VladimirovKD_HW3.ipynb

{
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   "cell_type": "markdown",
   "id": "541a45e6-bbf9-4ff6-9e07-2e2f1f55694a",
   "metadata": {},
   "source": [
    "# Домашнее задание 3. Pandas (HW)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f0356c-ebe1-41f1-a850-584bf42df063",
   "metadata": {},
   "source": [
    "## Описание задачи\n",
    "В приложенных файлах вы увидите данные по прохождению студентами тестов на образовательной платформе. Такие данные могут быть использованы для построения персонализированных ИИ-ассистентов для поддержки обучения студента. На текущем этапе сосредоточимся на начальном этапе — сборе и подготовке данных."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de18f69-496e-4d9d-a7a0-a1efd8561c31",
   "metadata": {},
   "source": [
    "## Цель"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "118e577d-cad4-467f-8dc3-221229ed4838",
   "metadata": {},
   "source": [
    "Используя библиотеку pandas (или аналоги), проанализировать как можно больше характеристик, влияющих на успеваемость студентов.\n",
    "\n",
    "- Проанализированы все возможные признаки\n",
    "- Код хорошо читается и задокументирован, соответствует pep-8\n",
    "- Код работоспособный"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555bb15e-f71f-4d55-8cc9-8f6978d810df",
   "metadata": {},
   "source": [
    "## Описание данных\n",
    "\n",
    "### train.csv\n",
    "- row_id: (int64) Идентификатор строки.\n",
    "- timestamp: (int64) время в миллисекундах между взаимодействием пользователя и первым завершением события от этого пользователя.\n",
    "- user_id: (int32) Идентификатор польователя.\n",
    "- content_id: (int16) Идентификатор пользовательского взаимодействия\n",
    "- content_type_id: (int8) 0, если событием был вопрос, заданный пользователю, 1, если событием был просмотр пользователем лекции.\n",
    "- task_container_id: (int16) Код идентификатора для пакета вопросов или лекций. Например, пользователь может увидеть три вопроса подряд, прежде чем увидеть объяснения для любого из них. Все эти три вопроса будут иметь общий task_container_id.\n",
    "- user_answer: (int8) ответ пользователя на вопрос, если таковой имеется. Читать -1 как NULL, для лекций.\n",
    "- answered_correctly: (int8) если пользователь ответил правильно. Читайте -1 как NULL, для лекций.\n",
    "- prior_question_elapsed_time: (float32) Среднее время в миллисекундах, которое потребовалось пользователю для ответа на каждый вопрос в предыдущем пакете вопросов, игнорируя любые лекции между ними. Имеет значение null для первого пакета вопросов или лекции пользователя. Обратите внимание, что время — это среднее время, которое потребовалось пользователю для решения каждого вопроса в предыдущем пакете.\n",
    "- prior_question_had_explanation: (bool) Увидел ли пользователь объяснение и правильный ответ(ы) после ответа на предыдущий пакет вопросов, игнорируя любые лекции между ними. Значение является общим для одного пакета вопросов и равно NULL для первого пакета вопросов или лекции пользователя. Обычно первые несколько вопросов, которые видит пользователь, были частью диагностического теста на вступление, где он не получал никакой обратной связи.\n",
    "\n",
    "\n",
    "### questions.csv: метаданные вопросов, заданных пользователям.\n",
    "- question_id: внешний ключ для обучающего/тестового столбца content_id, когда тип контента — вопрос (0).\n",
    "- bundle_id: код, по которому вопросы подаются вместе.\n",
    "- correct_answer: ответ на вопрос. Можно сравнить с train user_answer столбцом, чтобы проверить, был ли пользователь прав.\n",
    "- part: соответствующий раздел теста TOEIC.\n",
    "- tags: один или несколько подробных кодов тегов для вопроса. Значение тегов не будет предоставлено, но этих кодов достаточно для кластеризации вопросов.\n",
    "\n",
    "\n",
    "### lectures.csv: метаданные лекций, просмотренных пользователями по мере продвижения своего образования.\n",
    "- lecture_id: внешний ключ для обучающего/тестового столбца content_id, когда типом контента является лекция (1).\n",
    "- part: код категории верхнего уровня для лекции.\n",
    "- tag: один тег коды для лекции. Значение тегов не будет предоставлено, но эти коды достаточны для кластеризации лекций вместе.\n",
    "- type_of: краткое описание основной цели лекции."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1e2d56c-df81-4c31-9c22-45407eba49be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb62967a-ee06-424f-bcc1-18a464bb2717",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c9085bdc-e251-4687-8227-4b9074413017",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79aed4ad-3c8e-4156-b0a1-bb154827e2c4",
   "metadata": {},
   "source": [
    "## Library import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e00c126d-037d-4d58-afb7-2ed5558b8ed8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from omegaconf import OmegaConf\n",
    "from collections import Counter\n",
    "\n",
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Options for pandas\n",
    "pd.options.display.max_columns = 50\n",
    "pd.options.display.max_rows = 30\n",
    "\n",
    "# Visualizations\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib.style as style\n",
    "style.use('fivethirtyeight')\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "colorMap = sns.light_palette(\"blue\", as_cmap=True)\n",
    "\n",
    "# Autoreload extension\n",
    "if 'autoreload' not in get_ipython().extension_manager.loaded:\n",
    "    %load_ext autoreload\n",
    "    \n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "532234d9-7fb5-4ac5-8cf4-189e69fdc5a2",
   "metadata": {},
   "source": [
    "## Parameter definition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb084423-80dd-482f-af30-5067666d6561",
   "metadata": {},
   "source": [
    "Здесь приведены все параметры для блокнота"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "07c863b0-5f88-4673-bce1-21052303e0b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "cfg = OmegaConf.create({\n",
    "    \"train_path\" : \"./data/train.csv\", \n",
    "    \"lectures_path\" : \"./data/lectures.csv\", \n",
    "    \"questions_path\" : \"./data/questions.csv\", \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87031cef-4b9b-4937-9e4b-3dce31a60ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_path': './data/train.csv', 'lectures_path': './data/lectures.csv', 'questions_path': './data/questions.csv'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cfg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c048fcd8-c2b1-4bf0-b8e6-10b5844f24a3",
   "metadata": {},
   "source": [
    "# Data import"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c43298-d846-4355-9c1e-152222591d9c",
   "metadata": {},
   "source": [
    "### Загрузка тренировочных данных"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e4cb52b-4e07-46ba-a4d2-1512b282e451",
   "metadata": {},
   "source": [
    "Тренировочные данные имеют большой размер. Можно попробывать 2 способа их загрузки"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498f1d15-cb2e-4ce6-af90-eca3514ba2db",
   "metadata": {},
   "source": [
    "1. Загрузить без параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e172c2f3-531f-4236-a609-0260b8b09555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1min 33s\n",
      "Wall time: 1min 43s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_df = pd.read_csv(cfg.train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b65e151-591d-4c5b-bd08-fde516e8b6d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101230332 entries, 0 to 101230331\n",
      "Data columns (total 10 columns):\n",
      " #   Column                          Dtype  \n",
      "---  ------                          -----  \n",
      " 0   row_id                          int64  \n",
      " 1   timestamp                       int64  \n",
      " 2   user_id                         int64  \n",
      " 3   content_id                      int64  \n",
      " 4   content_type_id                 int64  \n",
      " 5   task_container_id               int64  \n",
      " 6   user_answer                     int64  \n",
      " 7   answered_correctly              int64  \n",
      " 8   prior_question_elapsed_time     float64\n",
      " 9   prior_question_had_explanation  object \n",
      "dtypes: float64(1), int64(8), object(1)\n",
      "memory usage: 7.5+ GB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a57c3c2-7230-4173-88d2-6cde3d36e6ac",
   "metadata": {},
   "source": [
    "2. Загрузка с перечесление типов данных для котлонок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5abdda11-cf34-47fe-86f9-cff1e61c53a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dtypes = {\n",
    "    \"row_id\": \"int64\",\n",
    "    \"timestamp\": \"int64\",\n",
    "    \"user_id\": \"int32\",\n",
    "    \"content_id\": \"int16\",\n",
    "    \"content_type_id\": \"boolean\",\n",
    "    \"task_container_id\": \"int16\",\n",
    "    \"user_answer\": \"int8\",\n",
    "    \"answered_correctly\": \"int8\",\n",
    "    \"prior_question_elapsed_time\": \"float32\", \n",
    "    \"prior_question_had_explanation\": \"boolean\"\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1123d18d-fb03-4068-8a9d-d16c31404d4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 3min 9s\n",
      "Wall time: 4min 9s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "train_df = pd.read_csv(cfg.train_path, dtype=dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f038b08d-359d-4e01-9c40-9c07e2bdca1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101230332 entries, 0 to 101230331\n",
      "Data columns (total 10 columns):\n",
      " #   Column                          Dtype  \n",
      "---  ------                          -----  \n",
      " 0   row_id                          int64  \n",
      " 1   timestamp                       int64  \n",
      " 2   user_id                         int32  \n",
      " 3   content_id                      int16  \n",
      " 4   content_type_id                 boolean\n",
      " 5   task_container_id               int16  \n",
      " 6   user_answer                     int8   \n",
      " 7   answered_correctly              int8   \n",
      " 8   prior_question_elapsed_time     float32\n",
      " 9   prior_question_had_explanation  boolean\n",
      "dtypes: boolean(2), float32(1), int16(2), int32(1), int64(2), int8(2)\n",
      "memory usage: 3.2 GB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6c1b01-d169-4386-bf03-169a8ed8a236",
   "metadata": {},
   "source": [
    "Вывод: во втором случае сократилось использование памяти в 2 раза."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a18752c8-196a-425b-94e4-41e97a03c785",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6aeb9e8-ee8b-430f-bf6b-0cad68283089",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>user_id</th>\n",
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       "      <th>content_type_id</th>\n",
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       "      <th>user_answer</th>\n",
       "      <th>answered_correctly</th>\n",
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       "      <th>prior_question_had_explanation</th>\n",
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       "   row_id  timestamp  user_id  content_id  content_type_id  task_container_id  \\\n",
       "0       0          0      115        5692            False                  1   \n",
       "1       1      56943      115        5716            False                  2   \n",
       "2       2     118363      115         128            False                  0   \n",
       "3       3     131167      115        7860            False                  3   \n",
       "4       4     137965      115        7922            False                  4   \n",
       "\n",
       "   user_answer  answered_correctly  prior_question_elapsed_time  \\\n",
       "0            3                   1                          NaN   \n",
       "1            2                   1                      37000.0   \n",
       "2            0                   1                      55000.0   \n",
       "3            0                   1                      19000.0   \n",
       "4            1                   1                      11000.0   \n",
       "\n",
       "   prior_question_had_explanation  \n",
       "0                            <NA>  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                           False  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "894017d7-af9c-4e76-949f-c4020dbd084c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101230332, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9605713d-ee4a-4154-ae87-18ddc7523ce2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f045889-b3b5-4eab-b54c-0ff6aaba56a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3171357a-a9a4-46b6-a291-1ae8f2ffb2d7",
   "metadata": {},
   "source": [
    "### Загрузка метаданных лекций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ecd23921-3eae-4187-803d-810f157ad104",
   "metadata": {},
   "outputs": [],
   "source": [
    "lectures_df = pd.read_csv(cfg.lectures_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "75169cdc-59eb-43b3-878b-7dce71682b54",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>lecture_id</th>\n",
       "      <th>tag</th>\n",
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       "      <th>type_of</th>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>192</td>\n",
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       "      <td>solving question</td>\n",
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       "      <th>4</th>\n",
       "      <td>317</td>\n",
       "      <td>156</td>\n",
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       "   lecture_id  tag  part           type_of\n",
       "0          89  159     5           concept\n",
       "1         100   70     1           concept\n",
       "2         185   45     6           concept\n",
       "3         192   79     5  solving question\n",
       "4         317  156     5  solving question"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ee4bda8f-cd58-4e29-a3a0-723863569eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 4)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0a8ecca-8f24-4f26-b6bf-6d02af631af0",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9208003-72d5-4940-97fc-53cb22853125",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "27a418bd-d6c6-4635-afd2-2129abe0431c",
   "metadata": {},
   "source": [
    "### Загрузка метаданных вопросов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4d58ffdb-a0b2-44da-9dbf-19e8b2ba7fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_df = pd.read_csv(cfg.questions_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d4f3ca52-1834-451b-aa6f-a9b9f1b1928a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question_id</th>\n",
       "      <th>bundle_id</th>\n",
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       "      <td>131 36 81</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>131 149 162 29</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>131 5 162 38</td>\n",
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       "   question_id  bundle_id  correct_answer  part            tags\n",
       "0            0          0               0     1   51 131 162 38\n",
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       "2            2          2               0     1  131 101 162 92\n",
       "3            3          3               0     1  131 149 162 29\n",
       "4            4          4               3     1    131 5 162 38"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5da08451-0959-4800-8529-3d0cfd8111c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13523, 5)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1713a6d0-69c0-4891-814f-fb3a26a2d5a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95b9fe6c-b5a5-4ba8-9fde-2a1cfdb2eb43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9d463485-8ee4-4bfd-b2d6-bab863b6c841",
   "metadata": {},
   "source": [
    "# Data processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29402a5-f447-419c-8a0d-881e138f6619",
   "metadata": {},
   "source": [
    "## EDA Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6ccc06dc-3466-4bd0-8d67-a3b027a7d7d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>user_id</th>\n",
       "      <th>content_id</th>\n",
       "      <th>content_type_id</th>\n",
       "      <th>task_container_id</th>\n",
       "      <th>user_answer</th>\n",
       "      <th>answered_correctly</th>\n",
       "      <th>prior_question_elapsed_time</th>\n",
       "      <th>prior_question_had_explanation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>37000.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>118363</td>\n",
       "      <td>115</td>\n",
       "      <td>128</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>131167</td>\n",
       "      <td>115</td>\n",
       "      <td>7860</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>False</td>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>137965</td>\n",
       "      <td>115</td>\n",
