ml_in_business.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Привет, меня зовут Артем Хуршудов. Сегодня я проверю твой проект.\n",
    "<br> Дальнейшее общение будет происходить на \"ты\" если это не вызывает никаких проблем.\n",
    "<br> Желательно реагировать на каждый мой комментарий ('исправил', 'не понятно как исправить ошибку', ...)\n",
    "<br> Пожалуйста, не удаляй комментарии ревьюера, так как они повышают качество повторного ревью.\n",
    "\n",
    "Комментарии будут в <font color='green'>зеленой</font>, <font color='blue'>синей</font> или <font color='red'>красной</font> рамках:\n",
    "\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Если все сделано отлично\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет: </b> Если можно немного улучшить\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\">\n",
    "<b>Ошибка:</b> Если требуются исправления. Работа не может быть принята с красными комментариями.\n",
    "</div>\n",
    "\n",
    "### <font color='orange'>Общее впечатление</font>\n",
    "* Большое спасибо за проделанную работу. Видно, что приложено много усилий.\n",
    "* Выводы и рассуждения получились содержательными, их было интересно читать.\n",
    "* Радует, что тебе удалось разобраться с техникой бутстрапа.\n",
    "* Отлично, что доверительный интервал и риск убытков посчитаны верно.\n",
    "* Я оставил несколько советов, надеюсь, что они будут учтены в будущих проектах.\n",
    "* Редко, кто сдает эту работу с первого раза, молодец!\n",
    "* Проект зачтен. Удачи в будущих проектах!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Описание проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Допустим, вы работаете в добывающей компании «ГлавРосГосНефть». Нужно решить, где бурить новую скважину.\n",
    "\n",
    "Вам предоставлены пробы нефти в трёх регионах: в каждом 10 000 месторождений, где измерили качество нефти и объём её запасов. Постройте модель машинного обучения, которая поможет определить регион, где добыча принесёт наибольшую прибыль. Проанализируйте возможную прибыль и риски техникой *Bootstrap.*\n",
    "\n",
    "Шаги для выбора локации:\n",
    "\n",
    "- В избранном регионе ищут месторождения, для каждого определяют значения признаков;\n",
    "- Строят модель и оценивают объём запасов;\n",
    "- Выбирают месторождения с самым высокими оценками значений. Количество месторождений зависит от бюджета компании и стоимости разработки одной скважины;\n",
    "- Прибыль равна суммарной прибыли отобранных месторождений."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Содержание\n",
    "* [Загрузка и подготовка данных](#open_file)\n",
    "* [Обучение и проверка модели](#train_model)\n",
    "* [Подготовка к расчёту прибыли](#calculation_preparation)\n",
    "* [Расчёт прибыли и рисков](#calculate_risks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import f1_score, roc_auc_score, mean_squared_error, r2_score\n",
    "\n",
    "from numpy.random import RandomState\n",
    "\n",
    "RANDOM_STATE = RandomState(12345) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Отлично, что все импорты собраны в первой ячейке ноутбука! Если у того, кто будет запускать твой ноутбук будут отсутствовать некоторые библиотеки, то он это увидит сразу, а не в процессе!\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PredictionData(object):\n",
    "    __slots__ = (        \n",
    "        'features_train', \n",
    "        'target_train', \n",
    "        'features_valid', \n",
    "        'target_valid',\n",
    "    )\n",
    "    \n",
    "    def __init__(\n",
    "        self, features_train, target_train, \n",
    "        features_valid, target_valid, \n",
    "    ):\n",
    "        self.features_train = features_train\n",
    "        self.target_train = target_train \n",
    "        self.features_valid = features_valid\n",
    "        self.target_valid = target_valid\n",
    "        \n",
    "    def __str__(self):\n",
    "        return '\\n'.join('размер {}: {}'.format(name, data.shape) for name, data in (\n",
    "            ('features_train', self.features_train),\n",
    "            ('target_train', self.target_train),\n",
    "            ('features_valid', self.features_valid),\n",
    "            ('target_valid', self.target_valid),\n",
    "        ))\n",
    "    \n",
    "    def __iter__(self):\n",
    "        return iter((\n",
    "            self.features_train,\n",
    "            self.target_train,\n",
    "            self.features_valid,\n",
    "            self.target_valid,\n",
