ML_program.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "# Информация о пользователях курса Stepik. \n",
    "### Общая цель - найти закономерности и определять пользователей, способных закончить успешно курс на сайте для предоставления определенных привилегий и скидок на другие образовательные программы (по результату анализа поведения юзера за перые 3 дня на сайте)\n",
    "\n",
    "То есть надо добиться высокого показателя recall, считая positive классом людей, способных закончить курс.\n",
    "1) Первоначально проанализируем данные, чтобы определить тех, кто из пользователей вообще смог закончить курс. Определим требуемую метрику, по которой будем классифицировать как тех, кто смог закончить курс, а кто нет.\\\n",
    "2) Изучим поведение пользователей за первые 3 дня с момента начала курса. Создадим фичи, по которым будем обучать модель.\\\n",
    "3) Зададим разные модели и определим лучшую по параметру recall\n",
    "\n",
    "**Дополнительно изучим датасет, чтобы определить id создателя курса и номер задания, который больше всего доставил сложностей и лишил многих мотивации решать курс далее**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1cddb8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize': (12, 6)})  # задание размера графиков"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8762380",
   "metadata": {},
   "source": [
    "# Предворительная обработка данных\n",
    "\n",
    "### Для предворительнойобработки надо найти ключевые параметры про каждого из пользователей курса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dfed80cf",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>action</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32815</td>\n",
       "      <td>1434340848</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32815</td>\n",
       "      <td>1434340848</td>\n",
       "      <td>passed</td>\n",
       "      <td>17632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32815</td>\n",
       "      <td>1434340848</td>\n",
       "      <td>discovered</td>\n",
       "      <td>17632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>discovered</td>\n",
       "      <td>17632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step_id   timestamp      action  user_id\n",
       "0    32815  1434340848      viewed    17632\n",
       "1    32815  1434340848      passed    17632\n",
       "2    32815  1434340848  discovered    17632\n",
       "3    32811  1434340895  discovered    17632\n",
       "4    32811  1434340895      viewed    17632"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data=pd.read_csv('https://getfile.dokpub.com/yandex/get/https://disk.yandex.ru/d/oG0XwOIrrMyQ2w')\n",
    "events_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e3e1d49e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>submission_status</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31971</td>\n",
       "      <td>1434349275</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31972</td>\n",
       "      <td>1434348300</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31972</td>\n",
       "      <td>1478852149</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31972</td>\n",
       "      <td>1478852164</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31976</td>\n",
       "      <td>1434348123</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   step_id   timestamp submission_status  user_id\n",
       "0    31971  1434349275           correct    15853\n",
       "1    31972  1434348300           correct    15853\n",
       "2    31972  1478852149             wrong    15853\n",
       "3    31972  1478852164           correct    15853\n",
       "4    31976  1434348123             wrong    15853"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissions_data = pd.read_csv('https://getfile.dokpub.com/yandex/get/https://disk.yandex.ru/d/2xY4JaB5JzrI6w')\n",
    "submissions_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9dce6fa",
   "metadata": {},
   "source": [
    "### Перевод времени из unix date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b3e3685",
   "metadata": {},
   "outputs": [],
   "source": [
    "events_data['date'] = pd.to_datetime(events_data.timestamp, unit='s')\n",
    "events_data['day'] = events_data['date'].dt.date\n",
    "submissions_data['date'] = pd.to_datetime(submissions_data.timestamp, unit='s')\n",
    "submissions_data['day'] = submissions_data['date'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ff5d313",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>timestamp</th>\n",
       "      <th>action</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
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       "      <th>0</th>\n",
       "      <td>32815</td>\n",
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       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:00:48</td>\n",
       "      <td>2015-06-15</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32815</td>\n",
       "      <td>1434340848</td>\n",
       "      <td>passed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:00:48</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32815</td>\n",
       "      <td>1434340848</td>\n",
       "      <td>discovered</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:00:48</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>discovered</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:01:35</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:01:35</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>passed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:01:35</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32929</td>\n",
       "      <td>1434340921</td>\n",
       "      <td>discovered</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:02:01</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step_id   timestamp      action  user_id                date         day\n",
       "0    32815  1434340848      viewed    17632 2015-06-15 04:00:48  2015-06-15\n",
       "1    32815  1434340848      passed    17632 2015-06-15 04:00:48  2015-06-15\n",
       "2    32815  1434340848  discovered    17632 2015-06-15 04:00:48  2015-06-15\n",
       "3    32811  1434340895  discovered    17632 2015-06-15 04:01:35  2015-06-15\n",
       "4    32811  1434340895      viewed    17632 2015-06-15 04:01:35  2015-06-15\n",
       "5    32811  1434340895      passed    17632 2015-06-15 04:01:35  2015-06-15\n",
       "6    32929  1434340921  discovered    17632 2015-06-15 04:02:01  2015-06-15"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "afec2ef5",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>step_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>submission_status</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31971</td>\n",
       "      <td>1434349275</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:21:15</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31972</td>\n",
       "      <td>1434348300</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:05:00</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31972</td>\n",
       "      <td>1478852149</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15853</td>\n",
       "      <td>2016-11-11 08:15:49</td>\n",
       "      <td>2016-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31972</td>\n",
       "      <td>1478852164</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2016-11-11 08:16:04</td>\n",
       "      <td>2016-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31976</td>\n",
       "      <td>1434348123</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:02:03</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31976</td>\n",
       "      <td>1434348188</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:03:08</td>\n",
       "      <td>2015-06-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31976</td>\n",
       "      <td>1478852055</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2016-11-11 08:14:15</td>\n",
       "      <td>2016-11-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   step_id   timestamp submission_status  user_id                date  \\\n",
       "0    31971  1434349275           correct    15853 2015-06-15 06:21:15   \n",
       "1    31972  1434348300           correct    15853 2015-06-15 06:05:00   \n",
       "2    31972  1478852149             wrong    15853 2016-11-11 08:15:49   \n",
       "3    31972  1478852164           correct    15853 2016-11-11 08:16:04   \n",
       "4    31976  1434348123             wrong    15853 2015-06-15 06:02:03   \n",
       "5    31976  1434348188           correct    15853 2015-06-15 06:03:08   \n",
       "6    31976  1478852055           correct    15853 2016-11-11 08:14:15   \n",
       "\n",
       "          day  \n",
       "0  2015-06-15  \n",
       "1  2015-06-15  \n",
       "2  2016-11-11  \n",
       "3  2016-11-11  \n",
       "4  2015-06-15  \n",
       "5  2015-06-15  \n",
       "6  2016-11-11  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissions_data.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1d152d6",
   "metadata": {},
   "source": [
    "### Обработка events_data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d7c43a2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49593429832996383"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.query(\"action == 'passed'\").groupby('user_id').count().shape[0] * 100 / events_data.shape[0] \n",
    "# Процент людей, решивших хотя бы один номер"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9d4f155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.kdeplot(data=events_data.query(\"action == 'passed'\").groupby('user_id').count().action,color='#003e4d')\n",
    "ax.set_xlabel('Число пройденных задач курса')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1bc44213",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = events_data.groupby(['day', 'action'], as_index=False).agg({'user_id': 'nunique'}).\\\n",
    "    query(\"action == 'viewed'| action == 'passed'\")\n",
    "x_v = x.query(\"action == 'viewed'\")\n",
    "x_p = x.query(\"action == 'passed'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93a248bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0.1, 0.1, 1.1, 0.8])\n",
    "ax.plot(x_v['day'], x_v['user_id'])\n",
    "ax.plot(x_p['day'], x_p['user_id'])\n",
    "ax.legend(['Количество просмотров номеров', 'Количество верно решенных номеров'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f1364d0",
   "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>action</th>\n",
       "      <th>user_id</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19229</th>\n",
       "      <td>26790</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19230</th>\n",
       "      <td>26793</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19231</th>\n",
       "      <td>26794</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>34</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>26797</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>26798</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19234 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "action  user_id  discovered  passed  started_attempt  viewed\n",
       "0             1           1       0                0       1\n",
       "1             2           9       9                2      10\n",
       "2             3          91      87               30     192\n",
       "3             5          11      11                4      12\n",
       "4             7           1       1                0       1\n",
       "...         ...         ...     ...              ...     ...\n",
       "19229     26790           8       8                1       9\n",
       "19230     26793           1       0                1       1\n",
       "19231     26794          69      69               34     180\n",
       "19232     26797          10      10                2      13\n",
       "19233     26798          12      12                3      12\n",
       "\n",
       "[19234 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_events_data = events_data.pivot_table(index='user_id', columns='action',\n",
    "    values='step_id', aggfunc='count', fill_value=0).reset_index()\n",
    "users_events_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "943f2116",
   "metadata": {},
   "source": [
    "Проверка на потерю данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b93a88c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check(df):\n",
    "    if events_data.user_id.nunique() == df.user_id.nunique():\n",
    "        print('Пользователи не потеряны')\n",
    "    else:\n",
    "        print('Количествопользователй отличается от исходного числа')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e4a59d0",
   "metadata": {},
   "source": [
    "### Обработка submissions_data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "14dc416f",
   "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>submission_status</th>\n",
       "      <th>user_id</th>\n",
       "      <th>correct</th>\n",
       "      <th>wrong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9935</th>\n",
       "      <td>26787</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9936</th>\n",
       "      <td>26790</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9937</th>\n",
       "      <td>26794</td>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9938</th>\n",
       "      <td>26797</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9939</th>\n",
       "      <td>26798</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9940 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "submission_status  user_id  correct  wrong\n",