       "      <td>7922</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id  timestamp  user_id  content_id  content_type_id  task_container_id  \\\n",
       "0       0          0      115        5692            False                  1   \n",
       "1       1      56943      115        5716            False                  2   \n",
       "2       2     118363      115         128            False                  0   \n",
       "3       3     131167      115        7860            False                  3   \n",
       "4       4     137965      115        7922            False                  4   \n",
       "\n",
       "   user_answer  answered_correctly  prior_question_elapsed_time  \\\n",
       "0            3                   1                          NaN   \n",
       "1            2                   1                      37000.0   \n",
       "2            0                   1                      55000.0   \n",
       "3            0                   1                      19000.0   \n",
       "4            1                   1                      11000.0   \n",
       "\n",
       "   prior_question_had_explanation  \n",
       "0                            <NA>  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                           False  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab7543e3-bd32-434d-b422-49981b267d90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101230332, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e14cdf31-e3b5-4cee-9055-9dd4b3d20ffc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cbe7d7ca-464c-4e76-a9f1-468ae06e561d",
   "metadata": {},
   "source": [
    "### Поиск дубликатов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4b70def8-4561-47dc-902d-f9a7db94e92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df) - len(train_df.drop_duplicates())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71cad8e7-5514-4bc2-b1a3-bcff966991c0",
   "metadata": {},
   "source": [
    "Вывод:\n",
    "- Дубликатов нет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7836c2bb-3f2c-42bd-be98-97c19e9d94f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "917eae15-569b-48b7-8be9-74999fa89ca3",
   "metadata": {},
   "source": [
    "### Анализ пропусков в данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27eec46f-c1f4-48d5-99f5-5f8e0cf53c58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "row_id                            101230332\n",
       "timestamp                         101230332\n",
       "user_id                           101230332\n",
       "content_id                        101230332\n",
       "content_type_id                   101230332\n",
       "task_container_id                 101230332\n",
       "user_answer                       101230332\n",
       "answered_correctly                101230332\n",
       "prior_question_elapsed_time        98878794\n",
       "prior_question_had_explanation    100837826\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfbea2e-06c2-4a0b-b205-b92df54c038c",
   "metadata": {},
   "source": [
    "Вывод:\n",
    " - NULL содержат только колонки prior_question_elapsed_time и prior_question_had_explanation, как и было написано в их описании\n",
    " - prior_question_elapsed_time - Имеет значение null для первого пакета вопросов или лекции пользователя.\n",
    " - prior_question_had_explanation - Значение является общим для одного пакета вопросов и равно NULL для первого пакета вопросов или лекции пользователя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63b27da6-4f88-4b4c-ba7f-6f8504e2a23e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7c1c6694-1e92-4b4e-b371-9af0377834dd",
   "metadata": {},
   "source": [
    "### Анализ целевой переменной"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0ae1a81-4efd-4d4c-b3fb-6d1d53d5982c",
   "metadata": {},
   "source": [
    "Так как задача заключается в анализе как можно большего количества характеристик, влияющих на успеваемость студентов,\n",
    "то целевым параметром является answered_correctly - правильность ответа студента на вопрос"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0c8ca914-eff9-4001-9d32-cd2882d5c91f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "answered_correctly\n",
       " 1    0.644517\n",
       " 0    0.336131\n",
       "-1    0.019352\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"answered_correctly\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9d3eb1-93a2-426b-9d80-31b35f742866",
   "metadata": {},
   "source": [
    "Значение -1 не относится к прохождению тестов, его нужно исключить"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b9ed4406-0c05-459b-afa1-8990acc9d1eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c7351c1-6876-4e18-acc3-93be3da40bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "answered_correctly\n",
       "1    0.657236\n",
       "0    0.342764\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"answered_correctly\"].loc[answered_question_mask].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7434c136-60e9-4896-9e90-0c3dd827a8f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "answered_correctly\n",
       "1    65244627\n",
       "0    34026673\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answered_questions = train_df[\"answered_correctly\"].loc[answered_question_mask].value_counts()\n",
    "answered_questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "55cdc4f2-b5ca-4f17-843b-ba5063920ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "answered_questions = train_df[\"answered_correctly\"].loc[answered_question_mask].value_counts()\n",
    "# sns.countplot( x='answered_correctly', data=answered_questions)\n",
    "plt.figure(figsize=(5, 5))\n",
    "sns.barplot(x=answered_questions.index.astype('int'), y=answered_questions.values, order=answered_questions.index.astype('int'))\n",
    "plt.title('Соотношение правильных и неправильных ответов на вопросы')\n",
    "plt.xlabel('1 - правильный, 0 - неправильный')\n",
    "plt.ylabel('количество');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8103d2-19d9-49a0-9f4e-ad966cc790e2",
   "metadata": {},
   "source": [
    "Выводы:\n",
    " - около 33% ответов были даны неправильно"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a152096e-75c6-45d9-aed0-905f44d5b288",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b7fae41-3012-4dd5-b7ca-1dbae0b5e456",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9b19d11f-6aaf-455f-a0a1-09ce0af76597",
   "metadata": {},
   "source": [
    "### Анализ переменной user_answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "81374242-c00c-4ef4-abc9-089297523f47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_answer\n",
       " 0    28186489\n",
       " 1    26990007\n",
       " 3    26084784\n",
       " 2    18010020\n",
       "-1     1959032\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"user_answer\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12af8efb-8ce8-4abf-9249-ab668399e664",
   "metadata": {},
   "outputs": [],
   "source": [
    "Читать -1 как NULL, для лекций."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "40ffb4f0-16bd-4b0a-baf4-5f20c6328dfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='user_answer', ylabel='count'>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='user_answer', hue='answered_correctly',data=train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0bc94e61-8247-4ed1-afa6-737ae7845305",
   "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>user_answer</th>\n",
       "      <th>answered_correctly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>user_answer</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answered_correctly</th>\n",
       "      <td>0.004881</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    user_answer  answered_correctly\n",
       "user_answer            1.000000            0.004881\n",
       "answered_correctly     0.004881            1.000000"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)\n",
    "user_answer_mask = (train_df[\"user_answer\"] != -1)\n",
    "train_df[['user_answer', 'answered_correctly']].loc[answered_question_mask & user_answer_mask].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "f838e1db-fef2-4c67-975f-c47c05e20bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)\n",
    "user_answer_mask = (train_df[\"user_answer\"] != -1)\n",
    "plt.matshow(train_df[['user_answer', 'answered_correctly']].loc[answered_question_mask & user_answer_mask].corr())\n",
    "plt.title('Матрица корреляции между ответами и результатом')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8908579-d9f5-4bbb-90a8-0ada4d39f147",
   "metadata": {},
   "source": [
    "Вывод:\n",
    "- Колонка user_answer, скорее всего, отвечает на вопрос - какой из представленных вариантов ответов выбрал студет. \n",
    "- Похоже, что вопросы имеют множественный выбор - ответы 0-3.\n",
    "- Значение -1 означает отсутствие ответа при просмотре лекции, должно быть исключено.\n",
    "- Гораздо больше контента, связанного с вопросами, чем с лекциями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cab9ca2-7cc0-449b-9c6a-772b34fdad20",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cf8bdf45-4108-494f-bac7-a7179cd4ecde",
   "metadata": {},
   "source": [
    "### Анализ переменной content_type_id"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd6a55b-08b4-4389-8949-c45deb42f658",
   "metadata": {},
   "source": [
    "Нужно привести тип данных к int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f6b138ab-eb37-46bb-848e-7c6d8536e969",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"content_type_id\"] = train_df[\"content_type_id\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ba5187e6-4e19-4001-98d3-bf55fe58df7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='content_type_id', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='content_type_id', hue='answered_correctly', data=train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaae1e9b-9c90-4757-8da6-47d1b22e2cfd",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "- content_type_id = 0, для вопроса, 1 для лекции\n",
    "- обозначает тип образовательного контента, не влияет на целевую переменную"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1b9fe92-966e-4e9a-b670-b2825386ad9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a598e21f-be29-4875-a395-783a3d974159",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f89ece10-414a-497e-9e2e-f3b83f4f55fa",
   "metadata": {},
   "source": [
    "### Анализ переменной timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10894752-5f5e-4824-b7e6-d2f4f6d7de64",
   "metadata": {},
   "outputs": [],
   "source": [
    "ms_per_year = 31_556_952_000\n",
    "ms_per_month = ms_per_year/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23146bfc-7cb3-46dd-9fa1-6c67313e69d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4eHhku71cLsebN2/w+PFjhISEICQkBA8fPhR+/N69e1fU+XWhTCydnZ2z7V/XoEEDABm30sPCwjRuo0+dOH78ONLT02FqaprtnQVlWVJTU3VuzUtKShJaj7t06YKvv/5aVPkKMt4O/kytX79e6Jg/adIkra15n1LeBgYybo3kNWV/slKlSqm1Vn2qbt26ePToEcLCwhAXF6fxOY7Vq1fP8hZvmTJl4OTkhIiIiCxHzGYWExMjfEH3799fbaJfTZo1a4br168jMTERnTt3xrRp01CjRg2VPoH6yJxc5uR9M2TcjYyMsv1SqFu3Lk6ePInk5GQ8efJEY8tZYmIiQkJChL+NjY1haWkJJycnnetzXnj+/DnWrl0LABg/fnyOb+vnpF7lFUN/LjOP0NRncNCHDx9U6oiJiQmsra11ns/t9u3baNSokcZ1ffr0Ee4UKCnjACDbH1p169YVHuUZEhKi8Q5AWlqaSvmTk5Px+PFjrF+/Hnfv3oWZmZnWfqYKhQJ79uzBtm3bcO3aNXz8+FFrWZS3rw1JmUCFhYWJGvzy9u1bjX3x9KkTyrKkpqbCwcFBVFl0sXLlSoSFhaFYsWKYPn26qLIVdEwCP0OxsbFYsWIFAKBq1aro2LGjzvtm7oujrXk/N0VHRwPQrX9c5vJFR0dr/LIpWbJktscpWbIkIiIidL5Qzp8/H1FRUbCzs8PkyZN12ufHH39EUFAQLl26hGvXrol6T3SRefLvnLxvhoi78kvXysoq24v4p8fQ5MaNG8Kv9szMzc3RoEEDDBs2LEeDeQxtypQpSElJQYUKFbT2/dJFTupVXjH05zLze65PX9ijR49qvNVpaWmJJk2a4McffxTdKl6kSBFMnDgRQ4cOVbsTIqbcutTxN2/eaKzjQEZ/upkzZ6JGjRpq65KSktCnTx+cOHEiyzIoZZUg5lROHzig7RasPnXC0GXJLDw8XOjmMHr06Gx/BBU2TAI/Q6tXrxY+UJMnTy6UE1waqsyGfu2PHj0S+jpNmjRJ55Y8KysrHDlyBFu3bsW2bdsKbKdifeKlnEojt+tbUlISAgMDERgYiMmTJwt9fPLDmTNnhCRk9uzZOe4+kdN6ldcM9d5mboHR5YeaWPHx8fj7779x9OhRrFq1SusE+ZUqVRL6eUVFReHRo0fYuHEjZsyYgZ07d2Lnzp1a+5fldj0/c+YMxo4diw0bNqgM6gAyuh8oE0AvLy8MGjQINWrUgIODA4oWLSpMz+Pr64uLFy9mO+dhTij7KlaqVEnUKH9tfaL1qRPKslhbW+PYsWM676dLn/bp06cjISEBZcuWVWsZ/hwwCfzMREdHC7em6tSpI3rkZfHixYX/j4yMzPEktTml/PJ79+5dttsqB1tk3u9TujT3K8+V+bVrM3nyZKSlpcHDw0P03ILGxsYYMGAAXr58KYxE9vf3R5kyZQDoN0WMvb298P+Z46IrQ8RdWVdiY2ORlJSUZWtg5mNoi7uXlxeOHDki/J2amirMfzZr1izEx8dj/vz5+Pbbb7V+UeemTwdxtGrVKsfH0qde5QVDfy6VUxSZm5vrNbisZ8+ewvUOyPiBEB4ejn379mHhwoVIS0vD+PHj0bp1a43XMnNzc5VRqA0bNkTfvn3RuXNnnD17FoMGDcKpU6eEhC/z63n37h2sra21lk2XODg7O6vM15meno63b9/i9OnTmDVrFq5duwZfX19cuXJFmFpFoVAI05TUr18fhw8f1jonY270P1ayt7fH48ePER8fr/fIegAq01aVL19edFmAjOTf3d1dp6nQdHHlyhXs3bsXQEYy+OkUOZ8DDgz5zCxfvlyYyHLKlCmi969Zs6bw/5rmVsttyk7Qb968EUYjanPt2jUAGRdSTbecgIw+P5mnHvhUeHi4MM1Ndhey48ePC9M1zJ07V9R0MkpXr17FqlWrAABjxowR5vjy9PTU67ZYuXLlhC+5nLxvhoi78hhyuTzblk7lMYoUKaJ1uopPmZqawsXFBUOGDBFm6ZfL5QgICNBpf0PbsmULQkJC9B7EYYh6ldty43MJAJUrVzbo6zU3N4ebmxt++eUX/PTTTwCAhIQEUZ8JU1NTDBo0CEDGk4YyD+rIPEjj6tWrWR5HGQcg+2uLkrGxMUqVKoWePXti48aNADJ+yGaemy46OlpIMDt27Kg1AYyPj8fjx491Om9OKJ+qER4eLnrgiybKOmFvby/8MBZbFrlcjuDgYL3LAvz/vJ8KhQL16tVDly5dDHLcgoZJ4Gfk7du3woWjfv36aNasmehjNGzYUJiPaePGjVkmULmhadOmwv9v375d63aXLl3Cw4cP1fb5VExMjEpr0qcyn0M5YlWTtLQ0Ialu1apVlufUJjk5GSNGjEB6ejo8PDwMehvTyMhImE5E+VQQMQwR98wd7LM6xsuXL4WRow0aNMjR6NDMA09yo9N7dmJiYoREdMCAATkexGGIepUXDPm5/PDhg1A/GzZsaMBSqtKnjmSeriRzP+mGDRsKSWtWcYiNjRUm03dzc8vRtFCZH12mjCmgOp9eVn3a/Pz8cvX6nXnk9Jo1a/Q6VlpamjA7g7e3t+j9fX19hdZafcui5O/vj2vXrkEmkxX6R8NlhUngZ2Tjxo3C9C45nabCxsZGuB0VEhKCsWPHan3+pHJaAkNq06aN8CtwxYoVGp/tGxMTgzFjxgDI6JejnINKmylTpqhMaq10584dYQBN2bJl4evrq/UYmzZtwpMnT2BmZoY5c+bo+GpUzZs3Dw8fPoSxsTFWr15t8KkxRo8eLYyaHTRoULbPf83MEHGvW7eu8MXl7++v8ZGDykRY+eWU04EUyik8gIwRq3lt4cKFeP/+Pezs7PR6bKIh6lVeMNTnMj09HT///LMwLVDnzp1zrcza6khSUpJKkvep2NhYLF++HEDGaOPM/fGcnJzQrl07ABnTxShH/2amUCgwbtw4IfEcPHhwjsqfedBH5hbVEiVKCLeG9+3bp3GKpevXr+f6FENNmjQRBt1s2LAhyzkAgYyniyhvrX5q9uzZCA8PB5CzOlGhQgXhaSL//POP8KhUbSIjI+Hn56d1fVJSkjDxfs+ePVGrVi3RZSosCt59B8ox5S/Wpk2bwsvLK8fHmTx5Mk6dOoW7d+/Cz88P165dw4ABA1CtWjUUKVIEkZGRwvNnu3btatBnB5uammL58uXo2rUrEhIS0KZNGwwbNgw+Pj4qj6dSzjU1atQorU8lADJGRz98+BCNGzcWHhun/NW5cuVKxMfHQyaTYfHixVl26lf22xkyZIhaJ21dXLt2DStXrgQAjBw5MsfPrcxK1apVMWXKFMycORMvXryAt7c3+vfvj6ZNmwqPlLp//z7++ecfPHnyROWL3FBxX7p0KVq1aoW0tDT06tULAwcORJs2bWBtbY379+9j5cqVwpQYXbp0QcuWLbW+Hrlcjvj4eOHvtLQ0vHnzBkeOHBGehFO0aNEsj5FblPVh8uTJeg3i0Lde5RV960dMTAz+/vtvbNq0Sah3PXr0UOl+khPp6ekqdSQ5ORmvXr3Cvn37hG4XDg4OKiNwHz16hB49eqB9+/bCI85MTU0RGRmJ69ev448//hBut3bt2lWtL+HcuXNx5swZvH//HuPGjUNwcDC6du0Ke3t7vHjxAuvXrxcS0K+++kq4tazJp1PEZO4TmLl/cPv27YX/NzIyQrdu3bBx40bcu3cPrVq1wogRI+Du7o7Y2Fj8+++/2Lx5MywsLODk5IQnT57kILK62bhxI3x8fBAVFYURI0bgzz//RJcuXVCpUiWYmJjg/fv3uHv3Lk6cOIHz58+jbdu26Nq1K4CMEctHjx6Fn58fzpw5AyCjH7AyyRZryZIluHHjBp49e4a5c+fi33//Re/eveHp6Qlzc3PExMTg/v37OHXqFAIDA1GlShWtcz0q+75aWloKj+H8XDEJ/AzpM1ktkPHFevjwYXz//fc4c+YM7t27h3HjxhmodNlr3rw5NmzYgFGjRiEhIQGLFy8WvvQzGzRoULZzNlWrVg2DBw/G2LFjNc63ZWxsjPnz5+vUqb9kyZIYP368zq9DKfNt4IoVKxo0af7U2LFjYWpqipkzZyIuLg4rVqwQWjsz0zRPlyHiXrduXfj5+WHQoEFISEjA+vXrsX79erXtOnXqpPHZpJldvHgxy+kYlImJoZ9aoytDDeLIab3Ka/rUj6CgIJVnAXfq1EmYzF4fe/bswZ49e7Sut7S0xMaNG4WR60rh4eFYt26d2jN1M2vRogUWLVqktlz5WLLu3bvj9evX2LVrF3bt2qW2Xf369eHv75/lnJZZTREDZLSoTp06VXh6iNLUqVNx6dIl3LlzBzdu3FBrdbWzs4Ofnx/mzp2bq0mgq6srTpw4gb59++Lu3bvCqH1tMrdoPn78WOX5wN7e3ti2bVuOR13b2tri+PHjGDhwIM6ePYurV69m2W9TW3/VzMaNG5cvU6XlJSaBn5lWrVqhTp06eh/Hzs4OBw8exLFjx7B3714EBwfj3bt3UCgUKFmyJKpVq4ZWrVpl+UBvfXTt2hX169fHunXrEBgYiLCwMKSkpAi/6gcMGKDzrO3Kx3itXbsWFy9exLt372BnZwcvLy+MHj1a59aIqVOnZjkaUJv58+fjwYMHMDIywqpVq/SaHFcXo0aNQrt27bB582acOnUKoaGhSExMhJWVFSpWrIiGDRtqfYKMIeLeunVrXLt2DevWrcOJEycQFhaG5ORklCxZEl9++SW+++47+Pj4iH5dMpkMVlZWcHFxgbe3N3744QedB5Xkhrlz5xpk0uqc1qv8kNP6YWxsjIoVK6J27dro169flomPPoyMjGBlZQV3d3c0bdoUP/zwg1p3gQoVKmD16tUICgrCrVu38PbtW0RHR6No0aIoXbo0ateujS5duqB58+Zaz1O1alUEBwdjy5YtOHLkCB4+fIj4+HgUL14cNWrUQNeuXdGlSxfRCY1MJkOxYsVQtmxZ1KtXD/3799d4fbKxscHx48exevVq7N+/H8+ePYOJiQnKlCmDFi1aYOjQoaIHV+SUm5sbzp49i0OHDuHgwYO4du0a3r17h7S0NNja2sLd3R1ffvklfH19Vd53mUyGcuXKoWbNmujVqxdatGih97Q7JUuWxKFDhxAQECB8b719+xZJSUmwtraGq6sr6tSpgxYtWmTbZ75cuXIYPny4XuUpDGQxMTGGn0CIKJ9Vq1YNYWFhalNIEBERUQYODCEiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCeLoYCIiIiIJYksgERERkQQxCSQiIiKSICaBRERERBLEx8blwO7du3Hx4kXcvHkTISEhSElJwerVq9G7d2+9j/3pw8o1uXv3bpbPVCUiIiLKDpPAHJg9ezbCwsJgb28PR0dHhIWFGezYEyZM0Lj8+fPn2LNnDypXrswEkIiIiPTGJDAHVq5cCTc3N7i4uGDp0qWYMWOGwY49adIkjcvHjx8PAPjuu+8Mdi4iIiKSLvYJzIEmTZrAxcVF5+3fvXuHSZMmoVatWnBwcICbmxv69OmDkJAQnfZPSkrC3r17YWZmhh49euS02EREREQCtgTmsufPn6Nt27Z4/fo1mjVrhjZt2uDdu3c4fPgwAgMDcfDgQdStWzfLYxw+fBgxMTHo0KEDSpQokUclJyIios8Zk8BcNnToUERERODPP/+Ej4+PsHz8+PFo2rQpRo8ejQsXLmR5jG3btgEA+vbtm6tlJSIiIung7eBcdOvWLVy+fBk9e/ZUSQABoEKFCujbty9CQkKyvC384sULnDt3DmXLlkXTpk1zu8hEREQkEWwJzEVXr14FkNEncN68eWrrHz9+LPzX09NT4zG2b98OhUKB3r17w8iIOTsREREZBpPAXBQdHQ0AOH78OI4fP651u4SEBI3L5XI5/P39YWRkxFHBREREZFBMAnORlZUVAGDhwoUYPHiw6P1PnjyJ169fw8fHB87OzoYuHhEREUkY7y/mIuWo3ytXruRofw4IISIiotzCJDAX1alTB3Xr1sW+ffvw119/qa2Xy+UICgrSuO9///2HY8eOoUSJEvD19c3tohIREZHE8HZwDvj5+eHixYsAIIzs3bZtm5DQ1a9fX2i927RpE9q1a4cBAwZg7dq1qFGjBszNzfHq1StcuXIF//33HyIjI9XO4e/vj9TUVHTv3h1mZmZ59MqIiIhIKmQxMTGK/C5EYTNs2DD4+/trXd+zZ0+sXbtW+DsmJgarVq3C0aNH8fz5cxgbG8PR0RG1a9dG+/bt0a5dO7VjfP3113j48CEuX76ML774IldeBxEREUkXk0AiIiIiCWKfQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBBUhSUhKePXuGpKSk/C5KocB4icN4icN4icN4icN4iceYGR6TwAImPT09v4tQqDBe4jBe4jBe4jBe4jBe4jFmhsUkkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEmeR3AUjVl0HFAESpLIvpXyZ/CkNERESfLbYEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJUIFPApOSkjB58mT4+vqicuXKcHR0RKVKldCyZUts374dqampOh9LLpdj/fr1aNCgAZycnODu7o6BAwfixYsXufcCiIiIiAqgAp8EJiQkYMuWLZDJZGjRogVGjBiBtm3bIjw8HCNHjkT37t0hl8t1OtaYMWMwYcIEKBQKDBkyBD4+Pjh8+DCaNm2Kp0+f5vIrISIiIio4Cvyzg+3s7BAaGgozMzOV5WlpaejYsSMCAwNx4sQJtGzZMsvjnD17Fn5+fmjQoAEOHDggHK9r167o2rUrxo8fj7/++ivXXgcRERFRQVLgWwKNjIzUEkAAMDExQdu2bQEAz549y/Y4fn5+AIApU6aoHO+bb76Bt7c3AgMDERYWZqBSExERERVsBT4J1EYulyMgIAAA4Onpme32QUFBsLCwQL169dTW+fj4AADOnz9v2EISERERFVAF/nawUkpKCpYsWQKFQoHo6GicOXMGjx49Qu/evdG4ceMs901ISEBERAQ8PT1hbGystt7NzQ0AdO4XmJSUJP4F6CAlJSVPz1fYKeOlLW6kivESh/ESh/ESh/ESjzHLnrm5uajtC1USuGDBAuFvmUyGUaNG4X//+1+2+8bGxgIArK2tNa5XLldul53w8HCkp6frtK14xdSW8DZ11iIjI/O7CIUK4yUO4yUO4yUO4yUeY6aZsbGx0Kilq0KTBFpaWiImJgZyuRxv3rzBsWPHMHPmTFy5cgV79uzRmuDlhtKlS+fKcTN+3cSpLXd2ds6V8xV2KSkpiIyMhKOjo8Z+o6SK8RKH8RKH8RKH8RKPMTO8QpMEKhkZGaFMmTIYOHAg7O3t8f3332PJkiWYMWOG1n2ya+nLrqXwU2KbW/WV1+crbMzMzBgjERgvcRgvcRgvcRgv8Rgzwym0A0MAoGnTpgAyBn1kxcLCAk5OTnj58qXG27jK0cXu7u6GLyQRERFRAVSok8CIiAgAgKmpabbbenl5ISEhAZcuXVJbpxxl3KBBA8MWkIiIiKiAKvBJ4IMHD5CYmKi2PDExEVOmTAGQMdefUlRUFB49eoSoqCiV7fv16wcAmDNnjsrIohMnTiAoKAjNmjWDi4tLbrwEIiIiogKnwPcJ3L9/P9asWYN69erBxcUFVlZWCA8Px8mTJ/H+/XvUr18fw4cPF7bfsGEDFixYgAkTJmDSpEnC8kaNGqFv377w8/ND48aN0aJFC0RERGD//v2ws7PDwoUL8+PlEREREeWLAp8EtmrVChEREQgODkZwcDASEhJgbW2NKlWqoHPnzvjuu+9gYqLby1i2bBk8PT2xdetWrFu3DhYWFmjbti2mTZuG8uXL5/IrISIiIio4CnwSWKtWLdSqVUvn7SdNmqTSApiZkZERhg4diqFDhxqqeERERESFUoHvE0hEREREhsckkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgliEkhEREQkQUwCiYiIiCSISSARERGRBDEJJCIiIpIgJoFEREREEsQkkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgkq8ElgeHg41qxZg2+//RZVq1ZFyZIlUalSJfTp0wdXr17V+Tjnzp2Dra2t1n87duzIxVdBREREVLCY5HcBsrNhwwYsW7YM5cuXR9OmTVGiRAk8ffoUR44cwZEjR7Bp0yZ06tRJ5+N5eXnB29tbbXm1atUMWWwiIiKiAq3AJ4G1a9fG33//rZa4XbhwAR06dMDYsWPRpk0bFClSRKfjeXt7Y9KkSblRVCIiIqJCo8DfDm7fvr3GlrsGDRqgYcOGiImJQUhISD6UjIiIiKjwKvAtgVkxNTUFABgbG+u8z7Nnz7BmzRokJSWhdOnSaNSoEUqXLp1bRSQiIiIqkAptEhgWFobTp0/DyckJVapU0Xm/vXv3Yu/evcLfJiYmGDx4MGbNmqVzMpmUlCS6vLpISUnJ0/MVdsp4aYsbqWK8xGG8xGG8xGG8xGPMsmdubi5q+0KZBKampmLIkCFITk7G9OnTdUreSpQogenTp6Nly5ZwcXFBYmIigoODMWPGDKxZswYymQxz5szR6fzh4eFIT0/X92VoUUxtSVhYWC6d6/MQGRmZ30UoVBgvcRgvcRgvcRgv8RgzzYyNjeHm5iZqH1lMTIwil8qTK+RyOYYMGYK9e/eiX79+WL58uV7Hi4yMhJeXF2JiYnD//n2ULFky231ysyXQ5c84teURPe1z5XyFXUpKCiIjI+Ho6AgzM7P8Lk6Bx3iJw3iJw3iJw3iJx5hl77NuCZTL5RgxYgT27t2Lbt26YenSpXof09HREa1bt4afnx+uXr0KX1/fbPcRG2R95fX5ChszMzPGSATGSxzGSxzGSxzGSzzGzHAKTRIol8sxfPhw7Nq1C126dMHatWthZGSYwc329hktbYmJiQY5HhEREVFBV+CniAFUE8BOnTph/fr1okYEZ0f55BEXFxeDHZOIiIioICvwSaDyFvCuXbvQsWNHbNiwIcsEMCoqCo8ePUJUVJTK8ps3b2rcfu3atTh37hzc3d1Ru3ZtQxadiIiIqMAq8LeDFyxYAH9/f1haWqJChQpYtGiR2jZt2rRB9erVAWQ8Zm7BggWYMGGCypNB+vTpA1NTU9SqVQulS5dGYmIirly5gtu3b8PGxibb5JKIiIjoc1Lgk8DQ0FAAQHx8PBYvXqxxGxcXFyEJ1GbgwIEICAjAhQsX8P79exgZGcHZ2RnDhg3DyJEjUaZMGYOXnYiIiKigKnRTxHzOkpKS4OQfpbY8pj8TVE2SkpIQFhYGZ2dnjhTTAeMlDuMlDuMlDuMlHmNmeAW+TyARERERGR6TQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgliEkhEREQkQUwCiYiIiCSISSARERGRBDEJJCIiIpIgJoFEREREEsQkkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgliEkhEREQkQSb67BwWFiZ6H2dnZ31OSUREREQGoFcSWL16dchkMp23l8lkiIqKEnWO8PBwHDhwACdOnMDjx48RGRkJOzs7fP311/jxxx9Rt25dnY8ll8uxceNGbN26Fc+ePYOFhQWaNGmCadOmwdXVVVS5iIiIiAozvW8HKxQKnf/J5XLRx9+wYQMmT56MFy9eoGnTphg5ciTq1auHo0ePokWLFvjrr790PtaYMWMwYcIEKBQKDBkyBD4+Pjh8+DCaNm2Kp0+fii4bERERUWGlV0sgANSrVw///POPyjI7OzuNy3Oidu3a+Pvvv+Ht7a2y/MKFC+jQoQPGjh2LNm3aoEiRIlke5+zZs/Dz80ODBg1w4MABmJmZAQC6du2Krl27Yvz48aISSiIiIqLCrMAPDGnfvr1aAggADRo0QMOGDRETE4OQkJBsj+Pn5wcAmDJlipAAAsA333wDb29vBAYG5qiPIxEREVFhlGtJYFRUFBISEnLr8AAAU1NTAICxsXG22wYFBcHCwgL16tVTW+fj4wMAOH/+vGELSERERFRA6XU7uGjRovjvv/9Ulin/fvLkCRo1aoSNGzeidu3a+pxGo7CwMJw+fRpOTk6oUqVKltsmJCQgIiICnp6eGhNGNzc3ANC5X2BSUpL4AusgJSUlT89X2CnjpS1upIrxEofxEofxEofxEo8xy565ubmo7fVKAsuUKYNnz54hJCQEnp6eAICDBw8CAEqXLo1nz56hVatW+OWXXzBu3DhRI4mzkpqaiiFDhiA5ORnTp0/PtiUwNjYWAGBtba1xvXK5crvshIeHIz09XUSJxSimtoS3qbMWGRmZ30UoVBgvcRgvcRgvcRgv8RgzzYyNjYVGLV3plQQ2bdoUT548QceOHdGzZ098+PABO3fuhEwmw+bNmxEREYHRo0dj7ty5CAwMxIYNG1C2bFl9Tgm5XI7hw4fjwoUL6NevH3r06KHX8XKidOnSuXLcjF83cWrLObeiZikpKYiMjISjo6NKP0/SjPESh/ESh/ESh/ESjzEzPL2SwLFjx+LQoUOIjIzEypUroVAoAGSMuP36668BZIzuHTx4MC5evAhvb2+8ePEix+eTy+UYMWIE9u7di27dumHp0qU67ZddS192LYWfEtvcqq+8Pl9hY2ZmxhiJwHiJw3iJw3iJw3iJx5gZjl5JoJOTE86cOYN169bh7t27sLa2hre3N77//nthG2dnZxw5cgQLFy7E4sWLc3wuZQvgrl270KVLF6xduxZGRrqNa7GwsICTkxNevnyJ9PR0tdvHz549AwC4u7vnuHxEREREhYne8wQ6Ojrif//7X5bbGBkZYeLEiWjatGmOzpE5AezUqRPWr1+v04jgzLy8vPDnn3/i0qVL8PLyUlkXEBAAIGPaGSIiIiIpyNN5ApW3iMVQ3gLetWsXOnbsiA0bNmSZAEZFReHRo0dqj6fr168fAGDOnDkqI4tOnDiBoKAgNGvWDC4uLqLLR0RERFQY6d0SmNsWLFgAf39/WFpaokKFCli0aJHaNm3atEH16tUBZDxmbsGCBZgwYQImTZokbNOoUSP07dsXfn5+aNy4MVq0aIGIiAjs378fdnZ2WLhwYZ69JiIiIqL8VuCTwNDQUABAfHy81j6FLi4uQhKYlWXLlsHT0xNbt27FunXrYGFhgbZt22LatGkoX768QctNREREVJDJYmJiFDnduXjx4uJOJpOp3aal/5eUlAQnf/X4xPQvkw+lKfiSkpIQFhYGZ2dnjhTTAeMlDuMlDuMlDuMlHmNmeHq1BCoUCshkMmFqGF22JyIiIqL8p1cSaGVlhbi4OFhbW2PChAlo27atocpFRERERLlIr9HB165dQ8+ePREXF4epU6fip59+QnJyMlxcXLT+IyIiIqL8p1cSWLJkSaxZswb//vsvatSogcDAQHh5eWHatGmIi1N//BkRERERFQwGmSewbt26CAwMxPLly2FtbY1Vq1bhyy+/hL+/vyEOT0REREQGZrDJomUyGfr27Ytr165h0KBB+O+//zBixAi0aNECN2/eNNRpiIiIiMgADP7EEBsbGyxcuBBnzpxBgwYNcOXKFfj4+ODHH3/k9DBEREREBYReo4PDwsK0rrO2tsbatWvx119/YfHixdi2bRsOHTqE58+f63NKIiIiIjIAvZLAGjVq6LytQqHAhw8f9DkdERERERmI3pNFExEREVHho1cSGB0dbahyEBEREVEeMvjAECIiIiIq+PRKAs+fP487d+4YqixERERElEf0SgLbtm2LCRMmGKosRERERJRH9L4dzMEhRERERIUP+wQSERERSRCTQCIiIiIJ0muKGABISUnJ8skhn3J2dtb3lERERESkJ72TwBs3buj85BCZTMbnBxMREREVAHongWIGhnAQCREREVHBoHcSWK9ePfzzzz+GKAsRERER5REODCEiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUmQXgNDoqOjDVUOIiIiIspDbAkkIiIikiC9WgLPnz8veh8vLy99TklEREREBqBXEti2bVvIZDKdt+cTQ4iIiIgKBr2SwLJly4pKAomIiIioYNArCbxz546hykFEREREeUjvx8ZpI5fLERISgtTUVFSqVAkWFha5dSoiIiIiEilXRgcHBATAw8MDjRo1go+PDypWrIjVq1fnxqmIiIiIKAcMngRGRkaiX79+ePv2LRQKBQDg48ePmDZtGgICAgx9OiIiIiLKAYMngbt370ZCQgK++uor3L59G2FhYfjpp5+gUCiwefNmQ5+OiIiIiHLA4EngjRs3IJPJMGfOHDg7O8PCwgLTpk1DmTJlcOPGDUOfjoiIiIhywOBJ4H///QcAqFKlirBMJpPBw8ODcwQSERERFRAGTwLT0tIAAEWLFlVZXqxYMWEdEREREeUvPjuYiIiISIL0micwLCxMbVlycjIA4NWrV8LoYCBjhDARERERFQx6JYE1atTQuq569er6HJqIiIiIcpFeSWDmlj5d8DnDRERERAWDXklgdHS0ocpBRERERHmIA0OIiIiIJKhQJIG7d+/GmDFj0KRJEzg4OMDW1hY7duwQdYxz587B1tZW6z+xxyMiIiIqzPS6HZzZ69evERAQgEePHiE+Ph5WVlaoVKkSfHx8ULp0ab2OPXv2bISFhcHe3h6Ojo4aRyXrysvLC97e3mrLq1Wrpk8RiYiIiAoVnZLAlJQUHD16FGlpaejSpYvKusTEREyaNAk7d+5Eeno6gIwBI8pBICYmJujbty9mz54Nc3PzHBVy5cqVcHNzg4uLC5YuXYoZM2bk6DgA4O3tjUmTJuV4fyIiIqLPgU5J4Pv379G/f394e3urJIHp6eno3r07zp8/D4VCgTJlyuCLL76Ao6MjIiMj8fDhQ7x+/RpbtmzBs2fPsG/fPhgZib8D3aRJE9H7EBEREZF2OiWBJUuWBAC1x75t27YNQUFBsLOzw4IFC9C5c2eVJE+hUODPP//ExIkTcfr0aezcuRPfffedAYsv3rNnz7BmzRokJSWhdOnSaNSokd63q4mIiIgKG52SQGNjY5iZmakt3717N2QyGfz8/DT2s5PJZOjSpQucnJzQrl077Nq1K9+TwL1792Lv3r3C3yYmJhg8eDBmzZoFY2NjnY6RlJSUK2VLSUnJ0/MVdsp4aYsbqWK8xGG8xGG8xGG8xGPMsie2253OA0NsbGyQmJiosuz+/fsoV66cxgQwM29vb5QvXx53794VVThDKlGiBKZPn46WLVvCxcUFiYmJCA4OxowZM7BmzRrIZDLMmTNHp2OFh4cL/R8Nr5jaEn0GwkhBZGRkfhehUGG8xGG8xGG8xGG8xGPMNDM2Noabm5uofXROAt3c3HD37l2kpqbC1NQUQMbzgHU9oa2tLV6/fi2qcIbk4eEBDw8P4W8LCwu0adMGdevWhZeXF9avX48xY8YIt76zklu3jzN+3cSpLXd2ds6V8xV2KSkpiIyMhKOjo8aWalLFeInDeInDeInDeInHmBmezklg/fr1ERwcjF27dqFPnz4AACcnJzx+/BiJiYkoVky9BUspMTERjx490inBymuOjo5o3bo1/Pz8cPXqVfj6+ma7T05HOedUXp+vsDEzM2OMRGC8xGG8xGG8xGG8xGPMDEfnobq9e/eGTCbD1KlT8eTJEwBAo0aNEB8fj4kTJ2a57+TJk5GYmJjtbeP8Ym9vDwBqt7uJiIiIPlc6J4EVKlTAiBEjEBsbizZt2uDgwYMYPnw4ihUrhu3bt6N+/frYtm0brl+/jlevXuH69evYvn07vLy84OfnBxMTE4waNSo3X0uOXb16FQDg4uKSzyUhIiIiyhuinhgyY8YMfPz4EZs2bUL//v1RtGhRKBQKAMDDhw/x448/qu2jUChgZmaGFStWoEqVKoYpdRaioqIQFRUFe3t7oYUPAG7evImaNWuqbb927VqcO3cO7u7uqF27dq6Xj4iIiKggEJUEymQyLFq0CB06dMDatWtx6tQpfPz4Uev2Tk5OaN68OUaMGIHKlSvnuJB+fn64ePEiACAkJATA/89RCGT0V+zbty8AYMOGDViwYAEmTJig8mSQPn36wNTUFLVq1ULp0qWRmJiIK1eu4Pbt27CxscGGDRt0niKGiIiIqLDL0bODvb29hf590dHRiItTHdFqbGwMa2trWFlZ6V9CABcvXoS/v7/KskuXLuHSpUvC38okUJuBAwciICAAFy5cwPv372FkZARnZ2cMGzYMI0eORJkyZQxSViIiIqLCQBYTE6PI70JQhqSkJDj5R6ktj+nPBFWTpKQkhIWFwdnZmSPFdMB4icN4icN4icN4iceYGV6OWgK1SUtLQ2hoKOLi4mBlZQUXFxeYmBj0FERERERkAAbJ0K5fv45FixbhzJkzKo84Mzc3R9OmTfHzzz+jVq1ahjgVERERERmAzlPEaLN161a0bNkSx48fx8ePH6FQKIR/Hz9+xNGjR9GiRQv4+fkZorxEREREZAB6JYG3bt3CuHHjkJaWhnr16mHnzp24efMmIiIicPPmTezcuRP169dHWloaxo4di1u3bhmq3ERERESkB72SwFWrViE9PR0jR47E0aNH4evri3LlyqFIkSIoV64cfH19cfToUYwaNQrp6elYvXq1ocpNRERERHrQKwm8cOECbGxs8Ouvv2a53bRp02BtbY3z58/rczoiIiIiMhC9ksB3797B3d0dpqamWW5namqKChUq4L///tPndERERERkIHolgZaWloiMjNRp28jISFhYWOhzOiIiIiIyEL2SwOrVqyM8PBxHjx7NcrsjR47g9evXqF69uj6nIyIiIiID0SsJ/O6776BQKDB48GCsWrUKiYmJKusTExOxcuVKDBkyBDKZDH369NGrsERERERkGHpNFt2lSxccPnwYhw4dwq+//oq5c+fCxcUFDg4OePv2LUJDQ5GUlASFQoEOHTqgc+fOhio3EREREelB78mit2zZggkTJsDS0hIfP37Ew4cPce7cOTx8+BAfP36EpaUlJk6ciM2bNxuivERERERkAHo/Ns7Y2BgTJ07E6NGjcfHiRTx+/Bjx8fGwtLREpUqVUK9ePRQrVswQZSUiIiIiAzHIs4MBoFixYvDx8YGPj4+hDklEREREuUT07eA2bdqgePHiWLJkiU7bL1myBMWLF8e3334runBERERElDtEJYEXLlzAhQsXULNmTYwbN06nfcaNG4eaNWvizJkzCA4OzlEhiYiIiMiwRCWBf/75J2QyGX766SdRJxk3bhwUCgX27t0raj8iIiIiyh2iksDLly/D3Nwc33zzjaiTNG/eHObm5rh8+bKo/YiIiIgod4hKAkNDQ+Hi4gJzc3NRJylSpAjKlSuHly9fitqPiIiIiHKHqCRQOe9fTijnESQiIiKi/CcqCbS1tUVUVFSOThQVFQUbG5sc7UtEREREhiUqCVTe0n337p2ok7x9+xYvX75EuXLlRO1HRERERLlDVBLYsGFDABD9CLjNmzdDoVCgUaNGovYjIiIiotwhKgns168fjI2NsWzZMgQFBem0z7lz57Bs2TKYmJigb9++OSokERERERmWqCTQ1dUVQ4cORXJyMjp37oy5c+dq7SMYFRWFOXPmoEuXLkhNTcXgwYPh6upqiDITERERkZ5EPzt4xowZeP78OY4cOYLFixfjt99+Q+XKleHq6goLCwskJCTgxYsXePDgAeRyORQKBVq3bo1Zs2blRvmJiIiIKAdEJ4FGRkbYvn07Vq5ciaVLlyI6Ohr37t3DvXv3IJPJoFAohG3t7OwwZswYjB492qCFJiIiIiL9iE4ClUaNGoWBAwfixIkTuHjxIsLDwxEXFwcrKyuULl0a9evXR/PmzWFhYWHI8hIRERGRAeQ4CQSAYsWKoUOHDujQoYOhykNEREREeUDUwBAiIiIi+jwwCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSpNezgylv2P7+Wm1ZTP8y+VASIiIi+lywJZCIiIhIgpgEEhEREUkQk0AiIiIiCSoUSeDu3bsxZswYNGnSBA4ODrC1tcWOHTtEH0cul2P9+vVo0KABnJyc4O7ujoEDB+LFixeGLzQRERFRAVYoBobMnj0bYWFhsLe3h6OjI8LCwnJ0nDFjxsDPzw8eHh4YMmQI3rx5gwMHDiAwMBAnT56Eu7u7gUtOREREVDAVipbAlStX4vbt23j69CkGDBiQo2OcPXsWfn5+aNCgAc6cOYMZM2Zgw4YN2LFjB6KjozF+/HgDl5qIiIio4CoULYFNmjTR+xh+fn4AgClTpsDMzExY/s0338Db2xuBgYEICwuDs7Oz3uciIiIiKugKRUugIQQFBcHCwgL16tVTW+fj4wMAOH/+fF4Xi4iIiChfFIqWQH0lJCQgIiICnp6eMDY2Vlvv5uYGAHj69KlOx0tKSjJo+ZRSUlJ03ja3ylCYKOMlJm5SxniJw3iJw3iJw3iJx5hlz9zcXNT2kkgCY2NjAQDW1tYa1yuXK7fLTnh4ONLT0w1TODXFdNoqp4NjPkeRkZH5XYRChfESh/ESh/ESh/ESjzHTzNjYWGjU0pUkkkBDK126dK4cN+PXTZxO27LvYka8IiMj4ejoqNLPkzRjvMRhvMRhvMRhvMRjzAxPEklgdi192bUUfkpsc2tuKAhlKCjMzMwYDxEYL3EYL3EYL3EYL/EYM8ORxMAQCwsLODk54eXLlxpv4z579gwAOE8gERERSYYkkkAA8PLyQkJCAi5duqS2LiAgAADQoEGDvC4WERERUb747JLAqKgoPHr0CFFRUSrL+/XrBwCYM2eOysiiEydOICgoCM2aNYOLi0uelpWIiIgovxSKPoF+fn64ePEiACAkJAQAsG3bNgQFBQEA6tevj759+wIANmzYgAULFmDChAmYNGmScIxGjRqhb9++8PPzQ+PGjdGiRQtERERg//79sLOzw8KFC/P4VRERERHln0KRBF68eBH+/v4qyy5duqRya1eZBGZl2bJl8PT0xNatW7Fu3TpYWFigbdu2mDZtGsqXL2/wchMREREVVLKYmBhFfheCMiQlJcHJPyr7DQHE9C+Ty6Up+JKSkoRH/XGkWPYYL3EYL3EYL3EYL/EYM8P77PoEEhEREVH2mAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiCT/C4A5Yzt76/VlsX0L5MPJSEiIqLCiC2BRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgliEkhEREQkQUwCiYiIiCSISSARERGRBDEJJCIiIpIgJoFEREREEsQkkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSIJP8LgAZju3vr9WWxfQvkw8lISIiooKOLYFEREREEsQkkIiIiEiCmAQSERERSVChSQKvX7+Orl27wsXFBaVLl0bz5s2xf/9+nfffsWMHbG1ttf47d+5cLpaeiIiIqGApFANDzp49i86dO8Pc3BydOnWCpaUlDh06hP79++PVq1cYNWqUzsdq3bo1qlWrprbcxcXFkEUmIiIiKtAKfBKYlpaGH3/8EUZGRjhy5AiqV68OAPjll1/g4+ODWbNmoUOHDjoncW3atEHv3r1zs8hEREREBV6Bvx189uxZPH/+HF26dBESQACwsbHB2LFjkZKSAn9//3wsIREREVHhU+BbAoOCggAAzZo1U1vn4+MDADh//rzOx7t9+zbev3+P9PR0uLi4oEmTJihevLhhCktERERUSBT4JPDp06cAAHd3d7V1jo6OsLS0xLNnz3Q+3vr161X+Llq0KCZMmIAxY8bofIykpCSdtxUjJSXF4MfMrbIWBMp45UbcPkeMlziMlziMlziMl3iMWfbMzc1FbV/gk8DY2FgAgLW1tcb1VlZWwjZZKVeuHBYuXAgfHx+ULl0a0dHROHv2LGbOnInp06ejaNGiGDJkiE5lCg8PR3p6uu4vQpRiBj1aWFiYQY9XEEVGRuZ3EQoVxkscxkscxkscxks8xkwzY2NjuLm5idpHFhMTo8il8hjEt99+i1OnTuH69esaX5yHhwcSEhIQGhqao+Pfv38fTZs2RdGiRfH48WOYmGSfF+dmS6DLn3EGPWZET3uDHq8gSUlJQWRkJBwdHWFmZpbfxSnwGC9xGC9xGC9xGC/xGLPsfXYtgcoWQG2tfXFxcbC1tc3x8T08PFCvXj2cPn0aDx8+RJUqVbLdR2yQ81NhKmtOmZmZSeJ1GgrjJQ7jJQ7jJQ7jJR5jZjgFfnSwsi+gsm9gZpGRkYiPjxfd/Pkpe/uM1rLExES9jkNERERUWBT4JNDLywsAEBgYqLYuICBAZZucSE9Px40bNwAAzs7OOT5OQWX7+2u1f0REREQFPgls3LgxXF1dsW/fPty+fVtY/uHDB/z2228wMzNDjx49hOURERF49OgRPnz4oHKcmzdvqh07PT0d06dPx7Nnz9CwYUM4OTnl2usgIiIiKkgKfJ9AExMTrFixAp07d0abNm1UHhsXFhaGWbNmoVy5csL2M2bMgL+/P1avXq3yZJAmTZqgSpUqqFKlijA6+Pz583jy5AnKlCmDlStX5sfLIyIiIsoXBT4JBIBGjRrh2LFjmDdvHvbv34/U1FR4enpixowZ6NSpk07HGDlyJK5evYrTp08jOjoaZmZmKF++PH7++WeMHDlSr8ElRERERIVNoUgCAaBOnTrYt29fttutXbsWa9euVVs+e/bs3CgWERERUaFU4PsEEhEREZHhMQkkIiIikiAmgUREREQSxCSQiIiISIKYBBIRERFJEJNAIiIiIgliEkhEREQkQYVmnkAyHE3PD47pXyYfSkJERET5hS2BRERERBLEJJCIiIhIgpgEEhEREUkQk0AiIiIiCWISSERERCRBTAKJiIiIJIhJIBEREZEEMQkkIiIikiAmgUREREQSxCeGEAA+RYSIiEhq2BJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSIA4MIa04WISIiOjzxZZAIiIiIgliEkhEREQkQUwCiYiIiCSIfQJJFPYTJCIi+jywJZCIiIhIgpgEEhEREUkQbweT3niLmIiIqPBhSyARERGRBLElkHKFptZBgC2EREREBQVbAomIiIgkiC2BlKfYf5CIiKhgYBJI+Y6JIRERUd5jEkgFkrY+hZ+64p3LBSEiIvpMMQmkQu3LoGIAorLdji2LREREqpgEkiTwljMREZEqJoEkWUwMiYhIypgEEmXCxJCIiKSCSSBRNpgYEhHR54hJIFEOMDEkIqLCjkkgkYEwMSQiosKESSBRLtJ1vkMmi0RElNcKTRJ4/fp1zJs3D5cvX0ZaWho8PT0xYsQIfPvttzofIzk5GcuWLcPu3bvx+vVr2NnZoWXLlpg6dSpKliyZi6UnypquySLAhJGIiAyjUCSBZ8+eRefOnWFubo5OnTrB0tIShw4dQv/+/fHq1SuMGjUq22PI5XL06tULAQEB+PLLL9G+fXs8ffoUfn5+OHPmDE6ePIkSJUrkwash0o+YhFEdJ9cmIqIMBT4JTEtLw48//ggjIyMcOXIE1atXBwD88ssv8PHxwaxZs9ChQwe4uLhkeZydO3ciICAAXbp0wcaNGyGTyQAAW7ZswdixYzF79mwsW7Yst18OUaGgX6KZc0w+iYjyToFPAs+ePYvnz5+jd+/eQgIIADY2Nhg7diyGDx8Of39/TJgwIcvj+Pn5AQB+/fVXIQEEgP79+2PFihXYu3cv5s2bh6JFi+bOCyGibOmTfDKBJCISp8AngUFBQQCAZs2aqa3z8fEBAJw/fz7LYyQlJeHq1auoWLGiWouhTCZD06ZN8fvvv+PGjRto0KCBgUqeM/ZFjPL1/ESFlfvON7lwVEvgSnS2Wz3tVSoXzl34GBsb53cRChXGSzzGzLAKfBL49OlTAIC7u7vaOkdHR1haWuLZs2dZHuP58+eQy+Vwc3PTuF65/OnTp/maBJqbm/PLhIgKJXNzc63XWFLHeInHmBlegW92io2NBQBYW1trXG9lZSVsk90xbGxsNK5XHju74xARERF9Lgp8EkhEREREhlfgk8DsWuni4uK0thJ+eowPHz5oXJ9dayMRERHR56bAJ4HKvoDKvoGZRUZGIj4+Pts+Aq6urjAyMtLad1C5XFO/QyIiIqLPUYFPAr28vAAAgYGBausCAgJUttGmaNGiqFOnDh4/fozQ0FCVdQqFAqdOnYKFhQVq1aploFITERERFWwFPgls3LgxXF1dsW/fPty+fVtY/uHDB/z2228wMzNDjx49hOURERF49OiR2q3ffv36AQBmzpwJhUIhLP/999/x4sULdO3alXMEEhERkWTIYmJiFNlvlr+0PTYuLCwMs2bNUnls3LBhw+Dv74/Vq1ejd+/ewnK5XI6uXbsKj43z8vLCs2fPcPjwYbi4uCAgIICPjSMiIiLJKPAtgQDQqFEjHDt2DF9//TX279+PLVu2wMHBAVu2bNHpucEAYGRkhJ07d2LixIn477//sGbNGly+fBl9+vTBiRMn8jUBvH79Orp27QoXFxeULl0azZs3x/79+/OtPAVZtWrVYGtrq/FfmzZt8rt4+Wb37t0YM2YMmjRpAgcHB9ja2mLHjh1at4+NjcXkyZNRtWpVODg4oFq1apg2bRri4+PzsNT5R0y85s2bp7XO2dra4uXLl3lc+rwVHh6ONWvW4Ntvv0XVqlVRsmRJVKpUCX369MHVq1c17iPl+iU2XlKvX0DGAx0mT54MX19fVK5cGY6OjqhUqRJatmyJ7du3IzU1VW0fKdcxQyoULYGfMzGtnJSRBH748AHDhg1TW+fi4qLS+isl1apVQ1hYGOzt7VGsWDGEhYWptYYrJSQkoFWrVrhz5w6aNWuG6tWr4/bt2wgMDETt2rVx9OhRmJub58OryDti4jVv3jwsWLAAPXv21PiM8mHDhsHW1jYPSp0/pk+fjmXLlqF8+fLw9vZGiRIl8PTpUxw5cgQKhQKbNm1Cp06dhO2lXr/Exkvq9QsAoqKiUKVKFdSuXRsVKlRAiRIlEBMTgxMnTiAsLAzNmjXDvn37YGSU0W4l9TpmSAX+iSGfs7S0NPz4448wMjLCkSNHhGcj//LLL/Dx8cGsWbPQoUMHjRcGKbOxscGkSZPyuxgFysqVK+Hm5gYXFxcsXboUM2bM0Lrt8uXLcefOHYwZMwbTp08Xliu/vNasWYOxY8fmQanzj5h4KfXq1QsNGzbMg9IVLLVr18bff/8Nb29vleUXLlxAhw4dMHbsWLRp0wZFihQBwPolNl5KUq1fAGBnZ4fQ0FCYmZmpLE9LS0PHjh0RGBiIEydOoGXLlgBYxwypUNwO/lydPXsWz58/R5cuXYQEEMhIcsaOHYuUlBT4+/vnYwmpsGjSpIlOPxYUCgW2bdsGS0tLjB8/XmXd+PHjYWlpCT8/v9wqZoGha7wIaN++vVpCAwANGjRAw4YNERMTg5CQEACsX4C4eFEGIyMjtQQQAExMTNC2bVsA/z+VG+uYYbElMB8FBQUBAJo1a6a2zsfHBwBw/vz5PC1TYZCSkoIdO3YgIiICVlZWqF27NurWrZvfxSoUnj59ijdv3sDHxwcWFhYq6ywsLPD1118jICAAr169QtmyZfOplAXThQsXcO3aNRgZGcHNzQ1NmjSBpaVlfhcrX5mamgIAjI2NAbB+ZefTeGXG+qVOLpcLU8F5enoCYB0zNCaB+Ug5AbamSaodHR1haWmpdYJrKYuMjMSIESNUltWuXRubN29G+fLl86lUhYOyzmmbYN3NzQ0BAQF4+vQpL6CfmDdvnsrfNjY2mD9/Pnr27JlPJcpfYWFhOH36NJycnFClShUArF9Z0RSvzFi/Mn7gL1myBAqFAtHR0Thz5gwePXqE3r17o3HjxgBYxwyNSWA+yu5xdVZWVloflydVvXv3Rv369eHp6QkLCws8efIEq1evxu7du9G+fXtcuHABVlZW+V3MAktZn2xsbDSuz+4xjVJUtWpVrFq1Ct7e3nByckJkZCSOHz+OuXPnYvjw4bCxsUHr1q3zu5h5KjU1FUOGDEFycjKmT58utGyxfmmmLV4A61dmKSkpWLBggfC3TCbDqFGj8L///U9YxjpmWEwCqVCZOHGiyt/Vq1fH+vXrAWRM+7F161aMHDkyP4pGn6l27dqp/F2uXDkMHjwYX3zxBTp27IjZs2dL5ksayLhFN3z4cFy4cAH9+vVTmayf1GUXL9av/2dpaYmYmBjI5XK8efMGx44dw8yZM3HlyhXs2bNHa4MJ5RwHhuSj7H6xxMXFsdLrqH///gCAy5cv53NJCjZlffr0iTpK2bVO0/9r3Lgxypcvj5CQEMm0OsjlcowYMQJ79+5Ft27dsHTpUpX1rF+qsotXVqRYv5SMjIxQpkwZDBw4EMuXL8elS5ewZMkSAKxjhsYkMB8p+wIq+zhkFhkZifj4eK39HkiVvb09ACAxMTGfS1KwKeuctr6myuWa+qmSOmW9+/jxYz6XJPcpW7T8/f3RpUsXrF27Vpi3TYn16//pEq/sSKl+adO0aVMA/z+QknXMsJgE5iMvLy8AQGBgoNo65Ygo5TaUNeVM/Jz2I2vu7u4oVaoULl++jISEBJV1CQkJuHz5MsqVK8cO1TpISEjAgwcPYGFhIXxZf66UCc2uXbvQqVMnrF+/XuMIV9avDLrGKytSql9ZiYiIAPD/I6tZxwyLSWA+aty4MVxdXbFv3z7cvn1bWP7hwwf89ttvMDMzY3+bTB49eqSxpe/Ro0fChKFdunTJ41IVLjKZDH369EF8fDwWLVqksm7RokWIj49Hv3798ql0BU9cXByePHmitvzjx4/48ccfERcXh44dO8LE5PPtXq28pblr1y507NgRGzZs0JrQsH6JixfrV4YHDx5ovLYnJiZiypQpAIBvvvkGAOuYofGxcfmMj43T3bx587BmzRo0aNAAzs7OKFasGJ48eYITJ04gNTUVY8eOxa+//prfxcwXfn5+uHjxIgAgJCQEt27dQr169YQpc+rXr4++ffsCyPi13LJlS9y9exfNmjVDjRo1cOvWLeGRS0eOHEHRokXz7bXkBV3j9fLlS9SsWRO1a9dGpUqV4OjoiLdv3+LMmTN4/fo1PD098ffff6N48eL5+XJylfKxZpaWlhg6dKjGhKZNmzbChPdSr19i4sX6lUF5ba9Xrx5cXFxgZWWF8PBwnDx5Eu/fv0f9+vXx119/CfVG6nXMkJgEFgDXrl3DvHnzEBwcjNTUVHh6emLEiBEqz5ekjD4hmzdvxu3bt/Hu3TskJibC3t4ederUwQ8//KBx0m2pGDZsWJZPl+nZsyfWrl0r/P3hwwfMnz8fhw8fRmRkJBwdHdGxY0dMmDBBElPs6Bqv2NhYzJo1C9euXUNoaChiYmJQtGhRVKpUCR06dMCgQYM++y+b7GIFQO25y1KuX2LixfqV4caNG/jjjz8QHByM8PBwJCQkwNraGlWqVEHnzp3x3XffqbWGSrmOGRKTQCIiIiIJYp9AIiIiIgliEkhEREQkQUwCiYiIiCSISSARERGRBDEJJCIiIpIgJoFEREREEsQkkIiIiEiCmAQSERERSRCTQCKiQighIQHz5s1DaGioyvJz585h06ZN+VQqIipMmAQaUJs2bWBrawtbW1vUrl072+1bt24tbP/ll1/mQQmJ6HNRrFgxrFy5Ej///DNevHiB5ORkXLt2DcOHD8fx48fzu3hEVAgwCcwlz549Ex5Qr8nz58+zXE9ElBWZTIZhw4bh33//Rc2aNeHo6AgfHx+8evUKI0eOzO/iEVEhYJL9JiTWF198gYcPH2LHjh2oX7++xm127NgBhUIhbEtEJNbUqVPh4eGB48eP4927d3BxccH333+v050IIiK2BOaCdu3awdLSEgcPHkRiYqLaerlcjl27dsHY2Bjdu3fPhxIS0edAJpOhS5cu2LhxIw4cOIAVK1YwASQinTEJzAUWFhbo0KED4uLicPDgQbX1Z86cwatXr9C8eXM4Ojpme7yzZ8+iX79+8PDwQMmSJVG+fHl06tQJR44cyXK/iIgITJ8+Hd7e3nB2dkapUqVQu3ZtDBgwAP/884/a9klJSVi2bBmaNm0KZ2dn2NnZCX0Wlf927Nihss+8efNga2uLYcOGaS3HsGHDYGtri3nz5qmt02X/zHbs2AFbW1u0adNGp+3FePjwIUaMGIFq1arBwcEB5cqVQ+vWreHn54f09HSN5db1n67lffnypc7HrFatmtbjHDp0CN27d0fFihVRsmRJVKxYEb169cL58+c1bp/5fUhKSsKcOXNQt25dODk5oUKFChg4cCAeP36scV/l+5v5X/HixeHu7o5vv/0Whw4d0lrOhIQELF26FE2aNBHq6JdffonJkycjIiJC5/NlV08B4MqVK+jfvz88PDzg4OAANzc3dOrUSeNnVIxz587lqDxKz549w7hx41CnTh2UKlUKZcuWRdOmTbFmzRokJyerbZ+5jmgSEhKCEiVKZFlHFAoF/v77b/Ts2ROVK1eGg4MDKlasiObNm2PhwoVC7PWp55r2tbOzQ7ly5eDr6ws/Pz8oFAq1sin7VmuKmUKhQIsWLYTjnTt3Tm2bnFzHsmKIz2RO6rmS8pqX1b9P45DVNVeb5ORkjB8/Hr6+vvDw8ICjoyPc3NzQqFEjLF68GNHR0Vr3PXnyJHr06CFcbypVqoRevXrhzJkzGrfX9pkpU6YMvLy8MHfuXMTFxantFx8fj927d2PgwIH46quv4OLiAicnJ9SuXRvjxo3Dixcv1PbJ3Fdfl3+aYpbT72Bd6o6m86Wnp+P3339Hq1atUK5cORQvXlyn/XKCt4NzSe/evbFjxw7s2LEDPXv2VFmnvAD16tUL8fHxWo+hUCgwYcIEbNiwAQBga2sLDw8PREREIDAwEIGBgRg0aBAWLVqktu/JkycxYMAAxMbGwsjICBUrVoS5uTlCQ0Px119/4cqVK/D19VU5V9euXYWLSalSpVC5cmUYGxsDyPhiiY2N1S8oBdj+/fsxZMgQpKSkwMLCAp6enoiOjsaFCxdw4cIF7N+/Hzt37kTRokUBAGXLlkW9evVUjvHq1Su8evUKJUuWhLu7u8o6T09P0WX69PhKsbGxCAkJ0bguOTkZgwYNEhKvEiVKwMPDA2FhYTh69Cj++ecfzJw5E6NGjdK4f2pqKtq3b4/g4GC4ubnhiy++wIMHD/Dnn3/i2LFj2Lt3Lxo0aKBx37Jly6Js2bIAMr6Inz17hlOnTuHUqVOYM2cORowYobL9mzdv8O233+LBgweQyWSoVKkSihQpgvv372PNmjXYtWsX9uzZg7p162Z7vk85ODio/L169WpMnToVCoUCtra2qFKlisrnqEePHlizZg2MjHL+u7hIkSKoVauWyrLsPjd79uzBqFGjkJycjKJFi6J8+fJITEzErVu3cOPGDRw4cAB//vknrKysdC7HhAkTkJaWpnX9x48fMXDgQBw9ehQAULx4cVSpUgUxMTG4desWrl69ijJlyqB3794GqeeZt0tNTcWLFy9w8eJFXLx4Effu3cOCBQt0fm3+/v4IDg7Wuj63r2M5+UzqW8+VrK2t1eJ748YNjT8UcuLjx4/YuHEjzMzMULp0aVSpUgWxsbG4c+cObt++jT/++AOBgYFqn62JEydi3bp1ADLe62rVquHly5c4evQojh49ip9//hlTp07VeM7Mnxm5XI43b97g3r17uHfvHv7991+cOHECpqamwvZBQUEYMmQITExMhB9yiYmJCAsLw+bNm7F3717s378fderUEfbx9PRU+xGvrAfu7u4oWbKkyrrM1xR9voM/9Wndefr0Kd69e6dx25EjR8Lf3x9AxjW8Vq1aQhyy2i8nmATmkgYNGsDNzQ3nz5/Hixcv4OrqCgCIiYnB33//jeLFi8PX1xd79+7VeowVK1Zgw4YNKFOmDJYsWYJWrVoJ6wICAjB06FBs3LgRderUQY8ePYR1Dx48QN++fZGYmIgOHTpg/vz5KFWqlMr6Y8eOqZwrICAA586dg5GREdauXat2m7pNmzZaW5EKu0ePHmHYsGFISUlB3759MW/ePFhYWAAATp8+jb59++LUqVP49ddfhQ97nz590KdPH5XjzJs3DwsWLEDz5s2xdu1avcv16XukdO7cObRr107jusmTJ+PQoUPw8PDA0qVLVS48e/bswZgxY/Drr7+iVq1a8Pb2Vtv/4MGDsLa2xrFjx4R9o6OjMXToUBw/fhwDBgzA5cuXYWNjo7Zv7969MWnSJOHv5ORkTJo0CVu2bMGqVavUksBBgwbhwYMHcHd3x7Zt24QvuLdv3+KHH37A2bNn0bdvX1y8eFGn82lz9uxZIQH85ZdfMH78eOGCunfvXowYMQK7du1ClSpVtCbHWVG2Zjk4OKi9Z1l9bi5duoThw4fDyMgI8+bNw8CBA2FmZgYAePLkCQYPHozg4GBMmjQJq1at0qks+/fvx7lz52Btba012Rk7diyOHj0KGxsbrFixAu3atROS348fP+LgwYMoV64cAMPU80+3S09Px2+//YY5c+Zgy5Yt+N///odixYple5zY2FjMmDEDxsbGKFKkiMauNrl9HcvJZ1Lfei6XywEA1apVU2t5qlatGsLCwnL8ejKzsLBAQEAAqlatiiJFigjLX7x4gT59+uDOnTvYsmULJk6cKKzbuXMn1q1bB2NjYyxatAjff/89jIyMkJ6ejrVr12LatGlYvHgxqlWrhg4dOqidU9NnRvmj7ObNmwgKCkLTpk2Fde7u7ti6dSt8fHxgaWkpLI+Li8OKFSuwaNEiDB8+HJcuXYJMJgMAjQmash6MHTsWvXv31hqTnH4HK2Vu6f70dQ4bNkxI9DJ79OiRsHz69OkYNWqU8CMmq/1yireDc1GvXr2gUCiwc+dOYdlff/2FpKQkdO3aVbjgaxITE4NFixbB2NgY27dvV6l8AODj44MlS5YAAJYuXaqybs6cOUhMTISXlxd+//13lQQQACpXrowxY8aoLLt79y4AoEaNGpLrp7hixQokJSXB09MTy5cvFxJAAGjSpAlmz54NAPjjjz+yvXWTnx4/fozff/8d1tbW2L17t9ovz27dumHy5MlQKBRYvny5xmOkpqZi/vz5Kvva2dlh8+bNsLW1RUREBLZt26ZTeYoUKYLvv/8eANRavC9cuICgoCAAwMaNG1VaOBwcHODn5wdra2uEh4fDz89Pp/Nps3jxYuE24uTJk1VaFrp27YrRo0cDAJYtW5ajVhXlPpkv1LqYPn060tLS8L///Q/Dhg1TuR5UqFABfn5+sLCwgL+/P968eZPt8RITEzFt2jQYGRlh/PjxGre5e/eu8AXi5+eHDh06qLR+Fi1aFD169NA6oM0QjI2NMWjQIAAZ9U3XmC9YsACRkZEYOHAg7O3tNW5T0K5jhqjnqampAAATk9xtszE1NUWdOnVUEkAAcHV1Rbdu3QAA79+/V1mnTLD69++PAQMGCHXJ2NgYI0eORNeuXQFAVGtvs2bNUKlSJQBQuyVcsWJFdOjQQSUBBAArKytMmTIF9erVw8OHD3Ht2jWdz6eNPt/BSikpKQDEXRuUddje3h5jxowRfV0Ri0lgLurRoweMjIywa9cu4ReB8lbwd999l+W+//77L+Lj41GrVi21W0xKvr6+MDU1xcOHD4XkJCkpCf/++y8AYNy4cTrf3rK2tgaAz/qWrzYnTpwAAAwdOlT49ZhZz549UbJkSaSmpuLUqVN5XTydHTx4EHK5HM2bN4eLi4vGbdq3bw8g47bKp7dIAMDR0RGdOnVSW25paYm+ffsCgFC/sqO8vQQADRs2VFmnPEb9+vU1DmSwtbUVPiO6nk+ThIQEoeXn05ZIpREjRsDY2BhRUVG4evWq6HMokxhlVwFdhIeH49KlSzAxMRHi+qmyZcuiVq1aSE9P16n16rfffsOrV6/w3XffoWbNmhq3OXz4MADgq6++QuPGjXUuryGlpaUJLYNVq1aFnZ1dtvs8fPgQGzZsQIkSJTB58mSt2xW065gh6rkykTA3N8+lUmoXExODQ4cOCS3RmbsQPXr0CM+fPweg/bOl/IEVEhKic4vlv//+i4cPH6Jo0aIa589NT0/HkSNHMH78eHTr1g2+vr5o1aoVWrVqhadPnwIAbt++rfuLzKIcOfkOziwn752yDiclJQn75ybeDs5FZcuWRePGjXHq1CmcPXsWjo6OuHbtGqpXr55lp37g/38NvHz5Uu0XSGbKpOX169dwcnLC06dPhS+lr776Suey1q9fH8bGxnj69CnGjh2L0aNHw9nZOdd/hSidPHlSeJ0ymQwWFhZwdXVFixYt0KJFi1w774cPHxAZGQlAe789U1NTVKxYEe/evdM6OKIgUNaZ4OBgrXVG+WPk48ePeP/+vVp/mEqVKml9zytXrgwg4+KvyY4dO4SO4Mo+gXK5HN27d8ecOXNUtlXG0cPDQ+vrUb4f+sT82bNnQrKr7Vx2dnYoVaoUXr16hcePH8PLy0vUOf777z8AUGlBzo7yvTI2NhZaSzR58uQJgIzPd1aeP3+OlStXwsbGBr/++ivu37+vcTtlvzUx1wZ9Zf5sp6am4vnz50hISEDr1q0xd+5cnY4xYcIEpKamYtq0abDVMiAGyP/r2KcMUc9jYmIAiPuRoZT5MymTyWBlZYUKFSqgXbt2Wbb2fhrjL774AvPmzVO5Nassr7IvqybK/pjp6el4/PgxnJ2dVda/fftWqBtyuRzh4eF49eoVvvzyS0yZMkXtLlZERAS6deuWbZL3aYtlTuT0OziznLx3NWvWhKWlJeLj4zFw4EBMnToVbm5uKncwDIlJYC7r3bs3Tp06hR07dggjgbPqg6CkrDzv3r3TqROosn+Msvnc2NhYrck8Kx4eHpgzZw6mTZuGLVu2YMuWLTrvawjaXuemTZvwzTffYMeOHVnePs+pzLcpP+3wnJnyw61pxFpBoawzyo772dHUpyqrGCjXaRvMpOm8xsbGePPmDd69e4cSJUoIy5XH0CXmWQ2eyo5yXyMjI7WE99NzvXr1KkfvrzJBK1OmjM77KN+r5ORkXLp0KdvtNb1XmU2aNAnJycmYMWOGSpw/pXx9mvqe5RZtn+3IyEiEh4cL/aW1OXToEE6fPo2aNWuq9U/8VH5fxz5liHquTGiyqr/aaPpM/vvvv1izZg369OmDlStXatyvXr16UCgUePv2LV6+fAljY2O1z4ayvFmVy8TEBPb29nj79q3Gz5a2+v/27Vs8f/4cTZo0UVk+YsQI3L59G66urpg2bRq++uorODg4CLewhwwZgt27dwu30PWR0+/gzJTvXVbv/6dKlCgh9KE+fPiw0HqfW3g7OJe1bdsWNjY2+Pvvv7Fr1y6YmZll+ctfSdmq0KNHD8TExGT7T3m7TTmKMD09XfSX59ChQxEcHCx88BwcHFCvXj3Uq1dPaKLOLT179hRey/v373H//n3MmjULxsbGOHHiBH7//fdcOW/mRPnt27dat1M29YsZpZnXlHXml19+0anOKDv/Z5ZVDJTrtP24mDBhgnDsqKgo3L59G2PGjMHZs2fRsmVLlX5tymPoEnMxP2Y+pdxXLpdneSHX5/1Vthi4ubnpvI/yvSpbtqxO71VWA2BOnDiBY8eOwdPTEz/88EOW51W+vg8fPuhcVn19+tl+8OAB5s6di1u3bqF9+/ZZtup8/PgRU6ZMgUwmw8KFC3Xq3pKf17FPGaKeK2+5ftqKpotPP5O3bt0S+oNv27ZN41RhQMYghuPHj+PGjRu4cuUKTE1N8eOPP2LZsmXCNsryZvW5SktLQ1RUFADNny1nZ2eVev78+XNs374dKSkp+Omnn4RRuUDGj4aAgAAAGaPEO3fuDGdnZ5U+jFlNYyNWTr+DM1O+d9pmMdCmY8eOuHr1qtAX08bGRqjDOfkxkBUmgbnM3NwcnTp1QmJiIt69ewdfX18UL1482/2Utwju3bsn6nwVKlQQ+h9kNZWCNjKZDNevX4eZmRn27duHY8eO4dixY9nevjYkIyMjlCpVCqNGjRI6d2ubb0pfNjY2Qguttike0tLShFsfyg7LBVFO60xmjx8/1thXEMgYVQ7oFgNjY2O4uLhg2rRpaNKkCWJjY7F9+3ZhvfIY2m5bAv//fugTczc3N6FDvbZzxcTECAmq2HPJ5XKhH6GYwRRVqlQBkNE3UJ8vrpSUFCFBnDdvXraDB5Tnzcm1wRCMjIzg5OSE4cOHo1evXkhLSxP6jWqybNkyhIWFoXv37qJuYReE6xhgmHqu/JGhb9mNjY1Rrlw5Ye5YQLfrqru7O+bPnw8AKkmZsrwfP34Ukp1PPXjwQLie6PLZsrOzQ9u2bTFjxgwAUBlV/vLlS2EbTbfX09LScOPGjWzPoStDXE/v3LkDIGfvXdGiRXHhwgUAGXFX1uHmzZvnuDyaMAnMA99//z0aN26Mxo0bZ/tLXalVq1YoWrQo7ty5I2owQpEiRdCyZUsAGSOWNE3Gqk1aWhp++OEHxMbGYtq0aahevbrO++YW5ShAQzTva6Psc7hu3TqN8dq1axfevXsHU1NTlT4xBU3Hjh0hk8nw77//CgmbWBEREThw4IDa8vj4eGFUsNg+msoWgMwtgcpjXLx4EdevX1fbJyYmRkga9ekTamFhIfTxW716tcZt1qxZg/T0dNjb26vML6aL06dP4927d7C2thbVl9DV1RU1a9aEXC7XefoXTdasWYMnT56gY8eOOg30aN++PWQyGYKDgzVOtJyXNNWLzEJDQ7FixQpYWVkJSYEuCtJ1TN96HhwcjDdv3sDCwgJff/21wcol9rqalJQEQLUFuWLFikLrt7bPlrJue3p6imoN01Q3lP3q4uLiNN569ff3N+j8eTn9DlaKi4tDYGAgAOToe2P06NF49eoVBg0aJHyn5wYmgXmgRo0aOHjwIA4ePKixyViTkiVL4ueffwYA9OvXD/7+/moTwEZHR8Pf3x/Tpk1TWT558mQUK1YM586dw8CBA9VGLT148EClWV9p7ty5uHr1Kho3blwgHkD//PlzYR7FGjVqZLt95pn1xRg1ahTMzc0REhKCMWPGICEhQVh35swZYaLT77//XqcnvOSXKlWqoG/fvkhNTUWnTp1w7NgxtaT2zZs32LRpk9YpDUxNTTFx4kSVlqKYmBgMGjQI0dHRcHR0zHZke2YnTpwQRl9n/vVev359oTVi0KBBKi0l7969Q//+/REbG4vSpUtn2w8sO+PGjROS43nz5ql88f3111/CdDljxoxRmx4jK+/evRNa4Xr37i1qYAgAzJ49GyYmJvjtt98we/ZsoQ+SUlJSEk6cOKF19DCQMf1N0aJFMWvWLJ3O6enpiV69egEA+vbti8OHD6vUkaSkJOzevRsXL14U9VrEunHjhjBVjbZBE6tXr8bHjx/xyy+/iPrcFaTrmD71/Pr16xg8eDCAjOnGdJlLURfXrl0Tbqtmvq6eO3cOFy9eVLtmXLlyBWPHjgUAtYnild9Rv//+O37//XdhX7lcjrVr12L37t0AMm5L6yoiIgK//fYbANW64eHhAXt7e6SlpWH8+PFCYgpkzIwwYcIEg46g1uc7ODw8HP3798f79+9RpUoVrRPsa/PHH3/g0KFDqFy5MmbOnKnfC8kGB4YUYGPHjsWHDx+wYsUKDBs2DOPHj4e7uztMTEzw9u1bvHr1CgqFQq0F4osvvoCfnx/69++Pv/76CwcOHBBmqQ8LC8P79+/h7OysMlfg2bNnsWzZMtjZ2WHt2rUap0rJSlBQkNYvK2UT/YEDB3D//n1MmjRJ7cKfeX+5XI63b9/i+vXrSEtLg4uLC4YOHSqqPGJUqlQJa9euxZAhQ7B161b8+eefqFixIqKjo4XHEDVt2jTXP4yGsGjRInz8+BF79uxBjx49YGtrK4zci4iIEH5Zf/oUG6UOHTogNDQULVq0gLu7O6ysrPDgwQMkJSWhWLFi2LRpk9YkO/NIxPT0dISHhwuDJqpXr66WPG7cuFF4kkKDBg3wxRdfwMzMDPfv30dqairs7Ozg5+en9yCGRo0aYdasWZg2bRoWLFiA9evXw83NDREREQgPDwcAdO/eXes0F5osWbIEq1atEm7lnjlzRuMIQuWtvt9++w3btm3D1q1bhYTG29sbGzduxMiRI7F48WIsW7YMFStWhKWlJWJiYvDixYtsW2ri4+MxefJkUf3FFi9ejPfv3+Off/5Bnz59ULx4cbi6uiImJgZhYWFITU3F6tWrDTZXYObRwQqFApGRkcLnytnZWesE3fHx8ahYsaKoz76+17HckJN67uvrKyTi9evXx6+//pqjcyuvucD/fyZv3rwJhUKhNpdiUFAQFixYACsrK5QqVQoWFhZ48+aN0Ijg6OioNpq7V69euH37NtatW4effvoJ8+bNQ9myZREaGiqMmv/55581ThQNqI4OBjJ+cD59+hSpqakwNzdXueaamJgIkyfv2LEDhw8fhpubG96+fYvw8HD4+PjA3t4ee/bsyVGsNMnJd/DMmTOxfPlypKeno3Tp0ti4caOoJxE9fPgQkydPRpEiRbBx48YcjQoXg0lgASaTyTBz5kx07NgRmzdvxoULF/Dw4UOkp6ejRIkS8PHxQYsWLTQ+l7Z58+a4fPkyVq9ejYCAAISGhkImk8HR0RE+Pj4qg1Pev3+PoUOHQi6XY/ny5ShdurTosoaFhWU7D9TDhw/x8OFDYaLYrPYvVqwYKlWqhJYtW2LUqFE69aNUTvWSk9s/3377LTw8PLBixQqcPXsW9+7dg7m5OerXr4+ePXuid+/e+TbNhBhmZmbYsGEDevXqBT8/PwQHBwuJiIODA9q0aYNWrVqhdevWGvc3NTXFoUOHsHjxYuELxNLSEr6+vpg4cSK++OILrefOPBJRJpPB0tIStWrVQrt27TBkyBC1i1mpUqUQEBCA9evX4+DBg3j69CnS0tJQrlw5fPPNNxg9erTaFBE5NXLkSHz99ddYvXo1Ll26hDt37sDS0hJNmzbF999/r/VLSpsnT56o9OXT1p9U6enTp3j69KlK6wWQUe+++uorbNiwAQEBAXj58iUSExNhY2OD2rVro0mTJlk+d7pcuXLCXGy6Klq0KHbu3ImDBw9ix44duHnzJu7cuQM7OzvUrFkTLVu2NGi/o09HVxYrVgyenp46fbbnz5+v89QYhriO5Yac1POEhAQ0bNgQXbt2Rc+ePXM8PYjymgv8/2eyZs2awmcyc8t3ixYt8ObNG1y9ehXh4eGIi4uDpaUl6tSpg+bNm2PIkCEa36v58+fDx8cHmzZtwrVr13D79m3Y2dmhdevWGDJkSJbdFD4dHax8ZJ2XlxdGjRql1ljQp08f2NnZYcWKFbhz5w4eP36M8uXLY9iwYRg2bFiOnviTlZx8B3/48AHVq1dHq1at8MMPP2id2FyT5ORkDBw4EImJiZg9e3ae9GGVxcTE6N5pjEgPO3bsEIa963pbXIwuXbrg5MmT+OOPP9CxY0eDH/9zpnwUWM+ePQ3yyLvPnfLRTZ/ewtVGGd9bt25pHJVNRJQf2CeQPgtyuRzBwcGoUKGC8FQMIiIi0o5JIH0W7ty5g9jYWIwePVpU/wsiIiKpYp9AyjMNGzbE1q1bs3yEUk7VqFFD51tzRPr6+eefsxy1+6nvvvsOTZo0UXusFBFRfmISSHnGxcUFLi4u+V0MIr25u7vD3d1d5+2dnZ1z9MQHIqLcxIEhRERERBLEzlNEREREEsQkkIiIiEiCmAQSERERSRCTQCIiIiIJYhJIREREJEFMAomIiIgkiEkgERERkQQxCSQiIiKSICaBRERERBL0f3Iif1gkFrVSAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = train_df['timestamp']/ms_per_month\n",
    "ts.plot.hist(bins=100)\n",
    "plt.title(\"История событий пользователей\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.xlabel(\"Месяцы, от первого действия пользователя\")\n",
    "plt.ylabel(\"События\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5da2256c-266a-4290-a41c-78fc65f295ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c0ceadbb-20b0-4c97-b20a-bc059444ccd2",
   "metadata": {},
   "source": [
    "Проверка связи между 'timestamp' и успеваимостью 'answered_correctly',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9833c2de-e512-4d06-9019-80a65f48748f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 21.5 s\n",
      "Wall time: 26.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "bin_labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n",
    "train_df['ts_bin'] = pd.qcut(train_df['timestamp'], q=10, labels=bin_labels)\n",
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)\n",
    "answered_question_ts_df = (\n",
    "    train_df.loc[answered_question_mask]\n",
    "    .groupby([\"ts_bin\", 'answered_correctly'], as_index=False)\n",
    "    .size()\n",
    ")\n",
    "answered_question_ts_df = answered_question_ts_df.pivot(index=\"ts_bin\", columns='answered_correctly', values='size')\n",
    "answered_question_ts_df['correct_answers'] = answered_question_ts_df.iloc[:, 1] / (answered_question_ts_df.iloc[:, 0] + answered_question_ts_df.iloc[:, 1])\n",
    "answered_question_ts_df = answered_question_ts_df.sort_values(by=\"correct_answers\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4bd4b7c8-2c25-4b62-87b4-b47640fdf5d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>answered_correctly</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>correct_answers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3176412</td>\n",
       "      <td>6753723</td>\n",
       "      <td>0.680124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3192168</td>\n",
       "      <td>6742420</td>\n",
       "      <td>0.678681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3188573</td>\n",
       "      <td>6708007</td>\n",
       "      <td>0.677811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3195985</td>\n",
       "      <td>6713079</td>\n",
       "      <td>0.677469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3208426</td>\n",
       "      <td>6712255</td>\n",
       "      <td>0.676592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "answered_correctly        0        1  correct_answers\n",
       "ts_bin                                               \n",
       "9                   3176412  6753723         0.680124\n",
       "10                  3192168  6742420         0.678681\n",
       "6                   3188573  6708007         0.677811\n",
       "7                   3195985  6713079         0.677469\n",
       "8                   3208426  6712255         0.676592"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answered_question_ts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "579376b0-ffff-4bc9-b360-8ca991d6b994",
   "metadata": {},
   "outputs": [],
   "source": [
    "answered_question_ts_df[\"correct_answers\"] = answered_question_ts_df[\"correct_answers\"].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "89675835-d7a2-4e73-bd4b-92ab8afb12fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>answered_correctly</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>correct_answers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ts_bin</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3176412</td>\n",
       "      <td>6753723</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3192168</td>\n",
       "      <td>6742420</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3188573</td>\n",
       "      <td>6708007</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3195985</td>\n",
       "      <td>6713079</td>\n",
       "      <td>0.68</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3208426</td>\n",
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       "      <td>0.68</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "answered_correctly        0        1  correct_answers\n",
       "ts_bin                                               \n",
       "9                   3176412  6753723             0.68\n",
       "10                  3192168  6742420             0.68\n",
       "6                   3188573  6708007             0.68\n",
       "7                   3195985  6713079             0.68\n",
       "8                   3208426  6712255             0.68"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answered_question_ts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ec68c1cf-69e1-4997-ba4c-d3c4e4086e81",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins_correct_answers = answered_question_ts_df.iloc[:, 2].sort_index()\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "plt.bar(bins_correct_answers.index, bins_correct_answers.values)\n",
    "for i, v in zip(bins_correct_answers.index, bins_correct_answers.values):\n",
    "    plt.text(i, v, v, color='white', fontweight='bold', fontsize=14, va='top', ha='center')\n",
    "plt.title(\"Процент корректных ответов\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f75705c-74cd-4107-9b33-6a3f0608ded6",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "- timestamp - это время между этим взаимодействием пользователя и завершением первого события от этого пользователя, т.е от старта регистрации на платформе обучения.\n",
    "- Большинство взаимодействий происходит от пользователей, которые еще не были активны на платформе очень долго.\n",
    "- Успеваимость студетов, которые только зарегистрировались ниже, чем студентов, учащихся на платформе уже некоторое время.\n",
    "- Хорошей дополнительной переменной может стать \"количество месяцев, прошедших с момента ррегистрации\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd7b9a79-9d56-41e9-97dc-fec9b8936426",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "afd46889-b10b-4315-9809-c7e94ea06101",
   "metadata": {},
   "source": [
    "### Анализ переменной prior_question_had_explanation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4afb83c-b352-4cac-ba89-2e5ff036c156",
   "metadata": {},
   "source": [
    "Заполню пропуски в 'prior_question_had_explanation' = False, так как это соответствует тому что студент не видел ответ предыдущего вопроса или смотрел лекцию - прерывал сессию ответов на вопросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "54b15f5b-c1f0-4bbe-b2f6-b5c80becb939",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['prior_question_had_explanation'] = train_df['prior_question_had_explanation'].fillna(value=False).astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7eb0e732-f8c1-4a85-a4c1-80d93cc30a0a",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">answered_correctly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prior_question_had_explanation</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>0.507977</td>\n",
