project_used_cars_yuliya_klimushina_from_repo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><br>\n",
    "## Открытый курс по машинному обучению. Сессия № 3\n",
    "<img src=\"https://article.images.consumerreports.org/c_lfill,w_1920,ar_32:11/prod/content/dam/CRO%20Images%202017/Cars/March/CR-Cars-Hero-Used-Car-Sales-03-17\">\n",
    "\n",
    "\n",
    "### <center> Автор материала: Юлия Климушина</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <center> Прогноз цен на подержанные автомобили </center>\n",
    "### <center> Индивидуальный проект по анализу данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом проекте мы будем решать задачу восстановления регрессии. Данные, используемые в этом проекте, можно скачать [тут](https://www.kaggle.com/orgesleka/used-cars-database). Датасет содержит информацию о подержанных автомобилях, выставленных на продажу на Ebay в марте-апреле 2016. Данные представлены на немецком языке. \n",
    "Цель исследования: создание модели, предсказывающую цену автомобиля на вторичном рынке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "from sklearn.model_selection import GridSearchCV, learning_curve, validation_curve, KFold\n",
    "from sklearn.preprocessing import StandardScaler, PolynomialFeatures\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from xgboost import XGBRegressor \n",
    "from scipy.sparse import csr_matrix, hstack\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_SEED = 17\n",
    "kf = KFold(n_splits=5, shuffle=True, random_state=RANDOM_SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. Описание набора данных и признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Список признаков:\n",
    "- **dateCrawled**: дата и время первого просмотра объявления \n",
    "- **name** : название машины (сформировано из названия марки, модели и другой информации)\n",
    "- **seller** : кто продает ('privat' - частное лицо, 'gewerblich' - диллер)\n",
    "- **offerType** : тип предложения ('Angebot' - продажа, 'Gesuch' - покупка)\n",
    "- **price** : цена\n",
    "- **abtest** : A/B тест. Покопавшись в интернете, я выяснила, что A/B тестирование - это тактика, с помощью которой макетологи выясняют, какие заголовки объявления, текст, изображения, призывы к действию будут лучше работать для целевой аудитории.\n",
    "- **vehicleType** : тип авто ('coupe', 'suv', 'kleinwagen', 'limousine', 'cabrio', 'bus', 'kombi', 'andere' - \"купе\", \"внедорожник\", \"миниавто\", \"седан\", \"кабриолет\", \"автобус\", \"комби\", \"другое\", соответственно)\n",
    "- **yearOfRegistration** : в каком году машина была впервые зарегистрирована\n",
    "- **gearbox** : тип коробки передач ('manuell' - ручная, 'automatik' - автоматическая)\n",
    "- **powerPS** : мощность\n",
    "- **model** : модель\n",
    "- **kilometer** : пробег в километрах\n",
    "- **monthOfRegistration** : в каком месяце машина была впервые зарегистрирована\n",
    "- **fuelType** : тип топлива\n",
    "- **brand** : марка\n",
    "- **notRepairedDamage** : есть ли повреждения, требующие ремонта ('ja' - да, 'nein' - нет)\n",
    "- **dateCreated** : дата и время создания объявления на eBay\n",
    "- **nrOfPictures** : количество фотографий автомобиля (к сожалению, это поле везде содержит нули и поэтому бесполезно)\n",
    "- **postalCode** : почтовый индекс\n",
    "- **lastSeenOnline** : дата и время последнего просмотра объявления\n",
    "\n",
    "Целевая переменная: **price** - цена автомобиля. Перед нами стоит задача восстановления регрессии."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  2. Первичный анализ и обработка признаков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dateCrawled</th>\n",
       "      <th>name</th>\n",
       "      <th>seller</th>\n",
       "      <th>offerType</th>\n",
       "      <th>price</th>\n",
       "      <th>abtest</th>\n",
       "      <th>vehicleType</th>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>powerPS</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <th>fuelType</th>\n",
       "      <th>brand</th>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <th>dateCreated</th>\n",
       "      <th>nrOfPictures</th>\n",
       "      <th>postalCode</th>\n",
       "      <th>lastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>Golf_3_1.6</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>480</td>\n",
       "      <td>test</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manuell</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:45</td>\n",
       "      <td>A5_Sportback_2.7_Tdi</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>18300</td>\n",
       "      <td>test</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manuell</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>diesel</td>\n",
