Uber_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZAhAFGo-ngNP"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AFRbb1917_Jo"
   },
   "source": [
    "# <p style=\"padding:10px;background-color:#9619EC;margin:0;color:#FFFFFF;font-family:newtimeroman;font-size:150%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Uber&Lyft Price Prediction</p>\n",
    "\n",
    "<p style=\"text-align:center; \">\n",
    "<img src=\"https://arc-anglerfish-arc2-prod-gmg.s3.amazonaws.com/public/LQMSPWQ3VJG7LHDHQQKDNYQWOY.jpg\" style='width: 650px; height: 350px;'>\n",
    "</p>\n",
    "\n",
    "# **Предсказание ценника для поездки в аггрегаторе такси Uber и Lyft.**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PkHDWeOQ7_J7"
   },
   "source": [
    "<a id=\"1\"></a>\n",
    "# <p style=\"padding:10px;background-color:#9619EC;margin:0;color:#FFFFFF;font-family:newtimeroman;font-size:100%;text-align:center;border-radius: 15px 50px;overflow:hidden;font-weight:500\">Импорт библиотек для проекта</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "MqoHF_nx7_J8"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import chi2_contingency\n",
    "import time\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.pipeline import make_union, make_pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer, StandardScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import make_union, make_pipeline\n",
    "import holidays\n",
    "pd.set_option('display.precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6VnHY_WTaLh"
   },
   "source": [
    "# **Базовый EDA и знакомство с датасетом.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "qiTaf5Osbkw5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 693071 entries, 0 to 693070\n",
      "Data columns (total 57 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   id                           693071 non-null  object \n",
      " 1   timestamp                    693071 non-null  float64\n",
      " 2   hour                         693071 non-null  int64  \n",
      " 3   day                          693071 non-null  int64  \n",
      " 4   month                        693071 non-null  int64  \n",
      " 5   datetime                     693071 non-null  object \n",
      " 6   timezone                     693071 non-null  object \n",
      " 7   source                       693071 non-null  object \n",
      " 8   destination                  693071 non-null  object \n",
      " 9   cab_type                     693071 non-null  object \n",
      " 10  product_id                   693071 non-null  object \n",
      " 11  name                         693071 non-null  object \n",
      " 12  price                        637976 non-null  float64\n",
      " 13  distance                     693071 non-null  float64\n",
      " 14  surge_multiplier             693071 non-null  float64\n",
      " 15  latitude                     693071 non-null  float64\n",
      " 16  longitude                    693071 non-null  float64\n",
      " 17  temperature                  693071 non-null  float64\n",
      " 18  apparentTemperature          693071 non-null  float64\n",
      " 19  short_summary                693071 non-null  object \n",
      " 20  long_summary                 693071 non-null  object \n",
      " 21  precipIntensity              693071 non-null  float64\n",
      " 22  precipProbability            693071 non-null  float64\n",
      " 23  humidity                     693071 non-null  float64\n",
      " 24  windSpeed                    693071 non-null  float64\n",
      " 25  windGust                     693071 non-null  float64\n",
      " 26  windGustTime                 693071 non-null  int64  \n",
      " 27  visibility                   693071 non-null  float64\n",
      " 28  temperatureHigh              693071 non-null  float64\n",
      " 29  temperatureHighTime          693071 non-null  int64  \n",
      " 30  temperatureLow               693071 non-null  float64\n",
      " 31  temperatureLowTime           693071 non-null  int64  \n",
      " 32  apparentTemperatureHigh      693071 non-null  float64\n",
      " 33  apparentTemperatureHighTime  693071 non-null  int64  \n",
      " 34  apparentTemperatureLow       693071 non-null  float64\n",
      " 35  apparentTemperatureLowTime   693071 non-null  int64  \n",
      " 36  icon                         693071 non-null  object \n",
      " 37  dewPoint                     693071 non-null  float64\n",
      " 38  pressure                     693071 non-null  float64\n",
      " 39  windBearing                  693071 non-null  int64  \n",
      " 40  cloudCover                   693071 non-null  float64\n",
      " 41  uvIndex                      693071 non-null  int64  \n",
      " 42  visibility.1                 693071 non-null  float64\n",
      " 43  ozone                        693071 non-null  float64\n",
      " 44  sunriseTime                  693071 non-null  int64  \n",
      " 45  sunsetTime                   693071 non-null  int64  \n",
      " 46  moonPhase                    693071 non-null  float64\n",
      " 47  precipIntensityMax           693071 non-null  float64\n",
      " 48  uvIndexTime                  693071 non-null  int64  \n",
      " 49  temperatureMin               693071 non-null  float64\n",
      " 50  temperatureMinTime           693071 non-null  int64  \n",
      " 51  temperatureMax               693071 non-null  float64\n",