    "        ))\n",
    "    \n",
    "class ProfitAndVolume(object):\n",
    "    __slots__ = (        \n",
    "        'profit', \n",
    "        'volume', \n",
    "    )\n",
    "    \n",
    "    def __init__(self, profit, volume):\n",
    "        self.profit = profit\n",
    "        self.volume = volume \n",
    "        \n",
    "    def __str__(self):\n",
    "        return '\\n'.join((\n",
    "            'Прибыль (млн. руб): {:.2f}'.format(to_mil(self.profit)),\n",
    "            'Суммарный объем сырья (тыс. баррелей): {:.2f}'.format(self.volume),\n",
    "        ))\n",
    "    \n",
    "    def __iter__(self):\n",
    "        return iter((self.profit, self.volume))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_df(df):\n",
    "    rows_count, columns_count = df.shape\n",
    "    print('Количество стобцов:', columns_count)\n",
    "    print('Количество строк:', rows_count)\n",
    "    display(df.head(10))\n",
    "    display(df.describe())\n",
    "    df.info()\n",
    "    \n",
    "def to_mil(num):\n",
    "    return num / 1e6 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"open_file\"></a>\n",
    "## Загрузка и подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Описание данных**:\n",
    "- *id* — уникальный идентификатор скважины\n",
    "- *f0*, *f1*, *f2* — три признака точек (неважно, что они означают, но сами признаки значимы);\n",
    "- *product* — объём запасов в скважине (тыс. баррелей)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Загрузим данные из 3-х датасетов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "geo_df_0 = pd.read_csv('/datasets/geo_data_0.csv') \n",
    "geo_df_1 = pd.read_csv('/datasets/geo_data_1.csv') \n",
    "geo_df_2 = pd.read_csv('/datasets/geo_data_2.csv')\n",
    "\n",
    "dfs = (geo_df_0, geo_df_1, geo_df_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем данные в них"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 5\n",
      "Количество строк: 100000\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>txEyH</td>\n",
       "      <td>0.705745</td>\n",
       "      <td>-0.497823</td>\n",
       "      <td>1.221170</td>\n",
       "      <td>105.280062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2acmU</td>\n",
       "      <td>1.334711</td>\n",
       "      <td>-0.340164</td>\n",
       "      <td>4.365080</td>\n",
       "      <td>73.037750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>409Wp</td>\n",
       "      <td>1.022732</td>\n",
       "      <td>0.151990</td>\n",
       "      <td>1.419926</td>\n",
       "      <td>85.265647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>iJLyR</td>\n",
       "      <td>-0.032172</td>\n",
       "      <td>0.139033</td>\n",
       "      <td>2.978566</td>\n",
       "      <td>168.620776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Xdl7t</td>\n",
       "      <td>1.988431</td>\n",
       "      <td>0.155413</td>\n",
       "      <td>4.751769</td>\n",
       "      <td>154.036647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>wX4Hy</td>\n",
       "      <td>0.969570</td>\n",
       "      <td>0.489775</td>\n",
       "      <td>-0.735383</td>\n",
       "      <td>64.741541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>tL6pL</td>\n",
       "      <td>0.645075</td>\n",
       "      <td>0.530656</td>\n",
       "      <td>1.780266</td>\n",
       "      <td>49.055285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>BYPU6</td>\n",
       "      <td>-0.400648</td>\n",
       "      <td>0.808337</td>\n",
       "      <td>-5.624670</td>\n",
       "      <td>72.943292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>j9Oui</td>\n",
       "      <td>0.643105</td>\n",
       "      <td>-0.551583</td>\n",
       "      <td>2.372141</td>\n",
       "      <td>113.356160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>OLuZU</td>\n",
       "      <td>2.173381</td>\n",
       "      <td>0.563698</td>\n",
       "      <td>9.441852</td>\n",
       "      <td>127.910945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        f0        f1        f2     product\n",
       "0  txEyH  0.705745 -0.497823  1.221170  105.280062\n",
       "1  2acmU  1.334711 -0.340164  4.365080   73.037750\n",
       "2  409Wp  1.022732  0.151990  1.419926   85.265647\n",
       "3  iJLyR -0.032172  0.139033  2.978566  168.620776\n",
       "4  Xdl7t  1.988431  0.155413  4.751769  154.036647\n",
       "5  wX4Hy  0.969570  0.489775 -0.735383   64.741541\n",