       "      <td>audi</td>\n",
       "      <td>ja</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:21</td>\n",
       "      <td>Jeep_Grand_Cherokee_\"Overland\"</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>9800</td>\n",
       "      <td>test</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>automatik</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>diesel</td>\n",
       "      <td>jeep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:04</td>\n",
       "      <td>GOLF_4_1_4__3TÜRER</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>1500</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2001</td>\n",
       "      <td>manuell</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>benzin</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:20</td>\n",
       "      <td>Skoda_Fabia_1.4_TDI_PD_Classic</td>\n",
       "      <td>privat</td>\n",
       "      <td>Angebot</td>\n",
       "      <td>3600</td>\n",
       "      <td>test</td>\n",
       "      <td>kleinwagen</td>\n",
       "      <td>2008</td>\n",
       "      <td>manuell</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>diesel</td>\n",
       "      <td>skoda</td>\n",
       "      <td>nein</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dateCrawled                            name  seller offerType  \\\n",
       "0 2016-03-24 11:52:17                      Golf_3_1.6  privat   Angebot   \n",
       "1 2016-03-24 10:58:45            A5_Sportback_2.7_Tdi  privat   Angebot   \n",
       "2 2016-03-14 12:52:21  Jeep_Grand_Cherokee_\"Overland\"  privat   Angebot   \n",
       "3 2016-03-17 16:54:04              GOLF_4_1_4__3TÜRER  privat   Angebot   \n",
       "4 2016-03-31 17:25:20  Skoda_Fabia_1.4_TDI_PD_Classic  privat   Angebot   \n",
       "\n",
       "   price abtest vehicleType  yearOfRegistration    gearbox  powerPS  model  \\\n",
       "0    480   test         NaN                1993    manuell        0   golf   \n",
       "1  18300   test       coupe                2011    manuell      190    NaN   \n",
       "2   9800   test         suv                2004  automatik      163  grand   \n",
       "3   1500   test  kleinwagen                2001    manuell       75   golf   \n",
       "4   3600   test  kleinwagen                2008    manuell       69  fabia   \n",
       "\n",
       "   kilometer  monthOfRegistration fuelType       brand notRepairedDamage  \\\n",
       "0     150000                    0   benzin  volkswagen               NaN   \n",
       "1     125000                    5   diesel        audi                ja   \n",
       "2     125000                    8   diesel        jeep               NaN   \n",
       "3     150000                    6   benzin  volkswagen              nein   \n",
       "4      90000                    7   diesel       skoda              nein   \n",
       "\n",
       "  dateCreated  nrOfPictures  postalCode            lastSeen  \n",
       "0  2016-03-24             0       70435 2016-04-07 03:16:57  \n",
       "1  2016-03-24             0       66954 2016-04-07 01:46:50  \n",
       "2  2016-03-14             0       90480 2016-04-05 12:47:46  \n",
       "3  2016-03-17             0       91074 2016-03-17 17:40:17  \n",
       "4  2016-03-31             0       60437 2016-04-06 10:17:21  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parse_dates = ['dateCrawled', 'dateCreated', 'lastSeen']\n",
    "df = pd.read_csv('data/autos.csv', sep=',', parse_dates=parse_dates, encoding = 'ISO-8859-1')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(371528, 20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 371528 entries, 0 to 371527\n",
      "Data columns (total 20 columns):\n",
      "dateCrawled            371528 non-null datetime64[ns]\n",
      "name                   371528 non-null object\n",
      "seller                 371528 non-null object\n",
      "offerType              371528 non-null object\n",
      "price                  371528 non-null int64\n",
      "abtest                 371528 non-null object\n",
      "vehicleType            333659 non-null object\n",
      "yearOfRegistration     371528 non-null int64\n",
      "gearbox                351319 non-null object\n",
      "powerPS                371528 non-null int64\n",
      "model                  351044 non-null object\n",
      "kilometer              371528 non-null int64\n",
      "monthOfRegistration    371528 non-null int64\n",
      "fuelType               338142 non-null object\n",
      "brand                  371528 non-null object\n",
      "notRepairedDamage      299468 non-null object\n",
      "dateCreated            371528 non-null datetime64[ns]\n",