      " 52  temperatureMaxTime           693071 non-null  int64  \n",
      " 53  apparentTemperatureMin       693071 non-null  float64\n",
      " 54  apparentTemperatureMinTime   693071 non-null  int64  \n",
      " 55  apparentTemperatureMax       693071 non-null  float64\n",
      " 56  apparentTemperatureMaxTime   693071 non-null  int64  \n",
      "dtypes: float64(29), int64(17), object(11)\n",
      "memory usage: 301.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# импорт датасета и первая оценка\n",
    "data = pd.read_csv(\"./data/uber_lyft_price_prediction.csv\")\n",
    "# from google.colab import files\n",
    "# data = pd.read_csv('uber_lyft_price_prediction.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "gicrHBiOVKXK"
   },
   "outputs": [],
   "source": [
    "# проверяем и удалаем строки с пропущенными значениями\n",
    "def check_missing(dataset, output_path=None):\n",
    "    result = pd.concat([dataset.isnull().sum(), dataset.isnull().mean()], axis=1)\n",
    "    result = result.rename(index=str, columns={0: 'total_missing', 1: 'proportion'})\n",
    "    if output_path:\n",
    "        result.to_csv(output_path+\"missing_data.csv\")\n",
    "        print(output_path, 'missing_data.csv')\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "E2XKY2cTVT6e"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_missing</th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
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       "      <th>day</th>\n",
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       "      <th>month</th>\n",
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       "      <th>source</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>destination</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cab_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>name</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>55095</td>\n",
       "      <td>0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surge_multiplier</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperature</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>short_summary</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_summary</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensity</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipProbability</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windSpeed</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windGust</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windGustTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureHigh</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureHighTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureLow</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureLowTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureHigh</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureHighTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureLow</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureLowTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icon</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dewPoint</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pressure</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windBearing</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudCover</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uvIndex</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility.1</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ozone</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunriseTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunsetTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moonPhase</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipIntensityMax</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uvIndexTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMin</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMinTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMax</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperatureMaxTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMin</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMinTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMax</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparentTemperatureMaxTime</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             total_missing  proportion\n",
       "id                                       0        0.00\n",
       "timestamp                                0        0.00\n",
       "hour                                     0        0.00\n",
       "day                                      0        0.00\n",
       "month                                    0        0.00\n",
       "datetime                                 0        0.00\n",
       "timezone                                 0        0.00\n",
       "source                                   0        0.00\n",
       "destination                              0        0.00\n",
       "cab_type                                 0        0.00\n",
       "product_id                               0        0.00\n",
       "name                                     0        0.00\n",
       "price                                55095        0.08\n",
       "distance                                 0        0.00\n",
       "surge_multiplier                         0        0.00\n",