       "6  tL6pL  0.645075  0.530656  1.780266   49.055285\n",
       "7  BYPU6 -0.400648  0.808337 -5.624670   72.943292\n",
       "8  j9Oui  0.643105 -0.551583  2.372141  113.356160\n",
       "9  OLuZU  2.173381  0.563698  9.441852  127.910945"
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.500419</td>\n",
       "      <td>0.250143</td>\n",
       "      <td>2.502647</td>\n",
       "      <td>92.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.871832</td>\n",
       "      <td>0.504433</td>\n",
       "      <td>3.248248</td>\n",
       "      <td>44.288691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-1.408605</td>\n",
       "      <td>-0.848218</td>\n",
       "      <td>-12.088328</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-0.072580</td>\n",
       "      <td>-0.200881</td>\n",
       "      <td>0.287748</td>\n",
       "      <td>56.497507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.502360</td>\n",
       "      <td>0.250252</td>\n",
       "      <td>2.515969</td>\n",
       "      <td>91.849972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.073581</td>\n",
       "      <td>0.700646</td>\n",
       "      <td>4.715088</td>\n",
       "      <td>128.564089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>2.362331</td>\n",
       "      <td>1.343769</td>\n",
       "      <td>16.003790</td>\n",
       "      <td>185.364347</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        0.500419       0.250143       2.502647      92.500000\n",
       "std         0.871832       0.504433       3.248248      44.288691\n",
       "min        -1.408605      -0.848218     -12.088328       0.000000\n",
       "25%        -0.072580      -0.200881       0.287748      56.497507\n",
       "50%         0.502360       0.250252       2.515969      91.849972\n",
       "75%         1.073581       0.700646       4.715088     128.564089\n",
       "max         2.362331       1.343769      16.003790     185.364347"
      ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      "id         100000 non-null object\n",
      "f0         100000 non-null float64\n",
      "f1         100000 non-null float64\n",
      "f2         100000 non-null float64\n",
      "product    100000 non-null float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(geo_df_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 5\n",
      "Количество строк: 100000\n"
     ]
    },
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       "      <td>0</td>\n",
       "      <td>kBEdx</td>\n",
       "      <td>-15.001348</td>\n",
       "      <td>-8.276000</td>\n",
       "      <td>-0.005876</td>\n",
       "      <td>3.179103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>62mP7</td>\n",
       "      <td>14.272088</td>\n",
       "      <td>-3.475083</td>\n",
       "      <td>0.999183</td>\n",
       "      <td>26.953261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>vyE1P</td>\n",
       "      <td>6.263187</td>\n",
       "      <td>-5.948386</td>\n",
       "      <td>5.001160</td>\n",
       "      <td>134.766305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>KcrkZ</td>\n",
       "      <td>-13.081196</td>\n",
       "      <td>-11.506057</td>\n",
       "      <td>4.999415</td>\n",
       "      <td>137.945408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>AHL4O</td>\n",
       "      <td>12.702195</td>\n",
       "      <td>-8.147433</td>\n",
       "      <td>5.004363</td>\n",
       "      <td>134.766305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>HHckp</td>\n",
       "      <td>-3.327590</td>\n",
       "      <td>-2.205276</td>\n",
       "      <td>3.003647</td>\n",
       "      <td>84.038886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>h5Ujo</td>\n",
       "      <td>-11.142655</td>\n",
       "      <td>-10.133399</td>\n",
       "      <td>4.002382</td>\n",
       "      <td>110.992147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>muH9x</td>\n",
       "      <td>4.234715</td>\n",
       "      <td>-0.001354</td>\n",
       "      <td>2.004588</td>\n",
       "      <td>53.906522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>YiRkx</td>\n",
       "      <td>13.355129</td>\n",
       "      <td>-0.332068</td>\n",
       "      <td>4.998647</td>\n",
       "      <td>134.766305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>jG6Gi</td>\n",
       "      <td>1.069227</td>\n",
       "      <td>-11.025667</td>\n",
       "      <td>4.997844</td>\n",