      "nrOfPictures           371528 non-null int64\n",
      "postalCode             371528 non-null int64\n",
      "lastSeen               371528 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(7), object(10)\n",
      "memory usage: 56.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте избавимся от пропусков и не информативных признаков.\n",
    "**abtest** явно лишний признак, так как не имеет отношения к автомобилям как таковым. Это показатель, используемый для определения эффективности рекламы. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нас интересуют только объявления о продаже, поэтому удалим строки о покупке и признак **offerType**.<br>\n",
    "Признак **vehicleType** содержит пропуски. Можно заменить их значением, обозначающим \"другое\" (andere)<br>\n",
    "Строки с пропусками в **gearbox** удалим."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([ 'abtest'], axis=1, inplace=True)\n",
    "df = df[df['offerType'] != 'Gesuch'].drop('offerType', axis=1)\n",
    "\n",
    "df['vehicleType'].fillna(axis=0, value='andere', inplace=True)\n",
    "\n",
    "df.dropna(axis=0, subset=['gearbox'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте посмотрим на признак **brand**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['volkswagen', 'audi', 'jeep', 'skoda', 'bmw', 'peugeot', 'ford',\n",
       "       'mazda', 'nissan', 'renault', 'mercedes_benz', 'opel', 'seat',\n",
       "       'citroen', 'honda', 'fiat', 'mini', 'smart', 'hyundai',\n",
       "       'sonstige_autos', 'alfa_romeo', 'subaru', 'volvo', 'mitsubishi',\n",
       "       'kia', 'suzuki', 'lancia', 'porsche', 'toyota', 'chevrolet',\n",
       "       'dacia', 'daihatsu', 'saab', 'chrysler', 'jaguar', 'daewoo',\n",
       "       'rover', 'land_rover', 'trabant', 'lada'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['brand'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"sonstige_autos\" означает \"прочие автомобили\". Строк с такими значения немного и анализ показывает, что это старые и/или редкие машины, информации по которым не достаточно, чтобы строить прогноз, к тому же поле **model** у них не заполнено, поэтому удалим такие строки."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В признаке **model** 13433 пропусков, зато **brand** всегда заполнен. Учитывая, что **name** часто содержит в себе информацию о марке и модели, достанем модель оттуда. Строки, которые не подойдут под алгоритм, удалим. После этой операции удалим переменную **name**, он нам больше не пригодится."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['brand'] != \"sonstige_autos\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_nan_idx = df[pd.isnull(df[\"model\"])].index\n",
    "def model_extractor(x):\n",
    "    x = x.apply(str.lower)\n",
    "    name = x['name'].split(sep='_')\n",
    "    try:\n",
    "        if name[0] == x[\"brand\"]:\n",
    "            return name[1]\n",
    "        else: return np.nan\n",
    "    except:\n",
    "        return np.nan\n",
    "\n",
    "df.loc[model_nan_idx, 'model'] = df.loc[model_nan_idx][['name', 'brand']].apply(model_extractor, axis=1) \n",
    "df.dropna(axis=0, subset=['model'], inplace=True)\n",
    "df.drop('name', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разберемся с топливом **fuelType**. Заменим пустующие значения 'andere' ('другое'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "benzin     211809\n",
       "diesel     102533\n",
       "NaN         22228\n",
       "lpg          5002\n",
       "cng           546\n",
       "hybrid        241\n",
       "andere        114\n",
       "elektro        66\n",
       "Name: fuelType, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fuelType'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fuelType'].fillna(axis=0, value='andere', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Признак **notRepairedDamage** имеет 56335 пропусков. Можно исходить из предположения, что если владелец не упомянул в объявлении про повреждения, то он продает её как не требующую ремонта. Пометим такие пропуски как 'nein' и приведем к бинарному формату."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['notRepairedDamage'].fillna(value='nein', inplace=True)\n",
    "df['notRepairedDamage'] = df['notRepairedDamage'].map({'ja': 1, 'nein': 0}).astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на статистику."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dateCrawled</th>\n",
       "      <td>342539</td>\n",
       "      <td>264509</td>\n",
       "      <td>2016-03-24 14:49:47</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-03-05 14:06:22</td>\n",
       "      <td>2016-04-07 14:36:58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seller</th>\n",
       "      <td>342539</td>\n",
