Выбор локации для скважины.ipynb

{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\"><div class='alert btn-success'>\n",
    "Максим, оставила еще пару полезных комментарием, надеюсь, пригодятся ))<br>\n",
    "Критичных ошибок нет. Молодец!)<br> \n",
    "Здорово, что обращаешь внимание на советы и применяешь на практике, значит не зря я их раздаю:) <br>\n",
    "С удовольствием принимаю проект!<br> \n",
    "\n",
    "</div></div>\n",
    "\n",
    "Очень рекомендую посмотреть видео от Глеба Михайлова!)<br>\n",
    "**[Линейная Регрессия для Дата Саентиста](https://www.youtube.com/watch?v=QZJ94igWVxQ&t=19s)**     <br> \n",
    "    \n",
    "**[Вот здесь есть отличное объяснение с примером по мультиколлинеарности.](https://habr.com/ru/company/akbarsdigital/blog/592493/)** <br>\n",
    "    \n",
    "**[И здесь интересная статься - коротко о мультиколлинеарности и что с этим делать.](https://medium.com/analytics-vidhya/removing-multi-collinearity-for-linear-and-logistic-regression-f1fa744f3666)** <br>   \n",
    "   \n",
    "**[Если интересна статистика, можно посмотреть короткие видео-лекции Карпова на степике](https://stepik.org/lesson/8095/step/1?unit=1371)**    <br>\n",
    "\n",
    "\n",
    "<br>\n",
    "<div class=\"alert alert-success\"><div class='alert btn-success'>    \n",
    "Надеюсь, ты найдешь для себя что-то интересное и полезное😊  <br>\n",
    "Желаю тебе удачи в дальнейшей учебе! 😉 </div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Максим, привет! Меня зовут Ирина Литвинова. Сегодня я проверю твой проект.<br>\n",
    "<br> Ты можешь обращаться ко мне на \"ты\"😏 Надеюсь, ты не будешь против, если и я буду обращаться к тебе на \"ты\". Но если для тебя это неприемлeмо, дай знать, пожалуйста) <br>\n",
    "<br> Мне очень важна твоя реакция на мои комментарии, особенно <font color='red'>красные</font>. Можешь реагировать, например, так ('исправлено', ...)<br>\n",
    "<br> Ты можешь реагировать на мои комментарии своими, выделяя их цветами и наиболее понравившимся тебе способом оформления, но явно  отличающимся от моих комментариев. <br>\n",
    "Это нужно, чтобы не создавалась путаница😉<br>\n",
    "Например, так:<br>\n",
    "    \n",
    "<div class=\"alert alert-block alert-info\"><b>Комментарий студента</b>\n",
    "    \n",
    "<b>Изменения:</b> Были внесены следующие изменения ...<br></div> \n",
    "\n",
    "\n",
    "<br> Пожалуйста, не удаляй мои комментарии, так как они повышают качество повторного ревью и делают его быстрее)<br>\n",
    "<br> Увидев у тебя неточность, в первый раз я лишь укажу на ее наличие и дам тебе возможность самостоятельно найти и исправить ее. На реальной работе твой руководитель будет поступать также, и я пытаюсь подготовить тебя именно к работе. Но если ты пока не справишься с такой задачей - при следующей проверке я дам более точную подсказку!🤓<br>\n",
    "\n",
    "Для твоего удобства мои комментарии будут в <font color='green'>зеленой</font>, <font color='orange'>желтой</font> или <font color='red'>красной</font> рамках:<br>\n",
    "<br>\n",
    "\n",
    "<div class=\"alert alert-block alert-danger\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Ошибка:</b><br>\n",
    "Если требуются исправления. Работа не может быть принята с красными комментариями.<br></div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Если можно немного улучшить.<br></div>\n",
    "\n",
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "Если все сделано отлично.<br></div>   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выбор локации для скважины"
   ]
  },
  {
   "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": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 Есть введение в проект. Отлично!<br>\n",
    "\n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загрузка и подготовка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объявим все нужные библиотеки для данного проекта:"
   ]
  },
  {
   "attachments": {
    "2023-02-07_10-06-40.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![2023-02-07_10-06-40.png](attachment:2023-02-07_10-06-40.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера V-2</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Максим, у тебя ячейки начинались не с первой. Я проверила - код работает. Но лучше перед отправкой проекта проверять работоспособность кода - это можно сделать, нажав на панели Jupiter Hub 👉 **Kernel** 👉  **Restart & Run All** .<br> \n",
    "Потом обязательно сохрани. Для этого сделай так: 👉  **File**  👉   **Save and Checkpoint** .   \n",
    "Когда сохранишь, пожалуйста, проверь - обрати внимание на сообщение \"**Последняя контрольная точка: несколько секунд назад\n",
    "(автосохранение)**\" <br>  \n",
    "</div>"
   ]
  },
  {
   "attachments": {
    "2022-06-25_09-17-28.png": {
     "image/png": 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TSfv7KKKDfX1bH+kL/WqwfM9I89XocPy3Eg8PjrZcGzq9p/fPv+j762PSqxXsPPbYYyQ4ij2l+R0dHaTU06dP8x9ZtLe3t7W18QLcxa2trcPDw2TzU6dOXbp0SUwdiJB/33zzTdIohc8u7/eh1ZWWln7zzTfUAOlP1IDKykr669mzZyl4F3U/NDTUpuP4vmbtJUptN7hJzfmdjdp82zfcCO3Abz+jr7HYyyVGhrRinY1RY42jd5pH2+pY/me0s2nowhES+siNb6PDXn6kDgDIECz37kY7G/ryplEI1vv6vf1bH40czhu6eHSk6YomlM7Gkabvhy8VRY78pf+931EB7b3y634y2prgxUAkL5Lj7Nmz+at8kkd0cTLlSa+0IopeRaEDAMD4R36OYqT+O+2NB9rlcJwXyRtTX96vyc4uVVNQSQ5dtWoVXexv3Sr/MCEZkncxRcRVVVXsUbni4uIU1gUAAAHi8EzbSPutyOfre17+YTwL97z0w8jBdaOtN1yylhQFv/HGG6RREvGiRYvoej+FxiXv4rfffpsi4hkzZuzcuXMsLzgGAIBAMF08NDRUW1t74/oNlg8dvV0/8PeVPWv+nczbvfKfaKIPvat+PLBvyUib7YdnTjzxxBNk0g0bNkivskyeS5cuUSVLlixJmHN45ZVXHnzwwT179qSQCQEAgMAxx4EuPlb87pZ3afqy8EvzGduBnpGGy8O1FcPXTtMHT8+01tXV3b6d4LaeO+zRY8fnJSRqamocHm8AAIAMIebi7u7ufXv3MRfT1NXVFWyzAAAgq4i5eHBw8PDhw0zEn/z9E1zpAwCASsx8cU9Pz7lz56qqqqR3mwEAAPAbFe8GAgAA4A5cDAAAwQMXAwBA8MDFAAAQPHAxAAAED1wMAADBAxcDAEDwwMUAABA8cDEAAAQPXAwAAMEzqREAAEDQpBIX02IDAICUCPqUTydB78sJBVwMgFKC9mc6CXpfTijgYgCUErQ/00nQ+3JCARcDoJSg/ZlOgt6XEwq4GAClBO3PdBL0vpxQwMUAKCVof6aToPflhAIuBkApQfsznQS9LycUcDEASgnan+kk6H05oYCLAVBK0P5MJ0HvywmFGhd/u+1BmW1nBwZuHVr+4PJDtwYGztLft33rywZ6bhi1qF4qxZunsp3auvS9JDZ3q968wHaXG/UHlzvsugxC643x+4AV7UBspSNQr/Xgg9422qvvjufNmpV33Pz3xX0vzrLOSQtatS/uu+htoRT2sHV30QmoG8BbJR4XCZCz2yx9ST6pY/2HdSd1Lpa0ojE+XOzQMAm7i1V0CK1tto6rz8kmFxvW8xnrSRL7zkvcKvUuPr5p1qwX917wWkliVLnY2nUVuVhRL7JjbS2dI4TZEuOv48DFnExxMUfJl7NshHGpYE4mu9iu1AQdIyAXX9i32IeImKHIxZqLhAPq3cUpEZiL9R5idCT6vPzQWe1/sR5jjZrHX1xsXioqOEhup5x2/Fg6ZasUF/PMhs8t1FZnrsLsT267Szz24tb53ulNF+uHddtWo138HDjLE0JCS+yHW5uzbZu+8/O22Srxg0TfrLwn8GYE4mLtw+J91pBYC5MZm46xOZqvN+Xps/OO68tuMsoIqmWJDnGmKhdrMnLulvVCksjh6jO2z8WZvEdt3WZ2dTHXpB8vrWdaDp+ZmUwch40ZarbRQ2i9tF26kfX2aw3Tm6Q6LrakZFnjHFws6ENFGBgnkW1ZO+si6nMUA9Y0heOecZjJDzDNMS+IpLyVD1hdbPT7W/zEE846szFOG2XNKqqIaFx7mhDvm+ZV7+ILe0mem6whsabdWL7CNKkeOxvK1pY1DCuoXDN4zN3HNsWqVebiW+IuNXuF2W/NvSruXqOrmKee2XmYbfXPQiwinKRCLxLqVHMW895OHwzz8l7EmjoOchR2F1uUIYZ4PhE3LrYoIJh8sbUZoi/cd5cxkzrotrPfbtM/C1/OfiHFxcbO4XtY/9qTrOrYfsdoyFfcXGzthEaDVbtYD2BfXGy/iWfamYrpXhYEbc01azZnLib/mvG1HkcfU+piYb85Xq6ZIYilU0m9y3LUnE5k4bLS7EX6VZeZIvH/vOBr1JLFbF0xKbMw2Wze+HKxeTUhRdB+Ec/F1nOMHz/lLuZrdPxuiLO7WP9ml0Li//1taSIXD1iuQqSLR7H96l3sdjTtV07i4VDmYt2SemLBvHF3jOcnDDTtJnaxHmJb0MordTEXpfVqyegFyy3hcCyUloJZ6/0Js5uZCSW9GtnFZ23HU8XdCOkc1C18y0wcj0cXK7iUTqph4yYuNlpobWei3aVf+8QiYi06PijcLvCNJFxswBMXju1X72IHpVouk+09JLDnKDT/ihneTbYbeV7jYgPFLo7tw23b5EhZPhx66PqtKTL3uFhchWOOwhoXqyF2Pprr1b18cJuUiBtnLhZLmqlG/4h/7066wAnMxfx7XuhAiXaXrsXly4Wsse8XYgldLM+UU35m+wNwsfxMmx6wx3a4GH/xJ1sCfL5YD5OZgkXt8mfdknCxmC/WY23lOQoGu+YwXSyGruZeZZGs7Tkw53yxcBboJ45rvtjhsVF/oHNw67blQjdmD7dZnqEcfy623Ab1fzc5/dZDPAlZM2g/yr/1iN3zVXAfVu6LA0nsLvH2l6qvjcRxsXB5KDXVMlNq8NnYCev7GRP/tx7mZa81wRfMbz1iz04wHZvPUYjPvSVysfgchf3unzIXs1PM/lDEt9YvYKs9nJ6jWH7wkD1HYbmatPQiOVfmO/YfuVhvUah0MQAghlcXj2eC3pccRc8p+wpcDIBSgvZnOglyPwqZXzGnlLnAxQAoJWh/ppNg96SQO8r4oHgALgZAMZLOGjKZoPflhAIuBkApme5fkX7gBfeOARcDoJSg/ZlOgpZbZiN1DLgYABXwMzCe18IZSB/wgruU4WIAfEc88TJavhJByy1TcdQxXAyAj0inHJ2Hks5uZTK9wDvxpAwXA+AXkoUZkn/rM5luEJ8eJxylDBcD4CN2EbMzUFLwTRt1mUMXSAJHQUtGTtHFzc3NVGnQXR2A8Us8EdNJeO7cuUqdbw2+ETgLJhDikeWHmx196gbcyKyHpOLizs5O0rHKn/cAkEHYn44QY+Hi4uIUTjowwaBuwPoD6xvUSVJxMQAgHqMCIzrDw8NDOoODg5FI5Lvvvgu6jSB4qBtQZ6AuwfoGdRK4GIB04i7igYGB8+fPB91GEDzUDagziDqGiwFIJ6KLh3VEEff39587dy7oNoLgoW7AHivmOoaLAUgbjkGxKOLe3l64GER1F1NnEHUMFwOQNuK5mIu4p6fn22+/DbqZIHioG7DnKJiOU3RxW1sbST2NT35QbVSnr2txXAXti2vXrqVrFWo4f/68H9XSfqC9oWb/XLhwIe11nlW7CfE4Y1Chc/r06fLy8rKystLS0lOnTp08efLEiRM7d+5M4aQDEwzqBtQZqEtQx6DuQZ0kFRezJ+PS2Cz20KWva3FcBZ2ld+7cSdcqROi09KNa/2qm/UB7Q5rp0/6ZAJvgSMKguLu7mxpDplbTHjCe+frrr+vq6urr68PhcENDQ1NTUyou9uNcsteZ9rUoWMUEq9mndU2ATXDE0cUsU9zX10fRQFdXV2dnJ1zsNzt27FifCCqT9mU9QddM7e3t1B+oV1DfSPG3HnBxCuvKxJrhYk9IT1Dwu3ZiUNzR0UEnobImZSeky5TLjGVZT1A3oM5AXYI6Bssaw8W+kGzNw5Guji42RYbTUHPEqE2akqkcLh4LCRMULCimUKi8vFxNk7KWjHAxdQMeF/vj4l5HF0RSqDPZs4itMfEaxp+LbxYsvm/y1Nlz5jxK08x7ny+U7y16rLnt89ycnJx7Z7AKjWn21Mk0d3lRl/eag3cx+65K4uDGqznuuiheTe7LL0lcXMwTFBQH3b59u6ysbIzrajuQm7OwIJnekp1khIupG1BnoC7hU46iavOUe2eyk5+4b6bmgvsnT77v3qlLCsOuXT9VEdAa9XUlITLPq2AiSN4Eydcc1USce/+cOb/KLWhm/64KJXd2ubn4gObiVUfaLF+EzUWraG4SlY/JxeGiDS9sKLyZVNmkq20rfF4/uI/tDSe3QPKbQPtq8uxVBRcTfkPJS+UY5B6w7NGEyWKWoGhra/PfxVWhnJxQ5RhXkhA1a0mFTHExdQaWpvDFxaGcUFVzQazD5lVF9X6TeyAc/nzx1OcLXHScsotDbF1pd01z4eL7KMaceS9Fmk/nV/faCnTV1jZ7WJcFTcSLC8PhgoWii/dWXayoKGdTVdi+xkQ1i6aQ8dXF4aJVM/S1TMktSELHybu4YCFrfagquQU8uZhVfe/T2yviH0cLlSGhJVrbRB27u5gli+malE6/0tLS5NYXF7jYHdGVVXuEm25bDjc5lUnXsp6gbkCdgboESxkrc7HWbXQdx41ex5uLte6+sUL7NNxWsnHmZLHlw13VX+bvPVUbLt+8+bhzYOVu+dwpU1fsL6koLww9arh4uELbkF/lbtiyefOWzRuef0AKu5Jts4V5K/TaNm/MfSBn5oZTCfZQioeAizgnWR2PKxfrTJ6zpqDaW4is15Bntiuei8Ubd3Titba2njp1ytuaHFfNOrz+9RDKE0N1vtMMUfLzMcfobNqc2FKhSq18KC/ETtiqvBy+UdJaCow9pldrW8t4wubKqo9JpY3uZdKwrCeoG1Bn4C6mTuKnix9bEdPK58wC4YL5ufFk7EkEkZsle5lltqyYx9ZlWGzzjoKqdKwiWrN9JtdKuGCBoYO2M/mhHRVtFODfLNq+v6orTqTvbswHnt+gN35z/gHdAMPhotfm5OaFFhjfKPwLzGvNVhcbCqMj8uiGDc/PcddxKi6WRJycjhNU21axd7++hyUXD7dV7M8v8Vhzci5mzZ6a+x5bbzJY4uKqPF5L7qeNuovPvvkjnX+Osf5UZ+3O3/9wzbG0u9hohvaZ2VaMWIXPPK7Xz1Cj8fpONjpefBcb853XMr7ILBd3dnb65uJoJFzJr7XFy+024ZI8cZ0uZ6zQ9R1I3mLuUggfWTVnin5v7f7JM1/bvPnNzdt3FbLoKXwk3z3V6G5MSwuZiD8Ja2eIfy6mmntr97rq2LOLHUWchI7dqo1UbdbrnKxltAQXa3tppl7zKpe7kGNyMeO+BaEvq+N9xerYLsVIT/RZD4rPvZWT89a5mIuf+XsDi4tv7P2D5uLOq7t+f/fa4rTHxeZRtluStc1YTguBK8WSUYcvFWcX28vDxWNi2Ueai2/fvu2ri+ORYS7WiLTVVlZUXQ9Xf75h1ScVJQcoEG4reX97RaKESPIurn5/Zs6MxSGWSTAyIWl2cVthLrtueH3B1JyZ22uSbXMy0iRtznu/MD8mzQ17P8mN3bmNL023aofDBc/HKpj8fCg0P3ZIVzER08zZ26vi30xNg4s17s39PPHNQlFb0ViOovXThTEXD0ku/qHm4ts1O5+6e/VHCl1s30ytX6XiYvO8pjJw8ZjbGP3Z3D9famlhLmaPtfng4kh1/gvWx6poejpU0pE2F0cj/DmBkg2si83fW53oWVrvz1F01R7fG3pp8Yo3QxvyCqquVlWcKdr7flH1xcK9X9a6P12RtItpn2wo4c88RBzLeKjZ2cX6DmOrqN6zID3fVTFpkoirI9ZkQljXsYs0E+x2LX6f7OjIybM3V8W5pel1E+K4WE8cd7itwoQbTUjI5rq7+PbVj568e9GHQcXFtpbrlSEuTteynlDiYjrSghnZVLKR3SVIk4sta9RJ+3MCumsmz16R/2VF4ZZ5k+9bsL28tvrA5s2f6xewXVUkZU+PhXBsLna4kki7i1OrOdF3VVv1RSHnKKyx7WK1S+I18ZF10nFCEXvaBLuL7306VFjj5f6dYTTmr1FrjiKOi2/X5D/1g0Vj/QWtBxeLn/XvDPcchb5bWIVCHjlxVnp8ARdHTRfbzEj9NbNc3HVkBU9h2VgAABtlSURBVNUZs61w7067g7QrlF/epn3YkR8vWeH+i4zJ95tXDFOnZKyLhVo9PfCQVLVWHScjYseak3Hx5Ptzt5cn7D6ipzQ0Beu9LvaBTNz0qVapq4tbLn006wc/SLwlCVvi5uJYEi92oM2wXZzj7GLzhKICB0KOT2tw/1rWMp7w+FzapaN7D5Q1pbZs6kw8F9fmz9BPp9fS/Lsy3cX51c1d4cq9q2ZMnrOj2vLn5qqCXfn5720vuu58He7FYmlzcdfXq6zBZCa7OGrqOEkRO9acwMVT5qw64H6nzkplyNy7vJNz2S389Bx9fPbTFhcXt9S8PnOsLlaNNV88/vH6e43m8qOOLh5L/Qnx28W1+bOdM30x7ltVkvT9nLT/ANdjjqKt4r3FM7XfKC9OJmjyULOFdLo47TUnva5I1XtzJudMnrom8Teil2o1HRfuL6xNTsSONcd38eIFHp5gS4z0fLH0YiD2MgrdxS07FsHF/uLRp81lR8uaHeaPqf6E+O3i1Bl3Lk73uuLQVbLRdp9TmxZsPz/GiDsNNfu0f1Tudv/WJZG0i4sX/WCWmialjQnu4rQt64lTp061wMX+rWKC1QwXJ09SLi5ee/fdd89a/4WaJmUtcPGYgIvHYc1wcfIkn6MY+289gDsZ8S55uNjfVUywmuHi5IGLgSfgYn9XMcFqhouTBy4GnkiPizH2aEIyzsUYe3SMwMXAE+lxcVtbG3ntbPqg2qhOX9fiuArFA7anhfPnz/tRrcoB7S9cuJD2Os+q3QRHzhhUVFScPn26vLy8rKystLS0pKTkxIkTxcXFRUVFR44c2b59ewonHZhgUDegzkBdgjoGdQ/qJKm4GAAggbgYeCI9cTEAQAIuBp6AiwHwBbgYeAIuBsAX4OLxQxY9XwwAkICLxw+Z+ru7Op0bN25cN6gFIH1cM7iqc+XKle+//76mpuby5cuXLl26ePHid999V1VVVVlZeTvDadNp1aHTrLm5ubGxsaGhob6+nk4xOrNo82nDCwsLx34mAxcywsXUDagzUJegjkHdgzrJJPrvpk6dwQ0A0of0Hc+kTEamjlhdXU1G5jruyHDadbiXycik46ampnA4zHRMm08b/tVXX43xNHYYYywvg17d4zsZ4WLqBtQZqEswEVMnmXRLp97gJgBpRfqOZ1ImI9t1fCeT6TTgXuY6puiY6Zg2nzb88OHDYzyNpaH2zAE7xlKJK+bb6zOBjHAxdQPqDNQlmIipk0yia6iwwS0A0o30Nc8TYkzH33//PddxdybTZcDVzGJkUce0+bThR44cGeNpbNeoJ7GmsEgmu7jp8BZ2y80yVFJyLva2rCeoG1BnoC7BREydZFKjQUMcwgCkA65mZmSm46tXrzIdX7p0qS+T6TXo0SE7cyNTgNzS0kKnGG0+bfvXX389xtPY3cXm8FGmPfnAK7FiZhm2lDAOk1GPtkgoTx/HZMVyW4XjGtGVVXvWbzmiD9rRSF79uMqpTLqW9QR1A+oM1CWoY1D3oE4yiaW0OI0ApBXpS53rmKJjnj5mOu7PZCQpk45ZjNzR0cGiY544Pnbs2BhPY7cchfDed15MM69o2EqpEnOmMBSeMPZoBsfFVR9zh577mOZ/fM5eJm3LeoK6ActOUMeg7kGdZBK74QuAT0hf80zKLEIUkxU1NTWRDGdAh6uZR8dMxyw0pm0vLi4e42lsv3fHx82iP5ljaGli1bysR8HyUJPOOQpzmGfb+NCZ6GKKZ/UxQ5uObFm/5fDhPV5c7H1ZT1A3COvZCfZkG3WSSez5GwB8QpIy1zF7tIBFxyw0HsxwJCPz6JhlKnhonB4XWzISfAAkIRdhWJopWBj3Whgr2qjElPvC3NwJ5+IqkukebUurvLvY07KeYC5mQTF1D+okk/gTkQD4gSRlrmMWGvPEMYXGQ5mM3cgsX8FDYzrRaPNpw48fPz7G01gKabV/GqK0xMXxltULm5WYeQk5R5HxLtbyDOuZTNmNOI85Cm/LeoK6AXUG6hLUMah7UCeZ1AaAn0hS5jpmoTFPHFNoPJzJSFKO6L+4Y5kKCo3FNMWJEyfGeBrb0gtVIZ6mEPLFPGSW42j9s9XFQhJ5QrlYiGe1nK/ne3eelvUEdQMxQUGdZJL0qyEA0kvyLh7JZCQpk455poJCYzFNcfLkyTGexvZUL0tBMJ/a0xHW3IWR0KgM8UcjeI4iVKlpXa/H4mLjQYvMGA3a6ko9vHV7Lu3S0b0HyppSWzZ1qBuICQrqJJPkH3ICkFYkKXMdszQF/33wtWvXRjMZScosNGaZCgqNxTRFSUnJ2M9k4ILX32s0lx+N4+LU608IdQMxQUGdBC4G/gIXw8WK8ejT5rKjZc0O88dUf0IcXCz+iB6AtAMXw8WKyYjfQMd1MQA+IUm5VXiHmfigcW1t7dj7d4BIUh7COzODIyNc7PDOzKBPVTDBgYvhYsVkxLvkCwsL2Yi0J0+epC5RVlYGFwN/gYvhYmAnPz//ypUrlt9AB32qggkOXAwXAzsffvih/D6KoE9VMMGBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi9PuYvsYS14HgU4j/BWdsRHznKkKmW01X7yZwvDViXBe0TgELgaqgYt9cXG8sUdTrsSVuO+S56+uF95hL6M3z/JaetvoqOlB2g/6C5oTfkN43W+pLiUDFwPVwMW+uzglqaXNxWy+Mc5pEisy31KfVhdbX37v3myjaXAxyCLgYsUuNsf1MDUkjOuhFzPLCMMsWdMd2iKhPP2Kf8VyW4UGRiga36pu5nIe/1Qayk8ao0Szfyi0MEce2Voctc8+UwzbYzPNfaI1Ty9QIOVbLHWyDbEuNQbgYqAauFhpjkKQDi/Gx7iLGbZSqsScKY09yuXrEmDGtB4