7. Выбор локации для скважины.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Выбор локации для скважины"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Привет! Меня зовут Антон, и я буду совсем не против общения на \"ты\".\n",
    "Хочу заранее поблагодарить за проверку работы. Я сторонник лаконичности и красивого кода :) Конечно, реализую это в меру своих способностей :)\n",
    "Буду очень благодарен за наводки, советы, какие-то рассказы из своего опыта!\n"
   ]
  },
  {
   "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": [
    "## Загрузка и подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler,MaxAbsScaler,RobustScaler\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error \n",
    "from scipy import stats as st "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df0 = pd.read_csv('/datasets/geo_data_0.csv')\n",
    "df1 = pd.read_csv('/datasets/geo_data_1.csv')\n",
    "df2 = pd.read_csv('/datasets/geo_data_2.csv')\n",
    "\n",
    "dfs = [df0, df1, df2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "f0         0.143536\n",
       "f1        -0.192356\n",
       "f2         0.483663\n",
       "product    1.000000\n",
       "Name: product, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df0.corr().iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
      "None\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "          id        f0        f1        f2    product\n",
      "10501  QlXPj -0.773416  0.624082  1.079618  25.722552\n",
      "40707  Nybd2 -0.086190  0.953241 -1.905766   8.573203\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "                  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\n",
      "=*=*=*=*=*=*=*=*=*=*\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n",
      "<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",
      "None\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "          id        f0         f1        f2    product\n",
      "95136  Ks9rv -7.800490  -3.719361  2.000509  57.085625\n",
      "3612   gjDr9 -7.979947 -12.786267  1.003335  30.132364\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "                  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\n",
      "=*=*=*=*=*=*=*=*=*=*\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAqV0lEQVR4nO3deXwV1fn48c9DQAENQUTIAlZkqWKCIZIQ3IAqCbJphVYEEa3K7opWrVaUaotfEQVFIVo3Gqz9unwFQQgicYckBAGholQpZIOAQKLgD0Ke3x/3cklilhty79yby/Pua169M3PumWeO4cnJmTMzoqoYY4xxRpNAB2CMMScSS7rGGOMgS7rGGOMgS7rGGOMgS7rGGOMgS7rGGOMgS7rGGFMDEXlJRHaJyFc17BcRmSMiW0Vkg4gk1FWnJV1jjKnZK8DAWvZfAXR1L+OA5+uq0JKuMcbUQFU/Bn6opciVwGvqshpoLSJRtdXZ1JcBVufw7u/slje3EQm3BTqEoLFy96ZAhxA0fi47FOgQgkbZoXxpaB31yTknndF5PK4e6lFpqppWj8PFADsqrOe5txXW9AW/J11jjAlW7gRbnyTbYJZ0jTGhpfyIk0fLBzpWWO/g3lYjG9M1xoSWI2XeLw23CLjePYshGdivqjUOLYD1dI0xIUa13Gd1icjrQD+grYjkAdOAZq7j6DxgKTAI2AocAG6sq05LusaY0FLuu6SrqtfWsV+ByfWp05KuMSa0+LCn6w+WdI0xocXZC2n1ZknXGBNarKdrjDHOUd/MSvAbS7rGmNDiwwtp/mBJ1xgTWmx4wRhjHGQX0owxxkHW0zXGGAfZhTRjjHGQXUgzxhjnqNqYrjHGOMfGdI0xxkE2vGCMMQ6ynq4xxjjoyOFAR1ArS7rGmNBiwwvGGOOgIB9eCPl3pD3411lcOngkV103IdCh+F3Pvgk8t2oe8z5OY/ikEb/Y3z3pPGYteZq3v3uXCwddVGnf2D/dyDMfzOXZlc9zyyPjfvHdYPbEzGms37iK1Wve5/z486otE98zljVZ77N+4yqemDnNs/200yJYtHgBX274kEWLF9C6dSsAunU7m5Wr3mLP3q+57fZbKtU1ecofyM5ZTlb2Ml5+ZTYnn3yS/07OR56aNZ2vN39K7toV9IyPrbZMQs841uV+wNebP+WpWdM92x95+B5y164gJzuD95csJCqqvVNhH5/ycu+XAAj5pHvVoAHMm/VooMPwuyZNmjD+0Yk8MnYaUy6bxCXD+tKxa8dKZXYXFDN76tN8/O5Hlbafc8E5nNvrXG5PuZXbBkymS49uxCbHORn+cUtJ7UfnLmdxflx/bp1yP0/Prv6/9dOzH2XK5Ps5P64/nbucxYCUvgDcNXUimZmfEd/jN2RmfsZdUycCsHfvfu65+xHmzH6xUj1R0e2ZOOkGLrl4GEmJAwkLC2PE74b69yQb6IqBv6Frl06c0/1iJk68l7nP/q3acnOf/RsTJvyRc7pfTNcunRiY2h+AmU8+T8IFA+iVmMKSpR/w4AN3Ohl+/VnSDaxe8XFEtAoPdBh+1zW+G0XbCtm5fSdlh8v4ZPHHJKUkVyqzK28X//16G+VVfthUodnJJ9G0WVOantSMps3C2Ld7r5PhH7chQwbwevrbAGRnf0lERCvaR55RqUz7yDNoFX4q2dlfAvB6+tsMHZoCwOAhA0hPfwuA9PS3GOLeXly8h9y1Gzh8+JcXZZo2DaNFi+aEhYXRomVzCgt3+ev0fGLo0FQWpL8JwJqsXCJaRxAZ2a5SmcjIdoS3CmdNVi4AC9LfZNiwgQCUlv7oKXfKKS1xvRYseOmRw14vgVDnmK6ItAdi3Kv5qrrTvyGZ43F65OnsLij2rO8p3E23+F979d0tuV+z8fMNvJzzGiLC0lffI29rnr9C9amo6Pbk5R1743VBfiHR0ZHsLDrWFtHRkeTnHyuTn19EVLTrT+R27dp6yu4sKqZdu7a1Hq+wYCdznn6Bf2/5jJ8P/szKlZ/w4cpPfHlKPhcTHUnejgLPen5eITHRkRQV7apUJr9COx4tc9Rfpt/LdaNHsL+khMsH/M6ZwI9XYx3TFZF4EVkNZAL/414+EpHVIpLgUHzGAZG/iqJjl47c1PsG/pA0lrgLz6d7UvVjo6Gurl5c69atGDxkALHdL6VL52RantKSa0Ze5UxwAfTnhx6nU+dEXn/9HSZPqvMt44HViIcXXgFuV9VzVfVy93IOcAfwcm2Visg4EckRkZwXX3vdd9GaGu0p2kPb6GN/Vp8e1ZY9O/d49d0+A/uwZd0Wfj7wMz8f+JnczBx+nXCOv0JtsHHjx/D56iV8vnoJRUXFdOgQ5dkXHRNFQUFRpfIFBUXExBwrExMTSWGB6w+2Xbt2e4Yj2keeQXFx7W3Wv//FbPvvDnbv/oGysjIWvbuc5OTg64NMnDCWnOwMcrIzKCzaSYeO0Z59MR2iyK/SRvkFRcRUaMfqygAsfP1tfvvbQf4L3Be03PslAGpLuqeo6pqqG1V1NXBKbZWqapqq9lLVXjdfX+tr442PfLv+G6I6RdOuY3uaNmvKJUMvJWvFL/7zVau4oJjY5FiahDUhrGkY5yXHkbd1h58jPn5p8xdwYfJgLkwezHuLM7h29NUAJCbGU1JSWmloAVzDBiWlP5KYGA/AtaOv5r33VgCwdMkHjB49HIDRo4ezxL29JjvyCkhK7EmLFs0B6NfvQrZ8/R9fnp5PPD/vVXolptArMYVFi5YzZrRrNkvvpARK9pdUGloAKCraRWlJKb2TXL9AxoweweLFywHo0qWTp9ywoals2RJ851tJkPd0paY/p0RkNtAFeA04+i+wI3A98L2qTvHmAId3fxfQUfd7ps0ge90G9u0r4fQ2rZl00xiGD00NSCwjEm7za/0X9O/FTdNuoUlYE1a+sYL/ffZfjLprNFs3fkvWiiy69OjK/S88wKkRp3Lo/x1iX/Febr18smvmw2MTOS8pFlByM3N56S8v1nm8hli5e5PP6pr11HQuH3ApBw8cZMKEP7IudyMAn69ewoXJgwHomRDH/PlP0LxFc1ZkfMTUu1zTxtq0ac1rC56lQ8dodmzP5/oxU9i7dz/t2rflk08XER5+KuXlyk8//USvhBRKS3/kgQfvYPjwIZSVlbF+/WYmT7qPQ4cOHXf8P5cd/3e9NWf2Y6Sm9OPAwYPcfPNdrM3dAEBOdga9El0XDy9I6MHf//4ULZo3Z9nyVdx+x4MA/OuNNLp160x5eTnbt+czafJ9v/hrwlfKDuVLQ+s4uPxZr3NOi9QpDT5efdWWdBcAC4F3gAxAgHxgkaou9fYAgU66wcTfSbcx8WXSbeycSLqNhU+S7pKnvU+6g+9wPOnWNnshAbgX+Aa4oeIOEWmjqj/4MS5jjDk+QT57obakOx9YCXQCcipsF0CBs/0YlzHGHJ/G+uwFVZ0DzBGR51V1ooMxGWPM8WvEPV0ALOEaYxqVxtrTNcaYRqmx93SNMaZRKbNXsBtjjHOC/IE8If+UMWPMCcaHd6SJyEAR2SIiW0Xkvmr2nykiq0RknYhsEJE675G2pGuMCS0+SroiEgbMBa4AugPXikj3KsUeBP6lqj2BkcBzdYVnSdcYE1p898CbJGCrqn6nqoeAfwJXVj0a0Mr9OQIooA42pmuMCS1HjviqphiOPXcGIA/oXaXMw0CGiNyK60Fgl9dVqfV0jTGhpR7DCxUfQ+te6vuCwGuBV1S1AzAIWCAiteZV6+kaY0JLPW6OUNU0IK2G3fm4nqx4VAf3topuAga66/pCRJoDbYEa3+FkPV1jTGjx3ZhuNtBVRDqJyEm4LpQtqlJmO3AZgIicCzQHiqmF9XSNMSFFy30zT1dVy0RkCrAcCANeUtVNIjIdyFHVRcBU4AURuRPXRbUbtI53PlnSNcaEFh8+e8H97PClVbY9VOHzZuCi+tRpSdcYE1p8N3vBLyzpGmNCiz1lzBhjHGRJ1xhjHBTkD7yxpGuMCS3W0zXGGAf5aMqYv/g96dprx495M3dOoEMIGq3P/E2gQwgazcKs7+NTNnvBGGOcoza8YIwxDjrRhxeMMcZR9mJKY4xxkPV0jTHGQWV2Ic0YY5xjwwvGGOMgG14wxhjn2JQxY4xxkvV0jTHGQZZ0jTHGQXYbsDHGOMdX70jzF0u6xpjQYknXGGMcZLMXjDHGQdbTNcYYB1nSNcYY5+gRG14wxhjnWE/XGGOcY1PGjDHGSZZ0jTHGQcE9pGtJ1xgTWrQsuLOuJV1jTGgJ7pxLk0AH0FA9+ybw3Kp5zPs4jeGTRvxif/ek85i15Gne/u5dLhx0UaV9Y/90I898MJdnVz7PLY+McyrkgHnwr7O4dPBIrrpuQqBD8aknZk5jw8ZM1qx5n/j486otE98zlqysZWzYmMkTM6d5tp92WgSLFy9g/YZVLF68gNatWwFwzTVXsmbN+2RlLWPlh28RF3eu5zsREa34R/pz5K5bydrcD0hKSvDn6fnEk08+zFdffURW1jLi42OrLdOzZyzZ2cv56quPePLJhz3br756EGvXruCnn74nISHOoYiPn5ar10sgNOqk26RJE8Y/OpFHxk5jymWTuGRYXzp27VipzO6CYmZPfZqP3/2o0vZzLjiHc3udy+0pt3LbgMl06dGN2OTg/4FqiKsGDWDerEcDHYZPpab2o0uXTvSI68eUKX/i6dmPVVtu9uxHmTz5fnrEucqnpPQDYOrUiWRmfs75PfqTmfk5U6dOAmDbth2kpl5DUtJAHp/xDM88+zdPXU88MY0VKz4ioedlJPe+gi1btvr9PBsiNbU/nTt3Ija2L1Om3M+cOdX/DMyZ8xiTJ99HbGxfOnc+1kabNn3DyJHj+fTTNQ5G3QDl9VgCoFEn3a7x3SjaVsjO7TspO1zGJ4s/JikluVKZXXm7+O/X2yivcj+2KjQ7+SSaNmtK05Oa0bRZGPt273UyfMf1io8jolV4oMPwqcFDUliY/jYA2dnriIgIJzLyjEplIiPPIDw8nOzsdQAsTH+bIUNT3N8fQHr6mwCkp7/JkKEDAFizJpd9+0oAyMrKJSYmEoBWrcK56OIkXn3lDQAOHz7M/v0lfj7LhhkyZAALF74FQFbWOiIiWhEZ2a5SmcjIdoSHn0pWlruNFr7FUHcbbdmylW+//c7ZoBsgJHu6InKqrwM5HqdHns7ugmLP+p7C3Zze/nSvvrsl92s2fr6Bl3Ne45Wc11j3US55W/P8Farxk+jo9uTlFXjWC/KLiIqOrFQmKjqSgvxCz3p+fiHR0e0BaNfuDIqKXD9DRUXFtGtXOWEDjB17DRkZmQCcdVZHdu/ew/z5M/n8iyXMfW4GLVu28PVp+VR0dGSlNsrPL/Kc/7Ey7cnPL6pQppDoKu3YaIRoT3dzbTtFZJyI5IhIzrYftx/nIfwr8ldRdOzSkZt638AfksYSd+H5dE+qfjzQnDhUK/d+Lr20D9ePvYY/PzgDgLCmYcTHx/LCi//gwj6DOfDTQabePTEQoZoaaJn3S11EZKCIbBGRrSJyXw1lfi8im0Vkk4gsrKvOGmcviMhdNe0Cau3pqmoakAZw5ZlD/NaH31O0h7bRx3omp0e1Zc/OPV59t8/APmxZt4WfD/wMQG5mDr9OOIfNWZv8EqvxnXHjx3DjjdcCsHbtejp0iPbsi46JpLCgqFL5woIiomOiPOsxMVEUFOwEYNeuYiIjXb3dyMgzKC7e7SkXG3sOc5+bwW+vuoEfftgHuHrS+flF5GR/CcA77ywNyqQ7fvz13HjjSADWrt1QqY1iYiI9539UQcFOzxCKq0wUBVXasbHw1RvYRSQMmAsMAPKAbBFZpKqbK5TpCtwPXKSqe0WkXfW1HVNbT/evwGlAeJXl1Dq+55hv139DVKdo2nVsT9NmTblk6KVkrfBusL+4oJjY5FiahDUhrGkY5yXHkbd1h58jNr6QNn8BfZIH0Sd5EIsXZzBq9NUAJCb2pKSk1DNccFRRUTGlpaUkJvYEYNToq1nyXgYAS5d8wOjRrlkvo0ePYMl7KwDo0CGaha/P4+ab7mTr1u89de3cWUxeXgFdu54NQL/+F/H1v7/17wkfh/nzXyM5eRDJR9to1HAAkpKOttGuSuWLinZRWvojSUnuNho1nPfcbdHo+G54IQnYqqrfqeoh4J/AlVXK3ALMVdW9AKq6izrUNk83F/g/VV1