oprimal_tariff_and_analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d238695",
   "metadata": {},
   "source": [
    "# Выбор оптимального тарифного плана А1 (Беларусь) корпоративной линейки \"Своё решение\"  \n",
    "### Февраль 2024 ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "d7a46247",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "IPython.notebook.set_autosave_interval(30000)"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 30 seconds\n"
     ]
    }
   ],
   "source": [
    "#Ставим таймер автосохранения \n",
    "%autosave 30"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c811af97",
   "metadata": {},
   "source": [
    "<a id='contents'></a>\n",
    "## Содержание:\n",
    "1. [Подготовка и предобработка данных](#preprocessing)  \n",
    "1.1. [Импорт необходимых библиотек](#import)  \n",
    "1.2. [Формирования датафрейма с действующими тарифами для корпоративных клиентов](#tariff)  \n",
    "1.3. [Международные звонки](#international_tariff)  \n",
    "1.3.1 [Пороги выгоды для каждого пакета](#international_tariff_bep)  \n",
    "\n",
    "2. [Загрузка сырых данных](#raw_processing)  \n",
    "2.1 [Преобразование типов данных](#precooking)  \n",
    "\n",
    "3. [Анализ данных](#analyz)  \n",
    "3.1. [Определение неиспользуемых номеров](#an_unused_numbers)  \n",
    "3.2. [Определение оптимального тарифа](#an_optimal_tariff)  \n",
    "3.3. [Определение необходимости подключения пакетов для совершения международных звонков](#an_need_int_package)  \n",
    "3.4. [Анализ в разрезе одного пользователя](#an_personal)  \n",
    "\n",
    "4. [Комплексный анализ поведения пользователей](#an_complex)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80755e77",
   "metadata": {},
   "source": [
    "<a id='preprocessing'></a>\n",
    "## Подготовка и предобработка данных\n",
    "\n",
    "[Назад к содержанию](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1b81d49",
   "metadata": {},
   "source": [
    "<a id='import'></a>\n",
    "### Импорт необходимых библиотек\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "**os**: *Эта библиотека используется для взаимодействия с операционной системой. С её помощью можно выполнять операции, связанные с файловой системой, например, получение пути к файлам, перечисление файлов в директории и т.д.*\n",
    "\n",
    "**pandas**: *Одна из основных библиотек для анализа данных в Python. Предоставляет структуры данных DataFrame и Series для манипуляции с табличными данными, удобное чтение и запись данных из/в различные форматы (CSV, Excel и т.д.), а также множество функций для обработки и анализа данных.*\n",
    "\n",
    "**numpy**: *Библиотека для эффективных вычислений с массивами и матрицами в Python. Используется в качестве основы для многих других библиотек анализа данных и научных вычислений.*\n",
    "\n",
    "**matplotlib.pyplot**: *Модуль библиотеки Matplotlib, предназначен для создания статических, интерактивных и анимированных визуализаций в Python. plt является традиционным псевдонимом для обращения к функциям этого модуля.*\n",
    "\n",
    "**seaborn**: *Библиотека визуализации данных на основе matplotlib, предоставляющая более высокоуровневый интерфейс для создания статистических графиков. Улучшает внешний вид графиков и дополняет matplotlib дополнительными типами графиков.*\n",
    "\n",
    "**glob**: *Библиотека для создания списков файлов из директорий с использованием шаблонов. Позволяет выполнять поиск файлов по заданным шаблонам (например, все файлы Excel в директории).*\n",
    "\n",
    "**openpyxl**: *Библиотека для чтения и записи файлов Excel (xlsx/xlsm/xltx/xltm). Позволяет работать с электронными таблицами Excel, изменять данные, форматы, формулы и т.д.*\n",
    "\n",
    "**sklearn.preprocessing.StandardScaler**: *Компонент библиотеки scikit-learn, используемый для стандартизации признаков путём удаления среднего и масштабирования до единичной дисперсии. Это часто необходимо перед применением многих алгоритмов машинного обучения.*\n",
    "\n",
    "**sklearn.cluster.KMeans**: *Модуль библиотеки scikit-learn для кластеризации данных методом K-средних. Используется для разделения данных на предварительно определенное количество кластеров.*\n",
    "\n",
    "**warnings**: *Библиотека для управления предупреждениями в Python. Позволяет игнорировать определенные предупреждения, что может быть полезно для подавления несущественных сообщений, которые не влияют на выполнение программы.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b0e87bcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import glob\n",
    "import openpyxl\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Устанавливаем стиль визуализации по умолчанию для seaborn\n",
    "sns.set()\n",
    "\n",
    "import warnings\n",
    "\n",
    "# Игнорирование конкретного предупреждения из библиотеки openpyxl\n",
    "warnings.filterwarnings(\"ignore\", message=\"Workbook contains no default style, apply openpyxl's default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e75803",
   "metadata": {},
   "source": [
    "<a id='tariff'></a>\n",
    "### Формирования датафрейма с действующими тарифами для корпоративных клиентов\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Формирования данных о действующих тарифах для корпоративных клиентов компании А1 и их сверхлимитных ставках. \n",
    "\n",
    "Создается словарь `tariffs_options`, где каждому тарифу (Лидер, Эксперт, Мастер, Новатор, и специальный тариф Передача данных) соответствует список опций. Каждая опция описывает условия тарифа: количество минут (minutes), объем интернет-трафика в гигабайтах (gb) и цену в долларах (price). Эти данные предназначены для анализа доступных тарифных планов и их условий для корпоративных клиентов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c2ae881c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Действующие тарифы А1 \"Своё решение\" для корпоративных клиентов\n",
    "tariffs_options = {\n",
    "    'Лидер': [\n",
    "        {'minutes': 'unlimited', 'gb': 30, 'price': 33.81},\n",
    "        {'minutes': 1200, 'gb': 40, 'price': 33.81},\n",
    "        {'minutes': 1000, 'gb': 50, 'price': 33.81},\n",
    "        {'minutes': 800, 'gb': 'unlimited', 'price': 33.81},\n",
    "    ],\n",
    "    'Эксперт': [\n",
    "        {'minutes': 'unlimited', 'gb': 20, 'price': 27.37},\n",
    "        {'minutes': 800, 'gb': 30, 'price': 27.37},\n",
    "        {'minutes': 600, 'gb': 40, 'price': 27.37},\n",
    "        {'minutes': 400, 'gb': 'unlimited', 'price': 27.37},\n",
    "    ],\n",
    "    'Мастер': [\n",
    "        {'minutes': 'unlimited', 'gb': 0, 'price': 19.93},\n",
    "        {'minutes': 500, 'gb': 5, 'price': 19.93},\n",
    "        {'minutes': 300, 'gb': 15, 'price': 19.93},\n",
    "        {'minutes': 100, 'gb': 25, 'price': 19.93},\n",
    "    ],\n",
    "    'Новатор': [\n",
    "        {'minutes': 200, 'gb': 0, 'price': 10.50},\n",
    "        {'minutes': 100, 'gb': 5, 'price': 10.50},\n",
    "        {'minutes': 0, 'gb': 10, 'price': 10.50},\n",
    "    ],\n",
    "    'Передача данных': [\n",
    "        {'minutes': 0, 'gb': 0, 'price': 2.06}  # Специальный тариф\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009970d5",
   "metadata": {},
   "source": [
    "Создание датафрейма для сверхлимитных ставок:\n",
    "\n",
    "В словаре `overlimit_rates` хранится информация о сверхлимитных ставках для различных услуг (Интернет-трафик, Исходящие внутри сети и на голосовую почту, SMS исходящие международные) по каждому тарифу (Лидер, Эксперт, Мастер, Новатор). Этот словарь содержит три ключа: Тариф, Услуга и Цена за единицу, где указаны названия тарифов, типы услуг и цены за единицу сверх установленных лимитов.\n",
    "\n",
    "Далее, словарь `overlimit_rates` преобразуется в объект DataFrame с помощью функции pd.DataFrame(). Полученный датафрейм `overlimit_df` организует данные в табличной форме, что упрощает анализ сверхлимитных ставок по различным услугам для каждого тарифа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "a50ad241",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создание DataFrame для хранения информации о сверхлимитных ставках для каждого тарифа\n",
    "overlimit_rates = {\n",
    "    \"Тариф\": [\"Лидер\", \"Эксперт\", \"Мастер\", \"Новатор\"] * 3,\n",
    "    \"Услуга\": [\"Интернет-трафик\", \"Исходящие внутри сети и на голосовую почту\", \"SMS исходящие международные\"] * 4,\n",
    "    \"Цена за единицу\": [\n",
    "        # Лидер, Эксперт, Мастер, Новатор\n",
    "        0.0596, 0.06, 0.185, 0.0596, 0.06, 0.185, 0.0596, 0.06, 0.185, 0.0596, 0.06, 0.185,\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Преобразование словаря в DataFrame\n",
    "overlimit_df = pd.DataFrame(overlimit_rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1328ad5a",
   "metadata": {},
   "source": [
    "<a id='international_tariff'></a>\n",
    "### Международные звонки\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Определение стоимости международных звонков по регионам:\n",
    "\n",
    "Словарь `international_call_rates` хранит информацию о стоимости минуты международного звонка для различных регионов: СНГ, Европа и Другие страны. Значения указаны в за минуту.\n",
    "Описание пакетов международных звонков:\n",
    "\n",
    "Список `international_calls_packages` содержит словари, каждый из которых описывает определенный пакет международных звонков. Для каждого пакета указано название (`name`), количество минут (`minutes`), регионы, в которых действует пакет (`regions`), и цена пакета (`price`).\n",
    "\n",
    "Для каждого пакета рассчитывается и добавляется стоимость одной минуты разговора (`cost_per_minute`), которая получается делением общей цены пакета на количество минут. Это позволяет оценить, насколько выгодно использование каждого пакета с точки зрения стоимости одной минуты звонка.\n",
    "Расчет пороговых значений для каждого пакета:\n",
    "\n",
    "Для каждого пакета рассчитываются пороговые значения (`break_even_points`), которые показывают, сколько минут необходимо использовать по данному пакету в каждом регионе, чтобы пакет стал выгоднее оплаты звонков по стандартной ставке (рассчитанной из `international_call_rates`).\n",
    "\n",
    "Если пакет действует во всех странах, пороговые значения рассчитываются для каждого региона. Если пакет ограничен определенными регионами, расчет проводится только для этих регионов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "bfcfd8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Стоимость международных звонков за минуту в зависимости от региона\n",
    "international_call_rates = {\n",
    "    'СНГ': 0.805,\n",
    "    'Европа': 1.15,\n",
    "    'Другие страны': 2.30,\n",
    "}\n",
    "\n",
    "# Пакеты международных звонков\n",
    "international_calls_packages = [\n",
    "    {'name': 'Международный 100', 'minutes': 100, 'regions': ['СНГ', 'Европа'], 'price': 55.83},\n",
    "    {'name': '20 минут международных звонков', 'minutes': 20, 'regions': ['Все страны'], 'price': 13.50},\n",
    "    {'name': '50 минут международных звонков', 'minutes': 50, 'regions': ['Все страны'], 'price': 30.50},\n",
    "    {'name': 'Международный 250', 'minutes': 250, 'regions': ['СНГ', 'Европа'], 'price': 133.57},\n",
    "]\n",
    "\n",
    "# Добавление стоимости минуты внутри пакета\n",
    "for package in international_calls_packages:\n",
    "    package['cost_per_minute'] = package['price'] / package['minutes']\n",
    "\n",
    "# Расчет порогового значения для каждого пакета\n",
    "for package in international_calls_packages:\n",
    "    # Проверяем, действует ли пакет во всех странах\n",
    "    if 'Все страны' in package['regions']:\n",
    "        # Рассчитываем порог для каждого региона из international_call_rates\n",
    "        package['break_even_points'] = [(region, package['price'] / rate) for region, rate in international_call_rates.items()]\n",
    "    else:\n",
    "        # Рассчитываем порог только для указанных в пакете регионов\n",
    "        package['break_even_points'] = [(region, package['price'] / international_call_rates[region]) for region in package['regions']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e0f92e",
   "metadata": {},
   "source": [
    "<a id='international_tariff_bep'></a>\n",
    "#### Пороги выгоды для каждого пакета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "97d0e71e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пакет: Международный 100\n",
      "  Регион: СНГ, Порог: 69.35 минут\n",
      "  Регион: Европа, Порог: 48.55 минут\n",
      "-------\n",
      "Пакет: 20 минут международных звонков\n",
      "  Регион: СНГ, Порог: 16.77 минут\n",
      "  Регион: Европа, Порог: 11.74 минут\n",
      "  Регион: Другие страны, Порог: 5.87 минут\n",
      "-------\n",
      "Пакет: 50 минут международных звонков\n",
      "  Регион: СНГ, Порог: 37.89 минут\n",
      "  Регион: Европа, Порог: 26.52 минут\n",
      "  Регион: Другие страны, Порог: 13.26 минут\n",
      "-------\n",
      "Пакет: Международный 250\n",
      "  Регион: СНГ, Порог: 165.93 минут\n",
      "  Регион: Европа, Порог: 116.15 минут\n",
      "-------\n"
     ]
    }
   ],
   "source": [
    "# Вывод результатов\n",
    "for package in international_calls_packages:\n",
    "    print(f\"Пакет: {package['name']}\")\n",
    "    for region, break_even in package['break_even_points']:\n",
    "        print(f\"  Регион: {region}, Порог: {break_even:.2f} минут\")\n",
    "    print(\"-------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766e6f86",
   "metadata": {},
   "source": [
    "<a id='raw_processing'></a>\n",
    "## Загрузка сырых данных\n",
    "\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "---\n",
    "\n",
    "*Используем детализации, доступные в `Личном Кабинете` > `Раздел \"Финансы\"` > `\"Детализация на Email\"` в разрезе номеров и месяцев*\n",
    "\n",
    "---\n",
    "\n",
    "Установка пути к файлам с сырыми данными:\n",
    "\n",
    "Переменная `raw_data_path` содержит путь к файлам Excel, которые нужно обработать. В примере используется шаблон `./a1_office/*.xlsx`, что означает, что будут выбраны все файлы с расширением `.xlsx` в папке `a1_office`.\n",
    "Подготовка списка для хранения данных:\n",
    "\n",
    "Создается пустой DataFrame `raw_data`, в который будут собираться данные из всех обрабатываемых файлов.\n",
    "Загрузка и обработка файлов Excel:\n",
    "\n",
    "С помощью функции `glob.glob(raw_data_path)` получаем список всех файлов, соответствующих указанному шаблону пути.\n",
    "\n",
    "Для каждого файла в списке files выполняем следующие действия:\n",
    "\n",
    "Загружаем данные из файла в DataFrame df с помощью функции `pd.read_excel(file)`.\n",
    "Добавляем в df столбец Номер телефона, значение которого формируется из имени файла. Для этого извлекаем имя файла с помощью `os.path.basename(file)`, разделяем его по пробелу и берем первую часть, предполагая, что это номер телефона.\n",
    "С помощью функции `pd.concat([raw_data, df], ignore_index=True) `объединяем текущий DataFrame df с уже существующими данными в `raw_data`, обеспечивая непрерывную нумерацию индексов (благодаря параметру `ignore_index=True`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "20114b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Установите путь к вашим файлам с сырыми данными\n",
    "raw_data_path = './RAW/*.xlsx'  # Убедитесь, что путь указан корректно\n",
    "files = glob.glob(raw_data_path)\n",
    "\n",
    "# Список для хранения всех датафреймов с сырыми данными\n",
    "raw_data = pd.DataFrame()\n",
    "\n",
    "# Загрузка всех файлов Excel из директории\n",
    "for file in files:\n",
    "    df = pd.read_excel(file)\n",
    "    # Добавление столбца с идентификатором файла, извлекаемым из имени файла\n",
    "    df['Номер телефона'] = os.path.basename(file).split(' ')[0]\n",
    "    raw_data = pd.concat([raw_data, df], ignore_index=True)  # Исправляем на правильное имя переменной"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee1450e",
   "metadata": {},
   "source": [
    "<a id='precooking'></a>\n",
    "## Преобразование типов данных\n",
    "\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Преобразовываем и очищаем данные, чтобы подготовить их к дальнейшему анализу и обработке. Работа ведется в несколько этапов, каждый из которых направлен на улучшение качества данных и их структуры для обеспечения более эффективного и точного анализа."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fcd3513",
   "metadata": {},
   "source": [
    "1. Преобразование дат и времени:  \n",
    "\n",
    "`Дата звонка`: Преобразуем строковое представление даты звонка в формат `datetime`, что позволяет использовать мощные инструменты pandas для работы с временными рядами.  \n",
    "\n",
    "2. Преобразование числовых данных:  \n",
    "\n",
    "`Номер телефона`, Кто звонил: Преобразуем эти столбцы в числовой формат `int64` для обеспечения единообразия данных и возможности выполнения математических операций.  \n",
    "\n",
    "3. Преобразование продолжительности звонков:  \n",
    "\n",
    "`Длительность, сек.`: Конвертируем длительность звонков из формата `HH:MM:SS` в секунды для унификации и упрощения анализа.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "73a4add5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Преобразование 'Дата звонка' в datetime\n",
    "raw_data['Дата звонка'] = pd.to_datetime(raw_data['Дата звонка'])\n",
    "\n",
    "# Преобразование 'Номер телефона', 'Кто звонил', и 'Куда звонил' в int\n",
    "raw_data['Номер телефона'] = raw_data['Номер телефона'].astype('int64')\n",
    "raw_data['Кто звонил'] = raw_data['Кто звонил'].astype('int64')\n",
    "\n",
    "# Преобразование 'Длительность, сек.' из формата HH:MM:SS в секунды\n",
    "def process_duration(row):\n",
    "    if 'SMS' in row['Тип звонка'] or not isinstance(row['Длительность, сек.'], str):\n",
    "        # Если тип звонка - SMS, или если 'Длительность, сек.' не является строкой, возвращаем значение как есть.\n",
    "        # Это предполагает, что для SMS или некоторых других случаев длительность звонка может быть уже в секундах или не применима.\n",
    "        return row['Длительность, сек.']\n",
    "    else:\n",
    "        # В противном случае пытаемся преобразовать HH:MM:SS длительность в секунды.\n",
    "        try:\n",
    "            h, m, s = row['Длительность, сек.'].split(':')\n",
    "            return int(h) * 3600 + int(m) * 60 + int(s)\n",
    "        except ValueError:\n",
    "            # Если встречается ошибка (например, из-за неправильного формата), возвращаем 0 или другое логичное значение по умолчанию.\n",
    "            return 0\n",
    "\n",
    "# Применение исправленной функции к датафрейму\n",
    "raw_data['Длительность, сек.'] = raw_data.apply(process_duration, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0419f79e",
   "metadata": {},
   "source": [
    "4. Работа с информацией о пользователях:  \n",
    "  \n",
    "Загрузка и преобразование данных о пользователях: Загружаем данные о пользователях и их тарифах, преобразуем номера телефонов для соответствия с основным датафреймом, а также конвертируем другие релевантные поля. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deae6432",
   "metadata": {},
   "source": [
    "*Загрузка справочника абонентов*\n",
    "\n",
    "Получить можно в ЛК > Раздел \"Абоненты\" > Выгрузить в Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "eef8551c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Загрузка информации о пользователях и их текущих тарифах\n",
    "accounts_df = pd.read_excel('accounts.xlsx')\n",
    "\n",
    "# Преобразование 'Номер/Логин' к строковому типу и очистка от пробелов\n",
    "accounts_df['Номер/Логин'] = accounts_df['Номер/Логин'].astype(str).str.strip()\n",
    "\n",
    "# Преобразование 'SIM/Точка подключения' в int (если это возможно)\n",