       "      <td>9585740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>0.673189</td>\n",
       "      <td>89685560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               answered_correctly          \n",
       "                                             mean     count\n",
       "prior_question_had_explanation                             \n",
       "False                                    0.507977   9585740\n",
       "True                                     0.673189  89685560"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)\n",
    "\n",
    "prior_question_df = (\n",
    "    train_df.loc[answered_question_mask]\n",
    "    .groupby(['prior_question_had_explanation'], dropna=False)\n",
    "    .agg({'answered_correctly': ['mean', 'count']})\n",
    ")\n",
    "prior_question_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8e157ff2-816a-422a-970b-9ed763c665cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prior_question_had_explanation\n",
       "False    0.507977\n",
       "True     0.673189\n",
       "Name: (answered_correctly, mean), dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior_question = prior_question_df.iloc[:, 0]\n",
    "prior_question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cb86d58e-07ba-40e1-8ec3-e07a19015785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prior_question.plot.barh()\n",
    "plt.title(\"Процент правильных ответов в зависимости от того видел ли студент предыдущий ответ\")\n",
    "plt.xlabel(\"Процент правильных ответов\")\n",
    "plt.ylabel(None)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a2df34-3400-46f6-b2a8-4e90c3526336",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "- prior_question_had_explanation - Увидел ли пользователь объяснение и правильный ответ(ы) после ответа на предыдущий пакет вопросов, игнорируя любые лекции между ними.\n",
    "- prior_question_had_explanation - соответствует непрерывной сессии ответов на вопросы\n",
    "- Когда студент работает непрерывно - у него выше результаты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2a453fd-5711-415e-ba51-baa4a6c874f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae46d29e-6ed0-4385-a5a2-22c10264a0a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec4d71c4-414f-4cbb-878b-84d57285f427",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb6aa9a-de86-4b9e-9253-1fed40afbfd6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f995fe4f-00b6-4123-9614-301330462ad0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0610e9e-322e-4224-a21f-5067a038a286",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b51fe1ed-3d7b-4424-95cf-94bdd89a7c09",
   "metadata": {},
   "source": [
    "## EDA Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "410cabe0-8b86-4303-8601-2aec468d1dd1",
   "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>question_id</th>\n",
       "      <th>bundle_id</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>part</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51 131 162 38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>131 36 81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131 101 162 92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131 149 162 29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>131 5 162 38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   question_id  bundle_id  correct_answer  part            tags\n",
       "0            0          0               0     1   51 131 162 38\n",
       "1            1          1               1     1       131 36 81\n",
       "2            2          2               0     1  131 101 162 92\n",
       "3            3          3               0     1  131 149 162 29\n",
       "4            4          4               3     1    131 5 162 38"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "c151159e-7e81-4754-b9b1-4a28d5657a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13523, 5)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "726dc61c-0644-4ef9-bcb9-5a34d5e0d163",
   "metadata": {},
   "source": [
    "Проверка пропусков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "3d5e9402-20b5-4faa-937a-73c6f3e1fc8d",
   "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>question_id</th>\n",
       "      <th>bundle_id</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>part</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [question_id, bundle_id, correct_answer, part, tags]\n",
       "Index: []"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df[questions_df[\"correct_answer\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "461c564c-b32f-4dee-8df0-e8b6550fc156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>question_id</th>\n",
       "      <th>bundle_id</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>part</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [question_id, bundle_id, correct_answer, part, tags]\n",
       "Index: []"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df[questions_df[\"part\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c5230fad-7c03-4cf0-86ca-ce7d2a0cd0d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>question_id</th>\n",
       "      <th>bundle_id</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>part</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10033</th>\n",
       "      <td>10033</td>\n",
       "      <td>10033</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       question_id  bundle_id  correct_answer  part tags\n",
       "10033        10033      10033               2     6  NaN"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df[questions_df[\"tags\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "427cde3b-9a46-4124-ba73-906b20568336",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_df[\"tags\"] = questions_df[\"tags\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "ccceb598-2449-44d3-9305-19a7764b0dde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>question_id</th>\n",
       "      <th>bundle_id</th>\n",
       "      <th>correct_answer</th>\n",
       "      <th>part</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>51 131 162 38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>131 36 81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131 101 162 92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>131 149 162 29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>131 5 162 38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   question_id  bundle_id  correct_answer  part            tags\n",
       "0            0          0               0     1   51 131 162 38\n",
       "1            1          1               1     1       131 36 81\n",
       "2            2          2               0     1  131 101 162 92\n",
       "3            3          3               0     1  131 149 162 29\n",
       "4            4          4               3     1    131 5 162 38"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "ad4074fc-b4bd-447f-a2bf-8d68df2dd902",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13523 entries, 0 to 13522\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   question_id     13523 non-null  int64 \n",
      " 1   bundle_id       13523 non-null  int64 \n",
      " 2   correct_answer  13523 non-null  int64 \n",
      " 3   part            13523 non-null  int64 \n",
      " 4   tags            13523 non-null  object\n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 528.4+ KB\n"
     ]
    }
   ],
   "source": [
    "questions_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d0a18213-534b-4e9d-b29e-15a24f08f329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part\n",
       "5    5511\n",
       "2    1647\n",
       "3    1562\n",
       "4    1439\n",
       "6    1212\n",
       "7    1160\n",
       "1     992\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df[\"part\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "41ec00e8-c406-4cd4-8dc1-0d01dce7e4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "correct_answer\n",
       "0    3716\n",
       "3    3544\n",
       "1    3478\n",
       "2    2785\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_df[\"correct_answer\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5535e1b2-9bcd-4638-ad0e-5b7d23db1a08",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "961fff6e-b12b-4b0b-9334-8d239d81e191",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a3468bde-82b7-4509-a2ec-755eed5ef775",
   "metadata": {},
   "source": [
    "Построение списка тегов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e2483c9a-1920-4974-bcb2-d11cb338dd7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_df['tags'] = questions_df['tags'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "7edb60ee-dd2c-443d-b68a-178e229fb082",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags = [x.split() for x in questions_df[questions_df[\"tags\"] != 0].tags.values]\n",
    "tags = [item for tag_set in tags for item in tag_set]\n",
    "tags = list(set(tags))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "0d4711f3-9d33-4de7-868b-97a82c62895c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "188"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "6f82f842-c9e6-4e04-b9ee-31570f793f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tags = [x.split() for x in questions_df[questions_df[\"tags\"] != 0].tags.values]\n",
    "tags = [item for tag_set in tags for item in tag_set]\n",
    "tag_counter = Counter(tags)\n",
    "most_common_tags = 30\n",
    "y = [count for tag, count in tag_counter.most_common(most_common_tags)]\n",
    "x = [tag for tag, count in tag_counter.most_common(most_common_tags)]\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "plt.bar(x, y)\n",
    "plt.title(f\"{most_common_tags} самых употребляемых тегов\")\n",
    "plt.ylabel(\"Чстота\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "630f08e6-bf1e-45a2-b47f-f7e7bb9899c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "fb377e2b-2924-4471-9dee-c03d24bb74b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(x='part', data=questions_df, order=questions_df['part'].value_counts().index)\n",
    "plt.title('Распределение вопросов по категориям')\n",
    "plt.xlabel(\"Категории вопросов\")\n",
    "plt.ylabel(\"Количество вопросов\")\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "975fdfb9-2f29-4ece-ba0e-cd3f5b401cdf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d3c20a3f-757f-4283-9c0d-1f1b6b2479f5",
   "metadata": {},
   "source": [
    "Можно построить зависимость правильности ответа от тега вопроса. Можно построить корректность ответов для каждой категории."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "3d80121c-54c7-44f8-9178-b2e4b5c5179c",
   "metadata": {},
   "outputs": [],
   "source": [
    "answered_question_mask = (train_df[\"answered_correctly\"] != -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "9a8cc909-31a5-4f91-befc-cb12b1dc8d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "answered_correctly_df = (\n",
    "    train_df.loc[answered_question_mask]\n",
    "    .groupby([\"content_id\", 'answered_correctly'], as_index=False)\n",
    "    .size()\n",
    ")\n",
    "answered_correctly_df = answered_correctly_df.pivot(index= \"content_id\", columns='answered_correctly', values='size')\n",
    "answered_correctly_df.columns = ['wrong', 'right']\n",
    "answered_correctly_df = answered_correctly_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a79cc1d0-07ed-4b7f-8c85-06b17c76a4b3",
   "metadata": {},
   "outputs": [
    {
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       "              wrong    right\n",
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     "execution_count": 168,
     "metadata": {},
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   ],
   "source": [
    "answered_correctly_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "3a206e9e-d89a-40f1-878f-1939e8a9172b",
   "metadata": {},
   "outputs": [],
   "source": [
    "questions_answers_df = questions_df.merge(answered_correctly_df, left_on=\"question_id\", right_on=\"content_id\", how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "a5795b17-6b6c-4475-b34d-c99d7abd8b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "tags_list = [x.split() for x in questions_answers_df.tags.values]\n",
    "questions_answers_df['tags'] = tags_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "0d0edcb0-5ad4-4a85-9ae5-acfa6ab9312c",
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   "outputs": [
    {
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       "      <th>tags</th>\n",
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       "   question_id  bundle_id  correct_answer  part                 tags    wrong  \\\n",
       "0            0          0               0     1   [51, 131, 162, 38]    637.0   \n",
       "1            1          1               1     1        [131, 36, 81]    809.0   \n",
       "2            2          2               0     1  [131, 101, 162, 92]  20015.0   \n",
       "3            3          3               0     1  [131, 149, 162, 29]   5067.0   \n",
       "4            4          4               3     1    [131, 5, 162, 38]  12275.0   \n",
       "\n",
       "     right  \n",
       "0   6266.0  \n",
       "1   6589.0  \n",
       "2  24890.0  \n",
       "3  17906.0  \n",
       "4  19461.0  "
      ]
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     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "questions_answers_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "cf43de70-baef-4557-83bd-ca82109c1cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13523, 7)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions_answers_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9470696b-1b11-4e24-b53c-73c0ab979921",
   "metadata": {},
   "source": [
    "Теперь я могу сложить все Неправильные и Правильные ответы для всех вопросов, помеченных определенным тегом, и вычислить процент правильных ответов для каждого тега. \n",
    "Обратите внимание, что существует «двойной подсчет» вопросов; например, если у вопроса 5 тегов, его ответы суммируются в суммах каждого из 5 тегов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42aab204-cfd2-493c-ab63-d93d44d09e77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53d1d7cf-9f8f-40dd-a6a3-0b478e31a4f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5c6c126-5fa1-4214-8be4-1e10353b0a2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Выводы:\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "251568e0-068a-40ea-bcee-335797a7ee11",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "77e9f5e9-f647-4b2b-ba44-fda503167945",
   "metadata": {},
   "source": [
    "## EDA Lectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "42015a41-1671-46bc-8b7a-9c096f122088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lecture_id</th>\n",
       "      <th>tag</th>\n",
       "      <th>part</th>\n",
       "      <th>type_of</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>89</td>\n",
       "      <td>159</td>\n",
       "      <td>5</td>\n",
       "      <td>concept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>concept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>185</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>concept</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>192</td>\n",
       "      <td>79</td>\n",
       "      <td>5</td>\n",
       "      <td>solving question</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>317</td>\n",
       "      <td>156</td>\n",
       "      <td>5</td>\n",
       "      <td>solving question</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lecture_id  tag  part           type_of\n",
       "0          89  159     5           concept\n",
       "1         100   70     1           concept\n",
       "2         185   45     6           concept\n",
       "3         192   79     5  solving question\n",
       "4         317  156     5  solving question"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1c82874b-a309-4fba-a843-e706b55d751a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 4)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e8b49aa-6c49-472b-bd2e-938bd12672b7",
   "metadata": {},
   "source": [
    "Проверка пропусков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "c472495b-3c44-4316-b3d8-87cc73ec542f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(lectures_df.tag.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "490372c9-ecd7-4ee8-9e7c-80867cfc5cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(lectures_df.part.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2d3e8329-ddac-4df2-8b85-6a9326a0bf01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(lectures_df.type_of.isna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "75758ba4-e574-4d12-9665-4b6675d1b432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151,)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df[\"tag\"].value_counts().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcd1456c-e9b0-459e-851e-f89a3ac157a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1de7d0a0-076f-4b63-81c7-844a98902fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "117e1971-c1ee-49c5-a476-1c4605184c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type_of\n",