vvHVUpG1DhPqF8JYH3WJr42UenJMkxjeBg4ujlu8JNuKUuQq9gIOLo4iLQaYCFyu4d8evzS3jQBt5A12XshCdI1muoeTGHtVbEgrlOYXMZhsSulj4MohaFGygfzGYonRKhqTBxZbhAbVth4vBRAIu9jUuFm+FWccYtVza2wckdc4PLMzNTdrFwkz96YW8AnvGNrkchfjwg/AIhJk5yc1N6GLPOYqozcWWBzzgYjDRgIv9zlFUCWGpJS6Otywf+9lMFhvGseYo3F1saUb8gaJdstvOcbHTgtYcheNNQtd7d4iLAYCL/b93p9nBzLEa0uEhsxxHWzxoSTfrAXLSOQrRX0YM6yAp6zNt4lJO+WLrt4X5kEZCF7s+0yb8ScjYyPliYR+aBYSZcDHIZOBiBc9RsBSE4BrrsweW3IUhMna3ilsvZlIuGmuYGSfsldcVV5SJf+shJMHFAJZlTgqqHCPcpFck1p97oCAkfhkw2+rVhsz9YNtAPQMjfGMluP5ICFwMVAMX4zfQGUACxacfuBioBi6GizMAuBhMeOBiuDgDgIvBhAcuhouBHbgYqAYuhouBHbgYqAYuhouBHbgYqAYuhouBHbgYqAYuhouBHbgYqAYuhouBHbgYqCarXDx8pxMuBskAFwPVZJWL72zf0rZyceRGLVwM3IGLgWqyysVDrS3he//11k9yyMj9N67DxSAecDFQTVa5mGhb8cKte/6ZJpLy7fWv9dbfhIuBHbgYqCbbXNx/9jRzcczI//Fvrete6ay7ARcDEbgYqCbbXDwyMtI8d5aoY226b3LLWxvb6m7AxYABFwPVZKGLu/d/LLuYTf/5741vvl5WdHSM67KPd5e+ke090yaNnSxjHz8pDS9ijxpD7clvKDZGCRnjy4UVABcD1WShi4e7u8I/v8dZx/f8882p99zZ/u5oT0/K63IbBzrlSlyJOw40f72Z61vkxbZZB+hLGapWe/W7WDNcDIAbWehi4vbG1fFczKaG//6Prp0fjA4MpLAulxHkxlKJC24uNodBSsrFaRmjiI0UJbUKLgbAjex0cf+VmltTfuSuY5oaf/Nf3ft2jQ5GPK3L3cXmsECmp4Qxlvj4cmJywxxxmdejjweapycYViy3VWhghOTxze7mYrnZwihKIXNIJLtVjbFKrWM/w8UAuJGdLh4aGmp+el5CF8eMPOP+ngN/jw4PJ7kutxyFkCuwjOwpGlb2oDAMs3UcaC7fuHEx13rcENstRxHfxfZhQAWEGFwc9xouBh5oLTwUnjY1/F8/kadfT22Y+QtxCj/8QMNjD4lT47yHG555QpqaFi9sXPKcODWtXNy0eqVlWvtyc2iDPG39S/N771imD99r+ThfnFr37mo9+Kk0tRUdbjt21DKVnmyrKLdM35xpu3SRTa0XL8Sm67WtN663XK9trr3WdO1q49Ur4bq6Cezi7q8+T9LFMSPP+qVm5JGRhOuy37sTfWQdMFRzljDysbUSu0BN9yUxDrRxAy2U5xQyG0Vs9+7skax1FWLq2Rr58pKWUaXFAaThYpAk4Qf+09P5mW1T+D/+reH+n0pT04O/lKbm3z3Y/MQccWpZ8Fjrcwss0x+fvv3Si9LU/vqrHRtXi1PnWxvvbMmzTFvf7tr5gTT1fLqfRClOvV8e6vv6MJ96i77qLS7qryhjU2/5qe6S4w2/uNfrHmh6ZAbVRmp3OY1tGQmeqBXHe7aIzz44tPMYzAtzc5N2sTBTF64+UrJNgmJcrDdP+AJIycVxH8yAi4EHwr+6L3DfYRr/U/PcWZqR4yCFtFVCWGqJi+Mtqxc2KxF9Z81RuLvY0oxYxtl++07KUWj/tGUVrKtwd7EQCEuVwMXAA1qO4pc/C/xUx5QRU+ui+ZHvHPKwtvSCIDhBZDxklpVnMZcl3awHyEnnKMRkrnH3z/aAhO3enbCUENQL6WlXFzt82Rjl4WLgmds36+Tp+xqeYGXT7fPn5CTs6TI5UXvsaMvnn0n53Ja/75XSvi27PpRTwzTl/Y+cQd6wWko0N766TEpG09Tw7JNy2vrJ30qp7YZHZkjpby0Dbk2R3/r5lFs//ZfAfTeep5ZnHh84XWo/je2pXpaCYMqzpyOsuQtDc/otMjNGjpmUq9NqtDhhr7wuh6eM7c9RsMawYjzhkFtwIJTYxU7pY/5VBBcD4Ian5yhGBwdHuu5YpjudQ+Fb0jR4pWaw+pI4Rc5XDpwpt0ynS8V8biyre/BTKe3bvW+XnB3e8Vc5g7wlr+ONtVKiuX3VCikZ3bbsj63PLWhZNJ9PDVPj/uLDbZryI6pt8PvqoHUBfAQuBqrJ2mfaeiu/8Wzhn+S0r145eO1K0FsDfAcuBqrJWhe3rnzBg4V/+i8Uaw/dqg96O4Ai4GKgmux0caS+joLcZCwc/tmPO9/aONwYDnoLgFLgYqCa7HRx+1/eTGzh+/6fZuGmxqDbDgIALgaqyUIXD3e5vadNs/DPtVe1jbTfDrrVIDDgYqCaLHRx1/7d8S085cKrK0Y6O4JuLwgYuBioJutcPDTUOOuXDha+/6dN2za31N3AuB4gChcD9WSbi/tOfC1ZuOGB/2zLf7+zqRHj3QEOXAxUk20ubn32SdPC037e8bc9PZ0dGAcaSMDFQDVZ5eKBi98xCzc+9EDXoYKBnp7+/v7e3l64GEjAxUA1WeXi9tUrm377m54vPhvs7x8cHIxEInAxcAQuBqrJHhePDA72fHloOBJhv7uDi4ELcDFQTfa4WPoNNFwMXICLgWrg4rS72D7GktdBoEHgwMVANXCxLy6ON/boOMT5pcPZDlwMVAMX++7ieAOJjhPgYifgYqAauFixi82xNsRRkYwxkORhmK0pDr2eUEgqqQ+0ETIKC0NmmMOAilG5tdoquTagAxcD1cDFSnMUwgBFQjFhoCOjgDSKMzOsMN6dMKSpPhqTMJ6ebUA8IfIVqjUKIC52Ai4GqoGLFdy7E0dWtg5Sp3vZYRg656FFPQzMzCu3rNdprDm42Am4GKgGLvY1LhaGUo5axxg1MwNOozhbhgRNxcWVIfkLwchIyHcR4WIn4GKgGrjY7xyFmUxwHLI+qiIudqw2Ki0CBOBioBq42P97d2bCV/SmEDIL4aphxrj54iRdbNGuWYMYg8dqg4udgIuBauBiBc9RsGcnmG3N5yhEA5rPUZg5hDjPUSTp4qj4HIUcp1se5IhlTsbvE9BBABcD1cDF+A00sAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsAMXA9XAxXAxsGNz8WW4GPgLXAwXAzuIi4Fq4GI/XCy8pFh4d3Aa3truNDAH8AG4GKgGLk67i61j3AkvgPfiYvsL6VndcLEa4GKgGrjY93E9+NBzcHHmABcD1cDFfsfFJszFB0LW3IVFr0zBZorDDKjZqEihkFRYLFYZ4mPZiePaaZ+pgD4UU8hYBEJ3By4GqoGLfcgXx0aQE8aU02FKFfSqDzHn4OKoJS4WCmg+tY8iyocWpQ984NHc3IXmIKTaioRl9c8Yb9QNuBioBi728TkKHs865Iv52M+JXGxZihe2LGV42dAuVb6woOpArvaZZ0jiDlEKHICLgWrgYt+faeMhcGouFh2qzzdCYHPkZh4jm+kI6f9RuNgbcDFQDVycbhfzRIE4Zwwu9hAX64V5RKxHxwV5hrLhYi/AxUA1cLEvz1EIkax5K8/ZxXp5IfPrmC8Ws8zx88Xss5gpps9GS+BiL8DFQDVwse+/9eC37+K42Ewr54QKWJIhGrvVZnG0XiCUF/85CnNmzLmWB+PgYi/AxUA1cDF+Aw3swMVANXAxXAzswMVANXAxXAzs5OfnX7lyBS4G6oCL4WJgp7Cw8MSJE8XFxSdPnqQuUVZWBhcDf4GL4WJgh7oBdQbqEtQxqHtQJ4GLgb/AxXAxsAMXA9XAxXAxsAMXA9XAxXAxsCO6uLu7Gy4GvgMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMXw8XADlwMVAMX210cBlkPezfQ8ePH8W4goAi4GHExsMPemdnQ0IB3ZgJFwMVwMbDD3iUPFwN1wMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbADFwPVwMVwMbBjc/GXcDHwF7gYLgZ2EBcD1cDFPrm4Ki+HE6oUZuZVpaV+z43xdb3NBbk5uQXNPq5BMXAxUA1c7IOL2woW5uQsLGhj/9I8FdMxXBwckfD1tuRLw8VANXBx+l1cGSL3iuZrO5DL1AwXB0dVKGdy7oFwkqXhYqAauDjtLnYRn/6nAi1q1hDkpek7Ru4BPXzT7RbKy43NNSts44trfzWib033DB6POzZJD9Jjq9D0ZM2iaH8NhfJic/SlQkYZobV6JZaZmeFiIlkdw8VANXBxul2sudKQnYyeRI45S/sc8yZpwhAZj6mZ7wSBMl3yEDsmF/ZZiMQdvwliMy3GFNrJ51tM7dJaw92W1maEi5PVMVwMVAMXq3axIUrBqsLCWnhrtxvXn6VyMfVhrlGPbSUZ6ybNzZVjW7NYrAarUp1bS/41m621R2tYJrk4J2fKqqKuBKXhYqAauFh5jsLuYp52yMld6O5iISY1azAX53kOSYssws1dKDRMyIqYaZAkXGwmQ8SkSga5eEru3quRhKXhYqAauFjBvTs+J26kycs7X/V7iYudMNYrqNwpfPYeFzsvOC7RXZyciKNwMVAPXOz7M226BZgr47uYiYxFuC4uTpwvNrMcAuZ6zZKi1o0WJuNiWegZk6PYfF+yIo7CxUA9cHFQv/VwylHkFlSKt9EcXCwUDhUccHqOwsmJwnr1xWOfhRSqeZMwoYvF5yhsd/8mCnAxUA1cnLm/gdb8GMTTytkAXAxUAxdnlouF1LCZ+gBpBy4GqoGLM8vFYn4AQbF/wMVANXBxhrkYKAEuBqqBi+FiYAcuBqqBi+FiYAcuBqqBi+FiYIe7mM4FuBioAC6Gi4EduBioJjtdPDw8LLq4u7sbLgYikovpTIGLgb/AxdzFtDdo8+FiELW6mM6LdrgY+A1cDBcDOzt27KDODxcDdWSti4eGhkQX37lzp113cWlpadCNBcHDXExnAVwMFJHlLh4YGOjr6+vp6WEupj1QXl4edGNB8JCLqf/TWUCnA1wMVJDNLh4cHOQu7urq6ujooD1w5swZ+hB0e0GQUH/YuXMn9f+mpiY6HejsaIeLgd9km4uZjh1dzB6loC2l0PjkyZPHjx8/plNUVHT06NEjR44cNvgKZDj8UNJhpYNLh5gOdHFx8YkTJ0pKSqgDHDp0SPyhB309w8XAX7LExVFraCy6WEoZ007gm19fX19XV8c2/5rOVZ0rVr4H4x7pkLHjyI7p9evX6RDTgabDzfIS1AFIzewDXAwUkZ0utv/cg6WM6ZRjTxlTQERhEW0+nZ9sD1zXqdW5BjIcdhyZhQk6xEzE/IFiipFZgoIli+maCS4G/pK1LrbfvmNpinY9NOY6ZjuBGblO54bBdZBp3BBgR5NZmA4xi4i5f0+cOCEGxXAx8J3scXHU0LH99h1PU9h1TPuBG5lJmXkZZC71BuyYsnCYZSR4IHzy5En+mVxM10xwMfCXLHSxlDLmoTHPGtO5167rmPYG7YomHSZlRhhkOPxQsliYWZgON89IlJSUiEExfUnDxcBfstbFko4pNGaZCqZjFh2z3cL2CdstXM0g02FHkx1ZZmH27BodeuoAp06dEv8JFwPfyU4Xi6ExzxqLOmbJChYgtxtS5rsITAD4AWXO7dBh2qUOUFpaymayoJjmwMXAX7LKxVHX0JjrmD1W0aXDYuQOg6APF0gn7Jh26nAL06GnDlBeXi6KGC4GvpO1LuahsYuOeYzMvQwmGOzIMtsyCxPUB06fPs1EzAUNFwN/yXIXSzqWjMykzL3M7QwynR4r7CjT4e7XoQ5QUVEhORouBv6SbS6OuupYDJCZkRl9VnpBhiMeTX6U2UGP6Jw5c0YUcS9cDPwmC10cddKxeCtv0ICdlgM2+kEmYz+gXMHsuFMfOHv2rBgyw8XAd+BiScfcyJKXJUGDCYB0ZPlBHzbgHYM6CVwM/CU7XRy16dhuZLuXwcRGPO6ihRlwMfCXrHVx1EnHopHtXgYTG+nQSx0DLgb+ks0uZiRjZJBVOHYJuBj4C1zMcDz9YOcJT8LjTrAeAhcDf4GLRZI5M0H2IPaN/w+BoDrGdABN9QAAABR0RVh0U29mdHdhcmUAWWFuZGV4LkRpc2tOX/iRAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![2022-06-25_09-17-28.png](attachment:2022-06-25_09-17-28.png)"
   ]
  },
  {
   "attachments": {
    "2022-06-26_15-59-11.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![2022-06-26_15-59-11.png](attachment:2022-06-26_15-59-11.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy import arange\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объявим известную информацию о разработке скважин и добавим эти константы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 Здорово, что импортируешь необходимые библиотеки в первой ячейке. Так сразу понятно какие используются библиотеки и можно легко настроить свое окружение при необходимости. \n",