       "0                        2        2      0\n",
       "1                        3       29     23\n",
       "2                        5        2      2\n",
       "3                        8        9     21\n",
       "4                       14        0      1\n",
       "...                    ...      ...    ...\n",
       "9935                 26787        3      0\n",
       "9936                 26790        1      0\n",
       "9937                 26794       33      9\n",
       "9938                 26797        2      0\n",
       "9939                 26798        2      1\n",
       "\n",
       "[9940 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_scores = submissions_data.pivot_table(index='user_id', columns='submission_status',\n",
    "    values='step_id', aggfunc='count', fill_value=0).reset_index()\n",
    "users_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89453905",
   "metadata": {},
   "source": [
    "### Вычисление дат посещений курса пользователями"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "56d3a287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1                                             [1472827464]\n",
       "2                                 [1514383364, 1519226966]\n",
       "3        [1434358476, 1441257725, 1441440209, 144153391...\n",
       "5                                 [1466156809, 1499859621]\n",
       "7                                             [1521634660]\n",
       "                               ...                        \n",
       "26790     [1451484016, 1451722241, 1452616205, 1517495853]\n",
       "26793                                         [1510225031]\n",
       "26794    [1495287049, 1495373177, 1495509936, 149560483...\n",
       "26797                             [1508425316, 1509993306]\n",
       "26798                                         [1520975763]\n",
       "Name: timestamp, Length: 19234, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits_users_time = events_data[['user_id', 'day', 'timestamp']].drop_duplicates(subset=['user_id', 'day']).\\\n",
    "    groupby('user_id')['timestamp'].apply(list)\n",
    "visits_users_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5eedc5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "1                                                       []\n",
       "2                                                [4843602]\n",
       "3         [6899249, 182484, 93710, 2768870, 171400, 78712]\n",
       "5                                               [33702812]\n",
       "7                                                       []\n",
       "                               ...                        \n",
       "26790                           [238225, 893964, 64879648]\n",
       "26793                                                   []\n",
       "26794    [86128, 136759, 94899, 1422583, 14347289, 5050...\n",
       "26797                                            [1567990]\n",
       "26798                                                   []\n",
       "Name: timestamp, Length: 19234, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visits_users_time.apply(np.diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bdff532",
   "metadata": {},
   "source": [
    "Все значения дельт по времени между заходами: (выражено в днях)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a84532f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "gap_data = visits_users_time.apply(np.diff).values\n",
    "gap_data = pd.Series(np.concatenate(gap_data, axis=0))\n",
    "gap_data = gap_data / (24 * 60 * 60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8ad61ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(gap_data[gap_data < 13], bins=40, kde=True, color='#1c6b72')\n",
    "ax.set_xlabel('Число дней между ближайшими заходами на сайт')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9752f29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95 процентов пользователей между заходами на курс длают интервал не более  59.8  дней\n"
     ]
    }
   ],
   "source": [
    "print('95 процентов пользователей между заходами на курс длают интервал не более ',\n",
    "    gap_data.quantile(0.95).round(1), ' дней')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ee4a1355",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>last_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  last_timestamp\n",
       "0        1      1472827464\n",
       "1        2      1519226966\n",
       "2        3      1444581588\n",
       "3        5      1499859939\n",
       "4        7      1521634660\n",
       "5        8      1481466913\n",
       "6        9      1484999497"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Дата последнего захода на сайт каждого пользоателя\n",
    "users_data = events_data.groupby('user_id', as_index=False)\\\n",
    "    .agg({'timestamp': 'max'}).rename(columns={'timestamp': 'last_timestamp'})\n",
    "users_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2942548a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1526772811 2592000\n"
     ]
    }
   ],
   "source": [
    "# Последняя дата в данных\n",
    "last_date = events_data.timestamp.max()\n",
    "# Считаем, что кикнулся с курса если нет более 30 дней\n",
    "drop_out_threshold = 30 * 24 * 60 * 60\n",
    "print(last_date, drop_out_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "58fe6db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Сколько прошло с момента последнего заходадо момента окончания исследования для каждого пользователя\n",
    "users_data['is_gone_user'] = (last_date - users_data.last_timestamp) > drop_out_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ed17390",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  last_timestamp  is_gone_user  correct  wrong\n",
       "0        1      1472827464          True      0.0    0.0\n",
       "1        2      1519226966          True      2.0    0.0\n",
       "2        3      1444581588          True     29.0   23.0\n",
       "3        5      1499859939          True      2.0    2.0\n",
       "4        7      1521634660          True      0.0    0.0\n",
       "5        8      1481466913          True      9.0   21.0\n",
       "6        9      1484999497          True      0.0    0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Объединение данных\n",
    "users_data = users_data.merge(users_scores, how='outer').fillna(0)\n",
    "users_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d7231bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пользователи не потеряны\n"
     ]
    }
   ],
   "source": [
    "check(users_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1d211be3",
   "metadata": {},
   "outputs": [],
   "source": [
    "users_data = users_data.merge(users_events_data, how='outer').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "531c9756",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>37</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>1484999497</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  last_timestamp  is_gone_user  correct  wrong  discovered  passed  \\\n",
       "0        1      1472827464          True      0.0    0.0           1       0   \n",
       "1        2      1519226966          True      2.0    0.0           9       9   \n",
       "2        3      1444581588          True     29.0   23.0          91      87   \n",
       "3        5      1499859939          True      2.0    2.0          11      11   \n",
       "4        7      1521634660          True      0.0    0.0           1       1   \n",
       "5        8      1481466913          True      9.0   21.0         109      84   \n",
       "6        9      1484999497          True      0.0    0.0           3       3   \n",
       "\n",
       "   started_attempt  viewed  day  \n",
       "0                0       1    1  \n",
       "1                2      10    2  \n",
       "2               30     192    7  \n",
       "3                4      12    2  \n",
       "4                0       1    1  \n",
       "5               37     156    2  \n",
       "6                0       4    1  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Найдем сколько суммарно дней пользователь побывал на сайте:\n",
    "users_days = events_data.groupby('user_id').day.nunique().to_frame().reset_index()\n",
    "users_data = users_data.merge(users_days, how='outer').fillna(0)\n",
    "users_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a7e3ddf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Пользователи не потеряны\n"
     ]
    }
   ],
   "source": [
    "check(users_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "597b9c41",
   "metadata": {},
   "source": [
    "### Определим условие успешного завершения курса:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "827f1dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(users_data.correct[users_data.correct < 100], bins=15, kde=True, color='#346f39')\n",
    "ax.set_xlabel('Значение правильно выполненных номеров курса')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9569dd1a",
   "metadata": {},
   "source": [
    "По распределению понятно, что есть те, кто брасают в самом начале, а есть те, кто в итоге доходят до заветного сертификата.\\ При этом сертификат выдается не при 100% решенных номеров. Судя по распределению можно сделать вывод, что после 60 номеров появляется всплеск пользователей, которые как раз оказались усердными и прошли до конца. Поэтому, границей между сдавшими и отлетевшими пользователями можно считать число верных номеров, равное 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cbd6ec59",
   "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_id</th>\n",
       "      <th>last_timestamp</th>\n",
       "      <th>is_gone_user</th>\n",
       "      <th>correct</th>\n",
       "      <th>wrong</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "      <th>day</th>\n",
       "      <th>passed_course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1472827464</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1519226966</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1444581588</td>\n",
       "      <td>True</td>\n",
       "      <td>29.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>91</td>\n",
       "      <td>87</td>\n",
       "      <td>30</td>\n",
       "      <td>192</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1499859939</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>1521634660</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19229</th>\n",
       "      <td>26790</td>\n",
       "      <td>1517495987</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19230</th>\n",
       "      <td>26793</td>\n",
       "      <td>1510225034</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19231</th>\n",
       "      <td>26794</td>\n",
       "      <td>1512035039</td>\n",
       "      <td>True</td>\n",
       "      <td>33.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>69</td>\n",
       "      <td>69</td>\n",
       "      <td>34</td>\n",
       "      <td>180</td>\n",
       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19232</th>\n",
       "      <td>26797</td>\n",
       "      <td>1509995096</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19233</th>\n",
       "      <td>26798</td>\n",
       "      <td>1520978742</td>\n",
       "      <td>True</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19234 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       user_id  last_timestamp  is_gone_user  correct  wrong  discovered  \\\n",
       "0            1      1472827464          True      0.0    0.0           1   \n",
       "1            2      1519226966          True      2.0    0.0           9   \n",
       "2            3      1444581588          True     29.0   23.0          91   \n",
       "3            5      1499859939          True      2.0    2.0          11   \n",
       "4            7      1521634660          True      0.0    0.0           1   \n",
       "...        ...             ...           ...      ...    ...         ...   \n",
       "19229    26790      1517495987          True      1.0    0.0           8   \n",
       "19230    26793      1510225034          True      0.0    0.0           1   \n",
       "19231    26794      1512035039          True     33.0    9.0          69   \n",
       "19232    26797      1509995096          True      2.0    0.0          10   \n",
       "19233    26798      1520978742          True      2.0    1.0          12   \n",
       "\n",
       "       passed  started_attempt  viewed  day  passed_course  \n",
       "0           0                0       1    1          False  \n",
       "1           9                2      10    2          False  \n",
       "2          87               30     192    7          False  \n",
       "3          11                4      12    2          False  \n",
       "4           1                0       1    1          False  \n",
       "...       ...              ...     ...  ...            ...  \n",
       "19229       8                1       9    4          False  \n",
       "19230       0                1       1    1          False  \n",
       "19231      69               34     180    9          False  \n",
       "19232      10                2      13    2          False  \n",
       "19233      12                3      12    1          False  \n",
       "\n",
       "[19234 rows x 11 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Проверим прошел ли пользователь курс\n",
    "users_data['passed_course'] = users_data['correct'] > 60\n",
    "users_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dd484293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Сколько надо дней чтобы завершить курс?\n",