       "      <td>137.945408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id         f0         f1        f2     product\n",
       "0  kBEdx -15.001348  -8.276000 -0.005876    3.179103\n",
       "1  62mP7  14.272088  -3.475083  0.999183   26.953261\n",
       "2  vyE1P   6.263187  -5.948386  5.001160  134.766305\n",
       "3  KcrkZ -13.081196 -11.506057  4.999415  137.945408\n",
       "4  AHL4O  12.702195  -8.147433  5.004363  134.766305\n",
       "5  HHckp  -3.327590  -2.205276  3.003647   84.038886\n",
       "6  h5Ujo -11.142655 -10.133399  4.002382  110.992147\n",
       "7  muH9x   4.234715  -0.001354  2.004588   53.906522\n",
       "8  YiRkx  13.355129  -0.332068  4.998647  134.766305\n",
       "9  jG6Gi   1.069227 -11.025667  4.997844  137.945408"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.141296</td>\n",
       "      <td>-4.796579</td>\n",
       "      <td>2.494541</td>\n",
       "      <td>68.825000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>8.965932</td>\n",
       "      <td>5.119872</td>\n",
       "      <td>1.703572</td>\n",
       "      <td>45.944423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-31.609576</td>\n",
       "      <td>-26.358598</td>\n",
       "      <td>-0.018144</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-6.298551</td>\n",
       "      <td>-8.267985</td>\n",
       "      <td>1.000021</td>\n",
       "      <td>26.953261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.153055</td>\n",
       "      <td>-4.813172</td>\n",
       "      <td>2.011479</td>\n",
       "      <td>57.085625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>8.621015</td>\n",
       "      <td>-1.332816</td>\n",
       "      <td>3.999904</td>\n",
       "      <td>107.813044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>29.421755</td>\n",
       "      <td>18.734063</td>\n",
       "      <td>5.019721</td>\n",
       "      <td>137.945408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        1.141296      -4.796579       2.494541      68.825000\n",
       "std         8.965932       5.119872       1.703572      45.944423\n",
       "min       -31.609576     -26.358598      -0.018144       0.000000\n",
       "25%        -6.298551      -8.267985       1.000021      26.953261\n",
       "50%         1.153055      -4.813172       2.011479      57.085625\n",
       "75%         8.621015      -1.332816       3.999904     107.813044\n",
       "max        29.421755      18.734063       5.019721     137.945408"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      "id         100000 non-null object\n",
      "f0         100000 non-null float64\n",
      "f1         100000 non-null float64\n",
      "f2         100000 non-null float64\n",
      "product    100000 non-null float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(geo_df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество стобцов: 5\n",
      "Количество строк: 100000\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>fwXo0</td>\n",
       "      <td>-1.146987</td>\n",
       "      <td>0.963328</td>\n",
       "      <td>-0.828965</td>\n",
       "      <td>27.758673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>WJtFt</td>\n",
       "      <td>0.262778</td>\n",
       "      <td>0.269839</td>\n",
       "      <td>-2.530187</td>\n",
       "      <td>56.069697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>ovLUW</td>\n",
       "      <td>0.194587</td>\n",
       "      <td>0.289035</td>\n",
       "      <td>-5.586433</td>\n",
       "      <td>62.871910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>q6cA6</td>\n",
       "      <td>2.236060</td>\n",
       "      <td>-0.553760</td>\n",
       "      <td>0.930038</td>\n",
       "      <td>114.572842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>WPMUX</td>\n",
       "      <td>-0.515993</td>\n",
       "      <td>1.716266</td>\n",
       "      <td>5.899011</td>\n",
       "      <td>149.600746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>LzZXx</td>\n",
       "      <td>-0.758092</td>\n",
       "      <td>0.710691</td>\n",
       "      <td>2.585887</td>\n",
       "      <td>90.222465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>WBHRv</td>\n",
       "      <td>-0.574891</td>\n",
       "      <td>0.317727</td>\n",
       "      <td>1.773745</td>\n",
       "      <td>45.641478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>XO8fn</td>\n",
       "      <td>-1.906649</td>\n",
       "      <td>-2.458350</td>\n",
       "      <td>-0.177097</td>\n",