bdYeI3FxnuA4oP1JO2p/n8fCC6TQJa8LKN1aw45vtjLprNFs3fkvWiiy69OjK/S88wKkRp5J4eRLX3jWKWy+fzOdLPiPuwh7MyZgLKLmZuWR/kBXoU/Kre6bNIHvdBvbtK+Gyq65j0k1jGD40NdBhNcjyZatITe3Pxq8+4uCBg4yfcI9n3xerl9IneRAAd9zxZ9Lmz6R5i+ZkZGSyfHkmAE8++TwLFszl+rG/Z8f2fMaMmQzA/X+6jTZtTuPp2a4r/WVlZVxy8TAA7p76MC+9/DQnNWvG99t2MGH83Q6ecf0tW/Yhqan92bTpYw4cOMj4CvGuXr2UZHcb3X77g6SlPUkLTxutAmDYsFRmzXqEtm3b8PbbL7Nhw2aGDbs+IOfijfr0dEVkHFBxvmia+y91gBigYk8sD+hdpYpu7no+A8KAh1V1Wa3HrDqGVSGY/wUmAqNVdXaVfe1VdWe1X6zCn8MLjc2buXMCHULQaH3mbwIdQtAor+Hf4Ino4MH/SkPr2HVZX68btN3Kj2o8noiMAAaq6s3u9TFAb1WdUqHMe8Bh4PdAB+BjIE5V99VUb2093e7AScAfROQ1XGO5Rx2u82yMMSYA9EiD8/ZR+UDFif8d3NsqygPWqOph4HsR+QboCmTXVGltSXc+sBI4G1hL5aSr7u3GGBNUfHUhDVfi7CoinXAl25HAqCpl/g+4FnhZRNriGm6odVJzjUlXVecAc0TkeVUNvsuzxhhTDS33TU9XVctEZAqwHNd47UuquklEpgM5qrrIvS9FRDYDR4B7VLXWKVR1PvDGEq4xpjHxYU8XVV0KLK2y7aEKnxW4y714xZ4yZowJKao+G9P1C0u6xpiQ4suerj9Y0jXGhJRy381e8AtLusaYkOKrC2n+YknXGBNSLOkaY4yDgv0GP0u6xpiQYj1dY4xxkE0ZM8YYBx2x2QvGGOMc6+kaY4yDbEzXGGMcZLMXjDHGQdbTNcYYBx0pD4pXONbIkq4xJqTY8IIxxjio3GYvGGOMc2zKmDHGOOiEH15YuXuTvw/RaNhrx4/Zt/3DQIdgQpQNLxhjjINs9oIxxjgoyEcXLOkaY0KLDS8YY4yDbPaCMcY4KMhfBmxJ1xgTWhTr6RpjjGPKbHjBGGOcYz1dY4xxkI3pGmOMg6yna4wxDrKerjHGOOiI9XSNMcY5Qf62Hku6xpjQUh7kPd3gfhyPMcbUk9ZjqYuIDBSRLSKyVUTuq6XccBFREelVV52WdI0xIaW8HkttRCQMmAtcAXQHrhWR7tWUCwduB9Z4E58lXWNMSCkX8XqpQxKwVVW/U9VDwD+BK6sp9xfgceBnb+KzpGuMCSlH6rGIyDgRyamwjKtQVQywo8J6nnubh4gkAB1VdYm38dmFNGNMSKnP7AVVTQPSjuc4ItIEmAXcUJ/vWdI1xoQUH85eyAc6Vljv4N52VDgQC2SKa6giElgkIsNUNaemSi3pGmNCig9f15MNdBWRTriS7UhglOc4qvuBtkfXRSQTuLu2hAuWdI0xIcZXN0eoapmITAGWA2HAS6q6SUSmAzmquuh46m1UF9KemDmN9RtXsXrN+5wff161ZeJ7xrIm633Wb1zFEzOnebafdloEixYv4MsNH7Jo8QJat24FQLduZ7Ny1Vvs2fs1t91+S6W6Jk/5A9k5y8nKXsbLr8zm5JNP8t/J1dMTM6exYWMma9a8T3wtbZGVtYwNGzN/0RaLFy9g/YZVLK7QFtdccyVr1rxPVtYyVn74FnFx53q+ExHRin+kP0fuupWszf2ApKQEf56e3z3411lcOngkV103IdChBFQotoOvpowBqOpSVe2mqp1V9TH3toeqS7iq2q+uXi40oqSbktqPzl3O4vy4/tw65X6env1oteWenv0oUybfz/lx/enc5SwGpPQF4K6pE8nM/Iz4Hr8hM/Mz7po6EYC9e/dzz92PMGf2i5XqiYpuz8RJN3DJxcNIShxIWFgYI3431L8n6aXU1H506dKJHnH9mDLlTzw9+7Fqy82e/SiTJ99PjzhX+ZSUfgBMnTqRzMzPOb9HfzIzP2fq1EkAbNu2g9TUa0hKGsjjM57hmWf/5qnriSemsWLFRyT0vIzk3lewZctWv5+nP101aADzZlX/M3QiCcV2OCLeL4HQaJLukCEDeD39bQCys78kIqIV7SPPqFSmfeQZtAo/lezsLwF4Pf1thg5NAWDwkAGkp78FQHr6Wwxxby8u3kPu2g0cPnz4F8ds2jSMFi2aExYWRouWzSks3OWv06uXwUNSWOhpi3VERIQTWaUtIiPPIDw8nOzsdQAsTH/bc86utngTgPT0NxkydAAAa9bksm9fCQBZWbnExEQC0KpVOBddnMSrr7wBwOHDh9m/v8TPZ+lfveLjiGgVHugwAi4U28GXPV1/aDRJNyq6PXl5hZ71gvxCoqMjK5WJjo4kP/9Ymfz8IqKi2wPQrl1bdhYVA7CzqJh27dpSm8KCncx5+gX+veUz/vPdGkr2l/Lhyk98dToNEh3dnry8As96QX4RUVXaIio6koJKbVFItKctzqDI3RZFRcW0a1c5YQOMHXsNGRmZAJx1Vkd2797D/Pkz+fyLJcx9bgYtW7bw9WkZ4xONNumKSJyIrBaRHSKSJiKnVdiXVVulFSccHy4r9WW8PqNa+zXO1q1bMXjIAGK7X0qXzsm0PKUl14y8ypngHFa1LS69tA/Xj72GPz84A4CwpmHEx8fywov/4MI+gznw00Gm3j0xEKEaUycV75dAqK2n+zzwMBAHfAN8KiKd3fua1Vapqqapai9V7dWs6fH/6TJu/Bg+X72Ez1cvoaiomA4dojz7omOiKCgoqlS+oKCImJhjZWJiIiks2AnArl27PcMR7SPPoLh4T63H7t//Yrb9dwe7d/9AWVkZi95dTnJy4C4ejRs/hi9WL+WL1UspKtpFhw7Rnn3RMZEUVmmLwoIioiu1RRQFnrYo9gxHREaeQXHxbk+52NhzmPvcDK75/S388MM+wNWTzs8vIsc9bPPOO0uJj4/1x2ka02CNtqcLtFLVZaq6T1VnAlOAZSKSjE+nwtUsbf4CLkwezIXJg3lvcQbXjr4agMTEeEpKSj3DBUftLCqmpPRHEhPjAbh29NW8994KAJYu+YDRo4cDMHr0cJa4t9dkR14BSYk9adGiOQD9+l3Ilq//48vTq5e0+QvokzyIPsmDWLw4g1GetuhJSUmpZ7jgqKKiYkpLS0lM7AnAqNFXs+S9DOBoW4wAYPToEZ626NAhmoWvz+Pmm+5k69bvPXXt3FlMXl4BXbueDUC//hfx9b+/9e8JG3Oc6nMbcCBITX9mi8iXQF/3BOCj23oAbwFtVPV0bw5wastOPkvQs56azuUDLuXggYNMmPBH1uVuBODz1Uu4MHkwAD0T4pg//wmat2jOioyPmHqXa6pUmzateW3Bs3ToGM2O7flcP2YKe/fup137tnzy6SLCw0+lvFz56aef6JWQQmnpjzzw4B0MHz6EsrIy1q/fzORJ93Ho0KHjjl99+Ltq1lPTGTCgLwcPHGT8hHs8bfHF6qX0SR4EuNoibf5MmrdoTkZGZqW2WLBgrqctxoyZzN69+5n73AyuvPIKduxw3XRTVlbGJRcPA6BHj+7MfW4GJzVrxvfbdjBh/N2ei27HY9/2Dxty+g12z7QZZK/bwL59JZzepjWTbhrD8KGpAY0pEIKtHZq1PbvBf/TPPvM6r/+h3b79H44PMtSWdI8AFwG9VXV2he1nAn9W1Vuq/WIVvky6jZ0vk25jF+ika4KTL5LuU/VIuncGIOnWNrywBdgO/EFEThORNiLSBvgRuNeR6Iwxpp6CfUy3ttuAnwdWAmcDa6HSUyTUvd0YY4JKsP89WWPSVdVngGdE5HlVtflBxphGodG/mNISrjGmMQnUrARv2VPGjDEhpTzIBxgs6RpjQkqgLpB5y5KuMSakBHc/15KuMSbEWE/XGGMcVCbB3de1pGuMCSnBnXIt6RpjQowNLxhjjINsypgxxjgouFOuJV1jTIix4QVjjHHQkSDv61rSNcaEFOvpGmOMg4L9ZQGWdI0xIcV6usYY4yCbMmaMMQ4K7pRrSdcYE2LKgjztWtI1xoSUE/5C2s9lh/x9iEajWZj9jjPG3+xCmjHGOOiE7+kaY4yTgr2n2yTQARhjjC8dUfV6qYuIDBSRLSKyVUTuq2b/XSKyWUQ2iMhKEflVXXVa0jXGhJRy1OulNiISBswFrgC6A9eKSPcqxdYBvVS1B/Am8D91xWdJ1xgTUrQe/6tDErBVVb9T1UPAP4ErKx1LdZWqHnCvrgY61FWpJV1jTEgpr8ciIuNEJKfCMq5CVTHAjgrree5tNbkJeL+u+OxCmjEmpNTnNmBVTQPSGnpMEbkO6AX0rausJV1jTEjx4ZSxfKBjhfUO7m2ViMjlwANAX1X9f3VVaknXGBNSvJmV4KVsoKuIdMKVbEcCoyoWEJGewHxgoKru8qZSS7rGmJDiq6eMqWqZiEwBlgNhwEuquklEpgM5qroIeAI4FfhfEQHYrqrDaqvXkq4xJqT48uYIVV0KLK2y7aEKny+vb52WdI0xIcVuAzbGGAfZQ8yNMcZB6rsLaX5hSdcYE1LsFezGGOMgG14wxhgH2fCCMcY4yHq6xhjjIJsyZowxDvLhbcB+YUnXGBNSbHjBGGMcFOxJNyQeYv7UrOl8vflTcteuoGd8bLVlEnrGsS73A77e/ClPzZru2f7Iw/eQu3YFOdkZvL9kIVFR7Z0K2y+efPJhvvrqI7KylhFfQ1v07BlLdvZyvvrqI5588mHP9quvHsTatSv46afvSUiIcyjiwHjwr7O4dPBIrrpuQqBDCahQbAdV9XoJhEafdK8Y+Bu6dunEOd0vZuLEe5n77N+qLTf32b8xYcIfOaf7xXTt0omBqf0BmPnk8yRcMIBeiSksWfoBDz5wp5Ph+1Rqan86d+5EbGxfpky5nzlzHq223Jw5jzF58n3Exvalc+dOpKT0A2DTpm8YOXI8n366xsGoA+OqQQOYN6v69jmRhGI7+Oodaf7S6JPu0KGpLEh/E4A1WblEtI4gMrJdpTKRke0IbxXOmqxcABakv8mwYQMBKC390VPulFNaBv0cv9oMGTKAhQvfAiArax0REa2qb4vwU8nKWgfAwoVvMXRoCgBbtmzl22+/czboAOkVH0dEq/BAhxFwodgOPnxHml94NaYrIidXfSJ6ddsCISY6krwdBZ71/LxCYqIjKSraValMfl7hL8oc9Zfp93Ld6BHsLynh8gG/cyZwP4iOjiQvr0Jb5BcRHd2+UltER7cnP7+oQplCoiu0hTGN3RH15cMdfc/bnu4XXm5rlP780ON06pzI66+/w+RJNwY6HGNMAzTqMV0RiRSRC4AWItJTRBLcSz+gZS3f87xhs7z8J99GDEycMJac7AxysjMoLNpJh47Rnn0xHaLILyiqVD6/oIiYDlG1lgFY+Prb/Pa3g3werz+NH389q1cvZfXqpRQV7aJDhwptERNJQcHOSuULCnYSExNZoUwUBdW0hTGNVWMf000FZuJ6IduTFZY7gT/V9CVVTVPVXqraq0mTU3wVq8fz816lV2IKvRJTWLRoOWNGjwCgd1ICJftLKv05DVBUtIvSklJ6JyUAMGb0CBYvXg5Aly6dPOWGDU1ly5b/+Dxef5o//zWSkweRnDyIxYszGDVqOABJST0pKSmtvi1KfyQpqScAo0YN5733VjgetzH+EuxjuuJNF1tEhqvqW8dzgKYnxfj9zObMfozUlH4cOHiQm2++i7W5GwDIyc6gV6LrItEFCT34+9+fokXz5ixbvorb73gQgH+9kUa3bp0pLy9n+/Z8Jk2+z289v2Zh/p8W/dRTfyElpS8HDhxk/Pi7yc3dCMDq1UtJTnb14hMS4khLe5IWLZqTkZHJnXe63j4ybFgqs2Y9Qtu2bdi3r4QNGzYzbNj1fomzZMcqv9TrrXumzSB73Qb27Svh9DatmXTTGIYPTQ1oTIEQbO3QrO3Z0tA6Ytsne51zvtq5usHHqy9vk+5fgf9R1X3u9dOAqar6YF3fdSLpNhZOJN3GItBJ1wQnXyTd89r39jrnbNq5xvGk6+2FtCuOJlwAVd0LNK7BT2PMCeGIlnu9BIK3Xa+wilPERKQFcLL/wjLGmONTHuRz7b1NuunAShF52b1+I/Cqf0IyxpjjFxKPdlTVx0VkA3CZe9NfVHW5/8IyxpjjEyo9XVT1feB9P8ZijDENFhI9XREpBc+ZnAQ0A35S1Vb+CswYY47HET0S6BBq5e3wgueJGCIiwJVAsr+CMsaY4xXsD62q91PG1OX/cN2tZowxQSXYbwP2dnjh6gqrTYBewM9+icgYYxog2Hu63l5IG1rhcxmwDdcQgzHGBJWQmL2gqva8Q2NMo9CoZy+IyDNQ8xmo6m0+j8gYYxqgsT/EPAdYCzQHEoBv3Us8rqljxhgTVIL9Iea19nRV9VUAEZkIXKyqZe71ecAn/g/PGGPqx5djuiIyEJgNhAEvquqMKvtPBl4DLgD2ANeo6rba6vR2ythpQMUbIU51bzPGmKDiq56uiIQBc4ErgO7AtSLSvUqxm4C9qtoFeAp4vK74vJ29MANYJyKrAAEuBR728rvGGOMYH86/TQK2qup3ACLyT1yztjZXKHMlx3Lhm8CzIiJaS0b3dvbCyyLyPtAb14W1e1XVXqxljAk69RmrFZFxwLgKm9JUNc39OQbYUWFfHq4cWJGnjKqWich+4HRgd03HrM+rDJKAS9yfFVhcj+8aY4wj6jN7wZ1g0+os6EPe3pE2A0jE9VxdgNtEpI+q1vhySmOMCQQfXkjLBzpWWO/g3lZdmTwRaQpE4LqgViNve7qDgHhV168QEXkVWEctbwQ2xphA8OFUsGygq4h0wpVcRwKjqpRZBIwFvgBGAB/WNp4L9RteaA384P4cUY/vGWOMY3x1R5p7jHYKsBzXlLGXVHWTiEwHclR1EfB3YIGIbMWVH0fWVa+3Sfev/HL2wn3HcR7GGONXvrzpQVWXAkurbHuowuefgd/Vp846k66INAHKcT0/N9G92WYvGGOCUrA/8Ea8+a0gIjmq2suBePxGRMZVmApyQrO2OMba4hhrC2d4e0faByJyt4h0FJE2Rxe/RuZ74+oucsKwtjjG2uIYawsHeDumew2uubmTqmw/27fhGGNMaPM26XbHlXAvxpV8PwHm+SsoY4wJVd4m3VeBEmCOe32Ue9vv/RGUn9hY1THWFsdYWxxjbeEAby+kbVbV7nVtM8YYUztvL6Tliojnlesi0hvXA86NMcbUg7c93X8Dvwa2uzedCWzB9ZJKVdUefovQGGNCiLdJ91e17VfV//osIh8RkduAiUAurgdQDAIOADeoam4gY3NahbbYDETjevXSA6o6M6CBBUCFtjgH2IjrDstSYKKqrg9kbIEmIjcAvVR1ynF+N0NVC3wdV6jx9nm6QZdUvTAJuBzoAdwKdMX1LMzn+eUzMUPd0bY4BPwKuCqg0QTW0bY4E/i3qu4VkStwXUQKyZ8LEQlT1SN+PswNwFeAJd06eDum26i43+F2NvA+8A7wmrqsBlqLSFRAA3RQlbYYrarZwOHARhUYVdqit6rude9ajeuxfY2OiJwlIl+LSLqI/FtE3hSRliKyTUQeF5Fc4Hcicq2IbBSRr0Tk8Qrfv1FEvhGRLOCiCttfEZERFdZ/rPD5Xndd60VkhrtcLyBdRL4UkRbOnH3jVJ+njDUaqjrB/UK5/sAr/PLp7zFAYQBCc1zFtlDVGp9mfyKopS1uwpWIG6tfAzep6mci8hLHbmLao6oJIhKN6xfLBcBeIENErgLWAI+4t+8HVuF6ZGuN3H8VXInrl9YBEWmjqj+4n8Z1t6raBfY6hGTSNcZbItIfV9K9ONCxNMAOVf3M/fkfwG3uz2+4/z8RyFTVYgARScf1pECqbH8D6FbHsS4HXlbVAwCq+kMd5U0VJ0LS9ebp7+YEJCI9gBeBK1S11qf9B7mqV8OPrv/UgDrLcA8/up80eFID6jIVhOSYbhWLgOvFJRnYr6onxNCCqZmInAm8DYxR1W8CHU8DnSkifdyfRwGfVtmfBfQVkbbu14pfC3yEa3ihr4icLiLNqPxc2G24hh0AhgHN3J9XADeKSEuACg++KgXCfXdKoetE6OkuxTVdbCuuKWM3BjacwBGRSFw3tbQCykXkDqC7qpYENLDAeAjXW1ufExGAskb8+NItwGT3eO5mXDN0bj26U1ULReQ+XGO2AixR1XcBRORhXK+a2Qd8WaHOF4B3RWQ9sAx3r1lVl4lIPJAjIodw/fv6E65rJ/NE5CDQR1UP+ulcGz2v5ukaY4KTiJwFvKeqsYGOxXjnRBheMMaYoGE9XWOMcZD1dI0xxkGWdI0xxkGWdI0xxkGWdI0xxkGWdI0xxkH/H1Nc3of4i8A6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n",
