbvTXl45bJAAAAAOM9sNisiIkLp6emSzu3FZDLRNAo4n2EYOnv2rNLT0xURESGz2VzusQhXAAAAVVh0dLQk2QMWgOJFRETY/30pL8IVvE6B1dD65BNKP5WjqNBAWrMDAOAEk8mkmJgYRUVFFdofFMDf/Pz8nJqxsiFcwWsYhqEJS3dr6poUZWT//R//mPBADe/WjE2FAQBwgtlsdskvjwBKRkMLeJz1/3dmWbXnmMYv3VMoWElSWmaOHpu1UYu3pXqiPAAAAMAhhCt41OJtqXpl4XZJkrWE7pe2wyMXbldBSScBAAAAHka4gscs3paqx2Zt1Nncgouea0hKzczR+uQT7i8MAAAAKAfCFTyiwGpo5MLtKus8VPqpHLfUAwAAADiLcAWPWJ98QqmZZQ9KUaGBbqgGAAAAcB7hCh5RnhmomPBzbdkBAAAAb0S4gkeUZwZqeLdm7HcFAAAAr0W4gke0iYtUTHigHIlKEcF++qB3K/a5AgAAgFcjXMEjzD4mDe/WTJJKDVhP3dZEv73UiWAFAAAAr0e4gsckxsdoUu9Wig4vvEQwNNBXkhQZ7KfBtzVmKSAAAAAqBV9PF4DqLTE+Rp2aRWt98gmln8pRVGigwoP81PXd1TKZCFUAAACoPAhX8Dizj0ntGtayf78r7ZQHqwEAAADKh2WBAAAAAOAChCsAAAAAcAHCFQAAAAC4AOEKXqfAakiSLPkFWrv3uP17AAAAwJsRruBVFm9LVZ9P1kmSTlsK1POjX3TjmOVavC3Vw5UBAAAApSNcwWss3paqx2Zt1LHTuYWOp2Xm6LFZGwlYAAAA8GqEK3iFAquhkQu3q7gFgLZjIxduZ4kgAAAAvBbhCl5hffIJpWbmlPi6ISk1M0frk09UXFEAAABAGRCu4BXST5UcrMpzHgAAAFDRCFfwClGhgS49DwAAAKhohCt4hTZxkYoJD5SphNdNkmLCA9UmLrIiywIAAAAcRriCVzD7mDS8W7NiX7MFruHdmsnsU1L8AgAAADyLcAWvkRgfo0m9W6l2iH+h49HhgZrUu5US42M8VBkAAABwcb6eLgA4X2J8jOpH1lDXd1crJMCsj/q0Vpu4SGasAAAA4PUIV/A6tiAV4GtWu4a17McLrIbWJ59Q+qkcRYUGEroAAADgVQhXqBQWb0vVyIXbC+2FFRMeqOHdmrFcEAAAAF6BZ67g9Rb9fliPztpYZJPhtMwcPTZroxZvS/VQZQAAAMDfCFfwWoZh6J0lu/WvTzcV//r//+vIhdtVYDWKPQcAAACoKIQreK2TZ/P0zrI9Ki02GZJSM3O0PvlERZVlV2A1tHbvcX29+ZDW7j1OwAMAAKjmeOYKXmfVnqOSVGqoulD6qZxij7ujCUaB1dDE5Xs0dU2KMrLz7Md5BgwAAKB6I1zBqyz6PVWjF+0o83VRoYFFjrm6CYYtVE1etU9ncwuKvG57Bow9uQAAAKonr1wWaLFY9Pzzzys2NlZBQUFKSEjQkiVLHLp2zpw5atWqlQIDA1WnTh31799fx44dc3PFcIXF21L1+KcbVdbVdTHh52akzufKJhgFVkMTlu5W8xHfa/zSPcUGK4lnwAAAAKo7rwxX/fr107hx49SrVy9NmDBBZrNZXbt21U8//VTqdZMmTVLPnj0VGRmpcePG6ZFHHtGcOXN06623Kien+GVj8A4FVkMjF24v17XDuzWzL/UrsLquCYajoerC8T31DBgAAAA8y+uWBa5fv15z5szR2LFjNWTIEElSnz59FB8fr+eee04///xzsdfl5ubqxRdfVPv27bVkyRKZTOd+2b7++uvVrVs3ffTRR3riiScq7H2gbNYnnygyy3QxPiZpYs9r1KlZtNbuPa6l29M059eDOmMpPQSdH4DO36RY+vsZLUfHKklJz4ABAACg6vK6cDVv3jyZzWYNGDDAfiwwMFD9+/fXiy++qIMHD6pevXpFrtu2bZsyMjLUo0cPe7CSpH/84x8KCQnRnDlzCFderDxhZGLPVvLxkW4cs7zMwezCe5bUpKK8insGDAAAAFWb14WrTZs2qUmTJgoLCyt0vE2bNpKkzZs3FxuuLBaLJCkoKKjIa0FBQdq0aZOsVqt8fLxyJWS1V9Yw8myXKyQZenRW8cv/HL3nxZpUlEdEsJ+sVkMFVsPpzoQAAACoPLwuaaSmpiompminNduxw4cPF3td48aNZTKZtGbNmkLHd+3apaNHjyo7O1snT54s8b4Wi0VZWVmFvlBx2sRFKiY8UKVFEZNJCg0wS5L2HztT4nNVjogJD9Qv+46V6XkqR2WczVOvKet045jlZWqcAQAAgMrN68JVdna2AgICihwPDAy0v16c2rVrq3v37po+fbrefvtt7du3T6tXr1aPHj3k5+dX6rWSNHr0aIWHh9u/ipsdg/uYfUwa3q2ZJJUYsN7r2UoBfufC1Re//VWmfbAulJmdpwnL/nRpqLpQeToTAgAAoPLyunAVFBRkX+J3Plu3v+KW/dlMnjxZXbt21ZAhQ9SwYUO1b99ezZs3V7du3SRJISEhJV47dOhQZWZm2r8OHjzo5DtBWSXGx2hS71aKDi+8RDAmPFAf9G4lydCx07lO3cP2OJ47Q5VNaZ0JC6yG1u49rq83H9Lavcdp3Q4AAFAFeN0zVzExMTp06FCR46mp5/70PzY2tsRrw8PD9fXXX+vAgQNKSUnRZZddpssuu0zXX3+96tSpo4iIiBKvDQgIKHbGDBUrMT5GnZpFa33yCaWfylFU6Lk9rL7flqZBn5V/GaCNUcEZ5sLOhCU1znBmc2MAAAB4B68LVy1bttSKFSuUlZVVqKnFunXr7K9fTP369VW/fn1JUkZGhn777Tfde++9bqkXrmf2MRVqkW7bXLgyW/PnsVLbu9uWEE7q3YqABQAAUEl53bLApKQkFRQU6MMPP7Qfs1gsmjp1qhISEuzPQh04cEA7d+686HhDhw5Vfn6+nnrqKbfVDPdxZnNhV4sI9lNSq0vLde3EFX9qypqUEvfNKsvmxgAAAPBOXjdzlZCQoPvuu09Dhw5Venq6GjVqpOnTpyslJUVTpkyxn9enTx+tXLlSxnnrvN544w1t27ZNCQkJ8vX11f/+9z/98MMPGjVqlFq3bu2JtwMnlXdz4Zub1NbyXcecvn9IgK+6X1dXnZpFq01cpL75/bDmbSy6bNUVStvcGAAAAN7P68KVJM2YMUPDhg3TzJkzdfLkSbVo0ULffPON2rdvX+p1zZs31/z587VgwQIVFBSoRYsW+uKLL3TfffdVUOVwtfJsLjzh/mv0+qIdTt032N+sge0batAtjQrtVVURmwOv+fOY2sRFskcWAABAJWMyjIp+xL9yyMrKUnh4uDIzM4tsaIyKs3bvcfX86BeHzo0I9tMb9zRXeJC/w9dcqKRQZVNgNXTjmOVKy8xxqhX8xVyswUWB1SjS9IMwBgAA4B6OZgOvnLkCbGybC5cWZkwm6clbGuvJWxvL7GPS15vLvmzvwjFKYtuP67FZG2WS3BawSmpwQbdBAAAA7+V1DS2A8zm6ufBTnZrYQ1F5lu5dOEZpStqPy5UubHBRYDU0YeluNR/xvcYv3VMoWElsWAwAAOANWBZYApYFepfF21I1cuH2Qs0tSpqtKcvSPdtSwvLM+BRYDU1bk6xXv3Xu+a6LSWp1qRZtS7voxscmSdHhgfrp+VtYIggAAOBCLAtElVLS5sLFhQhHlu5d7NkqR5h9TKod6v6Npx3tTki3QQAAAM8iXKHSuHBz4dLYlu5dONsVEeynh66PcypUna8iugeWVXk6LAIAAMB5hCtUWWWZ7SovRxpu2Pj7+ig33+qye5fEGwMfAABAdUBDC1Rpttmuu1peqnYNa7n8WSRHGm4E+5v11G1NNLWv+zeyjgk/FyABAABQ8Zi5Apzk6BLEAqvh8CxXeQ3v1qxIgGRPLAAAgIpBuAJcwJEliBW1R5YNe2IBAABULFqxl4BW7HCX4trKu4KtFfvKZztq0o9/avKqfcW2b7fFvQs3KAYAAEDxHM0GhKsSEK7gTgVWQ+OX7NLEFXtdPnaAr48sF2mcwZ5YAAAAjnM0G9DQAvAAs49JNzSq45axLxaspMJ7YgEAAMA1CFeAh9jauDsybxTsb1ZSq0tdXsPS7WkuHxMAAKC6IlwBHlKWNu5bR3TRmKSrFRPu2j2spqxJ0eJtqS4dsyQFVkNr9x7X15sPae3e4yqwsiIZAABULXQLBDzI0TbuNndeHaPJq5JdWsPIhdvVqVm02569KmvXQlrHAwCAyopwBXiYI23cpXOhY8EW188y2Z69atewVonn2AJPWlaOTpy2KLKGv6LDg0oMPrbzl25P05xfD+qMpWjXwrTMHD02a6Pee6CVatbwV1pWjtbsOaol248oMyfffh6t4wEAQGVBuAK8gNnHVGq4kaT1ySdc3r7dJv1U8eOWNOtkc2Hwudj557MtCvzXpxtL3fPLFsJoHQ8AALwd4QqoJEoKQK6QcuxMoe9tIamkvbJsUs+bfdqTfuqi5xfnYk9e2V539/JFAAAAZxGugEoiKtS1zSzON37pHl0RHapOzaIdClXnMyQ9/ulGt9Vmk5qZo4nL92jwbU3cfi8AAIDyIFwBlYStdXtaZs5FZ3vK45kvtsjQZp3Nvfg+WZ5yfgik6QUAAPA2JsMw6IdcDEd3YQYq0uJtqXps1rlZopL+xbV1GmwcVUP/+nSTW4KYJ9XwN8vf16STZ2l6AQAAKoaj2YBwVQLCFbzV4m2pRVq316rhr7taxqpTs+hCszgTlu7R+KW7PVVqhbHNWdH0AgAAuIOj2YBlgUAl42jrdklqUDvYAxVWPEPnAhZNLwAAgCcRroBKyJHW7ZJ7m2B4G0OO7dkFAADgLj6eLgCA+9iaYFSneRx3tqwHAAAoDeEKqMLMPiYN79bMY00tgv3NCgkwV+g9q9NsHQAA8C6EK6CKS4yP0f/d0KBC7xnsb9ZTtzXR1hFd9NZ9V8sklTh7Zjv3/Qeucfq+Pibp2stqOj0OAABAeRCugGqgU7Pocl0XEexnDz6lBaQLz986oosG39ZYZh+TEuNjNKl3K0WHB5Z6btcWsXrKyQ2CrYb02/6TTo0BAABQXjS0AKqBsmxAHBLgq+7X1S3S1n2Sj6lIC/iIID/ddmWUbmhUW9HhQSV2LXS0w+GgWxrps/X7lZZlKfd7deSZqwKrwSbEAADA5QhXQDVge/bqsVkbZVLxGxAH+5s1sH1DDbqlkVMBqbQaLtbFz+xj0og7r9JjszaW+zmxY6csKrAaxdZVYDU0cfkeTV2ToozsPPtxNiEGAACuwCbCJWATYVRFxW1AHBHsp4eujysxVHlCcXWWhS0s2cJgWlaO1uw5qm+3pio7z1rkfNu7/vdtTdSgdjCzWQAAoBBHswHhqgSEK1RVlWVJnK3OtKwcnTht0fEzuXr/x70OXWubnQv2N+tsbkG57u/obFZl+XkCAIDyczQbsCwQqGYc3YDY0y6s8+vNhxy+1vYnRuUNVpKUlpmjR2dt1P/d0KDI82cSSwwBAEBRzFyVgJkrwLus3XtcPT/6xWP3P3+p4cTlezR51b5iw5stfk3q3arEgMVsFwAAlQszVwCqlGsvqykf07l2656Q+v9nsvzNJuUWlFyEoXMBa+TC7erULNrls10EMwAAvBfhCkCl8Nv+kx4LVucrLVjZGDoXxn7Zd1w+JtNFG2qkZebosVkbLzrbxTJEAAC8G+EKQKXgyP5V3uaR6b/qbN7Fn/u6cLZLUqFmHn+dPKsvfvtLZyxFx3IkmAEAgIpBuAJQKUSFBnq6hDJzJFjZ2Ga7np+3RUt3pBeanbrYdSUtQwQAABXLx9MFAIAj2sRFKiY8UFU9OszbeMjhYGVjC2brk0+4pygAAOAQwhWASsHsY9Lwbs0kqcoHrPKqjEsnAQCoSrwyXFksFj3//POKjY1VUFCQEhIStGTJEoeuXbp0qTp27KjatWsrIiJCbdq00cyZM91cMYCKkBgfo0m9Wyk6vGxLBIP9zUpqdanGd79aT93WWNFhAW6q0LOSj572dAkAAFRrXrnPVc+ePTVv3jz9+9//VuPGjTVt2jRt2LBBK1as0I033ljidQsWLNA///lPtWvXTj179pTJZNIXX3yhVatWady4cXrqqaccroF9rgDvZWtHvnR7mqasSSnxvGB/swa2b6hBtzQq0hLd1s48+ehpvbPszwqoumI8dP1l6nxVDC3aAQBwIUezgdeFq/Xr1yshIUFjx47VkCFDJEk5OTmKj49XVFSUfv755xKv7dy5s/744w/t27dPAQHn/mQ6Pz9fTZs2VY0aNbRlyxaH6yBcAZXD4m2pGrlwu1Iz/14SFxHsp4eujysSqsoyRmVHi3YAAFyn0m4iPG/ePJnNZg0YMMB+LDAwUP3799eLL76ogwcPql69esVem5WVpZo1a9qDlST5+vqqdu3abq8bgGckxseoU7NopzbWPX+MH/5I1dSf97ux4oqRSot2AAAqnNeFq02bNqlJkyZFEmGbNm0kSZs3by4xXN18880aM2aMhg0bpr59+8pkMunTTz/Vr7/+qi+++KLU+1osFlksFvv3WVlZTr4TABXF7GNSu4a1XDJGu4a1lHB5raKzYUF+uu3KKN3QqLZOnMnVq9/ucLZstzNUeov285dHlieUAgCAwrwuXKWmpiompuifstqOHT58uMRrhw0bpuTkZL322msaNWqUJCk4OFhffvml7rrrrlLvO3r0aI0cOdKJygFUFRebDSuwGvr4p2SlZebIkXXVEUF+urpeuFbuPubewotha9FuC5/nP682f/MhnTjzd9t3lhICAOAcrwtX2dnZhZb12QQGBtpfL0lAQICaNGmipKQk3XPPPSooKNCHH36o3r17a8mSJWrbtm2J1w4dOlRPP/20/fusrKwSZ8gAVH2lzYbZ2sI/NmujTFKxASskwFfdr6urTs2i1SYuUpJ045jlDgcy6VxDjq7x0YoKC9T7P+4t1/uQpJ/2HFVaVo7W7DmqJduPKDMnv9jzUjNz9OisjXr/gVbq2oKABQBAWXlduAoKCiq0PM8mJyfH/npJBg0apF9++UUbN26Uj8+5LvPdu3fXVVddpcGDB2vdunUlXhsQEFBsqAOA4tjawpelmYYtkF3MhV0OC6yG5m86VO6GG++VMZgN+myjnjjSWA1q19CJ0xZF1vBXdHgQywYBALgIrwtXMTExOnToUJHjqampkqTY2Nhir8vNzdWUKVP03HPP2YOVJPn5+en222/XxIkTlZubK39/f/cUDqDaKWszjZICmU1Jwcw2U/aoA8HMFayGNGHZniLHWTYIAEDpvC5ctWzZUitWrFBWVlahpha2WaeWLVsWe93x48eVn5+vgoKCIq/l5eXJarUW+xoAOKOszTTOD2RpWTkOzwwlxsfo/QdaadBnG2X10AYadCAEAKB0XrfP1bp169S2bdtC+1xZLBbFx8erVq1a+uWXXyRJBw4c0NmzZ9W0aVNJUkFBgWrXrq2oqCht3brVPkN1+vRpXXnllQoJCdGOHY5392KfKwDeaNHvh/X4p5s8WkNMeKB+ev4WlggCAKqNSrvPVUJCgu677z4NHTpU6enpatSokaZPn66UlBRNmTLFfl6fPn20cuVK2bKh2WzWkCFD9NJLL6lt27bq06ePCgoKNGXKFP3111+aNWuWp94SALhM1xax+sDH5NFNjy/sQAgAAM7xunAlSTNmzNCwYcM0c+ZMnTx5Ui1atNA333yj9u3bl3rdf/7zH8XFxWnChAkaOXKkLBaLWrRooXnz5unee++toOoBwL1sSwtHLNimmb8c8EgNn284oLTMbBpdAABwHq9bFugtWBYIwNvN3/iXnvpii6fLoNEFAKDKczQb+JT4CgDAq0WHl7w1RUWyNbpYvC3V06UAAOBRhCsAqKTaxEUqJjzQ02VIOreR8siF21XgqVaGAAB4AcIVAFRStv2vHHnaKdjfrKRWl2p896v12SNtNfH+a+Tqx6RsjS4AAKiuvLKhBQDAMeXdmFiSfHzk8rbui7YeliSaXAAAqiUaWpSAhhYAKpMCq1HmjYklafG2VLe0dafJBQCgKnE0GxCuSkC4AlBd2IJZ+qkc7TlyShNX7HV6TFucm9S7FQELAFDp0S0QAOAQs49J7RrW0l0tL9UNjeq4ZExDNLkAAFQ/hCsAgJ2tA6GrnpaiyQUAoDohXAEA7GwdCCW5LGClZbn2eS4AALwV4QoAUIitA2G0i/bQOnHa4pJxAADwdrRiBwAUkRgfo07Nol3S6CKyhr+LqwMAwDsxcwUAKJarGl1EhblmBgwAAG9HuAIAXJRTjS5oFggAqCYIVwCAi3Km0UU6z1wBAKoJwhUAwCHlbXRBQwsAQHVBQwsAgMPOb3Txze+HNXvdgYtec+y0RQVWQ2YfVzV3BwDAOzFzBQAoE1uji8tr13Do/Ekr9+naV5dowtI9KrCW/QGsAquhtXuP6+vNh7R27/FyjQEAQEVg5goAUC4RwY63WM/IztP4pbs19edkvXFPcyXGx1z0mgKroYnL92jqmhRlZOfZj8eEB2p4t2YOjQEAQEUiXAEAyiXjbG45rsnTo7M26oPerQqFowKrofXJJ5SWlaMTpy366+RZffHbXzpjKSgyRmpmjh6dtVH/7XmNaocE2K+JrOGv6PAgtYmLZAkiAMAjCFcAgHIpy8zVhUYu3K5OzaIlqdjZKUc88dmmYo9HhwVqxJ3MbAEAKh7hCgBQLuWZubJJzcxRt3dXK/n4GWXnWV1YlZSWlVPs7BgAAO5GQwsAQLlE1ij/zJUkbU875fJgdb6hX22l+QUAoEIRrgAA5RIdHuTpEkp18myeftl33NNlAACqEcIVAKBc2sRFKqaMGwpXtLV7CVcAgIpDuAIAlIvZx6Th3ZrJm/vyGWJZIACg4hCuAADllhgfo0m9W3ntDFZaRranSwAAVCN0CwQAOCUxPkadmkVrffIJpZ/K0erdxzRv41+eLkuS9OWmw+p0VTRdAwEAFYKZKwCA08w+JrVrWEt3tbxUY5JaKDoswCXjhgQ4/2eAIxdup2sgAKBCEK4AAC5l9jFpxJ1XletZrGB/s5JaXarx3a/WZ4+01ZbhnfX+A9c4VU9qZo7WJ59wagwAABzBskAAgMvZnsV64autyjibd9HzI4L99ND1cRp0SyOZfQrHsq4tYvWBj0kjF25XamZOuepJyyrfdQAAlAXhCgDgFrZnsSYu36Opa1KUkf13yIoI8tNtV0bphka1FR0epDZxkUVCVXFj2Z7r2p12Su/9uNfhWk6ctjj1XgAAcAThCgDgNmYfkwbf1kSDbmlsD0ZRoYEXDVMljdWuYS1J5/avKku4OnDiTJnuBQBAeRCuAABud34wcgXbBsaOLhOcvvaA2jWsTddAAIBb0dACAFDp2DYwLgu6BgIA3I1wBQColBLjY5QYf4nD56dm5mji8j1urAgAUN0RrgAAlda19WuW6fzxS/do8bZUN1UDAKjuCFcAgEqrdkjZNytmeSAAwF0IVwCASis6PKjM17CpMADAXQhXAIBKy9Y1sKzYVBgA4A5eGa4sFouef/55xcbGKigoSAkJCVqyZMlFr2vQoIFMJlOxX40bN66AygEAFak8XQMlNhUGALiHV+5z1a9fP82bN0///ve/1bhxY02bNk1du3bVihUrdOONN5Z43TvvvKPTp08XOrZ//3699NJL6ty5s7vLBgB4QGJ8jJ66rYnGL93t8DURwf6SpAKr4fTmxgAA2JgMw/Cqp3rXr1+vhIQEjR07VkOGDJEk5eTkKD4+XlFRUfr