    "* Обрати внимание, есть повторы)    \n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "BUDGET = 10e9\n",
    "NUMBER_WELLS = 200\n",
    "PROFIT_BARREL = 45e4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера V-2</b><br>\n",
    "    \n",
    "Приятно, что прислушиваешься к советам))    \n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Датафреймы считаны и проведена оценка полученных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    },
    {
     "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>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "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",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f0</th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>product</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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",
      " #   Column   Non-Null Count   Dtype  \n",
      "---  ------   --------------   -----  \n",
      " 0   id       100000 non-null  object \n",
      " 1   f0       100000 non-null  float64\n",
      " 2   f1       100000 non-null  float64\n",
      " 3   f2       100000 non-null  float64\n",
      " 4   product  100000 non-null  float64\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 3.8+ MB\n"
     ]
    },
    {
     "data": {
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       "<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",
       "      <th>count</th>\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",
       "      <th>mean</th>\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",
       "      <th>std</th>\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",
       "      <th>min</th>\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",
       "      <th>25%</th>\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",
       "      <th>50%</th>\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",
       "      <th>75%</th>\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",
       "      <th>max</th>\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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('/datasets/geo_data_0.csv')\n",
    "df1.info()\n",
    "display(df1.describe())\n",
    "\n",
    "df2 = pd.read_csv('/datasets/geo_data_1.csv')\n",
    "df2.info()\n",
    "display(df2.describe())\n",
    "\n",
    "df3 = pd.read_csv('/datasets/geo_data_2.csv')\n",
    "df3.info()\n",
    "df3.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данных `нет пропусков`, столбцы `f0 f1 f2` требуют маштабирования для дальнейшего обучения.\n",
    "\n",
    "Также присутсвует неинформативный столбец `id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>id</th>\n",
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       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
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       "    <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>99995</th>\n",
       "      <td>DLsed</td>\n",
       "      <td>0.971957</td>\n",
       "      <td>0.370953</td>\n",
       "      <td>6.075346</td>\n",
       "      <td>110.744026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>QKivN</td>\n",
       "      <td>1.392429</td>\n",
       "      <td>-0.382606</td>\n",
       "      <td>1.273912</td>\n",
       "      <td>122.346843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>3rnvd</td>\n",
       "      <td>1.029585</td>\n",
       "      <td>0.018787</td>\n",
       "      <td>-1.348308</td>\n",
       "      <td>64.375443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>7kl59</td>\n",
       "      <td>0.998163</td>\n",
       "      <td>-0.528582</td>\n",
       "      <td>1.583869</td>\n",
       "      <td>74.040764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>1CWhH</td>\n",
       "      <td>1.764754</td>\n",
       "      <td>-0.266417</td>\n",
       "      <td>5.722849</td>\n",
       "      <td>149.633246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99980 rows × 5 columns</p>\n",
       "</div>"
      ],
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       "          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",
       "...      ...       ...       ...       ...         ...\n",
       "99995  DLsed  0.971957  0.370953  6.075346  110.744026\n",
       "99996  QKivN  1.392429 -0.382606  1.273912  122.346843\n",
       "99997  3rnvd  1.029585  0.018787 -1.348308   64.375443\n",
       "99998  7kl59  0.998163 -0.528582  1.583869   74.040764\n",
       "99999  1CWhH  1.764754 -0.266417  5.722849  149.633246\n",
       "\n",
       "[99980 rows x 5 columns]"
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       "      <th>0</th>\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",
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       "    <tr>\n",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
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       "      <th>4</th>\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",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>QywKC</td>\n",
       "      <td>9.535637</td>\n",
       "      <td>-6.878139</td>\n",
       "      <td>1.998296</td>\n",
       "      <td>53.906522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>ptvty</td>\n",
       "      <td>-10.160631</td>\n",
       "      <td>-12.558096</td>\n",
       "      <td>5.005581</td>\n",
       "      <td>137.945408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>09gWa</td>\n",
       "      <td>-7.378891</td>\n",
       "      <td>-3.084104</td>\n",
       "      <td>4.998651</td>\n",
       "      <td>137.945408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>rqwUm</td>\n",
       "      <td>0.665714</td>\n",
       "      <td>-6.152593</td>\n",
       "      <td>1.000146</td>\n",
       "      <td>30.132364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>relB0</td>\n",
       "      <td>-3.426139</td>\n",
       "      <td>-7.794274</td>\n",
       "      <td>-0.003299</td>\n",
       "      <td>3.179103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99992 rows × 5 columns</p>\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",
       "...      ...        ...        ...       ...         ...\n",
       "99995  QywKC   9.535637  -6.878139  1.998296   53.906522\n",
       "99996  ptvty -10.160631 -12.558096  5.005581  137.945408\n",
       "99997  09gWa  -7.378891  -3.084104  4.998651  137.945408\n",
       "99998  rqwUm   0.665714  -6.152593  1.000146   30.132364\n",
       "99999  relB0  -3.426139  -7.794274 -0.003299    3.179103\n",
       "\n",
       "[99992 rows x 5 columns]"
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>4GxBu</td>\n",
       "      <td>-1.777037</td>\n",