       "latitude                                 0        0.00\n",
       "longitude                                0        0.00\n",
       "temperature                              0        0.00\n",
       "apparentTemperature                      0        0.00\n",
       "short_summary                            0        0.00\n",
       "long_summary                             0        0.00\n",
       "precipIntensity                          0        0.00\n",
       "precipProbability                        0        0.00\n",
       "humidity                                 0        0.00\n",
       "windSpeed                                0        0.00\n",
       "windGust                                 0        0.00\n",
       "windGustTime                             0        0.00\n",
       "visibility                               0        0.00\n",
       "temperatureHigh                          0        0.00\n",
       "temperatureHighTime                      0        0.00\n",
       "temperatureLow                           0        0.00\n",
       "temperatureLowTime                       0        0.00\n",
       "apparentTemperatureHigh                  0        0.00\n",
       "apparentTemperatureHighTime              0        0.00\n",
       "apparentTemperatureLow                   0        0.00\n",
       "apparentTemperatureLowTime               0        0.00\n",
       "icon                                     0        0.00\n",
       "dewPoint                                 0        0.00\n",
       "pressure                                 0        0.00\n",
       "windBearing                              0        0.00\n",
       "cloudCover                               0        0.00\n",
       "uvIndex                                  0        0.00\n",
       "visibility.1                             0        0.00\n",
       "ozone                                    0        0.00\n",
       "sunriseTime                              0        0.00\n",
       "sunsetTime                               0        0.00\n",
       "moonPhase                                0        0.00\n",
       "precipIntensityMax                       0        0.00\n",
       "uvIndexTime                              0        0.00\n",
       "temperatureMin                           0        0.00\n",
       "temperatureMinTime                       0        0.00\n",
       "temperatureMax                           0        0.00\n",
       "temperatureMaxTime                       0        0.00\n",
       "apparentTemperatureMin                   0        0.00\n",
       "apparentTemperatureMinTime               0        0.00\n",
       "apparentTemperatureMax                   0        0.00\n",
       "apparentTemperatureMaxTime               0        0.00"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check_missing(dataset=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "XYRkrKAoVeZ5"
   },
   "outputs": [],
   "source": [
    "def drop_missing(dataset, axis=0):\n",
    "    data_copy = dataset.copy(deep=True)\n",
    "    data_copy = data_copy.dropna(axis=axis, inplace=False)\n",
    "    return data_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "ZemUt7KEViVm"
   },
   "outputs": [],
   "source": [
    "data = drop_missing(dataset=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "kbZwESsWVmLN"
   },
   "outputs": [],
   "source": [
    "# удаляем столбец id\n",
    "data.drop(columns=['id'],axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V8RGctR_W3_Q"
   },
   "outputs": [],
   "source": [
    "# оцениваем распределение числовых данных\n",
    "data.hist(figsize=(18, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8EJ-E_a_a-qw"
   },
   "source": [
    "# **Dataset summary**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NCtX_HG9YSdV"
   },
   "source": [
    " При оценке нашего датасета мы:\n",
    " 1. Рассмотрели данные, которые составили 693071 наблюдение, однако именно по нашей целевой переменной мы имели около 8% пропусков. Для дальнейшей работы мы удалили данные наблюдения (при глубокой оценке они включали в себя незначительные (около 100 метров) расстояния поездок или иные аномалии).\n",
    " 2. Были выявлены признаки, одинаковые для всех наблюдений (timezone), а также признаки,не оказывающие влияния на наши данные (id).\n",
    " 3. В датасете отражены признаки дублирующие друг друга: datetime  и day+month+hour; source  и   destination  отражают distance; name, icon и product_id фактически описывают класс автомобиля, выехавшего на вызов; short_summary и long_summary лписывают погодные условия.\n",
    " 4. Ни один числовой признак не имеет нормального распределения."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yZHgc80irnuD"
   },
   "source": [
    "# **Data processing с обоснованием значимости числовых колонок и их удаления.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "VHJTYaiGfrzp",
    "outputId": "9a961828-5974-4730-ba31-841cbe6555a8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-43-d3456ebf9f9d>:3: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  heatmap = sns.heatmap(data.corr()[['price']].sort_values(by='price', ascending=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Корреляция по Оценке счастья\n",
    "plt.figure(figsize=(8, 12))\n",
    "heatmap = sns.heatmap(data.corr()[['price']].sort_values(by='price', ascending=False)\n",
    ", vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