       "concept             222\n",
       "solving question    186\n",
       "intention             7\n",
       "starter               3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df[\"type_of\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "18e01400-a866-4d3a-a193-e6910a728b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aNc1jqlatqs2bNyssLEyenp6Ki4tTRESEvL29jYD5cPsHz5uZ27dvP85bQS7E9xoAkFs8PNYiM7kyHN67d0/Dhg3TnTt39NFHHxnBLjo6WpJUokSJNI8rXrx4inbJ/03enln7zFy6dEmJiYlZfBfIzcLDw80uAQCATDk7O6fbaZaeXBcOk5KSNGLECO3YsUMvvfSS+vbta3ZJhvLlyz9C65s5VgdyXnY/gwoAgKPIVeEwKSlJAQEB+uGHH/S3v/1Nn3/+eYr9yT19UVFRaR7/cE9hZj2DmfUsPuxRu22Re/G9BgDkVblmQEpyj+GSJUvUq1cvzZo1S05OKctPno/w9OnTaZ4jeXtyOxcXF5UrV07nzp1L83bww+0BAADyulwRDpOD4dKlS9WjRw99/fXXaQ4gqVatmp588knt3r1bcXFxKfbFxcVp9+7dqlSpkjw9PY3tfn5+iouL065du1Kdb/PmzZIkX1/fbH5HAAAAjsnhw2HyreSlS5eqW7dumjNnTprBUJIsFosGDhyo2NhYffLJJyn2ffLJJ4qNjdVLL72UYnvy64kTJ+ru3bvG9qCgIAUHB8vf319eXl7Z/K4AAAAck8NPgj158mRNnTpVxYoV0+uvv55mMOzYsaPq1asn6X4PYdu2bXX48GH5+/urfv36OnjwoLF83vr161WkSJEUxz+8fF5ERIRWrVolFxcXBQUFqXr16tn+vpgEO3djEmwAQF7l8ANSzp8/L0mKjY3Vp59+mmYbLy8vIxy6uLho/fr1mjJlitauXavt27erbNmyeuONN/TOO++kCoaS9MUXX8jb21sLFizQ7Nmz5eLiok6dOmncuHGqUqVKzr05AAAAB+PwPYd5FT2HuRs9hwCAvMrhnzkEAACA/RAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMBcwuAEDm6szcY3YJeExHRjQ1uwQAeCT0HAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAACGXBEOly1bptGjR6tVq1by8PCQm5ubFi1alGbbyZMny83NLd0/586dS/O4zZs3q0OHDvL09FTFihXVqVMnbdu2LSffFgAAgMMpYHYBWTFhwgSFh4erVKlSKlu2rMLDwzM9pl+/fvLy8kq1vUSJEqm2LVu2TMOGDVPp0qXVr18/SdKqVavUrVs3/fe//1XXrl1tfxMAAAC5QK4Ih1999ZWqVq0qLy8vff755xo/fnymx/Tv318tW7bMtF1kZKTefvttlSpVStu2bVOFChUkSaNHj9azzz6rt956S/7+/nJ1dbX5fQAAADi6XHFbuVWrVmn2AmaH1atXKyoqSq+99poRDCWpQoUKevXVV3X9+nWtW7cuR64NAADgaHJFOHwcO3bs0BdffKH//Oc/WrdunWJjY9NsFxwcLEny9/dPte+FF16QJIWEhORcoQAAAA4kV9xWfhyTJ09O8bpEiRKaMmWK8UxhsrCwMElStWrVUp0jeVtyGwAAgLwuz4XDunXravr06WrRooXKlSunK1eu6Oeff9akSZM0YsQIlShRQh06dDDaR0dHS5KKFy+e6lzJzxkmt8nM7du3s+EdIDfge42s4rMCwGyFCxd+pPZ5Lhx27tw5xetKlSrptdde01NPPaVu3bppwoQJKcJhdrp06ZISExNz5NxwLFkZMQ9IfFYAmMvZ2VlVq1Z9pGPyXDhMz3PPPacqVaro6NGjio6ONnoKk/8bHR2tkiVLpjgmJiYmRZvMlC9f/hEquvkIbeFoKlasaOcr8nnJrez/WQEA2+SbcChJpUqV0unTpxUfH28EvmrVqmn//v0KCwtLFQ4zeh4xLY/abYvci+81sorPCoDcJs+OVn5YXFycjh8/LhcXF5UqVcrY7ufnJ0nasmVLqmM2b96cog0AAEBel6fCYUxMjE6dOpVqe3x8vEaNGqWYmBh169ZNBQr8X4dp9+7dVbx4cc2ZM0cXL140tl+8eFHffPONSpUqpU6dOtmlfgAAALPlitvKgYGB2rlzpyTp6NGjkqTvvvvOmKOwefPmGjRokG7cuKEmTZqoYcOGqlmzpsqWLaurV69q27Ztunjxory9vfWvf/0rxbnd3Nz0ySefaNiwYXruuefUvXt3SfeXz7tx44bmz5/P6igAACDfyBXhcOfOnVqyZEmKbbt27dKuXbuM14MGDZK7u7teeeUV/fHHHwoKClJkZKSKFCmimjVratiwYXr11VdVpEiRVOfv06ePSpUqpc8++0yLFy+WxWJR/fr1NWbMGLVq1Sqn3x4AAIDDsERGRlrNLiI/qjNzj9klwAZHRjS16/X4vORe9v6sAICt8tQzhwAAALAN4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAabwuHUqVO1aNGiLLVdsmSJpk6dasvlAAAAkMNsCodTpkzRwoULs9R20aJFhEMAAAAHZ7fbylar1V6XAgAAwGOyWzj866+/VLRoUXtdDgAAAI+hwKM0jo6OVlRUVIptd+/eVXh4eLrHxMfHa+vWrTpx4oR8fHwer0oAAADYxSOFw5kzZ2ratGkptu3fv1/169fP0vF9+vR5lMsBAADAzh4pHFqt1hTPDloslkyfJSxatKiqVKmivn37asSIEY9XJQAAAOzikcLh2LFjNXbsWOO1u7u7mjVrpo0bN2Z7YQAAALC/RwqHD3vnnXfk6emZXbUAAADAZDaFw3fffTe76gAAAIADYPk8AAAAGGzqOZSkpKQkLV26VD/99JNOnz6t2NjYdAepWCwWHThwwNZLAgAAIIfYFA5jYmLUq1cv7d27N0sroFgsFlsuBwAAgBxmUzicOnWq9uzZo6JFi+rvf/+7mjZtqjJlysjJibvVAAAAuZFN4XDNmjVycnLSkiVL9Oyzz2ZXTQAAADCJTV18V65cUcWKFQmGAAAAeYRN4bBUqVJyd3fPrloAAABgMpvCob+/v44fP66YmJjsqgcAAAAmsikcvvvuuypUqJDeeecdJSYmZldNAAAAMIlNA1LOnTunsWPHaty4cdq/f78GDRqk6tWrq2jRouke4+fnZ8slAQAAkINsCoedOnUy5i48fvy43n///QzbWywWXb9+3ZZLAgAAIAfZFA49PT2Z2BoAACAPsSkchoaGZlcdAAAAcAAsZQIAAAAD4RAAAAAGwiEAAAAMNj1zWL9+/Udqb7FYdODAAVsuCQAAgBxkUzg8f/78I7VnZDMAAIBjsykcrl27Nt19t27d0qlTpxQYGKgzZ87oX//6l7y9vW25HAAAAHKYTeGwRYsWGe5v06aNhg0bpjfffFOTJ0/Wb7/9ZsvlAAAAkMNyfECKs7Ozpk6dqtu3b2vq1Kk5fTkAAADYwC6jlV1dXfXUU09p8+bN9rgcAAAAHpPdprKJjIzUjRs37HU5AAAAPAa7hMPt27fr/PnzKleunD0uBwAAgMdk04CUkJCQdPdZrVZdu3ZNe/bs0cKFCyVJXbt2teVyAAAAyGE2hcNOnTplae5Cq9Wqxo0b6+2337blcgAAAMhhNoVDT0/PdMOhxWKRi4uLqlatqvbt26tv375ydna25XIAAADIYTaFw9DQ0OyqAwAAAA7AbqOVAQAA4PgIhwAAADDYdFs5mdVq1bp167Rp0yadPHlSMTExcnV1Vc2aNdWmTRt17NgxSwNXAAAAYC6bw+HZs2c1aNAgHT58WNL9oJgseRobHx8fLViwQJUrV7b1cgAAAMhBNoXD6Ohode3aVefPn5ezs7Pat28vb29vlStXThERETp69Kg2btyoQ4cOqXv37tq2bZuKFy+eXbUDAAAgm9kUDmfMmKHz58/Lx8dH8+fPV7Vq1VK1OX36tF5++WUdPnxYM2fO1LvvvmvLJQEAAJCDbBqQsm7dOjk7OyswMDDNYChJVatWVWBgoCwWi9auXWvL5QAAAJDDbAqH586d01NPPZXps4SVK1dWrVq1dO7cOVsuBwAAgBxmUzi0Wq1ycsraKSwWS4rBKgAAAHA8NoXDSpUq6fjx47p06VKG7S5cuKDjx4+rUqVKtlwOAAAAOcymcNiuXTslJCRo8ODBioiISLPN5cuXNWTIECUlJalDhw62XA4AAAA5zKbRym+++aaWLl2qvXv3qkGDBurWrZu8vb3l4eGhq1ev6ujRo1q9erXu3Lmj8uXL64033siuugEAAJADbAqH7u7uWrlypQYOHKhTp07p+++/T7E/+RnDGjVqKDAwUG5ubrZcDgAAADnM5rWVa9WqpZCQEM2ePVvdu3dX3bp1VblyZdWtW1c9e/bU119/rZCQENWqVeuxr7Fs2TKNHj1arVq1koeHh9zc3LRo0aJ020dHR+u9995T3bp15eHhIR8fH40bN06xsbFptk9KStLXX38tX19flStXTtWqVdPQoUN19uzZx64ZAAAgN8qWtZWfeOIJ9enTR3369MmO06UyYcIEhYeHq1SpUipbtqzCw8PTbRsXF6eOHTsqNDRU/v7+6tWrlw4dOqSvvvpKISEh2rBhgwoXLpzimNGjRyswMFC1a9fWsGHDdPnyZa1evVpbtmzRL7/8ku4cjgAAAHmNTT2HV69e1ZIlS7R79+4M2+3atUtLlizRtWvXHus6X331lQ4dOqSwsDANGTIkw7ZffvmlQkNDNXr0aK1cuVIfffSRVq5cqdGjR2vfvn2aOXNmiva//fabAgMD5evrq23btmn8+PGaM2eOFi1apJs3b2rMmDGPVTMAAEBuZFM4XLBggQICAjKd3Pr8+fMKCAjQwoULH+s6rVq1kpeXV6btrFarvvvuOxUrVixVqBszZoyKFSumwMDAFNuTX7///vt64oknjO0vvviiWrRooS1btmTYUwkAAJCX2BQON23apIIFC6pr164ZtuvatasKFCign376yZbLZSosLEyXL1/WM888IxcXlxT7XFxc9Mwzz+js2bO6cOGCsT04OFguLi5q1qxZqvO98MILkqSQkJAcrRsAAMBR2PTM4blz5+Tp6alChQpl2K5QoUKqWLFiji+fFxYWJun+es5pqVq1qjZv3qywsDB5enoqLi5OERER8vb2lrOzc5rtHzxvZm7fvv2YlSO34XuNrOKzAsBsD4+1yIxN4TAqKkoVK1bMUls3N7cUPXY5ITo6WpJUokSJNPcXL148Rbvk/yZvz6x9Zi5duqTExMSsF4xci0cNkFV8VgCYydnZOd1Os/TYFA5LliyZ5d7As2fP5vl5DsuXL/8IrW/mWB3IeVn9pSj78HnJrez/WQEA29gUDhs0aKCff/5Za9euVefOndNtt27dOt24cUNt2rSx5XKZSu7pi4qKSnP/wz2FmfUMZtaz+LBH7bZF7sX3GlnFZwVAbmPTgJQBAwbIarVq5MiRCg4OTrNNSEiIRo4cKYvFov79+9tyuUwlz0d4+vTpNPcnb09u5+LionLlyuncuXNp3g5+uD0AAEBeZ1PPYadOndS+fXtt3LhRXbp0UdOmTdW0aVOVKFFCUVFR2rNnj/bs2SOr1aoOHTpkOqrZVtWqVdOTTz6p3bt3Ky4uLsWI5bi4OO3evVuVKlWSp6ensd3Pz08rVqzQrl275Ofnl+J8mzdvliT5+vrmaN0AAACOwubl87799lv17dtXVqtVu3fv1vTp0zVx4kRNnz7dmBx7wIABmjdvns3FZsZisWjgwIGKjY3VJ598kmLfJ598otjYWL300ksptie/njhxou7evWtsDwoKUnBwsPz9/bM0xyIAAEBeYImMjLRmx4mOHDmitWvX6ujRo4qJiZGrq6vq1KmjLl26qHbt2jadOzAwUDt37pQkHT16VAcPHlSzZs1UpUoVSVLz5s01aNAgSfd7CNu2bavDhw/L399f9evX18GDB7VlyxY1bNhQ69evV5EiRVKcf+TIkcbyeW3atFFERIRWrVolFxcXBQUFqXr16jbVn5Y6M/dk+zlhP0dGNLXr9fi85F72/qwAgK2yLRzmpOHDh2vJkiXp7u/Xr59mzZplvI6KitKUKVO0du1aXblyRWXLllW3bt30zjvvyNXVNdXxSUlJmjNnjhYsWKDTp0/LxcVFrVq10rhx44wAmt34YZ+7EQ6RVYRDALlNrgiHeRE/7HM3wiGyinAIILex+ZlDAAAA5B2EQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYMiT4dDHx0dubm5p/unYsWOq9nfu3NHUqVPVsGFDlS1bVrVq1dKoUaN07do1E6oHAAAwTwGzC8gpxYsX1/Dhw1Nt9/LySvE6KSlJ/fv31+bNm9WkSRN16dJFYWFhCgwM1LZt2/TLL7+odOnS9iobAADAVHk2HJYoUUJjx47NtN3ixYu1efNm9erVS998840sFosk6dtvv9Vbb72lCRMm6IsvvsjhagEAABxDnryt/CgCAwMlSR9++KERDCVp8ODBqly5sn744QfFx8ebVR4AAIBd5dlwePfuXS1atEifffaZ5syZo99//z1Vm9u3b+v3339XjRo1Ut1utlgsev755xUXF6f9+/fbq2wAAABT5dnbyleuXFFAQECKbQ0bNtS8efNUpUoVSdKZM2eUlJSkqlWrpnmO5O1hYWHy9fXN2YIBAAAcQJ4MhwMGDFDz5s3l7e0tFxcXnTp1SjNmzNCyZcvUpUsX7dixQ66uroqOjpZ0//nEtBQvXlySjHaZuX37dva8ATg8vtfIKj4rAMxWuHDhR2qfJ8Phu+++m+J1vXr19PXXX0uSli1bpgULFuiNN97I9uteunRJiYmJ2X5eOJ7w8HCzS0AuwWcFgJmcnZ3TvUOanjwZDtMzePBgLVu2TLt379Ybb7xh9AxGRUWl2T65xzC5XWbKly//CNXcfIS2cDQVK1a08xX5vORW9v+sAIBt8lU4LFWqlCTp1q1bkqTKlSvLyclJp0+fTrN98vZq1apl6fyP2m2L3IvvNbKKzwqA3CbPjlZOS/KI5eSRyUWKFFGjRo108uRJnT9/PkVbq9WqX3/9VS4uLnr66aftXisAAIAZ8lw4PHHihNEz+PD2jz76SJLUq1cvY/tLL70kSfr4449ltVqN7fPnz9fZs2fVu3dvFSlSJGeLBgAAcBB57rbyihUrNHPmTPn6+qpixYoqWrSoTp06paCgIN27d09vvfWW/Pz8jPb9+/fXqlWrtHz5cp07d05+fn46ffq01q5dq0qVKumDDz4w8d0AAADYV54Lhy1bttSJEyd06NAh7dy5U7du3VKpUqX04osv6pVXXpG/v3+K9k5OTlq8eLE+//xzLVu2TDNnzpS7u7sGDhyoDz74gHWVAQBAvmKJjIy0Zt4M2a3OzD1mlwAbHBnR1K7X4/OSe9n7swIAtspzzxwCAADg8REOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMBcwuAACQferM3GN2CXhMR0Y0NbsEQBI9hwAAAHgA4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4fsG/fPvXu3VteXl4qX768WrdurVWrVpldFgAAgN0wlc3/+u2339SzZ08VLlxYPXr0ULFixbRmzRoNHjxYFy5c0Jtvvml2iQAAZBumPcq9cnraI8KhpISEBI0aNUpOTk5av3696tWrJ0l6++239cILL+hf//qXunbtKi8vL5MrBQAAyFmEQ93vNTxz5owGDBhgBENJKlGihN566y2NGDFCS5Ys0TvvvJNt13QvzJceWcfnBVnFZwVZxWcF6eGTISk4OFiS5O/vn2rfCy+8IEkKCQnJ3msOaZit50PexucFWcVnBVnFZwXpYUCKpLCwMElStWrVUu0rW7asihUrptOnT9u7LAAAALsjHEqKjo6WJBUvXjzN/a6urkYbAACAvIxwCAAAAAPhUP/XY5he72BMTEy6vYoAAAB5CeFQ//esYfKzhw+6cuWKYmNjVbVqVXuXBQAAYHeEQ0l+fn6SpC1btqTat3nz5hRtAAAA8jJLZGSk1ewizJaQkKDGjRvr8uXLCgoKMuY6jIqK0gsvvKDz589r7969qlSpksmVAgAA5CzC4f9Kb/m88PBw/etf/2L5PAAAkC8QDh/wxx9/aPLkydqzZ4/u3bsnb29vBQQEqEePHmaXBgAA0rBkyRJ5eHgYi1bAdoRD2CwkJEReXl6qWLFium0uXLigc+fO8exmPrd161bNmDFD+/btU1RUlJKSklK1sVgsun79ugnVAciNSpcurVdffVWTJ082u5Q8g+XzYLPOnTvrnXfeyXDt6aVLl2rSpEm6ceOGHSuDI/nxxx81ZMgQJSUlqWLFiqpZs6acnZ3NLgu5wJUrV3TgwAFFRUUpMTExzTb9+vWzc1VwFOXKlVNCQoLZZeQphEPYzGrNvPM5KSlJFovFDtXAUU2bNk1FihTRokWL9Nxzz5ldDnKB27dva+TIkVq5cmWavczS/X9/LBYL4TAfa9++vbZu3ao7d+6oUKFCZpeTJxAOYRdhYWFMJJ7PnTp1Sn369CEYIss++ugj/fDDD6pevbp69uyp8uXLq0ABfmwhpQ8++EB79uzRwIEDNX78eNWuXdvsknI9/i/DYwkICEjxev369Tp//nyqdomJibp48aJ27Nih1q1b26s8OKCSJUuqSJEiZpeBXGT16tWqVauWtm7dSo8Q0vXss8/q7t27Cg0N1S+//KLChQurdOnSqe5WWSwWHThwwJwicxnCIR7L4sWLjb9bLBaFhoYqNDQ0zbYWi0UNGzbkYeF8rkuXLtq2bZsSEhLo/UGWREVFqVevXgRDZCgpKUkFCxaUp6dniu0PP/KUlUegcB+jlfFYknsJrVarGjRooOHDh+v1119P1c7Z2Vlubm5ycXGxd4lwMHFxcerRo4c8PDw0adKkDEe3A5LUsmVL1a5dW3PmzDG7FCBfIRzCZosXL1a9evVUt25ds0uBA6tfv74SEhJ0+fJlSVKJEiXSfA6VWz9I9sMPP+itt97Stm3bWN8esCPu7cBm/fv3T/E6ISFBsbGxKlasGLcPYUhKSpKzs3OKWz9p3ebh1g+SlS9fXv7+/vL399fw4cNVv359ubq6ptmWOVQhScePH9eJEyd069Yt9e3b1+xyci16DpEtEhMTNXv2bC1evFjHjx83ppeoVauWBgwYoNdee42gCOCRuLu7y2KxGL8wZDQdFnOo5m/79u3TyJEjdfToUWNb8mciJCREvXr10rx589ShQwezSsxVCIewWWxsrHr27Km9e/fKyclJFSpUkIeHh65evaqLFy8qKSlJTZs21YoVK3j2EECWTZ48Ocvzo7777rs5XA0c1bFjx/Tiiy/KyclJgwYN0smTJxUUFGSEQ6vVKh8fH/n6+vL8ahbRlQObTZo0SXv27FHv3r01bty4FAMNwsPD9fHHH2v58uWaNGmSJk6caGKlcBQJCQk6efKkYmJi5Orqqho1atCzjFTGjh1rdgnIBZJnwti6dauqVq2qKVOmKCgoyNhvsVjUpEkT7d+/36wScx0nswtA7rd69Wo9/fTTmjNnTqoRqBUrVtQ333yjBg0aaNWqVSZVCEdx8+ZNjRw5Ul5eXvLz81O7du3k5+enSpUqadSoUdwaBPDIQkJC1Llz5wwHLXl6eurKlSt2rCp341d12OzGjRuZPvjbqlUrzZ49204VwRHdvHlTrVu31unTp+Xu7q7mzZurbNmyunr1qvbv36/AwECFhIQoKChI7u7uZpcLBxIXF6f169crNDTU6G328fFRx44deVQFio2NVZkyZTJsc/v27XTX5UZqhEPYrGrVqrp27VqGbf766y9VqVLFThXBEU2bNk2nT5/WyJEj9fbbb6f4oX7r1i198skn+uKLL/Tpp5/y+AEMP/74o0aPHq2oqKgUI9ktFotKlCihL7/8Ul26dDGxQpitQoUKKQaipOXgwYOqXLmyfQrKA7itDJu9/vrrWrVqlY4dO5bm/iNHjmjlypUaPny4nSuDI9mwYYNatGih8ePHp+rtKVq0qP7nf/5HLVq00Lp160yqEI5m9+7dGjp0qG7duqVBgwZp7ty5Wrt2rebNm6eXXnpJ8fHxGjp0qPbs2WN2qTBR27ZttWXLFm3dujXN/atWrdLevXvVsWNH+xaWi9FzCJtVq1ZNLVu21PPPP69+/fqpWbNmxmjlnTt3aunSpfL391fVqlUVEhKS4ljmJss/IiIi1KtXrwzbNG3alB/0MPz73/9WoUKF9NNPP8nHxyfFvh49emjo0KFq27atPvvsMy1btsykKmG2f/7zn1qzZo169+6tfv36Gc8Wzp07V3v27NGKFSvk5eWlgIAAkyvNPZjKBjbLaC6yzOYnYwBC/lGjRg09//zzGU4l8dprr+nXX3/VyZMn7VgZHFWVKlXUsWNHTZ8+Pd02AQEB2rBhg86cOWPHyuBozp49q2HDhqX5y2Xjxo01d+5cVapUyYTKcid6DmGzt99+O8tzkSH/8vPz0+rVq9W/f3+1atUq1f5t27Zp9erV3PqBIT4+Xh4eHhm28fDwUHx8vJ0qgqOqXLmyfv75Zx06dEi///67bt68KVdXVzVu3FgNGzY0u7xch55DAHZx7NgxvfDCC7p9+7ZefPFFtWjRQmXKlNG1a9cUHBysoKAgFS1aVEFBQapdu7bZ5cIBNG3aVC4uLvr111/TbePv76/Y2FgeRwCyEQNSANhF7dq1tWLFClWqVEmbNm3Shx9+qBEjRujDDz/Upk2bVLlyZS1fvpxgCEO3bt104MABvf7667p8+XKKfRERERo+fLgOHDig7t27m1QhHEH9+vUznSrtm2++Uf369e1UUe7HbWVkm4MHD2rFihU6ceKE4uPj9eOPP0qSzp8/rz/++EOtWrVi/rp8rnnz5tq3b5927dqlQ4cOGXPW1atXT82aNePxBKQwevRobd68WcuWLdOqVatUtWpVo7f59OnTunv3rho1aqTRo0ebXSpMdP78eUVFRWXYJioqSuHh4XaqKPcjHCJbfPjhh5o+fXq6g1JeeeUVTZgwgelsIIvFoubNm6t58+ZmlwIHV7RoUW3YsEFffPGFli5dquPHj+v48eOS7j9j1q9fP40aNUqFChUyuVI4uujoaD4nj4BnDmGzhQsX6s0331S7du00btw4rVixQp9//nmKkcht2rRR4cKFtWbNGhMrBZCbxcTEGL3Nrq6uZpcDEz04LVqnTp3Uv39/9e/fP1W7xMREXbx4UR9//LFKlSql4OBge5aZa9FzCJvNmzdPTz31lL777jsVKFAgzQBYo0YNbdu2zYTqYJapU6fKYrHo1Vdflbu7u6ZOnZql4ywWi95+++0crg65EaEQyTp16mTcobJYLFqyZImWLFmSZlur1SqLxaL/+Z//sWeJuRrhEDb7888/NWjQIBUokP7HycPDI9Ml9pC3TJkyRRaLRT169JC7u7umTJmSpeMIhwAykzyFmtVq1bRp0+Tn56cWLVqkaufs7Cx3d3e1bNlSTz31lAmV5k6EQ9isQIECunfvXoZtLl++nGrJNORta9eulSR5enqmeA2kp379+rJYLFq9erUqV66c5dGlFotFBw4cyNni4FDGjh1r/D0kJEQDBgxQv379TKwobyEcwmbe3t767bfflJiYKGdn51T7b926pW3btqlBgwb2Lw6mefi3+LR+qwcelJSUlGIw28Ov05M8EA75k5eXF6ttZTPCIWw2YMAAjRw5Uv/4xz/0ySefpNgXHR2tkSNH6sqVK1m+rYi8acmSJfLx8VHdunXTbXP06FEdPHiQHoB8KjQ0NMPXQFpWrFihsmXLml1GnsIk2LDZwIED1bNnT3333XeqXr26Fi5cKOn+ygXe3t768ccf1a9fP3Xt2tXkSmGmESNGaP369Rm22bBhgwICAuxUEYC8oEqVKoqIiDC7jDyFcIhsMXfuXH3xxRfy8vLSpUuXZLVatX//fnl6eurf//63ZsyYYXaJyAUSExPl5MQ/S7ivc+fO6Y5ATbZs2TJ17tzZThXBEQ0YMECbNm3SpUuXzC4lz+C2MrLNSy+9pJdeeknx8fGKjIyUq6urihUrZnZZyEUOHTrEKjowBAcHZ/qsanh4eIo575D/dOnSRcHBwWrTpo1GjRqlhg0bqkyZMmk+r1qxYkUTKsx9CIfIdkWKFFGRIkXMLgMO4OEencWLF6c5CW1iYqIuXbqk8+fPs04uHsmtW7dUsGBBs8uAiRo0aGBMa/POO++k285isej69et2rCz3IhzCZrt27dKaNWs0atSoNB8KjoiI0H/+8x91795dTZo0MaFCmOXBIGixWHT+/HmdP38+VTsnJye5u7urW7dumjx5sj1LhIN5eP3b9NbETV75Ys2aNfLy8rJXeXBAffv2ZV32bMbyebDZwIEDdeTIEe3bty/dNo0aNVK9evU0f/58O1YGR+Lu7q533303w9/sAXd390f6QW+1WvXxxx/rzTffzMGqgPyFnkPYbP/+/Xr22WczbOPr66tff/3VThXBEa1du5YeHmQquRfIarVq6dKlqlu3rnx8fFK1S1754tlnn1Xr1q1NqBTIuwiHsNm1a9dUvnz5DNuULVtWf/31l50qgiNKb2BBdHS0/vjjDxUqVEjNmzfn9lA+N2vWLOPvyStfvP766yZWBOQ/hEPYrESJEmk+E/Sg8+fPs3xePrdgwQItW7ZMixcvlpubm6T7kxz37t1bV69elSQ1bdpUK1euVNGiRU2sFI7i0KFDZpeAXCImJkbffPONtm7dqoiICN25cydVG5ZZzDrCIWzWuHFjrV+/XhcuXDDW0X1QeHi4NmzYoJYtW5pQHRzF0qVLde/ePSMYStIHH3yga9euacCAAbp69aqCgoI0b948nh8DkGV//fWX2rRpozNnzsjV1VUxMTEqXry47t27p/j4eEnSk08+qQIFiDxZxWyzsFlAQIBu3bqldu3aacmSJcZM9REREVq8eLHatWun+Ph4vfHGGyZXCjOFhYWlWDrvxo0b2r59uwYNGqSvvvpKy5YtU8OGDfXDDz+YWCUczdatW9W7d29Vq1ZNpUuXVsmSJVP9KVWqlNllwkRTpkzRmTNnNHv2bJ07d07S/RWZLl26pM2bN6tRo0by8vLSrl27TK409yBGw2Z+fn6aOHGiPvjgA2Pps+QHyqX705RMmTJFfn5+ZpYJk0VFRal06dLG6x07dkhKORdis2bNjOUXgR9//FFDhgxRUlKSKlasqJo1a8rZ2dnssuBgNm3apOeee059+vRJta9hw4Zavny5fH19NXXqVI0fP96ECnMfwiGyxfDhw9WyZUvNnz9f+/btU3R0tEqUKKFGjRpp8ODB8vb2NrtEmKxkyZIp1j/dtm2bnJ2d9cwzzxjbrFarEhISzCgPDmjatGkqUqSIFi1apOeee87scuCgrly5om7duhmvnZ2ddfv2beO1m5ubWrdurVWrVhEOs4hwiGxTt25dffbZZ2aXAQdVp04dbdiwQUePHlXhwoW1fPlyPfPMMykGKp0/fz7NidSRP506dUp9+vQhGCJDyc8XJnNzc9PFixdTtHF1ddW1a9fsXVquxTOHAOxi5MiRioyMVIsWLdS4cWNFRUUZjyFIUlJSknbt2qUGDRqYVyQcSsmSJVmKE5mqXLlyipWX6tWrp61bt+rGjRuSpPj4eP30009pDphE2ug5RLa5cuWKDhw4oKioKCUmJqbZpl+/fnauCo7i2Wef1ZIlS7Ro0SJJUs+ePdW+fXtj/65du1SuXLlU6zEj/+rSpYu2bdumhIQERpoiXc8//7xmzZqlW7duqWjRonr55Zf10ksvqUWLFmrSpIkOHjyo8+fPa9y4cWaXmmuwfB5sdvv2bY0cOVIrV65UUlJSmm2sVqssFovxmxwAZCYuLk49evSQh4eHJk2apIoVK5pdEhxQRESEduzYoeeee84Yuf7VV1/p008/VXR0tIoUKaKhQ4fqo48+YkBTFhEOYbN3331XX3/9tapXr66ePXuqfPny6f6W379/fztXB0d18+ZNxcXFcasH6apfv74SEhJ0+fJlSfcn3C9evHiqdkxujLQkJibq+vXrKlOmDCsvPSLCIWxWq1Ytubu7a+vWrSpUqJDZ5cCBRUVFadKkSVq5cqWuX78ui8Wi69evS5J+//13TZ06Ve+//z7PHUKS5OPjk+Uf6qymkn+FhITIy8srw57lCxcu6Ny5c0yplkU8xAGbRUVFqVevXgRDZOjmzZtq06aNTp06pfr166t06dL6888/jf116tTR7t279f333xMOIen+8opAZjp37qx33nlH77zzTrptli5dqkmTJvFoUxYxWhk2q169urE2LpCeyZMn69SpU/r222+1detWde3aNcX+IkWKyNfXV7/99ptJFQLIjZIXXMhIUlISt5YfAeEQNhs5cqQ2btyo06dPm10KHNjGjRvVtm1bde/ePd02Xl5eunTpkh2rQm5x/PhxrVmzRkuXLjW7FORCYWFhaT6virRxWxk2K1++vPz9/eXv76/hw4erfv36cnV1TbMtz3vkX1euXFHPnj0zbFOoUCHdunXLThUhN9i3b59Gjhypo0ePGtv69u0r6f6zZr169dK8efPUoUMHs0qECR6cI1WS1q9fn2Kuw2SJiYm6ePGiduzYodatW9urvFyPcAibderUyVhLecqUKRl23fO8R/5VsmTJVKsWPOzEiROskALDsWPH1KVLFzk5OWnEiBE6efKkgoKCjP2+vr4qVaqUVq9eTTjMZxYvXmz83WKxKDQ0NN1nVC0Wixo2bKjJkyfbq7xcj3AIm7399ts8y4FM+fr6asOGDbp48aIqVKiQav/x48e1efNmDRgwwITq4IiSf5hv3bpVVatW1ZQpU1KEQ4vFoiZNmmj//v1mlQiTHDx4UNL95w0bNGig4cOH6/XXX0/VztnZWW5ubimW6UTmCIew2dixY80uAbnAP//5T23YsEFt27bVhx9+aExh8+eff2r37t2aMGGCChUqpJEjR5pcKRxFSEiIOnfurKpVq6bbxtPTU5s3b7ZjVXAEXl5ext9nzJihevXqpdgm3Q+Op0+f1s2bNwmHj4gBKQDsok6dOvr2228VHR2t119/XfPmzZPValXz5s01atQo3b59W/Pnz1e1atXMLhUOIjY2VmXKlMmwze3bt9NdrhP5Q7FixfTVV18pMjLS2JY8p2GTJk1Ur149DRkyhM/JI6DnENkmLi5O69evV2hoqGJiYuTq6iofHx917NiR39ogSerQoYMOHjyoxYsX648//tDNmzfl6uqqxo0ba8CAAcbSV4AkVahQIcVAlLQcPHhQlStXtk9BcEjffvutrl69Kjc3N2Pbe++9p2PHjunZZ5/VjRs3tHr1aj333HN66aWXzCs0FyEcIlv8+OOPGj16tKKiolLMOWWxWFSiRAl9+eWX6tKli4kVwlG4u7unGmkIpKVt27b6+uuvtXXrVrVq1SrV/lWrVmnv3r0aM2aM/YuDw/jzzz9TjESOiYnRpk2b1KNHD82bN0/37t3Ts88+q4ULFxIOs4hwCJvt3r1bQ4cOlbOzswYNGqSWLVuqbNmyunr1qrZv364lS5Zo6NChWr9+vZo2bWp2uQByiX/+859as2aNevfurX79+unKlSuSpLlz52rPnj1asWKFvLy8+GUjn7t582aKWQ527dqlhIQEY+qsggULqlWrVvrhhx/MKjHXIRzCZv/+979VqFAh/fTTT/Lx8Umxr0ePHho6dKjatm2rzz77TMuWLTOpSphtyZIlWW7br1+/HKwEuUXp0qW1fv16DRs2TN99952xPbmnsHHjxpo7d65KlChhVolwAK6urimmSdu+fbucnJzk6+trbCtYsCBzqD4CS2RkZObrzgAZqFKlijp27Kjp06en2yYgIEAbNmzQmTNn7FgZHIm7u3umUx5ZrVZZLBbmw0Qqhw4d0u+//57iOdWGDRuaXRYcQIcOHXT27FkFBwcbofDJJ59MMYp98ODB2rdvnzEFDjJGzyFsFh8fLw8PjwzbeHh4KD4+3k4VwRHNmDEjze3R0dE6ePCgli9frvbt26tdu3Z2rgy5Qb169VSvXj2zy4ADGjZsmF5++WV5e3urYMGCiouL0/vvv5+izd69e1W/fn2TKsx9CIewmZeXl3799Vd9+OGH6bbZtm1bqjmokL/0798/w/2DBw9Wly5dNHToUDtVBEdXv379dCc3TvbNN99o+vTp9AjlY127dtWnn35qPHrQs2fPFJPpBwcHKyYmhuXzHgHzHMJm3bp104EDB/T666/r8uXLKfZFRERo+PDhOnDggLp3725ShcgNmjZtqvbt22vSpElmlwIHcf78eUVFRWXYJioqSuHh4XaqCI5q6NCh2rp1q7Zu3ao333wzxb4WLVro3LlzGjx4sEnV5T70HMJmo0eP1ubNm7Vs2TKtWrVKVatWVZkyZXTt2jWdPn1ad+/eVaNGjTR69GizS4WDq1ixojZt2mR2GchFoqOjVahQIbPLAPIUwiFsVrRoUW3YsEFffPGFli5dquPHj+v48eOSpMqVK6tfv34aNWoU/4AjQ1arVTt27FDhwoXNLgUmCgkJSfH6/PnzqbZJUmJioi5evKgffviBVXWAbMZoZWS7mJgYY4UUV1dXs8uBg0jrB7wkJSQk6PLly1q6dKl+++039e3bVzNnzrRzdXAUWRnVnix5dPvMmTPVt2/fHK4MyD8Ih7DZrl27tGbNGo0aNSrFRKTJIiIi9J///Efdu3dXkyZNTKgQjiCzH/pWq1XNmjXTkiVLUiyDhfxl8uTJslgsslqtmjZtmvz8/NSiRYtU7ZydneXu7q6WLVvqqaeeMqFSIO8iHMJmAwcO1JEjR7Rv37502zRq1Ej16tXT/Pnz7VgZHEnyD/2HOTk5yc3NTQ0bNlTjxo1NqAyOqlOnThowYACTogN2xjOHsNn+/fv17