555/LNN6oUaM0bNgwrVmzRtdff73D12VlZSk8PFyZmZkKCwsr0z0BABWrwGrohjeWKS3LsRmpF29vquy8Ak1dk6KM7Dz78ZjwQA3v1ozNhgEAhTiaDbxuWeC8efNkNps1YMAA+7HAwED1799fa9eu1cGDB8s03qeffqq4uLgyBSsAQOVi9jFpxJ1XOXz+mMU7NX7pnkLBSpLSMnP02KyNtGsHAJSL14WrTZs2qUmTJkUSYZs2bSRJmzdvLtNYO3bs0AMPPHDRcy0Wi7Kysgp9AQAqj7JsKlxQwpoN4/9/0a4dAFAeXheuUlNTFRNTdDmG7djhw4cdHmv27NmSpF69el303NGjRys8PNz+Va9ePYfvAwDwDtfUK9umwiVJzczRxOV7XDIWAKD68LpwlZ2drYCAoptCBgYG2l93hNVq1Zw5c3TNNdfoyiuvvOj5Q4cOVWZmpv2rrMsPAQCel3nBMj9njF+6h+WBAIAy8bpwFRQUJIul6APJOTk59tcdsXLlSh06dMihWStJCggIUFhYWKEvAEDlYnJxo7+hX21leSAAwGFeF65iYmKUmlr0Twptx2JjYx0aZ/bs2fLx8VHPnj1dWh8AwHtFBPm5dLyTZ/P0y77jLh0TAFB1eV24atmypXbv3l2kocS6devsr1+MxWLRl19+qZtvvtnhMAYAqPxqhxRdVu6stXsJVwAAx3hduEpKSlJBQYE+/PBD+zGLxaKpU6cqISHB3mjiwIED2rlzZ7FjLFq0SBkZGQ4vCQQAVA3R4Y4tHS8LQywLBAA4xtfTBVwoISFB9913n4YOHar09HQ1atRI06dPV0pKiqZMmWI/r0+fPlq5cqWK2wN59uzZCggI0L333luRpQMAPKxNXKRiwgOVmpnjsjF3pmZq/sa/FB0epDZxkTL7nHuwq8BqaH3yCaWfylFUaGCh1wAA1ZPXhStJmjFjhoYNG6aZM2fq5MmTatGihb755hu1b9/+otdmZWXp22+/1R133KHw8PAKqBYA4C3MPiYN79ZMj83a6LL5pmU7j2nZzmOSpOiwQPVsU19Z2bmav/mQTpz5uzthRJCfHrohTo/d3FC/7T+ptKwcnThtUUSwvzLO5tr/GlnDv0hQAwBUDSajuKkfKCsrS+Hh4crMzKRzIABUMou3pWrkwu2FZrAigv3Ur10DtW4QqWNnLJr1y35tSDnpsRqjwwI14s5mSowvurcjAMC7OJoNvHLmCgAAZyTGx6hTs+hSl+2lZeZ4NFylZeXo0Vkb9UHvVgQsAKgiCFcAgCrJ7GNSu4a1SnzdlRsOO2PoV1sVGuCnY2csPLsFAJUc4QoAUC25esPh8jp5Nk+9pqyzf89yQQCovFwSrtavX68NGzYoIyNDBQUFRV43mUwaNmyYK24FAIBLuHrDYVdhuSAAVF5OhasTJ07on//8p9asWVNsS3QbwhUAwNu4Y8NhVxoy93fd0vQS+ft63ZaUAIASOBWunn76af3000+6+eab1bdvX9WtW1e+vqw0BAB4P3dsOOxKpy35umbk9xrQoZEG3dKI57AAoBJwqhV77dq11ahRI61du1Ymb1m87iK0YgeAqq3AaujGMctduuGwu0QE+emNe5uzTBAAPMTRbODUWoPs7Gy1b9++ygUrAEDVZ9twuDL8HywjO0+PztqoxdtSPV0KAKAUToWrli1bKiUlxUWlAABQsRLjYzSpdyvFhAcWOl7D31zqdQG+PgrwwLNQQ7/aqgJruRecAADczKkHpIYPH64777xTv/zyi9q2beuqmgAAqDAlbTi8ZHuaRi7cXmjZYESwnx66Pk6DbmkkSZq4fI+mrklRxnl7ZtnOeezmhpr+c4peW7TDZbWePJunX/Yd1w2NartsTACA6zj1zNWMGTP09ddf65tvvlGvXr3UqlWrEtcg9unTp9xFegLPXAEACqxGkdB1YWOJ0s4psBq6dtQSZZx13YbFgzo20pAuV7hsPADAxTmaDZwKVz4+PjKZTIXasF/4/JVhGDKZTMXuf+XNCFcAAFdYvC1Vj87a6LLx/tWxoZ7t0tRl4wEALs7RbODUssCpU6c6czkAAFVeYnyMPujdSiMW/KG0LIvT44UF+jk0owYAqHhOzVxVZcxcAQBc6cJAdPKMRY9/uqnM48SE+utsnlWZOfl/HwsP1PBuzWjVDgBuUiHLAqsywhUAwN0Wb0t12YyWJL3/QCt1bUHAAgBXq9BwlZKSotmzZ2vz5s3KyspSWFiYWrZsqV69eqlBgwbODu8RhCsAQEWwzWj9Z/7v2nfsrFNj+ZikiT2vUdcWsS6qDgAgVWC4mjBhgp577jnl5+frwqH8/Pz05ptvavDgwc7cwiMIVwCAijRm8Q5N+nGfS8b6oHcrlggCgAs5mg2c2gHxm2++0VNPPaXw8HCNGjVKP//8s5KTk7V27Vq9/vrrCg8P19NPP61vv/3WmdsAAFDlRQb7u2wsNhsGAM9wqlvguHHjFBkZqY0bN6pu3br245dddpkSEhLUq1cvXXPNNRo3bpzuuOMOp4sFAKCqqh0S4LKx2GwYADzDqXC1ceNG9erVq1CwOl+9evXUvXt3ffrpp87cBgCAKi86PMil4736zXYNbH+5osICJUM6dsZC23YAcDOnwlVubq5q1KhR6jkhISHKzc115jYAAFR5beIiFRMeqNTMHJeMtzPtlJ76YkuR49FhgRpxJ23bAcAdnHrmqkmTJlq4cKHy8/OLfT0/P1/ffPONmjRp4sxtAACo8sw+Jg3v1kzunlNKy8rRo7M2avG2VDffCQCqH6fCVZ8+fbRr1y516dJFv/32W6HXfv31V91+++3atWuX+vbt61SRAABUB4nxMZrUu5ViwgPdfi+aXgCA6znVir2goED33nuvFixYIJPJpODgYEVFRSk9PV1nz56VYRi666679OWXX8rHx6kcV+FoxQ4A8BTb3ldpWTlas+eoftiepqycApffZ/bDCTS9AAAHVOgmwjNmzND06dMLbSJ8zTXXqG/fvnrwwQedHd4jCFcAAG9hC1vpp3I065f92pBy0iXj3tAwUknX1lN0eBCNLgCgFBUarqoiwhUAwBut2XNMvaasc/m44UF+6nRllG5oVJuwBQAXcDQbONUtEAAAVKy2DWspIthPGWfzXDpuZnae5m08pHkbD0k6F7b+74Y4DbqlESELABxUuR6EAgCgmjP7mPTGPc3dfp/M7DyNX7pb1766hM6CAOCgMoUrHx8f+fr6avfu3fbvzWbzRb98fZkgAwDAVRLjY/RB71aKDgtw+70ysvNo3Q4ADipT6mnfvr29K+D53wMAgIqVGB+jTs2iK6SroHSudXunZtEsEQSAUtDQogQ0tAAAVDa2roKf/LRPS3aku3x8WrcDqK4czQY8cwUAQBVh9jGpXcNa6nd9nFvG/3nvMbeMCwBVhVsehkpJSdGSJUsUGBiou+++WyEhIe64DQAAKIa7OgoeOpnt0vEAoKpxaubq9ddfV1xcnE6e/Hszwx9//FHx8fF69NFH1a9fP7Vq1UonTpxwulAAAOAYd3UU3H44Q2v2HNPXmw9p7d7jKrDyZAEAnM+pZ67atGmjGjVqaMWKFfZjCQkJ2rp1q1588UWlpaXp/fff10svvaRXXnnFJQVXFJ65AgBUdou3pWrEgj+UlmVxy/jRYYEacWczJcbHuGV8APAWjmYDp8JVVFSU7rvvPr333nuSpMOHD6tu3bp65plnNHbsWElSp06dlJaWpq1bt5b3Nh5BuAIAVAW2JhdpWTk6cdqiiGB/rd17TEu2H1FmTr5L7vFB71YELABVmqPZwKlnrrKyshQREWH/ftWqVTKZTOrWrZv9WKtWrfTBBx84cxsAAFBOtiYX57v32rqFQteU1Xu17fCpct+DNu0AcI5Tz1xdcskl2r9/v/37JUuWKCAgQAkJCfZjOTk57IUFAICXsYWuu6+5VA3rONd46uTZPP2y77iLKgOAysupmavWrVvr66+/1jfffKPAwEB9/vnn6tixowIC/t4xPjk5WbGxsU4XCgAA3OPSmkFOj/Hz3mPsgQWg2nNq5urFF19Ufn6+7rrrLnXp0kU5OTl68cUX7a9bLBatWrWq0EyWIywWi55//nnFxsYqKChICQkJWrJkicPXf/7552rXrp1q1KihiIgIXX/99Vq+fHmZagAAoLq4oWEdp8egTTsAODlz1apVK/3yyy+aOXOmJKl79+5q06aN/fVNmzapY8eOeuCBB8o0br9+/TRv3jz9+9//VuPGjTVt2jR17dpVK1as0I033ljqtSNGjNArr7yipKQk9evXT3l5edq2bZsOHTpU9jcIAEA14Ip9sf46edaFFQFA5eRUt0B3WL9+vRISEjR27FgNGTJE0rnntuLj4xUVFaWff/65xGt/+eUXXX/99Xr77bf11FNPOVUH3QIBANXJ4m2penTWRqfGoGsggKrK0Wzg1LJAd5g3b57MZrMGDBhgPxYYGKj+/ftr7dq1OnjwYInXvvPOO4qOjtbgwYNlGIZOnz5dESUDAFDpJcbH6IPerRQdFnDxk0sw9KutbCwMoFor07LAGTNmSJLuvvtuhYaG2r93RJ8+fRw6b9OmTWrSpEmRRGhbbrh582bVq1ev2GuXLVum66+/Xu+++65GjRql48ePKzo6Wv/5z380aNCgUu9rsVhksfy9yWJWVpZD9QIAUFUkxseoU7Noe4v2NXuOav6mQypwMC/ZugbS2AJAdVWmcNWvXz+ZTCa1bdtWoaGh9u9LYxiGTCaTw+EqNTVVMTFFlxTYjh0+fLjY606ePKljx45pzZo1Wr58uYYPH6769etr6tSpeuKJJ+Tn56eBAweWeN/Ro0dr5MiRDtUIAEBVdf6+WHdfc6ly8wu04Pc0h6+nayCA6qxM4eqTTz6RyWSyB52