    "day_to_finish = users_data[users_data.passed_course == True].day\n",
    "ax = sns.histplot(day_to_finish[day_to_finish < 100], bins=50, kde=True, color='#02591c')\n",
    "ax.set_xlabel('Число дней на завершение курса')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7cde03",
   "metadata": {},
   "source": [
    "# Поиск создателя курса:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "97165ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "def func(x):\n",
    "    if x == True:\n",
    "        return ['passed']\n",
    "    else:\n",
    "        return [\"didn't pass\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fa73b9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x540 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = users_data.loc[1: 10000, :]\n",
    "sns.pairplot(x[['correct', 'wrong', 'day']]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1af86637",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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_id</th>\n",
       "      <th>last_timestamp</th>\n",
       "      <th>is_gone_user</th>\n",
       "      <th>correct</th>\n",
       "      <th>wrong</th>\n",
       "      <th>discovered</th>\n",
       "      <th>passed</th>\n",
       "      <th>started_attempt</th>\n",
       "      <th>viewed</th>\n",
       "      <th>day</th>\n",
       "      <th>passed_course</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>1046</td>\n",
       "      <td>1525160857</td>\n",
       "      <td>False</td>\n",
       "      <td>421.0</td>\n",
       "      <td>513.0</td>\n",
       "      <td>128</td>\n",
       "      <td>124</td>\n",
       "      <td>721</td>\n",
       "      <td>8122</td>\n",
       "      <td>478</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     user_id  last_timestamp  is_gone_user  correct  wrong  discovered  \\\n",
       "781     1046      1525160857         False    421.0  513.0         128   \n",
       "\n",
       "     passed  started_attempt  viewed  day  passed_course  \n",
       "781     124              721    8122  478           True  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = users_data.loc[users_data.day >= 0.9 * users_data.day.max()]\n",
    "# Определим по необычно высоким значениям дней захода и числу ошибок и верных ответов\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0f397bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Создатель курса под номером id = 1046\n"
     ]
    }
   ],
   "source": [
    "print('Создатель курса под номером id =', int(df.user_id));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c42076d3",
   "metadata": {},
   "source": [
    "## Отберем события пользователей, сделанные впервые 3 дня.\n",
    "### Целью является определять за 3 первых дня на сайте пройдет ли пользователь весь курс до конца"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ee167565",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_time_threshold = 3 * 24 * 60 * 60  # 3 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d6562a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>min_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>5</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  min_timestamp\n",
       "0        1     1472827464\n",
       "1        2     1514383364\n",
       "2        3     1434358476\n",
       "3        5     1466156809\n",
       "4        7     1521634660\n",
       "5        8     1480603427"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Дата когда пользователь впервые зашел на курс:\n",
    "user_min_time = events_data.groupby('user_id', as_index=False) \\\n",
    "    .agg({'timestamp': min}).rename({'timestamp': 'min_timestamp'}, axis=1)\n",
    "user_min_time.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "12d56672",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>1481466913</td>\n",
       "      <td>True</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>109</td>\n",
       "      <td>84</td>\n",
       "      <td>37</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "      <td>1480603427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>1484999497</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   user_id  last_timestamp  is_gone_user  correct  wrong  discovered  passed  \\\n",
       "0        1      1472827464          True      0.0    0.0           1       0   \n",
       "1        2      1519226966          True      2.0    0.0           9       9   \n",
       "2        3      1444581588          True     29.0   23.0          91      87   \n",
       "3        5      1499859939          True      2.0    2.0          11      11   \n",
       "4        7      1521634660          True      0.0    0.0           1       1   \n",
       "5        8      1481466913          True      9.0   21.0         109      84   \n",
       "6        9      1484999497          True      0.0    0.0           3       3   \n",
       "\n",
       "   started_attempt  viewed  day  passed_course  min_timestamp  \n",
       "0                0       1    1          False     1472827464  \n",
       "1                2      10    2          False     1514383364  \n",
       "2               30     192    7          False     1434358476  \n",
       "3                4      12    2          False     1466156809  \n",
       "4                0       1    1          False     1521634660  \n",
       "5               37     156    2          False     1480603427  \n",
       "6                0       4    1          False     1484999434  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_data = users_data.merge(user_min_time, how='outer')\n",
    "users_data.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b27beafe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>discovered</td>\n",
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       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   step_id   timestamp      action  user_id                date         day\n",
       "0    32815  1434340848      viewed    17632 2015-06-15 04:00:48  2015-06-15\n",
       "1    32815  1434340848      passed    17632 2015-06-15 04:00:48  2015-06-15\n",
       "2    32815  1434340848  discovered    17632 2015-06-15 04:00:48  2015-06-15\n",
       "3    32811  1434340895  discovered    17632 2015-06-15 04:01:35  2015-06-15\n",
       "4    32811  1434340895      viewed    17632 2015-06-15 04:01:35  2015-06-15"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8ef652fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>17632_1434340848</td>\n",
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       "      <td>2015-06-15</td>\n",
       "      <td>17632_1434340895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:01:35</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>17632_1434340895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step_id   timestamp      action  user_id                date         day  \\\n",
       "0    32815  1434340848      viewed    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "1    32815  1434340848      passed    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "2    32815  1434340848  discovered    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "3    32811  1434340895  discovered    17632 2015-06-15 04:01:35  2015-06-15   \n",
       "4    32811  1434340895      viewed    17632 2015-06-15 04:01:35  2015-06-15   \n",
       "\n",
       "          user_time  \n",
       "0  17632_1434340848  \n",
       "1  17632_1434340848  \n",
       "2  17632_1434340848  \n",
       "3  17632_1434340895  \n",
       "4  17632_1434340895  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Соединим timestamp (время какой-то операции на сайте) и user_id\n",
    "events_data['user_time'] = events_data.user_id.map(str) + '_' + events_data.timestamp.map(str)\n",
    "events_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "23378a1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1_1473086664\n",
       "1    2_1514642564\n",
       "2    3_1434617676\n",
       "3    5_1466416009\n",
       "4    7_1521893860\n",
       "5    8_1480862627\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_learning_time_threshold = user_min_time.user_id.map(str) + '_' + \\\n",
    "    (user_min_time.min_timestamp + learning_time_threshold).map(str)\n",
    "user_learning_time_threshold.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a6884b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id  min_timestamp user_learning_time_threshold\n",
       "0        1     1472827464                 1_1473086664\n",
       "1        2     1514383364                 2_1514642564\n",
       "2        3     1434358476                 3_1434617676\n",
       "3        5     1466156809                 5_1466416009\n",
       "4        7     1521634660                 7_1521893860"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_min_time['user_learning_time_threshold'] = user_learning_time_threshold\n",
    "user_min_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "83683855",
   "metadata": {},
   "outputs": [
    {
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       "      <td>17632</td>\n",
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       "      <td>2015-06-15</td>\n",
       "      <td>17632_1434340895</td>\n",
       "      <td>17632_1434600048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17632</td>\n",
       "      <td>2015-06-15 04:01:35</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>17632_1434340895</td>\n",
       "      <td>17632_1434600048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step_id   timestamp      action  user_id                date         day  \\\n",
       "0    32815  1434340848      viewed    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "1    32815  1434340848      passed    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "2    32815  1434340848  discovered    17632 2015-06-15 04:00:48  2015-06-15   \n",
       "3    32811  1434340895  discovered    17632 2015-06-15 04:01:35  2015-06-15   \n",
       "4    32811  1434340895      viewed    17632 2015-06-15 04:01:35  2015-06-15   \n",
       "\n",
       "          user_time user_learning_time_threshold  \n",
       "0  17632_1434340848             17632_1434600048  \n",
       "1  17632_1434340848             17632_1434600048  \n",
       "2  17632_1434340848             17632_1434600048  \n",
       "3  17632_1434340895             17632_1434600048  \n",
       "4  17632_1434340895             17632_1434600048  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data = events_data.merge(user_min_time[['user_id', 'user_learning_time_threshold']], how='outer')\n",
    "events_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4961ae19",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32811</td>\n",
       "      <td>1434340895</td>\n",
       "      <td>viewed</td>\n",
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       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <th>3480698</th>\n",
       "      <td>32814</td>\n",
       "      <td>1526762783</td>\n",
       "      <td>discovered</td>\n",
       "      <td>943</td>\n",
       "      <td>2018-05-19 20:46:23</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>943_1526762783</td>\n",
       "      <td>943_1527021864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480699</th>\n",
       "      <td>32814</td>\n",
       "      <td>1526762783</td>\n",
       "      <td>passed</td>\n",
       "      <td>943</td>\n",
       "      <td>2018-05-19 20:46:23</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>943_1526762783</td>\n",
       "      <td>943_1527021864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480700</th>\n",
       "      <td>32815</td>\n",
       "      <td>1526772750</td>\n",
       "      <td>viewed</td>\n",
       "      <td>5198</td>\n",
       "      <td>2018-05-19 23:32:30</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>5198_1526772750</td>\n",
       "      <td>5198_1527031950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480701</th>\n",
       "      <td>32815</td>\n",
       "      <td>1526772750</td>\n",
       "      <td>discovered</td>\n",
       "      <td>5198</td>\n",
       "      <td>2018-05-19 23:32:30</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>5198_1526772750</td>\n",
       "      <td>5198_1527031950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3480702</th>\n",
       "      <td>32815</td>\n",
       "      <td>1526772750</td>\n",
       "      <td>passed</td>\n",
       "      <td>5198</td>\n",
       "      <td>2018-05-19 23:32:30</td>\n",
       "      <td>2018-05-19</td>\n",
       "      <td>5198_1526772750</td>\n",
       "      <td>5198_1527031950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1014985 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         step_id   timestamp      action  user_id                date  \\\n",
       "0          32815  1434340848      viewed    17632 2015-06-15 04:00:48   \n",
       "1          32815  1434340848      passed    17632 2015-06-15 04:00:48   \n",
       "2          32815  1434340848  discovered    17632 2015-06-15 04:00:48   \n",
       "3          32811  1434340895  discovered    17632 2015-06-15 04:01:35   \n",
       "4          32811  1434340895      viewed    17632 2015-06-15 04:01:35   \n",
       "...          ...         ...         ...      ...                 ...   \n",
       "3480698    32814  1526762783  discovered      943 2018-05-19 20:46:23   \n",
       "3480699    32814  1526762783      passed      943 2018-05-19 20:46:23   \n",
       "3480700    32815  1526772750      viewed     5198 2018-05-19 23:32:30   \n",
       "3480701    32815  1526772750  discovered     5198 2018-05-19 23:32:30   \n",