      "<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",
      "None\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "          id        f0        f1        f2    product\n",
      "67544  glIDp -0.759761 -0.113697  1.788634  21.072034\n",
      "60065  KvqHJ  1.195022  0.787031  3.680542  98.889740\n",
      "=*=*=*=*=*=*=*=*=*=*\n",
      "                  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\n",
      "=*=*=*=*=*=*=*=*=*=*\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*\n"
     ]
    }
   ],
   "source": [
    "for df in dfs:\n",
    "    print(df.info())\n",
    "    print('=*'*10)\n",
    "    print(df.sample(2))\n",
    "    print('=*'*10)\n",
    "    print(df.describe())\n",
    "    print('=*'*10)\n",
    "    sns.heatmap(df.corr(), annot=True)\n",
    "    plt.show()\n",
    "    print('=*'*40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Похоже, что признаки уже отмасштабированы. Категориальных признаков нет (id мы удалим ниже, для обучения модели он не нужен). Во втором регионе имеем полную зависимость целевой переменной от признака f2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение и проверка модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функция, которая будет считать RMSE и выводить среднее предсказанное значение. Также в агрументах функции имеется возможность указать значения для удаления коррелирующих значений и применения масштабирования признаков. Как показали проверки ниже - это лишнее."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "RS=123"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_lr(df, drop_col=None, scaler=None): # Do Logistic Regression for df\n",
    "    \n",
    "    df = df.drop('id', axis=1)\n",
    "   \n",
    "    if drop_col:\n",
    "        print('dropped', drop_col)\n",
    "        df = df.drop(drop_col, axis=1)\n",
    "    X = df.drop('product', axis=1)\n",
    "    y = df['product']\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=RS)\n",
    "    \n",
    "    if scaler:\n",
    "        print(\"do scalling\")\n",
    "        #Creating Scaler Object\n",
    "        scaler_ = scaler\n",
    "        X_train = scaler_.fit_transform(X_train)\n",
    "        X_test = scaler_.transform(X_test)\n",
    "        \n",
    "    model = LinearRegression() \n",
    "    model.fit(X_train, y_train) \n",
    "    pred_test = pd.Series(model.predict(X_test)) \n",
    "    rmse = mean_squared_error(y_test, pred_test)**0.5 \n",
    "    mean_pred_test = pred_test.mean()\n",
    "    pred_all =  pd.Series(model.predict(X)) # чтобы сделать предсказания на всех данных и выбрать лучшие 200, нужно потом\n",
    "    \n",
    "    return  (rmse, mean_pred_test, pred_test, pred_all, y)   #2: добавим возвращаемое значение y\n",
    "\n",
    "\n",
    "def get_resume(args):\n",
    "    \n",
    "    print('Значение RMSE:', args[0])\n",
    "    print('Среднее предсказанное значение:', args[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Сохраним результаты обучения без предобработки данных\n",
    "lr_0 = do_lr(df0)\n",
    "lr_1 = do_lr(df1)\n",
    "lr_2 = do_lr(df2)\n",
    "lrs = [lr_0, lr_1, lr_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Значение RMSE: 37.64786282376177\n",
      "Среднее предсказанное значение: 92.54936189116306\n"
     ]
    }
   ],
   "source": [
    "get_resume(lr_0) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Варьируем параметры входных данных:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "do scalling\n",
      "Значение RMSE: 37.64786282376177\n",
      "Среднее предсказанное значение: 92.54936189116306\n"
     ]
    }
   ],
   "source": [
    "get_resume(do_lr(df0, scaler=RobustScaler())) # применим RobustScaler (https://machinelearningknowledge.ai/sklearn-feature-scaling-with-standardscaler-minmaxscaler-robustscaler-and-maxabsscaler/#Robust-Scaler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Эффекта от применения скалера нет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped ['f1']\n",
      "Значение RMSE: 38.196765022130705\n",
      "Среднее предсказанное значение: 92.50644418479048\n"
     ]
    }
   ],
   "source": [
    "get_resume(do_lr(df0, drop_col=['f1'])) # удалим коррелирующий признак"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Результат: удаление одного из признаков, который имеет обратную умеренную зависимость дает отрицательный эффект.  \n",
    "Модель работает неплохо и без лишних манипуляций с данными, соотвественно, не будем это использовать.\n",
    "Строим предсказания для трех регионов и анализируем результат:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Регион №1: \n",
      "Значение RMSE: 37.64786282376177\n",
      "Среднее предсказанное значение: 92.54936189116306\n",
      "Реальное среднее: 92.50000000000001\n",
      "\n",
      "Регион №2: \n",
      "Значение RMSE: 0.8954139804944313\n",
      "Среднее предсказанное значение: 69.28001860653976\n",
      "Реальное среднее: 68.82500000000002\n",
      "\n",
      "Регион №3: \n",
      "Значение RMSE: 40.12803006598514\n",
      "Среднее предсказанное значение: 95.09859933591373\n",
      "Реальное среднее: 95.00000000000004\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for n, df in enumerate(dfs):\n",
    "    print(f'Регион №{n+1}: ')\n",
    "    get_resume(do_lr(df))\n",
    "    print('Реальное среднее:', df['product'].mean())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: среднее значение предсказано весьма точно, показатель RMSE в 1 и 3 регионе достаточно высок и почти одинаков, а во втором регионе за счет полной корреляции признака f2 - минимален, модель будет очень точно предсказывать данные."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка к расчёту прибыли"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_points = 500 # При разведке региона исследуют 500 точек\n",
    "best_points = 200 #  с помощью машинного обучения выбирают 200 лучших для разработки\n",
    "budget = 10000 #(в млн руб) Бюджет на разработку скважин в регионе — 10 млрд рублей.\n",
    "income_1000b = 0.45 #(в млн руб) Доход с каждой единицы продукта составляет 450 тыс. рублей\n",
    "prob_loss = 0.025 #вероятность убытков меньше 2.5%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассчитаем достаточный объём сырья для безубыточной разработки новой скважины:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111.11111111111111"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_minuse_dev = budget / best_points / income_1000b\n",
    "non_minuse_dev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравнивая данный объем со средними запасами в регионах можно сделать вывод, что все три региона  не имеют средние показатели выше этого порога. То есть, если случайным образом вести разработку скважин, скорее всего они окажутся убыточными."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Расчёт прибыли и рисков \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_best_income(lr, to_print=0, region_n=None):\n",
    "    best_income = lr[3].sort_values(ascending=False).head(best_points).sum() * income_1000b - budget\n",
    "    if to_print:\n",
    "        print(f'Прогнозируемая прибыль (млн) с лучших {best_points} скважин в регионе №{region_n+1}: ')\n",
    "        print(best_income) \n",
    "    return best_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Прогнозируемая прибыль (млн) с лучших 200 скважин в регионе №1: \n",
      "4678.58361482675\n",
      "Прогнозируемая прибыль (млн) с лучших 200 скважин в регионе №2: \n",
      "2524.90649073436\n",
      "Прогнозируемая прибыль (млн) с лучших 200 скважин в регионе №3: \n",
      "4138.6421333428225\n"
     ]
    }
   ],
   "source": [
    "for n, lr in enumerate(lrs):\n",
    "    get_best_income(lr, to_print=1, region_n=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: по лучшим скважинам прибыль очень высокая. Но тут надо учитывать, что выбор происходит  лучших 200 точек не из 500 точек, как в условиях поиска, а из всего датасета с информацией о  100000 точках."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_distr_income(lr, n_samples=1000, n=search_points):\n",