pU6e6vKDs7OxCrdxtAgMD7a8Xx7YE8Pjx45ozZ4569OghSUpKSlLz5s01atSoUsPV0KFD9fTTT9u/z8rKKnGGDACA6qJuZHCZzqdrIIDqrMwzV+fr27evK2uRJAUFBRVanmeTk5Njf72k6yTJz89PSUlJ9uM+Pj7q0aOHhg8frgMHDqh+/frFXh8QEFBsqAMAoDq7oWEdvf/jPofPp2sggOrM6xpaxMTEKDU1tchx27GSNiSOjIxUYGCgatWqJbPZXOi1qKgoSeeWDgIAAMfZ2rQ76tf9GVq8rej/xwGgOnAqXK1Zs0ZPP/200tKKX4udmpqqp59+Wr/88ovDY7Zs2VK7d+8u0lBi3bp19teL4+Pjo5YtW+ro0aPKzc0t9JrtOa06dZzfJBEAgOrE7GPSG/c0L9M1dA0EUF05Fa7GjRunhQsXKjo6utjXY2Ji9M0332j8+PEOj5mUlKSCggJ9+OGH9mMWi0VTp05VQkKC/TmoAwcOaOfOnYWu7dGjhwoKCjR9+nT7sZycHM2ePVvNmjUrcdYLAACULDE+Rq0vi3D4fFvXQACobsr0zNWFNmzYoFtvvbXUc9q3b68lS5Y4PGZCQoLuu+8+DR06VOnp6WrUqJGmT5+ulJQUTZkyxX5enz59tHLlSp2/B/LAgQP18ccf61//+pd2796t+vXra+bMmdq/f78WLlxY9jcIAAAkSbERQdL+DIfPp2sggOrIqXCVnp6uSy+9tNRzoqOjlZ6eXqZxZ8yYoWHDhmnmzJk6efKkWrRooW+++Ubt27cv9bqgoCAtX75czz33nD755BOdOXNGLVu21LfffqsuXbqUqQYAAPC3S2sW31CqJHQNBFAdORWuIiIidODAgVLP2b9/v0JCQso0bmBgoMaOHauxY8eWeM6PP/5Y7PGoqChNmzatTPcDAAClK2vXwPNXlgBAdeHUM1dt27bV/PnzdfDgwWJfP3DggP73v//p+uuvd+Y2AADAw8raNfDQybMqsBpau/e4vt58SGv3HqfJBYAqz2Q48UdLq1atUseOHXXppZdq1KhR6tSpk72V+g8//KCXXnpJqampWr58+UWX9HmbrKwshYeHKzMzU2FhYZ4uBwAAj1u8LVWPztro8PmBvj7Kybfav48OC9SIO5spMT7GHeUBgNs4mg2cCleSNGHCBD3zzDP26X+TyWT/ex8fH73zzjv617/+5cwtPIJwBQBAUR3fWqHkY85tFPxB71YELACViqPZwKlnriRp8ODB6tixoz744ANt2LBBmZmZioiIUJs2bfToo48qPj7e2VsAAAAvERMW6HS4GvrVVnVqFi2zj8lFVQGAd3A6XElSixYt9P7777tiKAAA4MVqh/g7PcbJs3n677Ld+nenK1xQEQB4D6caWgAAgOqlbmSwS8Z5Z9mfWrwt1SVjAYC3cDpc5efna/z48WrTpo3CwsLk6/v3ZNjmzZv1+OOPa/fu3c7eBgAAeIEbGtZx2VgvfLWVDoIAqhSnwlV2drY6duyoIUOGaP/+/QoLCyu0r0VcXJymTp2qGTNmOF0oAADwvLK2ZC9Nxtk8PfnZby4ZCwC8gVPh6vXXX9eaNWs0evRopaWl6eGHHy70enh4uDp06KDvv//eqSIBAIB3MPuY9MY9zV023rdbj2j0ou3siQWgSnCqocXnn3+ujh076rnnnpN0rg37hS6//HJt2rTJmdsAAAAvkhgfow96t9LzX/6uzOx8p8ebvCpZn647oFOWAvuxYH+zusZH6/V7Wsjfl0fEAVQOToWrAwcO6O677y71nNDQUGVmZjpzGwAA4GUS42PUqVm0Ji7fo0/WJDsdss4PVpJ0NrdA8zYe0ryNh3RZZJBuaFxbDWuH6MF2DQhbALyWU+EqNDRU6enppZ6zd+9e1anjuodfAQCAdzD7mDT4tiYadEtjrU8+odSMbG06eFK/ppzQjrTTLrvP/hPZ2r/uoCTptUU79MhNcRratZnLxgcAV3EqXLVt21YLFy5URkaGIiIiirx+8OBBLVq06KKzWwAAoPIy+5jUrmEtSdI919aVJD0+6zct2pbm8ntZjXPLCPekn9YjNzVUm7hINiMG4DWcmld/9tlndfLkSd16661as2aN8vPPLQk4e/asli1bpi5duig/P19PP/20S4oFAACVw38faKVAX/eFnuU7j6rnR7/ohjeWs18WAK9hMs7vnV4OkyZN0uDBg1VQUFDkNbPZrPfff79IF8HKICsrS+Hh4crMzFRYWJinywEAoNJZvC1Vj87aWCH3ev+BVqpZw1/pp3JUOyRAMqQjWTnadPCkDElxtWrwvBaAcnM0GzgdriRpx44d+uCDD7Ru3TqdOHFCYWFhSkhI0OOPP66rrrrK2eE9gnAFAIDz7vzvav1+KMvTZUiSfEzieS0A5eJoNnDqmatVq1YpLCxMLVu21IQJE5wZCgAAVEHdro71mnBle15LEgELgFs4NTfesWNHffjhh66qBQAAVDF9r4+Tt7Wb+HBVslbvOspGxQBczqlwFRUVpcDAQFfVAgAAqhh/Xx8NaB/n6TIKMSQ9OHU9zTAAuJxT4apTp0768ccf5YLHtgAAQBU1tGszDWxf8gxWrRr+euiGBkpqdalCAyqu4URaVo4enbWRgAXAZZxqaHH48GG1a9dOnTt31pgxYxQZGenK2jyKhhYAALhWbr5VM9emKOX4WUmGWtaNUGzN4EJ7VRVYDf385zE9+Mn6CqurZrCffn2pE/tlAShRhXQLvOWWW3T8+HFt27ZN/v7+iouL0yWXXCKTqfB/nEwmk5YtW1be23gE4QoAAM8ZvWi7vflERZj9cIJuaFS7wu4HoHKpkG6BP/74o/3vLRaLdu7cqZ07dxY578KwBQAAUBpbN78PVyWrIh4+WLPnGOEKgNOcCldWq9VVdQAAABQytGszPdO5qWauTdGq3Ue1cs8xt91ry18ZbhsbQPVRrqdG165dq1tuuUWhoaEKDw9Xp06dtH59xa2NBgAA1YO/r4/633S5pvdP0Ae9W+mSUH+33Cc7L98t4wKoXso8c7V161bdeuutysnJsR9btmyZfv75Z61fv15XXXWVSwsEAACQpMT4GHVqFq31ySeUmpGtTQdPympIPiapRd0IZWXnKSLYXy8v2KYzloIyjR3oa3ZT1QCqkzKHqzfeeEM5OTn6z3/+oyeeeEKS9N577+nVV1/VmDFjNGPGDJcXCQAAIElmH5PaNawlSbrn2rrFnlMjwKxHZ20s07hsKwPAFcrcLbB+/fpq0KCBVq1aVeh4hw4dlJKSov3797u0QE+hWyAAAJXX4m2pGrHgD6VlWRw6v1W9cH31rxvdXBWAysrRbFDmZ66OHDmitm3bFjmekJCgI0eOlHU4AAAAl0uMj9GaF25Vg8ggh8635NOkC4Dzyhyu8vLyFBISUuR4jRo1lJeX55KiAAAAnGX2MalmsJ9D5/qb2TYGgPPK1S0QAACgMsgtcOzpB0fPA4DSlGufq1mzZumXX34pdOzPP/+UJHXt2rXI+SaTSd9++215bgUAAFBujs5IMXMFwBXKFa7+/PNPe5i60OLFi4scM5n4DxYAAKh4js5IHT3tWOMLAChNmcNVcnKyO+oAAABwOUu+Y5sD/5Vh0ciFf6huRJDCg/y05a8MGZLiatXQg+0ayOxj0vrkE0o/laOo0EC1iYuU2Yc/PAZQWJnD1WWXXeaOOgAAAFzOJMcD0NQ1KcUeH/XtDvn7+hTqKBgdFqgRdzZTYnyMsyUCqEJoaAEAAKosP7Pzv+oYKtqqPS0rR4/O2qjF21KdHh9A1UG4AgAAVVagn9mt47/w1VYVWOk0COAcwhUAAKiy6kUGu3X8jLN5evKz39x6DwCVB+EKAABUWUmt6rn9Ht9uPaLRi7a7/T4AvJ9XhiuLxaLnn39esbGxCgoKUkJCgpYsWXLR60aMGCGTyVTkKzAwsAKqBgAA3ub6xrUV7O/epYGS9OHqZOVe8FwWgOqnXPtcuVu/fv00b948/fvf/1bjxo01bdo0de3aVStWrNCNN9540esnTZqkkJAQ+/dms/v/owoAALyP2cekcd2v1qOzNrr1PoYhTf85RY+0v9yt9wHg3bwuXK1fv15z5szR2LFjNWTIEElSnz59FB8fr+eee04///zzRcdISkpS7dq13V0qAACoBBLjY/TvWxvrnWV73Hqf9cnHCVdANed1ywLnzZsns9msAQMG2I8FBgaqf//+Wrt2rQ4ePHjRMQzDUFZWlgyD7j0AAEB64tbGCg9y758pp2bmuHV8AN7P68LVpk2b1KRJE4WFhRU63qZNG0nS5s2bLzrG5ZdfrvDwcIWGhqp37946cuTIRa+xWCzKysoq9AUAAKoGs49JY+5t4dZ75OTmuXV8AN7P65YFpqamKiam6G7ntmOHDx8u8dqaNWtq0KBBateunQICArR69Wq99957Wr9+vX799dcige18o0eP1siRI51/AwAAwCslxsfog96tNGLBH0rLstiPhwf6qlOzS3RDo9o6cOKsPl23X0dO5ZZ5/NRMy8VPAlClmQwvWzvXsGFDXXHFFVq0aFGh4/v27VPDhg01fvx4/fvf/3Z4vE8//VS9evXS6NGj9cILL5R4nsVikcXy938Us7KyVK9ePWVmZpYaygAAQOVSYDW0PvmE0k/lKCo0UG3iImX2MZX4+qMzNijTUuDQ2APbx2lo12YXvQeAyiUrK0vh4eEXzQZeN3MVFBRUKOTY5OTk2F8viwceeEDPPPOMli5dWmq4CggIUEBAQNmKBQAAlY7Zx6R2DWs5/PqlNYOUmXbaobE/XJUsf18fzVh7QJnZfy8TjA4L1Ig7mykxvujqHABVh9c9cxUTE6PU1NQix23HYmNjyzxmvXr1dOLECadrAwAA1U9ugWOzVpJkSPrv