       "      <td>2</td>\n",
       "      <td>privat</td>\n",
       "      <td>342536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9193.5</td>\n",
       "      <td>522358</td>\n",
       "      <td>0</td>\n",
       "      <td>1200</td>\n",
       "      <td>3000</td>\n",
       "      <td>7500</td>\n",
       "      <td>1e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vehicleType</th>\n",
       "      <td>342539</td>\n",
       "      <td>8</td>\n",
       "      <td>limousine</td>\n",
       "      <td>91706</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yearOfRegistration</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2003.77</td>\n",
       "      <td>34.4349</td>\n",
       "      <td>1000</td>\n",
       "      <td>1999</td>\n",
       "      <td>2004</td>\n",
       "      <td>2008</td>\n",
       "      <td>9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gearbox</th>\n",
       "      <td>342539</td>\n",
       "      <td>2</td>\n",
       "      <td>manuell</td>\n",
       "      <td>267849</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>powerPS</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>119.978</td>\n",
       "      <td>188.264</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>109</td>\n",
       "      <td>150</td>\n",
       "      <td>20000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>342539</td>\n",
       "      <td>1027</td>\n",
       "      <td>golf</td>\n",
       "      <td>28612</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kilometer</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126253</td>\n",
       "      <td>39023.5</td>\n",
       "      <td>5000</td>\n",
       "      <td>125000</td>\n",
       "      <td>150000</td>\n",
       "      <td>150000</td>\n",
       "      <td>150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monthOfRegistration</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.89832</td>\n",
       "      <td>3.63351</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuelType</th>\n",
       "      <td>342539</td>\n",
       "      <td>7</td>\n",
       "      <td>benzin</td>\n",
       "      <td>211809</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>342539</td>\n",
       "      <td>39</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>72225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notRepairedDamage</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0984939</td>\n",
       "      <td>0.297982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dateCreated</th>\n",
       "      <td>342539</td>\n",
       "      <td>111</td>\n",
       "      <td>2016-04-03 00:00:00</td>\n",
       "      <td>13365</td>\n",
       "      <td>2015-03-20 00:00:00</td>\n",
       "      <td>2016-04-07 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nrOfPictures</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postalCode</th>\n",
       "      <td>342539</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51030.8</td>\n",
       "      <td>25789.4</td>\n",
       "      <td>1067</td>\n",
       "      <td>30826</td>\n",
       "      <td>49808</td>\n",
       "      <td>71706</td>\n",
       "      <td>99998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lastSeen</th>\n",
       "      <td>342539</td>\n",
       "      <td>171029</td>\n",
       "      <td>2016-04-06 01:15:23</td>\n",
       "      <td>16</td>\n",
       "      <td>2016-03-05 14:15:08</td>\n",
       "      <td>2016-04-07 14:58:51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count  unique                  top    freq  \\\n",
       "dateCrawled          342539  264509  2016-03-24 14:49:47       6   \n",
       "seller               342539       2               privat  342536   \n",
       "price                342539     NaN                  NaN     NaN   \n",
       "vehicleType          342539       8            limousine   91706   \n",
       "yearOfRegistration   342539     NaN                  NaN     NaN   \n",
       "gearbox              342539       2              manuell  267849   \n",
       "powerPS              342539     NaN                  NaN     NaN   \n",
       "model                342539    1027                 golf   28612   \n",
       "kilometer            342539     NaN                  NaN     NaN   \n",
       "monthOfRegistration  342539     NaN                  NaN     NaN   \n",
       "fuelType             342539       7               benzin  211809   \n",
       "brand                342539      39           volkswagen   72225   \n",
       "notRepairedDamage    342539     NaN                  NaN     NaN   \n",
       "dateCreated          342539     111  2016-04-03 00:00:00   13365   \n",