       "3480702    32815  1526772750      passed     5198 2018-05-19 23:32:30   \n",
       "\n",
       "                day         user_time user_learning_time_threshold  \n",
       "0        2015-06-15  17632_1434340848             17632_1434600048  \n",
       "1        2015-06-15  17632_1434340848             17632_1434600048  \n",
       "2        2015-06-15  17632_1434340848             17632_1434600048  \n",
       "3        2015-06-15  17632_1434340895             17632_1434600048  \n",
       "4        2015-06-15  17632_1434340895             17632_1434600048  \n",
       "...             ...               ...                          ...  \n",
       "3480698  2018-05-19    943_1526762783               943_1527021864  \n",
       "3480699  2018-05-19    943_1526762783               943_1527021864  \n",
       "3480700  2018-05-19   5198_1526772750              5198_1527031950  \n",
       "3480701  2018-05-19   5198_1526772750              5198_1527031950  \n",
       "3480702  2018-05-19   5198_1526772750              5198_1527031950  \n",
       "\n",
       "[1014985 rows x 8 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Остается просто проверить попадает ли событие в этот самый порог:user_time < или = user_learning_time_threshold\n",
    "events_data_train = events_data[events_data.user_time <= events_data.user_learning_time_threshold]\n",
    "events_data_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beabfbc0",
   "metadata": {},
   "source": [
    "## Интересное исследование"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660ad887",
   "metadata": {},
   "source": [
    "Слушатели онлайн курсов очень негативно реагируют на невозможность решить задачу, иными словами, если студент застрял на определенном шаге, то он, с высокой вероятность, вообще бросит курс, чем просто пропустит этот шаг и продолжит обучение.\n",
    "\n",
    "Давайте найдем такой стэп, используя данные о сабмитах. Для каждого пользователя найдем такой шаг, который он не смог решить, и после этого не пытался решать другие шаги. Затем найдем id шага, который стал финальной точкой практического обучения на курсе для максимального числа пользователей.\n",
    "\n",
    "То есть мы исследуем следующий сценарий: человек решает стэп, не может получить правильный ответ и больше не возвращается к практическим задачам. Что это за шаг такой, который отпугнул максимальное число пользователей?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99460d5c",
   "metadata": {},
   "source": [
    "### Сначала найдем степ который многие открывали и при этом не проходили:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ef2e8adf",
   "metadata": {},
   "outputs": [],
   "source": [
    "hard_step_search = events_data.groupby(['step_id', 'action'], as_index=False).count() \\\n",
    "    .rename({'user_id': 'users_amount_done'}, axis=1)[['step_id', 'action', 'users_amount_done']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1727214e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30456</td>\n",
       "      <td>viewed</td>\n",
       "      <td>16896</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30462</td>\n",
       "      <td>passed</td>\n",
       "      <td>2161</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30462</td>\n",
       "      <td>viewed</td>\n",
       "      <td>7747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30465</td>\n",
       "      <td>passed</td>\n",
       "      <td>2044</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30465</td>\n",
       "      <td>viewed</td>\n",
       "      <td>5782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31955</td>\n",
       "      <td>passed</td>\n",
       "      <td>7870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31955</td>\n",
       "      <td>viewed</td>\n",
       "      <td>16142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31956</td>\n",
       "      <td>passed</td>\n",
       "      <td>8317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31956</td>\n",
       "      <td>viewed</td>\n",
       "      <td>17702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   step_id  action  users_amount_done\n",
       "0    30456  passed               2936\n",
       "1    30456  viewed              16896\n",
       "2    30462  passed               2161\n",
       "3    30462  viewed               7747\n",
       "4    30465  passed               2044\n",
       "5    30465  viewed               5782\n",
       "6    31955  passed               7870\n",
       "7    31955  viewed              16142\n",
       "8    31956  passed               8317\n",
       "9    31956  viewed              17702"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hard_step_search = hard_step_search.query('action==\"viewed\" | action==\"passed\"')\n",
    "hard_step_search = hard_step_search.reset_index(drop=True)\n",
    "hard_step_search.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7324521e",
   "metadata": {},
   "outputs": [],
   "source": [
    "check = hard_step_search.pivot_table(index='step_id', columns='action', values='users_amount_done').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "39f372b7",
   "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>action</th>\n",
       "      <th>passed</th>\n",
       "      <th>viewed</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>step_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30456</th>\n",
       "      <td>2936</td>\n",
       "      <td>16896</td>\n",
       "      <td>13960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30462</th>\n",
       "      <td>2161</td>\n",
       "      <td>7747</td>\n",
       "      <td>5586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30465</th>\n",
       "      <td>2044</td>\n",
       "      <td>5782</td>\n",
       "      <td>3738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31955</th>\n",
       "      <td>7870</td>\n",
       "      <td>16142</td>\n",
       "      <td>8272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31956</th>\n",
       "      <td>8317</td>\n",
       "      <td>17702</td>\n",
       "      <td>9385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "action   passed  viewed   diff\n",
       "step_id                       \n",
       "30456      2936   16896  13960\n",
       "30462      2161    7747   5586\n",
       "30465      2044    5782   3738\n",
       "31955      7870   16142   8272\n",
       "31956      8317   17702   9385"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check['diff'] = check.viewed - check.passed\n",
    "check = check.set_index('step_id')\n",
    "check.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0574e277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check['diff'].to_frame().query('diff > 18000').plot.pie(y='diff', figsize=(7, 7));\n",
    "# Степы которые многие посмотрели, но не стали дальше"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337c13fc",
   "metadata": {},
   "source": [
    "### Найдем номер который многие пытались решить и именно после него бросили"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a3475e40",
   "metadata": {},
   "outputs": [],
   "source": [
    "an = submissions_data[submissions_data.submission_status == \"wrong\"].groupby(['user_id', 'step_id'], as_index=False)\\\n",
    "    .agg({'timestamp':'max'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c5d5fd7a",
   "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>step_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31978</th>\n",
       "      <td>3423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32031</th>\n",
       "      <td>3255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31972</th>\n",
       "      <td>3064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32202</th>\n",
       "      <td>2614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31971</th>\n",
       "      <td>2517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       step_id\n",
       "31978     3423\n",
       "32031     3255\n",
       "31972     3064\n",
       "32202     2614\n",
       "31971     2517"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "an = an.step_id.value_counts().sort_values(ascending=False).to_frame()\n",
    "an.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d68359d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "an.plot.pie(y='step_id', figsize=(7, 7), legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "41834fde",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cтеп номер  31978\n"
     ]
    }
   ],
   "source": [
    "a=an.idxmax()\n",
    "print('Cтеп номер ' ,a[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd9c9aa5",
   "metadata": {},
   "source": [
    "Преобразуем так же данные со второй таблицей, отобрав лишь где прошло не более 3 дней с начала прохождений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "96fb2fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "submissions_data['user_time'] = submissions_data.user_id.map(str) + '_' + submissions_data.timestamp.map(str) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6235f910",
   "metadata": {},
   "outputs": [],
   "source": [
    "submissions_data = submissions_data.merge(user_min_time[['user_id', 'user_learning_time_threshold']], how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "04b78262",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissions_data_train = submissions_data[submissions_data.user_time <= submissions_data.user_learning_time_threshold]\n",
    "submissions_data_train.groupby('user_id').day.nunique().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e223844f",
   "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>step_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>submission_status</th>\n",
       "      <th>user_id</th>\n",
       "      <th>date</th>\n",
       "      <th>day</th>\n",
       "      <th>user_time</th>\n",
       "      <th>user_learning_time_threshold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31971</td>\n",
       "      <td>1434349275</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:21:15</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>15853_1434349275</td>\n",
       "      <td>15853_1434605177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31972</td>\n",
       "      <td>1434348300</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:05:00</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>15853_1434348300</td>\n",
       "      <td>15853_1434605177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31976</td>\n",
       "      <td>1434348123</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:02:03</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>15853_1434348123</td>\n",
       "      <td>15853_1434605177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31976</td>\n",
       "      <td>1434348188</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 06:03:08</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>15853_1434348188</td>\n",
       "      <td>15853_1434605177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31977</td>\n",
       "      <td>1434347371</td>\n",
       "      <td>correct</td>\n",
       "      <td>15853</td>\n",
       "      <td>2015-06-15 05:49:31</td>\n",
       "      <td>2015-06-15</td>\n",
       "      <td>15853_1434347371</td>\n",
       "      <td>15853_1434605177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509099</th>\n",
       "      <td>120745</td>\n",
       "      <td>1501946959</td>\n",
       "      <td>wrong</td>\n",
       "      <td>2615</td>\n",
       "      <td>2017-08-05 15:29:19</td>\n",
       "      <td>2017-08-05</td>\n",
       "      <td>2615_1501946959</td>\n",
       "      <td>2615_1502205860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509100</th>\n",
       "      <td>120745</td>\n",
       "      <td>1503059927</td>\n",
       "      <td>correct</td>\n",
       "      <td>13177</td>\n",
       "      <td>2017-08-18 12:38:47</td>\n",
       "      <td>2017-08-18</td>\n",
       "      <td>13177_1503059927</td>\n",
       "      <td>13177_1503314487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509101</th>\n",
       "      <td>120745</td>\n",
       "      <td>1506348153</td>\n",
       "      <td>wrong</td>\n",
       "      <td>15253</td>\n",
       "      <td>2017-09-25 14:02:33</td>\n",
       "      <td>2017-09-25</td>\n",
       "      <td>15253_1506348153</td>\n",
       "      <td>15253_1506606463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509102</th>\n",
       "      <td>120745</td>\n",
       "      <td>1523035316</td>\n",
       "      <td>wrong</td>\n",
       "      <td>1424</td>\n",
       "      <td>2018-04-06 17:21:56</td>\n",
       "      <td>2018-04-06</td>\n",
       "      <td>1424_1523035316</td>\n",
       "      <td>1424_1523294469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509103</th>\n",
       "      <td>120745</td>\n",
       "      <td>1523382825</td>\n",
       "      <td>wrong</td>\n",
       "      <td>9422</td>\n",
       "      <td>2018-04-10 17:53:45</td>\n",
       "      <td>2018-04-10</td>\n",
       "      <td>9422_1523382825</td>\n",
       "      <td>9422_1523636200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>135599 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        step_id   timestamp submission_status  user_id                date  \\\n",
       "0         31971  1434349275           correct    15853 2015-06-15 06:21:15   \n",
       "1         31972  1434348300           correct    15853 2015-06-15 06:05:00   \n",
       "4         31976  1434348123             wrong    15853 2015-06-15 06:02:03   \n",
       "5         31976  1434348188           correct    15853 2015-06-15 06:03:08   \n",