8r2FgpUkpWXl6NFZG7V4W9HfcQBUHV4Xrlq2bKndu3cXaSixbt06++tlYRiGUlJSVKdOHVeVCAAAqpHwINetbHlm7hYVWL3qiQwALuR14SopKUkFBQX68MMP7ccsFoumTp2qhIQE1atXT5J04MAB7dy5s9C1R48eLTLepEmTdPToUSUmJrq3cAAAUCV1vuoSl411xlKgKav2EbCAKsrrGlpIUvfu3TV//nw99dRTatSokaZPn67169dr2bJlat++vSTp5ptv1sqVKwvtZRUcHKwePXqoefPmCgwM1E8//aQ5c+bo6quv1po1axQcHOxwDY4+tAYAAKq23Hyrmrz0nUvH5BksoHKptA0tJGnGjBkaNmyYZs6cqZMnT6pFixb65ptv7MGqJL169dLPP/+sL7/8Ujk5Obrsssv03HPP6T//+U+ZghUAAICNv6+PBraP0+RVyS4b0/YMVlKrS/X6PS3k7+t1i4kAlINXzlx5A2auAADA+Tq8uVz7T2S7fFyTpAH/v4U7AO/kaDbgj0kAAAAc0LvtZW4Z15A0eVWyXvt2u1vGB1BxCFcAAAAO6Ht9nNy5DfBHq5P1zeZDbrwDAHcjXAEAADjA39dHA9rHufUeg+ZsZi8soBIjXAEAADhoaNdmGtjevTNY7IUFVF40tCgBDS0AAEBJcvOtmrk2RcnHzuhIVo5qhwbI18ekv06c0Yrdx50e/5amtRUbEay4WjX0YLsGdBMEPMzRbEC4KgHhCgAAlMfoRdtd2rbdxyQ9chPdBAFPolsgAACABwzt2ky7R92upFaXyuyC9YNW41w3wdGL6CYIeDvCFQAAgIv5+/rore4ttfu1rurWItolY364Klm5+VaXjAXAPQhXAAAAbmL2Mem/D1yrbSO6OD2WIWnaGtctNwTgeoQrAAAANwsJ9NVAF7Rxn7PhgAuqAeAuhCsAAIAKMLRrM0WF+Ds1xr5jZzX8621au/c47doBL0S3wBLQLRAAALja5JV/avR3u1wyVrC/WV3jo/X6PS1k9jFpffIJpZ/KUVRooNrERcrs487duIDqhVbsTiJcAQAAV8vNt6rJS9+5fFw/H5PyzpvJig4L1Ig7mykxPsbl9wKqI0ezgW8F1gQAAFCt+fv6aGD7OJfugyWpULCSpLSsHD06a6MebFtf0rlmGGxIDLgfM1clYOYKAAC4i6s3GnYUGxID5cOyQCcRrgAAgDvl5ls1c22KUo6fVerJs1q662iF3XtgewIWUBaEKycRrgAAQEUpsBpqPuJ7nc0tqJD7mSTtGnU7SwQBBzmaDfg3CgAAwMPMPiaN6351hd2PDYkB9yBcAQAAeIHE+Bi9/0CrCrvf4m1pFXYvoLogXAEAAHiJri1i9P4D11TIvfakn6qQ+wDVCeEKAADAi3RtEasPerfSJaH+br3PKUuBFm9Ldes9gOqGhhYloKEFAADwpAKrofXJJ5SWlaMTpy0KD/LTJ2v2aXvqaZfdI8DXR9tfSZTZx+SyMYGqiE2EAQAAKjGzj0ntGtYqdCzpunr2Fu7Jx87oSFaO/jp5RjvSzpTrHpZ8q37ac1QdrohyRclAtUe4AgAAqET8fX3U/6bLCx3Lzbeq7ye/aO2+k2Ue78NVewlXgIvwzBUAAEAl5+/ro88GXK/do27XZTUDy3Ttn+nlm/UCUBThCgAAoIrw9/XRK3c2L9M1Z3Pz3FQNUP0QrgAAAKqQG6+oI39fx3/FM9HaDHAZwhUAAEAVYvYx6d37Wzp8vlWkK8BVCFcAAABVTGJ8jIJ8HWuvfjrX0Ev/26opq/cpN9/q5sqAqo1ugQAAAFWQuQx/hD7rlwOSpNcW7dAjN8VpaNdmbqoKqNqYuQIAAKiCCqxlX+5nNaTJq5I1etF2N1QEVH2EKwAAgCrIZHJsWWBxJq9K1updR8sV0IDqjGWBAAAAVVCAr0ln88ofjh6cul6hAb5qdVlNtW9cWw+2a1CmLoRAdUS4AgAAqIIa1ArRyb+ynBrjlCVfK3cf1crdR0t8HqvAamh98gmlZeXoxGmLImv4Kzo8SG3iImX2Kf/sGVAZEa4AAACqoMTmMdrkZLg6n+15rBW70hUa4KcAXx+dtuRr39HTOp1btMtgdFiARtx5lRLjY1xWA+DtTIZhsJi2GFlZWQoPD1dmZqbCwsI8XQ4AAECZ5OZb1eSl7zxdhj7o3YqAhUrP0WzAwlkAAIAqyN/XRwPbx3m6DA2es5nGGKg2CFcAAABV1NCuzTwesCz5Vn20ci8BC9UCywJLwLJAAABQVeTmW9X3k1+0dt9Jj9VQM9hPd10dqwLDkNWQfExSy7oRiq0ZTPMLeD1Hs4FXhiuLxaKXX35ZM2fO1MmTJ9WiRQuNGjVKnTp1KtM4nTp10tKlS/Wvf/1LEydOLNO1hCsAAFDV5OZbNf3nFL3x3Q4VeNFvgJeEBWgkzS/gxSr1M1f9+vXTuHHj1KtXL02YMEFms1ldu3bVTz/95PAYX331ldauXevGKgEAACoXf18fPdL+cr3Xq5WnSynkSJZFj87aqMXbUj1dCuAUrwtX69ev15w5czR69GiNHTtWAwYM0PLly3XZZZfpueeec2iMnJwcPfPMM3r++efdXC0AAEDlkxgfow96t9Ilof6eLqWQJz7bxLNZqNS8LlzNmzdPZrNZAwYMsB8LDAxU//79tXbtWh08ePCiY7z55puyWq0aMmSIO0sFAACotBLjY/Tz0Nv02SNtNb5HSw2740q9eW9z1Q7x81hNeQWGVu466rH7A87yuk2EN23apCZNmhRZy9imTRtJ0ubNm1WvXr0Srz9w4IDeeOMNffLJJwoKCnL4vhaLRRaLxf59VpbrNt0DAADwRmYfk9o1rFXoWPfW9fXIjA1asj3dIzUNmbdZG4d19si9AWd5XbhKTU1VTEzRhxltxw4fPlzq9c8884yuueYa3X///WW67+jRozVy5MgyXQMAAFAVfdSntbJzC/T6ou3ad/S0jp6yKDLYT3lW6fI6wdp39KwCfH0U6GfWchfPNJ04k6dXFv6hl7td5dJxgYrgdeEqOztbAQEBRY4HBgbaXy/JihUr9OWXX2rdunVlvu/QoUP19NNP27/PysoqdYYMAACgKgvyN+vVfza/6HnumOX6ZE2K9h8/rSn9Elw6LuBuXvfMVVBQUKHleTY5OTn214uTn5+vJ598Ug8++KBat25d5vsGBAQoLCys0BcAAABK91Gf1urULMrl4y7beUwPT9+gtXuP6+vNh7R273GaXcDred3MVUxMjA4dOlTkeGrqudacsbGxxV43Y8YM7dq1S5MnT1ZKSkqh106dOqWUlBRFRUUpODjY5TUDAABUZ7ZlhPdO+knbU0+7bNylO9K1dMffs2LRYQEawX5Y8GJeN3PVsmVL7d69u0hDCdtSv5YtWxZ73YEDB5SXl6cbbrhBcXFx9i/pXPCKi4vTDz/84NbaAQAAqqsgf7MWDe6g3aNuV1KrSxXka3L5PdLYDwtezmQYhlfNr65bt05t27bV2LFj7a3ULRaL4uPjVatWLf3yyy+SzoWps2fPqmnTppKknTt3aufOnUXGu/vuu9W1a1c98sgjSkhIKLZZRnEc3YUZAAAARRVYDa1PPqH0UzmqHRKgLQdO6s0fdrtk7ABfH21/JVFmH9cHOKA4jmYDr1sWmJCQoPvuu09Dhw5Venq6GjVqpOnTpyslJUVTpkyxn9enTx+tXLlStmzYtGlTe9C6UFxcnP75z39WRPkAAABQ0TbvbS+vpXeW/6ncfKvTY1vyreo7Za0+eait/H29biEWqjGv/DTOmDFD//73vzVz5kw9+eSTysvL0zfffKP27dt7ujQAAACUg9nHpHfvb+my8X7ae1JXvPSdRi/a7rIxAWd53bJAb8GyQAAAANdbvC1Vg2ZvVL4LfwO9JNRfT3dqovq1QtQmLpLlgnA5R7MB4aoEhCsAAAD3KLAaav3aEp04k+fysS8JC9BNjWorOMBXl0UG68F2DVg6CKcRrpxEuAIAAHCf7NwCXfnyYrffx8ckPXJTnIZ2beb2e6HqcjQbEOMBAABQ4YL8zW7ZfPhCVkOavCpZr33Ls1lwP8IVAAAAPOKjPq0rJGBJ0kerk/X0nI0u6VYIlIRlgSVgWSAAAEDFyM4t0OuLtmvf0dNKz7Joz9Ezbr3fP1rEaML919D4Ag7jmSsnEa4AAAA8Y/Si7Zq8Ktmt9wj299HA9o10aUSgvv8jTdl5BYqrXUMvdm2mIH+zW++Nyodw5STCFQAAgOdURMAqSd2IID10QwM6DcKOcOUkwhUAAIBn5eZb9eJXv2v+xkMq8MD96TQIG7oFAgAAoFLz9/XRW91bavfrXdWtRXSF39/WaXDkgj8q/N6onJi5KgEzVwAAAN4lN9+qmWtT9NWmv/TH4VMVem9fH+nVu65Sg9qhahMXSTOMaoZlgU4iXAEAAHiv3Hyr/vHfVdp9xL2dBYtzSViARt55lRLjYyr83vAMwpWTCFcAAADez9bGffnOdB3KyKnQe7dvUlsdGteh8UU1QLhyEuEKAACgcvFUh0EaX1R9NLQAAABAtTK0azPtHnW7klpdKt8KfCTK1vhi9KLtFXdTeCXCFQAAAKoMW4fBXa911VO3NVZFtp2YvCpZufnWCrwjvA3hCgAAAFWO2cekwbc10Z+vd9W1l0VU2H0HztxAwKrGeOaqBDxzBQAAUHXYGl/sO3pGOXkFahwdosZ1QnX3NXXV+Z0fdex0nkvv1y4uUtP7J9DoooqgoYWTCFcAAADVxyMzNmjJ9nSXjzuwPY0uqgIaWgAAAAAO+qhPa+14JVHNYkJcOi6NLqoXwhUAAAAgKcjfrEWDO2j3qNt1zzWxLht38qpkZZ517bJDeCdfTxcAAAAAeBN/Xx+N63GNaoX466PVKS4Z8+pXftC1l0WoT7sGigoNVJu4SJl9KrKXISoC4QoAAAAoxn/uuEo+JpPLNib+bX+Gftu/WZJk9pEuiwzWwPaXq36tEMJWFUFDixLQ0AIAAACSlJtvVdIHP+n3v0657R6XhAVo5J1XKTE+xm33QPnR0AIAAABwAX9fH30+4Aa33uNIlkWPztqoL3/7y633gXsRrgAAAICLCPI3q1OzKLff55m5W9T+zWVuvw/cg3AFAAAAOOCjPq0rJGAdOJGjDmOXu/0+cD0aWgAAAAAO+qhPa2XnFuj1RduVfOysTp7N1R+Hs1x+n/3Hs5V5Nk/hwX4uHxvuQ0OLEtDQAgAAAI5o/+YyHTiR4/Jx/XykP165Xf6+LDbzNBpaAAAAABVg1XO36rJaQS4fN88qNXnpO41etN3lY8M9CFcAAACAk1Y+e4u2vNxZYYFml489eVUyAauSIFwBAAAALhAe7KffRyS6penF5FXJys23unxcuBYNLQAAAAAXOr/pxb6jZ5STV6C4OsFatv2ITmYXlHvcyT/u1RO3NXZhpXA1GlqUgIYWAAAAcLXcfKuaDftO+eX4DTzAbNLWkYk0uPAAR7MB4aoEhCsAAAC4w4nTuWo1akm5r/f3NSnQ10dNo2oovEagOje7RHUja6hNXKTMPiYXVgobR7MBywIBAACAChQZ4q86If46ejq3XNfn5hvKzS/Q+gNZkrK0ZEe6JMnsI10VE6ZPH2mnkEB+zfcE5hQBAACACrbhpU6qE+Lv0jELrNLvh7IUP+J73TlxtUvHhmMIVwAAAIAHbHipk966J94tY//+VxYBywMIVwAAAICHJLW5zG1j//5Xlp6dt0nZueXvUIiyIVwBAAAAHtShYYTbxp7762Fd+fJidR63kn2yKoBXhiuLxaLnn39esbGxCgoKUkJCgpYsuXhHlfnz56tLly6KjY1VQECA6tatq6SkJG3btq0CqgYAAADK7r0HE9x+j93pp9Xkpe80etF2t9+rOvPKcNWvXz+NGzdOvXr10oQJE2Q2m9W1a1f99NNPpV63detW1axZU4MHD9b777+vxx57TJs2bVKbNm20ZcuWCqoeAAAAcFxIoK9a1K2YrX8mr0omYLmR1+1ztX79eiUkJGjs2LEaMmSIJCknJ0fx8fGKiorSzz//XKbxjhw5orp166p///764IMPHL6Ofa4AAABQke6cuFq//5VVIffaPep2NiMuA0ezgdf9ROfNmyez2awBAwbYjwUGBqp///5au3atDh48WKbxoqKiFBwcrIyMDBdXCgAAALjOgkE3aduILmpxaZjMbr7X5B/3uvkO1ZPX7S62adMmNWnSpEgibNOmjSRp8+bNqlevXqljZGRkKC8vT2lpaXrnnXeUlZWlW2+91W01AwAAAK4QEuirBU/cpAKrofXJJ3To5Fl9/0easvMKFBsWpK83/yWLC/pSvLtit564rbHzA6EQrwtXqampiomJKXLcduzw4cMXHaNt27batWuXJCkkJEQvvfSS+vfvX+o1FotFFovF/n1WVsVMyQIAAAAXMvuY1K5hLUm1lHTd3xMLb3a/Wtm5BXp0+gat3Hu83OPnFUijF23X0K7NXFAtbLxuWWB2drYCAgKKHA8MDLS/fjFTp07V4sWL9f777+vKK69Udna2CgpK7+8/evRohYeH278uNjsGAAAAeEKQv1mf9He+w+DkVcm0Z3cxrwtXQUFBhWaQbHJycuyvX0y7du3UpUsXPfbYY/r+++81a9YsDR06tNRrhg4dqszMTPtXWZ/tAgAAACqK2cekD3q3cnqcxPErXFANbLwuXMXExCg1NbXIcdux2NjYMo1Xs2ZN3XLLLZo9e3ap5wUEBCgsLKzQFwAAAOCtEuNj9EHvVooMNJV7jH3Hc/TIjA0urKp687pnrlq2bKkVK1YoKyurUMBZt26d/fWyys7OVmZmpqtKBAAAALxCYnyMOjWL1vrkE0o/laOn5mxWWRf6LdmeruzcAgX5u7tHYdXndTNXSUlJKigo0Icffmg/ZrFYNHXqVCUkJNifhTpw4IB27txZ6Nr09PQi46WkpGjZsmW67rrr3Fs4AAAA4AG25hd3tbxUgzs1LNcYw/631cVVVU9eN3OVkJCg++67T0OHDlV6eroaNWqk6dOnKyUlRVOmTLGf16dPH61cuVLn74HcvHlz3XrrrWrZsqVq1qypPXv2aMqUKcrLy9Mbb7zhibcDAAAAVJjHOjTR+CVl38NqwZbDeqt7S9cXVM14XbiSpBkzZmjYsGGaOXOmTp48qRYtWuibb75R+/btS73uscce07fffqvFixfr1KlTioqKUufOnfXiiy+qefPmFVQ9AAAA4Bn+vj4a2D5Ok1cll+m63ALj4ifhokzG+VM/sMvKylJ4eLgyMzNpbgEAAIBKZdj8bZq5bn+Zrkl54w43VVP5OZoNvO6ZKwAAAADOefXueIX486t+ReMnDgAAAFRB21653dMlVDuEKwAAAABwAcIVAAAAALgA4QoAAACAHpmxwdMlVHqEKwAAAABasj1d2bkFni6jUiNcAQAAAJAkXfnyYl336g9Ky8jxdCmVEuEKAAAAgN2xM3lq+8YyNXjhWx06ke3pcioVwhUAAABQRfk6ef0Nby5Xk/8sckkt1QHhCgAAAKii/ntfS6fHyC0wCFgOIlwBAAAAVVTna2JdMk5ugcESQQcQrgAAAIAqyuxj0ge9W7lkrBveXO6ScaoywhUAAABQhSXGx6hpnUCXjMXywNIRrgAAAIAqbt6/OrhkHJYHlo5wBQAAAFRxIYG+alE3zCVjdZmw0iXjVEWEKwAAAKAaWDDoJpcErNOWAhdUUzU52/oeAAAAQCWxYNBNOp2Tr6c+36QDJ7MVGx6oFbuOerqsKsNkGIbh6SK8UVZWlsLDw5WZmamwMNdMoQIAAADeJvNsnq5+5YcyXdOibpgWDLrJTRV5H0ezAcsCAQAAgGosPNhPl9UKKtM1v/+VpdM5+W6qqPIiXAEAAADV3MpnbynzNfEjvteBY2fdUE3lRbgCAAAAUC7t31qhy4d+qwIrTxpJhCsAAAAATrAaUsMXF2nxtlRPl+JxhCsAAAAAWjWko1PXPzproxZuOeyiaionWrEDAAAAUP3awfL1kfKt5R/jic82afPBk7rtymgdPHFWn284oFOWfF0WGazxPa5RSGDVjh+0Yi8BrdgBAABQHTV44Vu3jV1ZW7g7mg2qdnQEAAAAUCbBJumsm6Zffv8rS42Gfiuzj0k+MpRrlUyGFB0RqPmP36g6YQHuuXEF4ZkrAAAAAHbLn7/VrePnG5KlwFB2gVRgSPmS/srIUevXl6rFiO/dem93I1wBAAAAsIuOCFSQn2diQlZOvppX4oBFuAIAAABQyI5Xb/dYwDqVk6+h8zd55N7OIlwBAAAAKGLHq7frlxduVe0afhV+78/WHdboRdsr/L7OIlwBAAAAKFZ0RKB+HdZZKW/coeaXVmwH7cmrkpXrTF94DyBcAQAAALiohU/cpL7X16vQe1798ncVej9nEa4AAAAAOGTknS00sH1chd0v2yrd8MayCrufswhXAAAAABw2tGsz7R51uwLMpgq536GMHDVy48bGrkS4AgAAAFAm/r4+2vVaV2148TbVjQhUsJ9ZtWv46dW7rtRnj7SVq3NXvqQrh3n/EkFfTxcAAAAAoHKqExagn14ouunw3tF36MCxs0qcsFLZeVb5+khdrqyljX+d0eHMnHLdKzvPqrSMHEVHBDpbttuYDMMwPF2EN8rKylJ4eLgyMzMVFlaxnVEAAACAquqRGRu0ZHt6ua6NCDRr84hEF1d0cY5mA5YFAgAAAKgwH/Vpresvr1WuazNyClxcjWsRrgAAAABUqE8HtPV0CW7hleHKYrHo+eefV2xsrIKCgpSQkKAlS5Zc9LqvvvpKPXr00OWXX67g4GBdccUVeuaZZ5SRkeH+ogEAAAA4LOWNO1Qx/QYrjleGq379+mncuHHq1auXJkyYILPZrK5du+qnn34q9boBAwZox44d6t27t959910lJiZq4sSJateunbKzsyuoegAAAACOSH7jDk+X4FJe19Bi/fr1SkhI0NixYzVkyBBJUk5OjuLj4xUVFaWff/65xGt//PFH3XzzzYWOzZgxQ3379tVHH32khx9+2OE6aGgBAAAAVIwGDu5jlXBpoD5/omh3QnertA0t5s2bJ7PZrAEDBtiPBQYGqn///lq7dq0OHjxY4rUXBitJuvvuuyVJO3bscHmtAAAAAJy3bUQXh86b8kgHN1fiHK8LV5s2bVKTJk2KJMI2bdpIkjZv3lym8dLS0iRJtWvXdkl9AAAAAFwrJNBXLeqWvlqsRd0whQR69za9XheuUlNTFRMTU+S47djhw4fLNN6YMWNkNpuVlJRU6nkWi0VZWVmFvgAAAABUjAWDbioxYLWoG6YFg26q4IrKzuuiX3Z2tgICAoocDwwMtL/uqE8//VRTpkzRc889p8aNG5d67ujRozVy5MiyFQsAAADAZRYMukmnc/L11OebdOBkturXDNL4Htd4/YyVjddVGRQUJIvFUuR4Tk6O/XVHrF69Wv3791eXLl302muvXfT8oUOH6umnn7Z/n5WVpXr16jlYNQAAAABXCAn01Ud9W3u6jHLxunAVExOjQ4cOFTmempoqSYqNjb3oGFu2bNGdd96p+Ph4zZs3T76+F3+bAQEBxc6YAQAAAIAjvO6Zq5YtW2r37t1Fnnlat26d/fXS7N27V4mJiYqKitKiRYsUEhLirlIBAAAAwM7rwlVSUpIKCgr04Ycf2o9ZLBZNnTpVCQkJ9qV6Bw4c0M6dOwtdm5aWps6dO8vHx0fff/+96tSpU6G1AwAAAKi+vG5ZYEJCgu677z4NHTpU6enpatSokaZPn66UlBRNmTLFfl6fPn20cuVKnb8HcmJiovbt26fnnntOP/30k3766Sf7a5dccok6depUoe8FAAAAQPXhdeFKkmbMmKFhw4Zp5syZOnnypFq0aKFvvvlG7du3L/W6LVu2SJLefPPNIq916NCBcAUAAADAbUzG+VM/sMvKylJ4eLgyMzOLbGgMAAAAoPpwNBt43TNXAAAAAFAZEa4AAAAAwAUIVwAAAADgAoQrAAAAAHABwhUAAAAAuADhCgAAAABcgHAFAAAAAC7glZsIewPb9l9ZWVkergQAAACAJ9kywcW2CCZcleDUqVOSpHr16nm4EgAAAADe4NSpUwoPDy/xdZNxsfhVTVmtVh0+fFihoaEymUwerSUrK0v16tXTwYMHS90RGrDhM4Oy4jODsuIzg7LiM4Oy8LbPi2EYOnXqlGJjY+XjU/KTVcxclcDHx0d169b1dBmFhIWFecWHC5UHnxmUFZ8ZlBWfGZQVnxmUhTd9XkqbsbKhoQUAAAAAuADhCgAAAABcgHBVCQQEBGj48OEKCAjwdCmoJPjMoKz4zKCs+MygrPjMoCwq6+eFhhYAAAAA4ALMXAEAAACACxCuAAAAAMAFCFcAAAAA4AKEKwAAAABwAcKVB1ksFj3//POKjY1VUFCQEhIStGTJEoeuPXTokLp3766IiAiFhYXprrvu0r59+9xcMTytvJ+Zr776Sj169NDll1+u4OBgXXHFFXrmmWeUkZHh/qLhUc78d+Z8nTp1kslk0qBBg9xQJbyJs5+Zzz//XO3atVONGjUUERGh66+/XsuXL3djxfA0Zz4zS5cuVceOHVW7dm1FRESoTZs2mjlzppsrhiedPn1aw4cPV2JioiIjI2UymTRt2jSHr8/IyNCAAQNUp04d1ahRQx07dtTGjRvdV3AZEa48qF+/fho3bpx69eqlCRMmyGw2q2vXrvrpp59Kve706dPq2LGjVq5cqRdffFEjR47Upk2b1KFDBx0/fryCqocnlPczM2DAAO3YsUO9e/fWu+++q8TERE2cOFHt2rVTdnZ2BVUPTyjvZ+Z8X331ldauXevGKuFNnPnMjBgxQj179lS9evU0btw4jRo1Si1atNChQ4cqoHJ4Snk/MwsWLFDnzp2Vm5urESNG6LXXXlNQUJD69Omj8ePHV1D1qGjHjh3TK6+8oh07dujqq68u07VWq1V33HGHPv30Uw0aNEhvvvmm0tPTdfPNN2vPnj1uqriMDHjEunXrDEnG2LFj7ceys7ONhg0bGu3atSv12jFjxhiSjPXr19uP7dixwzCbzcbQoUPdVjM8y5nPzIoVK4ocmz59uiHJ+Oijj1xdKryEM5+Z889v0KCB8corrxiSjH/961/uKhdewJnPzNq1aw2TyWSMGzfO3WXCizjzmenUqZMRGxtr5OTk2I/l5eUZDRs2NFq0aOG2muFZOTk5RmpqqmEYhrFhwwZDkjF16lSHrv38888NScbcuXPtx9LT042IiAijZ8+e7ii3zJi58pB58+bJbDZrwIAB9mOBgYHq37+/1q5dq4MHD5Z6bevWrdW6dWv7saZNm+rWW2/VF1984da64TnOfGZuvvnmIsfuvvtuSdKOHTtcXiu8gzOfGZs333xTVqtVQ4YMcWep8BLOfGbeeecdRUdHa/DgwTIMQ6dPn66IkuFhznxmsrKyVLNmzUKbxPr6+qp27doKCgpya93wnICAAEVHR5fr2nnz5umSSy7RPffcYz9Wp04dde/eXV9//bUsFouryiw3wpWHbNq0SU2aNFFYWFih423atJEkbd68udjrrFarfv/9d1133XVFXmvTpo327t2rU6dOubxeeF55PzMlSUtLkyTVrl3bJfXB+zj7mTlw4IDeeOMNjRkzhl90qglnPjPLli1T69at9e6776pOnToKDQ1VTEyMJk6c6M6S4WHOfGZuvvlm/fHHHxo2bJj+/PNP7d27V6+++qp+/fVXPffcc+4sG5XUpk2b1KpVK/n4FI4wbdq00dmzZ7V7924PVfY3X08XUF2lpqYqJiamyHHbscOHDxd73YkTJ2SxWC567RVXXOHCauENyvuZKcmYMWNkNpuVlJTkkvrgfZz9zDzzzDO65pprdP/997ulPnif8n5mTp48qWPHjmnNmjVavny5hg8frvr162vq1Kl64okn5Ofnp4EDB7q1dniGM/+dGTZsmJKTk/Xaa69p1KhRkqTg4GB9+eWXuuuuu9xTMCq11NRUtW/fvsjx8z9vzZs3r+iyCiFceUh2dnahaXCbwMBA++slXSepXNeicivvZ6Y4n376qaZMmaLnnntOjRs3dlmN8C7OfGZWrFihL7/8UuvWrXNbffA+5f3M2JYAHj9+XHPmzFGPHj0kSUlJSWrevLlGjRpFuKqinPnvTEBAgJo0aaKkpCTdc889Kigo0IcffqjevXtryZIlatu2rdvqRuXkyt+F3IVw5SFBQUHFrgvNycmxv17SdZLKdS0qt/J+Zi60evVq9e/fX126dNFrr73m0hrhXcr7mcnPz9eTTz6pBx98sNCznaj6nP1/k5+fX6HZcB8fH/Xo0UPDhw/XgQMHVL9+fTdUDU9y5v9NgwYN0i+//KKNGzfal3l1795dV111lQYPHswf7qAIV/0u5E48c+UhMTExSk1NLXLcdiw2NrbY6yIjIxUQEFCua1G5lfczc74tW7bozjvvVHx8vObNmydfX/58pSor72dmxowZ2rVrlwYOHKiUlBT7lySdOnVKKSkpOnv2rNvqhuc48/+mwMBA1apVS2azudBrUVFRks4tHUTVU97PTG5urqZMmaI77rij0PMzfn5+uv322/Xrr78qNzfXPUWj0nLF70LuRrjykJYtW2r37t3KysoqdNz2pzQtW7Ys9jofHx81b95cv/76a5HX1q1bp8svv1yhoaEurxeeV97PjM3evXuVmJioqKgoLVq0SCEhIe4qFV6ivJ+ZAwcOKC8vTzfccIPi4uLsX9K54BUXF6cffvjBrbXDM5z5f1PLli119OjRIr8Q2565qVOnjusLhseV9zNz/Phx5efnq6CgoMhreXl5slqtxb6G6q1ly5bauHGjrFZroePr1q1TcHCwmjRp4qHK/ka48pCkpCT72mIbi8WiqVOnKiEhQfXq1ZN07pecnTt3Frl2w4YNhQLWrl27tHz5ct13330V8wZQ4Zz5zKSlpalz587y8fHR999/zy851UR5PzP333+/5s+fX+RLkrp27ar58+crISGhYt8MKoQz/53p0aOHCgoKNH36dPuxnJwczZ49W82aNfOKP1GG65X3MxMVFaWIiAjNnz+/UCA/ffq0Fi5cqKZNm3rFEi94Tmpqqnbu3Km8vDz7saSkJB05ckRfffWV/dixY8c0d+5cdevWrdjnsSqcpzfaqs7uu+8+w9fX13j22WeNyZMnG9dff73h6+trrFy50n5Ohw4djAv/MWVlZRkNGzY0oqKijDfffNMYP368Ua9ePSM2NtZIT0+v6LeBClTez8zVV19tSDKee+45Y+bMmYW+fvjhh4p+G6hA5f3MFEdsIlwtlPczc/bsWeOqq64y/Pz8jCFDhhjvvvuu0bp1a8NsNhuLFi2q6LeBClTez8yoUaMMScY111xjjB8/3njrrbeMK6+80pBkzJo1q6LfBirQf//7X+PVV181HnvsMUOScc899xivvvqq8eqrrxoZGRmGYRhG3759DUlGcnKy/br8/Hyjbdu2RkhIiDFy5EjjvffeM6666iojNDTU2Llzp4feTWGEKw/Kzs42hgwZYkRHRxsBAQFG69atjcWLFxc6p6Rfeg4ePGgkJSUZYWFhRkhIiPGPf/zD2LNnT0WVDg8p72dGUolfHTp0qMB3gIrmzH9nLkS4qh6c+cwcOXLE6Nu3rxEZGWkEBAQYCQkJRa5F1ePMZ2b27NlGmzZtjIiICCMoKMhISEgw5s2bV1Glw0Muu+yyEn8vsYWp4sKVYRjGiRMnjP79+xu1atUygoODjQ4dOhgbNmyo+DdRApNhGEYFTJABAAAAQJXGM1cAAAAA4AKEKwAAAABwAcIVAAAAALgA4QoAAAAAXIBwBQAAAAAuQLgCAAAAABcgXAEAAACACxCuAAAAAMAFCFcAAAAA4AKEKwAAKlCDBg3UoEGDQsemTZsmk8mkadOmeaQmAIBrEK4AAJVOSkqKTCZToS8/Pz9deuml6t69u3799VdPlwgAqIZ8PV0AAADl1bBhQ/Xu3VuSdObMGf3222+aO3eu/ve//2np0qVq3769hysEAFQnhCsAQKXVqFEjjRgxotCxN954Q0OHDtWwYcO0cuVKzxQGAKiWWBYIAKhS+vfvL0n67bffCh3Pzc3VuHHj1KpVK9WoUUOhoaG66aabtGDBgmLHyc3N1fjx49W6dWuFhoYqJCREzZo109NPP62TJ0/az1uxYoX+7//+T1dccYVCQkIUEhKi6667Th9++KH73iQAwCsxcwUAqJJ8ff/+X5zFYlFiYqJ+/PFHtWzZUv3791deXp6+/fZb3XXXXfrvf/+rQYMG2c/Pzs5Wp06dtGbNGjVu3FgPPfSQAgICtGfPHk2ePFl9+vRRzZo1JUljxozRn3/+qbZt2+ruu+9WRkaGFi9erIEDB2rXrl16++23K/y9AwA8g3AFAKhSPv74Y0nSjTfeaD/2yiuv6Mcff9SwYcM0cuRImUwmSdKpU6d0yy236JlnntE999yj2NhYSdKwYcO0Zs0aPfjgg5o6darMZrN9rMzMzELfT5o0SXFxcYVqyM/PV9euXTVhwgQNHjxY9evXd9v7BQB4D5YFAgAqrT///FMjRozQiBEj9Oyzz+qWW27Riy++qEsuuURjx46VJFmtVk2aNEkNGzYsFKwkKTQ0VC+//LJyc3P11VdfSToXjD788EOFh4drwoQJhYKUJIWHhyskJMT+/YXBSjo3a/boo4+qoKBAK1ascMdbBwB4IWauAACV1t69ezVy5MhCx6Kjo7V69Wo1atRIkrRr1y6dPHlSsbGxRc6VpKNHj0qSdu7caf/rqVOndNttt9mX/pXm1KlTeuutt/S///1Pe/fu1ZkzZwq9fvjw4XK9NwBA5UO4AgBUWl26dNHixYslnQtJ06dP1/PPP68777xT69evV0hIiE6cOCFJ+uOPP/THH3+UOJYtFGVmZkqSLr300ovePzc3VzfffLM2btyoa665Rg8++KBq1aolX19fpaSkaPr06bJYLM6+TQBAJUG4AgBUCXXq1NGQIUOUmZmpUaNG6aWXXtI777yjsLAwSf+vnftnaSyIwjD+LAhRlGAV1MZ0Iij4CewtRCQfQfAPKWwEMV2wUixExCCpVAQLLQRFQQiogQQiATEgFooWsbAL2EXJFrILYQV3l7u4hufX3jPDPeXLzByIxWLs7u5+uE97ezsA5XL5w9r9/X2KxSJjY2M/33r9sLOzw8bGxp83Ikn6snxzJUlqKIlEgq6uLtbW1ri/v6e3t5dwOMzFxQXVavXD9T09PYTDYQqFQt3I9ffc3t4CMDIy8su38/Pzv2tAkvRlGa4kSQ2lpaWF2dlZqtUq8/PzNDU1MTU1xcPDAzMzM+8GrFKpxNPTE/A2jGJiYoJKpcL09DSvr691tZVKhefnZwC6u7sByGazdTWnp6ek0+l/0Z4k6T/mtUBJUsMZHx9nYWGBzc1NEokEyWSSYrHIysoKh4eHDA4OEolEKJfLXF1dcXl5SS6XIxKJAG+j2/P5PFtbW+TzeYaGhgiFQtzd3XF8fEw2m2VgYIDh4WGi0SiLi4uUSiX6+vq4ubnh4OCA0dHR37qGKElqHJ5cSZIaTnNzM3Nzc7y8vJBMJgmFQhwdHbG+vk5HRwd7e3ssLy9zdnZGZ2cnqVSK/v7+uvUnJycsLS3R2tpKOp0mlUpxfX3N5OQk0WgUgLa2NjKZDLFYjEKhwOrqKo+Pj2xvbxOPxz+pe0nSZ/lWq9Vqn/0TkiRJkvTVeXIlSZIkSQEwXEmSJElSAAxXkiRJkhQAw5UkSZIkBcBwJUmSJEkBMFxJkiRJUgAMV5IkSZIUAMOVJEmSJAXAcCVJkiRJATBcSZIkSVIADFeSJEmSFADDlSRJkiQF4DsZh2UzVC8vwwAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "WCUrVArTmivN"
}
},
{
"cell_type": "code",
"source": [
"# Intento (2) Realizamos un GridSearchCV para explorar los hyperparametros de\n",
"# Gradient boosting\n",
"# =============================================================================\n",
"\n",
"# Definir variables\n",
"X = df.drop('churn', axis=1)\n",
"y = df['churn']\n",
"\n",
"# Columnas numéricas y categóricas\n",
"num_cols = ['credit_score', 'age', 'tenure', 'balance', 'products_number', 'estimated_salary']\n",
"cat_cols = ['country', 'gender', 'credit_card', 'active_member']\n",
"\n",
"# Preprocesamiento para columnas numéricas\n",
"num_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"# Preprocesamiento para columnas categóricas\n",
"cat_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"# Combinación de transformadores\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', num_transformer, num_cols),\n",
" ('cat', cat_transformer, cat_cols)\n",
" ]\n",
")\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Pipeline que incluye preprocesamiento y modelo\n",
"model = Pipeline(steps=[\n",
" ('preprocessor', preprocessor),\n",
" ('pca', PCA(n_components=10)),\n",
" ('classifier', GradientBoostingClassifier(random_state=42))\n",
"])\n",
"\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"param_grid = {\n",
" 'classifier__n_estimators': [100, 800],\n",
" 'classifier__learning_rate': [0.01, 0.1],\n",
" 'classifier__max_depth': [3, 5],\n",
" 'classifier__subsample': [0.6, 1.0],\n",
" 'classifier__min_samples_split': [2, 10]\n",
"}\n",
"\n",
"grid_search = GridSearchCV(\n",
" estimator=model,\n",
" param_grid=param_grid,\n",
" cv=5,\n",
" scoring='f1', # optimizamos por f1 ya que es como evaluamos el modelo\n",
" n_jobs=-1, # todos los núcleos disponibles\n",
" verbose=2\n",
")\n",
"\n",
"# Ejecutar la búsqueda\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Mejor combinación de hiperparámetros\n",
"print(f\"Mejores hiperparámetros: {grid_search.best_params_}\")\n",
"print(f\"Mejor puntuación en validación cruzada: {grid_search.best_score_}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lRb0ZRv2Z0rY",
"outputId": "4c8f3d04-750c-4d30-f2e5-282158b0bbfe"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fitting 5 folds for each of 32 candidates, totalling 160 fits\n",
"Mejores hiperparámetros: {'classifier__learning_rate': 0.1, 'classifier__max_depth': 3, 'classifier__min_samples_split': 10, 'classifier__n_estimators': 800, 'classifier__subsample': 0.6}\n",
"Mejor puntuación en validación cruzada: 0.5803762827385058\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Intento (3) con PCA[10] + RandomForest()\n",
"# =============================================================================\n",
"\n",
"# Definir variables\n",
"X = df.drop('churn', axis=1)\n",
"y = df['churn']\n",
"\n",
"# Columnas numéricas y categóricas\n",
"num_cols = ['credit_score', 'age', 'tenure', 'balance', 'products_number', 'estimated_salary']\n",
"cat_cols = ['country', 'gender', 'credit_card', 'active_member']\n",
"\n",
"# Preprocesamiento para columnas numéricas\n",
"num_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())\n",
"])\n",
"\n",
"# Preprocesamiento para columnas categóricas\n",
"cat_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
"])\n",
"\n",
"# Combinación de transformadores\n",
"preprocessor = ColumnTransformer(\n",
" transformers=[\n",
" ('num', num_transformer, num_cols),\n",
" ('cat', cat_transformer, cat_cols)\n",
" ]\n",
")\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Pipeline que incluye preprocesamiento y modelo\n",
"model = Pipeline(steps=[\n"
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