    "# Обратите внимание: если в этом столбце есть нечисловые значения, преобразование вызовет ошибку\n",
    "accounts_df['SIM/Точка подключения'] = pd.to_numeric(accounts_df['SIM/Точка подключения'], errors='coerce')\n",
    "\n",
    "# Заполнение NaN значений, если они есть, после попытки конвертации\n",
    "accounts_df['SIM/Точка подключения'] = accounts_df['SIM/Точка подключения'].fillna(0).astype(int)\n",
    "\n",
    "# Преобразование DataFrame в словарь\n",
    "accounts_info = accounts_df.set_index('Номер/Логин').to_dict('index')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29fbd5d1",
   "metadata": {},
   "source": [
    "5. Объединение данных:  \n",
    "\n",
    "Интеграция данных о пользователях с основным датафреймом: Объединяем информацию о пользователях с основным датафреймом для получения полной картины по каждому звонку, включая тариф и статус пользователя.  \n",
    "  \n",
    "6. Добавление и классификация новых атрибутов:  \n",
    "  \n",
    "Классификация типов соединений: Добавляем новые столбцы, такие как `Год звонка` и `Месяц звонка`, для удобства агрегации данных по временным периодам. Также классифицируем типы звонков и соединений для упрощения анализа паттернов использования услуг."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "c91dc26c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Проверяем, что 'Номер телефона' в raw_data и 'Номер/Логин' в accounts_df имеют одинаковый тип данных\n",
    "raw_data['Номер телефона'] = raw_data['Номер телефона'].astype(str)\n",
    "\n",
    "# Объединение основного датафрейма с информацией о пользователях\n",
    "data = raw_data.merge(accounts_df, how='left', left_on='Номер телефона', right_on='Номер/Логин')\n",
    "\n",
    "# Переименование столбцов и изменение порядка\n",
    "data = data.rename(columns={'Имя': 'ФИО', 'Тариф': 'Тариф', 'Статус': 'Статус'})\n",
    "\n",
    "# Добавление столбца 'Год звонка'\n",
    "data['Год звонка'] = data['Дата звонка'].dt.year\n",
    "\n",
    "# Добавление столбца 'Месяц звонка'\n",
    "data['Месяц звонка'] = data['Дата звонка'].dt.month\n",
    "\n",
    "data = data[['Номер телефона', 'ФИО', 'Тариф', 'Статус', 'Тип звонка', 'Год звонка', 'Месяц звонка', 'Дата звонка', 'Длительность, сек.', 'Объем, Кбайт', 'Куда звонил', 'Начислено']]\n",
    "\n",
    "# Удаление одного из дублирующихся столбцов 'ФИО', если они идентичны\n",
    "data = data.loc[:,~data.columns.duplicated()]\n",
    "\n",
    "def classify_connection_type(row):\n",
    "    if 'Интернет-трафик' in row['Тип звонка']:\n",
    "        return 'Интернет-трафик'\n",
    "    elif 'SMS входящие' in row['Тип звонка']:\n",
    "        return 'Входящие SMS'\n",
    "    elif 'SMS исходящие' in row['Тип звонка'] or 'SMS на сервисные номера компании' in row['Тип звонка']:\n",
    "        return 'Исходящие SMS'\n",
    "    elif 'Входящие' in row['Тип звонка']:\n",
    "        return 'Входящие звонки'\n",
    "    elif ('Исходящие' in row['Тип звонка'] or \n",
    "          'Бесплатные звонки' in row['Тип звонка'] or \n",
    "          'Звонки на справочные/ короткие номера' in row['Тип звонка'] or \n",
    "          'Международные звонки (СНГ)' in row['Тип звонка']):\n",
    "        return 'Исходящие звонки'\n",
    "    elif 'Звонки в ЗАГ' in row['Тип звонка'] or 'Команда' in row['Тип звонка']:\n",
    "        return 'Звонки в ЗАГ'\n",
    "    elif 'Переадресация на голосовую почту' in row['Тип звонка']:\n",
    "        return 'Переадресация на голосовую почту'\n",
    "    else:\n",
    "        return 'Другое'\n",
    "\n",
    "# Применяем обновленную функцию к каждой строке в DataFrame\n",
    "data['Категория соединения'] = data.apply(classify_connection_type, axis=1)\n",
    "\n",
    "\n",
    "def define_connection_type(row):\n",
    "    category = row['Категория соединения'].lower()  # Приведение к нижнему регистру для унификации\n",
    "    if 'sms' in category:\n",
    "        return 'SMS'\n",
    "    elif 'интернет-трафик' == category:\n",
    "        return 'Трафик'\n",
    "    elif 'звонки' in category or 'переадресация на голосовую почту' == category:\n",
    "        return 'Звонки'\n",
    "    else:\n",
    "        return 'Другое'\n",
    "\n",
    "# Применение исправленной функции к каждой строке DataFrame для обновления столбца 'Тип'\n",
    "data['Тип'] = data.apply(define_connection_type, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a49417",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='analyz'></a>\n",
    "## Анализ данных\n",
    "\n",
    "[Назад к содержанию](#contents)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0536290f",
   "metadata": {},
   "source": [
    "<a id='an_unused_numbers'></a>\n",
    "### Определение неиспользуемых номеров ###\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Выявление неиспользуемых номеров телефонов среди аккаунтов. Это позволяет определить, какие номера телефонов зарегистрированы в системе, но не участвовали в звонках или других активностях за рассматриваемый период.\n",
    "\n",
    "##### Создание списка уникальных номеров телефонов, использованных в данных: #####\n",
    "\n",
    "Из датафрейма `data`, содержащего информацию о звонках и других активностях, извлекается массив уникальных номеров телефонов (`used_phones`), что позволяет понять, какие номера были активны.\n",
    "\n",
    "##### Получение списка всех зарегистрированных номеров телефонов: #####\n",
    "\n",
    "Из датафрейма `accounts_df`, включающего сведения обо всех аккаунтах, извлекается массив всех зарегистрированных номеров телефонов (`all_phones`), что дает полное представление о доступных номерах.\n",
    "\n",
    "##### Выявление неиспользуемых номеров: #####\n",
    "\n",
    "Сравнивая массивы `used_phones` и `all_phones`, определяются номера, которые есть в списке всех номеров, но отсутствуют среди используемых (`unused_phones`). Это достигается с помощью функции np.isin, которая проверяет присутствие элементов одного массива в другом, и инвертирования результата для получения номеров, не встречающихся в данных о звонках.\n",
    "\n",
    "##### Преобразование массива в список для дальнейшей фильтрации: #####\n",
    "\n",
    "Массив `unused_phones` преобразуется обратно в список (`unused_phones_list`), что облегчает его использование для фильтрации в `pandas`.\n",
    "##### Фильтрация аккаунтов по неиспользуемым номерам: #####\n",
    "\n",
    "Проводится фильтрация датафрейма `accounts_df` для выявления записей, соответствующих неиспользуемым номерам. Результатом является датафрейм `unused_phones_with_names`, включающий номера телефонов и имена владельцев, что помогает идентифицировать владельцев неактивных номеров."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "1a72c3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Список уникальных номеров телефонов в data\n",
    "used_phones = data['Номер телефона'].unique()\n",
    "\n",
    "# Список всех номеров телефонов в accounts_df\n",
    "all_phones = accounts_df['Номер/Логин'].unique()\n",
    "\n",
    "# Находим номера телефонов из accounts_df, которые не используются в data\n",
    "unused_phones = all_phones[~np.isin(all_phones, used_phones)]\n",
    "\n",
    "# Преобразование массива unused_phones обратно в список для фильтрации\n",
    "unused_phones_list = unused_phones.tolist()\n",
    "\n",
    "# Фильтрация accounts_df для нахождения ФИО по неиспользуемым номерам\n",
    "unused_phones_with_names = accounts_df[accounts_df['Номер/Логин'].isin(unused_phones_list)][['Номер/Логин', 'Имя']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0c5bd912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Номер/Логин</th>\n",
       "      <th>Имя</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375291000001</td>\n",
       "      <td>Абонент 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375291000002</td>\n",
       "      <td>Абонент 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375291000004</td>\n",
       "      <td>Абонент 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>375291000006</td>\n",
       "      <td>Абонент 06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Номер/Логин         Имя\n",
       "0  375291000001  Абонент 01\n",
       "1  375291000002  Абонент 02\n",
       "3  375291000004  Абонент 04\n",
       "5  375291000006  Абонент 06"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вывод результатов\n",
    "unused_phones_with_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d448247",
   "metadata": {},
   "source": [
    "### Сохраняем список в файл ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "f814ba1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл 'unused_phones_with_names.xlsx' успешно сохранен.\n"
     ]
    }
   ],
   "source": [
    "# Указываем имя файла для сохранения\n",
    "excel_filename = \"unused_phones_with_names.xlsx\"\n",
    "\n",
    "# Сохранение DataFrame в Excel\n",
    "unused_phones_with_names.to_excel(excel_filename, index=False)\n",
    "\n",
    "# Вывод сообщения об успешном сохранении файла\n",
    "print(f\"Файл '{excel_filename}' успешно сохранен.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a24d201",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<a id='an_optimal_tariff'></a>\n",
    "### Определение оптимального тарифа ###\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Определение оптимального тарифа для пользователей на основе их паттернов использования услуг. Процесс включает в себя несколько ключевых этапов:\n",
    "\n",
    "#### Подготовка данных ####   \n",
    "\n",
    "##### Формирование базового DataFrame: #####  \n",
    "Создаем датафрейм `user_info`, включающий информацию о пользователях, такую как номер телефона, ФИО и текущий тариф. Это предоставляет основу для расчетов. \n",
    "\n",
    "##### Анализ исходящих звонков: #####   \n",
    "Выбираем данные по исходящим звонкам, исключая международные, и преобразуем длительность звонков в минуты. Далее, рассчитываем среднемесячную длительность исходящих звонков для каждого пользователя.  \n",
    "\n",
    "##### Анализ использования интернет-трафика: #####  \n",
    "Аналогично, для интернет-трафика конвертируем объем данных в гигабайты и рассчитываем среднемесячный объем трафика для каждого пользователя.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "5c9ea945",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Базовый DataFrame для рассчёта Оптимального тарифа\n",
    "user_info = accounts_df[['Номер/Логин', 'Имя', 'Тариф']].rename(columns={'Номер/Логин': 'Номер телефона', 'Имя': 'ФИО'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "8d60e139",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создаем копию для исходящих звонков, исключая международные\n",
    "outgoing_calls = data[(data['Категория соединения'] == 'Исходящие звонки') & \n",
    "                      (data['Тип звонка'] != 'Международные звонки (СНГ)')].copy()\n",
    "\n",
    "# Конвертация длительности звонков из секунд в минуты\n",
    "outgoing_calls['Длительность, мин'] = outgoing_calls['Длительность, сек.'] / 60\n",
    "\n",
    "# Группировка по номеру телефона, году и месяцу звонка и расчет суммы длительности звонков за месяц\n",
    "monthly_outgoing = outgoing_calls.groupby(['Номер телефона', 'Год звонка', 'Месяц звонка'])['Длительность, мин'].sum().reset_index()\n",
    "\n",
    "# Расчет среднего значения суммарной длительности звонков в месяц для каждого абонента (округляем до 2 знаков после запятой)\n",
    "average_monthly_outgoing = monthly_outgoing.groupby('Номер телефона')['Длительность, мин'].mean().apply(round, 2).reset_index()\n",
    "average_monthly_outgoing.rename(columns={'Длительность, мин': 'Исходящие, мин (сред. в месяц)'}, inplace=True)\n",
    "\n",
    "# Теперь у нас есть DataFrame average_monthly_outgoing с колонками 'Номер телефона' и 'Исходящие, мин (сред. в месяц)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "78eb87cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Фильтрация данных для получения только интернет-трафика\n",
    "internet_traffic = data[data['Тип'] == 'Трафик'].copy()\n",
    "\n",
    "# Конвертация объема данных из Кбайт в ГБ\n",
    "internet_traffic['Объем, ГБ'] = internet_traffic['Объем, Кбайт'] / (1024**2)\n",
    "\n",
    "# Группировка по номеру телефона, году и месяцу звонка и расчет суммы трафика за месяц\n",
    "monthly_traffic = internet_traffic.groupby(['Номер телефона', 'Год звонка', 'Месяц звонка'])['Объем, ГБ'].sum().reset_index()\n",
    "\n",
    "# Расчет среднего значения объема трафика в месяц для каждого абонента\n",
    "average_monthly_traffic = monthly_traffic.groupby('Номер телефона')['Объем, ГБ'].mean().reset_index()\n",
    "average_monthly_traffic.rename(columns={'Объем, ГБ': 'Трафик, ГБ (сред. в месяц)'}, inplace=True)\n",
    "average_monthly_traffic['Трафик, ГБ (сред. в месяц)'] = np.ceil(average_monthly_traffic['Трафик, ГБ (сред. в месяц)'])\n",
    "\n",
    "# Теперь у нас есть DataFrame average_monthly_traffic с колонками 'Номер телефона' и 'Трафик, ГБ (сред. в месяц)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b68edc48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Добавление информации о среднемесячных исходящих минутах\n",
    "user_info = user_info.merge(\n",
    "    average_monthly_outgoing,\n",
    "    on='Номер телефона',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# Добавление информации о среднемесячном трафике в ГБ\n",
    "user_info = user_info.merge(\n",
    "    average_monthly_traffic,\n",
    "    on='Номер телефона',\n",
    "    how='left'\n",
    ")\n",
    "\n",
    "# Замена NaN на 0 для случаев, когда данные отсутствуют\n",
    "user_info.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257ac27e",
   "metadata": {},
   "source": [
    "#### Расчет оптимального тарифа #### \n",
    "###### Объединение данных: ######\n",
    "Добавляем к `user_info` информацию о среднемесячных исходящих минутах и интернет-трафике.\n",
    "\n",
    "###### Определение оптимального тарифа: ######\n",
    "С помощью функции `find_optimal_tariff_and_options` для каждого пользователя определяем наиболее подходящий тариф на основе его среднемесячного использования услуг. Расчет включает сравнение стоимости текущего использования с тарифными опциями, предоставляемыми компанией.\n",
    "\n",
    "---\n",
    "\n",
    "#### Описание функций ####\n",
    "##### Функция format_option ######\n",
    "Эта функция предназначена для форматирования представления тарифной опции, делая ее более понятной и читаемой. Она принимает словарь `option`, содержащий информацию о количестве минут (minutes) и объеме интернет-трафика (gb), предоставляемых в рамках тарифа.\n",
    "\n",
    "Если количество минут или объем трафика указаны как 'unlimited', функция возвращает строку 'неограниченно' для соответствующего параметра.\n",
    "В противном случае она возвращает строковое представление лимитов с указанием единиц измерения (минуты или гигабайты).\n",
    "Результатом работы функции является строка, описывающая тарифную опцию в формате, удобном для восприятия (например, \"1000 мин, 10 Гб\").\n",
    "\n",
    "###### Функция find_optimal_tariff_and_options #####\n",
    "Эта функция выполняет основную работу по определению оптимального тарифа для конкретного пользователя. Она анализирует использование услуг пользователем и сравнивает его с доступными тарифными опциями для выбора наиболее выгодного варианта.\n",
    "\n",
    "Специальный случай для тарифа передачи данных: Если текущий тариф пользователя специализирован на передаче данных ('Передача данных'), функция сразу возвращает этот тариф, предполагая, что изменение не требуется.\n",
    "\n",
    "Расчет для остальных случаев: Для пользователей на других тарифах функция анализирует среднемесячное использование минут и интернет-трафика. Затем она перебирает все доступные тарифы (исключая специализированный тариф для данных) и опции внутри этих тарифов, рассчитывая общую стоимость для пользователя с учетом его потребления.\n",
    "\n",
    "Определение оптимального варианта: Для каждой тарифной опции функция сравнивает базовую стоимость с дополнительными расходами на сверхлимитное использование услуг. Оптимальным считается тариф с наименьшей общей стоимостью для пользователя.\n",
    "\n",
    "Форматирование результата: Выбранный тариф и его опция форматируются с помощью функции `format_option`, и результат возвращается в виде кортежа, включающего название тарифа, описание опции и общую стоимость.\n",
    "\n",
    "Функция `find_optimal_tariff_and_options` позволяет не только идентифицировать наиболее подходящий тарифный план на основе реального использования услуг пользователем, но и представить эту информацию в формате, максимально удобном для понимания и принятия решений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "a47c5e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_option(option):\n",
    "    minutes = 'неограниченно' if option['minutes'] == 'unlimited' else f\"{option['minutes']} мин\"\n",
    "    gb = 'неограниченно' if option['gb'] == 'unlimited' else f\"{option['gb']} Гб\"\n",
    "    return f\"{minutes}, {gb}\"\n",
    "\n",
    "def find_optimal_tariff_and_options(row):\n",
    "    # Проверяем, не является ли текущий тариф специализированным для передачи данных\n",
    "    if 'Передача данных' in row['Тариф']:\n",
    "        # Если это специализированный тариф для передачи данных, возвращаем его стоимость\n",
    "        data_tariff_cost = tariffs_options['Передача данных'][0]['price']\n",
    "        return row['Тариф'], \"Спец. тариф для данных\", data_tariff_cost\n",
    "\n",
    "    # Если это не специализированный тариф, вычисляем оптимальный тариф\n",
    "    usage = {'minutes': row['Исходящие, мин (сред. в месяц)'], 'gb': row['Трафик, ГБ (сред. в месяц)']}\n",
    "    optimal_choice = {'tariff': None, 'option': None, 'total_cost': float('inf')}\n",
    "    \n",
    "    # Исключаем из рассмотрения тариф \"Передача данных\"\n",
    "    for tariff_name, options in {k: v for k, v in tariffs_options.items() if k != 'Передача данных'}.items():\n",
    "        for option in options:\n",
    "            minutes_limit = float('inf') if option['minutes'] == 'unlimited' else option['minutes']\n",
    "            gb_limit = float('inf') if option['gb'] == 'unlimited' else option['gb']\n",
    "            extra_minutes_cost = 0 if usage['minutes'] <= minutes_limit else (usage['minutes'] - minutes_limit) * 0.1\n",
    "            extra_gb_cost = 0 if usage['gb'] <= gb_limit else (usage['gb'] - gb_limit) * 2\n",
    "            total_cost = option['price'] + extra_minutes_cost + extra_gb_cost\n",
    "            if total_cost < optimal_choice['total_cost']:\n",
    "                optimal_choice = {\n",
    "                    'tariff': tariff_name,\n",
    "                    'option': format_option(option),\n",
    "                    'total_cost': total_cost\n",
    "                }\n",
    "    return optimal_choice['tariff'], optimal_choice['option'], optimal_choice['total_cost']\n",
    "\n",
    "\n",
    "# Применение функции к DataFrame и создание новых столбцов\n",
    "user_info[['Оптимальный тариф', 'Опция внутри тарифа', 'Стоимость тарифа']] = user_info.apply(find_optimal_tariff_and_options, axis=1, result_type='expand')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b24ea58f",
   "metadata": {},
   "source": [
    "##### Дополнительная классификация и анализ #####\n",
    "**Статус активности:** Определяем активность пользователя на основе наличия данных о его звонках и интернет-сессиях, отмечая пользователей как \"Активные\" или \"Неактивные\".\n",
    "\n",
    "**Активность по месяцам:** Рассчитываем количество месяцев активности для каждого пользователя, что может служить дополнительным показателем для определения подходящего тарифа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "c09d3163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Список всех абонентов из accounts_df\n",
    "all_phones_set = set(accounts_df['Номер/Логин'].astype(str))\n",