       "      <td>1.125220</td>\n",
       "      <td>6.263374</td>\n",
       "      <td>172.327046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>YKFjq</td>\n",
       "      <td>-1.261523</td>\n",
       "      <td>-0.894828</td>\n",
       "      <td>2.524545</td>\n",
       "      <td>138.748846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>tKPY3</td>\n",
       "      <td>-1.199934</td>\n",
       "      <td>-2.957637</td>\n",
       "      <td>5.219411</td>\n",
       "      <td>157.080080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>nmxp2</td>\n",
       "      <td>-2.419896</td>\n",
       "      <td>2.417221</td>\n",
       "      <td>-5.548444</td>\n",
       "      <td>51.795253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>V9kWn</td>\n",
       "      <td>-2.551421</td>\n",
       "      <td>-2.025625</td>\n",
       "      <td>6.090891</td>\n",
       "      <td>102.775767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>99992 rows × 5 columns</p>\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",
       "...      ...       ...       ...       ...         ...\n",
       "99995  4GxBu -1.777037  1.125220  6.263374  172.327046\n",
       "99996  YKFjq -1.261523 -0.894828  2.524545  138.748846\n",
       "99997  tKPY3 -1.199934 -2.957637  5.219411  157.080080\n",
       "99998  nmxp2 -2.419896  2.417221 -5.548444   51.795253\n",
       "99999  V9kWn -2.551421 -2.025625  6.090891  102.775767\n",
       "\n",
       "[99992 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df1.drop_duplicates(subset = 'id', keep = False)\n",
    "display(df1)\n",
    "\n",
    "df2 = df2.drop_duplicates(subset = 'id', keep = False)\n",
    "display(df2)\n",
    "\n",
    "df3 = df3.drop_duplicates(subset = 'id', keep = False)\n",
    "display(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Были устранены дубликаты скважин, удалению подверглись все дубликаты, так как неизвестно исходя из чего они могли появиться, можно ли их сложить или же это исправление раннее ошибочных данных. Для обучения такие строки увеличивали вероятность ошибки предсказания."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 Хорошо, что обращаешь внимание на дубликаты по `id`. Так как нам не ясна природа таких дубликатов, то их можно как удалить, так и оставить. Но о таких фактах всегда надо сообщать коллегам.\n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Максим,    \n",
    "* Чтобы лучше узнать данные, можно посмотреть распределение признаков и целевой переменной для каждого региона, т.е. построить гистограмму.    \n",
    "* Еще важно уделять внимание мультиколлинеарности признаков в линейных моделях.<br>\n",
    "Мультиколлинеарность — явление, при котором наблюдается сильная корреляция между признаками, что может негативно отразиться на модели.<br>\n",
    "\n",
    "Рекомендую посмотреть отрывок из курса Статистики - [\"Корреляция и регрессия\".](https://stepik.org/lesson/8086/step/1?unit=1365) <br>\n",
    "Для удобной визуализации можно использовать - [\"Тепловая карта корреляции\".](https://www.delftstack.com/ru/howto/seaborn/correlation-heatplot-seaborn-python/) <br>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn==1.1.3\n",
      "  Downloading scikit_learn-1.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 30.8 MB 1.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.9/site-packages (from scikit-learn==1.1.3) (1.9.1)\n",
      "Requirement already satisfied: joblib>=1.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn==1.1.3) (1.1.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /opt/conda/lib/python3.9/site-packages (from scikit-learn==1.1.3) (1.21.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn==1.1.3) (3.1.0)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 0.24.1\n",
      "    Uninstalling scikit-learn-0.24.1:\n",
      "      Successfully uninstalled scikit-learn-0.24.1\n",
      "Successfully installed scikit-learn-1.1.3\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn==1.1.3\n",
    "target_1 = df1['product']\n",
    "target_2 = df2['product']\n",
    "target_3 = df3['product']\n",
    "features_1 = df1.drop(['id','product'], axis = 1)\n",
    "features_2 = df2.drop(['id','product'], axis = 1)\n",
    "features_3 = df3.drop(['id','product'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 `id`  - уникальный идентификатор скважины, он не является признаком, поэтому его действительно лучше удалить. \n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные разбиты на целевые и обучающие признаки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "features1_train, features1_valid, target1_train, target1_valid \\\n",
    "        = train_test_split(features_1, target_1,\\\n",
    "        test_size=0.25, random_state=12345)# делим тестовые выборки со свапом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "features2_train, features2_valid, target2_train, target2_valid \\\n",
    "        = train_test_split(features_2, target_2, \\\n",
    "        test_size=0.25, random_state=12345)# делим тестовые выборки со свапом"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features3_train, features3_valid, target3_train, target3_valid \\\n",
    "        = train_test_split(features_3, target_3, \\\n",
    "        test_size=0.25, random_state=12345)# делим тестовые выборки со свапом\n",
    "\n",
    "target2_train = target2_train.reset_index(drop=True)\n",
    "target2_valid = target2_valid.reset_index(drop =True)\n",
    "target1_train = target1_train.reset_index(drop =True)\n",
    "target1_valid = target1_valid.reset_index(drop =True)\n",
    "target3_valid = target3_valid.reset_index(drop =True)\n",
    "target3_train = target3_train.reset_index(drop =True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные разбиты на обучающие и валидационные выборки в соотношении 25:75. \n",
    "\n",
    "Также были сброшены индексы для feature и target, для простоты дальнейшей работы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 \n",
    "* Данные разделены на обучающую и валидационную выборку корректно - 75:25. Будет надежнее, если после разбиения проверять доли)\n",
    "    \n",
    "* Хорошо, что не забываешь про индексы, теперь у таргета и предсказаний они будут совпадать  \n",
    "   \n",
    "    \n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scaler_main(features_train,features_valid):\n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(features_train)\n",
    "\n",
    "    features_train = pd.DataFrame(scaler.transform(features_train),\\\n",
    "                                   columns = features_valid.columns).reset_index(drop = True)\n",
    "    features_valid = pd.DataFrame(scaler.transform(features_valid),\\\n",
    "                                   columns = features_train.columns).reset_index(drop = True)\n",