       "      <td>72.480640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>ybmQ5</td>\n",
       "      <td>1.776292</td>\n",
       "      <td>-0.279356</td>\n",
       "      <td>3.004156</td>\n",
       "      <td>106.616832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>OilcN</td>\n",
       "      <td>-1.214452</td>\n",
       "      <td>-0.439314</td>\n",
       "      <td>5.922514</td>\n",
       "      <td>52.954532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id        f0        f1        f2     product\n",
       "0  fwXo0 -1.146987  0.963328 -0.828965   27.758673\n",
       "1  WJtFt  0.262778  0.269839 -2.530187   56.069697\n",
       "2  ovLUW  0.194587  0.289035 -5.586433   62.871910\n",
       "3  q6cA6  2.236060 -0.553760  0.930038  114.572842\n",
       "4  WPMUX -0.515993  1.716266  5.899011  149.600746\n",
       "5  LzZXx -0.758092  0.710691  2.585887   90.222465\n",
       "6  WBHRv -0.574891  0.317727  1.773745   45.641478\n",
       "7  XO8fn -1.906649 -2.458350 -0.177097   72.480640\n",
       "8  ybmQ5  1.776292 -0.279356  3.004156  106.616832\n",
       "9  OilcN -1.214452 -0.439314  5.922514   52.954532"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.002023</td>\n",
       "      <td>-0.002081</td>\n",
       "      <td>2.495128</td>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.732045</td>\n",
       "      <td>1.730417</td>\n",
       "      <td>3.473445</td>\n",
       "      <td>44.749921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>-8.760004</td>\n",
       "      <td>-7.084020</td>\n",
       "      <td>-11.970335</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>-1.162288</td>\n",
       "      <td>-1.174820</td>\n",
       "      <td>0.130359</td>\n",
       "      <td>59.450441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.009424</td>\n",
       "      <td>-0.009482</td>\n",
       "      <td>2.484236</td>\n",
       "      <td>94.925613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.158535</td>\n",
       "      <td>1.163678</td>\n",
       "      <td>4.858794</td>\n",
       "      <td>130.595027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>7.238262</td>\n",
       "      <td>7.844801</td>\n",
       "      <td>16.739402</td>\n",
       "      <td>190.029838</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  f0             f1             f2        product\n",
       "count  100000.000000  100000.000000  100000.000000  100000.000000\n",
       "mean        0.002023      -0.002081       2.495128      95.000000\n",
       "std         1.732045       1.730417       3.473445      44.749921\n",
       "min        -8.760004      -7.084020     -11.970335       0.000000\n",
       "25%        -1.162288      -1.174820       0.130359      59.450441\n",
       "50%         0.009424      -0.009482       2.484236      94.925613\n",
       "75%         1.158535       1.163678       4.858794     130.595027\n",
       "max         7.238262       7.844801      16.739402     190.029838"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100000 entries, 0 to 99999\n",
      "Data columns (total 5 columns):\n",
      "id         100000 non-null object\n",
      "f0         100000 non-null float64\n",
      "f1         100000 non-null float64\n",
      "f2         100000 non-null float64\n",
      "product    100000 non-null float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "analyze_df(geo_df_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропусков или иных аномалий не видно, посмотрим сколько в датасетах уникальных значений `id`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Загрузка и первичный анализ данных проведены успешно.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальные ли значения \"id\" в датасете 1: False\n",
      "Уникальные ли значения \"id\" в датасете 2: False\n",
      "Уникальные ли значения \"id\" в датасете 3: False\n"
     ]
    }
   ],
   "source": [
    "for idx, df in enumerate(dfs):\n",
    "    print('Уникальные ли значения \"id\" в датасете {}: {}'.format(\n",
    "        idx + 1, \n",
    "        df['id'].unique().shape[0] == df.shape[0],\n",
    "    ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что значения не уникальны, поэтому удалим их "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for df in dfs:\n",
    "    df.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Молодец, что сделал эту проверку и удалил эту колонку. Абсолютно верное решение.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"train_model\"></a>\n",