    "    rs = np.random.RandomState(RS)\n",
    "    pred_reg = lr[3]\n",
    "    y = lr[4] # истинное значение запасов\n",
    "    distr = [] # distribution of best_samle_true_income\n",
    "        \n",
    "    for i in range(n_samples):\n",
    "        sample = pred_reg.sample(n=n, replace=True, random_state=rs)\n",
    "        # 2 исправляем логику расчета \n",
    "        # best_sample_income =  sample.sort_values(ascending=False).head(best_points).sum() * income_1000b - budget\n",
    "        best_sample_indexes = sample.sort_values(ascending=False).head(best_points).index #2\n",
    "        best_samle_true_income = y[best_sample_indexes].sum() * income_1000b - budget #2\n",
    "        distr.append(best_samle_true_income.mean())\n",
    "    return distr\n",
    "\n",
    "def get_resume_distr_income(distr:list, region_n=''):\n",
    "    risk = round(sum([i<0 for i in distr])/len(distr), 4) #Исправлено: поправил сравнение и сделал округление более точным\n",
    "    distr = pd.Series(distr)\n",
    "    print(f\"Результаты bootstrap-выборки в регионе {region_n+1}\")\n",
    "    print(\"-\"*40)\n",
    "    print(f\"График распределения прибыли:\")\n",
    "    distr.plot(kind='hist', bins=100)\n",
    "    plt.show()\n",
    "    mean_distr = round(distr.mean(), 3)\n",
    "    low = round(distr.quantile(0.025), 2)\n",
    "    up = round(distr.quantile(0.975), 2)\n",
    "    print(f\"Средняя прибыль: {mean_distr}\")\n",
    "    print(f\"95% доверительный интервал: от {low} до {up}\")\n",
    "    #print(f\"Риск убытков: {risk*100}%\")\n",
    "    \n",
    "    if risk < prob_loss:\n",
    "        print(f\"Вероятность убытков равна {risk*100}% и является меньше допустимой. Регион подходит!\")\n",
    "    else:\n",
    "        print(f\"Вероятность убытков равна {risk*100}% и является больше допустимой. Регион стоит исключить из разработки.\")   \n",
    "    print(\"*=\"*40)\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результаты bootstrap-выборки в регионе 1\n",
      "----------------------------------------\n",
      "График распределения прибыли:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль: 420.618\n",
      "95% доверительный интервал: от -68.35 до 945.86\n",
      "Вероятность убытков равна 5.3% и является больше допустимой. Регион стоит исключить из разработки.\n",
      "*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=\n",
      "\n",
      "Результаты bootstrap-выборки в регионе 2\n",
      "----------------------------------------\n",
      "График распределения прибыли:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль: 452.222\n",
      "95% доверительный интервал: от 42.53 до 850.21\n",
      "Вероятность убытков равна 1.3% и является меньше допустимой. Регион подходит!\n",
      "*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=\n",
      "\n",
      "Результаты bootstrap-выборки в регионе 3\n",
      "----------------------------------------\n",
      "График распределения прибыли:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Средняя прибыль: 371.707\n",
      "95% доверительный интервал: от -142.43 до 854.58\n",
      "Вероятность убытков равна 7.7% и является больше допустимой. Регион стоит исключить из разработки.\n",
      "*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for n, lr in enumerate(lrs):\n",
    "    get_resume_distr_income(get_distr_income(lr), n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод по результатам bootstrap:\n",
    "    из всех регионов исходя из ТЗ лучше остановится на регионе №2 со средней прибылью 452.222. Остальные регионы не подходят под условия задачи."
   ]
  }
 ],
 "metadata": {
  "ExecuteTimeLog": [
   {
    "duration": 43,
    "start_time": "2022-05-11T17:55:36.921Z"
   },
   {
    "duration": 356,
    "start_time": "2022-05-11T17:55:40.355Z"
   },
   {
    "duration": 326,
    "start_time": "2022-05-11T17:55:40.713Z"
   },
   {
    "duration": 15,
    "start_time": "2022-05-11T17:56:02.047Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-11T17:57:21.943Z"
   },
   {
    "duration": 14,
    "start_time": "2022-05-11T17:57:33.169Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-11T18:03:31.017Z"
   },
   {
    "duration": 322,
    "start_time": "2022-05-11T18:03:34.361Z"
   },
   {
    "duration": 15,
    "start_time": "2022-05-11T18:03:34.685Z"
   },
   {
    "duration": 33,
    "start_time": "2022-05-11T18:03:39.752Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-11T18:03:59.632Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-11T18:04:06.903Z"
   },
   {
    "duration": 31,
    "start_time": "2022-05-11T18:04:12.039Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-11T18:05:12.942Z"
   },
   {
    "duration": 32,
    "start_time": "2022-05-11T18:05:19.490Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-11T18:05:29.454Z"
   },
   {
    "duration": 1527,
    "start_time": "2022-05-11T18:10:06.599Z"
   },
   {
    "duration": 430,
    "start_time": "2022-05-11T18:10:08.128Z"
   },
   {
    "duration": 682,
    "start_time": "2022-05-11T18:10:23.278Z"
   },
   {
    "duration": 869,
    "start_time": "2022-05-11T18:10:36.040Z"
   },
   {
    "duration": 48,
    "start_time": "2022-05-11T18:12:22.566Z"
   },
   {
    "duration": 875,
    "start_time": "2022-05-11T18:12:34.277Z"
   },
   {
    "duration": 1080,
    "start_time": "2022-05-11T18:47:36.723Z"
   },
   {
    "duration": 1002,
    "start_time": "2022-05-11T18:49:24.151Z"
   },
   {
    "duration": 1235,
    "start_time": "2022-05-11T18:49:35.933Z"
   },
   {
    "duration": 20,
    "start_time": "2022-05-11T18:50:29.859Z"
   },
   {
    "duration": 12,
    "start_time": "2022-05-11T18:51:14.987Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-11T18:51:22.048Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-11T18:51:34.091Z"
   },
   {
    "duration": 13,
    "start_time": "2022-05-11T18:51:40.628Z"
   },
   {
    "duration": 12,
    "start_time": "2022-05-11T18:52:01.052Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-11T18:52:29.761Z"
   },
   {
    "duration": 17,
    "start_time": "2022-05-11T18:52:53.428Z"
   },
   {
    "duration": 294,
    "start_time": "2022-05-11T18:54:18.552Z"
   },
   {
    "duration": 18,
    "start_time": "2022-05-11T18:54:38.297Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-11T18:54:57.262Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-11T18:55:01.192Z"
   },
   {
    "duration": 4297,
    "start_time": "2022-05-11T18:55:22.782Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-11T18:55:27.081Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-11T18:55:42.937Z"
   },
   {
    "duration": 3218,
    "start_time": "2022-05-11T18:56:06.038Z"
   },
   {
    "duration": 16,
    "start_time": "2022-05-11T18:56:16.509Z"
   },
   {
    "duration": 1143,
    "start_time": "2022-05-11T19:03:12.341Z"
   },
   {
    "duration": 992,
    "start_time": "2022-05-11T19:03:55.317Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-11T19:04:11.056Z"
   },
   {
    "duration": 379,
    "start_time": "2022-05-11T19:04:11.061Z"
   },
   {
    "duration": 16,
    "start_time": "2022-05-11T19:04:11.442Z"
   },
   {
    "duration": 1043,
    "start_time": "2022-05-11T19:04:11.460Z"
   },
   {
    "duration": 1315,
    "start_time": "2022-05-11T19:04:39.029Z"
   },
   {
    "duration": 1673,
    "start_time": "2022-05-12T05:55:21.765Z"
   },
   {
    "duration": 331,
    "start_time": "2022-05-12T05:55:23.440Z"
   },
   {
    "duration": 14,
    "start_time": "2022-05-12T05:55:23.772Z"
   },
   {
    "duration": 974,
    "start_time": "2022-05-12T05:55:23.787Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T05:55:24.763Z"
   },
   {
    "duration": 103,
    "start_time": "2022-05-12T05:58:01.625Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T05:58:17.054Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-12T05:58:17.993Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T05:59:07.497Z"
   },
   {
    "duration": 50,
    "start_time": "2022-05-12T05:59:08.432Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T05:59:22.105Z"
   },
   {
    "duration": 114,
    "start_time": "2022-05-12T05:59:22.793Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T05:59:36.729Z"
   },
   {
    "duration": 79,