    "\n",
    "# Список абонентов, по которым есть данные в data\n",
    "active_phones_set = set(data['Номер телефона'].astype(str))\n",
    "\n",
    "# Добавление статуса в user_info на основе проверки наличия номера в active_phones_set\n",
    "user_info['Статус'] = user_info['Номер телефона'].apply(lambda x: 'Активный' if x in active_phones_set else 'Неактивный')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "3e411f36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Группировка по номеру телефона и подсчет уникальных месяцев за каждый год\n",
    "months_active = data.groupby('Номер телефона').apply(lambda x: x[['Год звонка', 'Месяц звонка']].drop_duplicates().shape[0]).reset_index(name='Активных месяцев')\n",
    "\n",
    "# Объединение с user_info\n",
    "user_info = user_info.merge(months_active, on='Номер телефона', how='left')\n",
    "\n",
    "# Заполнение пропущенных значений нулями для абонентов без данных\n",
    "user_info['Активных месяцев'] = user_info['Активных месяцев'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "866220c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Номер телефона</th>\n",
       "      <th>ФИО</th>\n",
       "      <th>Тариф</th>\n",
       "      <th>Исходящие, мин (сред. в месяц)</th>\n",
       "      <th>Трафик, ГБ (сред. в месяц)</th>\n",
       "      <th>Оптимальный тариф</th>\n",
       "      <th>Опция внутри тарифа</th>\n",
       "      <th>Стоимость тарифа</th>\n",
       "      <th>Статус</th>\n",
       "      <th>Активных месяцев</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375291000001</td>\n",
       "      <td>Абонент 01</td>\n",
       "      <td>Своё решение. Новатор</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Новатор</td>\n",
       "      <td>200 мин, 0 Гб</td>\n",
       "      <td>10.50</td>\n",
       "      <td>Неактивный</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375291000002</td>\n",
       "      <td>Абонент 02</td>\n",
       "      <td>Своё решение. Новатор</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Новатор</td>\n",
       "      <td>200 мин, 0 Гб</td>\n",
       "      <td>10.50</td>\n",
       "      <td>Неактивный</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>375291000003</td>\n",
       "      <td>Абонент 03</td>\n",
       "      <td>Своё решение. Новатор</td>\n",
       "      <td>303.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Мастер</td>\n",
       "      <td>неограниченно, 0 Гб</td>\n",
       "      <td>19.93</td>\n",
       "      <td>Активный</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375291000004</td>\n",
       "      <td>Абонент 04</td>\n",
       "      <td>Своё решение. Мастер</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Новатор</td>\n",
       "      <td>200 мин, 0 Гб</td>\n",
       "      <td>10.50</td>\n",
       "      <td>Неактивный</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375291000005</td>\n",
       "      <td>Абонент 05</td>\n",
       "      <td>Своё решение. Мастер</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Новатор</td>\n",
       "      <td>200 мин, 0 Гб</td>\n",
       "      <td>10.50</td>\n",
       "      <td>Активный</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Номер телефона         ФИО                  Тариф  \\\n",
       "0   375291000001  Абонент 01  Своё решение. Новатор   \n",
       "1   375291000002  Абонент 02  Своё решение. Новатор   \n",
       "2   375291000003  Абонент 03  Своё решение. Новатор   \n",
       "3   375291000004  Абонент 04   Своё решение. Мастер   \n",
       "4   375291000005  Абонент 05   Своё решение. Мастер   \n",
       "\n",
       "   Исходящие, мин (сред. в месяц)  Трафик, ГБ (сред. в месяц)  \\\n",
       "0                             0.0                         0.0   \n",
       "1                             0.0                         0.0   \n",
       "2                           303.0                         0.0   \n",
       "3                             0.0                         0.0   \n",
       "4                           160.0                         0.0   \n",
       "\n",
       "  Оптимальный тариф  Опция внутри тарифа  Стоимость тарифа      Статус  \\\n",
       "0           Новатор        200 мин, 0 Гб             10.50  Неактивный   \n",
       "1           Новатор        200 мин, 0 Гб             10.50  Неактивный   \n",
       "2            Мастер  неограниченно, 0 Гб             19.93    Активный   \n",
       "3           Новатор        200 мин, 0 Гб             10.50  Неактивный   \n",
       "4           Новатор        200 мин, 0 Гб             10.50    Активный   \n",
       "\n",
       "   Активных месяцев  \n",
       "0               0.0  \n",
       "1               0.0  \n",
       "2               1.0  \n",
       "3               0.0  \n",
       "4               1.0  "
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вывод первых пяти строк для просмотра результатов\n",
    "\n",
    "user_info.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b0df82b",
   "metadata": {},
   "source": [
    "### Сохранение в файл результатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "5cca707e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл 'user_info_optimal_tariff.xlsx' успешно сохранен.\n"
     ]
    }
   ],
   "source": [
    "# Указываем имя файла для сохранения\n",
    "excel_filename = \"user_info_optimal_tariff.xlsx\"\n",
    "\n",
    "# Сохранение DataFrame в Excel\n",
    "user_info.to_excel(excel_filename, index=False)\n",
    "\n",
    "# Вывод сообщения об успешном сохранении файла\n",
    "print(f\"Файл '{excel_filename}' успешно сохранен.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c804c03",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    " <a id='an_need_int_package'></a>\n",
    "### Определение необходимости подключения пакетов для совершения международных звонков ###\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Анализируем необходимость подключения пакетов для совершения международных звонков для пользователей, исходя из их истории звонков. Раздел состоит из нескольких ключевых шагов, направленных на анализ использования международных звонков и определение оптимальных пакетов для пользователей. \n",
    "\n",
    "\n",
    "##### Подготовка данных #####\n",
    "\n",
    "**Фильтрация международных звонков:** Из общего датафрейма `data` выбираются записи, относящиеся к международным звонкам, на основе содержания строки в столбце `Тип звонка`.\n",
    "\n",
    "**Преобразование типов данных:** В выбранных данных корректируется столбец `Тип звонка` для получения чистого направления звонка (`Направление`), а длительность звонков преобразуется из секунд в минуты, округляясь в большую сторону."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f8b90a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Номер телефона</th>\n",
       "      <th>Тип звонка</th>\n",
       "      <th>Год звонка</th>\n",
       "      <th>Месяц звонка</th>\n",
       "      <th>Дата звонка</th>\n",
       "      <th>Длительность, сек.</th>\n",
       "      <th>Начислено</th>\n",
       "      <th>Направление</th>\n",
       "      <th>Длительность, мин.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>375201000009</td>\n",
       "      <td>Международные звонки (СНГ)</td>\n",
       "      <td>2023</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-03-03 18:18:43</td>\n",
       "      <td>420</td>\n",
       "      <td>5.635</td>\n",
       "      <td>СНГ</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Номер телефона                  Тип звонка  Год звонка  Месяц звонка  \\\n",
       "949   375201000009  Международные звонки (СНГ)        2023             3   \n",
       "\n",
       "            Дата звонка Длительность, сек.  Начислено Направление  \\\n",
       "949 2023-03-03 18:18:43                420      5.635         СНГ   \n",
       "\n",
       "     Длительность, мин.  \n",
       "949                 7.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Фильтр для выбора исходящих международных звонков\n",
    "filtered_data_international = data[data['Тип звонка'].str.contains('Международные')]\n",
    "\n",
    "# Проверка на наличие данных после фильтрации\n",
    "if not filtered_data_international.empty:\n",
    "    # Создаем копию DataFrame для безопасной модификации\n",
    "    selected_columns_data = filtered_data_international[['Номер телефона', 'Тип звонка', 'Год звонка', 'Месяц звонка', 'Дата звонка', 'Длительность, сек.', 'Начислено']].copy()\n",
    "\n",
    "    # Преобразование \"Тип звонка\" в \"Направление\"\n",
    "    selected_columns_data['Направление'] = selected_columns_data['Тип звонка'].apply(\n",
    "        lambda x: x.replace('Международные звонки (', '').replace(')', '').replace('Др. страны', 'Другие страны')\n",
    "    )\n",
    "\n",
    "    # Преобразование длительности звонков из секунд в минуты и округление в большую сторону\n",
    "    selected_columns_data['Длительность, мин.'] = np.ceil(selected_columns_data['Длительность, сек.'].astype(float) / 60)\n",
    "\n",
    "    # Опционально: Удаление столбцов 'Тип звонка' и 'Длительность, сек.', если они больше не нужны\n",
    "    #selected_columns_data.drop(columns=['Тип звонка', 'Длительность, сек.'], inplace=True)\n",
    "\n",
    "    # Выводим первые строки для проверки\n",
    "    display(selected_columns_data.head(1))\n",
    "else:\n",
    "    print(\"Нет данных по международным звонкам для обработки.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d0e7007",
   "metadata": {},
   "source": [
    "##### Анализ использования #####  \n",
    "**Создание сводной таблицы:** Для удобства анализа создается сводная таблица, где значениями являются длительности звонков, агрегированные по номеру телефона, году, месяцу и направлению звонка.\n",
    "\n",
    "**Расчет среднего и количества месяцев:** Для каждого номера рассчитывается средняя длительность звонков по направлениям и количество месяцев, в которые совершались звонки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a28d8190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Инициализация пустого DataFrame для need_international_package\n",
    "need_international_package = pd.DataFrame()\n",
    "\n",
    "def define_priority(row):\n",
    "        # Функция определяет приоритетное направление международных звонков на основе среднего количества минут, \n",
    "    # потраченных на звонки в каждое направление\n",
    "    # Словарь для хранения направлений и соответствующих им средних значений длительности звонков\n",
    "    \n",
    "    directions = {direction: row[direction] for direction in ['Другие страны', 'Европа', 'СНГ'] if direction in row}\n",
    "    # Проверка на наличие данных по направлениям в строке\n",
    "    if directions:\n",
    "        # Находим направление с максимальной средней длительностью звонков\n",
    "        priority_direction = max(directions, key=directions.get)\n",
    "        return priority_direction\n",
    "    else:\n",
    "        return 'Нет данных'\n",
    "\n",
    "\n",
    "# Проверяем, что DataFrame содержит данные\n",
    "if 'Направление' in selected_columns_data.columns:\n",
    "    # Создание сводной таблицы\n",
    "    pivot_table_international = selected_columns_data.pivot_table(\n",
    "        values='Длительность, мин.',  # Значения для агрегации\n",
    "        index=['Номер телефона', 'Год звонка', 'Месяц звонка'],  # Индексы сводной таблицы\n",
    "        columns='Направление',  # Колонки для разбиения данных\n",
    "        aggfunc='sum',  # Функция агрегации - суммирование\n",
    "        fill_value=0  # Замена NaN на 0\n",
    "    ).reset_index()\n",
    "\n",
    "    # Проверяем наличие нужных столбцов перед расчетом\n",
    "    existing_columns = [col for col in ['Другие страны', 'Европа', 'СНГ'] if col in pivot_table_international.columns]\n",
    "\n",
    "    if existing_columns:\n",
    "        # Расчет среднего значения звонков по направлениям для каждого номера\n",
    "        average_calls_by_direction = pivot_table_international.groupby('Номер телефона')[existing_columns].mean().reset_index()\n",
    "\n",
    "        # Подсчет количества месяцев с звонками для каждого номера\n",
    "        calls_months_count = pivot_table_international.groupby('Номер телефона').size().reset_index(name='Количество месяцев')\n",
    "\n",
    "        # Объединение результатов в одну таблицу\n",
    "        need_international_package = average_calls_by_direction.merge(calls_months_count, on='Номер телефона')\n",
    "\n",
    "        # Проверяем наличие нужных столбцов перед определением приоритета\n",
    "        if existing_columns:\n",
    "            need_international_package['Приоритет'] = need_international_package.apply(define_priority, axis=1)\n",
    "\n",
    "        # Расчет медианы количества месяцев для определения низкой активности\n",
    "        if not need_international_package.empty:\n",
    "            median_months_int_usage = need_international_package['Количество месяцев'].median()\n",
    "        else:\n",
    "            print(\"Нет данных для расчета медианы.\")\n",
    "    else:\n",
    "        print(\"Нет данных о направлениях звонков для расчета средних значений.\")\n",
    "else:\n",
    "    print(\"Нет данных о международных звонках для обработки.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe8d44b",
   "metadata": {},
   "source": [
    "##### Определение оптимального пакета #####\n",
    "**Определение приоритета направления:** На основе средней длительности звонков по направлениям для каждого пользователя определяется приоритетное направление.\n",
    "\n",
    "**Подбор оптимального пакета:** Исходя из приоритетного направления и средней длительности звонков, для каждого пользователя анализируется необходимость подключения пакета для международных звонков. Рассматривается стоимость звонков по стандартному тарифу по сравнению с предложенными пакетами, выбирается оптимальный вариант.\n",
    "\n",
    "---\n",
    "\n",
    "#### Описание функции ####\n",
    "Функция `find_optimal_package` предназначена для определения оптимального пакета международных звонков для каждого пользователя на основе его истории международных звонков. Вот как работает эта функция:\n",
    "\n",
    "**Оценка активности пользователя:** Сначала функция проверяет, превышает ли количество месяцев, в течение которых пользователь совершал международные звонки, медианное значение (`median_months_int_usage`). Если количество месяцев меньше медианы, это указывает на низкую активность пользователя в совершении международных звонков, и функция рекомендует не подключать дополнительный пакет, возвращая соответствующее сообщение.\n",
    "\n",
    "**Подбор оптимального пакета:** Если активность пользователя выше медианы, функция переходит к выбору наиболее подходящего пакета. Она инициализирует переменную для хранения названия оптимального пакета (`optimal_package`) и переменную для минимальной разницы в стоимости (`min_cost_difference`) с очень большим начальным значением, чтобы любая первая проверяемая разница была меньше этого значения.\n",
    "\n",
    "**Расчет стоимости:** Для каждого доступного пакета функция рассчитывает стоимость международных звонков, учитывая предложенные условия пакета (количество минут и стоимость пакета) и стоимость звонков по стандартному тарифу. Это делается путем сравнения суммарной длительности звонков в предпочтительном направлении (Приоритет) с лимитом минут в пакете и рассчетом стоимости звонков, исходя из этих данных.\n",
    "\n",
    "**Выбор оптимального пакета:** Функция сравнивает рассчитанную стоимость использования каждого пакета с затратами по стандартному тарифу, исходя из фактического использования услуг пользователя. Оптимальным считается пакет, использование которого приводит к наименьшей разнице в стоимости по сравнению со стандартным тарифом. Если разница в стоимости для какого-то пакета оказывается меньше текущего значения `min_cost_difference`, значения `optimal_package` и `min_cost_difference` обновляются.\n",
    "\n",
    "**Возвращение результата:** По завершении работы функция возвращает название оптимального пакета, который предполагается наиболее выгодным для пользователя в зависимости от его истории совершения международных звонков.\n",
    "\n",
    "Таким образом, функция `find_optimal_package` позволяет автоматизировать процесс выбора наиболее подходящего пакета для международных звонков для каждого пользователя, исходя из его потребностей и паттернов использования услуг, что может способствовать оптимизации расходов на связь."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "be682a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_optimal_package(row):\n",
    "    # Если количество месяцев меньше медианы, рекомендуем не применять пакет\n",
    "    if row['Количество месяцев'] < median_months_int_usage:\n",
    "        return 'Не рекомендуется применять пакет'\n",
    "    \n",
    "    optimal_package = 'Стандартный тариф'\n",
    "    min_cost_difference = float('inf')\n",
    "    for package in international_calls_packages:\n",
    "        if row['Приоритет'] in package['regions'] or 'Все страны' in package['regions']:\n",
    "            standard_cost = row[row['Приоритет']] * international_call_rates[row['Приоритет']]\n",
    "            package_cost = package['price'] if row[row['Приоритет']] <= package['minutes'] else (row[row['Приоритет']] - package['minutes']) * international_call_rates[row['Приоритет']] + package['price']\n",
    "            cost_difference = package_cost - standard_cost\n",
    "            if cost_difference < min_cost_difference:\n",
    "                min_cost_difference = cost_difference\n",
    "                optimal_package = package['name']\n",
    "    return optimal_package"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b70ec7a",
   "metadata": {},
   "source": [
    "Результатом работы этого раздела является датафрейм, который для каждого пользователя содержит информацию о приоритетном направлении звонков, количестве месяцев активности и рекомендованном пакете для международных звонков. Это позволяет предложить пользователям наиболее подходящие пакеты услуг, исходя из их потребностей и паттернов использования, что может способствовать оптимизации расходов на связь."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "5021280c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Номер телефона</th>\n",
       "      <th>Европа</th>\n",
       "      <th>СНГ</th>\n",
       "      <th>Количество месяцев</th>\n",
       "      <th>Приоритет</th>\n",
       "      <th>Рекомендуемый пакет</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375201000009</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1</td>\n",
       "      <td>СНГ</td>\n",
       "      <td>20 минут международных звонков</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Номер телефона  Европа   СНГ  Количество месяцев Приоритет  \\\n",
       "0   375201000009     1.0  27.0                   1       СНГ   \n",
       "\n",
       "              Рекомендуемый пакет  \n",
       "0  20 минут международных звонков  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Проверяем, что DataFrame содержит данные перед применением функции подбора пакета\n",
    "if not need_international_package.empty and 'Приоритет' in need_international_package.columns:\n",
    "    # Применяем функцию подбора пакета\n",
    "    need_international_package['Рекомендуемый пакет'] = need_international_package.apply(find_optimal_package, axis=1)\n",
    "    \n",
    "    # Выводим обновленную таблицу с информацией\n",
    "    display(need_international_package)\n",
    "else:\n",
    "    print(\"Нет данных для определения рекомендуемого пакета международных звонков.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b78859",
   "metadata": {},
   "source": [