    "## Обучение и проверка модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию, которая разобъет датасет на обучающую и валидационную выборку"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_df(df):\n",
    "    # Выделим фичи и целевой показатель product\n",
    "    features = df.drop(columns=['product'])\n",
    "    target = df['product']\n",
    "    \n",
    "    # Данные нужно разбить на 2 части: обучающую и валидационную, в соотношении 75:25\n",
    "    features_train, features_valid, target_train, target_valid = train_test_split(features, target, test_size=0.25, random_state=RANDOM_STATE)\n",
    "    \n",
    "    data = PredictionData(\n",
    "        features_train,\n",
    "        target_train,\n",
    "        features_valid,\n",
    "        target_valid, \n",
    "    )\n",
    "    \n",
    "    print(data)\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию для обучения модели линейной регрессии и вычисления RMSE для каждого из датасетов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(df):\n",
    "    features_train, target_train, features_valid, target_valid = split_df(df)\n",
    "    \n",
    "    model = LinearRegression()\n",
    "    model.fit(features_train, target_train)\n",
    "    predictions_valid = pd.Series(model.predict(features_valid), index=features_valid.index)\n",
    "    \n",
    "    print('RMSE: {:.2f}'.format(mean_squared_error(target_valid, predictions_valid) ** 0.5))\n",
    "    print('Средний запас предсказанного сырья в регионе: {:.2f}'.format(predictions_valid.mean()))\n",
    "    \n",
    "    return target_valid, predictions_valid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим модель на каждом из датасетов и сохраним предсказания и правильные ответы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "размер features_train: (75000, 3)\n",
      "размер target_train: (75000,)\n",
      "размер features_valid: (25000, 3)\n",
      "размер target_valid: (25000,)\n",
      "RMSE: 37.58\n",
      "Средний запас предсказанного сырья в регионе: 92.59\n"
     ]
    }
   ],
   "source": [
    "target_valid_0, predictions_valid_0 = train_model(geo_df_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "размер features_train: (75000, 3)\n",
      "размер target_train: (75000,)\n",
      "размер features_valid: (25000, 3)\n",
      "размер target_valid: (25000,)\n",
      "RMSE: 0.89\n",
      "Средний запас предсказанного сырья в регионе: 68.77\n"
     ]
    }
   ],
   "source": [
    "target_valid_1, predictions_valid_1 = train_model(geo_df_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "размер features_train: (75000, 3)\n",
      "размер target_train: (75000,)\n",
      "размер features_valid: (25000, 3)\n",
      "размер target_valid: (25000,)\n",
      "RMSE: 39.96\n",
      "Средний запас предсказанного сырья в регионе: 95.09\n"
     ]
    }
   ],
   "source": [
    "target_valid_2, predictions_valid_2 = train_model(geo_df_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* исходя из значений RMSE погрешность модели меньше всего для 2 региона\n",
    "* среднее количество сырья примерно равно в 1 и 3 регионе (92.59 и 95.09 соотвественно), и больше в 1.4 раза, чем во 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> В целом, все сделано очень хорошо. Молодец, что напечатал размеры полученных наборов. Радует, что были использованы функции.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет: </b> Желательно проводить нормализацию признаков перед обучением линейной модели.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"calculation_preparation\"></a>\n",
    "## Подготовка к расчёту прибыли"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сохраним все ключевые значения для расчетов в переменные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# количество исследуемых точек при разведке\n",
    "RESEARCH_POINTS = 500 \n",
    "\n",
    "# количество лучших исследуемых точек \n",
    "BEST_RESEARCH_POINTS = 200 \n",
    "\n",
    "# бюджет на разработку скважин в регионе\n",
    "BUDGET = 10 * 1e9 \n",
    "\n",
    "# цена за 1000 баррелей сырья\n",
    "PRODUCT_PRICE = 1000 * 450\n",
    "\n",
    "# максимальная вероятность убытков\n",
    "MAX_LOSS = 0.025\n",
    "\n",
    "# количество выборок для bootstrap \n",
    "BOOTSTRAP_SAMPLES = 1000 \n",
    "\n",
    "# доверительный интервал \n",