    "heatmap.set_title('Корреляция признаков с price', fontdict={'fontsize':18}, pad=16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nHNIHvHVme7P"
   },
   "source": [
    "# **Дисперсионный анализ**\n",
    "Мы будем применять метод ANOVA для поиска признаков наиболее важных (интересных, насыщенных информацией) для обучения модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "nFbktkbdfQzQ",
    "outputId": "9135812a-5ea7-414f-a234-26843a585a17"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "  <div id=\"df-555f191b-8054-42de-a941-3e9acdd37254\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Feature</th>\n",
       "      <th>F_stat</th>\n",
       "      <th>P_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>distance</td>\n",
       "      <td>86228.75</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>surge_multiplier</td>\n",
       "      <td>39151.47</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>latitude</td>\n",
       "      <td>2.78</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>moonPhase</td>\n",
       "      <td>1.64</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>visibility.1</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>visibility</td>\n",
       "      <td>1.43</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>longitude</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>windBearing</td>\n",
       "      <td>1.22</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>humidity</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>windGust</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>precipIntensityMax</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>windSpeed</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>month</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>cloudCover</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>pressure</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>windGustTime</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>apparentTemperatureLow</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>apparentTemperatureMinTime</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>timestamp</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>temperatureMinTime</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>temperatureMaxTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>uvIndexTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>apparentTemperatureLowTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>sunsetTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>sunriseTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>temperatureHighTime</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>temperatureLowTime</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>apparentTemperatureMaxTime</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>apparentTemperatureHighTime</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>day</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>dewPoint</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>apparentTemperatureMin</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hour</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>temperatureMax</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>temperatureHigh</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ozone</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>temperatureMin</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>uvIndex</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>apparentTemperatureMax</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>apparentTemperatureHigh</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>precipProbability</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>apparentTemperature</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>temperatureLow</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>precipIntensity</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>temperature</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        Feature   F_stat  P_value\n",
       "4                      distance 86228.75     0.00\n",
       "5              surge_multiplier 39151.47     0.00\n",
       "6                      latitude     2.78     0.10\n",
       "34                    moonPhase     1.64     0.20\n",
       "30                 visibility.1     1.43     0.23\n",
       "16                   visibility     1.43     0.23\n",
       "7                     longitude     1.28     0.26\n",
       "27                  windBearing     1.22     0.27\n",
       "12                     humidity     0.98     0.32\n",
       "14                     windGust     0.95     0.33\n",
       "35           precipIntensityMax     0.79     0.37\n",
       "13                    windSpeed     0.62     0.43\n",
       "3                         month     0.53     0.47\n",
       "28                   cloudCover     0.50     0.48\n",
       "26                     pressure     0.48     0.49\n",
       "15                 windGustTime     0.46     0.50\n",
       "23       apparentTemperatureLow     0.44     0.51\n",
       "42   apparentTemperatureMinTime     0.42     0.52\n",
       "0                     timestamp     0.42     0.52\n",
       "38           temperatureMinTime     0.38     0.54\n",
       "40           temperatureMaxTime     0.37     0.54\n",
       "36                  uvIndexTime     0.37     0.54\n",