    "    return features_train,features_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features1_train, features1_valid = scaler_main(features1_train,features1_valid)\n",
    "features2_train, features2_valid = scaler_main(features2_train,features2_valid)\n",
    "features3_train, features3_valid = scaler_main(features3_train,features3_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция маштабирования обучена на обучающей выборке и применена для преобразования f0 f1 f2, для всех трех регионов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 Отлично, что используешь функцию для повторяющихся действий   \n",
    "   \n",
    "    \n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные готовы для дальнейшего обучения!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение и проверка модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся моделью линейной регрессии так как она лучше всего подходит для данной задачи и оценим качество подели по метрике R2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ML_linear(features_train,target_train,features_valid,target_valid,region):\n",
    "    best_R2 = 0\n",
    "    model = LinearRegression() \n",
    "    model.fit(features_train, target_train) \n",
    "    volume = model.predict(features_valid)\n",
    "    R2 = model.score(features_valid,target_valid)\n",
    "    best_R2 = R2 \n",
    "    mse = mean_squared_error(target_valid, volume)\n",
    "    print(f\"{region} регион\\nRMSE = {mse ** 0.5}\")\n",
    "    print(f\"Среднее значение объема скважины:{volume.mean()}+-{mse ** 0.5}\")\n",
    "    print(f\"R2 для {region} региона - {best_R2}\\n\")\n",
    "    return model,volume,mse ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 регион\n",
      "RMSE = 37.716904960382735\n",
      "Среднее значение объема скважины:92.42384109947359+-37.716904960382735\n",
      "R2 для 1 региона - 0.2766598499599884\n",
      "\n",
      "2 регион\n",
      "RMSE = 0.891490139034854\n",
      "Среднее значение объема скважины:68.98311857983121+-0.891490139034854\n",
      "R2 для 2 региона - 0.9996239095602003\n",
      "\n",
      "3 регион\n",
      "RMSE = 39.975543264382345\n",
      "Среднее значение объема скважины:95.11622302076478+-39.975543264382345\n",
      "R2 для 3 региона - 0.19558409993606407\n",
      "\n"
     ]
    }
   ],
   "source": [
    "modelLR1,volume1,mse1 = ML_linear(features1_train,target1_train,features1_valid,target1_valid,1)\n",
    "modelLR2,volume2,mse2 = ML_linear(features2_train,target2_train,features2_valid,target2_valid,2)\n",
    "modelLR3,volume3,mse3 = ML_linear(features3_train,target3_train,features3_valid,target3_valid,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся функцией для обучения модели на данных праметрах скважин."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После обучения модели на данных из трех регионов, можно сказать что лучше всего обучилась модель из второго региона, ее значенич `R2 - 0.99`, эта модель дает практически идеальные предсказания. Похуже обучена модель из первого региона с `R2 - 0.27`, а также третьего `R2 - 0.195` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Расчет `rmse`  и `среднего` можно добавить в функцию `ML_linear`\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h2> Комментарий студента <a class=\"tocSkip\"></h2>\n",
    "        \n",
    "<b> Замечения учтены, RMSE and mean добавлены в функцию!</b> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера V-2</b><br>\n",
    "    \n",
    "Отлично!)   \n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По результатам предсказания модели, можно сказать что самая малая погрешность у модели, которая обучалась на данных из второго региона, ее среднее значения запасов сырья: `68+-0.89`.\n",
    "\n",
    "Но во всех остальных регионах в среднем намного больше самого сырья:\n",
    "- Для первого региона : `92+-38`\n",
    "- Для третьего региона : `95+-40`\n",
    "\n",
    "Можно подумать, что предпочтение упадет на первый и второй регион, но тот факт что для второго региона мы можем предсказать объемы практически со 100 процентной веростностью может сыграть в будущем в его пользу "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "    👍 Молодец, что обращаешь внимание на размер ошибки, в двух других регионах она катастрофически большая - почти 40% от  среднего запаса. Если ошибка (RMSE) меньше, значит прогноз точнее и модель лучше обучилась. Если посмотреть корреляцию между признаками и целевой переменной, можно понять почему в одном из регионов ошибка минимальная. Высокий коэффициент корреляции `f2` и целевой переменной говорит о сильной прямой линейной зависимости.\n",
    "<br>    \n",
    "Т.к. средний запас в каждом регионе ниже объема безубыточности и при выборе скважин опираемся на предсказания, то качество предсказания имеет огромное значение - важно определить скважины с максимальным запасом.     \n",
    "</div>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка к расчёту прибыли"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получим минимальное значения запаса вярья для безубыточной разработки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "* Согласно руководству по написанию кода на Python [PEP-8](https://pythonworld.ru/osnovy/pep-8-rukovodstvo-po-napisaniyu-koda-na-python.html#section-29) названия констант принято задавать заглавными буквами, например, `BUDGET`. А еще важно объявлять их в начале проекта после импортов, затем загрузка данных. Так проще будет их найти и изменить, если возникнет такая необходимость.\n",
    "* Для записи больших чисел и более удобного чтения, чтобы не запутаться в нулях, можно использовать такую запись: `10_000_000_000` или `10e9`, что означает 10 * 10**9.    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h2> Комментарий студента <a class=\"tocSkip\"></h2>\n",
    "        \n",
    "<b> Замечения учтены, константы объявлены в начале работы!</b> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Минимальные запасы сырья в скважине для безубыточности разработки: 111\n",
      "\n",
      "Среднее количество запасов в первом регионе: 92.49916597893444\n",
      "Среднее количество запасов во втором регионе: 68.82414772665173\n",
      "Среднее количество запасов в третьем регионе: 94.99876686768079\n"
     ]
    }
   ],
   "source": [
    "product_min = (BUDGET/NUMBER_WELLS)/PROFIT_BARREL\n",
    "print('Минимальные запасы сырья в скважине для безубыточности разработки:',round(product_min))\n",
    "print(f\"\\nСреднее количество запасов в первом регионе: {target_1.mean()}\\n\\\n",
    "Среднее количество запасов во втором регионе: {target_2.mean()}\\n\\\n",
    "Среднее количество запасов в третьем регионе: {target_3.mean()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "👍 Достаточный объём сырья для безубыточной разработки новой скважины рассчитан верно.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можно сделать вывод, что не один регион `не подходит` для выгодной разработки скважин, так как минимальные запасы сырья `для безубыточности должны быть равны 111 тыс.бар`.\n",