    "CONF_INTERVAL = 0.95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Радует, что в названиях константных переменных использованы только большие буквы.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассчитаем достаточный объём сырья для безубыточной разработки новой скважины, перед этим надо рассчитать бюджет на разработку одной скважины."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Минимальный объём сырья для безубыточной разработки новой скважины: 111.11\n"
     ]
    }
   ],
   "source": [
    "budget_per_point = BUDGET / BEST_RESEARCH_POINTS\n",
    "min_product = budget_per_point / PRODUCT_PRICE\n",
    "print('Минимальный объём сырья для безубыточной разработки новой скважины: {:.2f}'.format(min_product))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Точка безубыточности найдена верно.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Напишем функцию для расчёта прибыли по выбранным скважинам и предсказаниям модели"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_profit(target_valid, predictions_valid):\n",
    "    research_points = predictions_valid.sample(RESEARCH_POINTS, replace=True, random_state=RANDOM_STATE)\n",
    "    best_research_points = research_points.sort_values(ascending = False)[:BEST_RESEARCH_POINTS]\n",
    "    \n",
    "    product_volume = target_valid[best_research_points.index].sum()\n",
    "    \n",
    "    profit = PRODUCT_PRICE * product_volume - BUDGET\n",
    "    \n",
    "    return ProfitAndVolume(profit, product_volume)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Совет: </b> В целом верно, но семплинг можно было сделать до функции.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выполним ее для каждого из 3-х регионов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Прибыль (млн. руб): 334.51\n",
      "Суммарный объем сырья (тыс. баррелей): 22965.57\n"
     ]
    }
   ],
   "source": [
    "print(calculate_profit(target_valid_0, predictions_valid_0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Прибыль (млн. руб): 338.61\n",
      "Суммарный объем сырья (тыс. баррелей): 22974.68\n"
     ]
    }
   ],
   "source": [
    "print(calculate_profit(target_valid_1, predictions_valid_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Прибыль (млн. руб): 407.97\n",
      "Суммарный объем сырья (тыс. баррелей): 23128.83\n"
     ]
    }
   ],
   "source": [
    "print(calculate_profit(target_valid_2, predictions_valid_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**: \n",
    "* получили, что минимальный объем сырья для безубыточной разработки новой скважины - 111.11 тыс. бареллей. Во всех 3-х регионах среднее предсказанное количество сырья меньше, следовательно не все скважины будут безубыточными.  \n",
    "* взяли 500 точек из предсказаний, из которых отобрали 200 лучших и получили для них суммарный объем сырья, а из него вычислили прибыль. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Полностью согласен с замечанием по поводу убыточности скважин.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"calculate_risks\"></a>\n",
    "## Расчёт прибыли и рисков "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим технику Bootstrap, чтобы найти распределение прибыли."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_profit_bootstrap(target_valid, predictions_valid):\n",
    "    values = []\n",
    "\n",
    "    for i in range(BOOTSTRAP_SAMPLES): \n",
    "        profit_and_volume = calculate_profit(target_valid, predictions_valid)     \n",
    "        values.append(profit_and_volume.profit)\n",
    "            \n",
    "    values = pd.Series(values)\n",
    "    profit_mean = values.mean()\n",
    "\n",
    "    conf_lower = values.quantile((1 - CONF_INTERVAL) / 2)\n",
    "    conf_upper = values.quantile((1 + CONF_INTERVAL) / 2) \n",
    "    \n",
    "    loss_probability = (values < 0).sum() / BOOTSTRAP_SAMPLES \n",
    "    \n",
    "    print('Средняя прибыль (млн. руб): {:.2f}'.format(to_mil(profit_mean)))\n",
    "    print('95% доверительный интервал (млн. руб): ({:.2f}, {:.2f})'.format(to_mil(conf_lower), to_mil(conf_upper)))\n",
    "\n",
    "    if loss_probability < MAX_LOSS: \n",
    "        print('Вероятность убытков равна {}, что меньше чем максимальная допустимая вероятность {}'.format(loss_probability, MAX_LOSS))\n",
    "    else:\n",
    "        print('Вероятность убытков равна {}, что больше чем максимальная допустимая вероятность {}'.format(loss_probability, MAX_LOSS))    \n",
    "\n",
    "    return profit_mean, conf_lower, conf_upper, loss_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "profit_and_risks = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль (млн. руб): 394.14\n",