       "24   apparentTemperatureLowTime     0.37     0.54\n",
       "33                   sunsetTime     0.37     0.54\n",
       "32                  sunriseTime     0.37     0.54\n",
       "18          temperatureHighTime     0.37     0.54\n",
       "20           temperatureLowTime     0.36     0.55\n",
       "44   apparentTemperatureMaxTime     0.36     0.55\n",
       "22  apparentTemperatureHighTime     0.35     0.55\n",
       "2                           day     0.33     0.56\n",
       "25                     dewPoint     0.24     0.62\n",
       "41       apparentTemperatureMin     0.21     0.64\n",
       "1                          hour     0.20     0.65\n",
       "39               temperatureMax     0.19     0.66\n",
       "17              temperatureHigh     0.17     0.68\n",
       "31                        ozone     0.14     0.71\n",
       "37               temperatureMin     0.12     0.73\n",
       "29                      uvIndex     0.11     0.74\n",
       "43       apparentTemperatureMax     0.05     0.82\n",
       "21      apparentTemperatureHigh     0.04     0.83\n",
       "11            precipProbability     0.04     0.85\n",
       "9           apparentTemperature     0.02     0.88\n",
       "19               temperatureLow     0.02     0.88\n",
       "10              precipIntensity     0.02     0.89\n",
       "8                   temperature     0.00     0.95"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Определим в датасете лишние признаки методом ANOVA (дисперсионный анализ)\n",
    "from sklearn.feature_selection import f_regression\n",
    "data = data.drop(['datetime'], axis=1).dropna()\n",
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "col_names = df.drop(['price'], axis = 1).select_dtypes(include=[np.number]).columns\n",
    "x = data.drop(['price'], axis = 1).select_dtypes(include=[np.number])\n",
    "y = data['price'].values\n",
    "column_names = col_names\n",
    "[f_stat, f_p_value] = f_regression(x,y)\n",
    "column_names = x.columns\n",
    "f_test_df = pd.DataFrame({'Feature':  x.columns,\n",
    "                          'F_stat': f_stat,\n",
    "                          'P_value': f_p_value\n",
    "                          })\n",
    "f_test_df.sort_values('P_value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q3iKZqM5oO67"
   },
   "source": [
    "# **Обоснование значимости категориальных колонок и их удаления.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1NahIr-slJ6j",
    "outputId": "1865a9c4-df0f-4eca-b53a-03e984195841"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column name - hour\n",
      "p value is 0.3568087232252539\n",
      "Both variables are Independent (H0 holds true)\n",
      "Column name - destination\n",
      "p value is 0.0\n",
      "Both variables are Dependent (reject H0)\n",
      "Column name - short_summary\n",
      "p value is 0.40058834184797343\n",
      "Both variables are Independent (H0 holds true)\n",
      "Column name - source\n",
      "p value is 0.0\n",
      "Both variables are Dependent (reject H0)\n",
      "Column name - cab_type\n",
      "p value is 0.0\n",
      "Both variables are Dependent (reject H0)\n",
      "Column name - name\n",
      "p value is 0.0\n",
      "Both variables are Dependent (reject H0)\n"
     ]
    }
   ],
   "source": [
    "cat_cols = ['hour', 'destination','short_summary','source', 'cab_type','name']\n",
    "\n",
    "def chi_sq_test(ct):\n",
    "    # input crosstab of 2 categorical variables\n",
    "    stat, p, dof, expected = chi2_contingency(ct)\n",
    "\n",
    "    # interpret p-value\n",
    "    alpha = 0.05\n",
    "    print(\"p value is \" + str(p))\n",
    "    if p <= alpha:\n",
    "        print('Both variables are Dependent (reject H0)')\n",
    "    else:\n",
    "        print('Both variables are Independent (H0 holds true)')\n",
    "\n",
    "def cat_col_test(df, cat_colname, target_colname):\n",
    "\n",
    "    print(f\"Column name - {cat_colname}\")\n",
    "    ct = pd.crosstab(df[cat_colname],df[target_colname])\n",
    "    chi_sq_test(ct)\n",
    "\n",
    "for c in cat_cols:\n",
    "    cat_col_test(data, c, 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S2hOtUenrfre",
    "outputId": "d53d2d9f-0fc0-4cbd-a4b0-7f6944e54260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 284271 entries, 0 to 693070\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   hour              284271 non-null  int64  \n",
      " 1   distance          284271 non-null  float64\n",
      " 2   surge_multiplier  284271 non-null  float64\n",
      " 3   short_summary     284271 non-null  object \n",
      " 4   source            284271 non-null  object \n",
      " 5   destination       284271 non-null  object \n",
      " 6   cab_type          284271 non-null  object \n",
      " 7   name              284271 non-null  object \n",
      " 8   price             284271 non-null  float64\n",
      " 9   is_holiday        284271 non-null  int64  \n",
      " 10  is_weekend        284271 non-null  int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 26.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Функция по предварительной очистки датасета. Работа с дублирующимися признаками и нечисловыми.\n",
    "def for_prepare(data):\n",
    "    data = data.loc[:, ['hour', 'distance', 'datetime','surge_multiplier','short_summary','source', 'destination', 'cab_type','name', 'price']]\n",