       "7         31977  1434347371           correct    15853 2015-06-15 05:49:31   \n",
       "...         ...         ...               ...      ...                 ...   \n",
       "509099   120745  1501946959             wrong     2615 2017-08-05 15:29:19   \n",
       "509100   120745  1503059927           correct    13177 2017-08-18 12:38:47   \n",
       "509101   120745  1506348153             wrong    15253 2017-09-25 14:02:33   \n",
       "509102   120745  1523035316             wrong     1424 2018-04-06 17:21:56   \n",
       "509103   120745  1523382825             wrong     9422 2018-04-10 17:53:45   \n",
       "\n",
       "               day         user_time user_learning_time_threshold  \n",
       "0       2015-06-15  15853_1434349275             15853_1434605177  \n",
       "1       2015-06-15  15853_1434348300             15853_1434605177  \n",
       "4       2015-06-15  15853_1434348123             15853_1434605177  \n",
       "5       2015-06-15  15853_1434348188             15853_1434605177  \n",
       "7       2015-06-15  15853_1434347371             15853_1434605177  \n",
       "...            ...               ...                          ...  \n",
       "509099  2017-08-05   2615_1501946959              2615_1502205860  \n",
       "509100  2017-08-18  13177_1503059927             13177_1503314487  \n",
       "509101  2017-09-25  15253_1506348153             15253_1506606463  \n",
       "509102  2018-04-06   1424_1523035316              1424_1523294469  \n",
       "509103  2018-04-10   9422_1523382825              9422_1523636200  \n",
       "\n",
       "[135599 rows x 8 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submissions_data_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "762b8d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_data_train.groupby('user_id').day.nunique().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc3bd0cf",
   "metadata": {},
   "source": [
    "### Создадим из данных по 3 дня фичи для машинного обучения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "80f29a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = events_data_train.groupby('user_id').day.nunique().to_frame().reset_index()\\\n",
    "    .rename(columns={'day': 'days'})\n",
    "# Задали значения числа дней захода за этот период (от 1 до 4 значения бывают)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c9b38823",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_tried=submissions_data_train.groupby('user_id').step_id.nunique().to_frame().reset_index()\\\n",
    "    .rename(columns={'step_id':'steps_tried'})\n",
    "# Задали значения пройденых степов за этот период "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "dbf30a45",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.merge(steps_tried)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a5d6c567",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.merge(submissions_data_train.pivot_table(index='user_id', columns='submission_status',\n",
    "    values='step_id', aggfunc='count', fill_value=0).reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "d4ff603b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>days</th>\n",
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       "      <th>correct</th>\n",
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       "      <th>2</th>\n",
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       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>27</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  days  steps_tried  correct  wrong  correct_ratio\n",
       "0        2     1            2        2      0           1.00\n",
       "1        3     1            4        4      4           0.50\n",
       "2        8     1           11        9     21           0.30\n",
       "3       14     1            1        0      1           0.00\n",
       "4       16     3           23       23     27           0.46"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['correct_ratio'] = X.correct / (X.correct + X.wrong)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "ad35b8b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.merge(events_data_train.pivot_table(index='user_id', columns='action',\n",
    "    values='step_id', aggfunc='count', fill_value=0)\\\n",
    "    .reset_index()[['user_id','viewed']], how='outer')\n",
    "X = X.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b7820940",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.merge(users_data[['user_id', 'passed_course', 'is_gone_user']], how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "c973185f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>steps_tried</th>\n",
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       "      <th>correct_ratio</th>\n",
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       "      <th>passed_course</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>154</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>9</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>132</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  days  steps_tried  correct  wrong  correct_ratio  viewed  \\\n",
       "0        2   1.0          2.0      2.0    0.0           1.00       9   \n",
       "1        3   1.0          4.0      4.0    4.0           0.50      20   \n",
       "2        8   1.0         11.0      9.0   21.0           0.30     154   \n",
       "3       14   1.0          1.0      0.0    1.0           0.00       9   \n",
       "4       16   3.0         23.0     23.0   27.0           0.46     132   \n",
       "\n",
       "   passed_course  is_gone_user  \n",
       "0          False          True  \n",
       "1          False          True  \n",
       "2          False          True  \n",
       "3          False          True  \n",
       "4           True          True  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9881d642",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[~((X.is_gone_user == False) & (X.passed_course==False))]\n",
    "# Тем самым найдем тех, кто уже не в процессе решения кура, а кто точно имеет тот или иной статус"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8efedb89",
   "metadata": {},
   "source": [
    "### Задаем значения для ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3e55149d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X.passed_course\n",
    "X = X.drop(['passed_course', 'is_gone_user'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0217e12d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>steps_tried</th>\n",
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       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         days  steps_tried  correct  wrong  correct_ratio  viewed\n",
       "user_id                                                          \n",
       "2         1.0          2.0      2.0    0.0           1.00       9\n",
       "3         1.0          4.0      4.0    4.0           0.50      20\n",
       "8         1.0         11.0      9.0   21.0           0.30     154\n",
       "14        1.0          1.0      0.0    1.0           0.00       9\n",
       "16        3.0         23.0     23.0   27.0           0.46     132"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X.set_index(X.user_id)\n",
    "X = X.drop('user_id', axis=1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b8824905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    1\n",
       "Name: passed_course, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y.map(int)\n",
    "y.head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deda9f26",
   "metadata": {},
   "source": [
    "## Tree без и с кросс-валидацией"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf45a22",
   "metadata": {},
   "source": [
    "Импорт библиотек для работы с моделями машинного обучения и критериями качества"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "df9e6a30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import recall_score, precision_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import roc_curve, auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "147e6e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Импортируем модули, необходимые для визуализации дерева решений.\n",
    "from IPython.display import SVG\n",
    "from graphviz import Source\n",
    "from IPython.display import display\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "83cb08d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "feec099b",
   "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>max_depth</th>\n",
       "      <th>Train_score</th>\n",
       "      <th>No_validation_score</th>\n",
       "      <th>Validation_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.915354</td>\n",
       "      <td>0.912593</td>\n",
       "      <td>0.915354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0.915354</td>\n",
       "      <td>0.912593</td>\n",
       "      <td>0.915354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>0.919472</td>\n",
       "      <td>0.917778</td>\n",
       "      <td>0.918426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>0.919472</td>\n",
       "      <td>0.917778</td>\n",
       "      <td>0.917576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>0.919668</td>\n",
       "      <td>0.915926</td>\n",
       "      <td>0.917315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>0.921825</td>\n",
       "      <td>0.915185</td>\n",
       "      <td>0.917968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>0.923328</td>\n",
       "      <td>0.913333</td>\n",
       "      <td>0.916073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.924766</td>\n",
       "      <td>0.912593</td>\n",
       "      <td>0.915223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0.927054</td>\n",
       "      <td>0.912593</td>\n",
       "      <td>0.913066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.929865</td>\n",
       "      <td>0.908148</td>\n",
       "      <td>0.912805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_depth  Train_score  No_validation_score  Validation_score\n",
       "0          1     0.915354             0.912593          0.915354\n",
       "0          2     0.915354             0.912593          0.915354\n",
       "0          3     0.919472             0.917778          0.918426\n",
       "0          4     0.919472             0.917778          0.917576\n",
       "0          5     0.919668             0.915926          0.917315\n",
       "0          6     0.921825             0.915185          0.917968\n",
       "0          7     0.923328             0.913333          0.916073\n",
       "0          8     0.924766             0.912593          0.915223\n",
       "0          9     0.927054             0.912593          0.913066\n",
       "0         10     0.929865             0.908148          0.912805"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Зададим диапазон исследуемых значений.\n",
    "max_depth_values = range(1, 51)\n",
    "scores_iris_data = pd.DataFrame()\n",
    "rs = np.random.seed(0)\n",
    "scores_data = pd.DataFrame()\n",
    "\n",
    "for max_depth in max_depth_values:\n",
    "    # Изменяем глубину обучения дерева по циклу от 1 до 99 с шагом 1.\n",
    "    clf = tree.DecisionTreeClassifier(criterion='entropy', max_depth=max_depth, random_state=rs)\n",
    "    # Обучаем дерево решений (с ограниченной глубиной) на подмножестве train.\n",
    "    clf.fit(X_train, y_train)\n",
    "    # Записываем в отдельную переменную число правильных ответов на обученной модели дерева\n",
    "    train_score = clf.score(X_train, y_train)\n",
    "    No_validation_score=clf.score(X_test, y_test)\n",
    "    # Записываем в отдельную переменную число правильных ответов на обученной модели дерева\n",
    "    test_val_score = cross_val_score(clf,X_train,y_train,cv=5).mean()\n",
    "    # Создаем временный DataFrame.\n",
    "    temp_score_data = pd.DataFrame({'max_depth':[max_depth],\n",
    "        'Train_score':[train_score],\n",
    "        'No_validation_score':[No_validation_score],  \n",
    "        'Validation_score':[test_val_score]})\n",
    "    # Наращиваем DataFrame \"scores_iris_data\".\n",
    "    scores_data = scores_data.append(temp_score_data)\n",
    "scores_data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "144ff0f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_data_long = pd.melt(scores_data, id_vars=['max_depth'],\n",
    "    value_vars=['Train_score', 'No_validation_score', 'Validation_score'],\n",
    "    var_name='set_type', value_name='score')\n",
    "sns.lineplot(x='max_depth', y='score', hue='set_type', data=scores_data_long);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "8777b820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_leaf': 2, 'min_samples_split': 2}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Подбор лучших параметров:\n",
    "par = {'max_depth': range(1, 8), 'min_samples_split': range(2, 6),\n",
    "    'min_samples_leaf': range(2, 6)}\n",
    "search = GridSearchCV(clf, par, cv=5)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5090b4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<title>Tree</title>\n",
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       "<text text-anchor=\"middle\" x=\"634.5\" y=\"-409.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">correct &lt;= 7.5</text>\n",
       "<text text-anchor=\"middle\" x=\"634.5\" y=\"-394.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">entropy = 0.418</text>\n",
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       "<text text-anchor=\"middle\" x=\"393.5\" y=\"-37.8\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">samples = 2307</text>\n",