    "#### Сохряняем в файл ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "23abcbf8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Файл 'need_international_package.xlsx' успешно сохранен.\n"
     ]
    }
   ],
   "source": [
    "# Указываем имя файла для сохранения\n",
    "excel_filename = \"need_international_package.xlsx\"\n",
    "\n",
    "# Сохранение DataFrame в Excel\n",
    "need_international_package.to_excel(excel_filename, index=False)\n",
    "\n",
    "# Вывод сообщения об успешном сохранении файла\n",
    "print(f\"Файл '{excel_filename}' успешно сохранен.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a19b1948",
   "metadata": {},
   "source": [
    " ---\n",
    " \n",
    " <a id='an_personal'></a>\n",
    "### Анализ в разрезе одного абонента ###\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Детальный анализ использования услуг связи одним конкретным абонентом. Целью анализа является получение подробной информации о паттернах использования звонков, интернет-трафика и SMS абонентом в разрезе месяцев и годов. Раздел включает в себя следующие ключевые этапы:\n",
    "\n",
    "#### Подготовка данных ####\n",
    "**Выбор целевого номера телефона:** Задается номер телефона (`target_phone_number`), для которого будет проводиться анализ.\n",
    "\n",
    "**Фильтрация данных:** Из общего датафрейма data выбираются все записи, соответствующие заданному номеру телефона. Создается копия этих данных (`personal_data`) для последующей обработки.\n",
    "\n",
    "**Создание уникального идентификатора месяца и года:** Для удобства агрегирования и анализа данных добавляется столбец Год-Месяц, объединяющий информацию о годе и месяце события.\n",
    "\n",
    "**Преобразование типов данных:** Корректируются типы данных для длительности звонков и объема интернет-трафика, а также заполняются пропущенные значения."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "b47a7d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Задаем номер телефона, по которому хотим получить информацию\n",
    "target_phone_number = 375291000003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "3a75593c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Фильтруем DataFrame для получения всех записей, относящихся к этому номеру телефона\n",
    "personal_data = data[data['Номер телефона'] == str(target_phone_number)].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6141d6b3",
   "metadata": {},
   "source": [
    "#### Агрегация и анализ данных ####  \n",
    "  \n",
    "**Агрегация данных по звонкам:** Для анализа звонков данные фильтруются по категориям соединений (исходящие и входящие звонки), группируются по месяцу и году, и суммируется длительность звонков. Результаты преобразуются в минуты и представляются в новом датафрейме."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "7518fbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Создание уникального идентификатора месяца и года для правильного агрегирования данных\n",
    "personal_data['Год-Месяц'] = personal_data['Год звонка'].astype(str) + '-' + personal_data['Месяц звонка'].astype(str).str.zfill(2)\n",
    "\n",
    "# Преобразование типов данных для длительности звонков и объема трафика\n",
    "personal_data['Длительность, сек.'] = personal_data['Длительность, сек.'].astype('float')\n",
    "personal_data['Объем, Кбайт'] = personal_data['Объем, Кбайт'].fillna(0)\n",
    "\n",
    "# Фильтруем данные, чтобы оставить только звонки\n",
    "personal_data_calls_data = personal_data[personal_data['Категория соединения'].isin(['Исходящие звонки', 'Входящие звонки'])]\n",
    "\n",
    "# Создаем новый DataFrame, группируя данные по месяцам и категориям соединений и суммируя длительность\n",
    "personal_data_calls_summary = personal_data_calls_data.groupby(['Год-Месяц', 'Категория соединения'])['Длительность, сек.'].sum().reset_index()\n",
    "\n",
    "# Перевод секунд в минуты\n",
    "personal_data_calls_summary['Длительность, мин.'] = (personal_data_calls_summary['Длительность, сек.'] / 60).round(2)\n",
    "\n",
    "# Удаляем колонку с секундами для чистоты визуализации\n",
    "personal_data_calls_summary.drop(columns=['Длительность, сек.'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b49fedd4",
   "metadata": {},
   "source": [
    "**Визуализация:** Создаются графики для наглядного представления динамики использования услуг абонентом:\n",
    "\n",
    "**Длительность звонков:** Линейный график суммарной длительности входящих и исходящих звонков по месяцам и годам.  \n",
    "**Объем интернет-трафика:** Линейный график объема использованного интернет-трафика в килобайтах по месяцам и годам.  \n",
    "**Количество SMS:** Столбчатый график количества входящих и исходящих SMS по месяцам и годам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "c08fdebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Создание визуализаций\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 18))\n",
    "\n",
    "# 1. Длительность входящих и исходящих звонков\n",
    "sns.lineplot(data=personal_data_calls_summary, x='Год-Месяц', y='Длительность, мин.', hue='Категория соединения', marker='o', ax=ax[0])\n",
    "ax[0].set_title('Суммарная длительность входящих и исходящих звонков по месяцам (в минутах)')\n",
    "ax[0].set_ylabel('Суммарная длительность, мин')\n",
    "ax[0].set_xlabel('Год-Месяц')\n",
    "ax[0].tick_params(axis='x', rotation=45)\n",
    "ax[0].legend(title='Категория соединения')\n",
    "\n",
    "\n",
    "# 2. Объем использованного интернет-трафика по месяцам и годам\n",
    "sns.lineplot(data=personal_data, x='Год-Месяц', y='Объем, Кбайт', estimator='sum', errorbar=None, ax=ax[1])\n",
    "ax[1].set_title('Объем использованного интернет-трафика по месяцам и годам')\n",
    "ax[1].set_ylabel('Объем, Кбайт')\n",
    "ax[1].set_xlabel('Год-Месяц')\n",
    "ax[1].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# 3. Количество входящих и исходящих SMS\n",
    "sms_data = personal_data[personal_data['Тип'] == 'SMS']\n",
    "sms_counts = sms_data.groupby(['Год-Месяц', 'Категория соединения']).size().reset_index(name='Количество SMS')\n",
    "sns.barplot(x='Год-Месяц', y='Количество SMS', hue='Категория соединения', data=sms_counts, ax=ax[2])\n",
    "ax[2].set_title('Количество входящих и исходящих SMS по месяцам и годам')\n",
    "ax[2].set_ylabel('Количество SMS')\n",
    "ax[2].set_xlabel('Год-Месяц')\n",
    "ax[2].tick_params(axis='x', rotation=45)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afc4d850",
   "metadata": {},
   "source": [
    " ---\n",
    " \n",
    " <a id='an_complex'></a>\n",
    "# Комплексный анализ поведения пользователей #\n",
    "[Назад к содержанию](#contents)\n",
    "\n",
    "Многоуровневый анализ поведения пользователей мобильного оператора, включая распределение по тарифам и статусам, анализ потребления услуг (минут и интернет-трафика), оценку экономической эффективности текущих тарифов по сравнению с потенциально оптимальными, и завершается кластеризацией пользователей для выявления общих паттернов поведения и предпочтений."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d150b5fa",
   "metadata": {},
   "source": [
    "**Анализ распределения пользователей по тарифам и статусам**\n",
    "\n",
    "Визуализация количества пользователей на различных тарифах помогает понять популярность каждого тарифа и может выявить тарифы с наибольшим и наименьшим числом абонентов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "60faf8a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Распределение пользователей по тарифам\n",
    "sns.countplot(data=user_info, x='Тариф')\n",
    "plt.title('Распределение пользователей по тарифам')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b784e4",
   "metadata": {},
   "source": [
    "#### Анализ активных и неактивных пользователей. ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "1a3055e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Распределение пользователей по статусам\n",
    "sns.countplot(data=user_info, x='Статус')\n",
    "plt.title('Распределение пользователей по статусам')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aba7d58",
   "metadata": {},
   "source": [
    "#### Анализ потребления (минуты, трафик) ####\n",
    "Сравнение среднемесячного потребления минут и интернет-трафика пользователями разных тарифов помогает оценить адекватность тарифных планов потребностям абонентов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "283d1766",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vffv2sXnzZubNm5fimDt49M9z06ZNCQwMZN++fVSuXJmdO3dy8+ZNmjRp8tjP50ldunSJQoUKJflOBChatCjwv8/6k9q8eTPTp0/n+PHjScb3pfSD8mH7Qd68eZOsu3DhAj/99BMjRozA2dk5ybqrV68CPNLJSvPly5dknOadO3eYMmUKgYGByb47EvaXxEqUKEGxYsVYtWoVnTp1YuXKlXTt2tVSQ2amUJSFlCtXzjL7LCXffvst33zzDS1atKB3797kzJkTOzs7xowZY/klZ0sJNXz88ceULFkyxW0S/yGLiYnhxo0b1KhR44Htms1mTCYTs2bNwt7e/oFtApZj9Q86v5PZbCZ37tx89dVXKa7/7/lxypcvT58+fZIsW7RoEb/99luK9//3339ZuXIlX375ZYpjk8xmM76+vknOT5XYs88+e9/aM4OEHsHGjRvTrFmzFLdJGHya+Ffuo7h58ybZs2d/YCgym834+fnRqVOnFNf/9xdxq1atkvWqJB7L81/Lli3j0qVLKQ7QBqhUqRIff/wxU6ZMeaSenftp0aIFzZo1IygoiPXr1z/SWLOvvvqKmjVrUr16dVasWJHiNo/6ea5ZsyZ58uRhzZo1VK5cmTVr1pA3b96H7rOZ1b59++jevTuVK1dm+PDh5M2bF0dHR5YvX55srJ41Jk6cSOHChWnWrBn79u1Lsu5xx/JERUVZ/t2nTx8OHDhAx44dKVmyJNmzZ8dsNtOpU6f7/m1o0aKFZZznzZs3qV+/PnPnzn38J5XBKBQ9RTZs2EDVqlUZM2ZMkuWhoaFJfokWLFiQQ4cOERsbmyqDhROcO3cuyW3DMDh37pzlj1vCoL8cOXI80pfmiRMniI2NTdL9nJKCBQtiGAbe3t4UKVLkoe2ePn0ak8n0wG0LFizIrl27qFSp0iP9Uc6VK1ey57Rp06b7bj9+/HhKlChx3z+IBQsW5MSJE1SvXv2JDmk+TP78+YF7g0sT3h+4F0gvXrxo9R+3/34W4F6PXrZs2SyB0tXVFbPZ/NDH8Pb2ZseOHYSFhSXpBTl79ixmsxkvL68k258+fdrSO3A/BQsWJCIi4pGfX6FChZJte7/QFRUVxZQpU3j33XeT1ZZYx44dOXfuHBs2bGDcuHE4OjpaDgk+quLFi1OyZEn69OlDrly5qFatGnv27Lnv9ps2beLgwYMPnZH1qJ9ne3t7GjZsyMqVKxkwYACbNm3i7bffTvHHSVrz8vLi5MmTmM3mJL1FQUFBwP8+609iw4YNODs7M2fOnCSHZ5cvX57i9o+yHyQ4duwY69atY+rUqSm+fgm9StevX39ondeuXeOZZ54B7h3i27VrF7169aJnz55J6niQRo0aMW7cOD7//HNef/31h/ZAZhYaU/QUsbe3T5b6f/nlF65du5ZkWb169bh9+zbff/99sjaepEdp1apVhIWFWW6vX7+eGzduWGaNlClThoIFC/Ldd99ZxlQk9t9DFuvXr8fe3p6XX375gY9br1497O3tmTJlSrL6DcNIMu00Li6OX3/9lXLlyiUbx5NY/fr1iY+PT/GXd1xcXJLp6o/r4MGD/PbbbwwYMOC+gad+/fpcu3aNJUuWJFsXFRVFRESE1Y+fWI0aNXB0dGThwoVJXrtly5Zx9+5d6tSpY1W7Bw4cSDJW4cqVK/z222/4+flhb2+Pvb09r7/+Ohs2bODUqVPJ7p/4s1CnTh3i4+OTfV4TfrUmnvFz5coV/vrrL8vYq/upX78+Bw4c4I8//ki2LjQ0lLi4uEd6nilZsGABkZGRdOvW7YHb/f777yxevNhyWgBrA2iLFi04efIkzZs3f2CATjik17Bhw/v21FqjSZMmhISEMGzYMCIiIu47TT+t1a5dmxs3bhAYGGhZFhcXx8KFC8mePTuVK1d+4sewt7fHZDIlGZpw8eLF+/YIP2w/SGz8+PFUqlSJV155JcW2ypYti4uLS5JZsidOnCA6OjrJobw7d+6wd+9ey/O9X0CdP3/+A5+rh4cHr7zyCidPnqRFixYP3DYzUU/RU+Sll15i6tSpDB48mIoVK3Lq1Cl+/vnnJD0AcG8cwKpVqwgICODw4cO88MILREZGsmvXLt555x1effVVqx4/Z86cvPvuuzRv3twyJb9QoUK8/fbbANjZ2TF69Gg6d+5Mw4YNad68Ofny5ePatWvs3r2bHDly8O233xIREcH333/PwoULKVy4cJJBxQlh4OTJkxw4cICKFStSsGBB+vTpw/jx47l06RKvvvoqrq6uXLx40fLLtWPHjuzcuZNvvvmGkydPJpui/V9VqlShVatWzJgxg+PHj+Pn54ejoyNnz55l/fr1fPLJJ/cdpPow27dvx8/P74F/BJs0acIvv/zC8OHD2b17N5UqVSI+Pt5ymGT27NkPPJT6qDw9PenatStTpkyhU6dO1K1blzNnzvDDDz9YzvNiDV9fXzp27JhkKjJAr169LNv079+f3bt38/bbb9OyZUuef/55QkJCOHr0KLt27bL0eCQEhgkTJnDx4kVKlCjB7t272bBhA61bt7ZMbf7hhx+YOXMm2bJlSzZI9b86duzI77//Trdu3WjWrBmlS5cmMjKSU6dOsWHDBn777TerLyGyfft2+vbt+8BxQgnT/lu2bGn1/pbg7bff5o033sDNze2B2129ehVHR8cUTwHxJEqVKoWvr69lckDp0qVTtf1H1apVKxYvXoy/vz9Hjx7Fy8uLDRs28NdffzFkyJBU6emoU6cOc+fOpVOnTjRs2JBbt27xww8/ULBgQU6ePJls+0fZDxJs377dsj4l2bNnp127dsycORMHBwdKlSrFTz/9hJ2dHTdu3KBr167UrVuXpUuXEhMTwwcffADc65mvXLkys2fPJjY2lnz58rFjx45k5ypLydixYxk2bFiWupyOQtFTpFu3bkRGRvLzzz8TGBhIqVKlmDFjBuPHj0+ynb29PbNmzWL69OmsXbuWX3/9FQ8PDypVqpTsJGKP+/gnT55k5syZhIeHU716dYYPH57kEiRVq1Zl8eLFTJs2jUWLFhEREUHevHkpV64crVq1Au71EiSM5fn3339TPHfLxo0byZEjBxUrVgTunSCvcOHCzJs3j6lTpwL3xt34+flZTrz2+++/W/4o1KpV66HPZ9SoUZQpU4affvqJCRMmYG9vj5eXF40bN6ZSpUpWv04mkynFGUmJ2dnZMXXqVObNm8fq1avZuHEj2bJlw9vbm7Zt2z7SYcJH1atXLzw9PVm0aBEBAQHkzJmTt99+m379+ll9eLVy5cpUqFCBqVOncvnyZZ5//nkCAgKSDDrOkycPS5cuZerUqWzcuJEff/wRDw8Pnn/+eQYMGGDZzmQyMXXqVL755hsCAwNZuXIl+fPnp3///knGBK1cuZLy5cvTu3fvhw5GzZYtGwsXLmTGjBmsX7+eVatWkSNHDgoXLkyvXr0eGjAeJG/evA88J45hGAwePBh3d3eGDBli9eMkcHBweOQ/Wu+88w7e3t5P/Jj/1aRJE7788kubDLBO4OLiwsKFC/nqq69YuXIlYWFhFClShICAAJo3b54qj1G9enU+//xzZs2axZgxY/D29mbAgAFcunQpxVD0KPtBgldeeeWh3yu9e/cmOjqaZcuWsXv3bkaNGsVHH31E7dq1yZMnD19//TW5c+dm0qRJSb7Lx48fz2effcYPP/yAYRj4+fkxa9ash34Puri4PPaYvozOZGSEEbaSpe3evZt27drxzTffWN17ktjFixd55ZVX+O233+77BT558mQuXbqkiyBmQMWLF+e9995j2LBhti5F0sn8+fMJCAjg999/T5WxO1lBeu0HuiDs49GYIhERSTOGYbBs2TIqV66sQCQZng6fSaaTPXt2GjVq9MAp1cWLF7fMrhCR9BcREcHvv//O7t27OXXq1COdDkDE1hSKJNPx9PS87/mBEtzvRI0ikj6Cg4Pp378/7u7udOvW7b6zpkQyEo0pEhEREUFjikREREQAhSIRERERQKFIREREBNBA68dmGAZms4ZhiYiIZBZ2dqZHuk6kQtFjMpsNgoOTX5dLREREMiZPT1fs7R8einT4TERERASFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERABwsHUBiW3dupVZs2Zx+vRpwsLCyJcvH6+++io9e/bEzc3Nst3vv//OxIkTOXPmDPnz56dLly60aNEiSVsxMTFMmDCBNWvWEB4eTsWKFfn000/x8fFJ76f11DEMg5iYaFuX8cQMwwDAZHr4lZUzMicn50z/HERE0kOGCkV37tyhXLlytG3bFg8PD/755x8mT57MP//8w3fffQfAvn376NmzJ2+99RZDhgzhzz//5JNPPsHV1ZU33njD0tbo0aMJDAzE39+ffPny8e2339K+fXvWrVuXJGBJ6jIMg4CAkZw+fcrWpcj/e/55XwYPHq5gJCLyEBkqFDVp0iTJ7apVq+Lk5MSnn37KtWvXyJcvH9OnT6dcuXKMGjUKgGrVqnHhwgUmTZpkCUVXr15l2bJlDB8+nLfeeguAsmXL8vLLL/PTTz/RuXPn9H1iIiIikuFlqFCUEg8PDwBiY2OJiYlh9+7dDBgwIMk2DRo0YO3atVy8eBFvb2+2b9+O2WxO0nPk4eGBn58f27ZtUyhKQyaTicGDh2f6w2fR0dH06dMdgIkTp+Ps7Gzjiqynw2ciIo8mQ4ai+Ph44uLiOH36NFOnTqVu3bp4e3tz+vRpYmNjk40LKlq0KABBQUF4e3sTFBRE7ty5yZkzZ7Ltli1b9sT1OThofPrDODpmt3UJTyTxe+zqmg1nZxcbViMiIukhQ4ail19+mWvXrgFQq1Ytxo8fD0BISAgA7u7uSbZPuJ2wPjQ0NMVxQ+7u7pZtrGVnZyJXLtcnakMyvqgoe8u/PTxccXFRKBIRyeoyZCiaOXMmkZGRnD59munTp9OtWzfmzp1r67IAMJsNQkMjbF2GpLHo6CjLv+/cCcfZOd6G1YiIyJNwd8+Gvf3Dj/JkyFBUokQJACpWrEjZsmVp0qQJGzdu5Pnnnwfg7t27SbYPDQ0FsBwuc3d3JywsLFm7oaGhyQ6pWSMuzvzEbUjGlvg9joszY2+v91xEJKvL8INjihcvjqOjI+fPn6dgwYI4OjoSFBSUZJuE2wljjXx8fLh582ayQ2VBQUE6T5GIiIikKMOHokOHDhEbG4u3tzdOTk5UrVqVDRs2JNkmMDCQokWL4u3tDUDNmjWxs7Pj119/tWwTEhLC9u3bqV27drrWLyIiIplDhjp81rNnT8qUKUPx4sVxcXHhxIkTzJkzh+LFi/Pqq68C0L17d9q1a8eIESOoX78+u3fvZu3atUyYMMHSzrPPPstbb73FuHHjsLOzI1++fMyYMQM3Nzdat25tq6cnIiIiGViGCkXlypUjMDCQmTNnYhgGXl5etGzZko4dO+Lk5ATAiy++yOTJk5k4cSLLli0jf/78jB49mvr16ydpa+jQobi6ujJ+/HjCw8OpVKkSc+fO1dmsRUREJEUmI+ECT/JI4uPNBAeH27oMSWPR0VF07/4BANOnf6fzFImIZGKenq6PNPssw48pEhEREUkPCkUiIiIiKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICgIM1dwoNDeXAgQOcPn2a27dvYzKZyJUrF0WLFqVChQrkzJkztesUERERSVOPHIpiYmJYu3YtK1euZP/+/ZjN5hS3s7Ozo1KlSjRv3pyGDRvi5OSUasWKiIiIpJVHCkU//vgj06dP5/bt2/j5+TF48GBKly5NgQIFyJkzJ4ZhEBISwsWLFzly5Ag7d+5k+PDhTJw4kQ8//JDWrVun9fMQEREReSImwzCMh2300ksv0b59e1q0aIGbm9sjNRwWFsayZctYsGABv//++xMXmlHEx5sJDg63dRmSxqKjo+je/QMApk//DmdnFxtXJCIi1vL0dMXe/uHDqB+pp2jTpk04ODze8KMcOXLQvn172rRp81j3ExEREbGFR5p99riBKLXuKyIiIpJe0iSxHD58mK+++spyu1y5cgwYMCAtHkpEREQkVVgViqZMmfLA9efPn2fv3r306NEDgIIFC1rzMCIiIiLpJk1CEYDJZKJnz57WNC8iIiKS7qwKRbt27Xro+v79+1tVkIiIiIgtWBWKcuXK9cD1jzptX0RERCSj0LXPRERERFAoEhEREQGsPHx29OjRB64/d+6cVcWIiIiI2IpVoahFixaYTKb7rjcM44HrRURERDIaq0JRQEBAatchIiIiYlNWhaJmzZqldh0iIiIiNqWB1iIiIiJY2VPUrVu3h25jMpmYPn26Nc2LiIiIpDurQtGWLVvIlSsX2bJlA+4NrL569Sq5c+fGyckJQAOtRUREJFOxKhQBDBkyhEaNGgEQHBxMjRo1+PLLL6levXqqFSciIiKSXlJlTFFCr9Ddu3dTozkRERGRdGdVKMqVKxdXr1613E7498CBA1m4cGHqVCYiIiKSjqw6fFaqVCnmz5+Pt7c3Hh4eTJ48mVy5ctGhQwe++OILtm7dytixY8mTJ09q1ysiIiKSJqwKRQMGDKBTp07069cPwzBwcHBg7NixNGzYED8/P/r370/jxo3ZuXPnY7X7yy+/sGbNGo4ePUpoaCiFChWibdu2Sc6g3bZtW/bs2ZPsvoGBgRQtWtRy++7duwQEBLBp0yZiY2OpVasWQ4cO5ZlnnrHmKYuIiEgWZ1UoKlmyJBs2bODQoUOEh4dTpkwZ8ufPD0Dp0qVZuXKlVWe9njdvHl5eXvj7+5MrVy527tzJp59+ytWrV+nZs6dlu0qVKjFo0KAk9/X29k5yu0+fPpw+fZoRI0bg7OzMxIkT6dy5M8uXL8fBwerx5SIiIpJFWZ0OcuTIgZ+fX4rrsmXLxqhRox67zenTp+Pp6Wm5Xb16de7cucPcuXP58MMPsbO7NwTK3d2dChUq3LedAwcOsH37dubMmUPNmjUBKFKkCA0aNODXX3+lQYMGj12biIiIZG1WDbSOi4sjLCzsvuvDwsKIi4t77HYTB6IEJUuWJCwsjIiIiEduZ9u2bbi7uycJbT4+PpQsWZJt27Y9dl0iIiKS9VnVUzR69Gj27dvH2rVrU1z/zjvvULVqVYYOHfpExQHs37+ffPnykSNHDsuyPXv2UKFCBeLj4ylfvjy9e/emcuXKlvVBQUEUKVIk2QkkfXx8CAoKeuKaHBx0dZSsLj7+f++xg4Od3nMRkaeAVaHojz/+oGnTpvdd//rrr7NmzRpra7LYt28fgYGBScYPVa5cmSZNmlC4cGGuX7/OnDlz6NChAwsXLqRixYoAhIaG4ubmlqy9nDlzcuTIkSeqyc7ORK5crk/UhmR8UVH2ln97eLji4uJiw2pERCQ9WBWKrl+/Tr58+e67/plnnuHatWtWFwX3zn3Ut29fqlatSrt27SzLP/rooyTbvfTSSzRs2JBp06Yxa9asJ3rMR2E2G4SGPvqhPMmcoqOjLP++cyccZ+d4G1YjIiJPwt09G/b2D+/xtyoUeXh4cObMmfuu//fff5Mc7npcoaGhdO7c2XIOpIQB1inJnj07derUYcOGDZZl7u7uSU4umSAkJIScOXNaXVeCuDjzE7chGVvi9zguzoy9vd5zEZGszqqBErVq1eKnn37i2LFjydYdPXqUJUuWULt2basKioqKomvXrty9e5fZs2eneBjsYXx8fDhz5gyGYSRZfubMGXx8fKyqS0RERLI2q3qKevfuzR9//EHLli2pW7cuzz//PAD//PMPmzdvxtPTk969ez92u3FxcfTp04egoCC+//77Bx6iSxAREcGWLVsoW7asZVnt2rWZNm0au3btokaNGsC9QHTs2DE6der02HWJiIhI1mdVKMqXLx/Lly9n/Pjx/Pbbb2zcuBG4d+6iRo0a0bdv30cKNP81cuRINm/ejL+/P2FhYRw8eNCyrlSpUhw+fJjZs2fz2muv4eXlxfXr15k7dy43btzgm2++sWxbsWJFatasyZAhQxg0aBDOzs5MmDCB4sWLU69ePWuesoiIiGRxJuO/x5gek2EYBAcHA/fOM/TfafCPo27duly6dCnFdb/99hvx8fGMGjWKkydPcufOHbJly0bFihXp2bMn5cqVS7J9wmU+Nm7cSFxcHDVr1mTo0KFWhbXE4uPNBAeHP1EbkvFFR0fRvfsHAEyf/h3Ozpp9JiKSWXl6uj7SQOsnDkVwL4Bkz54de3v7h2+cySkUPR0UikREso5HDUVWn5Hu77//pmPHjpQvX56qVataLtIaHBxM9+7d2b17t7VNi4iIiKQ7q0LRX3/9xbvvvsu5c+do3LgxZvP/pit7enoSFhbG4sWLU61IERERkbRmVSiaMGECRYsWJTAwkL59+yZbX7VqVQ4dOvTExYmIiIikF6tC0d9//03z5s1xcnJKcWB1vnz5uHnz5hMXJyIiIpJerApFDg4OSQ6Z/de1a9fInj271UWJiIiIpDerQlH58uWTXFYjsYiICFasWJHkqvUiIiIiGZ1Voeijjz7iyJEjdOnShW3btgFw8uRJli5dSvPmzQkODubDDz9M1UJFRERE0pLV5ynatWsXI0aM4Ny5c0mWFyxYkNGjR1OlSpVUKTCj0XmKng46T5GISNbxqOcpsuoyHwDVq1dnw4YNHD9+nLNnz2IYBgUKFKBMmTJPdFZrEREREVuwOhQlKFmyJCVLlkyNWkRERERsxqpQdPTo0UfarnTp0tY0LyIiIpLurApFLVq0eOAhMsMwMJlMHD9+3OrCRERERNKT1YfPnJ2dee2116hWrRp2dlZfQk1EREQkQ7AqFP3000+sWLGCwMBA9u/fT9OmTWnWrBkFChRI7fpERERE0oVVXTwVKlRg1KhRbN++nY8++oh9+/bx+uuv07ZtW1avXo2Vs/xFREREbOaJjnu5uLjQtGlTFixYwM8//0xoaCj+/v6EhISkVn0iIiIi6eKJp+QfPXqU5cuXs27dOhwdHenYsSM5cuRIjdpERERE0o1Voej27dusWbOGFStWEBQURJ06dRg7diy1a9fG3t4+tWsUERERSXNWhaKE8PPqq68yYMAAPD09AThx4kSS7XSeIhEREcksrApFsbGxxMbGsnbtWtatW5dsvc5TJCIiIpmNVaEoICAgtesQERERsSmrQlGzZs1Suw4RERERm9KpqEVERERQKBIREREBFIpEREREAIUiEREREUChSERERARQKBIREREBniAU7dq1i++++44jR44AsGrVKurXr88rr7zCF198QWxsbKoVKSIiIpLWrDpP0dKlSxk2bBiGYeDg4MCnn37KyJEjqV69OtHR0cybNw9XV1d69uyZ2vWKiIiIpAmreooWLFhA6dKl2bBhA2+99RZjxozh/fffZ86cOSxatIgGDRrw888/p3atIiIiImnGqlB04cIFWrRoQaFChejYsSPR0dFUq1bNsr5atWpcuXIl1YoUERERSWtWhaKoqChy5MgBYPm/k5OTZb2zs7PGFImIiEimYtWYIoA7d+5w+fJlQkJCAAgODuby5csA3L59O3WqExEREUknJsMwjMe9U4kSJTCZTJbbhmGkePv48eOpU2UGEh9vJjg43NZlSBqLjo6ie/cPAJg+/TucnV1sXJGIiFjL09MVe/uHHxyzqqcoICDAmruJiIiIZFhWhaJmzZqldh0iIiIiNqUzWouIiIigUCQiIiICKBSJiIiIAApFIiIiIoBCkYiIiAigUCQiIiICpEEoio+P5/Lly5azWz+OX375he7du1O7dm0qVKhAkyZNWLZsGf89v+TSpUt5/fXXKVu2LI0bN2bz5s3J2rp79y5DhgyhSpUqVKxYkY8++ojr169b/bxEREQka0v1UHTu3Dnq1q3Lq6+++tj3nTdvHtmyZcPf35/p06dTu3ZtPv30U6ZOnWrZZt26dXz66afUr1+fWbNmUaFCBXr27MnBgweTtNWnTx927NjBiBEj+Oqrrzhz5gydO3cmLi7uSZ+iiIiIZEFWX/vsftzc3GjatGmSy348qunTp+Pp6Wm5Xb16de7cucPcuXP58MMPsbOzY9KkSbz55pv06dMHgGrVqnHq1CmmTp3KrFmzADhw4ADbt29nzpw51KxZE4AiRYrQoEEDfv31Vxo0aPDkT1RERESylFTvKcqbNy9jx4616lIgiQNRgpIlSxIWFkZERAQXLlzg7Nmz1K9fP8k2DRo0YNeuXcTExACwbds23N3d8fPzs2zj4+NDyZIl2bZt22PXJSIiIllfqvcUpbb9+/eTL18+cuTIwf79+4F7vT6JFS1alNjYWC5cuEDRokUJCgqiSJEiyXqrfHx8CAoKeuKaHBw0Pj2ri4//33vs4GCn91xE5CmQJqFo586dfPvtt5bbfn5+dO3a9bHb2bdvH4GBgQwaNAiAkJAQANzd3ZNsl3A7YX1oaChubm7J2suZMydHjhx57DoSs7MzkSuX6xO1IRlfVJS95d8eHq64uLjYsBoREUkPVoWiwYMHP3D9xYsX2bdvH2PGjAGgQIECj/0YV69epW/fvlStWpV27dpZU2aaMJsNQkMjbF2GpLHo6CjLv+/cCcfZOd6G1YiIyJNwd8+Gvf3De/ytCkUrV67EyckJe3v7FNfHx9/7A9KsWTNrmic0NJTOnTvj4eHB5MmTsbO790Ry5swJ3Jtunzdv3iTbJ17v7u7O1atXk7UbEhJi2eZJxMWZn7gNydgSv8dxcWbs7fWei4hkdVYfPvv8889p1KhRiutWr16Nv7+/Ve1GRUXRtWtX7t69y+LFi5McBvPx8QEgKCjI8u+E246OjpYeKR8fH3bt2oVhGEnGFZ05cwZfX1+r6hIREZGsLU1Gj1ozHR8gLi6OPn36EBQUxOzZs8mXL1+S9QUKFKBw4cKsX78+yfLAwECqV6+Ok5MTALVr1yYkJIRdu3ZZtjlz5gzHjh2jdu3aVtUmIiIiWZvVPUXWBp8HGTlyJJs3b8bf35+wsLAkJ2QsVaoUTk5O9OrViwEDBlCwYEGqVq1KYGAghw8fZtGiRZZtK1asSM2aNRkyZAiDBg3C2dmZCRMmULx4cerVq5fqdYuIiEjmZzL+ew2NR1CiRAmcnZ1xcHDA2dkZNzc3vLy8KFu2LHXr1uXcuXMMGjSI48ePP1a7devW5dKlSymu++233/D29gbuXeZj1qxZXL58mSJFitCvXz9efvnlJNvfvXuXgIAANm7cSFxcHDVr1mTo0KHJep8eV3y8meDg8CdqQzK+6Ogounf/AIDp07/D2Vmzz0REMitPT9dHGmhtVShKmH0WGxtLTEwMt2/f5tKlS1y+fBmTyYSnpyfBwcGPHYoyA4Wip4NCkYhI1vGoociqw2f3O1t1cHAwv/zyC9988w1w7zxDhmGQO3fuJAOjRURERDKaVB1o7enpyXvvvUfv3r0xDIM2bdrQtm3bJCdyFBEREcmI0uSM1u+99x7vvfdeWjQtIiIikiaeuKfo+vXrnDhxgogIneVZREREMi+rQ9GmTZt44403qFOnDs2aNePQoUPAvXFFTZs2ZePGjalWpIiIiEhas+rw2e+//06vXr2oUKECDRs2ZMqUKZZ1np6e5MuXjxUrVvDaa6+lWqFPA8MwiImJtnUZAkRHR6f4b7EtJyfnNDlHmogIWBmKpk6dyosvvsjChQu5fft2klAEUKFCBRYvXpwqBT5NYmKiLdPAJePo06e7rUuQ/6fTI4hIWrLq8Nk///xD/fr177s+T5483Lp1y+qiRERERNKbVT1F2bJlIzIy8r7rL1y4gIeHh7U1CeBarCkmuzSZHCiPKOG8pjpcY1uGOY7wf1bZugwReQpY9Ve3atWqrFq1ivfffz/Zuhs3brBkyZJkl92Qx2Oyc1AosjFFIRGRp4tVh8/69OnD1atXeeutt1i8eDEmk4nt27czYcIEGjVqhGEY9OjRI7VrFREREUkzVoUiHx8ffvjhBzw8PPjmm28wDIM5c+YwY8YMfH19+eGHHywXbxURERHJDKw+PlOsWDHmzZtHSEgI586dwzAMChQogKenZ2rWJyIiIpIunnjQSs6cOSlXrlxq1CIiIiJiM1aHouDgYGbNmsXWrVu5dOkSAF5eXtSpU4eOHTuSJ0+eVCtSREREJK1ZfZ6iRo0aMXfuXNzc3HjjjTd44403cHNzY+7cuTRu3JhTp06ldq0iIiIiacaqnqJRo0YRHx/PkiVLkh06O3z4MJ07d+azzz5j4cKFqVKkiIiISFqzqqfo8OHDtGvXLsWxROXKlaNdu3YcPnz4iYsTERERSS9WhaLcuXPj7Ox83/XOzs7kzp3b6qJERERE0ptVoahdu3b8+OOP3LhxI9m6a9eu8eOPP9KuXbsnLk5EREQkvVg1psgwDLJnz069evV49dVXKVSoEABnz57lt99+o2DBghiGwdy5cy33MZlMtG/fPlWKFhEREUltJiPhqpePoUSJEo//QCYTx48ff+z7ZTTx8WaCg8PTpO3o6Ci6d/8AgBzF39K1z0S4d0HYsJPLAJg+/TucnV1sXJGIZDaenq7Y2z/84JhVf3V/++03a+4mIiIikmFZFYq8vLxSuw4RERERm7JqoPWFCxf4/fff77v+999/5+LFi1YXJSIiIpLerOopGjduHGFhYdStWzfF9d9//z3u7u5MmDDhiYoTERERSS9W9RQdOHCAGjVq3Hd99erV2bdvn9VFiYiIiKQ3q0JRaGgorq6u912fPXt27ty5Y21NIiIiIunOqlD03HPP8ddff913/f79+3n22WetLkpEREQkvVkViho2bMi6detYsGABZrPZsjw+Pp758+cTGBhIw4YNU61IERERkbRm1UDrrl27sn//fsaMGcO3335LkSJFADhz5gzBwcFUqVKF7t27p2qhIiIiImnJqlDk5OTEd999x8qVK9m4cSPnz58HoFy5ctSrV4+mTZtiZ2dVJ5SIiIiITVh9HQk7OztatGhBixYtUrMeEREREZtQd46IiIgIj9hT1LFjR7p160blypUfq/E///yTWbNmMWfOHKuKExGRjMUwDGJiom1dxhNLuBa6yWSycSVPxsnJOdM/h4zkkUJRgQIF6NChAwUKFKBBgwZUr16dkiVLJjtXUVhYGEePHmXnzp2sX7+ey5cv89Zbb6VJ4SIikr4MwyAgYCSnT5+ydSny/55/3pfBg4crGKWSRwpFI0aMoGPHjixYsIAffviBadOmYTKZyJkzJ+7u7gCEhIQQGhqKYRjkzJmTRo0a0a5dOwoUKJCmT0BEREQkNZiMhD7ERxQXF8e+ffs4ePAgQUFBljNXe3h44OPjQ4UKFXjhhRdwdHRMi3ptLj7eTHBweJq0HR0dRffuHwCQo/hbmOysHgcvkmUY5jjCTi4DYPr073B2drFxRU+3rHD4LDo6mj597p02ZuLE6Tg7O9u4Iuvp8Nmj8fR0xd7+4cOoH/uvroODA9WqVaNatWpWFSYiIpmXyWTKUsHU2dk5Sz0feTKafSYiIiKCQpGIiIgIoFAkIiIiAjzBGa3Twrlz55gzZw6HDh3in3/+wcfHh7Vr1ybZpm3btuzZsyfZfQMDAylatKjl9t27dwkICGDTpk3ExsZSq1Ythg4dyjPPPJPmz0NEREQynwwViv755x+2bt1K+fLlMZvN3G9iXKVKlRg0aFCSZd7e3klu9+nTh9OnTzNixAicnZ2ZOHEinTt3Zvny5Tg4ZKinLSIiIhlAhkoHdevW5dVXXwXA39+fI0eOpLidu7s7FSpUuG87Bw4cYPv27cyZM4eaNWsCUKRIERo0aMCvv/5KgwYNUr12ERERydysCkWXL19+pO3y58//WO3a2aXOEKdt27bh7u6On5+fZZmPjw8lS5Zk27ZtCkUiIiKSjFWhqG7duo90sqjjx49b0/xD7dmzhwoVKhAfH0/58uXp3bt3kuuyBQUFUaRIkWQ1+vj4EBQU9MSP7+CQNuPT4+M17l3kQRwc7NJs/5OnR+LvWn2mJDGrQtHAgQMtgSMiIoIpU6bw9ttvU7hw4dSsLUWVK1emSZMmFC5cmOvXrzNnzhw6dOjAwoULqVixIgChoaG4ubklu2/OnDnve0juUdnZmciVy/XhG1ohKso+TdoVySo8PFxxcdGJ9uTJJP6u1WdKErMqFHXs2NHy79u3bzNlyhTq169P9erVU62w+/noo4+S3H7ppZdo2LAh06ZNY9asWWn++GazQWhoRJq0HR0dlSbtimQVd+6E4+wcb+syJJNL/F2rz9TTwd09W9pc5iOjyZ49O3Xq1GHDhg2WZe7u7ly9ejXZtiEhIeTMmfOJHzMuzvzEbaRnuyJZRVycGXt77SfyZBJ/1+ozJYllyQOpPj4+nDlzJtmU/jNnzuDj42OjqkRERCQjS7VQZKur9EZERLBlyxbKli1rWVa7dm1CQkLYtWuXZdmZM2c4duwYtWvXtkWZIiIiksFZdfisUaNGln+bzfe6HYcOHUq2bNksy00mE2vWrHmsdiMjI9m6dSsAly5dIiwsjPXr1wNQpUoVgoKCmD17Nq+99hpeXl5cv36duXPncuPGDb755htLOxUrVqRmzZoMGTKEQYMG4ezszIQJEyhevDj16tWz5imLiIhIFmdVKPLw8Ehy29PTMzVq4datW/Tu3TvJsoTbCxYs4NlnnyU2NpYJEyZw584dsmXLRsWKFRk5ciTlypVLcr+JEycSEBDAsGHDiIuLo2bNmgwdOlRnsxYREZEUWZUQFi5cmNp1APcu1XHy5MkHbjNnzpxHasvNzY0xY8YwZsyY1ChNREREsrgsOdBaRERE5HFZ1VO0d+/eR9ou8VmmRURERDIyq0JR27ZtLbPNEqa9J559ZhgGJpMpzS7zISIiIpLarApFuXPn5tatW9SpU4e2bdvi5OSU2nWJiIiIpCurQtHGjRv57rvvmDt3LqdOneKjjz6iadOmNjtXkYiIiMiTsmqgdfbs2enZsycbN27klVdeYdiwYTRu3JgtW7akcnkiIiIi6eOJZp95enoydOhQAgMD8fX1pXv37rRp04ZDhw6lVn0iIiIi6SJVpuQXKFCA8ePHs2LFCpydnWndujW9evVKjaZFRERE0oVVY4pKlChx3/FDCbPRNm3aZH1VIiIiIunMqlDUo0cPDapOY4Y5ztYliGQI2hdEJL1YFYp0aCxtJPSyAYT/s8p2hYhkUIn3ERGR1KbLfIiIiIhgZU/R3LlzH7qNyWSiffv21jT/1Ep8SNK1WFNMdla9PSJZimGOs/Sc6rC9iKQlq/7qfvHFF8ku8/FfCkVPxmTnoFAkIiKSjqz6q+vm5kZMTAz16tWjefPmFCxYMLXrEhEREUlXVoWiHTt28Ouvv7J8+XI6depExYoVadGiBW+88QbZsmVL7RpFRERE0pxVA62dnJxo2LAhc+fO5ddff6VatWpMnToVPz8/PvnkE2JiYlK7ThEREZE09cSzz7y8vOjZsyfjx4+nQIECrFixgoiIiNSoTURERCTdPNFI3uDgYFavXs2KFSs4d+4cL7/8Mv3798fDwyOVyhMRERFJH1aFot9++40VK1awdetWihUrRsuWLWncuLHCkIiIiGRaVl/mw8XFhTfeeIPSpUsTHx/PypUrk2yjKfkiIiKSmVh9+CwqKoq1a9eydu3aFNcrFImIiEhmYvXhMxEREZGsxKpQ5OXlldp1iIiIiNiUriMhIpIODMMgJiba1mUIEB0dneK/xbacnJxtfn1DhSIRkXQQExNN9+4f2LoM+Y8+fbrbugT5f9Onf4ezs4tNa3jikzeKiIiIZAXqKRIRSWcDqubFyd62hwmedoZhANj8cM3TLibe4KvdN2xdhoVCkYhIOnOyNykU2Zxef0lOh89ERERESKNQNGXKFJYuXUpsbGxaNC8iIiKS6tIsFA0bNox69eqxdOnStHgIERERkVSVJmOKFixYQEREBHv27GHJkiW0bNkyLR5GREREJNWkSSiqUqUKAC+99FJaNC8iIiKS6jTQWkRERIQnCEVhYWHMnDmTjh070rRpUw4fPgzAnTt3mDt3LufOnUu1IkVERETSmlWHz65evUqbNm24evUqhQoVIigoiPDwcAA8PDz46aefuHTpEkOHDk3VYkVERETSilWhaNy4cYSHh7Nq1So8PT2pUaNGkvWvvvoqW7ZsSY36RERERNKFVYfPduzYQdu2bXn++edTPEV6gQIFuHLlyhMXJyIiIpJerApFUVFReHp63nd9wqE0ERERkczCqlBUtGhR9u7de9/1mzZtolSpUlYXJSIiIpLerApF77//PoGBgcycOZOwsDDg3hWHz507x8CBAzl48CDt27dPzTpFRERE0pRVA62bNGnC5cuX+eabb5g4cSIAnTp1wjAM7Ozs6Nu3L6+++upjt3vu3DnmzJnDoUOH+Oeff/Dx8WHt2rXJtlu6dCmzZ8/m8uXLFClShL59+/Lyyy8n2ebu3bsEBASwadMmYmNjqVWrFkOHDuWZZ56x5imLiIhIFmf1Ga27d+9OkyZN+PXXXzl37hxms5mCBQtSr149ChQoYFWb//zzD1u3bqV8+fKYzWYMw0i2zbp16/j000/p1q0b1apVIzAwkJ49e/L9999ToUIFy3Z9+vTh9OnTjBgxAmdnZyZOnEjnzp1Zvnw5Dg5pciJvERERycSeKB3kz58/VQ+T1a1b19LD5O/vz5EjR5JtM2nSJN5880369OkDQLVq1Th16hRTp05l1qxZABw4cIDt27czZ84catasCUCRIkVo0KABv/76Kw0aNEi1mkVERCRryFCX+bCze3A5Fy5c4OzZs9SvXz/J8gYNGrBr1y5iYmIA2LZtG+7u7vj5+Vm28fHxoWTJkmzbti31CxcREZFMz6qeopIlSz50G5PJxLFjx6xp/r6CgoKAe70+iRUtWpTY2FguXLhA0aJFCQoKokiRIsnOoeTj42NpQ0RERCQxq0KRYRiYTCZq1apFuXLlUrum+woJCQHA3d09yfKE2wnrQ0NDcXNzS3b/nDlzpnhI7nE5OKRNB1t8fIbquBPJcBwc7NJs/0tr2r9FHiwj7N9WhaIlS5bw1VdfsW3bNrJly0a/fv0oVKhQateWIdnZmciVyzVN2o6Ksk+TdkWyCg8PV1xcXGxdhlW0f4s8WEbYv60KReXKlWPBggVs3bqVCRMm8Oabb9KyZUt69er1wDNdP6mcOXMC96bb582b17I8NDQ0yXp3d3euXr2a7P4hISGWbaxlNhuEhkY8URv3Ex0dlSbtimQVd+6E4+wcb+syrKL9W+TB0nL/dnfPhr39w3uhnmj2WZ06dahTpw6rV69m0qRJrFq1ig4dOtCxY0dcXVO/N8XHxwe4N7Yo4d8Jtx0dHS2nAvDx8WHXrl2Ww3wJzpw5g6+v7xPXERdnfuI20rNdkawiLs6MvX3m3E+0f4s8WEbYv1Pl4F2TJk1Yv349ffr04ccff+S1115j4cKFqdF0EgUKFKBw4cKsX78+yfLAwECqV6+Ok5MTALVr1yYkJIRdu3ZZtjlz5gzHjh2jdu3aqV6XiIiIZH5W9RS1a9fuvuuee+45jh07xpgxY2jbtu1jtRsZGcnWrVsBuHTpEmFhYZYAVKVKFTw9PenVqxcDBgygYMGCVK1alcDAQA4fPsyiRYss7VSsWJGaNWsyZMgQBg0ahLOzMxMmTKB48eLUq1fPimcsIiIiWZ3Vs8/ux9XVlcqVK1tVzK1bt+jdu3eSZQm3FyxYQNWqVWnYsCGRkZHMmjWLmTNnUqRIEaZMmULFihWT3G/ixIkEBAQwbNgw4uLiqFmzJkOHDtXZrEVERCRFViWEtDg0BuDt7c3Jkycful3Lli1p2bLlA7dxc3NjzJgxjBkzJrXKExERkSxMJ84QERERIY1C0R9//EHJkiUt/3Xu3DktHkZEREQk1Vh1+OyVV1554Pro6GgANm3aBGDzkzGJiIiIPIxVoej5559Pdl2xxIKDg7l16xZeXl5WFyYiIiKSnqwKRTNmzHjg+m3bttG1a1erChIRERGxhTQZU/SgXiQRERGRjEizz0RERESw8vDZlClTHrj+/PnzVhUjIiIiYitpEopAh9BEREQkc7EqFCW+0Or91vfv39+qgkRERERswapQlCtXrgeud3Nzs6oYEREREVvRQGsRERERrOwpOnr06APXnzt3zqpiRERERGzFqlDUokWLBw6kNgxDA61FRO4jJt6wdQkiGUJG2xesCkUBAQGpXYeISJZmGP/78v9q9w0bViKSMSXeR2zFqlDUrFmz1K5DRERExKasCkUiIvJ4Eg8pGFA1L072GmIgEhNvWHpOM8KwG6tCUbdu3R66jclkYvr06dY0LyKSpTnZmxSKRDIgq0LRli1byJUrF9myZQPuHQe8evUquXPnxsnJCcgYiU9ERETkUVl9+GzIkCE0atQIgODgYGrUqMGXX35J9erVU604ERERkfSSKidvTOgVunv3bmo0JyIiIpLurApFuXLl4urVq5bbCf8eOHAgCxcuTJ3KRERERNKRVYfPSpUqxfz58/H29sbDw4PJkyeTK1cuOnTowBdffMHWrVsZO3YsefLkSe16RURERNKEVT1FAwYMwDAM+vXrR4cOHTh8+DCffPIJXbp0YfHixVy8eJHGjRundq0iIiIiacaqnqKSJUuyYcMGDh06RHh4OGXKlCF//vwAlC5dmpUrV+qs1yIiIpKpWD37LEeOHPj5+aW4Llu2bIwaNcrqokRERETSW5qc0To4OJh///3Xcjt37tz4+PikxUOJiIiIpAqrQtHly5cfuH7Tpk0EBATw3HPPAVCvXj38/f2teSgRERGRdGFVKKpbt+4Dz1htGAYmk4nff//d6sJERERE0pPVh88aNGhA6dKlU1x39OhRAgMDrS5KREREJL1ZHYpeeukly2U+/mvNmjUKRSIiIpKppMplPkREREQyO6t7irZu3cqtW7dwcnLCzc0NLy8vSpYsSbZs2VKzPhEREZF0YXUoWrt2LWvXrrXcNplM2NvbU716dby8vFKlOBEREZH0YlUo+u233wCIi4sjOjqa27dvc+nSJf7++282b97MH3/88cDZaSIiIiIZjVWh6H49Qc2bN2fIkCH069ePjRs30q5dOwD8/Pzo2rWr9VWKiIiIpLFUP6O1o6MjPXv2pHjx4pZlOpu1iIiIZHRpcpmP4sWLJwlFIiIiIhndE4WiPXv2sGXLFstlP/Lnz89LL71ElSpVUqU4ERERkfRiVSiKiYmhf//+bNq0CcMwcHd3ByA0NJS5c+fy2muvMX78eBwdHVO1WBEREZG0YtXJG6dOncrGjRvp0KED27dvZ8+ePezZs4cdO3bwwQcf8OuvvzJ16tTUrlVEREQkzVgVin7++WeaNWvGxx9/TJ48eSzLc+fOzcCBA2natClr1qxJtSJFRERE0ppVoejGjRuUK1fuvuvLlSvHjRs3rC5KREREJL1ZFYqeffZZ9uzZc9/1e/fu5dlnn7W6qAdZsWKFZXZb4v+++uqrJNstXbqU119/nbJly9K4cWM2b96cJvWIiIhI1mDVQOumTZsyefJk3NzcaN++PYUKFcJkMnH27Fnmz5/P+vXr6dWrV2rXmsTs2bNxc3Oz3M6XL5/l3+vWrePTTz+lW7duVKtWjcDAQHr27Mn3339PhQoV0rQuERERyZysCkXdunXjwoULLFmyhKVLl2Jnd6/DyWw2YxgGzZo1o1u3bqla6H+VLl0aT0/PFNdNmjSJN998kz59+gBQrVo1Tp06xdSpU5k1a1aa1iUiIiKZk1WhyN7enrFjx9K+fXu2bdvGpUuXgHuX/6hduzYlSpRI1SIfx4ULFzh79iwDBw5MsrxBgwaMGzeOmJgYnJycbFSdiIiIZFRPdPLGEiVK2CwANWzYkNu3b5M/f37efvttOnXqhL29PUFBQQAUKVIkyfZFixYlNjaWCxcuULRo0Sd6bAcHq4ZiPVR8fNq0K5JVODjYpdn+l9a0f4s8WEbYv60KRT/99BOtW7e+7/qoqCjGjRvHsGHDrC7sfvLmzUuvXr0oX748JpOJ33//nYkTJ3Lt2jWGDRtGSEgIgOWEkgkSbiest5adnYlcuVyfqI37iYqyT5N2RbIKDw9XXFxcbF2GVbR/izxYRti/rQpFI0aMYOPGjYwZMybJAGeAffv2MXjwYK5cuZImoahWrVrUqlXLcrtmzZo4Ozszf/78NB/HBGA2G4SGRqRJ29HRUWnSrkhWcedOOM7O8bYuwyrav0UeLC33b3f3bNjbP7wXyqp+qi+//JIjR47QsGFDVq5cCUB0dDSff/457dq1w8XFhcWLF1vTtFXq169PfHw8x48fJ2fOnADcvXs3yTahoaEAlvVPIi7OnGb/icj9peW+lx7/icj9ZYR9z6qeokaNGlG1alU+/fRThgwZQmBgIOfPn+fixYt06tSJXr162ey6Zz4+PgAEBQVZ/p1w29HRkQIFCtikLhEREcnYrB7R9MwzzzB58mTKli3LH3/8wfnz5xk4cCD9+vVL90AUGBiIvb09pUqVokCBAhQuXJj169cn26Z69eqaeSYiIiIpsnr22fHjx/n4448JCgrivffeY8+ePXz55Zdcv36dPn36pFn46NixI1WrVqV48eIA/PbbbyxZsoR27dqRN29eAHr16sWAAQMoWLAgVatWJTAwkMOHD7No0aI0qUlEREQyP6tC0eTJk5kxYwZeXl4sXLiQSpUqERMTw6RJk5g7dy7btm1j7NixlClTJrXrpUiRIixfvpyrV69iNpspXLgwQ4YMoW3btpZtGjZsSGRkJLNmzWLmzJkUKVKEKVOmULFixVSvR0RERLIGq0LRtGnTePfddxk4cKBl+pyTkxMDBgzgtddeY9CgQbRu3ZojR46karEAQ4cOfaTtWrZsScuWLVP98UVERCRrsioUzZ07l2rVqqW4rnz58qxevZoJEyY8UWEiIiIi6cmqUHS/QJTA2dkZf39/qwoSEcnqYuINW5fw1DOMe++ByWSycSVPt4y2L1gVinbu3Mmff/5Jv379Ulw/YcIEqlWrRvXq1Z+oOBGRrOir3TdsXYKIpMCqKfnTpk3jypUr911/7do1pk+fbnVRIiIiIunNqp6iU6dO8cYbb9x3fdmyZdm8ebPVRYmIZDVOTs5Mn/6drcsQ7l2BoU+f7gBMnDgdZ2dnG1ckcG8fsTWrQlFMTAyxsbEPXB8Vpev8iIgkMJlMODtnzovZZmXOzs56X8TCqsNnxYoVY+PGjSmuMwyDX3/9laJFiz5RYSIiIiLpyapQ1KZNG/766y8++ugjTp48SVxcHHFxcZw4cYLevXtz8ODBJCdTFBEREcnorDp81qRJEy5cuMC0adPYuHEjdnb3spXZbMZkMtG9e3eaNWuWqoWKiIiIpCWrr33Ws2dPGjduzMaNG7lw4QIABQsW5NVXX6VgwYKpVqCIiIhIerA6FMG9ENSxY8fUqkVERETEZqwaUyQiIiKS1TxyT1GjRo0eq2GTycSaNWseuyARERERW3jkUPTPP/9gMpkoUqQIuXPnTsuaRERERNLdI4eipk2b8vPPP3P58mVq1apF586dyZMnT1rWJiIiIpJuHnlM0dixY/nll1+oX78+33//Pa+++ioBAQHcunUrLesTERERSRePNdC6YMGCBAQEEBgYaAlHr7zyCgEBAdy4oas+i4iISOZl1eyzhHCUuOfotddeY8yYMQpHIiIikik90ZT8AgUKWMJRgwYN+OGHH3j11Vf5/PPPuX79emrVKCIiIpLmHnmg9dGjRx+4/r333qN27drMmDGDRYsWsXTpUg4ePPik9YmIiIiki0cORS1atMBkMj10O8MwAIiOjra+KhEREZF09sihKCAgIC3rEBEREbGpRw5Fuuq9iIiIZGW69pmIiIgICkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICJDFQ9G///5Lhw4dqFChAn5+fowbN46YmBhblyUiIiIZkIOtC0grISEhvP/++xQuXJjJkydz7do1xo4dS1RUFMOGDbN1eSIiIpLBZNlQ9NNPPxEeHs6UKVPw8PAAID4+npEjR9K1a1fy5ctn2wJFREQkQ8myh8+2bdtG9erVLYEIoH79+pjNZnbs2GG7wkRERCRDyrI9RUFBQbRo0SLJMnd3d/LmzUtQUJCNqnp0hjnO1iVYzTAMMOJtXYYkMNljMplsXYXVMvO+kBUZhkFMTLSty3gi0dHRKf47M3Jycs7U+3dGk2VDUWhoKO7u7smW58yZk5CQkCdq28EhbTrY4uP/1274P6vS5DFEMjMHB7s02//k4QzDYPToEfzzzylbl5Jq+vTpbusSnkixYsUZOnSEglEqybKhKK3Y2ZnIlcs1TdqOirJPk3ZFsgoPD1dcXFxsXcZTyzAMHBz0PZWRODjYkSuXq0JRKsmyocjd3Z27d+8mWx4SEkLOnDmtbtdsNggNjXiS0u7LMAxmzZqXJm2np3vd6zr1QUbh5OSUZb4wIyLiiIwMt3UZTzV//2GZ/vAZ/P9hfsj0+4aTkzN37qTN36SsxN09G/b2D+9lzrKhyMfHJ9nYobt373Ljxg18fHyeqO24OPMT3f9B7O2d0qzt9OTg4GzrEiQLio83AMPWZTz1ssr3VFagfSJ1ZdmD87Vr12bnzp2EhoZalq1fvx47Ozv8/PxsWJmIiIhkRFk2FLVu3RpXV1d69OjB9u3bWb58OePGjaN169Y6R5GIiIgkYzISDqxmQf/++y+fffYZBw4cwNXVlSZNmtC3b1+cnKzv+o2PNxMcrDENIiIimYWnp+sjjSnK0qEoLSgUiYiIZC6PGoqy7OEzERERkcehUCQiIiKCQpGIiIgIoFAkIiIiAigUiYiIiAAKRSIiIiKAQpGIiIgIoFAkIiIiAujkjY/NMAzMZr1kIiIimYWdnQmTyfTQ7RSKRERERNDhMxERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhFJJD4+3tYliEgq0j79eBxsXYCIZAxxcXE4ODgQExPDmTNnuHv3LpUqVcJsNuPgoK8KkcwmPj4ee3t7IiMjWbNmDRERERQsWJAXX3yRnDlz2rq8DMlkGIZh6yJExLYMw8BkMhEWFsb777/PhQsXCA0NpVChQrz11lu89dZb5MqVy9ZlisgjStinw8PDad68OaGhoQDcuXOHmjVr8s4771C3bl0bV5nx6PCZyFMuPj4ek8mE2WzG39+fHDlyMHLkSJYtW0bx4sVZtmwZY8eO5c6dO7YuVUQeQeJ9esGCBXh5eTF//nx+/vlnFixYwPnz55k6dSqbN2+2dakZjkKRyFPO3t6e6Oho1q9fD0DXrl2pX78+ZcqUYdKkSdSvX5+DBw+yfPlyjU8QyQQSDpktWLCAv/76i2rVquHr60uePHmoXLkyU6dOJSwsjB9//JG7d+/autwMRaFI5ClnNpsZPnw4Y8aM4eDBgxQpUgSA6OhoAPr06UOxYsVYtmwZ9vb2tixVRB7R4sWLGTt2LIcPH+a5554D7vUgxcfH8/zzzzNy5Ej++OMP9u/fb+NKMxaFIpGnnJ2dHU2aNKFAgQLcvHmTn3/+GQBnZ2dLMPrggw84c+YMBw8etGGlIvKo2rdvT69evQgJCWHu3LmcOXMmyY+awoUL4+7uzsWLF21YZcajUCTylDGbzcmWVa9enY8//piyZcuyePFiVq1aBdwLRgAXL14kZ86cuLq6pmepIvII7ndYu0ePHnTr1o0LFy4we/ZsgoKCsLe3xzAMgoODAciePXt6lprhafaZyFMk8bT7U6dOcfPmTQoWLIi3tzdOTk7s2bOH8ePHc+PGDVq1akX9+vU5c+YM06ZNw97enkWLFmFnp99SIhlFwj4dERHB8uXLuXTpEiVKlKBgwYJUqlQJgHHjxrFkyRK8vLx4//33uXLlCjt27CAsLIyVK1fqsHgiCkUiTwmz2YydnR1hYWF06NCB69evc+3aNfLkyUO5cuX46quvyJ49O3v27OHLL7/k77//xtPTkxdeeAFHR0cCAgJwdna2nPtERGwr8ak0WrZsSXx8PM7Ozty4cYPChQvz8ssv07VrVwCmTJnClClTyJkzJ5UrV6ZSpUq0adMGJycn7dOJ6CefyFPCzs6OmJgYPvjgA7Jnz86oUaPYsGEDzZo148SJE7z99ttERERQpUoVhgwZQvny5cmbNy9+fn58/fXXODs7ExMToy9PkQzCZDIRFxfH4MGDyZs3L9OnT+fnn3+2zCSdPn06hw8fBqBnz558+OGH2NnZ4e3tTb169XByciI2Nlb7dCIKRSJPkb///psrV67QtWtX6tSpQ6FChejRowcDBgwgIiKCjz/+mNjYWCpWrMjAgQPJli0bP/30E4GBgQA4OTnZ+BmISGKxsbGcPXuWOnXqULBgQQD27dvHoUOH6NOnD+XKlSMyMhKAjz76iObNm7Nq1Srmzp3LuXPncHR0tGX5GY5CkUgWFhcXl+T2zZs3CQ4OJnfu3ADExMTg4uJC3bp1eeONNzh06BBXrlwB4MUXX2TAgAG4urry1VdfsWHDhnSvX0SS+u8+ff36df79918KFCiAo6Mja9asoWfPnvTp04f27dsTERHB999/z7FjxwAYOHAgrVu35vvvv+enn35K1t7TTqFIJItKGIAZFhbG8ePHAShWrBgODg5s3boVuNfzkxCMWrRowY0bNzh//ryljRdffJGPPvqIIkWKUKpUKZs8DxG5J/E+PW3aNMxmM4UKFeKFF15g7dq1/PjjjwwaNIg+ffpYxhLt2rWLXbt2ERUVZZml1qdPH3r16sXbb7+t6xr+h14NkSwo8Zdn3bp1KVu2LNOnTyd//vw0adKEmTNnki9fPpo0aWI5JHbmzBmee+458uTJA/xvEGfVqlUpX748Li4utnxKIk+1xPt048aN8fX1JSIighw5clC7dm2+++47Nm7cSJcuXejWrRvx8fGWqfh58uShQoUK2NnZWQZV9+jRw9ZPKUNSKBLJYhK+PMPDw2nSpAkVK1Zk+PDhlvDz1ltvcfXqVUaNGsWVK1eoWbMmt27dYtasWXh5eeHr6wvcG8SZIOF8RSKS/hLv082bN6dgwYKMHj3aco6hzp07c+XKFX755ReCgoLYtm0bp06dYuPGjURFRTFhwgTs7Owwm80aVP0QmpIvkoUk/vJ85513uHnzJkuWLMHb29syJR/uDbhesWIFK1euJCYmhty5c1O4cGG+++47HB0dk2wrIraTuIeobdu2nDlzhvnz51O+fHng3rjAhB88M2fOZPfu3ezbt4+SJUtSpEgRPvvsMxwcHCztyIMpFIlkEYm/PJs0acKtW7coUKAA9evXp1OnTjg5OSX5YoyJieHq1aucOXOGXLlyUaZMGezs7PTlKZJBJD63WJMmTXBwcODSpUs0btyYHj164OXlBdybgZZ4Ftnly5fJly+fpVdI+/Sj009BkSwi8RiiYsWKsXr1asqWLcv69euZNWsWMTExODg4WAZbOjg4ULBgQerUqUO5cuUs4w305SmSMdjZ2REdHc0rr7zCc889x4YNG/jmm29YsWIFU6ZM4cKFCwA4OjomudTHc889ZwlEhmFon34MCkUiWcj58+cpW7Ysw4YNo1ChQgwbNozSpUuzYcMGSzCyt7e/7+ExjTcQsb3EAef27dt07tyZL7/8EsMweOWVV5gyZQorV65k2rRplmCUeN9NPB4w8b/l4XT4TCQTSyncJIwxSJg9FhkZyWeffcaRI0d44403LIfSEtaLSMaRcKgrMjKStWvXUqlSJQoUKGAZN5Swz2/atImePXvSvHlzPvzwQ7y9vW1cedagniKRTCouLg47OztiY2O5dOkSZ8+eJSwsDCcnJ8xmMyaTifj4eLJly8ann35KmTJl2LhxIxMnTiQ2NlaBSCSDSTh8HRYWxnvvvcfChQvZv39/ksNfdnZ2GIbBq6++ytSpU1m9ejUBAQFcv37dhpVnHeopEsmEEs41EhYWxoABAzh//jzR0dF4eHgwfvx4ChcubOkJStg2MjKSgQMH4urqytixYxWKRDKg6Oho3nvvPbJnz84nn3xCkSJFUry8TsL+HRgYyMKFC/n+++81YzQVKBSJZDIJX4YRERG0atWKnDlz0qVLF+Li4vjoo4/w8PBg+vTplC1bNlkwio6OxtHR0fJrU8FIJGP5448/+Oyzzxg5ciRVq1a9775qGAaGYSQJQjqVxpPTqyeSySSEnLFjx+Lp6cmkSZOoXbs2q1atInfu3Hh6etKpUyeOHDmCyWTCMAzL4GpnZ2fLSdwUiEQynsuXL3PlyhV8fHwsASdhP4Z7h83j4+MxmUyW9QnrFIienF5BkUwoMjKSZ599lhYtWuDp6Un//v05ePAgM2bMYOjQocTHx9OjRw+OHz9u+UJN/IWpL0+RjMnFxQU7OzvLrLIEJpOJqKgoli5dmuT6hAnrJHXom1EkE/jvUW5XV1dee+016tevz7Zt2zh06BCfffYZJUqUoEqVKtSqVYtr167RrFkzTp8+rS9NkQzGbDanuPz1118nT548TJs2jaioqCTbnz17lp9++olDhw6lV5lPHZ3RSSSDS5iiGx8fj2EYREZG4ubmRrFixQA4e/YskZGRFC1a1HIfJycn3nnnHdzd3SlcuLCNKheRlCTs0zExMZw8eZI7d+5QsmRJ8uTJg4uLC/7+/gwZMoTu3bvTvXt38ufPz5kzZ5gyZQrZs2enUaNGtn4KWZZCkUgGlvhaZsOGDeP8+fO4u7vTtWtXqlSpAtw7aZvJZOLEiRN4eHhw+/ZtLl++TIsWLWjatGmSdkTEthJPu+/ZsyeXLl0iKiqKmJgYhg8fzksvvcTLL79MQEAAAQEBlkkUzz33HHnz5mX+/PnY29tbJk9I6tLsM5EMKvG1zJo2bUqePHnw8vIiKCiInDlzMnnyZNzc3IiMjOS9997j+vXrPPPMM0RGRuLs7Mzy5cv1pSliYynNHIuIiOCdd94hZ86c9O/fnwIFCvDmm28C8OGHH9KyZUtcXFyIjo5m48aNxMTE8Oyzz1KtWjVdnzCN6VUVyaAcHByIiIigcePGlChRgqFDh5I/f37mz5/PsmXLLF3vxYsX5/vvv2f8+PHcvXsXNzc3/P399WtSJAOIjIwke/bslnBkNpuZOnUqnp6efPXVV+TOnZtevXqRLVs2SpUqxRdffIHJZKJBgwZ4enrSsGHDJO3p+oRpS6+sSAZlNpuZPHkyly9fpm/fvuTPnx+498vz8uXLfPDBB/z777906dKFjz76iCFDhiSZVaZfkyK2dejQIQYOHMhnn31G1apVLbNACxYsiIeHB7lz52bw4MEcOXKEqVOnUrBgQbp3787kyZNxcHDgjTfewMPDI0mb+pGTtnT4TCQD27VrF/PmzWPv3r388MMPBAcH88EHH9CjRw+qVq3KH3/8waxZswgMDMTHx8fW5YrI/wsJCeGDDz7g6NGjFChQgJEjR1KjRg3g3vUJTSYT//77Lz169KBfv368/vrrODg4MGXKFKZMmQLAhAkTqF+/vi2fxlNHPyNFMoiUzkZbvXp1HB0diY2NpVWrVkRHRzN8+HBatWqFnZ0d//zzD4UKFcLFxcVGVYtISpycnChYsCAXLlygWLFiDB06lM8++ww/Pz8cHR0xmUxcuHCBy5cvU6xYMUuvrslkonv37uTLl4/XXnvNxs/i6aPzFIlkAAkXd42JiWHv3r1s3bqV/fv3A/Diiy/Ss2dPateujaOjI97e3tjZ2XHp0iV+/vlnihQpwnPPPWfjZyAiCQzDIFu2bPTs2ROTyYSbmxslS5Zk6NCh7Nq1yzLwOl++fHh5ebFkyRKCg4M5ffo027dvx8nJidatW+Pg4EBcXJyNn83TRYfPRGwsoYcoLCyMDh06EBwczKVLl8iWLRsvv/wyQ4cOxdPTk/379zNt2jQOHjzIsGHDWLFiBTdv3mT16tU4ODjoukciGUhcXBxms5nx48cTGRlJzZo1WbhwIRcvXmTMmDFUr16dqKgoxo0bx+bNmwkPDydHjhy4u7uzbNkyjQe0EYUikQwgJiaGNm3a4OTkRI8ePciePTsHDhxgxowZPP/880yaNIlcuXKxb98+Zs2axdatW/Hx8WH16tU4OjpqULWIjf3zzz9ER0fj5eVFrly5LMt/+eUXRo4cyaJFi4iNjWXMmDFcvHiR0aNH4+fnR0REBJs3b+bff//FxcWFDz74wHKyVg2qTn8KRSLpLHGPTsI03b179zJo0CBGjx5tGYwZGxvLX3/9Re/evfHz82P8+PHAvcHXf/75J7169bJ0rysQidjOqVOnaNy4MdmzZ6dmzZrUq1cvyVT6jz76iPDwcL799lsOHDjAtGnTOHfuHKNGjaJWrVrJzmWkQGQ76msXSUdHjx5lxIgRhIaGAv+7kGNUVBTXr1/H0dERuBeWHB0dqVSpEp07d2bLli0cPHgQuDf4um/fvgpEIhnE1atX8fDwIDo6GmdnZwYPHkzfvn1ZuHAhAK1atSIiIoJjx45RpUoVOnbsSOHChRkxYgRbtmxJdnJHBSLbUSgSSUenT59myZIljBs3jrCwMMtyd3d3AA4fPmxZlhCMypcvT3h4OBEREcnaUyASsb1q1arxxRdfkCtXLqKiopg+fTouLi4sWbKE5s2bExYWxoULF1i8eDEAtWrVokuXLri5ubFs2TIbVy+J6RtVJB3Vr18fwzAYNmwY8fHxDBkyBDc3N8qXL89bb73FlClTKF68ODVr1rTcJyIiAi8vL7Jnz27DykXkfpycnPDz82PkyJEMHDgQV1dXevTogZOTE+PGjWPZsmVERUWxadMm2rRpQ6lSpahevTqfffYZpUuXtnX5kojGFImks5iYGNasWcPo0aNp2LAh/fr1w9PTk7NnzxIQEMDu3bvp0aMHZcqUITY2lkmTJuHs7MzChQs1u0wkA4uPj2fLli18/PHHVK5cmfHjx+Pq6sqBAwc4fPgw586do1+/fuTIkSPZ/XTILGNQKBJJQw/6sps8eTJTp06ldevWll+X58+fZ/78+SxZsgTDMHjmmWfw8vLiu+++w9HRUdPuRTK4hGDk7+9PxYoVGTdunOVSHbGxsTg6OqZ4kVjJGBSKRNLI8ePHmT59Op9++il58+YF/velePz4cdq0aUOJEiU4fPgw9evXZ/jw4bi6ugJw5MgRwsPDcXJyonz58roytkgmkjgYvfDCC4wePZo8efIAKBBlcPrJKZJGrl27xq+//srIkSO5desWZrMZR0dHjhw5wjvvvMObb77J9OnT+eyzz1i/fj2jRo2yzEorU6YMVatWpWLFitjZ2WE2mxWIRDIJe3t7XnrpJcaOHcuBAwcYPnw4N27cAFAgyuAUikTSSK1atZg+fTp79uzhk08+wc7OjmPHjtGmTRsaNWrE4MGDcXd3p2HDhowcOZINGzYwduxYQkJCkrWlQ2YimUtCMAoICOC3335j3rx5ti5JHoEOn4mkoYRu9EGDBlGsWDFOnDhBo0aNGDJkSJKLuMbGxrJu3Tr8/f3p3bs33bt3t2HVIpJa4uPjOXDgABUqVFBvbyagUCSSxsxmM1u2bOHzzz8nIiKCpUuX4u3tbVmX0AsUExPDn3/+SY0aNfTlKZIFaVxgxqdQJJIOYmJi2LFjB4MGDaJSpUqMGTMGT0/P+26vL0+RjEkDpbM2DVQQSQdOTk7Url2bgIAA9u/fz5AhQ7h169Z9t1cgErG9+Pj4ZMtSCkSJ+xbMZnOa1iRpSz1FIukoYYzRkCFDePHFFxkxYoRlur6IZBwJvbURERF8++233Lp1Czs7O9566y2ef/55XF1dMQwDs9mMvb094eHhllNq6HximZdCkUg6i4+PZ+vWrXz44Yd07NiRgQMH2rokEUkk4RBZeHg4zZs3x8XFhdy5c3Pjxg3Cw8Np0KAB7dq145lnngHg9u3bfPDBB5QsWZIxY8bYuHp5EuqjF0ln9vb21KlTh0WLFlGhQgVblyMi/2EymTCbzYwcORIPDw++/vprnn32Wezt7XnnnXdYtWoVderUsYSimJgYLly4QO7cuTXmKJNTKBKxAXt7e1588UVAg6pFMqK4uDjOnz9PzZo18fLyAmDTpk0cOHCAAQMGULlyZWJjY4mPjydfvnysX7+eXLlyYTKZFIwyMR30FEkFT3IUWoFIxPb+uw/HxMRw/fp1YmJiAAgMDKRnz5707duXTp06ER4ezuzZszl79iyGYZAnTx7s7e2Jj49XIMrEFIpEHpNmpIhkLQlBJjY2lqioKODej5XixYtz/Phx5s2bR79+/ejXrx+dOnUC4NixY/z+++9cvXo1yf6vq91nbhpoLfIYNCNFJGuKjIykY8eOlClTho8++ogcOXKwY8cOOnfujNlsplOnTgwYMACAM2fOMGTIENzd3Zk+fbr26yxEoUjkEWlGikjWtWnTJnr27EmxYsWoXbs2Xbp0IWfOnAQGBtKvXz/8/PyoUqUK0dHRbNmyBcMwWLp0KQ4ODvrBk4XoXRR5RP+dkTJt2jRmzZrFzz//TL58+Vi1ahXnzp2zbJ8wI+X69etPNOZIRFLff/fJIkWK4OTkhJ2dHfv372f27NncvXuXBg0aMHv2bJydnVmxYgWHDh2iYsWKlkAUFxenQJSFaISnyGPQjBSRzC+hZyfxzM+iRYvy+uuvU6BAAe7evcumTZsA6Ny5MzVr1uTFF1/EbDaTLVs2y34cHx+viRJZjOKtyANoRopI1mNnZ0dMTAydO3dm0qRJluUuLi7s3buXTz75hJo1a7JlyxZmz55NWFgYLi4uZM+e3bIfG4ahQdVZkEKRyH1oRopI1pJ4FujNmzfJnz8/y5cvZ+bMmQC0a9eOa9eucevWLT7++GMqV67M1q1b+frrr4mMjEzSln7kZE0KRSL3YW9vT2RkJO+//z5ff/215ddimzZt2LlzJ2PHjqVTp0506dIFe3t7zpw5w9dff42npye1a9e2dfkikkjCIbOYmBji4+PJnz8/nTt3pn79+ixYsIC5c+fi4+NDeHg469atw9HRkU8++QRfX1/Lvi9Znw6GijzAjh07+Ouvv7h79y6Ojo506dIFPz8/vvrqK/r168fx48eZMWNGkhkpCxcuxM7OTjNSRDKQhED00Ucf4ebmRkBAAIULF6Z169YYhsHMmTNxcnKiTZs2nD592nJajS+++AJA4wKfEvrGFklEM1JEsq7Y2Fjc3Nz4+++/CQgIIC4ujsKFC/POO+/QuHFjvvvuO5YsWcLevXuJiIgA7oWphB85CkRZn85TJPL/UpqRAjBw4EDLjJTt27fz6quv0rlzZ9zd3YmKikpxRorGEInYXsKfN5PJZNmvQ0NDmTBhAjt37qRWrVr4+/vj4OBAUFAQy5cvJzAwkCtXrjB37lyqV69u42cg6U0/ZUX+n2akiGQdCT07CZflcXBwICYmBnd3d4YPH463tzdr1qxh7NixxMXF4ePjw1tvvYWfnx9Vq1alSpUqNn4GYgsKRfLU04wUkawn4UdO69atWbBgAQCOjo4AbN68mR07dlCkSBG2bt1qOZRWpEgRevXqxbx58yyn0pCni0KRPNU0I0Uk67p69Sq5c+fm66+/ZsmSJZhMJgIDA+nevTtDhgxhxowZ1K5dmx07djB48GDLSVcTzl6vXt+nj2afyVNNM1JEso7/7osFCxakX79+zJ49m1GjRnHo0CFWrVpFv379aNWqFc7OzvTt25eQkBBiY2OT3FcTJZ5OGmgtT73w8HBGjBjB33//jZ+fH4MHD8bBwYGzZ8/y448/smnTJsxmMy4uLixdupQcOXJY7qtp9yIZQ8JA6vj4eKKjozGZTGTLlg2AEydOMHfuXNavX0+NGjWYPn16kvtERETg4uKiU2mIDp/J08cwDMuslLi4OFxdXfn000+pXr0627dvtwy8LFy4MK1ateKNN94A4MyZM/z9999J2tKXp4jtJVyDLCwsjEGDBtGmTRu6dOnCp59+Snx8PCVKlOCDDz6gfv36/PHHHyxduhTAEqKyZ8+uQCSAeorkKZPStPuYmBicnJwA6NixI3///TeNGze2TNU9c+YMc+bM4cKFC3z33XcaZyCSAUVERNCiRQty5sxpuTDz5s2bcXR05Msvv6RkyZL8888/zJo1iw0bNtCvXz/ef/99W5ctGYzGFMlTJWEM0bvvvkvjxo1p165dshkp5cuXZ+vWrRiGweDBgy0zUp555hnLFF8FI5GMIWEc0dy5c3F1dWXMmDH4+PgA4Onpyddff8358+cpWbIkxYoV44MPPiAkJIRNmzbRrl07jQeUJNRPKE8dzUgRyToSQk1QUBCurq4ULlwYgHXr1jFx4kT69+/P66+/zt27dwEoUaIEn3zyCfPnz7dMlBBJoJ4iyfI0I0Uka0k89scwDOLi4oiNjbUMll6zZg0ff/wx/fr1o3PnzsTExPDll1/i4+ND+/btKViwYLJ2REChSLK4+81IKV68OB07dsTOzo61a9dSu3ZtunTpYrlPjhw5GDVqlGakiGRAdnZ2REdHEx4ejqenJ46OjtStWxd/f38CAgJYtGgRvXv3tuzTJ0+e5OzZs/j6+iZrRyQxhSLJshLPSBkxYkSS7vURI0ZYZqSYTCbWrl3L0qVLadmyZZIZKaBfkyIZjdlspnPnzoSFhTF9+nTy5ctHnTp1qFevHgsWLKBRo0Z0796d+Ph4Tp8+zeeff46TkxPvvPOOrUuXDE6hSLIse3t7IiIiaNmyJTlz5sTPz88yI6VJkyaWGSkdO3bEbDYzevRoIiIieP/995OMG1IgEslYDMPg/fffZ/jw4QwZMoSxY8eSN29eOnToYDl8BnDr1i1u374NwMKFCy2X7tC4QLkfTcmXLClhHNHUqVPZvHkz48aNs8xImTlzJl9//TXffPMNr7/+OnDv5G4TJkwgIiKCBQsWaEaKSAaSEGQSjw+Mj49n586dfPzxxxQvXpzx48eTO3dubt++zZYtW/jjjz/Inj07xYoV47333sPBwSHJqThEUqJQJFla//79uXnzJnPnzsXOzo5169YxcOBA+vbtS+fOnbl79y5ubm4AnD9/Hm9vb+zs7HTpDpEMJjIyklGjRvHOO+9QtmxZTCYTcXFx7Nq1yxKMvvzyS/LmzQuQLACph0gehY4LSJaR+Gr3hmEQGxubbEZK//796dOnT5IZKfPmzQPuzUpLGFStQCSSsVy+fJmVK1cybtw4jh8/jmEYODg4UL16db744gtOnjyJv78/165dA0gWgBSI5FEoFEmWkTAjJTg4GJPJZJmRsnXrVgICAhg8eHCKM1L+252uMUQithcfH5/kdtGiRVmxYgVnz57ls88+SxKMatSowVdffcXx48cZMGAAt27d0g8bsYq+/SXLSJiR0qlTJ8uvxcQzUt58803LjJSTJ0/y+eefA2hGikgGZG9vT1RUFAcPHrQsK1WqFDNmzODChQt89tlnHDt2zBKMSpUqhZubG7dv3yZXrly2K1wyNY0pkiwjPj6eLVu2MHz4cIoXL26ZkXLgwAHmz5/P+vXrady4cZIZKYsXL8bR0VHjDUQyoPbt23P69GnGjRtHjRo1LMuPHj1K165dKVCgAMOHD+e5556jd+/e3Lhxg1WrVuHo6KhTaYhVFIok09KMFJGs7cKFC7z33nt4eHgwaNAg/Pz8LOsSB6PIyEiio6NZs2YNjo6O2qfFagpFkqlpRopI1nC/ffHixYu8/fbb5M6dG39/f2rUqGH5EbR3717atm1LuXLl+P777xWI5Impb1EyNc1IEcn84uLisLe3Jzo6msOHD7N582ZCQ0O5e/cu3t7eLF68mFu3bjF27Fh27NgB3PtBNHXqVIoVK8YPP/ygQCSpQj1Fkqmk9Gvy2LFjdOnShQIFCvDpp59SsmRJS4/R7t27GThwIEWLFmXixInkzp3bRpWLSEoSxv6EhYXRrl07bt26xc2bN8mVKxdNmzaladOmPP/881y8eJFWrVqRJ08eevXqxYIFC7h+/To///yzApGkGvUUSaaiGSkiWYudnR0xMTF06tQJDw8Pxo4dyx9//EGnTp1YtWoVX3zxBcHBwXh7e7N06VJu375Nz549uXv3rgKRpDqFIsl0unXrRs+ePdm5c6dlWenSpS3BaPTo0Zw8eZKQkBD69u2Lg4MDK1eutJyYUUQylpMnT3Lz5k06duzICy+8gKenJ15eXty8eZOXXnoJT09PYmJiyJ8/Pz/++CP16tVjyZIlCkSS6nT4TDIdzUgRyVrWrVvHgAED2Lt3Lzly5GD16tUMGjSIvn370rVrV8LCwti9ezeVK1fG3d3dcj/t05La1FMkGdp/z2oLUKBAAX744Qdu3rxpGXiZkO1Lly7NhAkTOHDgAE5OTgpEIhlYwn77/PPP4+7uzr59+wgMDEwSiAzDYNWqVWzatIno6Ogk99c+LalNoUgyLM1IEcla/vsjJ2FqvYeHB15eXowePZqPP/6Yfv360bVrV8xmM2fPnuWXX37BycmJPHny2KJseYro8JlkSJqRIpK1JMwcjYiIYNGiRdy5c4fnnnuOl19+GW9vb44cOUL79u3JkSMHH3/8MXXq1GHPnj3MmDGDmJgYlixZgoODQ5KTtYqkNoUiybBiYmJo164d2bNnp3PnzhQvXpw1a9Ywe/ZsSpYsyRdffIGnpyeXL1+mdevWXL9+nZIlS2oApkgGFRERQbNmzYiLiyMuLg7DMIiMjOTzzz+nXr16HDlyhGHDhnHnzh1u3rxJgQIFePbZZ/n22291OR5JF/qLIRlWwoyUkSNH8sILL+Dk5PTAGSlffPEF48ePVyASyUASB5kFCxaQL18+hg8fToECBTh16hTz58+nX79+TJgwgddee41p06Zx9+5dzp49S5EiRfDx8cHOzk77tKQL9RRJhqUZKSJZQ0REBFOmTCEiIoK8efPSo0cP4N5A67CwMEaOHMnWrVtZtWoVXl5eye6vi7tKetGnTDIczUgRyVqOHz/O/Pnz+emnn7h7965luclkIkeOHLRr1w5nZ2e2bt0K/O87IIECkaQXfdLE5jQjRSRrK1++PDNmzMDb25utW7fy77//WtaZTCaKFSsGYLlGoQZSi60oFIlNJZ6RMnPmTMaNG8fChQu5ePEi+fLlY9SoUdy5c4c8efLg7e1NeHg4W7duZfDgwURGRvLpp59iMpmS/bIUkYzDwcGBatWqMWzYMEJCQhg9enSSYHTu3Dns7e3x8PCwXZEiaEyRZACakSLydDCbzfzxxx8MHjwYd3d3atasiZubG7t27SIiIoIVK1bo8LfYlEKR2ETiIPPtt9+yc+fOZDNSfvnlF8uMlKtXr2pGikgGtHv3bjZv3oy/v/8jbW82m9m+fTujRo3i4sWLvP/+++TJk4cPPvgAe3t7/cgRm9JfE7GJhENmCTNSqlatStGiRYF7l+oYNmwYhmEwZMgQSpUqhZeXF88++6xl7AHc+3JVIBKxndjYWE6dOpXiuvvNGLOzs6NGjRoMHz6cTz75hMuXL9OxY0fs7e0xm80KRGJTGlMkNqMZKSKZ159//sn8+fNp1KgR/v7+xMTEcOLECfbs2UNERMQD7+vg4ECNGjX47LPP2Lt3L0OHDuXq1avap8Xm9AkUm9GMFJHMa//+/Vy/fh0PDw/CwsJo27YtXbp0oUuXLtSrV49FixZx8eLF+97f3t6eWrVq8eWXX7Jt2zbGjBmT4gWgRdKTxhSJTcXFxbFz5078/f0pXrw4Q4cOtRxGO3HiBF27dqV9+/Z06NDBxpWKSGIJ1yCLjo7m3Xffxc3NjQ4dOlCoUCFWrVrFggULqF+/Pv7+/ri5ud23HbPZzJ9//slzzz1HkSJF0vEZiCSnARliUw4ODtSsWZOAgAAGDx5Mjx49ksxIyZkzJ23btrV1mSLyHwk9t4cPHyYsLAx/f38qVqyIg4MDFSpU4Ntvv6VcuXIPDETwvzFGIhmBDp9Jmti9ezdjx459pG3t7OyoVasWY8eOJS4ujkWLFhEREcErr7zCypUrcXBwULe6SAZ19uxZLl26RJkyZXBwcGDVqlV069aNvn370qpVK8LCwti/fz+xsbG2LlXkoRSKJNU9bEZKShLPSHnmmWe4fPkyTZo00YwUkQwqYeRFgQIFcHd3599//2Xt2rX4+/tbLsdjNpv54YcfWLly5UMHX4tkBBpTJKnqzz//5MiRI7z11lt4eHgQExNDUFAQoaGhlClTBhcXlwfOMImPj2f79u0MGjSIcuXKMWrUKJ599tl0fAYikpL7nT/oypUrtGrVCldXVy5cuEDPnj3p1q2b5XI8w4YNo0SJEnzyySeaLCEZnkKRpKqpU6cSEhLCkCFDCAsLo2PHjly5coXQ0FBy5MhBly5dqFu3Lt7e3vdtw2w2s2PHDjp37ky9evWYMGGCeopEbCjhJKlRUVH8/vvvXLp0CS8vL8qWLUuBAgXYs2cPPXr0wM3NjYCAAMqWLcuBAweYNGkScXFxLF68GAcHB8vgbJGMSqFIUpVmpIhkLQknYQwLC+Odd94hPDycO3fuYG9vj6OjI1999RU1atRgz5499O/fn+zZs3P9+nW8vb3x9PRk9uzZuhyPZBqafSapSjNSRLIWOzs7YmNj6d27Nx4eHgQEBFC8eHF27tzJDz/8QLdu3fj222+pUaMGixcv5vLly1y8eJHnn3+eUqVK6XI8kqlooLWkCc1IEck6bt68yZkzZ3jzzTcpU6YMjo6O1KlTh8GDB1OtWjUGDhzIxYsXyZ8/Py+++CJNmzalTJky2NnZ6XI8kqkoFEmq0owUkazn7t273L59GycnJwBiYmIAKFy4MO+++y6xsbEcO3YM0OV4JHPTp1WeyH/PH5Rw+KxQoUI4ODgwcOBA/P396dOnjyUQnT17lm3btuHi4oK7u7styhaR+0jptBk+Pj54e3uzevVqAJycnCzBqFq1asTGxnL58mVAl+ORzE2hSKwWFxeHvb09UVFRBAYGMmvWLAIDA7lw4QLPPfccX331FTdv3uSZZ56hYsWKREREsGvXLgYPHkxkZCT+/v6YTKZkvyxFxDbi4uIsY4guXLjArVu3gHtnnu/evTt//fUXQ4YMAbD0Gp04cQIPDw/y589vs7pFUotmn4lVNCNFJGtJGAwdFhZGr169OH/+PKGhoQwePJjmzZtz69YtFi9ezPTp06lYsSJvvvkmsbGxrF69GrPZzJIlS7QvS6anUCRWi42NpVu3bsTExDBo0KAkM1J27dplmZFy+fJlzUgRycASB6ImTZrw3HPP8dJLL3H8+HE2btzI5s2byZ07N6GhoezatYspU6Zw6dIlcufOTbFixfjmm2/0I0eyBIUisdqVK1d477336NKlC61bt7YsP3v2LGPGjOHo0aMsXrw4xRM1JvQ0iUjGEBYWRosWLcidOzfffvstbm5uBAUF8eGHHzJs2DDMZjM+Pj54eXkRHx/PuXPncHV15ZlnnsFkMulHjmQJ+qskVtOMFJHM6dq1a+zbt48VK1ZYlq1evZpz585Rvnx5oqKiMJlM7Nu3j3PnzjFx4kR69OjBp59+yqlTp7C3t8fHx4d8+fJhMpk07V6yDH2K5ZGk1LOTeEZK8+bNLTNSnJycNCNFJIM6ePAgo0ePJioqCg8PD0qVKkWJEiV47733uHDhAnPnziVfvny4uLgwatQoevXqRfv27fnll18YMWIEwcHBydrUjxzJKhSK5KESusVjY2O5evUq2bNnJ3fu3JYZKYMGDWLIkCGMGTNGM1JEMrADBw7QoUMHmjVrRv369alSpUqSXlx/f3/i4uIYO3YsAN26daNHjx7AvdNsPPPMM8TFxdmkdpH0oFAkD/SwGSlVq1ale/fuTJ8+nYsXLyaZkeLp6ckrr7xi66cgIsDVq1cZNmwYjRs3pn///uTIkQP43/UKEwZJDx06FFdXV2bMmIGdnR3BwcEYhsGXX36Jl5eXLr8jWZpCkdxXSjNS3nnnHY4fP86IESOoU6cOuXPnpk2bNhQtWpQpU6bwxRdfJJmRYm9vrxkpIjaUEHpOnTpFTEwMjRs3tgQi+N+hL3t7e8s+37dvX8LDw5k2bRoRERHs2bOHqKgovv/+e+zs7LRPS5alUCT3lRCIWrRoQb58+Zg2bZplRsqRI0c4ceKEZUbK66+/zquvvqoZKSIZTMJ4vkOHDhESEkKZMmXuu23ifXXo0KEAzJs3jxIlSrB69WocHR21T0uWpk+2WFy7do0LFy5w/vx5mjdvDvxvRkrdunWJiorC3d09yYyUkydP8uKLL+Lv74+vry8+Pj6W9jQjRSTjSDiPUMKYoIQepMTMZjNxcXHs3r2bWrVqMXToUIoVK0aLFi1wcHBQIJIsT59uATQjRSSrSgg/5cuXJzw8nPnz59OjRw/LJXYSByM7OzvCw8MZOXIkw4YNo3bt2rRq1QpAgUieCvqEi2akiGRhCaHH19eXokWLsnz5ckqVKsXLL79sOcdQwg8Ys9nM1q1b8fDwoGjRoknaUSCSp4F+yj/l/jsjpUqVKsD/TrYYHx8P3Btf0LVrVwDLjJRbt25pRopIJpE7d27GjBnD7du3mTx5Mr/99htAkkB08eJFlixZQqFChXQ6DXkqKfo/pTQjReTpU7ZsWb755ht69+7NiBEj2LdvH++++y4xMTH8/fff/Pjjj0RGRvLFF18k60USeRro2mdPucmTJ/P999+zZcsWXFxcHuk+o0ePZtGiRZQoUYKlS5dqRopIJnPixAlGjhzJsWPHMJlMxMTEULRoUQoVKsTEiRM1qFqeWvrEP+U0I0Xk6VOiRAlmzJjBlStXOH78OI6Ojvj6+vL888/rVBryVFNP0VMqIfzs2rWLjh070qNHD8vg6ZSC0e3bt2nZsqVlRkoCfXmKZC06ZCZPM33yn1IpzUjZvHmzZZ3ZbLZsqxkpIk8PBSJ5munT/5TTjBQREZF7dPhMANi2bRu9e/cmR44cNGzYMMUZKStWrMDBwUHd6yIikiUpFImFZqSIiMjTTKFIkggNDdWMFBEReSopFMkj0SEzERHJ6hSKRERERNDsMxERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhEREREAdCErEcmwihcv/kjbLViwgKpVq6ZxNY8nODiY6tWr07NnT3r16mXrckTkESgUiUiGNW7cuCS3V69ezY4dO5ItL1q0aHqWJSJZlEKRiGRYTZo0SXL70KFD7NixI9lyEZHUoDFFIpIlxMTE8M0339C8eXNeeOEFKlSowLvvvsuff/6ZZLuLFy9SvHhx5syZw7x583j55ZcpV64cbdq04dSpU0m29ff3p27dukmWXblyhXLlylG8eHEuXrxoWR4ZGcngwYOpUKECDRo04OjRowDExcUxePBgypcvT+PGjfn777/T6BUQkSelUCQiWUJYWBhLly6lSpUqDBgwgJ49exIcHEynTp04fvx4su1XrVrFggULePfdd+nSpQv//PMP77//Pjdv3nzg40yaNIno6OhkywMCAlj5f+3dSyi0bRzH8e+Y2IkSkxwXY0HMrCyUJmUUsUCo2alxWNhgIRaUlI3TxgYlh41ThpVjRFayQLKhHIsRpQg5Pounpmea8bwL3l7v9Pvs5n//57rue1a/ruu6G5eLkpISioqKaG1tBWB8fJzHx0dqa2u5u7ujsrKS+/v773loEflW2j4TkYAQFhbGysoKISEhnlpZWRl5eXmMjo7S3t7u1X96esri4iImkwkAm81GaWkpAwMDNDU1+Z3j4OCAmZkZbDYb6+vrnvr19TXT09NUVVVRX18PQFJSEtXV1VgsFnp6ejAYDNhsNvLz85mamqK8vPybfwER+SqtFIlIQDAajZ5A9P7+zu3tLa+vr6SmprK/v+/Tb7fbPYEIwGKxYLVaWVtb+3SOrq4uUlJSyM3N9apvbW3x8vJCTk6O13gAaWlpGAwG4PeBcLPZ7LOlJyI/g1aKRCRguFwuBgcHOTo64uXlxVOPjY316U1ISPCpJSYmMjc353fsra0tVldXGRoa4uLiwuva5eUlgFfI+ozJZPL5voj8DApFIhIQZmdnaWxsxG6343Q6iYiIwGg00tfXx9nZ2ZfH7+zsJDMzk4yMDKanp72u+Ttj9DdPT09fvh8R+X4KRSISEBYWFoiLi6O3t9ezXQW/D0b7c3Jy4lM7Pj4mJibGp768vMz29jYul8vvWJGRkQBcXV0RFRX11/t0u93/2CMi/w2dKRKRgGA0GgH4+Pjw1HZ2dtje3vbbv7y8jNvt9nze3d1lZ2cHm83m1ff29kZ3dzcFBQUkJyf7HSs9PR2ApaUlr/EAr1fwj46OODw89PSLyM+ilSIRCQhZWVksLi5SU1NDVlYW5+fnjI2NYTabeXh48OmPj4/H4XDgcDh4fn5mZGSE8PBwKioqvPouLy8JDg6mv7//07nj4uLIz8+nv7+fh4cHTCYTExMTAOzt7VFXV4fVamVkZITQ0FAcDsf3PryIfAuFIhEJCMXFxVxfXzM+Ps7GxgZms5mOjg7m5+fZ3Nz06S8sLCQoKIjh4WFubm6wWCw0Nzf73dpyOBx+D2v/qa2tDaPRyOTkJNHR0bS0tOB0OiktLeXq6oqenh7i4+Pp6urybLeJyM9i+PhzrVlEJMCdn5+TnZ1NQ0MDTqfzX5tHfwgr8v+jM0UiIiIiKBSJiIiIAApFIiIiIoDOFImIiIgAWikSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEgF9BmeQDZZ9m9AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Распределение потребления минут\n",
    "sns.boxplot(data=user_info, x='Тариф', y='Исходящие, мин (сред. в месяц)')\n",
    "plt.title('Распределение потребления минут по тарифам')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# Распределение потребления трафика\n",
    "sns.boxplot(data=user_info, x='Тариф', y='Трафик, ГБ (сред. в месяц)')\n",
    "plt.title('Распределение потребления трафика по тарифам')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79b8e0d",
   "metadata": {},
   "source": [
    "#### Экономическая оценка тарифов: ###  \n",
    "Расчет текущих расходов пользователей и сравнение с расходами по оптимальным тарифам позволяет выявить возможности для оптимизации и экономии средств абонентов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "5f1a820a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Добавление стоимости текущего тарифа к user_info\n",
    "tariff_prices = {tariff: options[0]['price'] for tariff, options in tariffs_options.items()}\n",
    "\n",
    "# Определение функции для получения текущей стоимости тарифа\n",
    "def get_tariff_price(tariff_name):\n",
    "    # Если тариф специальный для передачи данных\n",
    "    if 'Передача данных' in tariff_name:\n",
    "        return tariffs_options['Передача данных'][0]['price']\n",
    "    \n",
    "    # Для стандартных тарифов удаляем \"Своё решение. \" и возвращаем стоимость\n",
    "    tariff_name_cleaned = tariff_name.replace(\"Своё решение. \", \"\")\n",
    "    if tariff_name_cleaned in tariffs_options:\n",
    "        return tariffs_options[tariff_name_cleaned][0]['price']\n",
    "    return 0  # Возвращаем 0, если тариф не найден\n",
    "\n",
    "# Применяем функцию к столбцу 'Тариф'\n",
    "user_info['Текущая стоимость тарифа'] = user_info['Тариф'].apply(get_tariff_price)\n",
    "\n",
    "# Расчет суммарных начислений для каждого пользователя из данных детализации\n",
    "total_charges = data.groupby('Номер телефона')['Начислено'].sum().reset_index()\n",
    "total_charges.rename(columns={'Начислено': 'Суммарные начисления'}, inplace=True)\n",
    "\n",
    "# Объединение информации о пользователях с суммарными начислениями\n",
    "user_info = user_info.merge(total_charges, on='Номер телефона', how='left')\n",
    "\n",
    "# Расчет текущих расходов как суммы абонентской платы и суммарных начислений\n",
    "user_info['Текущие расходы'] = user_info['Текущая стоимость тарифа'] + user_info['Суммарные начисления'].fillna(0)\n",
    "\n",
    "# Добавление стоимости оптимального тарифа из предыдущего расчета\n",
    "# Предположим, что стоимость оптимального тарифа уже добавлена в user_info как 'Стоимость тарифа'\n",
    "\n",
    "# Расчет разницы в расходах\n",
    "user_info['Разница в расходах'] = user_info['Текущие расходы'] - user_info['Стоимость тарифа']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "df2a2c07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nfU6kBxdG7ty589Tu1/fv37f5Ow8ODn7strHbPK3F42EJPd48KiwsTNOmTdOHH35ovbCUWB5+z+/fv6+goCCVLl063nNbQs5Fj3rcZ+/kyZO6ffu2zbmoQIECSpUqlQoWLGj9vWbMmFFly5bVH3/8EW/NERERunPnjooXLy5JOnr0aJwakuP7jheHFiske0OGDFGuXLlkMpnk4+OjXLly2XwhMJvNWrBggZYsWaLLly/bHHAf7rIUq06dOtZuAo0bN7ZelXpYmzZtnqnGS5cu6ccff9SwYcMS1JXnSZ719cRn7ty5Ni0Xb731ls0V+yd59dVX4yzLmTOnNaxlypRJ4eHhmjlzplauXKkbN27YjLV4+AvJxYsXE9wqMGDAAG3dulXHjx/XxIkTrV2kYl29etV6MntYbHeTq1ev2nQPuXv3rlxdXZ86c+TVq1c1adIkbd26NU6AfvSLjqenpwYPHqyePXvKx8dHgwcPTtBri1WgQAHly5dPq1evVrt27bRq1Sp17NhR169ft9kudrxU69at491PunTp4iwLDg5WeHi4li9fbh2PN2jQIJ04cUKZMmWS2WyOd18xMTEKDAyM94vnoxo0aKCRI0fqypUr8vX11caNGxUVFZWg1oaEuHDhgs6cOfPYiwC3b9+2+fm3337TkSNHJOmxFz8aNmyogIAA7d+/X2XLltXu3bt169atRKv5UQk9FtSvX18TJkzQ4cOH5eXlpX379mnEiBH666+/nrj/iRMnqkyZMqpatapNa2ZCPevjr1y5oldeeSXO31yePHms61+EoKAgzZgxQ23atIlzbHhY5cqVZTKZtGXLFlWpUkUbNmzQtGnTtGbNmjjbHjhwQJMnT9ahQ4cUFhYW5/ke/kw83OL2NA93j3R3d1e1atU0cOBAa5fXWI+2+j9OaGiozWcia9as+vjjjx97fIiV0OPNo+bNm6eIiAh17NhRY8eOTVCNCbVt2zZNnz5dx48ftxk3Gd+FmYScix72pM9e7GtOSFf6zJkz68CBA9af7927pylTpiggICDOMSi+Vt/k+L7jxSFYIdkrVqyYdVbA+MyYMUPffvutmjRpop49eypDhgwyGo0aPXp0nMHVkvTtt98qODhYR44c0ezZs/XKK6+od+/eNtvEhrlYwcHB6t69+2Nr+Oabb5QzZ041atRI+/fvt+NV2v964tOgQQM1bNhQZrNZly5d0rRp09SxY0d9//33idKyMHLkSK1cuVKtW7dWiRIllD59ehkMBvXu3TvBNT7q+PHj1pPW48ZQPIsrV64oa9asT3y9MTEx+vjjj3X//n21a9dOuXPnlru7u27cuCF/f/94w8jOnTslPfgif/36dXl4eDxTXU2aNNGSJUtUrFgx3bp1S7Vq1dK8efNstol9D8ePHx/ny4MkmUymOK9j+PDhKlGihGrXrq0vvvhCkvTvv/9q+vTp6t+/v7799tt4LyJcvXpVZrNZvr6+T629Tp06GjNmjNatW6dOnTpp7dq1KlKkiDXcOspsNuu1117TwIED412fJUsWm58PHz6s999/X2nSpNH06dP17rvvxqmlYsWK8vHx0dq1a1W2bFmtXbtWmTJl0ptvvpkoNT8qoccCLy8vVa1aVStWrJCPj49KlSoV7xfJh+3cuVO7d+/W0qVL7arN0ccnpdmzZ8toNKpt27a6d+/eY7dzdXVV/fr1tXLlSoWHhytjxox644034gSrixcvqk2bNsqdO7f8/f2VNWtWubq6aseOHfr+++/jfPZjL8Y9Go7i07VrV5UpU0ZRUVE6evSopk2bpsDAwDhdkSdPnmwTWM+dOxenpV560Oo+Y8YMSQ9aw1asWKHRo0crU6ZMql279hNrScjx5mF37tzR3Llz1bFjxwRfyEuo/fv3q3PnzipbtqyGDh2qTJkyydXVVStWrHhqF7mEeNJn71la+iTZTF7Uq1cvHTx4UG3btlXBggXl7u4us9msdu3aPfZ8l5zed7xYBCs4vU2bNqlcuXIaPXq0zfLAwMB4x77EdiWrUqWKDAaDpkyZog4dOtgMsH00zD06e9PDjh07pvXr12vq1KlxvvDa41lfT3yyZ89u88Uxffr06tu3rw4dOvTUGaYuXLgQZ9n58+eVJk0aeXl5WWts2LChzWxGERERca7e5ciRI84sdvEJDQ3VwIEDlTdvXpUsWVJz5sxRjRo1VKxYMes22bJl07lz5+I89uzZs9b1saKjo3XixAm99dZbT3zeU6dO6fz58xo3bpx1XJgk7dq1K97tf/vtNy1fvlzt2rXTunXr5O/vr2XLlsnFJeGH0nr16mn8+PH64osvVLNmzXhbn2IHTXt7eycoACxZskQnT57UypUrVaBAAevg6lGjRqlatWrq1auXRo8erSZNmqhAgQI2j41t8Xm4y83jeHp6qkqVKlq3bp3q1aunv/76K87kBY7IkSOHTpw4ofLlyyfoAkCFChU0bNgwRUREaMuWLRoyZIgWLlxo81iTyaS6detq1apV6tevn7Zs2aL3338/UT6rj3rWY0GTJk3Ur18/pU+f/ond26QHYXvixIl6++23VaJEiWeuzd7H+/r6as+ePQoODrb5W4393CUkkDvq5s2bWrBggfr06aN06dI9MVhJD97XBg0a6Pr162rYsGG8f0tbt25VZGSkpk+fbnPsiK+7qSSdPn1aXl5eCToGv/baa9bPbeXKlXXt2jWtWrVK0dHRNseKMmXKWI+pkh7bamwymWyOA5UrV1a5cuX0+++/PzVYJeR487Dp06crbdq0atWq1VNf57PatGmTUqVKpblz59p0qVyxYkW82yfkXBTraZ+92AtUN2/efGqdN27c0CuvvCLpwQW0PXv2qHv37jaf0YdnYY1Pcnrf8WIxxgpOz2QyxblqtGHDBptpuh/n7t27MpvN1okO7DFx4kSVKlVK1atXt3sfD3Pk9TxO7NW3R6esjs/Bgwdt+o1fu3ZNv/76qypUqGA9YcV34lq4cGGcfu/vvPOOTpw4oc2bN8fZ/uHXOGHCBF27dk1jx46Vv7+/fH195e/vb1Nv5cqVdfjwYR08eNC6LDQ0VMuWLZOvr691JjjpQTAKCgp66u8ktkvpw7VYLBYtWLAgzraBgYEaPHiwihUrpj59+mjUqFE6evSo9UpyQnl6eqp69eo6efKkmjRpEu82b731ltKlS6eZM2fGmbZXsg36t27d0rfffquWLVtaQ1NseI79f4sWLVSgQAENGzYszt/Wxo0b5eHhoddffz1B9Tdo0ECnT5/W+PHjZTKZVKdOnQQ9LiFq1aqlGzduaNmyZXHWhYeHKzQ01GZZyZIlZTKZ5O7uruHDh+vPP/+M97ENGjTQ/fv3NWTIEIWGhj5xBkNHPOux4K233lKaNGl079491apV64nbBgQE6OTJk+rTp49dtdn7+EqVKikmJkaLFy+2WR7b+l2pUiW76nkWU6dOlbe392NntHxUvnz5VLhwYZ0+fVqNGjWKd5vYY9ij3Zjj+5IfHBysHTt2WMf6PSuz2Syj0ZgovQUelpDwnpDjTazY2V27d+9uM64osZhMJhkMBpvzxOXLl/Xrr7/Gu31CzkWxnvbZK1q0qFKnTm1zLjpx4oQiIiJsuiXeu3dPf/75p3WCkse9x/Pnz3/ia01O7zteLFqs4PSqVKmiqVOnauDAgSpZsqROnTqldevWWa/6xxo2bJhcXFysY7QOHDign3/+WVWrVo13iuqE2rlzp3744YenbhcaGhpnoHdsC8yff/4pV1dXZc6cOcGv50lOnjypNWvWyGKx6NKlS1q4cKGyZMmSoFaJ1157TW3btrWZ4laSTVfIKlWqaM2aNUqXLp3y5s2rQ4cOaffu3XG6MLRt21abNm1Sz5491aRJExUuXFj379/X1q1bNXz4cBUoUEB79uzRkiVL1K1bNxUuXFiSNGbMGH300Uf65ptvrDPZdejQQevXr1f79u310UcfKUOGDFq9erUuX76syZMnW0NSQECAxo0bJzc3N4WHh9t0AQoKClJMTIy2bNmiGjVqKHfu3MqRI4fGjRunGzduKF26dNq0aVO8g86/+OIL3bt3T/PmzZPJZFKlSpXUtGlTzZgxQzVq1IjTEvQkY8eO1ZAhQ+JcdY2VLl06DRs2TP3791fjxo1Vu3ZteXl56erVq9qxY4dKlSplvW/UuHHjlDp1avXs2fOxz2cymTR06FA1a9ZMK1as0Hvvvadbt25p4cKF2rhxo8qWLatNmzZZt4+djerQoUMqVKiQzWurXLmyPD09tXHjRlWqVOmJ412eVYMGDbRhwwYNHTpUe/fuValSpRQTE6OzZ89q48aNmjNnzmO7Bb/11luqX7++vvzyS1WtWtV6xVmSChUqpNdee806OUbs39mjzp07Z/MZDQ0NlcFgsFn28Gc2Xbp0Nn/zCT0WxDKZTNqwYYMsFstTx+7s3LlT77//vt3dLu19fLVq1VSuXDl9/fXXunLlivLnz69du3bp119/VevWrZ84NXpi2blzpyZMmJCgiSNizZ8/X5GRkY/tVlWhQgW5urqqU6dOat68uUJCQrR8+XJ5e3tbJx2QHhxPpk6dqsDAQHXo0CFBz338+HG5u7srJiZGR48e1Zo1a1StWjW7W0ljYmKsf4MhISFauXKlQkNDEzxxztOON7H27dunPHnyqHHjxnbV+TSVK1fWvHnz1K5dO9WtW1e3b9/WkiVLlCNHDp08eTLO9gk5F8V62mfP3d1drVq10qxZs+Ti4qJChQrpxx9/lNFo1H///aeOHTuqWrVqWr58uSIjI62zAKZLl05ly5bVnDlzFBUVpcyZM2vXrl0JmrEvubzveLEIVnB6nTp1UlhYmNatW6eAgAAVKlRIM2fO1MSJE222y58/v5YsWWK9IpktWzZ169YtzjSqz6p69erxTgH9qCtXrqh9+/bxrvP399eYMWPUuHHjBL+eJ4m9N4zBYJCPj4/KlSunXr162XR3fJyyZcuqRIkSmjp1qq5evaq8efNqzJgxNl+uP/vsMxmNRq1bt04REREqVaqU9YT5sLRp02rx4sWaPHmyNm/erFWrVsnb21vly5dX5syZFRwcrM8++0yFChVSp06drI8rU6aMWrVqpXnz5umdd95RiRIl5OPjox9//FFffvmlFi1apIiICOXPn18zZsywmaZ3woQJ1kHC8Y0pkh7c+6lGjRpydXXVjBkzNGrUKM2cOVOpUqXS22+/rZYtW9pMbvDrr79a7ysVO2hfevB72717twYMGKCffvpJrq6uT31/pQezTD3tymS9evX0yiuvaNasWZo7d64iIyOVOXNmlSlTxnoC3rdvn9auXauJEyc+tatJ8eLF1bRpU02YMEE1atTQmTNnrK1tf/75Z5wbb0rS0qVLlSlTJpvfvZubm/VGqok9AYTRaNTUqVP1/fffa82aNdq8ebPSpEkjPz8/ffTRRzbjHuMzaNAg7dy5UyNGjIgzHXODBg305ZdfPrHm5cuXx3t/p/g+t/7+/lqwYIHNlNIJPRY87Gm/t1ipU6d+anfB5/F4o9Go6dOna9KkSQoICNDKlSvl6+ur/v37O3zsTKiCBQvGe4P1J3F3d39iWM2dO7cmTZqkb775RuPGjZOPj49atGghLy8vm+6tAQEBypYtm8aMGaOCBQsm6LljP1cuLi7KnDmzmjdvrh49ejxT/Q+LiIiw/g3GzhA7fvz4ONOTP05Cjjex+vTp81y6yUpS+fLl9cUXX2j27NkaPXq0/Pz81K9fP125ciXeYJWQc1GshHz2evbsqYiICP3000/au3evRowYoR49eqhSpUry8fHRV199JW9vb02aNMnmpsMTJ07UyJEjtWTJElksFlWoUEGzZ89+alfz5PK+48UyWOwdaQ4g0VSrVk3dunVL8itW+fPnV8uWLa2tIc7oae/l3r17NXDgQG3duvUFV5a87N27V61atYr3C02s2G6Zj14hHj16tH766Sft2rXrqbMuJhfz58/XmDFjtHXr1kSZyrhatWoaM2ZMvPfqAeCYF3UuKlmypGrWrMksfEg0jLECACRYRESE1q5dq5o1azpNqLJYLPrpp59UtmxZ7g8DAHhu6AoIJANly5ZN0P018HQ1atR44rgPHx+fZ7qp78vKx8dH9erVe+I2JUuWtM6Cdvv2be3evVubNm3SvXv3nGL2qtDQUG3dulV79+7VqVOnNG3atETbd9myZR0amwkAePkQrIBkYNy4cUldwkvjadN/58mTJ1GnCHdWefLk0YQJE564TbNmzaz/Pn36tPr16ydvb28NHjw4weNNktKdO3fUt29feXh4qFOnTok2c6fEZxYAEBdjrAAAAADAQYyxAgAAAAAHJdtgdeHCBQ0ZMkQNGjRQoUKFHjvV6vLly1WzZk0VLVpU9evX17Zt215wpQAAAABSumQ7xurff//Vjh07VLx4cZnNZsXXY3H9+vX6/PPP1alTJ73xxhsKCAhQt27dtHjxYpUoUcLu57ZYLDKb6SEJAAAApGRGo0EGgyFB2ybbMVZms1lG44MGNX9/fx05ckQ///yzzTY1a9ZUkSJFbG6c2rx5c6VPn16zZ8+2+7ljYsy6cyfE7scDAAAAcH5eXmllMiWsk1+y7QoYG6oe59KlSzp//rxq1apls7x27dras2ePIiMjn2d5AAAAAGCVbLsCPs3Zs2clSbly5bJZnidPHkVFRenSpUvKkyeP3ft3cUm2mRMAAABAMuO0wer+/fuSJA8PD5vlsT/HrreH0WhQxoxp7S8OAAAAQIritMHqeTKbLQoMDE3qMgAAAAAkIQ+PNAkeY+W0wSpDhgySpKCgIGXKlMm6PDAw0Ga9vaKjzQ49HgAAAEDK4bQDiXLnzi3pf2OtYp09e1aurq7Knj17UpQFAAAAIAVy2mCVPXt25cyZUxs3brRZHhAQoPLly8vNzS2JKgMAAACQ0iTbroBhYWHasWOHJOnKlSsKDg62hqjXX39dXl5e6t69u/r166ccOXKoXLlyCggI0OHDh7Vo0aKkLB0AAABACpNsbxB8+fJlVa9ePd51CxYsULly5SRJy5cv1+zZs3X16lXlypVLffr0UdWqVR16bm4QDAAAAOBZbhCcbINVUiJYAQAAAHiWYOW0Y6wAAAAAILkgWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADjIJakLAAAAz8ZoNMhoNCR1GQCQaMxmi8xmS1KX4RCCFQAATsRoNChjxjQyGk1JXQoAJBqzOUZ374Y5dbgiWAEA4EQetFaZdO7n2Qq7fS2pywEAh6XxzqpcddvLaDQQrAAAwIsVdvuawm5cTOoyAAD/j8krAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABzl9sPr111/VtGlTlSxZUhUrVlTPnj116dKlpC4LAAAAQAri1MFq79696tatm/LmzaupU6dq0KBBOnHihD755BOFh4cndXkAAAAAUgiXpC7AEevXr1e2bNk0evRoGQwGSZKXl5dat26tI0eOqEyZMklcIQAAAICUwKlbrKKjo5U2bVprqJKk9OnTS5IsFktSlQUAAAAghXHqFqvGjRtrzZo1Wrx4serXr6979+7pq6++UqFChVSqVCmH9u3i4tSZEwDwkjKZOD8BeDk5+/HNYHHypp1t27apb9++CgkJkSQVLFhQc+bMkY+Pj937tFgsNq1gAAAkN8fmj1DYjYtJXQYAOCxN5hwq1HpIUpfhMKdusfrrr7/Uv39/vf/++6pSpYru3bunadOmqUOHDlqyZIlSp05t137NZosCA0MTuVoAABxnMhnl4ZEmqcsAgEQXGBimmBhzUpdhw8MjTYJb0pw6WI0aNUpvvPGG/P39rctKlCihKlWqaM2aNWrWrJnd+46OTl6/VAAAAOBlFhNjdurv4E7dkfHMmTMqUKCAzbIsWbIoY8aMuniR7hEAAAAAXgynDlbZsmXTsWPHbJZduXJFd+/ela+vbxJVBQAAACClcepg1bx5c23ZskWjRo3S7t27FRAQoE6dOsnb21u1atVK6vIAAAAApBBOPcaqVatWcnNz0w8//KAVK1Yobdq0KlGihL755htlzJgxqcsDAAAAkEI4dbAyGAxq0aKFWrRokdSlAAAAAEjBnLorIAAAAAAkBwQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAc9FIEq1WrVqlhw4YqWrSoypUrp3bt2ik8PDypywIAAACQQrgkdQGOmj59umbPnq1OnTqpRIkSunv3rvbs2aOYmJikLg0AAABACuHUwers2bOaMmWKpk2bpsqVK1uX16xZMwmrAgAAAJDSOHVXwJUrV8rPz88mVAEAAADAi+bULVZ///23XnvtNU2bNk0LFy5UUFCQihQpooEDB6p48eIO7dvFxakzJwDgJWUycX4C8HJy9uObUwer//77T0eOHNGpU6c0dOhQpUmTRjNmzNAnn3yiX375Rd7e3nbt12g0KGPGtIlcLQAAAIDH8fBIk9QlOMSpg5XFYlFoaKi+/fZbFShQQJJUvHhxVatWTYsWLVLPnj3t2q/ZbFFgYGhilgoAQKIwmYxO/+UDAOITGBimmBhzUpdhw8MjTYJb0pw6WHl4eMjT09MaqiTJ09NThQoV0unTpx3ad3R08vqlAgAAAC+zmBizU38Hd+qOjHnz5n3suoiIiBdYCQAAAICUzKmDVdWqVXXv3j0dP37cuuzu3bs6evSoChcunISVAQAAAEhJnLorYI0aNVS0aFH16NFDvXv3VqpUqTRr1iy5ubnpgw8+SOryAAAAAKQQTt1iZTQaNWvWLJUoUUJDhgxRnz59lC5dOi1evFiZMmVK6vIAAAAApBBO3WIlSV5eXvryyy+TugwAAAAAKZhTt1gBAAAAQHJAsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABz03IJVTEzM89o1AAAAACQrdgWrPn36KDAw8LHrT5w4offee8/uogAAAADAmdgVrLZu3ao6depo27ZtNsvNZrOmTZumpk2bPjF4AQAAAMDLxK5gtWbNGvn5+alLly4aOHCggoODdfr0aTVt2lSTJk1Sw4YNtXbt2sSuFQAAAACSJRd7HvTqq69qyZIl+u677zRp0iT9/vvvCgwMlJeXl2bPnq233norsesEAAAAgGTL7skrDAaDatSooZw5c+rWrVuKjIxUvXr1VLFixcSsDwAAAACSPbuD1aJFi9SwYUPdvXtX3377rd577z3Nnj1bLVq00Pnz5xOxRAAAAABI3uwKVq1atdKoUaNUrVo1rVu3TjVr1tSoUaM0a9YsXb16VQ0bNtT8+fMTu1YAAAAASJbsClb//vuvvv32W02cOFEZMmSwLq9UqZLWr1+vmjVrauzYsYlWJAAAAAAkZ3ZNXrF+/Xp5eXnFuy59+vQaN26catas6VBhAAAAAOAs7Gqxelyoeli1atXs2TUAAAAAOB27WqxiXb9+XceOHVNQUJAsFkuc9Q0bNnRk9wAAAADgFOwKVhERERowYIB++eUXmc1mGQwGa7AyGAzW7QhWAAAAAFICu7oCfvXVV9q8ebN69eqlhQsXymKxaOzYsfruu+9UqVIlFShQQGvWrEnsWgEAAAAgWbIrWG3atEmNGzdWhw4dlDdvXklS5syZ9eabb2rmzJlKnz69Fi9enKiFAgAAAEByZVewun37tooVKyZJSp06tSQpLCzMur5mzZravHlzIpQHAAAAAMmfXcHKx8dHd+/elSSlSZNGGTJk0Llz56zrg4ODFRERkTgVAgAAAEAyZ9fkFcWKFdNff/1l/blq1aqaO3euMmXKJLPZrO+//14lSpRIrBoBAAAAIFmzK1h99NFH2rhxoyIjI+Xm5qaePXvq4MGD6t+/vyQpR44c+uyzzxK1UAAAAABIruwKVmXKlFGZMmWsP2fNmlUbNmzQqVOnZDQalTt3brm4OHSLLAAAAABwGomWfoxGowoUKJBYuwMAAAAAp/FcmpW2bNmiMWPGWH9+++235e/v/zyeCgAAAACSnF3BqlWrVk9cf/v2bV29elXz58+XJHl7e9vzNAAAAADgFOwKVvv27VOWLFmUPn36eNeHhIRIkl5//XX7KwMAAAAAJ2F3V8C+ffuqXr168a5bs2YNXf8AAAAApBh23SD4aQwGw/PYLQAAAAAkS3YHq7CwMN2/f1/h4eGJWQ8AAAAAOB27uwIOHTpUQ4cOfbATFxdly5ZNRYoUUfXq1RUVFZVoBQIAAABAcmdXsOrWrZskKSoqSpGRkbp7966uXLmi3377TQEBAXJzc0vUIgEAAAAgOXMoWD0qJiZGO3fu1NChQ3Xjxg2tXr1akpQ9e3aVLl3a7iIBAAAAIDlL1MkrTCaTKleurP79+ytr1qyaNGmSJk2apM2bNyfm0wAAAABAsmL3GKsnqV27tmrXrv08dg0AAAAAyY7Dwer06dO6cuWKJMnX11d58+Z1uCgAAAAAcCZ2B6stW7Zo7Nix1lAVy8/PT/7+/qpevbrDxQEAAACAM7ArWO3YsUM9evRQtmzZ1Lt3b+XJk0eSdObMGS1btkzdu3fXjBkzVKlSpUQtFgAAAACSI7uC1bRp05Q/f34tXrxY7u7u1uXVq1fXhx9+qA8++EBTp0594cEqJCREtWrV0o0bN/TTTz+paNGiL/T5AQAAAKRMds0KePLkSTVs2NAmVMVyd3dXo0aNdPLkSYeLe1bTpk1TTEzMC39eAAAAACmbXcEqVapUun///mPX379/X6lSpbK7KHucOXNGS5YsUffu3V/o8wIAAACAXcGqXLlyWrBggQ4ePBhn3d9//62FCxeqfPnyDhf3LEaNGqXmzZsrV65cL/R5AQAAAMCuMVaffvqpmjdvrg8++EDFihWzhplz587p8OHD8vb2Vr9+/RK10CfZuHGjTp06pcmTJ+vo0aOJsk8Xl0S9dzIAAInCZOL8BODl5OzHN7uCVfbs2bV27VrNnDlTv/32mwICAiRJ2bJlU6tWrdShQwd5e3snaqGPExYWprFjx6p3795Kly5douzTaDQoY8a0ibIvAAAAAE/n4ZEmqUtwiN33sfL29tagQYM0aNCgxKznmU2fPl3e3t5q0qRJou3TbLYoMDA00fYHAEBiMZmMTv/lAwDiExgYppgYc1KXYcPDI02CW9LsClb//fefMmXK9MRtDh8+rGLFitmz+wS7cuWKvvvuO02dOlVBQUGSpNDQUOv/Q0JClDatfS1P0dHJ65cKAAAAvMxiYsxO/R3crmBVt25dff7556pbt26cdVFRUfrmm2/0/fffJ9p4p8e5fPmyoqKi1KFDhzjrWrVqpeLFi2vZsmXPtQYAAAAAsCtYFSlSRJ9++ql++eUXDRs2TF5eXpKkI0eOyN/fX2fPnlWrVq0StdD4FCxYUAsWLLBZdvz4cY0ZM0bDhw/nBsEAAAAAXgi7gtXcuXP1448/avz48apbt64GDx6skydPas6cOfL19dWCBQtUpkyZxK41Dg8PD5UrVy7edYULF1bhwoWfew0AAAAAYPfkFc2bN1fFihXVvXt39e3bV5L0/vvvy9/fX2nSMKgWAAAAQMph92TxFotF69ev1+nTp+Xt7S2DwaCDBw/qwoULiVnfMytXrpxOnjxJN0AAAAAAL4xdwers2bNq1qyZvv76azVu3Fi//PKLFixYoLCwMDVt2lTTpk2T2ey8M3oAAAAAwLOwK1g1bNhQN2/e1Ny5czV8+HC5u7urTJkyWrt2rZo2barJkyfr/fffT+xaAQAAACBZsitY1a5dW+vWrVOFChVslqdJk0ZDhgzRd999pzt37iRKgQAAAACQ3Nk1ecXYsWOfuL58+fJat26dXQUBAAAAgLOxq8Xq3r17OnHixGPXnzx5UtHR0XYXBQAAAADOxK5gNWbMGA0ZMuSx64cOHapx48bZXRQAAAAAOBO7gtUff/yhatWqPXZ91apVtWfPHruLAgAAAABnYlewunPnjjJmzPjY9Z6enrp9+7bdRQEAAACAM7ErWGXKlEnHjh177PqjR4/Ky8vL7qIAAAAAwJnYFaxq1KihFStW6Ndff42zbsuWLVq5cqVq1KjhcHEAAAAA4Azsmm69e/fu2rNnj7p166YCBQooX758kqR///1XJ06cUJ48edSjR49ELRQAAAAAkiu7WqzSp0+vpUuXqnPnzoqOjtamTZu0adMmRUdHq0uXLlq2bJk8PDwSu1YAAAAASJbsarGSJHd3d/Xo0YOWKQAAAAApnl0tVgAAAACA/7G7xSoiIkKbNm3SsWPHFBQUJLPZbLPeYDBo9OjRDhcIAAAAAMmdXcHqypUratWqla5cuSIPDw8FBQUpQ4YMCgoKUkxMjDJmzCh3d/fErhUAAAAAkiW7ugKOHz9ewcHBWrZsmTZu3CiLxaKvv/5aBw8eVL9+/ZQ6dWrNnTs3sWsFAAAAgGTJrmD1xx9/qEWLFipWrJiMxv/tws3NTe3atdMbb7xBN0AAAAAAKYZdwSo8PFy+vr6SpHTp0slgMCgoKMi6vmTJkjpw4EDiVAgAAAAAyZxdwSpr1qy6ceOGJMnFxUWZM2fWoUOHrOtPnz6tVKlSJUqBAAAAAJDc2TV5xRtvvKFff/1V3bp1kyQ1atRIs2bNUmBgoMxms9auXasGDRokaqEAAAAAkFzZFaw6dOigf/75R5GRkXJzc1OnTp108+ZNbdq0SUajUXXr1tXAgQMTu1YAAAAASJYMFovFktRFJDcxMWbduROS1GUAABCHi4tRGTOm1bH5IxR242JSlwMADkuTOYcKtR6iu3dDFB1tfvoDXiAvr7QymRI2esruGwTHslgsunPnzv8/sZcMBoOjuwQAAAAAp2J3sDp9+rQmTZqk33//XeHh4ZKk1KlT66233lK3bt302muvJVqRAAAAAJCc2RWs9u/fr/bt28tsNqt69erKmTOnJOncuXPaunWrfvvtN82ZM0dlypRJzFoBAAAAIFmyK1iNHj1aXl5eWrRokbJmzWqz7tq1a2rZsqXGjBmjFStWJEqRAAAAAJCc2XUfq9OnT+uDDz6IE6qkB/e4atGihU6fPu1wcQAAAADgDOwKVtmyZVNkZORj10dFRSlLlix2FwUAAAAAzsSuYNW1a1ctXLhQx48fj7Pu2LFjWrRokbp37+5wcQAAAADgDOwaY/X333/L29tbjRs3VsmSJfXqq69Kks6fP69Dhw4pX758OnTokA4dOmTzuMGDBztcMAAAAAAkN3bdILhAgQLP/kQGQ7wtXMkRNwgGACRX3CAYwMsmRd8g+MSJE/Y8DAAAAABeSnaNsQIAAAAA/A/BCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHGTXdOuP8/fffysyMlJFihRRmjRpEnPXAAAAAJBs2RWsgoKCNGrUKB07dkzly5dXv3791KFDB+3du1eSlDVrVs2dO1e5cuVK1GIBAAAAIDmyqyvgl19+qTVr1ihdunRavny5unXrprNnz2rChAkaOXKkQkJC9M033yRyqQAAAACQPNnVYrV9+3Y1a9ZMw4cP16ZNm9SrVy8NGzZMderUkSRdu3ZNy5YtS9RCAQAAACC5sqvF6tatWypZsqQkqXTp0rJYLMqePbt1fY4cOXT79u3EqRAAAAAAkjm7gpXZbJbJZJIk6/8NBoN1vcFgkMViSYTyAAAAACD5s3tWwM2bN+vChQsKDw+XwWDQmjVrdODAAUnSqVOnEq1AAAAAAEju7A5Wv/zyi3755Rfrz6tXr7ZZ/3ALFgAAAAC8zOwKVidOnEjsOgAAAADAaSXqDYJftA0bNmjt2rU6evSoAgMD9eqrr+qjjz5SkyZNaDEDAAAA8MI4dbD6/vvv5evrK39/f2XMmFG7d+/W559/ruvXr6tbt25JXR4AAACAFMKpg9X06dPl5eVl/bl8+fK6d++e5s2bpy5dushotGvSQwAAAAB4Jk6dPB4OVbEKFiyo4OBghYaGJkFFAAAAAFIip26xis+BAweUOXNmpUuXzqH9uLg4deYEALykTCbOTwBeTs5+fHupgtX+/fsVEBCgAQMGOLQfo9GgjBnTJlJVAAAAAJ7GwyNNUpfgkJcmWF2/fl29e/dWuXLl1KpVK4f2ZTZbFBhIV0IAQPJjMhmd/ssHAMQnMDBMMTHmpC7DhodHmgS3pNkdrP777z/99NNPOnbsmIKCgmQ2274JBoNB8+fPt3f3zyQwMFDt27eXp6enJk+enCiTVkRHJ69fKgAAAPAyi4kxO/V3cLtvENyqVSuFh4crV65cOnXqlPLmzavAwEDduHFDOXLkUJYsWRK71niFh4erY8eOCgoK0tKlS5U+ffoX8rwAAAAAEMuupp2JEyfK3d1dGzdu1Lx582SxWDRo0CDt2LFDX3/9te7fv69+/foldq1xREdHq1evXjp79qzmzJmjzJkzP/fnBAAAAIBH2RWs/vrrLzVr1kzZsmWzdruzWCySpFq1aqlevXoaP3584lX5GMOHD9e2bdvUqVMnBQcH69ChQ9b/IiMjn/vzAwAAAIBkZ1dAs9ksHx8fSZKHh4dMJpPu3btnXZ8/f36tWLEiUQp8kl27dkmSxo4dG2fdr7/+Kj8/v+deAwAAAADYFaz8/Px0+fJlSZLRaJSfn5/27Nmj2rVrS3rQovUixjpt3br1uT8HAAAAADyNXcGqYsWK2rhxo3r37i1JatGihcaOHatLly7JYrFo3759+vjjjxO1UAAAAABIruwKVp06dVKdOnUUFRUlV1dXtW7dWqGhofrll19kNBrVpUsXdezYMbFrBQAAAIBkya5glSFDBmXIkMH6s8FgUJcuXdSlS5dEKwwAAAAAnIXdNwh+khs3bmjPnj3WnzNnzqzy5cs/j6cCAAAAgCRnV7C6evXqE9fv27dPAwcOVNasWSVJ5cqVI1gBAAAAeGnZFayqVasmg8Hw2PUWi0UGg4FZ+wAAAACkCHYFq08//fSJwers2bMv5D5WAAAAAJAc2BWs2rZt+8T1v//+O8EKAAAAQIphTOoCAAAAAMDZEawAAAAAwEEEKwAAAABwkF1jrOrVq/fE9aGhoXYVAwAAAADOyK5g5enp+dT12bJls2fXAAAAAOB07ApWCxcuTOw6AAAAAMBpMcYKAAAAABxkV4vVn3/+maDtypYta8/uAQAAAMCp2BWsPvroIxkMBkmSxWKRJOvPscsMBoOOHz+eCCUCAAAAQPJmV7Dy9vbW7du3VblyZX300Udyc3NL7LoAAAAAwGnYFaw2b96s7777TvPmzdOpU6fUo0cPNWzY0KbVCgAAAABSCrsmr3B3d1e3bt20efNmVa9eXUOGDFH9+vW1ffv2RC4PAAAAAJI/h2YF9PLy0uDBgxUQEKDXXntNnTt31ocffqi///47seoDAAAAgGQvUaZbz549uyZOnKiVK1cqVapUat68ubp3754YuwYAAACAZM+uMVYFChR47Hiq2FkCt2zZYn9VAAAAAOBE7ApWXbt2ZaIKAAAAAPh/dgUruvkBAAAAwP8kyhgrAAAAAEjJ7GqxmjJlylO3MRgM6tq1qz27BwAAAACnkijBymAwWCeteHgZwQoAAABASmBXsNqzZ4/13/fu3VOtWrU0ZcoUlS5dOtEKAwAAAABnYVewypgxY5xladOmjXc5AAAAALzsmLwCAAAAABxEsAIAAAAAB9nVFfDo0aPWfwcFBUmSLly4IA8PD5vtChcu7EBpAAAAAOAc7ApWTZo0kcFgsFk2fPhw678tFosMBoOOHz/uWHUAAAAA4ATsClZjxoxJ7DoAAAAAwGnZFawaNWqU2HUAAAAAgNNK1MkrwsLCrGOuAAAAACClsCtYRUZGatSoUapfv75GjRols9msb775RmXKlNHrr7+uLl26KDw8PLFrBQAAAIBkya6ugJMmTdKiRYtUuXJlrVu3TqGhoQoICFDXrl11584dLV68WPPmzVPnzp0Tu14AAAAASHbsClYbN27Ue++9p1GjRmnLli3q3r27unfvri5dukiS7t27p4CAAIIVAAAAgBTBrq6AN2/eVIkSJSRJxYsXl8VisblnVZkyZXT16tVEKRAAAAAAkju7gpW7u7t1DJWbm5t8fHyULl0663qz2SyLxZI4FQIAAABAMmdXV8DcuXPr1KlTkqQMGTJo586dNuvPnTun7NmzO14dAAAAADgBu4JV3759FRIS8tj10dHRatGihd1FAQAAAIAzsStYlS5d+onrhw4dalcxAAAAAOCMEvUGwQAAAACQEtnVYiVJ//33n3766ScdO3ZMQUFBMpvNNusNBoPmz5/vcIEAAAAAkNzZFaxOnDihVq1aKTw8XLly5dKpU6eUN29eBQYG6saNG8qRI4eyZMmS2LXG68yZMxo1apQOHjyotGnTqkGDBurVq5fc3NxeyPMDAAAAgF1dASdOnCh3d3dt3LhR8+bNk8Vi0aBBg7Rjxw59/fXXun//vvr165fYtcZx//59tW7dWlFRUZo8ebJ69+6tZcuWaezYsc/9uQEAAAAgll0tVn/99ZfatWunbNmy6d69e5JkvW9VrVq1dODAAY0fP16LFi1KtELj8+OPPyokJERTpkyRp6enJCkmJkbDhw9Xx44dlTlz5uf6/AAAAAAg2dliZTab5ePjI0ny8PCQyWSyBixJyp8/v44ePZooBT7Jb7/9pvLly1tDlfQg2JnNZu3ateu5Pz8AAAAASHYGKz8/P12+fPnBDoxG+fn5ac+ePdb1f/31l9KnT584FT7B2bNnlTt3bptlHh4eypQpk86ePfvcnx8AAAAAJDu7AlasWFEbN25U7969JUktWrTQ2LFjdenSJVksFu3bt08ff/xxohYan8DAQHl4eMRZniFDBt2/f9+hfbu4JM+Z6A0Gg4xGQ1KXAQCJxmy2WLuT4+lMpgfnpzTeWZO4EgBIHLHHs9jjm7OyK1h16tRJderUUVRUlFxdXdW6dWuFhobql19+kdFoVJcuXdSxY8fErvWFMRoNypgxbVKXES+z2UKwAvBS4bj27Cxms3LVbZ/UZQBAorGYzfLwSJPUZTjErmCVIUMGZciQwfqzwWBQly5d1KVLl0QrLCE8PDwUFBQUZ/n9+/dt6ntWZrNFgYGhjpT2XJhMRnl4pNHUH3bpyk3HWuQAIDnwfSWDuraooMDAMMXEmJ/+AEii9wKAl09y7b3g4ZEmwS1pzxSsIiIi9Ouvv+ry5cvy9PRUlSpV9Morr9hVZGLInTt3nLFUQUFB+u+//+KMvXpW0dHJ9wR/5eZ9nb9yN6nLAIBEExNjTtbHXQAAnibBwer27dtq3ry5Ll++bE2TadKk0dSpU/Xmm28+twKfpFKlSpoxY4bNWKuNGzfKaDSqQoUKSVITAAAAgJQnwSPEpk2bpitXrqhNmzaaOXOmBg0apFSpUmnIkCHPs74nat68udKmTauuXbtq586dWrFihcaPH6/mzZtzDysAAAAAL0yCW6x27typBg0aaMCAAdZlPj4+6tu3b7zTnr8IGTJk0Pz58zVy5Eh17dpVadOm1XvvvWedrRAAAAAAXoQEB6tr166pdOnSNstKly4ti8Wi27dvJ0mwkqQ8efLo+++/T5LnBgAAAADpGYJVZGSkUqVKZbPMzc1NkhQdHZ24VQFIMtHh9xV06Q9JUvrsb8gltf0zbAIAAKQUzzQr4JUrV3T06FHrz7FTnV+4cCHeG/UWLlzYwfIAvGhBl/5QZOCV///3XmXM904SVwQAAJD8PVOw+vbbb/Xtt9/GWT58+HCbny0WiwwGg44fP+5YdQBeuOjw+w/9+17SFQIAAOBEEhysxowZ8zzrAAAAAACnleBg1ahRo+dZBwAAAAA4rQTfxwoAAAAAED+CFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOcknqAuwVExOj7777Ttu3b9fp06dlsViUP39+9ezZU2XKlEnq8gAAAACkIE7bYhUeHq5Zs2apcOHCGjdunCZMmKAMGTKoVatW2rNnT1KXBwAAACAFcdoWq9SpU2vLli3KkCGDdVmFChVUt25dzZ8/X+XLl0/C6gAAAACkJE7bYmUymWxCVeyy/Pnz6+bNm0lUFQAAAICUyGlbrOITHR2tv//+W6VLl3Z4Xy4uyS9zmkwPavJ9JcNTtgTsd/eYUZH//28Xk1E5fTMmaT14ucUez2KPbwAAOKuXKljNmTNHN27cUJs2bRzaj9FoUMaMaROnqERmNlvUtUWFpC4DL7EP9i7SjbBASVJGjzQa3bN2EleEl53ZbJGHR5qkLgMAAIckq2AVFBSUoG582bNnl5ubm82yXbt2afLkyerSpYuKFCniUB1ms0WBgaEO7eN5MRgMMhoNSV0GXmJms8Xm34GBYUlYDVICs9kii8Xy9A0BAHjBPDzSJLhXRbIKVhs3btTgwYOful1AQIDy5Mlj/fno0aPq3r276tatq27duiVKLdHR5kTZD+BsHv6Ca7FYFBERnYTVAAAAOIdkFayaNm2qpk2bPtNjLly4oPbt26tkyZIaNWrUc6oMAAAAAB7PqUcL37x5U5988omyZs2qSZMmydXVNalLAgAAAJACJasWq2cRHh6u9u3b6+7du/rss8/077//Wte5ubmpUKFCSVgdAAAAgJTEaYPVrVu3dOLECUlS586dbdb5+vpq69atSVEWAAAAgBTIaYOVn5+fTp48mdRlAAAAAIBzj7ECAAAAgOSAYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgoJcmWB05ckQFCxZUyZIlk7oUAAAAACnMSxGsLBaLRo4cKS8vr6QuBQAAAEAK9FIEqxUrVuju3btq0qRJUpcCAAAAIAVy+mAVGBioiRMnauDAgXJ1dU3qcgAAAACkQC5JXYCjvvnmGxUuXFhVq1bVkSNHEm2/Li5OnzkBuxgMBpt/81kAAAB4OqcOVsePH9dPP/2kVatWJep+jUaDMmZMm6j7BJyF0Wiw+TefBQAAgKdLVsEqKChIN2/efOp22bNnl6urq4YPH64PPvhAefLkSdQ6zGaLAgNDE3WfgLMwmy02/757NyQJqwEAAEg6Hh5pZDIlrPdOsgpWGzdu1ODBg5+6XUBAgE6cOKGzZ89q4sSJCgwMlCRFRERIejDuKlWqVEqVKpXdtURHm+1+LODMLBaLzb/5LAAAADxdsgpWTZs2VdOmTRO0bUBAgO7fv69q1arFWVe2bFm1b99e/fr1S+wSAQAAACCOZBWsnkWjRo30+uuv2yxbtWqVAgICNHv2bGXLli2JKgMAAACQ0jhtsPLz85Ofn5/Nsn379slkMqlcuXJJVBUAAACAlIh5lAEAAADAQS9VsOrevbsOHjyY1GUAAAAASGFeqmAFAAAAAEmBYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAbvr7Zrf/288v+hC0BAAAQy2nvYwXg+Wjduq2+/94sSWrVqm0SVwMAAOAcDBaLxZLURSQ3MTFm3bkTktRlAAAAAEhCXl5pZTIlrJMfXQEBAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAAAAAHAQwQoAAAAAHESwAgAAAAAHEawAAAAAwEEEKwAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBovFYknqIpIbi8Uis5m3BQAAAEjJjEaDDAZDgrYlWAEAAACAg+gKCAAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADiJYAQAAAICDCFYAAAAA4CCCFQAAAAA4iGAFAAAAAA4iWAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAkmJiYpK6BAAA4MQIVgBSvOjoaJlMJkVGRurkyZPav3+/zGazoqOjk7o0AMBzxoU1JBaDxWKxJHURAJBULBaLDAaDgoOD1bp1a126dEmBgYF69dVX9d577+m9995TxowZk7pMAMBzEBMTI5PJpLCwMK1du1ahoaHKkSOHypQpowwZMiR1eXAyBCsAKVbsCdVsNqtHjx4KCgpS8+bNlT17ds2aNUsnT55UiRIlNHDgQHl6eiZ1uQCARBR7YS0kJESNGzdWYGCgJOnevXuqWLGiWrRooWrVqiVxlXAmdAUEkGKZTCZFRERo48aNkqSOHTuqVq1aKlKkiCZNmqRatWrp0KFDWrFiBV1FAOAlEhMTI4PBILPZrAULFsjX11fz58/XunXrtGDBAl28eFFTp07Vtm3bkrpUOBGCFYAUy2w2a+jQoRo9erQOHTqkXLlySZIiIiIkSb169VK+fPn0008/yWQyJWWpAIBEFNv9b8GCBfrrr7/0xhtv6LXXXpOPj4/Kli2rqVOnKjg4WD/88IOCgoKSulw4CYIVgBTLaDSqQYMGyp49u27duqV169ZJklKlSmUNV5988onOnTunQ4cOJWGlAIDEtnTpUo0dO1aHDx9W1qxZJT1oyYqJiVHevHk1fPhw/f777zpw4EASVwpnQbACkGKYzeY4y8qXL6/+/furaNGiWrp0qVavXi3pQbiSpMuXLytDhgxKmzbtiywVAPCctWnTRt27d9f9+/c1b948nTt3zqZ3Qs6cOeXh4aHLly8nYZVwJgQrAClCdHS0jEajIiMjdeTIEW3fvl1nz55VZGSkSpYsqU8//VQ+Pj6aNGmSZs6cqYsXL2rHjh1avHix8uTJozx58iT1SwAA2Olx42S7du2qTp066dKlS5ozZ47Onj0rk8kki8WiO3fuSJLc3d1fZKlwYswKCOClZzabZTQaFRwcrI8//lg3b97UjRs35OPjo2LFimnChAlyd3fXvn379OWXX+qff/6Rl5eXSpcuLVdXV40ZM0apUqWyziIIAHAe0dHRcnFxUWhoqFasWKErV66oQIECypEjh0qVKiVJGj9+vJYtWyZfX1+1bt1a165d065duxQcHKxVq1Zx7EeCEKwApAiRkZH68MMPlSZNGn3yySfKmTOnfvrpJ61fv17u7u5atmyZ3N3ddfDgQY0dO1bh4eFq0aKFmjdvbn28m5tbEr8KAMCzePhehU2bNlVMTIxSpUql//77Tzlz5lTVqlXVsWNHSdKUKVM0ZcoUZciQQWXLllWpUqX04Ycfys3NjQtrSBC6AgJIEf755x9du3ZNHTt2VOXKlfXqq6+qa9eu6tevn0JDQ9W/f39FRUVZuwWmSZNGP/74owICAiSJUAUATshgMCg6OloDBw5UpkyZNH36dK1bt856m43p06fr8OHDkqRu3bqpS5cuMhqN8vPz0zvvvCM3NzdFRUURqpAgBCsAL6Xo6Gibn2/duqU7d+7I29tb0oMWqNSpU6tatWp699139ffff+vatWuSpDJlyqhfv35KmzatJkyYoE2bNr3w+gEAiSMqKkrnz59X5cqVlSNHDknS/v379ffff6tXr14qVqyYwsLCJEk9evRQ48aNtXr1as2bN08XLlyQq6trUpYPJ0KwAvDSie1PHxwcrOPHj0uS8uXLJxcXF+3YsUPSgxao2HDVpEkT/ffff7p48aJ1H2XKlFGPHj2UK1cuFSpUKEleBwDg2T16Ye3mzZs6c+aMsmfPLldXV61du1bdunVTr1691KZNG4WGhmrx4sU6duyYJOnTTz9V8+bNtXjxYv34449x9gc8jktSFwAAienhUFWtWjUVLVpU06dPV7Zs2dSgQQPNmjVLmTNnVoMGDazd+86dO6esWbPKx8dH0v/65JcrV07FixdX6tSpk/IlAQAS6OFzwIIFC9SpUye9+uqrKl26tH7++Wfdvn1bI0aMUK9evaxjq/bs2aM9e/aoVKlS1rFUvXr1kqurq2rXri0XF74uI2H4SwHw0og9oYaEhKhBgwYqWbKkhg4dag1Q7733nq5fv64RI0bo2rVrqlixom7fvq3Zs2fL19dXr732mqQHffJjxd7PCgCQvD0cqurXr6/XXntNoaGhSpcunSpVqqTvvvtOmzdvVocOHdSpUyfFxMRYp1n38fFRiRIlZDQareGqa9euSf2S4GSYFRDAS+HhUNWiRQvdunVLy5Ytk5+fn3W6denBJBYrV67UqlWrFBkZKW9vb+XMmVPfffedXF1dbbYFADiHh88BjRo1UrZs2TRhwgR5eXlZj+kjRozQhg0bVKZMGTVt2lSnTp3S5s2bFR4erhUrVsjFxYVzABxCsALg9B6+StmgQQPdvn1b2bNnV61atdSuXTu5ublZt5EeTFxx/fp1nTt3ThkzZlSRIkVkNBpttgEAOIeHzwEfffSRzp07p/nz56t48eKSbG+XMWvWLO3du1f79+9XwYIFlStXLo0cOVIuLi6cA+AwghWAl0LsmKpSpUpp4MCBmjlzpo4cOaKaNWuqffv2Nvchie+KJPcoAQDn8/AN4Bs0aCAXFxdduXJF9evXV9euXeXr6yvpwcyAD8/ud/XqVWXOnNl63CdUITHQ1gngpXDx4kUVLVpUQ4YM0auvvqohQ4aocOHC2rRpk2bPnq3IyMjHhipJhCoAcEJGo1ERERGqXr26smbNqk2bNunbb7/VypUrNWXKFF26dEmS5OrqqpiYGOvjsmbNaj3uWywWQhUSBS1WAJxSfAEptrtH7Kx+YWFhGjlypI4cOaJ3333X2i0wdj0AwDk93Mvg+vXr+vnnn1WnTh1lyZJFBoNBW7ZsUbdu3dSoUSN16dJF2bNnT+KKkRIQrAA4ndguG1FRUbp586aioqLk4+OjdOnSWQNX7Ek3NlwdP35c5cuXV+/evbnZIwA4sdhzQFhYmH7++WeVKlVK2bNnt46jij0PxIarxo0bq0uXLvLz80viyvGyo90TgFOJiYmxDlLu16+fLl68qIiICHl6emrixInKmTOnLBaLTCaTYmJilCZNGn3++ef69NNPdfv2bbp7AIATe/gc0KpVK0VHR8tisShXrlzWbYxGoywWi2rUqKGpU6eqR48eun//voYOHapXXnklCavHy44WKwBOI7YLX2hoqJo1a6YMGTKoQ4cOio6OVo8ePeTp6anp06eraNGi1m1jW64iIiLk6upqPeHSFRAAnFNERIRatmwpd3d3ffbZZ8qVK5e1tephscf6gIAALVy4UIsXL2YqdTxX/HUBcBqxQWns2LHy8vLSpEmTVKlSJa1evVre3t7y8vJSu3btdOTIERkMBmvLldlsVqpUqWQ0GmU2mwlVAODE9u3bp8DAQHXu3Fn58uWzjp2Nj9lsVu3atfXDDz9YzwHA80KwAuBUwsLClCVLFjVp0kReXl7q27evDh06pJkzZ2rw4MGKiYlR165ddfz4cWu4evgKJVcrAcC5Xb16VdeuXVPu3Lmtx/TY4730YAxWTEyMDAaDdX3sOs4BeJ746wKQrD16FTJt2rR6++23VatWLf3222/6+++/NXLkSBUoUECvv/663nrrLd24cUONGjXS6dOnaZ0CgJdM6tSpZTQarVOpxzIYDAoPD9fy5ct18eLFOOuA541gBSDZio6Otnb/i46OVlBQkAwGg/LlyydXV1edP39eYWFhypMnj/Uxbm5uatGihTp27KicOXMmXfEAAIc8rttezZo15ePjo2nTpik8PNxm+/Pnz+vHH3/U33///aLKBKwIVgCSpdjpdENCQtS/f3+1aNFCvXr10r59+6zbmEwmGQwGnThxQsHBwbp06ZKuXr2q4sWLq3fv3nJxcVF0dHQSvgoAgD2io6NlNBoVGRmpf/75R7///rtu3bol6UGLlb+/v/755x917txZ+/bt0+XLl7Vr1y4NHTpU7u7uqlevXhK/AqREzAoIINmJDVXBwcFq2LChfHx85Ovrq7NnzypDhgyaPHmy0qdPr7CwMLVs2VI3b97UK6+8orCwMKVKlUorVqyw3jgSAOBcYmdzDQ4OVrdu3XTlyhWFh4crMjJSQ4cOVZUqVeTm5qbt27drzJgxun37tqKjo5U1a1ZlypRJ8+fPl6urq81NhIEXgWAFIFkKDQ1V3bp1VaBAAQ0ePFjZsmXT/Pnz9dNPP+n777/XrVu3lD9/foWFhWnixIkKCgpS+vTp5e/vLxcXF06oAOAk4rsFRmhoqFq0aKEMGTKob9++yp49u+rUqSNJ6tKli5o2barUqVMrIiJCmzdvVmRkpLJkyaI33nhDRqPReoEOeJH4iwOQ7JjNZk2ePFlXr15V7969lS1bNkkPTr5Xr17VJ598ojNnzqhDhw7q0aOHBg0aZDPTEydUAHAeYWFhcnd3twYss9msqVOnysvLSxMmTJC3t7e6d++uNGnSqFChQho3bpwMBoNq164tLy8v1a1b12Z/sTcRBl40/uoAJDtGo1GVKlXS2bNnNXToUOXLl0937tzR2LFj1bVrV5UrV06///67pk2bprp16yp37tw2j+eECgDO4e+//9ann36qkSNHqly5ctZbZOTIkUOenp7y9vbWwIEDdeTIEU2dOlU5cuRQ586dNXnyZLm4uOjdd9+Vp6enzT7prYCkwrcPAEnObDbHubdI+fLl5erqqqioKDVr1kwREREaOnSomjVrJqPRqH///VevvvqqUqdOnURVAwAccf/+fY0YMUIXL17U4MGDNXz4cL355puSpEaNGlknJ9q3b5/69eun1157TS4uLipXrpz+/PNPDRs2TBkyZFCtWrWS+JUADzArIIAk9fDMT3/++ad27NihAwcOSJLKlCmjbt26qVKlSnJ1dZWfn5+MRqOuXLmidevWKVeuXMqaNWsSvwIAgD3c3NyUI0cOeXh4KF++fBo8eLB27dolSXJ1dZWrq6t1ttd8+fJZeyMYDAZ17txZw4YN09tvv52ULwGwQYsVgCRjNputs/99/PHHunPnjq5cuaI0adKoatWqGjx4sEqVKiWLxaLQ0FD16tVLQ4YM0cqVKxUUFKRFixZZ++M/2uIFAEi+LBaL0qRJo27dumn37t1Knz69ChYsqMGDB2v06NEqX768JClz5szy9fXVsmXL1KVLF925c0c7d+5UpUqV1Lx5c0mMq0XywayAAJJUZGSkPvzwQ7m5ualr165yd3fXwYMHNXPmTOXNm1eTJk1SxowZtX//fs2ePVs7duxQ7ty5tWbNGrm6unJCBQAnFR0dLbPZrIkTJyosLEwVK1bUwoULdfnyZWu4Cg8P1/jx47Vt2zaFhIQoXbp08vDw0E8//cSxH8kOwQrAC/Nwy1Ls7E9//vmnBgwYoFGjRln71kdFRemvv/5Sz549VaFCBU2cOFGStGfPHv3xxx/q3r279ea/nFgBwDn8+++/ioiIkK+vrzJmzGhdvmHDBg0fPlyLFi1SVFSURo8ercuXL2vUqFGqUKGCQkNDtW3bNp05c0apU6fWJ598wm01kCwRrAC8EEePHtXSpUvVr18/eXh4WJf//vvv6ty5s+bNm6eyZctaA1dUVJQWLFigadOmae7cuSpRooTN/ghVAOA8Tp06pfr168vd3V0VK1bUO++8YzNNeo8ePRQSEqIZM2bo4MGDmjZtmi5cuKARI0borbfeinOvK0IVkiMGJQB4IU6fPq1ly5Zp/PjxCg4Oti6PDVmHDx+2LrNYLHJ1dVXx4sUVEhKi0NDQOPsjVAGA87h+/bo8PT0VERGhVKlSaeDAgerdu7cWLlwoSWrWrJlCQ0N17Ngxvf7662rbtq1y5sypYcOGafv27XFuIEyoQnJEsALwQtSqVUtjx47V2rVr9cUXXygoKEiSVLx4cb333nuaMmWKdu7cKYPBYD2BhoaGytfXV+7u7klZOgDAQW+88YbGjRunjBkzKjw8XNOnT1fq1Km1bNkyNW7cWMHBwbp06ZKWLl0qSXrrrbfUoUMHpU+fXj/99FMSVw8kDF0BAbwwkZGRWrt2rUaNGqW6deuqT58+8vLy0vnz5zVmzBjt3btXXbt2VZEiRRQVFaVJkyYpVapUWrhwIbP+AYCTi46O1o4dO/Tpp5/qnXfeUdeuXeXm5qbx48crMDBQBw8elNFo1Pfff69ChQpJkv755x8VLlyYcwCcAsEKwHPxpP7vkydP1tSpU9W8eXN9+umnSps2rS5evKj58+dr2bJlslgseuWVV+Tr66vvvvtOrq6uTKkOAC+BmJgYbd++Xf3791fZsmU1ceJEpU2bVgcPHtThw4d14cIF9enTR+nSpYvzOLr/IbkjWAFIdMePH9f06dP1+eefK1OmTJIezPTn6uqq48eP68MPP1SBAgV0+PBh1apVS0OHDlXatGklSUeOHFFISIjc3NxUvHhxGY1GJqoAgJdIbLjy9/dXyZIlNX78eHl6ekr637ni0ckqAGfA5V8Aie7GjRv65ZdfNHz4cN2+fVtms1murq46cuSIWrRooTp16mj69OkaOXKkNm7cqBEjRigwMFCSVKRIEZUrV04lS5aU0Wi03kQYAPByMJlMqlKlisaOHauDBw/K399ft27dkiRCFZwaLVYAEl1MTIx+++03DRgwQKVKldKMGTN07NgxffDBB6pXr54GDRqkNGnSKDo6WuvWrdPw4cNVu3ZtDRgwQBkyZEjq8gEAL0Bsy9WgQYNUpkwZDRs2zNrLAXBGBCsAz0XsCXPAgAHKly+fTpw4YQ1VqVOntm4XFRWl9evXy9/fXz179lTnzp2TsGoAwIsUExOjHTt2qEuXLmrbtq0+/fTTpC4JsBvBCsBzYzabtX37dn3xxRcKDQ3V8uXL5efnZ10XOxlFZGSk/vjjD7355pt0+wOAFCYmJkYHDx5UiRIlOAfAqRGsADxXkZGR2rVrl7Vb4OjRo+Xl5fXY7ZmoAgBSLs4BcGYEKwDP3cMzQJUuXVpffPGFvL29k7osAMBzwOQTSKmYFRDAc/foDFBDhgzRf//9l9RlAQAcFBMTE2dZfKHq4ev4ZrP5udYEJBVarAC8MAxSBoCXR2y3vdDQUM2YMUO3b9+W0WjUe++9p7x58ypt2rSyWCwym80ymUwKCQmx3rOQm77jZUSwAvBCMUgZAJxfbHe/kJAQNW7cWKlTp5a3t7f+++8/hYSEqHbt2mrVqpVeeeUVSdLdu3f1ySefqGDBgho9enQSVw88H3yrAfBCmUwmlSlTRhKDlAHAWRkMBpnNZg0fPlyenp766quvlCVLFplMJrVo0UKrV69W5cqVrcEqMjJSly5dkre3N2Ow8NLiGw0AhzhygiRUAYDzio6O1sWLF1WxYkX5+vpKkrZs2aKDBw+qX79+Klu2rKKiohQTE6PMmTNr48aNypgxowwGA+EKLyU6twJIMAYpA0DK9ejokcjISN28eVORkZGSpICAAHXr1k29e/dWu3btFBISojlz5uj8+fOyWCzy8fGRyWRSTEwMoQovJS4XA0gQewcpG41GBikDgJOLiYmRyWSytkClTp1aLi4uyp8/v44fP67vv/9eY8eOVZ8+fdS2bVtJ0rFjx7R161YVLFhQBQoUsO7LZDIl1csAnismrwDwVAxSBgCEhYWpbdu2KlKkiHr06KF06dJp165dat++vcxms9q1a6d+/fpJks6dO6dBgwbJw8ND06dP5+IaUgRarAA8FYOUAQC7du3SX3/9paCgILm6uqpDhw6qUKGCJkyYoD59+uj48eOaOXOmIiIitH37dlksFi1cuJCeC0gx+AsHkCCPDlI2mUzWQcpt2rSxDlIODw+3DlKeOXOmdZAyAMC5PHrszpUrl9zc3GQ0GnXgwAHNmTNHQUFBql27tubMmaNUqVJp5cqV+vvvv1WyZEktX75cLi4uio6OJlQhRaDFCkC8Hm1pim+Qcp8+fWwGKS9YsEBVq1ZV/vz55ePjI+l//fIBAM4jtoXp4dti5MmTRzVr1lT27NkVFBSkLVu2SJLat2+vihUrqkyZMjKbzUqTJo31/BETE8MMsEgxuHwAII7YGZtiW6AkxRmk3KdPH/Xp00ft2rWT9L9BytevX7cJZIQqAHA+RqNRkZGRat++vSZNmmRdnjp1av3555/67LPPVLFiRW3fvl1z5sxRcHCwUqdOLXd3d+s5wGKxcA5AikKwAhCHyWRSWFiYWrdura+++sp6wvzwww+1e/dujR07Vu3atVOHDh1kMpl07tw5ffXVV/Ly8lKlSpWSunwAgJ0evkXGrVu3lC1bNq1YsUKzZs2SJLVq1Uo3btzQ7du31b9/f5UtW1Y7duzQV199pbCwMJt9Mb4WKQ1tswDixSBlAEhZYo/dkZGRMplMypYtm9q3b6/06dNrwYIFcnV1VatWrRQSEqL169erVatW+uyzz+Tv72+9AAekZEy3DkBS3DFVZ86cUaNGjZQrVy6lSZNGZcuWVYcOHZQ+fXrt3LlTS5Ys0ZkzZ+Tn56ecOXNq4MCB1kHK9KcHAOcUGRmpHj16KH369BozZoxcXFx0/vx5/fDDD1q7dq26deumwMBAXbt2TcOGDbNeTJMedB9kJlikZHz7AcAgZQCAJCkqKkrp06fXP//8ozFjxmjgwIHKmTOnWrRoIUn67rvvZDablTp1aoWGhipdunTWHgr0VkBKx18/AAYpA0AKZbFYrNOqR0dHK23atPr8889Vvnx57dy5U2PHjlV0dLRy5sypZs2a6d1335X04AbA//zzj82+CFVI6fgEACkYg5QBIOUym80yGAyKiYmR9GD218jISHl4eGjo0KHy8/PT2rVrreEqd+7ceu+991ShQgWVK1dOr7/+ehK/AiB5IVgBKdTDg5RjYmKsg5Rr1aqlBQsWaN68ecqdO7d1kLKrq6s+++wzvfbaawxSBoCXQOw5oHnz5lqwYIEkydXVVZK0bds27dq1S7ly5dKOHTs0ZswYRUdHK1euXOrevbu+//57mUwmaygDwBgrIMWKPaE+PEg5Z86cat68uSwWi2bNmiU3Nzd9+OGHOn36tMxms0wmk8aNGyfpQQsVg5QBwLldv35d3t7e+uqrr5Q6dWq9//771hvADxo0SPXr19fkyZO1a9cuDRw4UGPHjlXmzJklyXpeAPAAwQpIwRikDAApy6MXxHLkyKE+ffpozpw5GjFihP7++2+tXr1affr0UbNmzZQqVSr17t1b9+/fV1RUlM1jOQcAtphuHUhBYj/uBoPBOgNgYGCgvv76a+3evVtvvfWW/P395eLiorNnz2rFihUKCAjQtWvXNG/ePJUvXz6JXwEAwF6xx/2YmBhFRETIYDAoTZo0kqQTJ05o3rx52rhxo958801Nnz7d5jGhoaFKnTo19yoEnoBgBaQQ8U2pHhkZKTc3N0lS27Zt9c8//6h+/frWcHXu3DnNnTtXly5d0nfffUeXDwBwUjExMTKZTAoODtawYcN09uxZpU2bVjlz5tSwYcNkMpl08uRJzZs3Tz///LOGDh2qpk2b2jxWorcC8CQEKyAFiYyM1AcffKD69eurVatW1i4h27ZtU+fOnVW8eHHduXNHlSpVst7w98aNG3rllVesM0cRrgDAOYWGhqpJkybKkCGDypYtq6ioKG3btk2urq768ssvVbBgQf3777+aPXu2Nm3apD59+qh169ZJXTbgNLjkAKQgDw9SXrZsmQwGgwICAtS5c2cNGjRIM2fOVKVKlayDlGNiYpQ5c2YZDAYGKQOAk4q9hj5v3jylTZtWo0ePVt++feXv768mTZro9OnTunjxoiQpX758+uSTT/TGG29oy5Yt4vo7kHBMXgG8xBikDACIPZY/3P1PktavX69vvvlGffv2Vc2aNRUUFKT06dOrQIEC+uyzz+Tn58cMsMAzIFgBL6nHDVLOnz+/2rZtK6PRqJ9//lmVKlVShw4drI9Jly6dRowYwSBlAHBiDx+7LRaLoqOjFRUVZT22r127Vv3791efPn3Uvn17RUZG6ssvv1Tu3LnVpk0b5ciRI85+ADwZwQp4CcXExMjFxeWxg5QLFCigTz75RAaDQT///LOWL1+upk2bWoOYu7u7JE6oAOCsjEajIiIiFBISIi8vL7m6uqpatWry9/fXmDFjtGjRIvXs2dN6Ye3kyZM6f/68XnvttTj7AZAwBCvgJWQymRQaGqqmTZsqQ4YMqlChgnWQcoMGDayDlNu2bSuz2axRo0YpNDRUrVu3thlHxQkVAJyT2WxW+/btFRwcrOnTpytz5syqXLmy3nnnHS1YsED16tVT586dFRMTo9OnT+uLL76Qm5ub9T6GAJ4dwQp4ycT2hX94kHLu3LklSV5eXvrqq6908eJFFSxY0DpI+f79+9qyZYtatWpFP3oAeAlYLBa1bt1aQ4cO1aBBgzR27FhlypRJH3/8sbUroCTdvn1bd+/elSQtXLhQJpOJGWABOzHdOvCS6tu3r27duqV58+bJaDRq/fr1+vTTT9W7d2+1b9/eOkhZki5evCg/Pz8ZjUYGKQOAE4oNQw8fw2NiYrR79271799f+fPn18SJE+Xt7a27d+9q+/bt+v333+Xu7q58+fKpZcuWcnFxsbnXIYBnQ7ACXgLxDVLu27evIiIiNHPmTJtByh06dFBkZKRGjRplHaQc334AAM4lLCxMI0aMUIsWLVS0aFEZDAZFR0drz5491nD15ZdfKlOmTJIUJ0TRUgU4hm9QwEsgdpDynTt3ZDAYrIOUd+zYoTFjxmjgwIHxDlJ+9KokoQoAnNfVq1e1atUqjR8/XsePH5fFYpGLi4vKly+vcePG6eTJk/L399eNGzckKU6IIlQBjuFbFPASiB2k3K5dO+sJ8+FBynXq1LEOUj558qS++OILSWKQMgA4sZiYGJuf8+TJo5UrV+r8+fMaOXKkTbh68803NWHCBB0/flz9+vXT7du36fYNJDK6AgIvgZiYGG3fvl1Dhw5V/vz5rYOUDx48qPnz52vjxo2qX7++zSDlpUuXytXVla4fAODEwsPDdeLECZUoUcK67OjRo+rYsaOyZ8+uwYMHq1ChQjIYDLp7966aN28uV1dXrV27ll4KQCIjWAFOiEHKAABJatOmjU6fPq3x48frzTfftC5/OFwNHTpUWbNmVc+ePfXff/9p9erVcnV1ZVwtkMgIVoCTYpAyAODSpUtq2bKlPD09NWDAAFWoUMG67uFwFRYWpoiICK1du1aurq5cWAOeAy5TAE6KQcoAkLI8OqZKkrJnz64lS5bo1q1bGjt2rHbt2qXYa+aFCxfW119/rYMHD8rNzY1QBTxntFgBTiK+FqZjx46pQ4cOyp49uz7//HMVLFjQ2nK1d+9effrpp8qTJ4+++eYbeXt7J1HlAABHxYahiIgInTx5Urdv31bp0qVlMBiUPn16Xbp0Sc2aNZO3t7cGDBigihUrKiwsTJ07d9bt27e1atUquoADzxnBCnAiDFIGgJQndixUcHCwWrVqpdu3b+vWrVvKmDGjGjZsqIYNGypv3ry6fPmymjVrJh8fH3Xv3l0LFizQzZs3tW7dOlqqgBeAYAU4EQYpA0DKFBkZqVatWsnd3V3t27dX/vz5tXbtWs2ZM0cFCxbUuHHj5OXlpatXr6p58+a6efOmChYsqGXLlhGqgBeEb1mAExk5cqSMRqO1H32swoULa+bMmbp06ZKGDx+u1q1b68aNG9ZQFR0dTagCACd28uRJ3bp1S23btlXp0qXl5eUlX19f3bp1S1WqVJGXl5ciIyOVLVs2/fDDD3rnnXcIVcALxjctIJlikDIAINbFixd15coVFS9eXG5ublqzZo26d++u3r17q2XLlgoODtbvv/+uwMBA+fr6atKkSZwDgBeMYAUkQ9HR0TKZTIqIiNDhw4e1bds2BQYGKigoSH5+flq6dKlu375t03IVFhamqVOnKl++fFqyZAknVAB4CcRePMubN688PDy0f/9+BQQEaMCAAerdu7c6duwoi8Wi1atXa8uWLYqIiLB5POcA4MXh0wYkM2azWS4uLk8dpLxs2TI1a9ZMX375pcLDw20GKTPzEwA4p0dngI29Cbynp6d8fX01atQoXb9+XX369FGHDh1kNpt14cIFbdiwQXnz5pWPj09SlQ6keExeASRDDFIGgJQnNlSFhoZq0aJFunfvnrJmzaqqVavKz89PR44cUZs2bZQuXTr1799flStX1r59+zRz5kxFRkZq2bJlcnFxkcVisQYyAC8OwQpIhv755x/17t1bw4cPV9myZeXm5qbNmzere/fu+vzzz9WyZUtFRkbKzc1NV65c0bhx4zRx4kRCFQA4udDQUDVq1EjR0dGKjo6WxWJRWFiYvvjiC73zzjs6cuSIhgwZonv37unWrVvKnj27smTJohkzZsjV1TXeex4CeDEIVkAytH79evXr109//vmn0qVLpzVr1tj0pw8ODtbevXtVtmxZeXh4WB9HqAIA5/NwGJoxY4Z2796toUOHKnv27Dp16pTmz5+vDRs26Ouvv9bbb7+t69evKygoSOfPn1euXLmUO3duGY1GzgFAEuPTByQjsd03Hh6kHBoaGu8g5aNHj6pYsWI2j+eECgDOJ7b735QpUxQaGqpy5copT548kh7M+DpkyBBZLBYNGjRIhQoVkq+vr7JkyaJ8+fJZ9xE7PhdA0mFWQCAJPTqlenyDlPv3768+ffqoY8eOMpvNOn/+vDZs2CA3NzcGKQPAS+L48eOaP3++fvzxRwUFBVmXGwwGpUuXTq1atVKqVKm0Y8cOSf+bLTAW9yoEkh6fQiCJPDxIedasWRo/frwWLlyoy5cvK3PmzBoxYoTu3bsnHx8f+fn5KSQkRDt27NDAgQMVFhamzz//XAaDIc7JFQDgfIoXL66ZM2fKz89PO3bs0JkzZ6zrDAaDtXXqxo0b1mUAkhfGWAFJiEHKAIBY0dHR2r17t/z9/ZU/f34NHjzY2iXwxIkT6tixo9q0aaOPP/44iSsFEB+CFfCCMUgZAPA4ZrNZv//+uwYOHCgPDw9VrFhR6dOn1549exQaGqqVK1dy7AeSKYIVkAQeHqScKVMmde3aVdKDPvPBwcEaPny4duzYodWrV8vX1zfO481mM/3pAcBJ7N27V9u2bZO/v3+Ctjebzdq5c6dGjBihy5cvq3Xr1vLx8dEnn3wik8lEbwUgmeKbGZAEGKQMAClDVFSUTp06Fe86s9kc73Kj0ag333xTQ4cO1SuvvKKrV6+qQYMGMplMMpvNhCogmeLbGZAEGKQMAC+/P/74Q/Pnz1e9evXk7++vyMhInThxQvv27VNoaOgTH+vi4qI333xTI0eO1J9//qnBgwfr+vXrXFgDkjE+nUAScHFx0RtvvKEhQ4bo/v37GjVqlE24unDhgkwmkzw9PZOuSACAQw4cOKCbN2/K09NTwcHB+uijj9ShQwd16NBB77zzjhYtWqTLly8/9vEmk0lvvfWWvvzyS/32228aPXp0nNt0AEg+GGMFJCEGKQPAyyv2pu8RERH64IMPlD59en388cd69dVXtXr1ai1YsEC1atWSv7+/0qdP/9j9mM1m/fHHH8qaNaty5cr1Al8BgGdBixWQyPbu3auxY8cmaFuj0ai33npLY8eOVXR0tBYtWqTQ0FBVr15dq1atkouLC1cnAcBJxXbjPnz4sIKDg9W1a1dVqFBBOXPmVIkSJRQaGqpixYo9MVRJ/xtzRagCkjeCFZCIGKQMAHjU+fPndeXKFRUpUkQuLi5avXq1OnXqpN69e6tZs2YKDg7WgQMHFBUVldSlAnAAfYyARPLHH3/oyJEjeu+99+Tp6anIyEidPXtWgYGBKlKkiFKnTv3Yxz48SHnAgAEaPHiwRowYoSxZsrzAVwAASEyxXQGzZ88uDw8PnTlzRufPn5e/v7969+6tjh07ymw2a8mSJbp48aLy5s2rDBkyJHXZAOxEsAISyYEDB3T//n3rIOW2bdvq2rVrCgwMVLp06dShQwdVq1ZNfn5+8T7+4UHK7du31+jRo/X111/TYgUATuLR+0vFdgV89dVX5eLiok8//VSXLl1Sr169rKHq/Pnz+u2331SgQAF5eHgkVekAEgGTVwCJhEHKAJByRUdHy8XFReHh4dq6dauuXLkiX19fFS1aVNmzZ9e+ffvUtWtXpU+fXmPGjFHRokV18OBBTZo0SdHR0Vq6dKlcXFys5xIAzocWKyCRPDpI2d/fXyVLlpSLi4tKlCihGTNmPNMgZQCAczCbzXJxcVFwcLBatGihkJAQ3bt3TyaTSa6urpowYYLefPNNTZ06VX379tWQIUN08+ZN+fn5ycvLS3PmzLFOVkQvBcB5EayARBbfIOXY/vSxg5RPnjypYsWKydXVNanLBQA4yGg0KioqSj179pSnp6fGjBmj/Pnza/fu3VqyZIk6deqkGTNm6M0339TSpUt19epVXb58WXnz5lWhQoVkNBqtLV4AnBefYCCRMEgZAFKuW7du6dy5c+rQoYOKFCkiSapcubJeffVVjR49Wp9++qmWLl0qPz8/ZcuWTWXKlLE+NrbFC4BzY7p1wE6P3l8qvkHK/v7+8Q5STp06NYOUAeAlEhQUpLt378rNzU2SFBkZKUnKmTOnPvjgA0VFRenYsWOSHlyIe5jRyNcx4GXAJxmwQ3R0tEwmk8LDwxUQEKDZs2crICBAly5dUtasWTVhwgTdunVLr7zyikqWLKnQ0FDt2bNHAwcOVFhYmPz9/WUwGOKcXAEAyV989yXMnTu3/Pz8tGbNGkmSm5ubNVy98cYbioqK0tWrVyWJySmAlxTtzsAzYpAyAKRcsWOhoqKidP36dbm7u8vb21suLi7q3LmzBgwYoEGDBmn06NHW1qsTJ07I09NT2bJlS+LqATxPTLcO2CEqKkqdOnVSZGSkBgwYYDNIec+ePdZBylevXmWQMgC8JGKP3cHBwerevbsuXryowMBADRw4UI0bN9bt27e1dOlSTZ8+XSVLllSdOnUUFRWlNWvWyGw2a9myZVxQA15iBCvADteuXVPLli3VoUMHNW/e3Lr8/PnzGj16tI4ePWodpPwos9lMf3oAcDIPh6oGDRooa9asqlKlio4fP67Nmzdr27Zt8vb2VmBgoPbs2aMpU6boypUr8vb2Vr58+fTtt9/K1dWV3grAS4xL5oAd4huk7ObmZh2k3L9/fx07dkx+fn5xbvZIqAIA5xMbqpo0aaLMmTNr2rRpSp8+vc6ePasjR47oxIkTMpvNyp07t2rWrKkaNWrowoULSps2rV555RUZDAZ6KwAvOb7hAU/BIGUASHlu3Lih/fv3a+XKldZla9as0YULF1S8eHGFh4fLYDBo//79unDhgr755ht17dpVn3/+uU6dOiWTyaTcuXMrc+bMMhgMTKkOpAB8woEnYJAyAKQ8hw4d0qhRoxQeHi5PT08VKlRIBQoUUMuWLXXp0iXNmzdPmTNnVurUqTVixAh1795dbdq00YYNGzRs2DDduXMnzj7prQC8/AhWwGM8bZByuXLl1LlzZ02fPl2XL1+2GaTs5eWl6tWrJ/VLAAA8o4MHD+rjjz9Wo0aNVKtWLb3++us2t8bw9/dXdHS0xo4dK0nq1KmTunbtKunBfQxfeeUVRUdHJ0ntAJIWk1cA8WCQMgCkPNevX1f79u1VsmRJ9e/fX+nSpZP0v0mHHj6mf/3115o5c6a6dOmiDz/8UBaLRZ07d1aqVKk0f/58WqiAFIgWKyAeDFIGgJQjdpKhU6dOKTIyUvXr17eGKul/3fhMJpP12N67d2+FhIRo2rRpCg0N1b59+xQeHq7FixfHCWEAUga+9QF6MEj50qVLunjxoho3bizpf4OUq1WrpvDwcHl4eNgMUj558qTKlCkjf39/vfba/7V370FVVf0fxz8ox9SQJLyljiIIRw1HpfBG8ChokpFo3qjEbBhHHGuc0mm8EEhZjGiGkqijIIi3JDRI08krhjqkNqmlDGNJSZN4OYp2UEnh94c/znSec7zgwQeT92vGP/ba373OWswAfthr7e0jT09PS39sUgaAf4/qhwwdPXpUZWVl8vX1vWPtP3+2x8TESJLS09PVpUsX5eTkyGAw8Ic1oJ7iux71HpuUAQCSLEu4q/dI/ffrMqTbfzi7efOmCgoKFBgYqJiYGHl7e2vkyJFydnYmVAH1GN/5qNfYpAwAqA5QPXr0kNlsVkZGhqZMmSInJye77yI0m82Kj49XbGysgoKCNHbsWEkiVAH1HN/9qLfOnj2r2NhYDRs2TNOmTbOsp6/+JVq9Pj4mJkZPPvmkli9frgYNGshkMqmqqkrz589Xu3bt1L9//zqeCQDAEdXBycfHR15eXsrOzla3bt00cOBAyzuoqlciVFZWKi8vT82bN5eXl5dVP4QqoH7jJwDqHTYpAwDscXd31yeffKLx48crOTlZlZWVCgkJsQpVJSUl2rhxozp27Mj7CgFY4XHrqLeSk5O1du1a7d27V40bN76va+bOnas1a9aoS5cuysrKYpMyADyG9u3bp6lTp8rFxUVhYWF6/fXXVVFRoePHj2v9+vW6du2aNm3aJGdnZ6u7WQDqN4IV6q1ly5YpNTVVe/bskYuLy31tUpakL774gk3KAPCYKywsVHx8vE6cOCEnJydVVFTIy8tLHTt2VFJSEr8DANggWKHeqQ5QBw8eVFRUlKZMmWJ5IIW9cHXp0iWNHj3askm5Gr9QAeDxduXKFf355586efKkDAaDfHx81LlzZ95VCMAufiKg3mGTMgDgfri6usrV1VVGo9GqnXcVArCHRcGot6o3KV+6dEnJycnatWuXJLFJGQBwV+ypAmAPSwFR77FJGQAAAI4iWAFikzIAAAAcQ7AC/h+blAEAAPCgCFbAPbD8DwAAAPdCsAIAAAAAB/FneAAAAABwEMEKAAAAABxEsAIAAAAABxGsAAAAAMBBBCsAAAAAcBDBCgAAAAAcRLACAAAAAAcRrAAAAADAQQQrAADuoKioSDt37rQcnzx5Unv37q27AQEAHlnOdT0AAMCjwWg03lfd6tWr1adPn4c8mkeD2WxWbGysWrRooebNm+vjjz9WUFCQBgwYUNdDe2jCwsLk5uamzMzMuh4KAPyrEKwAAJKkxMREq+OcnBzt37/fpt3Ly+t/Oaw61atXL/Xs2VNjx46VJHl4eGj06NF1PCoAwKOIYAUAkCSFh4dbHR89elT79++3aa9vUlJSdOrUKV2/fl0+Pj5q1KhRXQ8JAPAIYo8VAOCBVFRUaPHixRo8eLB8fX31n//8R4mJiaqoqLCqMxqNSk5OthzfvHlTEydOVO/evXXq1ClJ0rhx4zRs2DC7nzNkyBBFRUVJkkpKSmQ0GpWammpVExYWpsjISKu21NRUGY1GlZSUWNqSk5NtljyazWYFBATIaDSqoKDA0h4ZGWnps3PnzvL19VVhYaGMRuM9l01GRkZa6u70r1p2drbGjx+vfv36ydfXV0OHDtW6dets+gwODtakSZOUn5+v8PBwde/eXUOHDtW3335rVbdp0yabeVdWVuqVV16R0WjUpk2bbL5Offv2Vf/+/a3OZWRkqHfv3goKCtLmzZvvOl8AAHesAAAPoLKyUpMnT9aRI0c0ZswYeXl5qaioSBkZGSouLlZKSsodr42JidH333+vtLQ0de7cWdLtu2UxMTEqKiqSj4+PpfbYsWMqLi7W5MmTH9pcVq1apQsXLtxX7YIFC+6rLjo6WqNGjZIkXbp0SQkJCRo7dqyee+45m9r169fL29tbwcHBcnZ21p49exQfH6+qqiq98cYbVrXFxcV69913FRERoREjRig7O1tTp07VypUrFRAQcMfx5OTkqKioyKZ9y5YtSkxM1MCBAxUYGKi0tDSVlpbq/PnzMpvNmjp1qrZu3aqZM2eqQ4cOdscPALiNYAUAqLGvv/5aBw4cUGZmpp5//nlLu7e3t+Li4vTDDz/Iz8/P5rqFCxcqNzdXixcvtvpPemhoqD766CPl5uZq+vTplvbc3Fw1bdpUL7744kOZh8lkUlpamoKCgrRv37671ubl5amgoECBgYH67rvv7lr7z5BTUlKihIQE9ezZ0+6yyjVr1qhx48aW43HjxikqKkqrVq2yG6ySk5MtX49Ro0YpNDRUCxYsuGOwqr6zaG+OK1askJ+fn5YuXSonJycNGjRIL7/8sgwGg9LT0+Xq6qrRo0crNDRUqampBCsAuAuWAgIAamz79u3y8vKSp6enTCaT5V/fvn0lyWpJXbU1a9Zo+fLlmj17tgYNGmR1rlmzZgoJCdHWrVtVVVUlSbp165a2bdumkJAQNW3a9KHMIyUlRc2aNbNZRvjfqqqqtHDhQg0ZMkQ9evSo1TH8M1RdvXpVJpNJvXv31pkzZ3T16lWr2latWmnw4MGWYxcXFw0fPlwnTpzQ+fPn7fa/du1aXb58WW+//bZVu8lkUmFhoQYNGiQnJydJUuvWrdWmTRt16tRJrq6ukqRGjRppwIABOnjwYK3MFwAeV9yxAgDU2G+//aZffvlF/fr1s3v+4sWLVsf79u3TTz/9JEkqKyuze83w4cP1zTff6PDhw/L399eBAwd04cKFh/bwjDNnzmjDhg2aM2eOnnjiibvW5ubm6tSpU0pKStKWLVtqdRxHjhxRcnKyfvzxR127ds3q3NWrV9WsWTPLcceOHS0hqJqHh4ck6Y8//lDLli1trl+2bJkmTJggd3d3q3Nnz56VdDtM3Uvr1q1VXl6uK1euWAIXAMAawQoAUGOVlZXy8fHRzJkz7Z5v06aN1fGxY8c0ZswYNWnSREuXLlVoaKg8PT2tal544QW1aNFCubm58vf3V25urlq2bKn+/fs/lDkkJSXJw8NDI0aM0OHDh+9YV1FRoUWLFmnkyJHq1KlTrY7h999/14QJE+Tp6akZM2bomWeekcFgUF5entLT01VZWelQ/ytWrFCDBg0UFRWly5cvW527ceNGjfu7fv06wQoA7oBgBQCosQ4dOqiwsFD9+vWzuYNiT0BAgObMmaMbN25o586dio2NVWZmptW1DRs2VFhYmDZv3qzp06dr586dGjNmjBo2bFjr4z9x4oS2bt2qJUuW3LP/devWyWQy6Z133qn1cezevVsVFRVaunSp2rZta2m3t5RSun2nsKqqyurrVlxcLElq166dVe25c+e0evVqvffee3JxcbEJVtV3t86dO3fPcZaWlspgMMjNze1+pgUA9RJ7rAAANfbSSy+ptLRUGzdutDl3/fp1lZeXW7X16tVLDRs2VNOmTRUfH69Dhw7ZvTY8PFxlZWWKjY1VeXn5HR/B7qhPP/1Ufn5+CgkJuWud2WzWsmXL9Oabb9oss6sN1aGuel+ZdHv5XnZ2tt36c+fOaceOHZbjv/76S1999ZW6du1qM74lS5bI3d1dERERdvtq37692rZtq127dlk+v7S0VGfPnlVxcbGuXLki6fYdu7y8PPXs2VMGg+HBJwsAjznuWAEAaiw8PFzbtm1TXFycCgoK5Ofnp1u3bunXX3/V9u3btXLlSnXv3t3utYGBgRo2bJjmz5+vgQMHqlWrVpZz3bp1k4+Pj+XhGM8++6zdPk6fPm31hLvy8nI5OTlZtZ0+fVqSdOjQIbm4uKh58+aWc/n5+Vq/fv095/nzzz/Lzc1NEydOvGftgwgICJDBYFB0dLQiIiJkNpuVlZUld3d3uw+j8PDw0OzZs3X8+HG5u7srOztbFy9eVEJCgk1tfn6+FixYcNcXGk+aNElxcXGaPHmyAgMDtWHDBjk5Oenvv//WW2+9pVdffVXbtm1TSUmJ4uLianXuAPC4IVgBAGqsQYMGWrJkidLT05WTk6MdO3aoSZMmat++vSIjI++5F2nWrFnKz8/Xhx9+qM8//9zqXHh4uObPn3/Xh1ZkZWUpKyvLpt1eAJoxY4ZWr16tPn36WNpCQkLsPg7enujoaLm4uNxXbU15enpq8eLFSkpK0rx589SiRQu99tprevrppzVr1iybeg8PD33wwQdKTEzU6dOn1b59e3322WcKDAy0qe3atavCwsLu+vkREREqKytTWlqajh07pvfff18rV66Um5ubBg8erEWLFqlRo0aaO3eugoKCam3eAPA4cqr65/oDAADqWEZGhhISErR7926rfUcPKjg4WAkJCVbB6t8oODhY3t7eWr58+UP9nLCwMLm5uSkzM/Ohfg4APG7YYwUAeGRUVVXpyy+/lL+/f62EKgAA/ldYCggAqHPl5eXavXu3CgoKVFRUpJSUlFrr29/fX0899VSt9QcAgD0EKwBAnTOZTJo2bZpcXV0VHR19z6f11cS8efNqrS8AAO6EPVYAAAAA4CD2WAEAAACAgwhWAAAAAOAgghUAAAAAOIhgBQAAAAAOIlgBAAAAgIMIVgAAAADgIIIVAAAAADiIYAUAAAAADvo/u9QIae4IAIwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Визуализация разницы в расходах\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=user_info, x='Тариф', y='Разница в расходах')\n",
    "plt.title('Разница в расходах между текущими и оптимальными тарифами')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Разница в расходах')\n",
    "plt.xlabel('Текущий тариф')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788a5893",
   "metadata": {},
   "source": [
    "#### Кластеризация пользователей: ####  \n",
    "Применение методов машинного обучения (K-means) для группировки пользователей по схожим паттернам использования услуг. Это помогает выделить типичные профили поведения для составления локального нормативного документа для описания принципов предоставления сотрудникам услуг мобильной связи."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "64bcaddc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/md/.local/lib/python3.10/site-packages/sklearn/base.py:1151: ConvergenceWarning: Number of distinct clusters (7) found smaller than n_clusters (8). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/home/md/.local/lib/python3.10/site-packages/sklearn/base.py:1151: ConvergenceWarning: Number of distinct clusters (7) found smaller than n_clusters (9). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n",
      "/home/md/.local/lib/python3.10/site-packages/sklearn/base.py:1151: ConvergenceWarning: Number of distinct clusters (7) found smaller than n_clusters (10). Possibly due to duplicate points in X.\n",
      "  return fit_method(estimator, *args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Выбираем данные для кластеризации\n",
    "X = user_info[['Исходящие, мин (сред. в месяц)', 'Трафик, ГБ (сред. в месяц)', 'Суммарные начисления']].fillna(0)\n",
    "\n",
    "# Стандартизация данных\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "inertia = []\n",
    "for n_clusters in range(1, 11):\n",
    "    # Явное указание n_init=10 для предотвращения предупреждений\n",
    "    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)\n",
    "    kmeans.fit(X_scaled)\n",
    "    inertia.append(kmeans.inertia_)\n",
    "\n",
    "plt.plot(range(1, 11), inertia, marker='o')\n",
    "plt.title('Метод локтя для определения количества кластеров')\n",
    "plt.xlabel('Количество кластеров')\n",
    "plt.ylabel('Сумма квадратов расстояний')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "ecf07d65",
   "metadata": {},
   "source": [
    "График метода локтя показывает изменение суммы квадратов расстояний между точками и центрами их кластеров при увеличении числа кластеров. Чтобы выбрать оптимальное количество кластеров, необходимо найти точку, в которой уменьшение суммы квадратов расстояний начинает замедляться, и эта точка больше не приносит значительного улучшения. Это часто выглядит как \"локоть\" на графике.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "На представленном графике мы видим, что сумма квадратов расстояний резко уменьшается, когда количество кластеров увеличивается с 1 до 2, и продолжает уменьшаться уже менее интенсивно при увеличении количества кластеров до 3. После этого изменения становятся ещё менее значительными. Таким образом, \"локоть\", или точка изгиба, находится где-то между 2 и 3 кластерами.\n",
    "\n",
    "На основании этого графика можно предложить, что оптимальное количество кластеров для данного набора данных лежит в диапазоне от 2 до 3. Выбор между этими значениями может зависеть от дополнительных соображений, таких как интерпретируемость кластеров и специфика задачи. Если 3 кластера предоставляют более полезное и информативное разделение данных, и эти кластеры могут быть четко интерпретированы и использованы в бизнес-процессах, тогда предпочтение следует отдать трём кластерам. Если же 2 кластера уже достаточно для выполнения аналитических задач, то можно остановиться на этом количестве, чтобы избежать излишней сложности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "d00297e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Применяем K-means с выбранным количеством кластеров\n",
    "optimal_clusters = 4  # Примерное оптимальное количество кластеров\n",
    "kmeans = KMeans(n_clusters=optimal_clusters, n_init=10, random_state=42)  # Явно указываем n_init=10\n",
    "user_info['Кластер'] = kmeans.fit_predict(X_scaled)\n",
    "\n",
    "# Визуализация результатов кластеризации\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(data=user_info, x='Исходящие, мин (сред. в месяц)', y='Трафик, ГБ (сред. в месяц)', hue='Кластер', palette='viridis')\n",
    "plt.title('Результаты кластеризации пользователей')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "471e103f",
   "metadata": {},
   "source": [
    "#### Анализ кластеров: ####   \n",
    "Оценка средних значений ключевых показателей (использование минут, трафика, расходы) в каждом кластере, определение размеров кластеров и наиболее подходящих тарифов для них. Этот анализ предоставляет глубокое понимание разнообразия потребностей пользователей и может служить основой для индивидуализированных предложений."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "46eb5db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Кластер</th>\n",
       "      <th>Исходящие, мин (сред. в месяц)</th>\n",
       "      <th>Трафик, ГБ (сред. в месяц)</th>\n",
       "      <th>Суммарные начисления</th>\n",
       "      <th>Количество абонентов</th>\n",
       "      <th>Оптимальный тариф для кластера</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Новатор</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>229.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3</td>\n",
       "      <td>Новатор</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>Новатор</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1</td>\n",
       "      <td>Новатор</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Кластер  Исходящие, мин (сред. в месяц)  Трафик, ГБ (сред. в месяц)  \\\n",
       "0        0                        0.000000                         0.0   \n",
       "1        1                      229.333333                         0.0   \n",
       "2        2                      170.000000                         1.0   \n",
       "3        3                      101.000000                         0.0   \n",
       "\n",
       "   Суммарные начисления  Количество абонентов Оптимальный тариф для кластера  \n",
       "0                   NaN                     4                        Новатор  \n",
       "1                  0.00                     3                        Новатор  \n",
       "2                  0.00                     2                        Новатор  \n",
       "3                  0.06                     1                        Новатор  "
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Анализ средних значений признаков по кластерам\n",
    "cluster_means = user_info.groupby('Кластер')[['Исходящие, мин (сред. в месяц)', 'Трафик, ГБ (сред. в месяц)', 'Суммарные начисления']].mean()\n",
    "\n",
    "# Добавление информации о количестве абонентов в каждом кластере\n",
    "cluster_sizes = user_info.groupby('Кластер').size().reset_index(name='Количество абонентов')\n",
    "\n",
    "# Определение наиболее часто встречающегося оптимального тарифа в каждом кластере\n",
    "cluster_optimal_tariffs = user_info.groupby('Кластер')['Оптимальный тариф'].agg(lambda x: x.mode()[0]).reset_index(name='Оптимальный тариф для кластера')\n",
    "\n",
    "# Соединение данных о средних значениях и количестве абонентов\n",
    "cluster_analysis = cluster_means.merge(cluster_sizes, on='Кластер')\n",
    "\n",
    "# Добавление информации об оптимальном тарифе\n",
    "cluster_analysis = cluster_analysis.merge(cluster_optimal_tariffs, on='Кластер')\n",
    "\n",
    "# Вывод результата\n",
    "cluster_analysis"
   ]
  }
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