    "    data['datetime'] = pd.to_datetime(data['datetime'])\n",
    "    holiday_dates = holidays.US(years=2018, state='NY').keys() #ориентируясь на регион определяем значимые часы и часы-пик для влияния на цену\n",
    "    data['is_holiday'] = data['datetime'].dt.date.isin(holiday_dates).astype(int)\n",
    "    data['is_weekend'] = data['datetime'].dt.weekday.isin([5, 6]).astype(int)\n",
    "    data = data.drop(['datetime'], axis=1).dropna()\n",
    "    data = data.drop_duplicates(subset=['hour', 'destination', 'distance', 'price']) #удаляем дублирующие значения для сжатия датасета и дальнейшей реализации пайплайна\n",
    "    return data\n",
    "\n",
    "df = for_prepare(data)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BNNQxDLWob_p"
   },
   "outputs": [],
   "source": [
    "y = df['price']\n",
    "x = df.drop(['price'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JfnF_PGslVnP",
    "outputId": "d466f05e-e54d-4da5-9631-13a309b05bf2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284271, 40)\n",
      "None\n",
      "(284271,)\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def get_onehot_col(df):\n",
    "    return df[['short_summary','source', 'cab_type','name','is_holiday','is_weekend']] # признаки 'hour', 'destination' не были включены после оценки работы модели с их использованием\n",
    "\n",
    "def get_scaler_cols(df):\n",
    "    return df[['distance', 'surge_multiplier']]\n",
    "\n",
    "my_prepare = make_union(*[\n",
    "    make_pipeline(FunctionTransformer(get_onehot_col, validate=False),  OneHotEncoder(sparse_output=False)),\n",
    "    make_pipeline(FunctionTransformer(get_scaler_cols, validate=False), StandardScaler())\n",
    "    ])\n",
    "\n",
    "\n",
    "x = my_prepare.fit_transform(x)\n",
    "print(print(x.shape))\n",
    "y = df['price']\n",
    "print(print(y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M0XboQ8fe6VX"
   },
   "source": [
    "# **Dimentionality reduction**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "zSJqTfFWgV8_",
    "outputId": "7073de69-97e9-483c-869e-57ad74c0491e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Рассчет собсвтенных чисел (PCA)\n",
    "from sklearn.decomposition import PCA\n",
    "scaler = StandardScaler()\n",
    "x = scaler.fit_transform(x)\n",
    "ev, _ = np.linalg.eig(np.cov(x.T))\n",
    "ev = np.sort(ev)[::-1]\n",
    "plt.scatter(range(1,x.shape[1]+1),ev)\n",
    "plt.plot(range(1,x.shape[1]+1),ev)\n",
    "plt.title('Scree Plot')\n",
    "plt.xlabel('Factors')\n",
    "plt.ylabel('Eigen Value')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eysiomulgV8_",
    "outputId": "9a57fb2b-3e92-46bf-8c0e-537bff4eea67"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7550819774898749"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', '{:.2f}'.format)\n",
    "pca = PCA(n_components=20)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fPdYxeoQgV8_",
    "outputId": "dc1614dd-204e-4b62-dc27-b485ab5ec570"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер выборки training: 170562, размер выборки для настройки параметров: 56854, размер выборки для тестирования: 56855\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "x = principalComponents\n",
    "y = df['price']\n",
    "\n",
    "\n",
    "# Поэтапно поделим выборку на 3 части для обучения, настройки гиперпараметров и тестирования\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(x,y,test_size=0.2, random_state=12)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X_trainval, y_trainval, random_state=12)\n",
    "print(\"Размер выборки training: {}, размер выборки для настройки параметров: {}, размер выборки для тестирования:\"\n",
    "      \" {}\\n\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qo3t0rwKfK08"
   },
   "source": [
    "# **Regression. Testing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gxt9C9DIwFPz",
    "outputId": "f3729f0f-2946-49b7-d499-4fe893cd16a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 5.29823039700656\n",
      "Mean Absolute Error: 1.4806763281654578\n",
      "r2_score 0.9509040609150119\n"
     ]
    }
   ],
   "source": [
    "# Обучаем ExtraTreesR на данных после факторного анализа\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error,r2_score\n",
    "model_FA_ExraTR = ExtraTreesRegressor()\n",
    "model_FA_ExraTR.fit(X_train, y_train)\n",
    "\n",
    "# сделаем прогноз\n",
    "y_pred = model_FA_ExraTR.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "r2_score = r2_score(y_test, y_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"Mean Absolute Error:\", mae)\n",
    "print(\"r2_score\", r2_score)\n",
    "# r2_score 0.9418459666063844"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "huN323qHfZ6G"
   },
   "source": [
    "# **Summary.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6o7buXYUfe5K"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fa9Tr1uOffRt"
   },
   "source": [
    "В данной работе были проанализированы более 693000 наблюдений с целью создания модели, предсказывающей стоимость поездки. В результате анализа и обработки данных фактически значимыми оказались лишь 8 признаков из 55.\n",
    "При работе наиболее информативной показала себя модель Extra Trees Regressor, ключевой параметр оценки R2 равен более 0,95, что свидетельствует о хорошей работе данной модели."
   ]
  }
 ],
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