    "start_time": "2022-05-12T05:59:37.529Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T05:59:46.752Z"
   },
   {
    "duration": 28,
    "start_time": "2022-05-12T05:59:47.081Z"
   },
   {
    "duration": 31,
    "start_time": "2022-05-12T06:00:52.480Z"
   },
   {
    "duration": 43,
    "start_time": "2022-05-12T06:01:01.657Z"
   },
   {
    "duration": 28,
    "start_time": "2022-05-12T06:01:07.968Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T06:42:52.274Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-12T06:44:16.211Z"
   },
   {
    "duration": 194,
    "start_time": "2022-05-12T06:44:20.554Z"
   },
   {
    "duration": 23,
    "start_time": "2022-05-12T06:44:20.929Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T06:55:04.440Z"
   },
   {
    "duration": 15,
    "start_time": "2022-05-12T06:55:04.954Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T06:55:17.920Z"
   },
   {
    "duration": 34,
    "start_time": "2022-05-12T06:55:18.400Z"
   },
   {
    "duration": 23,
    "start_time": "2022-05-12T06:56:02.601Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T06:56:34.552Z"
   },
   {
    "duration": 38,
    "start_time": "2022-05-12T06:56:34.874Z"
   },
   {
    "duration": 27,
    "start_time": "2022-05-12T06:56:38.607Z"
   },
   {
    "duration": 27,
    "start_time": "2022-05-12T06:58:08.376Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T06:59:02.984Z"
   },
   {
    "duration": 25,
    "start_time": "2022-05-12T06:59:03.431Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T06:59:14.056Z"
   },
   {
    "duration": 73,
    "start_time": "2022-05-12T06:59:14.727Z"
   },
   {
    "duration": 35,
    "start_time": "2022-05-12T07:00:03.016Z"
   },
   {
    "duration": 65,
    "start_time": "2022-05-12T07:00:14.639Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T07:21:13.132Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-12T07:21:13.916Z"
   },
   {
    "duration": 24,
    "start_time": "2022-05-12T07:21:14.924Z"
   },
   {
    "duration": 14,
    "start_time": "2022-05-12T07:21:23.387Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-12T07:21:31.939Z"
   },
   {
    "duration": 23,
    "start_time": "2022-05-12T07:21:32.332Z"
   },
   {
    "duration": 105,
    "start_time": "2022-05-12T07:21:49.500Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T07:23:41.548Z"
   },
   {
    "duration": 29,
    "start_time": "2022-05-12T07:23:41.868Z"
   },
   {
    "duration": 119,
    "start_time": "2022-05-12T07:24:14.083Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-12T07:24:30.203Z"
   },
   {
    "duration": 92,
    "start_time": "2022-05-12T07:24:43.515Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T07:24:53.235Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-12T07:24:53.731Z"
   },
   {
    "duration": 22,
    "start_time": "2022-05-12T07:24:57.755Z"
   },
   {
    "duration": 35,
    "start_time": "2022-05-12T07:25:15.052Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T07:49:04.344Z"
   },
   {
    "duration": 80,
    "start_time": "2022-05-12T07:49:04.719Z"
   },
   {
    "duration": 21,
    "start_time": "2022-05-12T07:49:05.616Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-12T07:52:24.999Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-12T07:52:40.214Z"
   },
   {
    "duration": 198,
    "start_time": "2022-05-12T07:55:42.447Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-12T07:55:42.855Z"
   },
   {
    "duration": 193,
    "start_time": "2022-05-12T07:55:56.087Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-12T07:55:56.502Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-12T07:56:05.366Z"
   },
   {
    "duration": 58,
    "start_time": "2022-05-12T07:56:05.750Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T07:57:13.663Z"
   },
   {
    "duration": 77,
    "start_time": "2022-05-12T07:57:14.127Z"
   },
   {
    "duration": 109,
    "start_time": "2022-05-12T07:57:21.494Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-12T07:57:38.374Z"
   },
   {
    "duration": 30,
    "start_time": "2022-05-12T07:57:38.798Z"
   },
   {
    "duration": 20,
    "start_time": "2022-05-12T07:57:39.494Z"
   },
   {
    "duration": 21,
    "start_time": "2022-05-12T07:58:30.231Z"
   },
   {
    "duration": 41,
    "start_time": "2022-05-12T07:59:17.415Z"
   },
   {
    "duration": 20,
    "start_time": "2022-05-12T07:59:38.238Z"
   },
   {
    "duration": 375,
    "start_time": "2022-05-12T08:02:34.126Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-12T08:03:58.478Z"
   },
   {
    "duration": 299,
    "start_time": "2022-05-12T08:04:23.613Z"
   },
   {
    "duration": 171,
    "start_time": "2022-05-12T08:04:41.134Z"
   },
   {
    "duration": 390,
    "start_time": "2022-05-12T08:06:00.215Z"
   },
   {
    "duration": 183,
    "start_time": "2022-05-12T08:11:36.220Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-12T08:13:07.628Z"
   },
   {
    "duration": 29,
    "start_time": "2022-05-12T08:13:42.276Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T08:15:48.108Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-12T08:15:48.531Z"
   },
   {
    "duration": 32,
    "start_time": "2022-05-12T08:15:49.395Z"
   },
   {
    "duration": 21,
    "start_time": "2022-05-12T08:15:50.428Z"
   },
   {
    "duration": 304,
    "start_time": "2022-05-12T08:15:51.604Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-12T08:18:30.468Z"
   },
   {
    "duration": 26,
    "start_time": "2022-05-12T08:18:30.810Z"
   },
   {
    "duration": 32,
    "start_time": "2022-05-12T08:18:31.715Z"
   },
   {
    "duration": 20,
    "start_time": "2022-05-12T08:18:32.171Z"
   },
   {
    "duration": 353,
    "start_time": "2022-05-12T08:18:33.355Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-12T08:50:19.704Z"
   },
   {
    "duration": 2,
    "start_time": "2022-05-12T10:57:18.273Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-12T14:19:58.649Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T14:20:06.830Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-12T14:20:36.018Z"
   },
   {
    "duration": 227,
    "start_time": "2022-05-12T14:21:53.094Z"
   },
   {
    "duration": 500,
    "start_time": "2022-05-12T14:33:43.516Z"
   },
   {
    "duration": 370,
    "start_time": "2022-05-12T14:34:06.052Z"
   },
   {
    "duration": 457,
    "start_time": "2022-05-12T14:35:41.854Z"
   },
   {
    "duration": 526,
    "start_time": "2022-05-12T14:36:01.677Z"
   },
   {
    "duration": 356,
    "start_time": "2022-05-12T14:38:05.061Z"
   },
   {
    "duration": 549,
    "start_time": "2022-05-12T14:40:51.675Z"
   },
   {
    "duration": 307,
    "start_time": "2022-05-12T14:41:40.611Z"
   },
   {
    "duration": 456,
    "start_time": "2022-05-12T14:42:29.867Z"
   },
   {
    "duration": 406,
    "start_time": "2022-05-12T14:42:56.204Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-12T14:46:51.371Z"
   },
   {
    "duration": 374,
    "start_time": "2022-05-12T14:47:13.346Z"
   },
   {
    "duration": 669,
    "start_time": "2022-05-12T14:47:26.434Z"
   },
   {
    "duration": 619,
    "start_time": "2022-05-12T14:55:23.482Z"
   },
   {
    "duration": 537,
    "start_time": "2022-05-12T14:58:58.264Z"
   },
   {
    "duration": 215,
    "start_time": "2022-05-12T15:02:10.208Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T10:52:56.300Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T10:53:01.117Z"
   },
   {
    "duration": 5959,
    "start_time": "2022-05-13T10:53:01.124Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-13T10:53:07.085Z"
   },
   {
    "duration": 1191,
    "start_time": "2022-05-13T10:53:07.097Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-13T10:53:08.291Z"
   },
   {
    "duration": 98,
    "start_time": "2022-05-13T10:53:08.304Z"
   },
   {
    "duration": 126,
    "start_time": "2022-05-13T10:53:08.404Z"
   },
   {
    "duration": 181,
    "start_time": "2022-05-13T10:53:08.532Z"
   },
   {
    "duration": 604,
    "start_time": "2022-05-13T10:53:08.715Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T10:53:09.323Z"
   },
   {
    "duration": 117,
    "start_time": "2022-05-13T10:53:09.401Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T10:53:09.522Z"
   },
   {
    "duration": 223,
    "start_time": "2022-05-13T10:53:30.796Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T10:54:12.147Z"