    "\n",
    "Единственным вариантом заработка с минимальными рисками в данной ситуации является предсказание самых больших по объему скважин с помощью `машинного обучения`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Корректнее объём сырья для безубыточной разработки скважины сравнивать со средним реальным запасом продукта во всем регионе, а не с предсказанными значениями) Но результаты будут совпадать:)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h2> Комментарий студента <a class=\"tocSkip\"></h2>\n",
    "        \n",
    "<b> Замечения учтены, посчитано среднее реальных значений!</b> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b><br>\n",
    "    \n",
    "В каждом регионе средний объем сырья меньше достаточного объёма для безубыточности и при выборе скважин опираемся на предсказания, поэтому точность прогноза имеет большое значение и без модели нам не обойтись.  <br>   \n",
    "\n",
    "Есть 25 000 точек, из которых в результате надо выбрать 200. В результате,  среди этих скважин могут быть скважины с достаточным и недостаточным запасом сырья. <br>\n",
    "    \n",
    "Имеено техника \"Bootstrap\" опираясь на предсказания позволяет оценить риск убытков при случайным отборе 500 точек. 1000 раз отбирая случайным образом точки, считаем какой % случаев принесет убытки. \n",
    "    \n",
    "   \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расчёт прибыли и рисков "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном параграфе нужно написать функцию для рассчета прибыльности выбранных 200 скавжин на основе предсказания модели машинного обучения, исходя из начального набора исследуемых 500 скважин.\n",
    "\n",
    "А дальше провести анализ рисков и прибыльности на полученных данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit(target,predictions):\n",
    "    result = pd.concat([target,predictions], axis = 1)\n",
    "    result = result.sort_values(by = ['predict']).reset_index(drop = True)\n",
    "    choise = result[result.index >= 300]\n",
    "    return choise['profits'].sum()*PROFIT_BARREL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "В функции `profit` нет необходимости предсказывать, уже есть предсказания для валид.выборки, можно их использовать.\n",
    "* В фукцию `profit` лучше передать датасет с таргетом и предсказаниями для валид.выборки, например, для первого региона - создать датасет с `target1_valid` и `volume1` и передать в функцию прибыли.\n",
    "* Отсортировать по предсказаниям по убыванию.\n",
    "* Взять первые 200 значений таргета (они будут соответствовать лучшим предсказаниям). \n",
    "* И рассчитать прибыль.    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h2> Комментарий студента <a class=\"tocSkip\"></h2>\n",
    "        \n",
    "<b> Замечения учтены, функция скорректирована!</b> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера V-2</b><br>\n",
    "    \n",
    "Молодец, что скорректировал функцию!)\n",
    "* Чтобы  взять первые 200 значений таргета (к-е соответствуют лучшим предсказаниям), можно  `result` отсортировать по убыванию и применить  `[:200]`  \n",
    "</div> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция profit передает на выход суммарную прибыль с выбранных 200 самых больших по объему скважин."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся техникой `Bootstrap с 1000 выборок`, чтобы найти распределение прибыли.\n",
    "\n",
    "Далее найдем `среднюю прибыль`, `95%-й доверительный интервал` и `риск убытков`, который должен быть меньше **2.5%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def characteristics(target,volume,BUDGET):\n",
    "    state = np.random.RandomState(12345)\n",
    "    lesion = []\n",
    "    for i in range(1000):\n",
    "        part_target = target.sample(n = 500, replace=True, random_state=state)\n",
    "        volume = pd.Series(volume, name = 'predict')\n",
    "        part_target = pd.Series(part_target, name = 'profits')\n",
    "        volume_boot = volume.iloc[part_target.index] \n",
    "        part_target.reset_index(drop = True)\n",
    "        volume_boot.reset_index(drop = True)\n",
    "        boot = profit(part_target,volume_boot)\n",
    "        lesion.append(boot-BUDGET)\n",
    "\n",
    "    lesion = pd.Series(lesion)\n",
    "    neg_count = len(list(filter(lambda x: (x < 0), lesion)))\n",
    "    loss_probability = (neg_count/len(lesion))*100\n",
    "    \n",
    "    lower = lesion.quantile(0.025)\n",
    "    upper = lesion.quantile(0.975)\n",
    "    mean = lesion.mean()\n",
    "    print(\"Риски убытка:\", loss_probability)\n",
    "    print(f\"Средняя прибыль рублей: {round(mean)} рублей\")\n",
    "    print(f\"95%-ый доверительный интервал: {round(lower)} : {round(upper)} рублей\\n\")\n",
    "    return loss_probability, mean, lower, upper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\"><b>Комментарий ревьюера V-2</b>\n",
    "    \n",
    "<b>Совет:</b><br>\n",
    "Обрати внимание на строки 👇   \n",
    "    \n",
    "```python\n",
    "part_target.reset_index(drop = True)\n",
    "volume_boot.reset_index(drop = True)    \n",
    "```  \n",
    "Чтобы сбросить индекс, надо присвоить, т.е.   `part_target = part_target.reset_index(drop = True)`  или использовать `inplace=True`\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Воспользуемся функцией для получения характеристик скважин `characteristics`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Риски убытка: 5.5\n",
      "Средняя прибыль рублей: 431538187 рублей\n",
      "95%-ый доверительный интервал: -80924627 : 941037638 рублей\n",
      "\n",
      "Риски убытка: 2.0\n",
      "Средняя прибыль рублей: 477948839 рублей\n",
      "95%-ый доверительный интервал: 51741836 : 897944131 рублей\n",
      "\n",
      "Риски убытка: 12.3\n",
      "Средняя прибыль рублей: 322147249 рублей\n",
      "95%-ый доверительный интервал: -173464434 : 843529290 рублей\n",
      "\n"
     ]
    }
   ],
   "source": [
    "loss_probability1, lesion_mean1, lower1, upper1 = characteristics(target1_valid,volume1,BUDGET)\n",
    "loss_probability2, lesion_mean2, lower2, upper2 = characteristics(target2_valid,volume2,BUDGET)\n",
    "loss_probability3, lesion_mean3, lower3, upper3 = characteristics(target3_valid,volume3,BUDGET)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "    👍 Отлично, что при семплировании данных устанавливаешь `replace = True`, это необходимо чтобы наблюдение в выборку попало несколько раз, что позволяет добиться независимости при каждом выборе, иначе вероятность достать элемент из выборки будет с каждым разом увеличиваться. Чем больше размер подвыборки, тем выраженнее будет зависимость, поэтому без возврата не удастся получить разнообразные варианты, а подвыборку бОльшего размера вообще не получить.\n",