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_tree = search.best_estimator_\n",
    "clf_best = best_tree\n",
    "graph = Source(tree.export_graphviz(best_tree,out_file=None,\n",
    "    feature_names=list(X_train),\n",
    "    class_names=['Покинет курс', 'Пройдет курс'],\n",
    "    filled=True)) \n",
    "display(SVG(graph.pipe(format='svg')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "24e55f1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = best_tree.predict_proba(X_test)\n",
    "y_pred_class = best_tree.predict(X_test)\n",
    "pd.Series(y_pred_class).hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "bc0ed13b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(pd.Series(y_pred[:, 1]), bins=10, kde=True, color='#1c6b72')\n",
    "ax.set_xlabel('Процент уверенности в выдаче класса (чем ближе к нулю тем больше false)')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1a9f31",
   "metadata": {},
   "source": [
    "Мы хотим большее число людей считать оставшимися на курсе чтобы быть наверняка уверенным, что тот, кого мы нашли однозначно покинет курс. Так что сместим границу уверенности от 50% до 20%\n",
    "\n",
    "Наша цель- увеличить recall (то есть увеличить уверенность, что все те, кто пройдут будут обнаружены)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "eb81fcd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9177777777777778"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ed83c535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07627118644067797"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "f3f72dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_new = np.where(y_pred[:, 1] > 0.1, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d691105f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7288135593220338"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_class_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b5b91ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(y_pred_class_new).hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80601cb2",
   "metadata": {},
   "source": [
    "### Создадим Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "67af1c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 6,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 6,\n",
       " 'n_estimators': 6}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(random_state=0)\n",
    "par={'n_estimators': range(6, 11), 'max_depth': range(3, 10), 'min_samples_split': range(2, 7),\n",
    "    'min_samples_leaf': range(1, 5)}\n",
    "search = GridSearchCV(clf, par, cv=5, n_jobs=-1)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007278ca",
   "metadata": {},
   "source": [
    "Произвели поиск лучших параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "659f0f7f",
   "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>featured</th>\n",
       "      <th>feature_importances</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>correct</td>\n",
       "      <td>0.482422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>steps_tried</td>\n",
       "      <td>0.182991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>days</td>\n",
       "      <td>0.107273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wrong</td>\n",
       "      <td>0.098040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>correct_ratio</td>\n",
       "      <td>0.070784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>viewed</td>\n",
       "      <td>0.058490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        featured  feature_importances\n",
       "2        correct             0.482422\n",
       "1    steps_tried             0.182991\n",
       "0           days             0.107273\n",
       "3          wrong             0.098040\n",
       "4  correct_ratio             0.070784\n",
       "5         viewed             0.058490"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best = search.best_estimator_\n",
    "feature_importances = clf_best.feature_importances_\n",
    "clf_best.fit(X_train, y_train)\n",
    "feature_importance_df = pd.DataFrame({'featured': list(X_train),\n",
    "    'feature_importances': feature_importances})\n",
    "feature_importance_df.sort_values('feature_importances', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7c74ad0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp = pd.DataFrame(clf_best.feature_importances_, index=X_train.columns, columns=['importance'])\n",
    "imp.sort_values('importance').plot(kind='barh', figsize=(12, 8));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d88d8cd",
   "metadata": {},
   "source": [
    "Мы определили самые значимыйе параметры в разработанной модели. Ими оказалось значение правильно решенных номеров за первые 3 дня прохождения курса и значение числа попыток решить степы"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d77c2e9",
   "metadata": {},
   "source": [
    "Определим зачение accuracy полученное с помошью random forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "bc2d1dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.917037037037037"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_best.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc4df10",
   "metadata": {},
   "source": [
    "Опять определим параметр recall:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8e55c1f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07627118644067797"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "68613ab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf_best.predict_proba(X_test)\n",
    "y_pred_class = clf_best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "669d11c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAFoCAYAAAAyzMHhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAfn0lEQVR4nO3df5iVZYE38O+BQdR1R7NmANF1y7BaSGHVfdW2sR8bUIgK2y8w8bVtUxMyK1sElKU0ySVtDbHWrFfTfqCt8Go2tttu9pa2i+6uZPmWmaOvIMOwkuOg0Pw47x9us06Uj/gow/B8Pl7nwnPPOT33meuS69v3vu/n1Or1ej0AAPAshg32BAAA2PUJjQAAFBIaAQAoJDQCAFBIaAQAoJDQCABAoYadebG9Js3dmZcDhrDNa5YP9hSAIWDPnZpkfreyGeepf9/1/87bRX7VAABDWG33X7zd/T8hAAClaRoBAMqq1QZ7Bi86oREAoKwKLE8LjQAAZVWgadz9YzEAAKVpGgEAyrI8DQBAoQosTwuNAABlaRoBAChUgaZx94/FAACUpmkEACjL8jQAAIUqsDwtNAIAlKVpBACgUAWaxt0/FgMAUJqmEQCgLMvTAAAUEhoBACg0zJ5GAADQNAIAlGZ5GgCAQhW45Y7QCABQlqYRAIBCFWgad/9YDABAaZpGAICyLE8DAFCoAsvTQiMAQFmaRgAAClWgadz9YzEAAKVpGgEAyrI8DQBAoQosTwuNAABlVaBp3P0/IQAApWkaAQDKqkDTKDQCAJRlTyMAAIU0jQAAFKpA07j7x2IAAErTNAIAlGV5GgCAQhVYnhYaAQBKqgmNAAAUqUJo3P0X4AEAKE3TCABQ1u5fNAqNAABlVWF5WmgEACipCqHRnkYAAAppGgEASqpC0yg0AgCUJDQCAFBs98+MQiMAQFlVaBodhAEAoJCmEQCgpCo0jUIjAEBJQiMAAIWqEBrtaQQAKKtW8rEDli9fnmnTpmXatGm55JJLkiR33HFHpk+fnsmTJ+eyyy7rf+19992XmTNnZsqUKVm4cGF6enqSJOvXr8/JJ5+cqVOn5swzz8yWLVsKrys0AgAMEXfccUe+//3v56abbsqqVavy4x//OLfccksWLFiQFStW5NZbb829996b22+/PUly7rnn5oILLshtt92Wer2elStXJkmWLFmS2bNnp7W1NRMmTMiKFSsKry00AgCUVKvVSj2eq6ampsyfPz977LFHRowYkUMOOSRtbW05+OCDc9BBB6WhoSHTp09Pa2tr1q1bl61bt2bixIlJkpkzZ6a1tTXd3d1Zs2ZNpkyZMmC8iD2NAAAlld3T2NnZmc7Ozu3GGxsb09jY2P983Lhx/f/e1taWb33rW3nPe96Tpqam/vHm5ua0t7dn48aNA8abmprS3t6ezZs3Z5999klDQ8OA8SJCIwBASWVD4zXXXJPly5dvNz537tzMmzdvu/H7778/p59+ej72sY9l+PDhaWtr6/9ZvV5PrVZLX1/fgHn9evzXf+7o/IVGAIBBduqpp2bGjBnbjT+zZfy1u+++Ox/84AezYMGCTJs2Lf/6r/+ajo6O/p93dHSkubk5o0ePHjC+adOmNDc3Z//9988TTzyR3t7eDB8+vP/1RexpBAAoq+Tp6cbGxhx44IHbPX4zND766KM566yzsmzZskybNi1Jcvjhh+fBBx/MQw89lN7e3txyyy1paWnJ2LFjM3LkyNx9991JktWrV6elpSUjRozIkUcemVtvvTVJsmrVqrS0tBR+RE0jAEBJO+s+jVdffXW2bduWpUuX9o+9+93vztKlSzNv3rxs27Ytxx13XKZOnZokWbZsWRYtWpSurq6MHz8+c+bMSZIsXrw48+fPz5VXXpkxY8bk0ksvLbx2rV6v11+cj7W9vSbN3VmXAoa4zWu239sD8Jv23EXqr9F/eWOp92+46u0v0ExePLvIrxoAYOjyjTAAABBNIwBAaVVoGoVGAICydv/MKDQCAJSlaQQAoFAVQqODMAAAFNI0AgCUVIWmUWgEAChr98+MQiMAQFlVaBrtaQQAoJCmkR3y7rcdlXNOfXPq9eSprb/KRy65Mf/2k4cHvGbph2dk5p9NymOdTyZJ7m9rzynzv/S8r/myl+yTL3zilPzBmP3T11fP3Au/mh/e82CS5Ix3teQv3/H61Ov1/OKRTTnr419Jx+au5/8BgV1GvV7P+QvmZ9yhh+bU0/4ivb29+fQlS/ODH/yf9Pb0Zs5p78073zVrsKcJSarRNAqNPGfjDm7OJz90Uo6d/als2NSZKX/6R/nasvfl0LddMOB1Rx/+isw570v9wa6sz8x/Z37wbw/kpC9emcMOHZubPntmJpy4JK9++eh8aM6b8yfvujidXVtz8TkzcsEHjs+8i772glwXGDy/eOCBfPLCJfnRj9Zm3KGHJkluXPm1PPRQW76x6pY8uWVLTjn5XXnNa8bntYcdNsizBaERBtj2q5584ONfyYZNnUmSf/vxwxn1ssaMaBie7p7eJMkeIxpy+KsOzIdP/bO8/MCm/PzhjfnYsm/k/23YnBENw3PR2SfmT48Yl+HDa7nn/z6Sj1xyY57YsrX/Gn+35D353l3357qb/yVJMnz4sLz19RPyoaUrkyRrf7YuP3+4I5OP/aOs/qd7MuHEJenp6cvIPRpyQPO+aVv3nzv5twK8GL721esz88/fkTFjDugf+6fv/GP+/B3vTENDQxr33TdT3zot37zlfwuN7BKExiQPPPBAbrvttmzYsCHDhg1Lc3NzXv/61+e1r33tzpgfu5CHH30sDz/6WP/zT31kZr55+4/6A2OSjGnaN99d87P89RW35CcPPJpz5rw5Ky97f46Z9al89L1vSU9vX46d/akkyZK50/OJD56QD1288nde82X7/V6GDatl0zOWnNe1b87YUfslSXp6+jL9DYdlxQWz86vunnz8ym++wJ8aGAwLFj29gnHnHT/oH9uw4dGMHj2m//moUaPzs5/9dKfPDX6r3T8zPntovP7667Ny5cpMmTKlPyR2dHTk/PPPzwknnJD3vve9O2WS7Fr23nOPXPXx9+TAUS/JCWetGPCzh9b/Z2bMu7L/+WXXfifz/3JqDj7gpXnb6ydk39/fO286+tVJkj1GDE/HY0+Hwe9d+9HssUdDDhr9krzhqEMz9+Q35s7/+EUuufq21Ov1Adeo1Wrp7f3vsZu/uzY3f3dtTptxbG6+4qyMP2HJdu8Bhr6+vnryjDanXq9n+DDnOWFnedbQeO2112bVqlXZa6+9BoyfdtppmTFjhtBYQQeNfklu/NvT89MH2zPl/Zdn67buAT+fMO6AvPbQsfnqN9f0j9VqtfT09GbYsGH56N/cmG//4CdJkt/ba4/sOXJEkqRlzrIkv315ular5SWNe2fzfx2sGdO0b9Zt3JxXHPSyjH5pY+74j18kSa5ZfWc+u/DdeUnj3nns8S0v7i8C2OnGjBmTjo0b+593dGzMqFGjB3FG8N+qsDz9rP8XraGhIT09PduNb926NSNGjHjRJsWuaZ+9R+a2q87O6u/ckznzv7RdYEyebgI+/bF35OADXpokef87Xp9771+XdRt/mX+8876c8a6WjGgYnlqtlhUXzM7H553wrNfs7e1L6/d/nL/489cleTqUvvoVo/O9u+7PmJftm2uXnpaX7vd7SZ4+2f3jB9YLjLCbesOb3pxVf/+N9PT0pLOzM63f+mbe+OY/G+xpQZKnQ2OZx1DwrE3jGWeckZNOOinHHHNMmpqaUqvVsnHjxvzwhz/MOeecs7PmyC7ijHcflz8Ys39OeNPhOeFNh/ePz7vwq/nsolk5+t1L85MHHs2HP3VDvvG3p2f4sGFZt/GXOfW8/5Ukufiq1lx8zoz88GvzM3xYLWt/ti7zL71pwDXev/i67a579ie/nhUXzM5dNyxIvZ78xaJr09m1NT/49wfyqatvy21XnZ2e3r482vF43nnOVS/q7wAYPO9816w88vDDecfME9PT3Z23v/NdOfKoPxnsaUGSATsndlu1esHmr/b29tx5553ZuHFj+vr6Mnr06BxzzDEZNWrUDl9sr0lzn/dEgWrZvGb5YE8BGAL23EXuA/PKj36r1Pt/vuytL9BMXjyFv+pRo0blpJNO2glTAQAYmobKEnMZu0g+BwAYuiqQGYVGAICyNI0AABSqQGZ89lvuAABAomkEACht2LDdv2oUGgEASqrC8rTQCABQkoMwAAAUqkBmdBAGAIBimkYAgJIsTwMAUEhoBACgUAUyoz2NAAAU0zQCAJRkeRoAgEIVyIxCIwBAWZpGAAAKVSAzOggDAEAxTSMAQEmWpwEAKFSBzCg0AgCUpWkEAKBQBTKjgzAAABTTNAIAlGR5GgCAQhXIjEIjAEBZVWga7WkEAKCQphEAoKQKFI1CIwBAWVVYnhYaAQBKEhoBAChUgczoIAwAAMU0jQAAJVmeBgCgUAUyo9AIAFCWphEAgEIVyIwOwgAAUExoBAAoaVitVuqxo7q6unL88cfnkUceSZKcd955mTx5ck488cSceOKJ+Yd/+IckyX333ZeZM2dmypQpWbhwYXp6epIk69evz8knn5ypU6fmzDPPzJYtW4o/4w7PEgCAAWq1co8dcc8992TWrFlpa2vrH7v33ntz3XXXZfXq1Vm9enXe8pa3JEnOPffcXHDBBbnttttSr9ezcuXKJMmSJUsye/bstLa2ZsKECVmxYkXhdYVGAICSarVaqUdnZ2ceeeSR7R6dnZ3bXWvlypVZvHhxmpubkyRPPfVU1q9fnwULFmT69Om5/PLL09fXl3Xr1mXr1q2ZOHFikmTmzJlpbW1Nd3d31qxZkylTpgwYL+IgDADAILvmmmuyfPny7cbnzp2befPmDRi76KKLBjzftGlTjj766CxevDi///u/n9NPPz033nhjxo0bl6ampv7XNTU1pb29PZs3b84+++yThoaGAeNFhEYAgJKGlTw9feqpp2bGjBnbjTc2Nha+96CDDsoVV1zR//yUU07JqlWrcsghhwy4FVC9Xk+tVuv/85meyy2DhEYAgJLK3qexsbHxOQXE3+anP/1p2tra+peb6/V6GhoaMnr06HR0dPS/btOmTWlubs7++++fJ554Ir29vRk+fHg6Ojr6l7qfjT2NAAAl7cyDML+pXq/nk5/8ZB5//PF0d3fn61//et7ylrdk7NixGTlyZO6+++4kyerVq9PS0pIRI0bkyCOPzK233pokWbVqVVpaWgqvo2kEACiplsG7u/erX/3qvP/978+sWbPS09OTyZMn5/jjj0+SLFu2LIsWLUpXV1fGjx+fOXPmJEkWL16c+fPn58orr8yYMWNy6aWXFl6nVq/X6y/qJ3mGvSbN3VmXAoa4zWu23xAO8Jv23EXqr+M/v6bU+285/agXaCYvnl3kVw0AMHSVPQgzFAiNAAAllT0IMxQIjQAAJVUgMwqNAABlPZ/vjx5q3HIHAIBCmkYAgJIqUDQKjQAAZTkIAwBAoQpkRnsaAQAopmkEACipCqenhUYAgJJ2/8goNAIAlOYgDAAAharw3dMOwgAAUEjTCABQkuVpAAAKVSAzCo0AAGVpGgEAKOQgDAAARNMIAFCa5WkAAArt/pFRaAQAKK0K3z1tTyMAAIU0jQAAJVWgaBQaAQDKchAGAIBCFciMQiMAQFkOwgAAQDSNAAClVaBoFBoBAMpyEOYF9uB3L9uZlwOGsHp9sGcA8NxVYb+fphEAoKQqNI1VCMYAAJSkaQQAKGnY7l80Co0AAGUJjQAAFLKnEQAAomkEACjN8jQAAIUqsDotNAIAlDWsAqlRaAQAKKkKh0Sq8BkBAChJ0wgAUFIFVqeFRgCAsuxpBACgUAUyoz2NAAAU0zQCAJTk5t4AABSypxEAgEIVyIxCIwBAWVVYnnYQBgCAQppGAICSatn9q0ahEQCgpCosTwuNAAAlCY0AABSqVeD4tIMwAAAU0jQCAJRUheVpTSMAQEm1WrnHjurq6srxxx+fRx55JElyxx13ZPr06Zk8eXIuu+yy/tfdd999mTlzZqZMmZKFCxemp6cnSbJ+/fqcfPLJmTp1as4888xs2bKl8JpCIwBAScNqtVKPHXHPPfdk1qxZaWtrS5Js3bo1CxYsyIoVK3Lrrbfm3nvvze23354kOffcc3PBBRfktttuS71ez8qVK5MkS5YsyezZs9Pa2poJEyZkxYoVxZ9xx34lAAAMppUrV2bx4sVpbm5OkqxduzYHH3xwDjrooDQ0NGT69OlpbW3NunXrsnXr1kycODFJMnPmzLS2tqa7uztr1qzJlClTBowXsacRAKCksnsaOzs709nZud14Y2NjGhsbB4xddNFFA55v3LgxTU1N/c+bm5vT3t6+3XhTU1Pa29uzefPm7LPPPmloaBgwXkRoBAAoqewdd6655posX758u/G5c+dm3rx5z/revr6+Abf8qdfrqdVqv3P8138OnH/xBxAaAQBKGlbyawRPOfXUzJgxY7vx32wZf5vRo0eno6Oj/3lHR0eam5u3G9+0aVOam5uz//7754knnkhvb2+GDx/e//oi9jQCAJRU9vR0Y2NjDjzwwO0ezyU0Hn744XnwwQfz0EMPpbe3N7fccktaWloyduzYjBw5MnfffXeSZPXq1WlpacmIESNy5JFH5tZbb02SrFq1Ki0tLYXX0TQCAAxhI0eOzNKlSzNv3rxs27Ytxx13XKZOnZokWbZsWRYtWpSurq6MHz8+c+bMSZIsXrw48+fPz5VXXpkxY8bk0ksvLbxOrV6v11/UT/IMGx7v3lmXAoa4ffceMdhTAIaAvXaRvyo+d2dbqfefccwfviDzeDFpGgEAStrRey0ORUIjAEBJFciMQiMAQFlVaBqdngYAoJCmEQCgpAoUjUIjAEBZVVi6FRoBAEp6Ll/DN9RVIRgDAFCSphEAoKTdv2cUGgEASqvCLXeERgCAknb/yCg0AgCUVoGi0UEYAACKaRoBAEqqwi13hEYAgJKqsHQrNAIAlKRpBACg0O4fGavRpgIAUJKmEQCgJMvTAAAUqsLSrdAIAFBSFZrGKgRjAABK0jQCAJS0+/eMQiMAQGkVWJ0WGgEAyhpWga5RaAQAKKkKTaODMAAAFNI0AgCUVLM8DQBAkSosTwuNAAAlOQgDAEChKjSNDsIAAFBI0wgAUFIVmkahEQCgJKenAQAoNGz3z4z2NAIAUEzTCABQkuVpAAAKOQgDAEAhTSMAAIUchAEAgGga2UHf/tbN+dqXv5RarZaRe+6ZD37kvLz6jyZs97p6vZ6LlyzMK145Lu9+z2mlrvnLzY/lor9ekPYN6zOsNiwfXbA4Ew6btEPzAYaWb968Otd86eqkVsuee+6VvzpvYQ555bhcfOGS3Hvvj1Kv1/Pa1x6W8xYtzp577jnY0wXL0/BMDz/0YK68/NP5wpdvyEtf1pQf/uB7Of+vPpQbbv7HAa9re/CBfOaSi3Lfj3+UV7xyXOnrXnbJRTls4h/nlNM+l/t/9n8z/5wP5PpvfDMb2zc8p/kAQ0vbg7/IZZ/+m3z1hr9PU1Nz/s/3bs9HPjQvx59wYnp7e3PD3//v1Ov1LJx/br74hc/nA3PPHuwpg4Mw8EwjRuyRjy1ckpe+rClJ8qrXjM9j/7kp3d3dGTFiRP/rVt34tUw78c8zavSYAe/v7u7O5z97af7j3+9KX29vxr3qNfngR87L7+2zT/9rLl6yMBOPOCpvPf6kJElPT0/u/P7tOedjC5Mk4w59dQ486A/yr3d+P+Ne9UfPaT7A0DJijz1ywZIL09TUnCQZP35CNm3alD8+4qiMHTs2w4Y9vbPqVa95TR74+c8Hc6rQrwKZ0Z5GnrsxB4zNMX96XJKnl5+v+MwleV3LG7cLaB86d2HeMnXadu+//povZHjD8Fx17cp88St/n5c2NeXzV1z2rNd8/Je/TL3el/1esn//WFPzqGzc2P6c5wMMLWPHHpiW496Q5On/tpddcnHe8MY35djX/WkO/sOXJ0nWr1+Xr3z5mkyePHUQZwr/bVitVuoxFGga2WFPPfVkLl6yKB0bN+SSv/3cc37fnd+/PV1PPJG7/uXOJEl3T3de8l9h8IzTZqX7V79Ke/uj+be7/iU3fvXLmXD4pJxy2vu36/zr9WT4sOGl5wPs2p568smcv2h+2jdsyBWf+0L/+E9+fG