   },
   {
    "duration": 123,
    "start_time": "2022-05-13T10:54:12.996Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T10:54:56.459Z"
   },
   {
    "duration": 201,
    "start_time": "2022-05-13T10:54:57.215Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T10:55:14.652Z"
   },
   {
    "duration": 74,
    "start_time": "2022-05-13T10:55:15.251Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T10:55:25.013Z"
   },
   {
    "duration": 415,
    "start_time": "2022-05-13T10:55:25.787Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T10:55:47.555Z"
   },
   {
    "duration": 594,
    "start_time": "2022-05-13T10:55:48.227Z"
   },
   {
    "duration": 180,
    "start_time": "2022-05-13T11:27:19.094Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-13T11:27:25.958Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-13T11:27:50.534Z"
   },
   {
    "duration": 186,
    "start_time": "2022-05-13T11:27:54.638Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-13T11:27:54.967Z"
   },
   {
    "duration": 209,
    "start_time": "2022-05-13T11:28:02.808Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T11:28:24.719Z"
   },
   {
    "duration": 466,
    "start_time": "2022-05-13T11:28:25.758Z"
   },
   {
    "duration": 221,
    "start_time": "2022-05-13T11:28:47.526Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T11:28:57.366Z"
   },
   {
    "duration": 712,
    "start_time": "2022-05-13T11:28:57.990Z"
   },
   {
    "duration": 533,
    "start_time": "2022-05-13T11:29:12.574Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T11:30:05.503Z"
   },
   {
    "duration": 480,
    "start_time": "2022-05-13T11:30:06.029Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T11:30:08.222Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T11:41:25.717Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T11:43:14.196Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T11:43:27.141Z"
   },
   {
    "duration": 12,
    "start_time": "2022-05-13T11:43:49.036Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T11:44:15.269Z"
   },
   {
    "duration": 41,
    "start_time": "2022-05-13T11:44:23.061Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T11:44:32.814Z"
   },
   {
    "duration": 227,
    "start_time": "2022-05-13T11:44:33.476Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T11:46:24.741Z"
   },
   {
    "duration": 298,
    "start_time": "2022-05-13T11:46:26.780Z"
   },
   {
    "duration": 201,
    "start_time": "2022-05-13T11:56:25.131Z"
   },
   {
    "duration": 327,
    "start_time": "2022-05-13T11:56:39.387Z"
   },
   {
    "duration": 400,
    "start_time": "2022-05-13T11:56:44.915Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-13T11:58:52.315Z"
   },
   {
    "duration": 307,
    "start_time": "2022-05-13T11:58:55.218Z"
   },
   {
    "duration": 211,
    "start_time": "2022-05-13T11:58:55.609Z"
   },
   {
    "duration": 384,
    "start_time": "2022-05-13T12:21:02.440Z"
   },
   {
    "duration": 251,
    "start_time": "2022-05-13T12:21:03.114Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:21:15.239Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:21:19.695Z"
   },
   {
    "duration": 3417,
    "start_time": "2022-05-13T12:21:20.167Z"
   },
   {
    "duration": 10,
    "start_time": "2022-05-13T12:22:16.327Z"
   },
   {
    "duration": 37,
    "start_time": "2022-05-13T12:22:25.542Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:24:38.734Z"
   },
   {
    "duration": 473,
    "start_time": "2022-05-13T12:25:31.256Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T12:25:45.094Z"
   },
   {
    "duration": 127,
    "start_time": "2022-05-13T12:26:26.574Z"
   },
   {
    "duration": 35,
    "start_time": "2022-05-13T12:26:27.430Z"
   },
   {
    "duration": 535,
    "start_time": "2022-05-13T12:27:36.894Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T12:28:00.711Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-13T12:28:06.893Z"
   },
   {
    "duration": 302,
    "start_time": "2022-05-13T12:28:12.608Z"
   },
   {
    "duration": 38,
    "start_time": "2022-05-13T12:28:19.342Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:29:05.597Z"
   },
   {
    "duration": 28,
    "start_time": "2022-05-13T12:29:05.997Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T12:29:18.878Z"
   },
   {
    "duration": 37,
    "start_time": "2022-05-13T12:29:19.221Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T12:33:31.486Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-13T12:34:17.437Z"
   },
   {
    "duration": 546,
    "start_time": "2022-05-13T12:34:22.460Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-13T12:34:28.813Z"
   },
   {
    "duration": 478,
    "start_time": "2022-05-13T12:34:33.868Z"
   },
   {
    "duration": 548,
    "start_time": "2022-05-13T12:34:53.277Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:37:07.181Z"
   },
   {
    "duration": 4099,
    "start_time": "2022-05-13T12:37:07.868Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-13T12:38:43.901Z"
   },
   {
    "duration": 1207,
    "start_time": "2022-05-13T12:38:43.906Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:45:18.604Z"
   },
   {
    "duration": 722,
    "start_time": "2022-05-13T12:45:18.931Z"
   },
   {
    "duration": 826,
    "start_time": "2022-05-13T12:45:25.204Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:51:01.547Z"
   },
   {
    "duration": 395,
    "start_time": "2022-05-13T12:51:02.235Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:51:21.827Z"
   },
   {
    "duration": 413,
    "start_time": "2022-05-13T12:51:22.219Z"
   },
   {
    "duration": 372,
    "start_time": "2022-05-13T12:51:30.939Z"
   },
   {
    "duration": 374,
    "start_time": "2022-05-13T12:51:50.068Z"
   },
   {
    "duration": 481,
    "start_time": "2022-05-13T12:52:35.164Z"
   },
   {
    "duration": 181,
    "start_time": "2022-05-13T12:54:01.508Z"
   },
   {
    "duration": 930,
    "start_time": "2022-05-13T12:54:04.843Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T12:59:09.443Z"
   },
   {
    "duration": 399,
    "start_time": "2022-05-13T12:59:10.106Z"
   },
   {
    "duration": 385,
    "start_time": "2022-05-13T12:59:35.721Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T12:59:42.633Z"
   },
   {
    "duration": 863,
    "start_time": "2022-05-13T12:59:45.345Z"
   },
   {
    "duration": 891,
    "start_time": "2022-05-13T12:59:55.522Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:00:35.074Z"
   },
   {
    "duration": 1289,
    "start_time": "2022-05-13T13:00:35.835Z"
   },
   {
    "duration": 1306,
    "start_time": "2022-05-13T13:01:07.785Z"
   },
   {
    "duration": 866,
    "start_time": "2022-05-13T13:01:13.314Z"
   },
   {
    "duration": 865,
    "start_time": "2022-05-13T13:01:17.914Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:02:15.257Z"
   },
   {
    "duration": 879,
    "start_time": "2022-05-13T13:02:15.857Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T13:05:52.153Z"
   },
   {
    "duration": 863,
    "start_time": "2022-05-13T13:05:52.698Z"
   },
   {
    "duration": 865,
    "start_time": "2022-05-13T13:06:07.706Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T13:06:23.386Z"
   },
   {
    "duration": 865,
    "start_time": "2022-05-13T13:06:23.913Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T13:06:36.097Z"
   },
   {
    "duration": 855,
    "start_time": "2022-05-13T13:06:36.777Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:07:14.497Z"
   },
   {
    "duration": 848,
    "start_time": "2022-05-13T13:07:15.200Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T13:08:56.593Z"
   },
   {
    "duration": 856,
    "start_time": "2022-05-13T13:08:57.025Z"
   },
   {
    "duration": 377,
    "start_time": "2022-05-13T13:09:23.408Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T13:09:58.521Z"
   },
   {
    "duration": 728,
    "start_time": "2022-05-13T13:09:59.224Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:10:12.329Z"
   },
   {
    "duration": 723,
    "start_time": "2022-05-13T13:10:13.017Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T13:10:23.449Z"
   },
   {
    "duration": 944,
    "start_time": "2022-05-13T13:10:24.273Z"
   },
   {
    "duration": 568,