LPPZtjG19dXv/76q50qgiMaO3as2SUgl1m3bp3ZJQD5EuEQNrt27ZrKly+fYZuyZcvqr7/+slNFAPKauLg4RUVFKTExMc39FStWtHNFQN5FOITNSpQokek8Y+fPn5eLi4udKoIjsmUuOn7w51+BgYGaMWOGTp48mW4bi8Wi69ev27EqIG8jHMJmjRs31vr163XhwgV5enqm2h8eHq4NGzaoZcuWJlQHR1GvXr0sj0J9ED/486958+bp//2//6cCBQrI19dX5cuXV4EC/NgCchr/l8FmAQEB+umnn9SuXTu9//77ev7551WuXDlFRERoy5YtmjhxouLj4/XGG2+YXSpM1LdvX507d047d+5UiRIl5OPjIw8PD129elWhoaGKioqSr6+vKlWqZHapcBCzZs1SqVKl9NNPP6l69epmlwPkG4xWRraYNWuWPvjgA1mt9z9OyVNRSPdHo06ePFmvvvqqmSXCZMeOHVPbtm312muv6R//+EeKxwzi4uL02Wefad68efr5559Vq1YtEyuFoyhbtqwGDRqkTz75xOxSgHyFcIhsc/jwYc2fP1/79u1TdHS0SpQooUaNGmnw4MHy9vY2uzyY7G9/+5vu3bunVatWpdumW7duKlSokJYtW2bHyuCo6tWrp9atW+vf//632aUA+Qq3lZFt6tatq88++8zsMuCgdu3apddeey3DNo0aNdI333xjp4rg6Pr166fFixcrLi6OAW2AHTmZXQCA/MFqter06dMZtgkLC7NTNcgNxowZo4YNG6p79+4KCQlRbGys2SUB+QI9hwDswtfXV2vWrNGKFSvUs2fPVPuXL1+utWvXqnXr1iZUB0fk4eEh6f4vFp07d063HSPagezFM4cA7OL48eNq06aNYmNjVadOHTVr1kxlypTRtWvXtGvXLh05ckTFihXTpk2bGJACSVLHjh2zPP0Rq6kA2YdwCMBujh49qjFjxmjHjh2p9vn6+urTTz9V7dq1TagMAJCMcAjA7i5cuKDDhw8rOjpaxYsXV926ddOcQB0AYH+EQwCAQwgICJDFYtGHH34oDw8PBQQEZOk4i8Wi6dOn53B1QP5BOAQAOAR3d3dZLBbt2bNH1atXl7u7e5aOs1gsunHjRg5XB+QfjFYGADiEgwcPSpLKly+f4jUA+6LnEAAAAAYmwQYAAICBcAgAAAAD4RAAAAAGwiEAAAAMjFYGYBdTp07NtI2Tk5NcXV1Vo0YNtWjRQoUKFbJDZQCABzFaGYBdJM9hl8xq/b9/eh7ebrFY5ObmpokTJ6pfv352rRMA8jvCIQC7CA4O1owZM7R161b17dtXzzzzjDw8PHT16lXt3r1bS5cu1fPPP6++ffvq0KFDmjNnjuLi4rRixQq1atXK7PIBIN8gHAKwi8DAQH3wwQf65ZdfVLNmzVT7T5w4odatW2vixIkaOHCgjh8/rueee04tW7bU8uXLTagYAPInBqQAsIvZs2erR48eaQZDSapZs6a6d++uWbNmSZJq1aqldu3a6Y8//rBnmQCQ7xEOAdjF6dOnM10rt2TJkjpz5ozxukqVKoqLi8vp0gAADyAcArCL0qVLKygoKMVAlAdZrVYFBQWpZMmSxrbIyEgVL17cXiUCAEQ4BGAnPXr00JEjR9S3b18dPnw4xb7Dhw+rX79+Onr0qHr27Gls/+OPP9K9DQ0AyBnMcwjALsaOHav9+/dr06ZNCgoKkouLi0qVKqXr168rLi5OVqtVvr6+Gjt2rCTpypUr8vLyUvfu3U2uHADyF0YrA7CbpKQkLVy4UN9//72OHDmimJgYubq6qm7duurdu7f+/ve/y8mJGxoAYCbCIQAAAAz8ig4AAAADzxwCsKuEhASdPHlSUVFRSkxMTLONn5+fnasCACQjHAKwC6vVqokTJ2rOnDmKjY3NsO2NGzfsVBUA4GGEQwB2MW3aNH322WcqUaKE+vbtq/Lly6tAAf4JAgBHw4AUAHbh4+Mji8WirVu3ppjoGgDgWBiQAsAurl69qo4dOxIMAcDBEQ4B2EWlSpUUExNjdhkAgEwQDgHYxZAhQ/Tzzz/r2rVrZpcCAMgAT4MDsIsOHTpo586datOmjd5++23Vr19frq6uabatWLGinasDACRjQAoAu3B3d5fFYpHVapXFYkm3ncVi0fXr1+1YGQDgQfQcArCLvn37ZhgKAQCOgZ5DAAAAGBiQAgAAAAPhEAAAAAaeOQSQIzp37iyLxaJZs2apQoUK6ty5c5aOs1gsWrNmTQ5XBwBID+EQQI4IDg6WxWJRfHy88TorGLQCAOZiQAoAAAAMPHMIAAAAA+EQgF0EBgYqOjra7DIAAJngtjIAu3B3d1fhwoXVvn17/e1vf9OLL74oZ2dns8sCADyEcAjALqZPn65ly5bp8OHDslgsKlmypHr06KE+ffqoUaNGZpcHAPhfhEMAdnXs2DEtXbpUK1as0MWLF2WxWFS1alX16dNHvXv3VuXKlc0uEQDyNcIhAFNYrVb99ttvWrZsmdatW6eYmBhZLBY988wz2rhxo9nlAUC+RTgEYLrbt29r1qxZmjx5shISEnTjxg2zSwKAfItJsAGYJioqSqtXr9ayZcu0e/duJSUlqXjx4maXBQD5GuEQgF3du3dPP//8s5YtW6agoCDduXNHBQoUUOvWrdWnTx916NDB7BIBIF8jHAKwi507d+r777/Xjz/+qMjISFmtVjVo0EB9+vRRr169VLp0abNLBACIZw4B2Im7u7skydPTU3/729/Up08f1axZ0+SqAAAPo+cQgF38/e9/V58+fdSiRQuzSwEAZICeQwAAABhYWxkAAAAGbisDyBGdO3d+rOMsFovWrFmTzdUAALKK28oAckTyAJRHZbFYmAQbAExEOAQAAICBZw4BAABgIBwCAADAQDgEYFfff/+9unXrpmrVqsnDw0PVqlVT9+7d9cMPP5hdGgBAPHMIwE4SExP18ssva/369bJarSpcuLDKlCmja9eu6fbt27JYLOrYsaMWLFggJyd+bwUAs/AvMAC7mD17ttatW6dnnnlGP//8sy5fvqxDhw7p8uXL2rRpk5o1a6b169fr66+/NrtUAMjX6DkEYBctWrTQnTt3tGPHDhUsWDDV/nv37snPz09PPPGEgoODTagQACDRcwjATsLCwtS+ffs0g6EkFSxYUO3atVNYWJidKwMAPIhwCMAuChYsqFu3bmXY5tatW+mGRwCAfRAOAdhFvXr1tGrVKl2+fDnN/REREVq1apXq169v58oAAA8iHAKwi4CAAN24cUPPP/+8vvrqK+3fv18XLlzQ/v379dVXX6lVq1a6efOmAgICzC4VAPI1BqQAsJvp06dr/PjxSkxMTLHdarWqQIEC+uijjwiHAGAywiEAuzp79qy+//57hYaGKiYmRq6urqpXr5569+6typUrm10eAOR7hEMAAAAYeOYQgKmsVqvCwsJ04cIFs0sBAIhwCMBO1qxZo2HDhikyMtLYdu7cOfn5+alJkyaqV6+ehgwZkup5RACAfREOAdjFt99+q9DQULm5uRnb3nvvPR07dkwtW7ZUnTp1tHr1ai1cuNC8IgEAhEMA9vHnn3+qUaNGxuuYmBht2rRJPXr00I8//qgtW7boqaeeIhwCgMkIhwDs4ubNmypbtqzxeteuXUpISFDPnj0l3V9BpVWrVjpz5oxZJQIARDgEYCeurq66ceOG8Xr79u1ycnKSr6+vsS0rS+wBAHIW4RCAXdSoUUM//fSTbty4ocjISC1fvlwNGjRI8QxieHi4ypQpY16RAADCIQD7GDZsmC5fvixvb2/5+PgoIiJCQ4YMSdFm7969qlu3rkkVAgAkqYDZBQDIH7p27apPP/1U3333nSSpZ8+eGjBggLE/ODhYMTExat26tVklAgDECikAAAB4ALeVAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEACQJZGRkRozZox8fHxUunRpubm5qWPHjmaXBSCbMZUNgHxn5syZioqKUv/+/VWpUiWzy8k1BgwYoJCQEBUpUkR16tRRkSJF5O3tbXZZALIZ4RBAvjNr1iyFh4erRYsWhMMsOnr0qBEMd+3axdcNyMO4rQwAyNSff/4pSapduzbBEMjjCIcAgEzFx8dLkooUKWJyJQByGuEQQL6xaNEiubm5KTw8XJLUuXNnubm5GX+GDh2qKlWqyM3NTb/++mu65zlz5ozc3d1VunRpRUREGNt9fHzk5uam7du36+jRo3r55ZdVs2ZNlS1bVk2aNNG0adN0+/btdM+bmJiohQsXqkuXLqpatarKlCmj2rVr69VXX1VoaGi2fR1u3Lihjz/+WM2bN1f58uVVoUIF+fr6atKkSYqKikrRNvlrNmLECElSSEhIiq/Z9u3bs60uAI6BZw4B5BseHh5q1qyZ9u/frzt37sjb21vFixc39nt7e6ts2bKaOXOmAgMD9fzzz6d5nsDAQFmtVrVp00blypVLtf+PP/7QtGnTlJiYqFq1aqlYsWI6efKkJk2apF9++UWrVq2Si4tLimMiIyPVr18/7dy5U5L05JNPytPTU2fOnNEPP/yg1atXa/bs2erZs6dNX4Pjx4+rR48eunTpkpydnVWrVi1ZrVYdP35cR48e1dKlS7V69WpVrVo1xdfs2rVrCgsLU/HixVMMQnnw6wcgb2BtZQD5jo+Pj8LDw7V27Vq1bNkyxb4TJ06oadOmeuKJJ3T8+HGVLFkyxf6EhATVrVtXERERWrZsmdq2bZvqvAULFpS/v79mz54td3d3SdLOnTv197//XdevX9crr7yiTz/9NMV5e/furaCgIDVv3lyfffaZEcCSkpI0e/ZsffDBB3riiScUHBys6tWrP9b7vnPnjlq0aKGTJ0+qcePGmj9/vipWrCjpfm/owIEDdfjwYdWtW1fbtm2Ts7OzceyiRYsUEBAgPz8/rV+//rGuDyB34LYyADygZs2a8vPz0927d7VkyZJU+3/++WdFRESoQoUKat26dZrnKFasmObNm2cEQ0lq3ry5pkyZIklasGCBrl69auzbunWrgoKC5OnpqSVLlqTomXNyctKIESP0yiuv6Pbt25o1a9Zjv7dVq1bp5MmTeuKJJ7RgwQIjGEpSlSpV9N///lfOzs46fPiw1q1b99jXAZC7EQ4B4CGDBw+WJC1cuDDVvsDAQElS//79U/SsPWjgwIEqVqxYqu09evRQ2bJlde/ePW3ZssXYvnLlSklSr1695ObmluY5u3TpIknatm1b1t/IQzZt2iRJ6tatmypUqJBqf/Xq1dW+ffsUbQHkPzxzCAAP6dy5s0qVKqVjx45p7969atKkiSTp0qVL+uWXX+Tk5KSBAweme3zt2rXT3O7s7KwaNWroypUrOnHihLH98OHDkqS1a9dq165daR6bPJDl4sWLj/WeJOnkyZOSlOHE1d7e3lq3bp3RFkD+QzgEgIcUKlRI/fv311dffaXAwEAjHC5atEiJiYl64YUX5OXlle7xHh4eme6LiYkxtkVGRkqSwsLCFBYWlmFtyVPKPI7Y2NhM60seYJPcFkD+w21lAEjDyy+/LIvFolWrVik2NlZWq9W4zTxo0KAMj33wecL09rm6uhrbkkcuT58+XZGRkZn+eVzJt7ozqi95ap60bosDyB8IhwDyHYvFkmmbatWqqWXLloqNjdXKlSu1bds2nTt3TmXKlFGHDh0yPPb48eNpbk9MTNSpU6ck3R/4kiz5Nu+RI0ey+hYeS/I1jx49mm6b5H0P1gcgfyEcAsh3ihYtKinzW7TJA1MCAwONgSj9+vVTwYIFMzwuMDBQcXFxqbavWrVKERERKliwYIo5FLt37y5JWrp0aYa9erZq06aNJGn16tVpPrt4+vRpbdy4MUVbAPkP4RBAvlOlShVJmY/87dSpk8qUKaPff/9da9askZT5LWXp/vN6r7zySopbwLt379bYsWMl3R/NXLZsWWNfu3bt5O/vr5s3b6pz587GRNgPOnv2rL788ksjpD6O7t27q0aNGrp7965efvllY6WY5PO//PLLSkxMVN26ddWxY8fHvg6A3I0BKQDynb59+2rjxo2aMWOG1q9fryeffFJOTk5q3bq1/vGPfxjtChYsqAEDBuiLL75QQkKC/Pz8sjQB9fvvv69p06apVq1aqlWrlmJiYoyBJo0bN9b48eNTHfPtt9/q5Zdf1tatW9W+fXuVKVNGFStWVGJioi5evKi//vpLkvTOO+889vt+4oknFBgYqB49emjv3r1q0KBBihVSkpKS5OXlpcDAwHSn6QGQ99FzCCDf6dq1q6ZPn67GjRvr+vXr2rVrl0JCQlJML5PspZdeSvPvGWnUqJF++eUXtW3bVhcvXlR4eLiqV6+ud999V2vXrk0xGCWZm5ubVq5cqQULFqhDhw5ydnZWaGioTpw4IVdXV/Xq1Uvz5s1TQEDA479x3Z9mJyQkRG+99ZZq1Kih06dP6+zZs6pVq5bGjBmj3377zVg6D0D+xPJ5AJCB3bt3q23btnJzc9Px48dVuHDhdNtmtCwfAOQW9BwCQAbmz58vSerTp0+GwRAA8grCIQCkY9++fVqxYoWcnZ312muvmV0OANgFA1IA4CEdO3ZUfHy8Dh06pISEBL366quqVq2a2WWl0K5du0dqv2DBghQjpAEgPYRDAHhISEiILBaLKlSooN69extT0DiS9NZgTk/y2swAkBkGpAAAAMDAM4cAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMPx/4bDwZHfBaGwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='type_of', data=lectures_df, order=lectures_df['type_of'].value_counts().index)\n",
    "plt.title('Типы лекций')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "194d4548-7c1b-4554-9ed2-dc12377956c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "part\n",
       "5    143\n",
       "6     83\n",
       "2     56\n",
       "1     54\n",
       "7     32\n",
       "4     31\n",
       "3     19\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lectures_df[\"part\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "1828b256-29ef-418b-a3ed-8dd0e98c98d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2++CDD3Tq1ClJ0vPPP1/gM2vffPON1q1bl2v7jRs3NG7cOCP4vPTSS/keIzExUf/7v/8r6fakhfvvv79wH+gv0tPT9d5770kq2UkLy5cvN0ZeBw0aVOT9XV1d9fLLL0uSjh07pvHjxxf4isDk5GR98sknRTqHo6OjFi9erAoVKujMmTMWn61dEEt+D2+99Zak2/9x0a9fP4vUB5iDZw6BcsTNzU1z5szRiBEjlJKSovHjx+dYPLpHjx6qWbOmLl++rOnTpys+Pl59+/ZV9erVFRcXp5UrV2r37t1q166d8Wq+ktCkSRN9+OGHevPNN3XmzBl16tRJzz//vLp37y53d3fduHFDFy5c0OHDh7V582bFx8dr7dq1Rb51mZ8JEyZo69atio2N1bx58/Sf//xHAQEB8vDw0IULFxQaGmosNF63bl3jL+f8tG7dWv/zP/+jAwcOaMCAAXJxcdGpU6e0cOFCY51If39/9ejRI99jnD59WpLk6empV199tdifLfv5RicnJ7377rvFPk5eMjMztX37dq1bt06bN2+WJDVo0KDA0FuQt956S/v371dUVJRWrlypyMhIBQQEqFWrVnJyclJKSopOnTqlffv26euvv1blypXznWyVnw4dOmj06NFasmSJPv30Uz311FPq0qVLnn2zsrKUmppa4PGyJ2hlZGQoNTVVDg4OeY7kWfJ7OH78uOzs7Ep0FBgoCsIhUM74+/urX79+2rx5s3bs2KH169cbIzuOjo5aunSphg0bpvT0dH3++ef6/PPPc+zfqVMnzZkzR+3bty/ROl9++WU5OjrqzTffVGpqqpYuXaqlS5fm2bdChQoWnVDh5OSkr776Ss8884xiYmL09ddf53rrjCQ98MADWrdu3V1vmS5fvlz9+/fXihUrtGLFilztXbp00ZIlSwpV27Rp0ywys/b111/PsRSPJaSkpOj55583fm7atKnWrl1b7NucFStW1IYNGzRu3Dht2LBBJ0+eNN7Ok5eizojO9s4772jnzp06ffq0XnnlFR04cCDP5YHOnz9f6GVm1q1bp3Xr1mno0KEFfreW+h4CAgIs9h9HgLm4rQyUQ3PnzjVm17755ps51pTr0aOHvv/+ez3zzDOqU6eOHBwcVKNGDXXs2FELFizQ5s2bS2Rma16GDRum6OhoTZkyRR06dFCNGjVkb2+vqlWrqm7duurdu7c+/PBDRUdH5zvaU1yenp7avXu35s2bp65du6pGjRpycHBQ9erV1aVLF3300Ufau3evvL2973qsunXrateuXZowYYKaNGkiR0dHVatWTW3atNH8+fO1adOmQl3Tdu3aFesW7V/5+PgoMDDQ7OP8lclkkoeHh3r37q1//OMf2rNnT6GuT0GcnJy0fPly7dy5UyNHjlTjxo3l7OwsOzs7ubi4qEWLFho+fLhWrlxZ7LfkVKlSRSEhIapQoYLi4+ON91WXNEt9D87Oznr77bctUBFgGaakpCTLLnIGADZgxowZxttdst9cg3tHWFiYxo4de9eRQ8AWMXIIAAAAA+EQAAAABsIhAAB/4eDgIEdHR1WuXNnapQCljtnKAAD8xTPPPKNnnnnG2mUAVsHIIQAAAAzMVgYAAICBkUMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBgb+0CbF2zkChrl2BVPwW2sXYJAACgCMrFyOGXX36poKAgdevWTbVq1ZKrq6vCwsIKte/Zs2d1//33y9XVVa+99lq+/datW6fu3bvLw8NDPj4+GjJkiI4ePWqhTwAAAFA+lItwOH36dK1YsUIJCQlyd3cv9H6ZmZkaM2bMXfvNnTtXL7/8si5fvqyRI0eqf//+OnDggHr16qVDhw6ZUzoAAEC5Ui7C4cKFCxUdHa3Y2Fi98MILhd5v8eLFOnz4sN5+++18+8TGxmrmzJlq0KCB9u3bpw8++EALFizQtm3bJEmvvvqqMjMzzf4MAAAA5UG5CIfdunWTt7d3kfY5deqUPvjgA7322mtq0aJFvv3CwsJ069Ytvf7663JxcTG2P/jggxo0aJB+/vlnHTx4sNi1AwAAlCflIhwWVUZGhsaMGaP69etrwoQJBfbdt2+fJKl79+652nr06CFJ2r9/v+WLBAAAKINscrbyxx9/rGPHjum7775TxYoVC+wbGxsrJyenPJ9l9PX1NfoAAADcC2wuHB4/flyzZ8/WuHHj1KpVq7v2T0lJUc2aNfNsq1atmtHnbtLT04tU572C6wIAgHVVrly5SP1tKhzeuHHDuJ08ceLEUj33hQsXlJGRUarnLA8SEhKsXQIAAPcsOzs71a9fv0j72FQ4/PjjjxUTE6Nvv/1WlSpVKtQ+zs7O+Y4MXrt2zehzNx4eHvm0/F6oOmyVl5eXtUsAAABFYFPhMDo6WpmZmXr88cfzbP/888/1+eefq0+fPvriiy8k3X6uMCoqSomJibmeO8x+1jD72cOCFHXI9l7BdQEAoHyxqXD42GOPqXr16rm2JyYm6ttvv1WjRo3Utm1bPfjgg0Zbx44dFRUVpYiICA0dOjTHfuHh4UYfAACAe4FNhcOXXnopz+179+7Vt99+q44dO2revHk52oYNG6aFCxfqo48+Up8+fYy1DqOjo7V+/Xo1btxY7du3L/HaAQAAyoJyEQ5DQ0ONhahjYmIkSatWrTLWKGzfvr0CAgKKdewGDRpo0qRJmj59ujp16qR+/fopNTVVGzZskCQtWLBAFSrY5HKQAAAAuZSLcHjw4EGtWbMmx7ZDhw7leO9xccOhJL3xxhvy9vbWkiVLtHz5cjk4OKh9+/aaPHlyoZbDAQAAsBWmpKSkLGsXYcuahURZuwSr+imwjbVLAAAARcD9UgAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAoVyEwy+//FJBQUHq1q2batWqJVdXV4WFheXqd/PmTX311Vf6n//5H7Vp00b333+/PD091aNHD3322WfKyMjI9xzr1q1T9+7d5eHhIR8fHw0ZMkRHjx4twU8FAABQ9thbu4DCmD59uhISElS9enW5u7srISEhz35nzpzRiBEj5OTkpC5duujJJ59USkqKvv76a73++uv69ttvtXbtWplMphz7zZ07V9OnT5eXl5dGjhyp1NRUbdiwQb169dJXX32ldu3alcbHBAAAsDpTUlJSlrWLuJtdu3apfv368vb21rx58zRt2jQtXrxYw4YNy9HvwoUL2r59u4YOHSpHR0dje1pamvr27asff/xRK1asUP/+/Y222NhYtW3bVnXr1lV4eLhcXFwkSdHR0XriiSdUt25dHTx4UBUqFG+QtVlIVLH2sxU/BbaxdgkAAKAIysVt5W7dusnb2/uu/Tw8PPTiiy/mCIaS5OjoqLFjx0qS9u/fn6MtLCxMt27d0uuvv24EQ0l68MEHNWjQIP388886ePCgBT4FAABA2VcuwqElODg4SJLs7OxybN+3b58kqXv37rn26dGjh6TcgRIAAMBWlYtnDi1h9erVknKHwNjYWDk5Ocnd3T3XPr6+vkafu0lPT7dAlbaH6wIAgHVVrly5SP3viXC4YsUK7dy5U126dFHPnj1ztKWkpKhmzZp57letWjWjz91cuHChwNnQ96r8Jg8BAICSZ2dnp/r16xdpH5sPh19//bUmTJggLy8vLVu2rMTO4+HhkU/L7yV2zvLAy8vL2iUAAIAisOlw+O2332rEiBGqVauWtmzZotq1a+fq4+zsnO/I4LVr14w+d1PUIdt7BdcFAIDyxWYnpHzzzTcaPny4qlevri1btqhu3bp59vP19VVqaqoSExNztWU/a5j97CEAAICts8lw+M033yggIEBubm7asmVLgffaO3bsKEmKiIjI1RYeHp6jDwAAgK2zuXC4c+dOBQQEyNXVVVu2bLnrqN+wYcNkb2+vjz76SMnJycb26OhorV+/Xo0bN1b79u1LumwAAIAyoVw8cxgaGmosRB0TEyNJWrVqlbFGYfv27RUQEKBTp07p+eef159//qlOnTrpX//6V65jeXt753izSoMGDTRp0iRNnz5dnTp1Ur9+/YzX50nSggULiv12FAAAgPKmXITDgwcPas2aNTm2HTp0SIcOHTJ+DggIUGJiov78809J0vr16/M8VseOHXO9du+NN96Qt7e3lixZouXLl8vBwUHt27fX5MmT1apVK8t+GAAAgDKsXLxbuTzj3cq8WxkAgPKE+6UAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAEO5CIdffvmlgoKC1K1bN9WqVUuurq4KCwvLt39KSoomT56s5s2bq1atWmrRooWmTp2q1NTUPPtnZmbqk08+UYcOHVS7dm35+vpq1KhROnv2bAl9IgAAgLKpXITD6dOna8WKFUpISJC7u3uBfdPS0uTn56eQkBA1atRIgYGBatiwoRYuXKh+/fopPT091z5BQUGaOHGisrKyNHr0aPXo0UNbtmzRY489ptjY2JL6WAAAAGVOuQiHCxcuVHR0tGJjY/XCCy8U2HfBggU6fvy4goKCtGHDBgUHB2vDhg0KCgrSkSNHFBISkqP/nj17FBoaqg4dOmj37t2aNm2ali1bprCwMP3++++aMGFCSX40AACAMqVchMNu3brJ29v7rv2ysrK0atUqOTk55Qp1EyZMkJOTk0JDQ3Nsz/757bffVsWKFY3tTzzxhDp16qSIiAglJCRY4FMAAACUfeUiHBZWbGysLl68qLZt28rR0TFHm6Ojo9q2bauzZ8/q/PnzxvZ9+/bJ0dFR7dq1y3W8Hj16SJL2799fsoUDAACUEfbWLsCSsp8PrF+/fp7t9evXV3h4uGJjY+Xp6am0tDRdunRJTZs2lZ2dXZ797zxuQfJ6lhFcFwAArK1y5cpF6m9T4TAlJUWS5OLikme7s7Nzjn7Z/5u9/W79C3LhwgVlZGQUreB7ALfkAQCwHjs7u3wHzfJjU+HQmjw8PPJp+b1U6yhrvLy8rF0CAAAoApsKh9kjfcnJyXm2/3Wk8G4jg3cbWbxTUYds7xVcFwAAyhebmpDi6+srSYqLi8uzPXt7dj9HR0fVrl1b586dy/OW8F/7AwAA2DqbC4d16tRRZGSk0tLScrSlpaUpMjJSPj4+8vT0NLZ37NhRaWlpOnToUK7jhYeHS5I6dOhQsoUDAACUETYVDk0mk4YPH67U1FTNmTMnR9ucOXOUmpqqESNG5Nie/fMHH3ygGzduGNt37typffv2qXv37oVaYxEAAMAWmJKSkrKsXcTdhIaG6uDBg5KkmJgYHTt2TO3atVO9evUkSe3bt1dAQICk2yOEvXr10okTJ9S9e3e1bNlSx44dU0REhFq3bq1t27apSpUqOY4/btw4hYaGqkmTJurZs6cuXbqkjRs3ytHRUTt37lSDBg2KXXuzkKhi72sLfgpsY+0SAABAEZSLcDhmzBitWbMm3/ahQ4dqyZIlxs/JycmaOXOmtmzZosTERLm7u6t///6aOHGiqlWrlmv/zMxMLVu2TCtXrlRcXJwcHR3VrVs3TZ061QigxUU4JBwCAFCelItwWJ4RDgmHAACUJzb1zCEAAADMQzgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAwaxwOGvWLIWFhRWq75o1azRr1ixzTgcAAIASZlY4nDlzplavXl2ovmFhYYRDAACAMq7UbitnZWWV1qkAAABQTKUWDn/77TdVrVq1tE4HAACAYrAvSueUlBQlJyfn2Hbjxg0lJCTku8/169e1a9cunTp1Si1atChelQAAACgVRQqHISEhmj17do5tP/74o1q2bFmo/YcMGVKU0wEAAKCUFSkcZmVl5Xh20GQy3fVZwqpVq6pevXp69tlnFRgYWLwqAQAAUCpMSUlJxZ4p4ubmpnbt2mnHjh2WrMmmNAuJsnYJVvVTYBtrlwAAAIqgSCOHfzVx4kR5enpaqhYAAABYmVnhcNKkSZaqA8gTI6+MvAIASpdNvj4vKytLmzdvVt++fdW4cWPVqVNHjzzyiIKCgnT27Nlc/VNSUjR58mQ1b95ctWrVUosWLTR16lSlpqaWfvEAAABWZNbIoSRlZmZq7dq1+vrrrxUXF6fU1NR8J6mYTCYdPXrU3FPe1ZQpU7R48WLVrl1bfn5+qlatmk6cOKGVK1dq/fr1+uabb9S0aVNJUlpamvz8/HT8+HF1795dgwcPVnR0tBYuXKj9+/dr+/btqly5conXDAAAUBaYFQ6vXbumwYMH6/Dhw4V6A4rJZDLndIWSmJioJUuWyMvLS/v27ZOLi4vRtnjxYr399ttavHixFi9eLElasGCBjh8/rqCgIAUHBxt9g4ODNX/+fIWEhGj8+PElXjcAAEBZYFY4nDVrlqKiolS1alU9//zzatOmjWrWrKkKFax3tzo+Pl6ZmZlq165djmAoSb1799bbb7+t3377TdLt28+rVq2Sk5OTJkyYkKPvhAkT9Omnnyo0NJRwCAAA7hlmhcPNmzerQoUKWrNmjbp06WKpmszi6+urihUr6tChQ0pJSZGzs7PR9vXXX0uSunbtKkmKjY3VxYsX1aNHDzk6OuY4jqOjo9q2bavw8HCdP3+eWdkAAOCeYFY4TExMlJeXV5kJhpJ033336d1339WUKVPUpk0b9enTx3jmcM+ePXrxxRf18ssvS7odDiWpfv36eR6rfv36Cg8PV2xsLOEQAADcE8wKh9WrV5ebm5ularGYsWPHysPDQ+PGjdPy5cuN7e3bt9fgwYNlb3/7Y6ekpEhSrtvP2bJHHbP7FSQ9Pd3csm0S18U8XD8AgLmKOrHWrHDYvXt3rV+/XteuXVO1atXMOZRFzZo1S3PnztXkyZP1zDPPyMXFRcePH9fkyZPVt29fhYaGqk+fPhY954ULF5SRkWHRY9qChIQEa5dQrnH9AADmsLOzy/cOaX7MXgR769atmjhxohYuXCg7OztzDmcRu3bt0owZMxQYGKjXXnvN2N6+fXutXbtWrVq10pQpU9SnTx9jZDA5OTnPY2WPGN753GJ+PDw88mn5vWgfwMZ4eXmZeQSuHwAApcmscHju3Dm99dZbmjp1qn788UcFBASoQYMGqlq1ar77dOzY0ZxT3tXOnTslSZ07d87V5u7uroYNGyo6Olqpqany9fWVJMXFxeV5rOzt2f0KwlqIeeO6mIfrBwAobWaFw759+xprF548eVJvv/12gf1NJpOuXLlizinv6saNG5JkLFfzV1euXFGFChXk4OAgX19f1alTR5GRkUpLS8sxYzktLU2RkZHy8fFhMgoAALhnmLUgoaenp/GPl5dXjp/z+uf++++3VN35ateunSQpJCQk1+3i5cuX65dfflGbNm1UqVIlmUwmDR8+XKmpqZozZ06OvnPmzFFqaqpGjBhR4jUDAACUFaakpKS7v9qkHMnIyNBTTz2lAwcOqGbNmnryySfl4uKiY8eOac+ePapSpYq2bt2qhx9+WNLtEcJevXrpxIkT6t69u1q2bKljx44pIiJCrVu31rZt21SlSpVi19MsJMpSH61c+imwjVn7c/3Mu34AABSV2e9WLmvs7Oy0ceNGhYSEaOPGjfrXv/6lGzduqFatWnrmmWf0+uuvq3HjxkZ/R0dHbdu2TTNnztSWLVu0d+9eubu765VXXtHEiRPNCoYAAADljc2NHJY1jHwxcmgORg4BAKXNei9BBgAAQJlj1m3lli1bFqm/yWTS0aNHzTklAAAASpBZ4TA+Pr5I/bOXvQEAAEDZZFY43LJlS75tf/zxh06fPq3Q0FCdOXNG77//vpo2bWrO6QAAAFDCzAqHnTp1KrC9Z8+eGj16tP7+979rxowZ2rNnjzmnAwAAQAkr8QkpdnZ2mjVrltLT0zVr1qySPh0AAADMUCqzlatVq6bGjRsrPDy8NE4HAACAYiq1pWySkpJ09erV0jodAAAAiqFUwuHevXsVHx+v2rVrl8bpAAAAUExmTUjZv39/vm1ZWVm6fPmyoqKitHr1akmSv7+/OacDAABACTMrHPbt27dQaxdmZWXpkUce0ZtvvmnO6QAAAFDCzAqHnp6e+YZDk8kkR0dH1a9fX08++aSeffZZ2dnZmXM6AAAAlDCzwuHx48ctVQcAAADKgFKbrQwAAICyj3AIAAAAg1m3lbNlZWVp69at+vbbb/Xf//5X165dU7Vq1dSoUSP17NlTfn5+hZq4AgAAAOsyOxyePXtWAQEBOnHihKTbQTFb9jI2LVq00MqVK1W3bl1zTwcAAIASZFY4TElJkb+/v+Lj42VnZ6cnn3xSTZs2Ve3atXXp0iXFxMRox44dio6O1oABA7R79245OztbqnYAAABYmFnhcPHixYqPj1eLFi30+eefy9fXN1efuLg4/e1vf9OJEycUEhKiSZMmmXNKAAAAlCCzJqRs3bpVdnZ2Cg0NzTMYSlL9+vUVGhoqk8mkLVu2mHM6AAAAlDCzwuG5c+fUuHHjuz5LWLduXT3wwAM6d+6cOacDAABACTMrHGZlZalChcIdwmQy5ZisAgAAgLLHrHDo4+OjkydP6sKFCwX2O3/+vE6ePCkfHx9zTgcAAIASZlY47N27t27duqWRI0fq0qVLefa5ePGiXnjhBWVmZqpPnz7mnA4AAAAlzKzZyn//+9+1du1aHT58WK1atVL//v3VtGlT1apVS7/++qtiYmK0adMm/fnnn/Lw8NArr7xiqboBAABQAswKh25ubtqwYYOGDx+u06dPa926dTnas58xbNiwoUJDQ+Xq6mrO6QAAAFDCzH5DygMPPKD9+/dr48aNxuvzUlNT5eTkZLw+b8CAAXJwcLBEvQAAAChBFnm3csWKFTVkyBANGTLEEocDAACAlZg1IeXXX3/VmjVrFBkZWWC/Q4cOac2aNbp8+bI5pwMAAEAJMyscrly5UmPHjr3r4tbx8fEaO3asVq9ebc7pAAAAUMLMCofffvutHBwc5O/vX2A/f39/2dvb6+uvvzbndAAAAChhZr8+z9PTU5UqVSqwX6VKleTl5cXr8wAAAMo4s8JhcnJyoZencXV11e+//27O6QAAAFDCzAqH9913X6FHA8+ePcs6hwAAAGWcWeGwVatWunr1qrZs2VJgv61bt+rq1atq1aqVOacDAABACTMrHA4bNkxZWVkaN26c9u3bl2ef/fv3a9y4cTKZTHruuefMOR0AAABKmFmLYPft21dPPvmkduzYoX79+qlNmzZq06aNXFxclJycrKioKEVFRSkrK0t9+vS566xmAAAAWJfZb0hZvny5XnvtNa1du1aRkZGKiooy2rKysmQymTRs2DDNnTvX3FMV2ZYtW/TZZ5/p2LFj+uOPP+Tu7q5HH31U06ZNk6enp9EvJSVFM2fO1ObNm/Xrr7/K3d1d/fv318SJE+Xk5FTqdQMAAFiL2eGwcuXKWrJkiV555RVt2bJFMTExunbtmqpVq6ZmzZqpX79+atKkiSVqLbSsrCy99tprWrFiherVq6dBgwbJyclJFy9e1P79+5WQkGCEw7S0NPn5+en48ePq3r27Bg8erOjoaC1cuFD79+/X9u3bVbly5VKtHwAAwFos8m5lSWrWrJmaNWtmqcOZZenSpVqxYoVefPFFzZo1S3Z2djnab926Zfx5wYIFOn78uIKCghQcHGxsDw4O1vz58xUSEqLx48eXVukAAABWZUpKSsqydhGWdP36dTVp0kSurq7697//LXv7/PNvVlaWmjZtqmvXrunnn3+Wo6Oj0ZaWlqbGjRurRo0aOnr0aLHraRYSdfdONuynwDZm7c/1M+/6AQBQVBYbOSwrIiIilJSUpGHDhikjI0Pbt29XbGysXFxc1K1bN9WvX9/oGxsbq4sXL6pHjx45gqEkOTo6qm3btgoPD9f58+dzPKMIAABgq2wuHGaP8tnZ2aljx446ffq00VahQgUFBgZq+vTpkm6HQ0k5AuOd6tevr/DwcMXGxhIOAQDAPcHmwuFvv/0mSVq8eLFatmypiIgINWrUSNHR0QoKCtKiRYtUr149jRo1SikpKZIkFxeXPI/l7OwsSUa/gqSnp1voE9gWrot5uH4AAHMVdWKtzYXDzMxMSVLFihUVFhamOnXqSJI6dOigFStWqFOnTlq0aJFGjRpl0fNeuHBBGRkZFj2mLUhISLB2CeUa1w8AYA47O7t875Dmx+bCYfZoX6tWrYxgmK1p06aqW7eu4uLilJSUZPRNTk7O81jZI4bZ/Qri4eGRT8vvhazcNnl5eZl5BK4fAAClyebCYcOGDSXlf6s4e3t6erp8fX0lSXFxcXn2zd6e3a8grIWYN66Lebh+AIDSZnPhsHPnzpKkU6dO5Wq7efOm4uLi5OjoqBo1asjd3V116tRRZGSk0tLSci1lExkZKR8fHyajAACAe0YFaxdgafXq1VP37t0VFxen0NDQHG3z5s1TcnKy/Pz8ZG9vL5PJpOHDhys1NVVz5szJ0XfOnDlKTU3ViBEjSrN8AAAAq7K5RbAl6cyZM+rZs6cuX76sXr16qWHDhoqOjtaePXvk5eWl7777Tu7u7pJujxD26tVLJ06cUPfu3dWyZUsdO3ZMERERat26tbZt26YqVaoUuxYWcWYRbHOwCDYAoLTZ3MihdHv08Pvvv9dzzz2no0eP6pNPPlFcXJxeeuklRUREGMFQur3Y9bZt2zRmzBidOnVKixYt0qlTp/TKK6/oq6++MisYAgAAlDc2OXJYljDyxcihORg5BACUNpscOQQAAEDxEA4BAABgsLmlbADcxi15bskDQHEwcggAAAAD4RAAAAAGwiEAAAAMhEMAAAAYmJACAHlgQg8TeoB7FSOHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAMM9Ew7nz58vV1dXubq66vDhw7naU1JSNHnyZDVv3ly1atVSixYtNHXqVKWmplqhWgAAAOu4J8JhTEyMZsyYIUdHxzzb09LS5Ofnp5CQEDVq1EiBgYFq2LChFi5cqH79+ik9Pb2UKwYAALAOmw+HN2/e1JgxY9SiRQv5+fnl2WfBggU6fvy4goKCtGHDBgUHB2vDhg0KCgrSkSNHFBISUspVAwAAWIfNh8O5c+fq5MmTWrRokezs7HK1Z2VladWqVXJyctKECRNytE2YMEFOTk4KDQ0trXIBAACsyqbD4dGjR/XRRx9p4sSJeuCBB/LsExsbq4sXL6pt27a5bjs7Ojqqbdu2Onv2rM6fP18aJQMAAFiVzYbDP//807id/Oqrr+bbLzY2VpJUv379PNuzt2f3AwAAsGX21i6gpHz44YeKjY3Vrl278rydnC0lJUWS5OLikme7s7Nzjn75YdJK3rgu5uH6FR/XzjxcP8B2VK5cuUj9bTIcRkVFaeHChZo0aZKaNm1aKue8cOGCMjIySuVc5UlCQoK1SyjXuH7Fx7UzD9cPsA12dnb53h3Nj82Fw1u3bmnMmDFq1qyZXnvttbv2zx4ZTE5OzrM9e8Qwu19+PDw88mn5/a412DIvLy8zj8D1Kz6unXm4fgDuTTYXDlNTU43nA2vWrJlnnyeeeEKStHr1amOiSlxcXJ59s7f7+voWeN6iDtneK7gu5uH6FR/XzjxcP+DeZXPhsFKlSho+fHiebQcOHFBsbKyefPJJ1ahRQ97e3vL19VWdOnUUGRmptLS0HDOW09LSFBkZKR8fH3l6epbWRwAAALAamwuHVapU0cKFC/NsGzNmjGJjYzV+/Hg9+uijxvbhw4dr9uzZmjNnjoKDg43tc+bMUWpqqsaPH1/SZQMAAJQJNhcOi+PVV1/V9u3bNX/+fEVHR6tly5Y6duyYIiIi1Lp1a40ZM8baJQIAAJQKm13nsCgcHR21bds2jRkzRqdOndKiRYt06tQpvfLKK/rqq69UpUoVa5cIAABQKu6pkcMlS5ZoyZIleba5uLhoxowZmjFjRilXBQAAUHYwcggAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGOytXQAAwPY0C4mydglW81NgG2uXAJiFkUMAAAAYCIcAAAAwcFsZAIAy5F6+JS9xW74sYOQQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgsLlweOHCBYWEhGjAgAFq3ry5atasqUaNGmn48OH697//nec+KSkpmjx5spo3b65atWqpRYsWmjp1qlJTU0u5egAAAOuyt3YBlrZs2TLNnz9f9erV02OPPaYaNWooNjZW27Zt07Zt2/Tpp59q4MCBRv+0tDT5+fnp+PHj6t69uwYPHqzo6GgtXLhQ+/fv1/bt21W5cmUrfiIAAIDSY3PhsHXr1tq6das6deqUY/uBAwfk7++v8ePHy8/PT5UqVZIkLViwQMePH1dQUJCCg4ON/sHBwZo/f75CQkI0fvz40vwIAAAAVmNzt5X79euXKxhKUocOHdS5c2clJSUpJiZGkpSVlaVVq1bJyclJEyZMyNF/woQJcnJyUmhoaKnUDQAAUBbYXDgsiIODgyTJzs5OkhQbG6uLFy+qbdu2cnR0zNHX0dFRbdu21dmzZ3X+/PlSrxUAAMAabO62cn4SEhK0a9cu1a5dW82aNZN0OxxKUv369fPcp379+goPD1dsbKw8PT0LPH56erplC7YRXBfzcP2Kj2tnHq5f8XHtzMP1s7yizp24J8LhzZs3NXr0aP35558KDg42Rg5TUlIkSS4uLnnu5+zsnKNfQS5cuKCMjAwLVWw7EhISrF1Cucb1Kz6unXm4fsXHtTMP18+y7Ozs8h0Ey4/Nh8PMzEwFBgbqwIEDGjFihJ599tkSOY+Hh0c+Lb+XyPnKCy8vLzOPwPUrPq6debh+5rl3rx/XzjzmXz+Yy6bDYWZmpsaOHat//vOfeuaZZzRv3rwc7dkjg8nJyXnunz1imN2vICx3kzeui3m4fsXHtTMP16/4uHbm4fpZn82Gw+wRw7Vr12rw4MFasmSJKlTIOf/G19dXkhQXF5fnMbK3Z/cDAABlW7OQKGuXYDU/BbaxyHFscrbyncFw4MCB+uSTT4znDO/k6+urOnXqKDIyUmlpaTna0tLSFBkZKR8fn7tORgEAALAVNhcOs28lr127Vv3799eyZcvyDIaSZDKZNHz4cKWmpmrOnDk52ubMmaPU1FSNGDGiNMoGAAAoE2zutvKsWbO0Zs0aOTk5qUGDBrlCnyT5+fnpwQcflCS9+uqr2r59u+bPn6/o6Gi1bNlSx44dU0REhFq3bq0xY8aU9kcAAACwGpsLh/Hx8ZKk1NRUzZ07N88+3t7eRjh0dHTUtm3bNHPmTG3ZskV79+6Vu7u7XnnlFU2cOFFVqlQptdoBAACszebC4ZIlS7RkyZIi7ePi4qIZM2ZoxowZJVQVAABA+WBzzxwCAACg+AiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGwiEAAAAMhEMAAAAYCIcAAAAwEA4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYCAcAgAAwEA4BAAAgIFwCAAAAAPhEAAAAAbCIQAAAAyEQwAAABgIhwAAADAQDgEAAGAgHAIAAMBAOAQAAICBcHiHI0eO6Omnn5a3t7c8PDz0+OOPa+PGjdYuCwAAoNTYW7uAsmLPnj0aNGiQKleurIEDB8rJyUmbN2/WyJEjdf78ef3973+3dokAAAAljnAo6datW3r11VdVoUIFbdu2TQ8++KAk6c0331SPHj30/vvvy9/fX97e3lauFAAAoGQRDnV71PDMmTMaNmyYEQwlycXFRePHj1dgYKDWrFmjiRMnFvnYbpW5xObg+hUf1848XD/zcP2Kj2tnHq6f+UxJSUlZ1i7C2t577z19/PHH+uyzzzRo0KAcbYmJiWrcuLG6dOmizZs3W6lCAACA0sGEFEmxsbGSJF9f31xt7u7ucnJyUlxcXGmXBQAAUOoIh5JSUlIkSc7Oznm2V6tWzegDAABgywiHAAAAMBAO9X8jhvmNDl67di3fUUUAAABbQjjU/z1rmP3s4Z0SExOVmpqq+vXrl3ZZAAAApY5wKKljx46SpIiIiFxt4eHhOfoAAADYMpay0e1FsB955BFdvHhRO3fuNNY6TE5OVo8ePRQfH6/Dhw/Lx8fHypUCAACULMLh/5ff6/MSEhL0/vvv8/o8AMA9Yfv27fL29lbz5s2tXQqshHB4hx9++EEzZsxQVFSUbt68qaZNm2rs2LEaOHCgtUuDlaSkpCg9PV01atRQhQo8hQHA9rm5uWnEiBGaP3++tUuBlfCOmTs8/PDD+te//mXtMiwiLS1NK1euVGRkpP744w95e3tr8ODBat++vbVLK1MuXLighIQEPfrooznC3+eff65FixbpzJkzkiRHR0f17dtX7733nmrWrGmtcsukmzdv6qeffpK9vb2aNWsmk8mUZ78TJ07o+PHjGjp0aClXCFvTv39/9ejRQ0OHDlWNGjWsXU658sMPPxSq3+XLl3P0ffjhh0uqpHJp586diouLU5MmTdSlSxdJUmpqqj766CPt27dP9vb26tWrl8aMGaNKlSpZudqiY+SwnHvqqaf03HPP5fgL9+zZs/L391dCQoKysv7v6zWZTBo/frymTJlijVLLpBdffFFRUVGKjo42tr3zzjtatGiRTCaT6tatKxcXF8XFxSk5OVk+Pj4KDw9X9erVrVh12bFp0yaNHz9eSUlJkqQ6deooODhYTz/9dK6+M2fO1OzZs3X16tVSrtI2hIaGKjIyUosXL7Z2KVbn5uYmk8kke3t79e7dWwEBAerRo0e+/2GC/5N97YqKf29vu3Xrlp599llFREQoKytLJpNJzz//vObPny8/Pz8dOnTI6GsymdShQwdt3ry53N15YuSwnNu3b586deqUY9uoUaMUHx+vIUOGaOTIkapevboOHz6s4OBgffzxx+rcubO6du1qpYrLlsOHD+eYiX7mzBktXrxYjRo10ueff64mTZpIuv1/CB9//LFmzJih2bNna9asWdYqucz44YcfNGrUKNnZ2emxxx6Tvb29du/erdGjR+vgwYP6+OOPrV2iTTl06JDWrl1LOPz/HnjgASUmJmrLli3aunWrPDw8NGzYMA0bNkze3t7WLq9Mc3R0VJ8+fWRnZ5erLSsrS2vXrpWvr6/atGljherKti+//FLh4eHq1KmT+vbtq++++06rV6+Wm5ub/vOf/2jFihV67LHHdPHiRb399tuKiIjQ6tWrFRAQYO3Si4RwaGN++OEHHTlyRM8++6yWLFlibG/QoIEefvhhderUSZ9++inh8P9LTExUnTp1jJ937dqlrKwszZ8/3wiGkmRvb68333xT+/fv1/bt2wmHkhYsWKAKFSpo8+bNateunSQpISFBo0eP1ooVK3T9+nWFhIQwmoMS4e/vr/Hjx2vr1q0KDQ3Vnj17NHv2bM2dO1ddu3ZVQECA/Pz85ODgYO1Sy5R3331XM2fO1JkzZ7Ro0SI1atQoV5+1a9eqY8eOWrBggRUqLNtWrVolb29vffXVV6pQoYJefvlltWvXTosWLdLcuXPl7+8v6fbLNVatWqXmzZtr/fr1hENY15EjR2QymTR27NhcbY0aNdITTzyhf//731aorGyqWrWqrl27ZvycnJwsScZyRn/14IMP5rhtcC+LjIyUn5+fEQwlycvLS5s3b9aYMWO0du1aZWRk6JNPPiEg5mHNmjVF6p/9/Cv+j4ODgwYMGKABAwYoISFBq1evVlhYmL7//nvt2rVLbm5uGjJkiIYPH57jP/buZUFBQerZs6fGjBmjrl27atKkSRo3bhz/jhbSmTNn9NRTTxm3iU0mk7p06aL//ve/6t27d46+VapU0eOPP26sl1yeEA5tTHbQadCgQZ7tDRo00M6dO0uzpDLtwQcfzPHsSPabcE6dOqVWrVrl6n/q1Cndd999pVxl2fT7778bbxe6k729vZYtWyZ7e3utXbtWWVlZ+uSTT6xQYdkWGBhYpL+Qs39HkTcvLy+99dZbmjRpkr7//nuFhoZqx44dWrJkiZYuXapHHnlE3377rbXLLBOaNm2q8PBwzZkzR9OnT9eWLVu0ePFiNW7c2NqllXnJyclydXXNsS3774Q770Jl8/DwyPfVvGUZ4dAG3PkXRu3atSXdnq1cuXLlXH3/+OMPValSpdRqK+tGjRqlgIAATZw4UTNnzlTv3r3l6+urCRMmKCwsTLVq1TL6hoaGaufOnXruueesWHHZUatWLf322295tplMJoWEhCgrK0tffvmlMjMzVa9evVKusGyrWLGiateurZEjRxaq/6ZNm3JMnELeTCaTunfvru7du+v333/XmjVrtHr1au6Y/IW9vb3eeustPfnkk8Yo4oQJExQUFGTt0so0Nze3PP9/787Jn3e6du2aHB0dS7osiyMc2oCQkBCFhYVJkm7cuCFJiomJUefOnXP1jY+PzxF47nVPPfWURowYoX/84x8KDw9Xnz591KdPHy1evFgPPfSQWrVqJRcXF506dUpxcXGqWbOmJk+ebO2yy4RGjRpp3759+babTCbjudcvv/xS1apVK63SyoWmTZvq/Pnzhf7L+NSpU4TDInJzc1NgYKACAwN15MgRa5dTJrVq1Uq7d+/WBx98oA8//FCbN29mhLoA9erV0+nTp3Nse+ONNzRu3Lg8+8fHx+c5oljWla+51cjF09NTzs7OysrKUlZWlhwcHOTp6amDBw/m6nv9+nUdPHgw3+fp7lXz58/Xhx9+qKtXr2rRokVauHChMjIy9Mcff+jAgQPasWOHYmNj9dhjj+mbb76Rh4eHtUsuEx5//HHFxcXpwIED+fbJDojPPvtsjmc7cfsv5StXruj8+fPWLuWe0Lp1a2uXUGZVrFhR06ZN044dO5SamprvKBhu/3t75MgR3bp1y9jm4OCQ5+jg9evXdejQIbVt27Y0S7QIRg7LuePHjxe67/nz5xUYGJjniOK9bsyYMRo5cqQiIiJ09OhR/fbbb8rMzJSzs7MaNGigLl26qG7dutYus0wZMGCALl++fNf1z7JvMXt7eyshIaGUqiv72rdvr/DwcMXGxsrT0/Ou/e+c+HOvW7x4sVq0aGHtMmxOmzZtFBUVpdTU1HK5cHNpePfdd/XGG2/I3v7u8Sk+Pl6BgYG5JqqUByyCDQAAAAO3lQEAAGAgHAIAAMBAOAQAAICBcAgAAAAD4RAAAAAGlrIBABs0Y8YMSbeXafrr674AoCAsZQMANig7EB47dkw+Pj7WLQZAucJtZQAAABgIhwAAADAQDgHADC1atJCrq6v27t2rmJgY/e1vf1OjRo3k7u6uRx99VLNnz1Z6enqu/Y4dO6YPPvhAvXr1UtOmTVWzZk3Vq1dPTz31lNasWZPv+239/Pzk6uqqsLAwXbp0Sa+//roefPBB1apVS35+fpoxY0aOZwxbtmwpV1dX45/sZxEBID9MSAEAC/jhhx80e/ZsZWRk6IEHHpCTk5P++9//6sMPP9R3332njRs3ytHR0ej/6quv6ujRo3J2dlbt2rXl7u6uS5cuae/evdq7d6/Cw8P16aef5nu+M2fOqGPHjkpKSlLjxo3VuHFjVaxYUZ6enmrXrp0OHTokSXrooYdyvCe3MO9xBnBvY0IKAJihRYsWSkhIkIODg7p3766lS5fKzc1NknTw4EE9//zzunLlil588UXNnTvX2O+f//ynmjVrpqZNm+Y43pEjR/TSSy8pNjZWn332mQYNGpSj3c/PT/v375ednZ06d+6spUuXqnbt2pKk69evq0qVKpKYkAKg+LitDAAW4OTkpM8++8wIhpLUvn17zZw5U5K0cuVK/frrr0bb008/nSsYSlLr1q310UcfSZLWrFmT7/nc3Ny0cuVKIxhKMoIhAJiD28oAYAHDhw+Xk5NTru0DBw7UlClTlJiYqIiICD377LNGW3x8vDZs2KBjx47pypUrunHjhiTpzz//lCRFR0fnez5/f3+5uLhY+FMAAOEQACyiSZMmeW63s7NTw4YNlZiYqFOnThnbly5dqnfeeccIhHm5evVqvm0PPPBA8YsFgAJwWxkALKBWrVp3bbt27ZokKSoqSpMmTdKNGzf00ksvKSIiQmfPntWVK1eUlJSko0ePSpJu3bqV7zGrVq1queIB4A6MHAKABdz5PGF+bdWqVZP0f88S+vv7a86cObn6FzRiCAAljZFDALCAkydP5rk9IyNDp0+fliQ1atRIknTu3DlJUocOHfLc5/DhwyVQIQAUDuEQACwgNDRUaWlpubZv3LhRly5dkoODgx577DFJ/zerODExMVf/9PR0LVu2zOx6sm87X79+3exjAbi3EA4BwAJSU1P14osvKikpydgWGRmpt956S9Lt2czu7u6SpI4dO0qSPv30Ux05csTof/nyZY0YMUK//PKL2fXUq1dPkrRr1y6zjwXg3kI4BAALePvtt7V792498MAD6tatmx5++GH16tVLly9f1iOPPKJp06YZfUeMGKHGjRsrJSVFPXr00COPPKIuXbqoadOm2rVrl2bPnm12PdlL5kyaNElt2rRRnz595Ofnp7CwMLOPDcC2EQ4BwAIefvhhfffdd+rVq5d++eUXJSQkqEGDBpo0aZK2bNliTEaRJEdHR+3YsUOjRo1S7dq1de7cOSUmJqpv374KDw9X165dza5n7Nixev/999W8eXOdP39eBw4c0P79+xUfH2/2sQHYNl6fBwBmyH593pYtW9S5c2drlwMAZmPkEAAAAAbCIQAAAAyEQwAAABgIhwAAADAwIQUAAAAGRg4BAABgIBwCAADAQDgEAACAgXAIAAAAA+EQAAAABsIhAAAADIRDAAAAGAiHAAAAMBAOAQAAYPh/mrpsb3W5O0sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='part', data=lectures_df, order=lectures_df['part'].value_counts().index)\n",
    "plt.title('Категории лекций')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1a1a1949-913c-4663-b660-d7b87439db4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 5))\n",
    "sns.countplot(x='tag', data=lectures_df, order=lectures_df['tag'].value_counts().iloc[:50].index)\n",
    "plt.title('Количество употреблений тегов')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b20cbe04-adba-40ca-8597-d485cac5da7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.countplot(x='part', data=lectures_df, hue='type_of', order=lectures_df['part'].value_counts().index)\n",
    "plt.title('Связь категории и типа лекции')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a3a7c4-0ba4-4163-afae-34ec5bd775f1",
   "metadata": {},
   "source": [
    "Посмотрю на связь оценки и просмотра лекций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "fbd15654-eae2-4147-8e57-3ef5eada6d29",
   "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>answered_correctly</th>\n",
       "      <th>-1</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5382</th>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8623</th>\n",
       "      <td>3.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "answered_correctly   -1     0     1\n",
       "user_id                            \n",
       "115                 NaN  14.0  32.0\n",
       "124                 NaN  23.0   7.0\n",
       "2746                1.0   8.0  11.0\n",
       "5382                3.0  41.0  84.0\n",
       "8623                3.0  39.0  70.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_lecture_df = (\n",
    "    train_df.groupby([\"user_id\", \"answered_correctly\"])\n",
    "    .size()\n",
    "    .unstack()\n",
    ")\n",
    "user_lecture_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3b11918a-c6f1-4231-a2c4-373ac4c7859f",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_lecture_df.columns = ['lecture', 'wrong', 'right']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "9fa6b2c2-767d-4f67-a32d-562380bcc852",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_lecture_df['lecture'] = user_lecture_df['lecture'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "be5d393c-83df-4891-b5be-2032d72423c6",
   "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>lecture</th>\n",
       "      <th>wrong</th>\n",
       "      <th>right</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2746</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5382</th>\n",
       "      <td>3.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8623</th>\n",
       "      <td>3.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lecture  wrong  right\n",
       "user_id                       \n",
       "115          0.0   14.0   32.0\n",
       "124          0.0   23.0    7.0\n",
       "2746         1.0    8.0   11.0\n",
       "5382         3.0   41.0   84.0\n",
       "8623         3.0   39.0   70.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_lecture_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "66f5d530-a4f8-4ef9-822b-d9680ebbe6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_lecture_df['watch_lecture'] = user_lecture_df['lecture'].apply(lambda x: x > 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "7edd1bda-9d11-4892-94fa-ff3bca998cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "         lecture  wrong  right  watch_lecture\n",
       "user_id                                      \n",
       "115          0.0   14.0   32.0          False\n",
       "124          0.0   23.0    7.0          False\n",
       "2746         1.0    8.0   11.0           True\n",
       "5382         3.0   41.0   84.0           True\n",
       "8623         3.0   39.0   70.0           True"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_lecture_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "57a7a308-79c3-4075-8e79-016c148ccad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sum</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>watch_lecture</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>False</th>\n",
       "      <td>5330919.0</td>\n",
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       "      <td>28695754.0</td>\n",
       "      <td>58058832.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    wrong       right\n",
       "                      sum         sum\n",
       "watch_lecture                        \n",
       "False           5330919.0   7185795.0\n",
       "True           28695754.0  58058832.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "watch_lecture_df = (\n",
    "    user_lecture_df.groupby(\"watch_lecture\")\n",
    "    .agg({\n",
    "        'wrong': ['sum'], \n",
    "        'right': ['sum']\n",
    "    })\n",
    ")\n",
    "watch_lecture_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "81e74a03-0df7-401a-a87e-3cdf936eaaf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "watch_lecture_df['correct'] = watch_lecture_df[\"right\"] / (watch_lecture_df[\"right\"] + watch_lecture_df[\"wrong\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1b87d9d5-e166-4719-a487-25b9b0cb3a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                    wrong       right   correct\n",
       "                      sum         sum          \n",
       "watch_lecture                                  \n",
       "False           5330919.0   7185795.0  0.574096\n",
       "True           28695754.0  58058832.0  0.669231"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "watch_lecture_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "dc04202e-c9d5-493f-8cb8-98b46652cde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "watch_lecture = watch_lecture_df.iloc[:, 2]\n",
    "watch_lecture.plot.barh()\n",
    "for i, v in zip(watch_lecture.index, watch_lecture.values):\n",
    "    plt.text(v, i, round(v, 2), color='white', fontweight='bold', fontsize=14, ha='right', va='center')\n",
    "\n",
    "plt.title(\"Правильность ответа в зависимости от просмотра лекции\")\n",
    "plt.xlabel(\"Процент правильных ответов\")\n",
    "plt.ylabel(None)\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99d40104-a665-4115-80db-f3bb3140ef1d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1984513e-d68d-4792-af7a-f9c0de4c2568",
   "metadata": {},
   "source": [
    "Выводы:\n",
    "- Всего тегов для лекций - 151\n",
    "- Больше всего лекций по концепциям и решениям задач\n",
    "- В каждой категории больше всего лекций, связанных с концепциям и решениям задач\n",
    "- Успеваимость выше у тех студентов, которые смотрели лекции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "197eb40d-c075-4f30-8e47-5182062ea5d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bffb3c0-99f8-44fe-8cfb-745d85928555",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7052967f-783f-481e-a64f-16cee3cba125",
   "metadata": {},
   "source": [
    "# END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09423925-5e7d-4018-8f0e-28d8d7ebeb0c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "668ee98e-83a1-45c3-b66d-60b26149c6da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bdd1ae91-fd32-4126-a7b0-cf34954e6f02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cbefaaa-c5eb-4528-8074-263f1c6949cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "271a7630-d2b1-4627-9302-651b136f38fa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1853fe3a-cdb0-4247-8a66-03507825ac2c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6d34993-e5bc-4a1a-a2e5-084fcd0ea2c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1969ff1e-c9c0-4f7d-91a7-0836e23b3987",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6198bc59-9934-4020-bcff-742fa267318c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d722747e-a51d-44c0-9933-f864a85ad577",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d69d8f0-7c20-4e31-b4f8-2caf5f26e1be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78cc4f3c-7b2f-4e6d-9f87-b8d3235a683f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "769f4487-0618-4bbe-bfbb-2b3c857f3e66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b19cae87-9295-422d-b4ca-e07a3f13de43",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}