      "95% доверительный интервал (млн. руб): (-69.45, 915.50)\n",
      "Вероятность убытков равна 0.061, что больше чем максимальная допустимая вероятность 0.025\n"
     ]
    }
   ],
   "source": [
    "profit_and_risks.append(calculate_profit_bootstrap(target_valid_0, predictions_valid_0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль (млн. руб): 454.46\n",
      "95% доверительный интервал (млн. руб): (64.53, 855.13)\n",
      "Вероятность убытков равна 0.007, что меньше чем максимальная допустимая вероятность 0.025\n"
     ]
    }
   ],
   "source": [
    "profit_and_risks.append(calculate_profit_bootstrap(target_valid_1, predictions_valid_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль (млн. руб): 353.74\n",
      "95% доверительный интервал (млн. руб): (-162.65, 847.76)\n",
      "Вероятность убытков равна 0.076, что больше чем максимальная допустимая вероятность 0.025\n"
     ]
    }
   ],
   "source": [
    "profit_and_risks.append(calculate_profit_bootstrap(target_valid_2, predictions_valid_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>profit_mean</th>\n",
       "      <th>conf_lower</th>\n",
       "      <th>conf_upper</th>\n",
       "      <th>loss_probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3.941415e+08</td>\n",
       "      <td>-6.945057e+07</td>\n",
       "      <td>9.154962e+08</td>\n",
       "      <td>0.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.544582e+08</td>\n",
       "      <td>6.452857e+07</td>\n",
       "      <td>8.551335e+08</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3.537407e+08</td>\n",
       "      <td>-1.626509e+08</td>\n",
       "      <td>8.477618e+08</td>\n",
       "      <td>0.076</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profit_mean    conf_lower    conf_upper  loss_probability\n",
       "0  3.941415e+08 -6.945057e+07  9.154962e+08             0.061\n",
       "1  4.544582e+08  6.452857e+07  8.551335e+08             0.007\n",
       "2  3.537407e+08 -1.626509e+08  8.477618e+08             0.076"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profit_and_risks_df = pd.DataFrame(profit_and_risks, columns = ['profit_mean', 'conf_lower', 'conf_upper', 'loss_probability'])\n",
    "profit_and_risks_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Все статистики посчитаны верно. Радует, что все регионы были собраны в одну таблицу, так мы можем намного проще их сравнить между собой.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Выводы**:\n",
    "* Из таблицы видно, что единственный регион, где вероятность убытков ниже, чем допустимое значение - **второй** (первый, если использовать индексацию с 0), поэтому для разработки скважин надо выбрать его.\n",
    "* Также у второго региона самый узкий доверительный интервал и самая выскокая средняя прибыль."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Успех:</b> Согласен с выбором региона. Молодец, что уточнил по поводу нумерации.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Чек-лист готовности проекта"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поставьте 'x' в выполненных пунктах. Далее нажмите Shift+Enter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [x]  Jupyter Notebook открыт\n",
    "- [x]  Весь код выполняется без ошибок\n",
    "- [x]  Ячейки с кодом расположены в порядке исполнения\n",
    "- [x]  Выполнен шаг 1: данные подготовлены\n",
    "- [x]  Выполнен шаг 2: модели обучены и проверены\n",
    "    - [x]  Данные корректно разбиты на обучающую и валидационную выборки\n",
    "    - [x]  Модели обучены, предсказания сделаны\n",
    "    - [x]  Предсказания и правильные ответы на валидационной выборке сохранены\n",
    "    - [x]  На экране напечатаны результаты\n",
    "    - [x]  Сделаны выводы\n",
    "- [x]  Выполнен шаг 3: проведена подготовка к расчёту прибыли\n",
    "    - [x]  Для всех ключевых значений созданы константы Python\n",
    "    - [x]  Посчитано минимальное среднее количество продукта в месторождениях региона, достаточное для разработки\n",
    "    - [x]  По предыдущему пункту сделаны выводы\n",
    "    - [x]  Написана функция расчёта прибыли\n",
    "- [x]  Выполнен шаг 4: посчитаны риски и прибыль\n",
    "    - [x]  Проведена процедура *Bootstrap*\n",
    "    - [x]  Все параметры бутстрепа соответствуют условию\n",
    "    - [x]  Найдены все нужные величины\n",
    "    - [x]  Предложен регион для разработки месторождения\n",
    "    - [x]  Выбор региона обоснован"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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