    "start_time": "2022-05-13T13:39:45.317Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:42:36.668Z"
   },
   {
    "duration": 561,
    "start_time": "2022-05-13T13:42:37.147Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:42:44.060Z"
   },
   {
    "duration": 559,
    "start_time": "2022-05-13T13:42:44.435Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:43:37.636Z"
   },
   {
    "duration": 567,
    "start_time": "2022-05-13T13:43:38.027Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:45:52.236Z"
   },
   {
    "duration": 718,
    "start_time": "2022-05-13T13:45:52.747Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:47:35.643Z"
   },
   {
    "duration": 568,
    "start_time": "2022-05-13T13:47:35.939Z"
   },
   {
    "duration": 595,
    "start_time": "2022-05-13T13:48:47.586Z"
   },
   {
    "duration": 564,
    "start_time": "2022-05-13T13:49:10.746Z"
   },
   {
    "duration": 2190,
    "start_time": "2022-05-13T13:49:59.923Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:50:19.414Z"
   },
   {
    "duration": 2273,
    "start_time": "2022-05-13T13:50:21.258Z"
   },
   {
    "duration": 1886,
    "start_time": "2022-05-13T13:50:32.835Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:51:03.346Z"
   },
   {
    "duration": 1695,
    "start_time": "2022-05-13T13:51:04.050Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T13:51:55.963Z"
   },
   {
    "duration": 1848,
    "start_time": "2022-05-13T13:51:59.683Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:52:28.916Z"
   },
   {
    "duration": 1760,
    "start_time": "2022-05-13T13:52:29.290Z"
   },
   {
    "duration": 139,
    "start_time": "2022-05-13T13:53:00.267Z"
   },
   {
    "duration": 1752,
    "start_time": "2022-05-13T13:53:00.890Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T13:54:07.762Z"
   },
   {
    "duration": 2531,
    "start_time": "2022-05-13T13:54:08.218Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T13:58:30.842Z"
   },
   {
    "duration": 1804,
    "start_time": "2022-05-13T13:58:31.315Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-13T13:59:06.137Z"
   },
   {
    "duration": 2165,
    "start_time": "2022-05-13T13:59:06.498Z"
   },
   {
    "duration": 195,
    "start_time": "2022-05-13T14:08:12.169Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T14:18:51.680Z"
   },
   {
    "duration": 682,
    "start_time": "2022-05-13T14:18:52.240Z"
   },
   {
    "duration": 13,
    "start_time": "2022-05-13T14:19:07.537Z"
   },
   {
    "duration": 2305,
    "start_time": "2022-05-13T14:19:08.016Z"
   },
   {
    "duration": 151,
    "start_time": "2022-05-13T14:19:38.271Z"
   },
   {
    "duration": 1805,
    "start_time": "2022-05-13T14:19:38.663Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T14:20:57.616Z"
   },
   {
    "duration": 2001,
    "start_time": "2022-05-13T14:20:58.023Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-13T14:21:21.471Z"
   },
   {
    "duration": 1887,
    "start_time": "2022-05-13T14:21:21.847Z"
   },
   {
    "duration": 204,
    "start_time": "2022-05-13T14:22:51.695Z"
   },
   {
    "duration": 223,
    "start_time": "2022-05-13T14:22:54.632Z"
   },
   {
    "duration": 210,
    "start_time": "2022-05-13T14:22:57.671Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T14:24:43.368Z"
   },
   {
    "duration": 3249,
    "start_time": "2022-05-13T14:24:43.377Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T14:25:44.464Z"
   },
   {
    "duration": 1966,
    "start_time": "2022-05-13T14:25:44.920Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T14:28:39.663Z"
   },
   {
    "duration": 1936,
    "start_time": "2022-05-13T14:28:39.983Z"
   },
   {
    "duration": 1546,
    "start_time": "2022-05-13T14:29:08.566Z"
   },
   {
    "duration": 335,
    "start_time": "2022-05-13T14:29:10.114Z"
   },
   {
    "duration": 14,
    "start_time": "2022-05-13T14:29:10.453Z"
   },
   {
    "duration": 999,
    "start_time": "2022-05-13T14:29:10.468Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T14:29:11.468Z"
   },
   {
    "duration": 24,
    "start_time": "2022-05-13T14:29:11.476Z"
   },
   {
    "duration": 404,
    "start_time": "2022-05-13T14:29:11.502Z"
   },
   {
    "duration": 94,
    "start_time": "2022-05-13T14:29:11.908Z"
   },
   {
    "duration": 195,
    "start_time": "2022-05-13T14:29:12.006Z"
   },
   {
    "duration": 117,
    "start_time": "2022-05-13T14:29:12.205Z"
   },
   {
    "duration": 603,
    "start_time": "2022-05-13T14:29:12.401Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-13T14:29:13.006Z"
   },
   {
    "duration": 111,
    "start_time": "2022-05-13T14:29:13.012Z"
   },
   {
    "duration": 22,
    "start_time": "2022-05-13T14:29:13.125Z"
   },
   {
    "duration": 94,
    "start_time": "2022-05-13T14:29:13.149Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-13T14:29:13.245Z"
   },
   {
    "duration": 2117,
    "start_time": "2022-05-13T14:29:13.253Z"
   },
   {
    "duration": 1585,
    "start_time": "2022-05-13T14:30:09.839Z"
   },
   {
    "duration": 361,
    "start_time": "2022-05-13T14:30:11.426Z"
   },
   {
    "duration": 15,
    "start_time": "2022-05-13T14:30:11.789Z"
   },
   {
    "duration": 1080,
    "start_time": "2022-05-13T14:30:11.806Z"
   },
   {
    "duration": 14,
    "start_time": "2022-05-13T14:30:12.887Z"
   },
   {
    "duration": 38,
    "start_time": "2022-05-13T14:30:12.902Z"
   },
   {
    "duration": 273,
    "start_time": "2022-05-13T14:30:12.942Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-13T14:30:13.217Z"
   },
   {
    "duration": 138,
    "start_time": "2022-05-13T14:30:13.303Z"
   },
   {
    "duration": 118,
    "start_time": "2022-05-13T14:30:13.503Z"
   },
   {
    "duration": 677,
    "start_time": "2022-05-13T14:30:13.623Z"
   },
   {
    "duration": 96,
    "start_time": "2022-05-13T14:30:14.311Z"
   },
   {
    "duration": 24,
    "start_time": "2022-05-13T14:30:14.412Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-13T14:30:14.438Z"
   },
   {
    "duration": 42,
    "start_time": "2022-05-13T14:30:14.449Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-13T14:30:14.493Z"
   },
   {
    "duration": 1933,
    "start_time": "2022-05-13T14:30:14.501Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-14T16:29:41.778Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-14T16:29:42.438Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T16:32:16.742Z"
   },
   {
    "duration": 233,
    "start_time": "2022-05-14T16:34:18.933Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-14T16:36:14.276Z"
   },
   {
    "duration": 188,
    "start_time": "2022-05-14T16:41:24.288Z"
   },
   {
    "duration": 1503,
    "start_time": "2022-05-14T16:54:20.320Z"
   },
   {
    "duration": 355,
    "start_time": "2022-05-14T16:54:21.825Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-14T16:54:22.182Z"
   },
   {
    "duration": 977,
    "start_time": "2022-05-14T16:54:22.203Z"
   },
   {
    "duration": 18,
    "start_time": "2022-05-14T16:54:23.182Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T16:54:23.202Z"
   },
   {
    "duration": 396,
    "start_time": "2022-05-14T16:54:23.210Z"
   },
   {
    "duration": 93,
    "start_time": "2022-05-14T16:54:23.608Z"
   },
   {
    "duration": 196,
    "start_time": "2022-05-14T16:54:23.704Z"
   },
   {
    "duration": 199,
    "start_time": "2022-05-14T16:54:23.904Z"
   },
   {
    "duration": 498,
    "start_time": "2022-05-14T16:54:24.106Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-14T16:54:24.607Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-14T16:54:24.701Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-14T16:54:24.712Z"
   },
   {
    "duration": 116,
    "start_time": "2022-05-14T16:54:24.717Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-14T16:54:24.834Z"
   },
   {
    "duration": 1763,
    "start_time": "2022-05-14T16:54:24.842Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:03:29.528Z"
   },
   {
    "duration": 1711,
    "start_time": "2022-05-14T17:03:31.310Z"
   },
   {
    "duration": 91,
    "start_time": "2022-05-14T17:03:46.866Z"
   },
   {
    "duration": 1659,
    "start_time": "2022-05-14T17:03:48.074Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:04:49.242Z"
   },
   {
    "duration": 1881,