8+fPbcvGvWe9LyhjcO4gyhWp41NK5fv/5Z33zAAQe8oJNh19e+4dGc9+GzcvDLX5HPrPhiRu7ABvS+vr7M+8j8HH3s65MkTz75ZH71q21Jks996atJfvvydOr1dD7+eBr33TdJsmnTxjQ1jyo9H2DX9eij63P2WWfk5a84JFd98dr+wy6tt34zn7xwSeYvPD9vmzZ9kGcJ/21odIXlPGtoPP3009PW1pbm5ubU6/UBP6vVavnOd77zok6OXcuTW7bk7DNOy9RpJ+R//uUHdvj9Rx19bG664Ss54qijM3z48PzNJxdnr732zscWLvmd72loaMjRr2vJzatuyMmnvi8P3P/TPPTgA5l4xFGl5wPsmrZs6cr7Tjsl00+YkTM+MLd//Pbv/lM+tfTCXPl3V2f8hNcO4gzht6hAaqzVfzMNPkNXV1dmz56dxYsX54gjjih9sQ2Pd5f+32DwXPe/rsrVn/tsXnHIwBPRHzlvcT598ZJcff03BoxfvGRhXn7IK/tvubNt69asuHxZ/uPuNenr68srD31VPnreXw84CPPbPPafm3LJRYuzYf26pFbLWWd/NEcd/brfOZ9Lr7g6++63X/kPzKDad297U6vq6qs+nys++5m8ctyhA8afeuqpdD3R2b/SkCQTJ/1xFixavLOnyC5kr13kr4p/eeDxUu//H4fs+wLN5MXzrKExSdauXZsbbrghn/jEJ0pfTGgEniuhEXguhMadp/AgzGGHHZbDDjtsZ8wFAGBIGiIHoEtxehoAoKQKZEahEQCgtAqkRqERAKCkKnz3tG+EAQCgkKYRAKAkB2EAAChUgcwoNAIAlFaB1Cg0AgAMIaecckoee+yxNDQ8HeM+/vGPZ8uWLbn44ouzbdu2vPWtb80555yTJLnvvvuycOHCbNmyJUceeWSWLFnS/74dJTQCAJS0s05P1+v1tLW15Z//+Z/7w9/WrVszderUfPnLX86YMWNy+umn5/bbb89xxx2Xc889NxdeeGEmTpyYBQsWZOXKlZk9e/bzurbT0wAAJdVq5R7P1S9+8YskyXvf+96ccMIJue6667J27docfPDBOeigg9LQ0JDp06entbU169aty9atWzNx4sQkycyZM9Pa2vq8P6OmEQCgpLI9Y2dnZzo7O7cbb2xsTGNj44DXHXPMMTn//PPT3d2dOXPm5H3ve1+ampr6X9Pc3Jz29vZs3LhxwHhTU1Pa29uf9xyFRgCAskqmxmuuuSbLly/fbnzu3LmZN29e//NJkyZl0qRJ/c/f/va35/LLL88RRxzRP1av11Or1dLX15faM2rMX48/X0IjAMAgO/XUUzNjxoztxp/ZMibJXXfdle7u7hxzzDFJng6CY8eOTUdHR/9rOjo60tzcnNGjRw8Y37RpU5qbm5/3HO1pBAAoqVbyn8bGxhx44IHbPX4zND7xxBO55JJLsm3btnR1deWmm27Khz/84Tz44IN56KGH0tvbm1tuuSUtLS0ZO3ZsRo4cmbvvvjtJsnr16rS0tDzvz6hpBAAoaWd9I8wb3/jG3HPPPTnppJPS19eX2bNnZ9KkSVm6dGnmzZuXbdu25bjjjsvUqVOTJMuWLcuiRYvS1dWV8ePHZ86cOc/72rV6vV5/oT5IkQ2Pd++sSwFD3L57jxjsKQBDwF67yF8V9z7SVer9Ew7c5wWayYtH0wgAUFYFvhHGnkYAAAppGgEAStpZ3wgzmIRGAICSdtZBmMEkNAIAlFSBzGhPIwAAxTSNAABlVaBqFBoBAEpyEAYAgEIOwgAAUKgCmdFBGAAAimkaAQDKqkDVKDQCAJTkIAwAAIUchAEAoFAFMqODMAAAFNM0AgCUVYGqUWgEACjJQRgAAApV4SCMPY0AABTSNAIAlFSBolFoBAAorQKpUWgEACjJQRgAAAo5CAMAANE0AgCUVoGiUWgEACirCsvTQiMAQGm7f2oUGgEASqpC0+ggDAAAhTSNAAAlVaBoFBoBAMqqwvK00AgAUFIVvhHGnkYAAAppGgEAytr9i0ahEQCgrApkRqERAKAsB2EAACjkIAwAAETTCABQ3u5fNAqNAABlVSAzCo0AAGU5CAMAQCEHYQAAIJpGAIDSqrA8rWkEAKCQphEAoCRNIwAARNMIAFBaFU5PC40AACVVYXlaaAQAKKkCmVFoBAAorQKp0UEYAAAKaRoBAEpyEAYAgEIOwgAAUKgCmVFoBAAorQKp0UEYAAAKaRoBAEpyEAYAgEJVOAhTq9fr9cGeBAAAuzZ7GgEAKCQ0AgBQSGgEAKCQ0AgAQCGhEQCAQkIjAACFhEYAAAoJjQAAFBIaAQAoJDQyaG6++ea87W1vy+TJk3P99dcP9nSAXVxXV1eOP/74PPLII4M9FagkoZFB0d7enssuuyxf+cpXsmrVqnz961/Pz3/+88GeFrCLuueeezJr1qy0tbUN9lSgsoRGBsUdd9yRo48+Ovvtt1/23nvvTJkyJa2trYM9LWAXtXLlyixevDjNzc2DPRWorIbBngDVtHHjxjQ1NfU/b25uztq1awdxRsCu7KKLLhrsKUDlaRoZFH19fanVav3P6/X6gOcAwK5FaGRQjB49Oh0dHf3POzo6LDsBwC5MaGRQHHvssbnzzjvz2GOP5amnnsq3v/3ttLS0DPa0AIDfwZ5GBsWoUaNyzjnnZM6cOenu7s7b3/72HHbYYYM9LQDgd6jV6/X6YE8CAIBdm+VpAAAKCY0AABQSGgEAKCQ0AgBQSGgEAKCQ0AgAQCGhEQCAQkIjAACF/j9hn7/NzFuVkwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = confusion_matrix(y_test, y_pred_class)\n",
    "sns.heatmap(mat, annot=True, cmap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8559d8fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZM5WXl6euXbtq0qRJkqTZs2dr2rRpeumll9SiRQs9++yznirZZ1gDy7uS5BYVKTw42MvVAAAAoDYeC9wzZ87UzJkzT7ts1apV1eZ16dJFy5cvrzY/Pj5er7/+er3X58u4+Q0AAIDvaFwdgOGWsJNjcTNSCQAAQONH4PZBZrO5fCxu+nADAAA0egRuHxUWFESXEgAAAB9A4PZRVgI3AACATyBw+yhrYKAKiovldDq9XQoAAABqQeD2UWFBQTIk5dGPGwAAoFEjcPuoipvfMFIJAABA40bg9lEVgZuRSgAAABo3ArePCq10t0kAAAA0XgRuH2WpGIubwA0AANCoEbh9mDUwkD7cAAAAjRyB24dx8xsAAIDGj8Dtw6xBQcovLpbTMLxdCgAAAGpA4PZh1pNjceczUgkAAECjReD2YWEVQwPSrQQAAKDRInD7MG5+AwAA0PgRuH1YGGNxAwAANHoEbh9mMZsVGhBA4AYAAGjECNw+jqEBAQAAGjcCt4+zBgUpj1FKAAAAGi0Ct48LCwpSXlERY3EDAAA0UgRuH1cxFncBZ7kBAAAaJQK3j7MyUgkAAECjRuD2cdz8BgAAoHEjcPs4AjcAAEDjRuD2cX5ms0ICApRLH24AAIBGicB9HrCeHKkEAAAAjY/bgbuoqEg7d+6UYRgqLCz0ZE04Q9z8BgAAoPFyK3B///33GjRokO644w6lpaXpuuuu07fffuvp2uAma2Cg8oqLGYsbAACgEXIrcM+bN09LlixRZGSk4uLiNG/ePM2ZM8fTtcFNYUFBMgyDsbgBAAAaIbcCd1FRkTp27Oiavvbaa+VwODxWFM6MlZFKAAAAGi23Arefn59ycnJkMpkkSXv27PFoUTgzFYE7jzPcAAAAjY6fOyvdeeeduvnmm5WRkaEHHnhAGzZs0GOPPebp2uCmMO42CQAA0Gi5FbgHDhyoDh06aMOGDXI6nZoyZYo6dOjg6drgJj+LRcEBAQRuAACARsitwL1582ZJUqdOnSRJWVlZysrK0uWXX+65ynBGrIGBjMUNAADQCLkVuCdNmqSIiAjZbLYq81evXu2RonDmwoKClJGb6+0yAAAAcAq3Avcbb7yhJ554QgMGDNCdd94pf39/T9eFM2QNCtK+jAwZhuG6uBUAAADe59YoJZdddpmWLVsmPz8/3XTTTdqyZYun68IZsgYFyWkYKigp8XYpAAAAqMStM9w//fSTJKl///5q06aNHnzwQfXv35+RShqRymNxh54ctQQAAADe51bgvueee6pMm81mbdiwwSMF4exUHhowLiLCy9UAAACggluBe926dZ6uA+eIu00CAAA0Tm4F7ieeeOK082fOnFmvxeDs+VksCvb3Z2hAAACARsatiyYjIyMVGRmp1NRU1+PIyEgPl4YzFRYUpFxu7w4AANCouHWG++6775YkffLJJ67HaHysQUHKzMvzdhkAAACoxK0z3BUY37lxswYFKbeoSE7D8HYpAAAAOOmM+nAfO3asSn9u+nA3LuEnx+LOLy52XUQJAAAA73IrcFf0154wYYIna8E5iggOliTlFBYSuAEAABoJt/twFxUVaf/+/erUqZOKi4sVfDLcofEIDwmRJJ0oLJSiorxcDQAAACQ3+3D/8MMPGjRokO644w6lp6fruuuu07fffuvp2nCGQgMCZDGblVNQ4O1SAAAAcJJbgfvpp5/WkiVLFBkZqbi4OM2bN09z5szxdG04QyaTSeHBwcphLG4AAIBGw63AXVRUpI4dO7qmr732Wjkcjjqfl5eXpxEjRujQoUOSpIcfflhDhgxRUlKSkpKS9PHHH0uStm/fruTkZCUkJGjGjBkqKyuTJB05ckQTJkzQ0KFDNXnyZOXn55/xC2xqIoKDy7uUAAAAoFFwK3D7+fkpJyfHNSzgnj176nzODz/8oHHjxmnfvn2uedu2bdPSpUuVkpKilJQUDR48WJI0depUzZo1S2vXrpVhGFq2bJkk6dFHH9X48eOVmpqqbt26adGiRWf6+pqcisDN0IAAAACNg1uB+84779TNN9+sY8eO6YEHHtC4ceM0efLkWp+zbNkyzZ49W7GxsZKkwsJCHTlyRNOnT1diYqL+9re/yel06vDhwyoqKlKPHj0kScnJyUpNTVVpaak2b96shISEKvNRu/DgYDkNg1u8AwAANBJujVIycOBAdejQQRs2bJDT6dSUKVPUoUOHWp9zah/vjIwM9enTR7Nnz5bVatUdd9yh5cuXq1OnTrLZbK71bDab0tLSlJ2drbCwMPn5+VWZj9pVDA14orBQ4YwkAwAA4HVuBe7NmzdLkjp16iRJysrKUlZWli6//HK3d9S6dWstXLjQNT1x4kStXLlSHTp0qHIHS8MwZDKZXH8rO5s7XcbEhJ3xc86VzWaVJIWGBDb4vluYIyRJRY5Sr+y/4rU3BU3ptTZltHPTQDs3DbRz09AY29mtwP3HP/5Rbdq0qTZ/9erVbu9o586d2rdvn6uLiGEY8vPzU1xcnOx2u2u9jIwMxcbGKjo6Wrm5uXI4HLJYLLLb7a7uKWciMzNPTmfD9We22ayy23Nls1mVX1DcYPt1MSSL2az0nFyv7N9uz23wfXpDRTvj/EY7Nw20c9NAOzcN3mpns9lU60letwJ3u3bttHLlynMqxDAMzZ07V3369FFISIjefvttjR49WvHx8QoMDNTWrVvVq1cvpaSkqH///vL391fv3r21Zs0aJSYmauXKlerfv/851dAUmEwmRioBAABoRNwK3GfTleNUXbp00e23365x48aprKxMQ4YM0YgRIyRJ8+fP18yZM5WXl6euXbtq0qRJkqTZs2dr2rRpeumll9SiRQs9++yz51xHUxAeHKxshlAEAABoFNwK3JmZmXr11VcllYdvPz8/9e7dW126dKnzuevWrXM9njBhgiZMmFBtnS5dumj58uXV5sfHx+v11193p0RUEhEcrAOZmXIahsz1cLAEAACAs+dW4L766qu1a9cu13Rpaan++c9/av369Z6qC+cgotLQgIxUAgAA4F1uBe4nn3yy2rw///nP9V4M6kdFyM5haEAAAACvc+vGN3a7XbfffrsSEhKUmZmpP/zhD/rLX/7i6dpwliqPxQ0AAADvcitwP/rooxo0aJACAwMVHh6uLl26aObMmZ6uDWcpJCBAfmazcgjcAAAAXudW4D58+LBuuukmmc1m+fv7a+rUqTp69Kina8NZqhgakMANAADgfW4FbpPJJKfT6ZrOy8urMo3GJ5yxuAEAABoFtwL3kCFD9OCDDyo3N1dvvfWWbrnlFl1//fWerg3nICI4WCeKijgwAgAA8DK3Rim58847tXLlSjmdTm3cuFG//e1vddNNN3m6NpyDyJAQGYahE0VFigwJ8XY5AAAATZZbgVuSRo0apVGjRrmmP/vsM+Xk5Ojyyy9XfHy8J2rDOYgKDZUkZefnE7gBAAC8yO0z3KfaunWrbrnlFnXr1q3ei8K5qwjZ2QUFauflWgAAAJoytwL3zp07de+997qmDcPQrl27dPfdd3usMJwbf4tF1qAgZeXne7sUAACAJs2twB0ZGanRo0dXmff66697pCDUn6iQEB0vKPB2GQAAAE2aW6OUlJaWKisrSyUlJZ6uB/Uo8mTgdhqGt0sBAABostw6w92lSxfNmjVLx44dU1FRka644gqdOHHC07XhHEWHhsppGDpRWMiFkwAAAF7iVuCeP3++63FJSYk+/fRTffDBB3r44Yc1btw4de/e3WMF4uwxUgkAAID3uT0s4I8//qiff/5ZycnJiouL08svv6yCggK1atXKk/XhHDBSCQAAgPe51Yf73Xff1cMPP6x//vOfys3N1V133aWff/5ZV1xxhaKjoz1dI85SxUgl2YxUAgAA4DVuBe6lS5fq7bffVlhYmGJiYrRixQq99tprnq4N9SAqJETZjFQCAADgNW4FbrPZrLCwMNd0ixYtZLFYPFYU6g8jlQAAAHiXW4E7MjJS27dvl8lkkiStWrVKERERHi0M9aPySCUAAABoeG5dNDl9+nT96U9/0oEDB9SvXz8FBgZq0aJFnq4N9YCRSgAAALzLrcDdoUMHpaSkaN++fXI4HGrXrp38/f09XRvqASOVAAAAeJdbgdvhcOitt97SV199JYvFooEDByo5OdnTtaEeMFIJAACAd7kVuJ944gn9+uuvSkpKkmEYWr58ufbv36/777/f0/WhHjBSCQAAgPe4Fbg3bNigDz74wNWNZOTIkRo5ciSB20dEhoToUHa2nIYh88kLXwEAANAw3BqlJDo6Wg6HwzVtMpkUHh7usaJQv6LDwuQ0DB3nLDcAAECDc+sMd5cuXTR+/HglJyfLYrFozZo1ioqK0quvvipJ+t3vfufRInFump0cQz0jL0/RJ0ctAQAAQMNwK3AXFxerc+fO+umnnyRJrVq1kiTt2rXLc5Wh3kSGhMhiNisjN1cXNm/u7XIAAACaFLcC95NPPunpOuBBZpNJzcLClJGX5+1SAAAAmhy3AndiYuJp569evbpei4HnNAsL0660NBmG4bpjKAAAADzP7S4lTzzxhKdrgQc1s1r105Ejyiks5I6TAAAADcitwB0SEqIrrrjC07XAg1wXTubmErgBAAAakFvDAtIFwfdFhYTIYjLJTj9uAACABuXWGe4dO3bosssuc01X9AP+9ttvPVYY6pfZbFZ0WJgycnO9XQoAAECT4lbg/uSTTzxdBxqAzWrVL1w4CQAA0KDc6lKSm5urRx99VPHx8crLy9Ndd92l4uJiT9eGetYsLEylDodOFBZ6uxQAAIAmw63A/de//lU33nijJKlz58665557NHv2bI8WhvpX+Y6TAAAAaBhuBe7CwkINHjzYNT1o0CDlEdp8TlRoqMwmk+z04wYAAGgwbo9SsmPHDtf07t27ZTa79VQ0IhazWdGhoZzhBgAAaEBuXTT5pz/9SRMnTtSFF14oSdqzZ4/mz5/v0cLgGc2sVu2x27lwEgAAoIG4FbgHDBig1NRUffvtt7JYLLr00ksVExPj6drgAc3CwrTj6FHlFhUpPDjY2+UAAACc99zuF5KWlqaoqChZrVb98ssvWrZsmSfrgoc0Dw+XJB3LyfFyJQAAAE2DW2e4Z86cqU8//VTFxcWKjY3VgQMH1KtXL910002erg/1LDo0VAEWi46dOKEL4+K8XQ4AAMB5z60z3Bs3btSnn36qwYMHa/HixXr11VcVFBTk6drgASaTSc0jIjjDDQAA0EDcCtw2m00hISFq3769du3apSuvvFLHjh3zdG3wkLiICB0vKFBRaam3SwEAADjvuRW4/f39tXnzZnXo0EFffPGFcnNzVVBQ4Ona4CFx9OMGAABoMHUG7l27dunqq6/Wiy++qGuvvVY7duxQnz59NHLkyIaoDx5gCw+X2WQicAMAADSAWi+afPfdd/X000+rbdu2OnDggDZt2qRly5YpNzdXVqu1oWpEPfMzm2WzWgncAAAADaDWwP36669r9erVat68ub777js999xz6tevH2H7PBAXEaH/HjqkModDfhaLt8sBAAA4b9XZpaR58+aSpJ49eyo7O9vjBaFhtIiIkNMwlJ6b6+1SAAAAzmu1Bu5Tb/1tOcMzoXl5eRoxYoQOHTokqXx4wcTERA0ZMkTPPfeca73t27crOTlZCQkJmjFjhsrKyiRJR44c0YQJEzR06FBNnjxZ+fn5Z7R/1Iwb4AAAADQMt+80KVUP4LX54YcfNG7cOO3bt0+SVFRUpOnTp2vRokVas2aNtm3bps8//1ySNHXqVM2aNUtr166VYRiuu1g++uijGj9+vFJTU9WtWzctWrToTMpFLQL9/RUVGkrgBgAA8LBaA/fOnTt12WWXuf6rmO7Zs6cuu+yyWje8bNkyzZ49W7GxsZKkH3/8UW3btlXr1q3l5+enxMREpaam6vDhwyoqKlKPHj0kScnJyUpNTVVpaak2b96shISEKvNRf+LCw5V24oSchuHtUgAAAM5btV40+fHHH5/1hufMmVNlOj09XTabzTUdGxurtLS0avNtNpvS0tKUnZ2tsLAw+fn5VZmP+tMiIkLbjx5VVl6emnEhLAAAgEfUGrjj4+PrbUdOp7NKlxTDMGQymWqcX/G3sjPp0lIhJibs7Is+SzZbeXgNDQls8H2fiQ4tmmvdjh1Kz8tV2+bN6mWbFa+9KWhKr7Upo52bBtq5aaCdm4bG2M61Bu76FBcXJ7vd7pq22+2KjY2tNj8jI0OxsbGKjo5Wbm6uHA6HLBaLa/0zlZmZJ6ez4bpM2GxW2e25stmsyi8obrD9ng2TpOjQUO1Jt+viFi3rZZt2e9MY9aSinXF+o52bBtq5aaCdmwZvtbPZbKr1JO8ZXTR5Li699FLt3btX+/fvl8Ph0Pvvv6/+/fsrPj5egYGB2rp1qyQpJSVF/fv3l7+/v3r37q01a9ZIklauXKn+/fs3VLlNRquoKB3LyVGpw+HtUgAAAM5LDRa4AwMD9dRTT+mee+7RsGHD1L59ew0dOlSSNH/+fD355JMaOnSoCgoKNGnSJEnS7NmztWzZMg0bNkxbtmzRfffd11DlNhmtoqPlNAwdPX7c26UAAACclzzepWTdunWux3379tWqVauqrdOlSxctX7682vz4+Hi9/vrrHq2vqYuLiJDFbNbB7Gy1iYnxdjkAAADnnQY7w43Gyc9sVsvISB3KyvJ2KQAAAOclAjfUKipKOYWFyi0q8nYpAAAA5x0CN9QqKkqSOMsNAADgAQRuKDIkRKGBgTqUne3tUgAAAM47BG7IZDKpVVSUDmdnc5t3AACAekbghiSpdXS0ShwOpZ844e1SAAAAzisEbkgq78dtNpm0PzPT26UAAACcVwjckCQF+PmpZWSk9mVkyKBbCQAAQL0hcMPlgmbNlFNYqOMFBd4uBQAA4LxB4IZL25N3mtybkeHlSgAAAM4fBG64hAYGKtZqpR83AABAPSJwo4oLmjWTPTdXedx1EgAAoF4QuFHFBc2aSZL2cZYbAACgXhC4UUVkSIgiQ0K0j37cAAAA9YLAjWouiInR0ePHVVRa6u1SAAAAfB6BG9Vc0KyZDImLJwEAAOoBgRvV2KxWWYOCtDs93dulAAAA+DwCN6oxmUzqYLPpcHa2CktKvF0OAACATyNw47Q6xMbKEDfBAQAAOFcEbpxWdGiookJC9CvdSgAAAM4JgRunZTKZ1CE2VsdycrgJDgAAwDkgcKNGHWJjJUl77HYvVwIAAOC7CNyoUURwsGxWK91KAAAAzgGBG7XqYLMpIy9POQUF3i4FAADAJxG4UauKbiW/cJYbAADgrBC4UavQwEDFR0bql7Q0GYbh7XIAAAB8DoEbdbowLk65RUU6mpPj7VIAAAB8DoEbdWrXrJn8LRbtOnbM26UAAAD4HAI36uRnsai9zaY9drtKHQ5vlwMAAOBTCNxwy4XNm6vM6dRexuQGAAA4IwRuuCUuIkLWoCDtSkvzdikAAAA+hcANt5hMJl3YvLmOHD+uXG71DgAA4DYCN9x2YVycJHHxJAAAwBkgcMNt1qAgxUdGasexY3IyJjcAAIBbCNw4Ixe1bKn84mIdysrydikAAAA+gcCNM9I2JkZB/v7acfSot0sBAADwCQRunBGL2azOcXHan5mp/OJib5cDAADQ6BG4cca6xMXJkLSTiycBAADqRODGGYsICVHLyEjtPHZMBhdPAgAA1IrAjbPSpUUL5RYV6VB2trdLAQAAaNQI3Dgr7Zo1U5C/v34+csTbpQAAADRqBG6cFYvZrC4tWuhAZiZ3ngQAAKgFgRtn7eKWLSVJPx0+7OVKAAAAGi8CN85aWGCgLmjWTDuPHVOZw+HtcgAAABolAjfOSbf4eBWXlemX9HRvlwIAANAoEbhxTuIiIhQdGqqfDh9miEAAAIDTIHDjnJhMJnWLj1dWfr7+8+seb5cDAADQ6BC4cc46xsYq0M9P//zsc2+XAgAA0OgQuHHO/CwWdW3ZUh/9+JP22O3eLgcAAKBRIXCjXnSNj1eQv7/+tXGDt0sBAABoVLwSuCdOnKjhw4crKSlJSUlJ+uGHH7Rx40YlJiZqyJAheu6551zrbt++XcnJyUpISNCMGTNUVlbmjZJRh+CAAP227xVa8+OPSjuR4+1yAAAAGo0GD9yGYWjfvn1KSUlx/de5c2dNnz5dixYt0po1a7Rt2zZ9/nl5f+CpU6dq1qxZWrt2rQzD0LJlyxq6ZLjp9oHXyjAM/fs///F2KQAAAI1GgwfuPXvKR7L4/e9/r5EjR2rp0qX68ccf1bZtW7Vu3Vp+fn5KTExUamqqDh8+rKKiIvXo0UOSlJycrNTU1IYuGW5q0yxGg7t21btbtyq3qNDb5QAAADQKDR64T5w4ob59+2rhwoVasmSJ3nrrLR05ckQ2m821TmxsrNLS0pSenl5lvs1mU1paWkOXjDNwy9VXq6CkRMs2b/Z2KQAAAI2CX0PvsGfPnurZs6dresyYMfrb3/6mXr16ueYZhiGTySSn0ymTyVRt/pmIiQk796LPkM1mlSSFhgQ2+L69rd8lF2pg14u09D9f667rByo8ONjbJXlMRTvj/EY7Nw20c9NAOzcNjbGdGzxwb9myRaWlperbt6+k8hAdHx8ve6Xh5Ox2u2JjYxUXF1dlfkZGhmJjY89of5mZeXI6G+4OiDabVXZ7rmw2q/ILihtsv42F3Z6rP1x9jdb9tF3Pr/5YkwcM8HZJHlHRzji/0c5NA+3cNNDOTYO32tlsNtV6krfBu5Tk5uZq3rx5Ki4uVl5ent577z098MAD2rt3r/bv3y+Hw6H3339f/fv3V3x8vAIDA7V161ZJUkpKivr379/QJeMMXdSipX5z0UV64z9fK7sg39vlAAAAeFWDn+EeMGCAfvjhB40aNUpOp1Pjx49Xz5499dRTT+mee+5RcXGxrr32Wg0dOlSSNH/+fM2cOVN5eXnq2rWrJk2a1NAl4yxMHjBA67Zv15KvNuj+IUO8XQ4AAIDXNHjglqT77rtP9913X5V5ffv21apVq6qt26VLFy1fvryBKkN9aW+L1bDu3bVs8ze6uW9f2ayNrz8VAABAQ+BOk/CY26+9Tg6nU/84OaY6AABAU0Tghse0jo7WDb16a8W3W7Xz2FFvlwMAAOAVBG541OQBAxQeHKyn16yRYTTcaDEAAACNBYEbHhUeHKx7fzNI3x88qA9+/NHb5QAAADQ4Ajc8bmTPHuoWH68XPv5IecVF3i4HAACgQRG44XFmk1nThg1TVn6+XvrsM2+XAwAA0KAI3GgQF7eM15jel+utTZu0ee9eb5cDAADQYAjcaDD3DR6sNjExmrXyPZ0oLPR2OQAAAA2CwI0GExwQoDnJycrMy9OTaz5g1BIAANAkELjRoC5uGa/br71Oa7dt05r/MmoJAAA4/xG40eB+16+ferRpoznvv6/tR494uxwAAACPInCjwVnMZs278UZFhoTovjffVNqJHG+XBAAA4DEEbnhFszCrXhg3XgXFxbrvzTdVUFLs7ZIAAAA8gsANr+nUvLmeGnOjfklL01/eeUfFZaXeLgkAAKDeEbjhVVd36qTpw0dow6+/6v4331RhSYm3SwIAAKhXBG54XXKvXno0aZS+2btXU95Yqtwibv8OAADOHwRuNAqJPXroyRvGaNuhQ7rjtSU6cjzb2yUBAADUCwI3Go3BXbvqubHjdCg7WxMWL9aGX37xdkkAAADnjMCNRuXqTp209PbbFRsernv//YYWrVunUkeZt8sCAAA4awRuNDptomO05A9/0IgePfTPL7/QhMWLte3wIW+XBQAAcFYI3GiUgv0D9GjSKD03dqxOFBbq1pdf1jOpH+pEYaG3SwMAADgjft4uAKjNtZ27qNcFF2jBJ5/orU2b9P4PP+j3/a7R2CuvUKCfv7fLAwAAqBNnuNHohQUG6eHhI/TmnXfq0tat9cInH2v0ggVa9f13cjid3i4PAACgVgRu+IwLm8fpb+MnaPEttygmLEx/TUnRuP/3d325a5cMw/B2eQAAAKdF4IbP6X1BO/3rj7fp6TE3qrisTH9689/6w5JX9d2B/d4uDQAAoBoCN3ySyWTS4K5dtfyuKXp42HAdysrSH159Vff++w3tPHbU2+UBAAC4cNEk6k2ZwyGbzdrg+71r2ED9blA/vfr5V1r08TqN+3//TyN79dCDw4eqXazNY/uNiAxWznFGTQEAALUjcKPe+FksevydVV6tYVTPy/TDwYNa892PWr31e3Vu0UK92rZVaGBgve4nNCRQDwxPqNdtAgCA8xOBG+eVQD8/XdGunbrFx+u7/fu1/ehR/ZKWpq4tW6pHmzYK8mcoQQAA0LAI3DgvhQQE6OpOnXRJq1baun+/fjx0SDuOHdNlbdqoa3y8LGYuXwAAAA2D1IHzWnhwsAZ06aIxvXqpudWq/+zZo2WbN+vX9HSGEgQAAA2CwI0mITosTNd3765hl1yiAItF67Zv18rvvtPR48e9XRoAADjP0aUETUqr6Gi1jIrSr2lp2rxvn1b/8IPaxsToyvbtFRkS4u3yAADAeYjAjSbHbDLpwrg4tbfZ9N/Dh/X9gQN6Z/NmXdSypS5r21YhAQHeLhEAAJxHCNxosvwsFvVs00Zd4uK09eSIJjuPHdPFLVro0jZtCN4AAKBeELjR5AUHBKjfyRFNvtu/X9sOH9bPR4/qohYt1KN1a4XU8xjeAACgaSFwAydFBAfrui5d1LNtW323f79+OnxY208G70tbt673m+cAAICmgcANnOJ0wfunI0fUvlkzdWvVSs3Dw71dIgAA8CEEbqAGFcH7srZt9dPhw9px7Jh22+1qFhamS9q0UnZ+gbdLrJXTcOp4QYHST+TKnlv1v9ziIhWWlKiwpESlDocMSYZhyM9sVkhgoEICAhQcEKAQ/wCFBAQoLChQUSGhigoNUeTJv1EhoQoLDJTJZPL2SwUAoFEjcAN1CA8OVt+OHdXrggv0S1qadhw7ps9+3qHeM/6qqzt20oAuF6n/hRcqPDjYK/WVOhw6lJ2lPXa79trt2nPyv30ZGSpxOKqtHxUSooiQEAX7+5eH6oAAyWSSSVKZ06kThYU6lpOjgpISFRQXq6CkRGVO52n37Wc2KzIkRFGhoYqq/LdSKC9fXv44PDiYu3wCAJocAjfgpgA/P3WNj1fX+HgVlJUo0N9Pq7d+r8927JCf2awebdro8gvaqXe7C9QtPl7+lvr9epU6ynQgM8sVqPdk2LUnPV37MzOrBOIWERFqb7Ppinbt1TIqUjarVbHWcNmsVsWEhZ5VXYWlJcrOL9Dxgnxl5xco++Tf4wUnHxcUKDs/Xz8fOaLs/HzlFRfXuC2zySSzySSL2SyL2ex67DpPfjL8lz+s+jjAz08hAQEK8vdXeHCwokJCFB0aKpvVqhYRkYqLiFCbmGhZg7xz8AMAwOkQuIGzYAu36t6hgzQreaR+OHBQH37/X32xY6deWv+ZtF4K8LPowrg4XdyqpS5sEadW0VGKj45S84gIWYPKu2yYK53pdTidOlFYVB5g8/J1IDNTBzOzdCAjUwcys3QwM0tHso/LcTJYm0wmtYmJVqe45kro0U2d4uLUKa65OsbFeuzizjaKUUlpmXKOF9a5bqmjrPy1nBLOjxcWyOF0yuk05DCcVR5LkmFIkuF6bJzyuKSsTAUlJSosLdXxggLttduVnZ+vorKyKvtvHh6uDrGx6mCLVcfYWHWIjVV7m01B/v71+p4AAOAOAjdwlvwsFj3+zirX9NUdOqlXmwt09PhxpefmKjMvT6u3fq/C0tLTPt9iNsswjPL/athH8MkzudagIHVv1UpRISGKDA1VZHCw/CwWSVJZiVPbDxzR9gNH6vslVvNw8nDZbFa31m2pKA9XU84wDOUUFupwVrYOZWVrT1q6dhw5pp1Hj2rZ5m9UfDKMW8xmdYprrktat1L3Nq10SetWurhVSwXXMd56RGSwWwcZAADUhMAN1KMgf3+1s9nUzmZzzSsuLVVecbHyioqUf/IixVKHQ2UOR3mXiZNdLAL9/BTo56cgf3+FBQXJGhQk/5OhurE49SCjsWoWEqZmHTqpb/uOOlFYqKz8fGXm5SkjN1erv/1e72zaLEkySYoMDZUtLEzNrFY1CwtTs7Aw18FMaEigHhie4MVXAgA4HxC4AQ8L9PdXoL+/YsLCvF1Kk2M2mRQZEqLIkBC1P3kQZBiG8ouLlZGXJ3turjLy8nQwK0u70tIknQzhISFqZrUqPjpSm3fvVfOg8DrPhAMAUBMCN4AmxWQyKSwoSGFBQbqgWTNJJ0N4SYkycnOVkZsre16eDmVl6Ze0NK3fvlOSFGu1qlV0tFpFRal1dLRaRZU/bmYNU1RIqAL8+OcUAHB6/D8EgCbPZDIpLDBQYYGBVUK4LFKfTh20Zdc+HcrO0sHsbG389Vdl5OVV20ZYYKBiwsJcQyOGBgYq2N9fQf7lo6oEB/gr2D9AFvP/uhGZZDrZraj8bLxkco3iYjabZDGZZTabZDaZZTnlb4Cfn6xBgbIGBSksMEhB/v6MiQ4AjRSBGwBOw2QyKTQoUAO7XqQh3btVWZZfXOwaQSYzt7xrSmZunjJy85SZl6fDOcddY5gXFJeoqIYLZ+uTxWyWNShIUaEhiouMUFxkhFpERiguIkJxUZFqGRmp1jFRigoNrTOYuzsaDQDAPQRuAKiFuxeK+suiFtYItbBGSC2qLjMMQ2VOp8ocDteoNOUjIBquu3zq5LyKx86KEWwqreOsNF3mcKjE4VBJWdn//nM4VFRaqj1pdm07eFj5xcVyGlXHwPG3WGQ9eVFueFCQrCdHwQk/Oc/PYtEjN448x3cNAFAZgRsAPMxkMsnfYmnwUWcMw1BRaanyi4uVV1ys3KIi5RYV6URhoU4UFupwdna1u4gG+/tr64F9ig0LV8vISMVHRSk+MkrxUZGKDQ+Xn7lxjZwDAL7AJwL36tWr9dJLL6msrEy33HKLJkyY4O2SAKDRM5lMCg4IUHBAgJpZq4+fbhiGCktLqwTx3KIiBfn764eDB/XRtm1yVDpD7mc2q3l4uKLDwlyjv0QGBysyJEQRwSEKO3lTp5CAk38DAxQSEKDQgED6mANo0hp94E5LS9Nzzz2nFStWKCAgQGPHjtWVV16pjh07ers0APBpJpPpZEAOUPPwcNf8R24cKbs9V6UOh9JPnNCh7GwdOZ6tw9nHdeR4trILCmQ/cUK/HDum44WFbvVRN0kKORn+QwMDy/9Wmg7xrwjogQo95W9IQEC1ecEB/jKbzHXuFwAag0YfuDdu3Kg+ffooMjJSkpSQkKDU1FTdfffd3i0MAM5z/hZLeZeSqNrvGlpUWqrjBQXKr7hQtKT8b35xiQpLSpRfUlz+t9J0+XrlQzEeyMw8uX5xjXdmPZ2Kg4XKAT4kMLD87ynBPfjkmfaQwOp//UIsyi8ulr/FIj+LudEH+fJ+/P/r0+80TnnsrGVZpcfuznMaTrefaxgnrz+QofL/VSyTpKrLK+ZVXGdQMWpPxUg9p3tc07zKo//UtF5GSa6OHy+oc3vu7K/yPIvJzK83qFOjD9zp6emyVbprX2xsrH788Ue3n282N/yXoGKfESHBDb5vb2sqrzkkuPwmKE3l9VbWlF5zU23nModDNlv1Lii1aa3oetm30+lUQUmpCkqKVVhcKaQXVw3tBaeZV/63WNlFBTqSk6OCkuKzGiHGbDLJYjbLz2yWxWyWv8Xyv2mL5X/hqtLFrhUqLnqtPL/yvIrp/wXVk6HU6awSYlV5WZV5hqpuDY1B+efDIovZdPLzYpGf2SSLxSL/k/P9Kn2O/CwW1+er4nNlVtW8cmpLV7542umsehDkqDTPOHnQ5VDF4/L5DqdTMiSHTl3XKYchGZUOrByuz6NcB1zlBxlmWUySyWyWxWSS2Ww+edBRMZyp2fXXIpNM5vJlpsrLTq5fZRsynRzy9H/rWUxmmU1yfd+qvBunfPcqf8Xa2KJ142WXN/hBUF15s9EHbqfTWeVNMwzjjN7EqKhQT5RVq5iY8jsK3jt8cIPv29ua2mtuaq9X4jXDs8xms8KCAhUWFOjtUgCg3jTu380kxcXFyW63u6btdrtiY2O9WBEAAADgvkYfuK+66ip9/fXXysrKUmFhoT766CP179/f22UBAAAAbmn0XUqaN2+u+++/X5MmTVJpaanGjBmj7t27e7ssAAAAwC0m49SrOQAAAADUm0bfpQQAAADwZQRuAAAAwIMI3AAAAIAHEbgBAAAADyJwAwAAAB5E4D5Lq1ev1rBhwzRkyBC98cYb1ZZv375dycnJSkhI0IwZM1RWVuaFKnGu6mrnTz75RElJSRo5cqTuuusu5eTkeKFKnKu62rnC+vXrNXDgwAasDPWprnbes2ePJk6cqJEjR+oPf/gD32cfVVc7//TTT7rhhhs0cuRI3XHHHTpx4oQXqkR9yMvL04gRI3To0KFqyxpdDjNwxo4dO2YMGDDAyM7ONvLz843ExETjl19+qbLO8OHDje+++84wDMN4+OGHjTfeeMMLleJc1NXOubm5xtVXX20cO3bMMAzDeP75543HH3/cW+XiLLnzfTYMw7Db7cbQoUONAQMGeKFKnKu62tnpdBpDhgwxPv/8c8MwDOOZZ54x5s2b561ycZbc+T6PGzfOWL9+vWEYhvHkk08azz77rDdKxTn6/vvvjREjRhhdu3Y1Dh48WG15Y8thnOE+Cxs3blSfPn0UGRmpkJAQJSQkKDU11bX88OHDKioqUo8ePSRJycnJVZbDN9TVzqWlpZo9e7aaN28uSercubOOHj3qrXJxlupq5wozZ87U3Xff7YUKUR/qaueffvpJISEhrjsZ33nnnZowYYK3ysVZcuf77HQ6lZ+fL0kqLCxUUFCQN0rFOVq2bJlmz56t2NjYassaYw4jcJ+F9PR02Ww213RsbKzS0tJqXG6z2aosh2+oq52joqI0ePBgSVJRUZEWL16sQYMGNXidODd1tbMk/etf/9LFF1+sSy+9tKHLQz2pq50PHDigZs2aafr06Ro9erRmz56tkJAQb5SKc+DO93natGmaOXOm+vXrp40bN2rs2LENXSbqwZw5c9S7d+/TLmuMOYzAfRacTqdMJpNr2jCMKtN1LYdvcLcdc3Nzdfvtt6tLly4aPXp0Q5aIelBXO+/atUsfffSR7rrrLm+Uh3pSVzuXlZXpm2++0bhx4/Tee++pdevWeuqpp7xRKs5BXe1cVFSkGTNmaMmSJfrqq680fvx4/eUvf/FGqfCgxpjDCNxnIS4uTna73TVtt9ur/KRx6vKMjIzT/uSBxq2udpbKj6LHjx+vzp07a86cOQ1dIupBXe2cmpoqu92uG264QbfffrurzeFb6mpnm82mtm3b6pJLLpEkjRgxQj/++GOD14lzU1c779q1S4GBgerevbsk6be//a2++eabBq8TntUYcxiB+yxcddVV+vrrr5WVlaXCwkJ99NFHrn5/khQfH6/AwEBt3bpVkpSSklJlOXxDXe3scDh055136vrrr9eMGTO8fvSMs1NXO997771au3atUlJStHjxYsXGxurf//63FyvG2airnXv27KmsrCzt2LFDkrRu3Tp17drVW+XiLNXVzm3bttWxY8e0Z88eSdKnn37qOsjC+aMx5jA/r+7dRzVv3lz333+/Jk2apNLSUo0ZM0bdu3fXbbfdpnvvvVeXXHKJ5s+fr5kzZyovL09du3bVpEmTvF02zlBd7Xzs2DH9/PPPcjgcWrt2rSSpW7dunOn2Me58n+H73GnnhQsXaubMmSosLFRcXJzmzZvn7bJxhtxp5yeffFL33XefDMNQTEyM5s6d6+2yUU8acw4zGYZheLUCAAAA4DxGlxIAAADAgwjcAAAAgAcRuAEAAAAPInADAAAAHkTgBgAAADyIYQHh8w4dOqTBgwfrwgsvrDI/IyNDCQkJmjVrlpcqA85fzzzzjPr27at+/fo1yP6WLFmi1NRUlZSUSJIuvfRSzZ49u0H2jZqtWrVKb7/9toqKimQYhtq0aaP58+fLz69xx4vZs2fryy+/VGJiou6///7TrrNp0yY9/vjjev/9989qH0899ZQGDBigK6+88lxKxXmicX8jADcFBQUpJSWlyrwFCxYoOzvbSxUB56/vv/9eu3fv1tSpUxtkf6tWrdKRI0f05ptvcoOpRmTr1q365JNPtGTJEvn7+3u7nDPy9ttva/369YqLi/PYPqZMmaLx48frnXfeUVBQkMf2A99AlxI0CRMnTtSTTz6pMWPG6De/+Y3+9re/uZZ98sknGjVqlEaOHKlx48a5bue8YMECPfbYY671Kk/n5uZq2rRpSk5OVmJioubOnauysjJJUufOnZWVleV6XsX0bbfdpqSkJHXr1k0JCQlKSkrSzp07XesZhqGEhARt2LDBNW/GjBl67bXXtGDBAvXp00dJSUkaOHCgHnzwQTmdTknld8S78cYbNWrUKI0dO1bfffedq94HH3xQN998sxISEvSnP/1JeXl5kqS0tDRNmTLFVf/f//53SeW/FvTs2dO1/8rTtb0fv/zyiyZOnKjExESNHDlSK1eudK23fPlyDR8+XImJiZo0aZKOHj2qGTNmKCkpST179tTAgQOVlJSkL7/8UhMnTlRqaqok6eDBg7r00ku1YsWK07bnwIEDlZiYqOuuu05Lliw5bbt37txZiYmJGjFihAYNGuS6hfOpr6XCtGnT9PLLL0uSysrK1LNnTyUlJblqrajts88+09ixY5WcnKzrrrtOzz//vCRp4MCB+u9//+vaXuXpmtqpssrv9/vvv68hQ4Zo+/bt2rRpk7p37+6q5ZprrtG0adNqraWm976m+U6nU0888YRuvPFGDRs2TNdff73rLm2nWrBggX77299We89Ond69e7d+//vfKzk5WUlJSVq+fLnrdXbu3Fm///3vXc/bsmWLOnfurAULFlTb3xdffKHo6GglJSUpMTFRkydPVlpammt/11xzjeu96d69uzZt2qRNmzZpxIgRrm3MmjVLAwcO1NGjR5WQkKClS5dWaaO9e/fq+uuv1zvvvKOSkhLNnTtXo0eP1siRIzVt2jTXd+fU9+Gxxx6TYRiaMmWKbr/99mrr1PSZmDlzpp577jnX/JSUFE2ZMkXS/z63SUlJSkhI0MCBA6u9t6tXr9bQoUM1fPhw3XzzzTp48GC1fdf0Pavp+1+x74SEBNf00aNHddFFF7k+b6e2S7t27TR27FglJibqlltu0e7duyVJpaWlevzxxzVs2DAlJiZqxowZrvfQ3Tar6X18+eWXXfXk5+erc+fOys/PlyS98847Sk5O1qhRo3Trrbe66qls/PjxMgxDt912m7Zs2VLrd6jCli1bNGbMGCUnJys5Odl1s7PaPitWq1U9e/bU22+/fdrXhKaFwI0mY+/evXrzzTf13nvvac2aNfrss8+0e/duzZ49WwsWLNCqVat077336q677jrt/7lWNnfuXHXt2lUrVqzQypUrlZ2drVdffbXW5/zjH/9QSkqKYmNjNX/+fKWkpKhz586u5SaTSePGjdOyZcskSXl5eVq3bp1Gjx4tSRo2bJhSUlL00Ucf6ccff9Q333yjffv26bnnntPixYu1cuVKPf7447rnnntUUFAgSdq8ebOef/55ffjhh/Lz89PChQslSVOnTtUNN9ygFStWaPny5dq4caPWrFlzVu9rWVmZJk+erIkTJ2r16tX6xz/+oWeffVbfffedduzYofnz5+uf//ynVq9erYEDB+qll17SnDlzlJKSom7duumhhx5SSkqKrrnmmirb/etf/6qwsLAa9/vQQw9p9erVWrp0qebPn+/qanCq1157Te+//77uuuuuKgdadSksLNRll12mlJQUV61S+YHRK6+8oqeeekorVqzQ22+/rcWLFysrK0sWi8V1IFRZXe10qoyMDD3//PN68803ddFFF0mS2rRp46rl1ltvrbOWmt77mub/8MMPSk9P19tvv601a9Zo9OjR+sc//lGtthMnTmjr1q26+uqrJUkWi0UOh6PaemVlZbr33nv15z//WStWrNDSpUv1yiuv6Pvvv5ckhYSE6OjRo66D05UrV6pVq1anfT8yMzP1wQcfaOnSpVq9erWuuOIKPfjgg67lt956q+u9adOmTbXnb9myRevWrZMktWjRQikpKXr99dd14MABSZLT6dSsWbP08MMP68Ybb9TixYtlsVi0YsUKrVq1yvWdrcny5csVGBioxYsX17jOqSZMmKB3333XdaC+bNkyjR071rX8tddeU0pKymn3e/ToUU2bNk1z5szRBx98oNGjR5/214aavmd1ff+Dg4O1fft2SeXt0rJly9O+hszMTL377rtauHChVq9erQkTJmjy5MkqLS3VSy+9pPT0dFe7OJ3OKnfurKvNzsY333yjlStX6o033tDKlSv1xz/+UXfffXe19f79739LKn+Pe/XqVeN3qLIFCxbod7/7nVasWKG5c+fqP//5jyTV+Vnp16+fPv7443p5ffBtdClBk/Hb3/5W/v7+8vf319ChQ/XVV1+pffv26tOnj1q3bi1J6tu3r6Kjo7Vt27Zat7V+/Xr997//dZ2xKyoqqrL8lltukdl85sezycnJWrhwobKyspSamqrrrrtO4eHhVdYpLCxUfn6+wsPDtWHDBqWnp7sCmFQe3CuCxNChQ9WsWTNJ0pgxYzR37lzdc8892rx5s3JycvTCCy9IkgoKCrRjxw51795dRUVFSkpKklR+lqqyNWvWuM56VvSR37dvn4qLizVkyBBJ5bdWHjJkiL788ktZrVb169dPLVq0kKQqddYmJSVFMTExrpBbm8zMTAUHB9fZZzQjI0NWq7XaazGZTOrdu3e1wHLkyBFFRERU247JZNLf//53rV+/Xu+//752794twzBUWFio9u3ba926dbrkkkuUn5/vOgiorZ26dOlSZftFRUUaNGiQZs+erZiYmFpfU221fP3116d971999dUa2yQiIkJvvfWWDh48qE2bNik0NLTaPvfv3y+bzaaAgABJUvv27fXFF19o0qRJMpvNys3NlVR+kHHgwAFNnz69ymv7+eef1b9/f0nSkCFD9PHHHyspKUlbtmxxhfjTGTVqlOu7MGHCBD3zzDN1HhhL5Wcgn3jiCT344IOuA66goCBNmzZNiYmJcjgcuvXWWzVkyBBXXevXr1dubq42btwoqfx7UFNbvPfee/r444/17rvv1ljDgw8+6OpSkJ6eLkm66KKL1KpVK61fv17t2rVTenq6W/3hlyxZorfeekvNmzdXr169JEkJCQmaPn26iouLFRgYWOvzCwoKavz+Dxs2TFL5wf2HH36oiy66SGvWrNHw4cNddZ9qyJAhrm4ZQ4YM0Zw5c7R371598cUXuv/++11dTSZOnOg6g1+bAwcOuP79adu2raZOner697ku69ev1/79+6scuJw4cULHjx9XZGTkaZ9T23eosuuvv16PPfaY1q1bp6uuukoPPPCAa5+1fVZatWqlvXv3ulU/zm8EbjQZlQOZYRgym81yOp3V+oQahuE661QTp9OpF154QR06dJBU/o965e289tprio6OlqQqZ7HrEh4erqFDh2rVqlVavXp1lYvCKgJiZmamrFarWrVqpa1bt6pv375VfgI9evSoYmNj9fHHH8tisVSpueI1G4aht956S8HBwZKkrKwsBQYGKjs7u0p/+EOHDikxMdG1jWHDhrkuQq3oI+9wOGp8Dy0WS5VlRUVFOnz4sOt9O53jx4/rX//6l5YuXaqHH364xvXmzZunRYsW6fDhw7ruuutqXO+WW26RYRjat2+fZsyYUe21lJaW6oEHHqjSLUKStm/frnbt2lXbXkFBgUaPHq1Bgwapd+/euuGGG/TJJ5/IMAw9/PDDmj17tkaMGKHo6GjXz9xOp7PGdjpVUFCQ3n77bd122226+uqrXeucru9ybbXU9N7XNP/gwYOaM2eOfve73+k3v/mN2rdvr1WrVlXbp8lkqnIWf9y4cdq1a5eSkpIUERGhjIwMXXbZZXI4HLJarVWurag46LHb7a42ePLJJxUaGqoBAwaouLi42v4kqVmzZlW+vw6Hw/U5rum9qbB48WL95je/qfKZy8vL09y5c/XKK69o6tSpmjNnjubOnasvv/xS11xzjZxOp6ZPn65rr71WUnm3hZpqGz16tMLDw/X000/r//7v/067zvz583XJJZdIkqt7iPS/s9wXXHCBbrrppiqvo6bXdOutt6p///764x//WGW+xWJx6yC/tu9/hcGDB2vKlCkaMGCA2rVrp4iIiNMG7mbNmlX7laa0tFROp7Pav61Op7PKAXxNr6/ilxyp/ODwkUceqbHL2OleW1JSkuvg2el0Kj09/bQHzhVq+w5VNnbsWA0YMEAbNmzQl19+qRdffFGpqal1flb8/PzO6uQLzj98CtBkrFq1Sk6nUzk5Ofrwww81cOBA9e3bV1999ZWr/+PXX3+to0eP6tJLL611W/369dOSJUtkGIZKSko0efJkV5/QczVhwgT961//kmEY6t69u2t+RZeSr776SpdccoleeeUV9e3bVxs2bHD1U/z88881cuRI1xn3Tz/9VLm5uXI6nVq2bJkGDBigsLAw9ejRw9UF5sSJExo3bpw+/fTTs6q3ffv28vPz00cffSSpvH/42rVrddVVV+nKK6/U119/7fo/67feekvPPPNMrdtbuHChbr/9dtcBS00eeughrVq1Sl9//bV++OEHbdq06bTrVXQpWbNmjR5//PFqXU/8/f0VFhZW7VeKNWvWuP5PtLL9+/crLy9P9913nwYOHKhNmzappKRETqdTF1xwgV577TWtWbNGS5cuVVRUlCTV2U6nuvDCC5WUlOR6r8rKyk57Br+2Wmp672uav2HDBg0YMEDjx49Xt27d9Mknn5y2q0ibNm2UmZnpChUhISF6+umntXbtWi1btkxXXHGFJKldu3ZVDt6OHj2qESNGVPn1qHPnzsrMzNSSJUtcXadOZ8iQIVq5cqXr7Pkrr7yiK664QlarVaWlpTX+umG32/Xxxx/rjjvuqDJ/4cKF6tu3r+sMcVhYmB5++GE9+uijKikpUb9+/fTGG2+43stHHnlEzz77bI313XXXXfr+++/19ddf17jO6SQkJGj79u1au3atbrjhBkn/+1WptosQL7jgAlksFm3ZskVS+b9tvXr1cuvCRXe+/xEREYqPj9fTTz9da7sMGjRIH330keuztHLlSoWFhenCCy/UNddcozfffNMVwN944w3XLxi1tVllkZGRNX5HTqdfv3764IMPXPW8+eabuuWWW2p9Tm3focrGjh2r7du3Kzk5WY8//rhOnDghu91e52fl0KFDat++vduvAecvznCjySgqKtKYMWOUn5+v8ePHq2/fvpLKh4e6++675XA4FBQUpL///e+urgendqFwOBwaPXq0ZsyYoTlz5igxMVGlpaW66qqrqp1xOltdunRRRERElZ9FK9dScab61ltvVceOHfXYY4/pgQcekGEY8vPz00svveTqCtCsWTPddtttys7O1uWXX64777xTUvkZt8cff1yJiYkqKSnRiBEjNHLkSB06dOiM6/X399eiRYv0xBNPaMGCBXI4HJoyZYr69Okjqby/eMV7Y7PZNHfu3Fq316FDB40aNarO/c6bN08vvfSSSktL1aZNG3Xt2vW0691yyy0ymUwqKirS+PHjXV0hKt7PwsJCtWnTRlOnTnX1MX3hhRdcF5dVOHDggObNm6dPPvlE1113na6//noFBATowgsvVMeOHbV///4a+6LW1U6nc/vtt2vIkCFavHixli5dqjFjxlRbp3PnzjXWUtFX99T3vnnz5qedn5eXpz//+c9KTExUWVmZrr76an300Ueuz1uF8PBw9erVS//5z39Oe0BSISAgQIsWLdKcOXP0z3/+U2VlZfrTn/6kXr16VfmcJSQkaN26ddWG9axs8ODB2rt3r8aMGSOTyaSWLVvqmWee0QMPPKAtW7boD3/4w2mfl5OTo4ULF7raXCoPQCtWrKg21NtVV12lCy64QP/+97911113ucKmw+Go8aLByq/1vvvu07x58/Tuu++6fUYzICBACQkJysjIUHR0tA4cOKA//vGP6tOnT63XL/j7++v555/XY489ppKSEsXExNT5vaqspu9/ZcOGDdP//d//6ZprrtGePXtOu51u3brp3nvvdXVLCgsL06JFi2Q2mzV58mQ9/fTTGjVqlMrKytS9e3c98sgjdbZZRZeS0tJSBQYG6tFHH3Ute+CBBxQYGKjjx4+ruLhYI0aMqBKM+/Xrp9tuu02///3vZTKZFBYWphdffLHWX0Bq+w5V/tw8+OCDmjt3rp5//nmZTCbdfffdatWqVZ2flS+//FJDhw6tuTHQZJiMU383Ac5DEydO1IQJE875H75p06Zp9OjRHh1X9cCBA67ROip+8j0bFV0+GIf8zNXUzgMHDnRdfNeUffvtt/r73/9+RhcJorqCggLdfPPNmjVrlnr06OHtclDP8vLyNHbsWL377rt19q3H+Y8uJcAZ6N69e50Xsp2LF154QePGjdMjjzxyTmEb52bcuHHq2LFjtfkzZ870QjWNz2WXXaZ27drpiy++8HYpPuvLL7/Uddddp2uuuYawfZ5asGCBpk+fTtiGJM5wAwAAAB7FGW4AAADAgwjcAAAAgAcRuAEAAAAPInADAAAAHkTgBgAAADyIwA0AAAB40P8HjDZgaUNjdOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(pd.Series(y_pred[:, 1]), bins=10, kde=True, color='#1c6b72')\n",
    "ax.set_xlabel('Процент уверенности в выдаче класса (чем ближе к нулю тем больше false)')\n",
    "ax.set_ylabel('Распределение');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e313283d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_class_new = np.where(y_pred[:, 1] > 0.1, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "242c30f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6779661016949152"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(y_test, y_pred_class_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ad6483b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(y_pred_class_new).hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "173600fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = confusion_matrix(y_test, y_pred_class_new)\n",
    "sns.heatmap(mat, annot=True, cmap='Blues');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "606acd6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % (roc_auc))\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb398372",
   "metadata": {},
   "source": [
    "**Общий вывод:**\n",
    "\n",
    "Благодаря смещению границы мы смогли значительо повысить recall. Это позволяет с высокой степенью вероятности быть убежденным, что среди определенных (найденных) пользователей точно будут почти все те, кто пройдут курс до конца. Эо позволит придумать способ для более персонального взаимодействия с аудиторией. По результату исследования видно, что метод рандомных деревьев лучше всего справился с задачей поиска потенциальных окончателей курса. При этом выбранные пользователи с высоким шансом прохождения составляют менее одной пятой общего числа людей, записавшихся на курс. В процссе иследования positive классом считался класс потенциальных окончателей курса на Степике."
   ]
  }
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