    "\n",
    "Почему именно так. Вспомним, что выборка - это некоторое представление о генеральной совокупности, ее часть. Значит, что где-то в регионе существуют и другие потенциальные точки добычи нефти, о которых мы можем не знать, но они являются частью этой генеральной совокупности. В этом случае повторяющиеся наблюдения можно считать разными точками добычи из всей генеральной совокупности с близкими характеристиками.\n",
    "    \n",
    "</div>  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<s> <b>Ошибка:</b><br>\n",
    "Максим, надо рассчитать среднюю прибыль, а у тебя выручка, т.е. `mean()` надо применить к `lesion`.  <br>\n",
    "    А еще надо рассчитать `quantile(0.025)`  и `quantile(0.975)`  для прибыли, т.е. для `lesion` </s> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "    <h2> Комментарий студента <a class=\"tocSkip\"></h2>\n",
    "        \n",
    "<b> Замечения учтены, рассчитана правильно прибыль!</b> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера </b><br>\n",
    "    \n",
    "\n",
    "Что такое  `quantile()` очень хорошо объясняет пример - 0.5-й квантиль, это, по сути, медиана, т.е. функция `quantile(0.5)` должна вернуть такое значение, которое меньше ровно половины элементов массива.<br>  \n",
    "`quantile(0.025)` находит такое значение прибыли,  меньше которого только 2,5% элементов. И если это значение прибыли положительное, значит и риск меньше 2,5%. <br>\n",
    "Выбирая наугад 1000 раз 500 случайных скважин и отбирая из них 200 лучших, убыток получаем менее, чем в 2,5% случаев.  \n",
    "    \n",
    "   \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для первого региона вероятность убытка равна 5.5 процентам. Также  95%-ый доверительный интервал  прибыли равен -80 млн.рублей : 941млн.рублей, а средняя выручка - 431 млн.рублей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для второго региона вероятность убытка равна 2 процентам. Также  95%-ый доверительный интервал  прибыли равен 51 млн.рублей : 897 млн.рублей, а средняя выручка - 477 млн.рублей.\n",
    "\n",
    "95%-ый доверительный интервал находится в положительном диапазоне - следоватьельно риск убытка меньше 2.5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для третьего региона вероятность убытка равна 12.3 процентам. Также  95%-ый доверительный интервал  прибыли равен -173 млн.рублей : 843 млн.рублей, а средняя выручка - 322 млн.рублей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основании риска убытков и доверительного интервала - безубыточной разработкой можно заниматься только во втором регионе, благодаря лучшей модели машинного обучения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Заключение"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В данном проекте основной задачей являлось построение модели для определения региона, где добыча нефти принесёт наибольшую прибыль. Заказчиком являлся «ГлавРосГосНефть», который предоставил пробы нефти в трёх регионах.\n",
    "\n",
    "Нужно было подготовить данные и разбить на выборки: убучающую и валидационную. Нужно было обучить модель и сделать предсказания на валидационной выборке. Далее требовалось рассчитать достаточный объём сырья для безубыточной разработки новой скважины. Была написана функция для расчёта прибыли по выбранным скважинам и предсказаниям модели. В результате были посчитаны риски и прибыль для каждого региона.\n",
    "\n",
    "Можно сделать вывод, что только в одном регионе можно получить достаточную прибыль при минмальных рисках, в данном случае это `второй регион`.\n",
    "\n",
    "Полученные данные для сравнения приведены ниже:\n",
    "\n",
    "- Первый регион: Риски убытка - `5.5%`; Средняя прибыль - `431 млн.рублей`; Доверительный интервал прибыли -  **-80 млн.рублей : 941млн.рублей**\n",
    "- Второй регион: Риски убытка - `2.0%`; Средняя прибыль - `477 млн.рублей`; Доверительный интервал прибыли -  **51 млн.рублей : 897 млн.рублей**\n",
    "- Третий регион: Риски убытка - `12.3%`; Средняя прибыль - `322 млн.рублей`; Доверительный интервал прибыли -  **-173 млн.рублей : 843 млн.рублей**\n",
    "\n",
    "В результате анализа, самой эффективным регионом оказался второй, с вероятной чистой прибылью равной от 51 млн.рублей до 897 млн.рублей , и рисками равными 2.0%.\n",
    "\n",
    "Такой результат можно объяснить очень хорошим качеством модели предсказания для второго региона, благодаря этому возможно по данным о скажинах предположить самые прибыльные с большой вероятностью и минмальными убытками."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\"><b>Комментарий ревьюера V-2</b>\n",
    "    \n",
    "<b>Успех:</b><br>\n",
    "    👍 Регион выбран абсолютно верно. В этом регионе минимальный % риска, т.к. модель предсказывает хорошо (ошибка минимальная). А при выборе 200 лучших скважин мы опираемся на предсказания, поэтому ничего удивительного  -  регион победил!)\n",
    "    \n",
    "</div>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"><b>Комментарий ревьюера</b>\n",
    "    \n",
    "<s> <b>Ошибка:</b><br>\n",
    "Максим, итоговый вывод после перерасчетов лучше скорректировать, чтобы он соответствовал полученным результатам)) </s>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"background: #cceeaa; padding: 5px; border: 1px solid green; border-radius: 5px;\">\n",
    "<font color='green'> \n",
    "    <b><u>Общий вывод по проекту</u></b>\n",
    "<font color='green'><br> \n",
    "\n",
    "\n",
    "Максим, приложено много усилий, ты - молодец! Есть несколько важных моментов, которые я отметила и которые можно было бы улучшить, подсказки ты найдешь в моих комментариях. \n",
    "\n",
    "**Что мне особенно понравилось в твоей работе:**:\n",
    " \n",
    "* Есть знакомство с данными.\n",
    "* Рассчитан средний запас предсказанного сырья и размер ошибки для каждого региона.\n",
    "* Рассчитан достаточный объём сырья для безубыточной разработки новой скважины.    \n",
    "* Написана функция для расчёта прибыли.\n",
    "* Использованы функции для повторяющихся действий.    \n",
    "* Рассчитаны риски для каждого региона.   \n",
    "* Есть выводы в каждом блоке.    \n",
    "* Код логичный и понятный. \n",
    "* Проект полностью соответствует плану!<br>\n",
    "\n",
    "\n",
    "**На что стоит обратить внимание**:\n",
    "    \n",
    "* Осталось совсем немного поправить, а именно: скорректировать `Bootstrap` (расчет средней прибыли и `quantile()`) и итоговый вывод. Подсказки ты найдешь в моих комментариях.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "**Доработка займет у тебя совсем немного времени) Желаю удачи и жду твой проект на повторное ревью!**😉 "
   ]
  },
  {
   "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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