    "start_time": "2022-05-14T17:04:50.094Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:05:48.892Z"
   },
   {
    "duration": 1735,
    "start_time": "2022-05-14T17:05:50.280Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:06:06.292Z"
   },
   {
    "duration": 1977,
    "start_time": "2022-05-14T17:06:07.320Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:14:37.215Z"
   },
   {
    "duration": 1640,
    "start_time": "2022-05-14T17:14:38.661Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-14T17:14:58.869Z"
   },
   {
    "duration": 1766,
    "start_time": "2022-05-14T17:15:00.286Z"
   },
   {
    "duration": 194,
    "start_time": "2022-05-14T17:15:30.642Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-14T17:16:47.649Z"
   },
   {
    "duration": 1466,
    "start_time": "2022-05-14T17:26:24.652Z"
   },
   {
    "duration": 339,
    "start_time": "2022-05-14T17:26:26.120Z"
   },
   {
    "duration": 17,
    "start_time": "2022-05-14T17:26:26.461Z"
   },
   {
    "duration": 991,
    "start_time": "2022-05-14T17:26:26.480Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-14T17:26:27.473Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-14T17:26:27.477Z"
   },
   {
    "duration": 599,
    "start_time": "2022-05-14T17:26:27.501Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-14T17:26:28.103Z"
   },
   {
    "duration": 200,
    "start_time": "2022-05-14T17:26:28.110Z"
   },
   {
    "duration": 293,
    "start_time": "2022-05-14T17:26:28.313Z"
   },
   {
    "duration": 499,
    "start_time": "2022-05-14T17:26:28.609Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-14T17:26:29.110Z"
   },
   {
    "duration": 92,
    "start_time": "2022-05-14T17:26:29.115Z"
   },
   {
    "duration": 93,
    "start_time": "2022-05-14T17:26:29.213Z"
   },
   {
    "duration": 49,
    "start_time": "2022-05-14T17:26:29.308Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-14T17:26:29.359Z"
   },
   {
    "duration": 1832,
    "start_time": "2022-05-14T17:26:29.369Z"
   },
   {
    "duration": 1384,
    "start_time": "2022-05-19T17:36:05.131Z"
   },
   {
    "duration": 797,
    "start_time": "2022-05-19T17:36:06.518Z"
   },
   {
    "duration": 13,
    "start_time": "2022-05-19T17:36:07.317Z"
   },
   {
    "duration": 1046,
    "start_time": "2022-05-19T17:36:07.332Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:36:08.379Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-19T17:36:08.383Z"
   },
   {
    "duration": 370,
    "start_time": "2022-05-19T17:36:08.393Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:36:08.765Z"
   },
   {
    "duration": 200,
    "start_time": "2022-05-19T17:36:08.853Z"
   },
   {
    "duration": 118,
    "start_time": "2022-05-19T17:36:09.057Z"
   },
   {
    "duration": 506,
    "start_time": "2022-05-19T17:36:09.255Z"
   },
   {
    "duration": 90,
    "start_time": "2022-05-19T17:36:09.762Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:36:09.854Z"
   },
   {
    "duration": 101,
    "start_time": "2022-05-19T17:36:09.859Z"
   },
   {
    "duration": 42,
    "start_time": "2022-05-19T17:36:09.961Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-19T17:36:10.004Z"
   },
   {
    "duration": 128,
    "start_time": "2022-05-19T17:36:10.013Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:37:33.537Z"
   },
   {
    "duration": 324,
    "start_time": "2022-05-19T17:37:33.542Z"
   },
   {
    "duration": 12,
    "start_time": "2022-05-19T17:37:33.868Z"
   },
   {
    "duration": 1103,
    "start_time": "2022-05-19T17:37:33.882Z"
   },
   {
    "duration": 2,
    "start_time": "2022-05-19T17:37:34.987Z"
   },
   {
    "duration": 15,
    "start_time": "2022-05-19T17:37:34.991Z"
   },
   {
    "duration": 454,
    "start_time": "2022-05-19T17:37:35.008Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:37:35.464Z"
   },
   {
    "duration": 198,
    "start_time": "2022-05-19T17:37:35.553Z"
   },
   {
    "duration": 113,
    "start_time": "2022-05-19T17:37:35.755Z"
   },
   {
    "duration": 590,
    "start_time": "2022-05-19T17:37:35.870Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:37:36.553Z"
   },
   {
    "duration": 101,
    "start_time": "2022-05-19T17:37:36.558Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:37:36.662Z"
   },
   {
    "duration": 44,
    "start_time": "2022-05-19T17:37:36.667Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-19T17:37:36.712Z"
   },
   {
    "duration": 3937,
    "start_time": "2022-05-19T17:37:36.721Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:38:38.226Z"
   },
   {
    "duration": 321,
    "start_time": "2022-05-19T17:38:38.232Z"
   },
   {
    "duration": 10,
    "start_time": "2022-05-19T17:38:38.555Z"
   },
   {
    "duration": 1098,
    "start_time": "2022-05-19T17:38:38.567Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:38:39.667Z"
   },
   {
    "duration": 9,
    "start_time": "2022-05-19T17:38:39.672Z"
   },
   {
    "duration": 375,
    "start_time": "2022-05-19T17:38:39.682Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:38:40.059Z"
   },
   {
    "duration": 229,
    "start_time": "2022-05-19T17:38:40.064Z"
   },
   {
    "duration": 115,
    "start_time": "2022-05-19T17:38:40.353Z"
   },
   {
    "duration": 590,
    "start_time": "2022-05-19T17:38:40.470Z"
   },
   {
    "duration": 89,
    "start_time": "2022-05-19T17:38:41.064Z"
   },
   {
    "duration": 107,
    "start_time": "2022-05-19T17:38:41.155Z"
   },
   {
    "duration": 6,
    "start_time": "2022-05-19T17:38:41.263Z"
   },
   {
    "duration": 45,
    "start_time": "2022-05-19T17:38:41.271Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-19T17:38:41.353Z"
   },
   {
    "duration": 2990,
    "start_time": "2022-05-19T17:38:41.362Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:39:40.574Z"
   },
   {
    "duration": 342,
    "start_time": "2022-05-19T17:39:40.581Z"
   },
   {
    "duration": 11,
    "start_time": "2022-05-19T17:39:40.925Z"
   },
   {
    "duration": 1173,
    "start_time": "2022-05-19T17:39:40.938Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:39:42.113Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-19T17:39:42.118Z"
   },
   {
    "duration": 329,
    "start_time": "2022-05-19T17:39:42.128Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:39:42.459Z"
   },
   {
    "duration": 206,
    "start_time": "2022-05-19T17:39:42.552Z"
   },
   {
    "duration": 207,
    "start_time": "2022-05-19T17:39:42.760Z"
   },
   {
    "duration": 597,
    "start_time": "2022-05-19T17:39:42.969Z"
   },
   {
    "duration": 84,
    "start_time": "2022-05-19T17:39:43.568Z"
   },
   {
    "duration": 5,
    "start_time": "2022-05-19T17:39:43.654Z"
   },
   {
    "duration": 95,
    "start_time": "2022-05-19T17:39:43.661Z"
   },
   {
    "duration": 44,
    "start_time": "2022-05-19T17:39:43.758Z"
   },
   {
    "duration": 8,
    "start_time": "2022-05-19T17:39:43.804Z"
   },
   {
    "duration": 2947,
    "start_time": "2022-05-19T17:39:43.813Z"
   },
   {
    "duration": 1280,
    "start_time": "2022-05-19T17:43:55.864Z"
   },
   {
    "duration": 350,
    "start_time": "2022-05-19T17:43:57.146Z"
   },
   {
    "duration": 16,
    "start_time": "2022-05-19T17:43:57.498Z"
   },
   {
    "duration": 1083,
    "start_time": "2022-05-19T17:43:57.516Z"
   },
   {
    "duration": 3,
    "start_time": "2022-05-19T17:43:58.601Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-19T17:43:58.606Z"
   },
   {
    "duration": 639,
    "start_time": "2022-05-19T17:43:58.615Z"
   },
   {
    "duration": 4,
    "start_time": "2022-05-19T17:43:59.256Z"
   },
   {
    "duration": 197,
    "start_time": "2022-05-19T17:43:59.262Z"
   },
   {
    "duration": 214,
    "start_time": "2022-05-19T17:43:59.463Z"
   },
   {
    "duration": 510,
    "start_time": "2022-05-19T17:43:59.751Z"
   },
   {
    "duration": 90,
    "start_time": "2022-05-19T17:44:00.263Z"
   },
   {
    "duration": 98,
    "start_time": "2022-05-19T17:44:00.355Z"
   },
   {
    "duration": 19,
    "start_time": "2022-05-19T17:44:00.455Z"
   },
   {
    "duration": 61,
    "start_time": "2022-05-19T17:44:00.476Z"
   },
   {
    "duration": 7,
    "start_time": "2022-05-19T17:44:00.539Z"
   },
   {
    "duration": 3191,
    "start_time": "2022-05-19T17:44:00.548Z"
   }
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}