       "nrOfPictures         342539     NaN                  NaN     NaN   \n",
       "postalCode           342539     NaN                  NaN     NaN   \n",
       "lastSeen             342539  171029  2016-04-06 01:15:23      16   \n",
       "\n",
       "                                   first                 last       mean  \\\n",
       "dateCrawled          2016-03-05 14:06:22  2016-04-07 14:36:58        NaN   \n",
       "seller                               NaN                  NaN        NaN   \n",
       "price                                NaN                  NaN     9193.5   \n",
       "vehicleType                          NaN                  NaN        NaN   \n",
       "yearOfRegistration                   NaN                  NaN    2003.77   \n",
       "gearbox                              NaN                  NaN        NaN   \n",
       "powerPS                              NaN                  NaN    119.978   \n",
       "model                                NaN                  NaN        NaN   \n",
       "kilometer                            NaN                  NaN     126253   \n",
       "monthOfRegistration                  NaN                  NaN    5.89832   \n",
       "fuelType                             NaN                  NaN        NaN   \n",
       "brand                                NaN                  NaN        NaN   \n",
       "notRepairedDamage                    NaN                  NaN  0.0984939   \n",
       "dateCreated          2015-03-20 00:00:00  2016-04-07 00:00:00        NaN   \n",
       "nrOfPictures                         NaN                  NaN          0   \n",
       "postalCode                           NaN                  NaN    51030.8   \n",
       "lastSeen             2016-03-05 14:15:08  2016-04-07 14:58:51        NaN   \n",
       "\n",
       "                          std   min     25%     50%     75%     max  \n",
       "dateCrawled               NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "seller                    NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "price                  522358     0    1200    3000    7500   1e+08  \n",
       "vehicleType               NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "yearOfRegistration    34.4349  1000    1999    2004    2008    9000  \n",
       "gearbox                   NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "powerPS               188.264     0      75     109     150   20000  \n",
       "model                     NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "kilometer             39023.5  5000  125000  150000  150000  150000  \n",
       "monthOfRegistration   3.63351     0       3       6       9      12  \n",
       "fuelType                  NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "brand                     NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "notRepairedDamage    0.297982     0       0       0       0       1  \n",
       "dateCreated               NaN   NaN     NaN     NaN     NaN     NaN  \n",
       "nrOfPictures                0     0       0       0       0       0  \n",
       "postalCode            25789.4  1067   30826   49808   71706   99998  \n",
       "lastSeen                  NaN   NaN     NaN     NaN     NaN     NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Основные выводы:\n",
    "**nrOfPictures** по нулям. Удаляем этот признак.\n",
    "в **price** наблюдаются большие выбросы (10 в восьмой многовато даже для Bloodhound SSC)\n",
    "**kilometer** - имеет скошенное влево распределение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('nrOfPictures', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Признак **postalCode** мы исключим."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['postalCode'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3. Визуальный анализ признаков. Особенности данных."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на разброс значений количественных признаков: цены, года первой регистации и мощности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eALwSOAjYLzPvLsfcA+xXtqcacx2LVYsVK1aMa1utoE4QEYcCzwW+wcyNtweU7Yn90qxrNpuMjo4CMDo6arWC2qqtEzVGxLKIGIyIQbNnkgSZeRPwfuALwOeA64AtE45JIGfqMx2LNZPGqhTGbNy4saZIpB0TEXsBq4G3ZuZDrftmerzdRgyOw5pR69atY2RkBICRkRHWrl1bc0TqJjORVLiT6q7amANL36Nk5vmZuSAzF8ydO3cGPlqSOl9mXpCZR2Xmi4EHgP8E7h17lKz8HC6HTzXmOharFvPmzRvX7u/vrykSafsiopcqofDJzFxTumdqvL2zbE/sfxTHYc20gYEBent7Aejt7WXhwoU1R6RuMhNJhbXA68oMuccAD7aUkEmzauLF7DOf+cyaIpGmLyL6ys+DqeZT+BTV2Do2o/hpwGfK9lRj7ueBYyPiKWXCsGNLnzSrVq5cOa59zjnn1BSJtG1lJYYLgJsy8+9bds3IeFv2PRQRx5TPel3LuaRZ1Wg06Omp/rTr6emh0WjUHJG6yZztHRARFwMvAZ4WEXdQzXbbC5CZ/wRcRfUM8BDwc+B/zFaw0kT33HPPuPZdd91VUyTSTlkdEfsCI8CbM/MnEfE+4LKIOB34EXBSOXbSMTcz74+I9wDfKse9OzO3NcmuNCPmz5/PvHnzGBoaor+/nyOOOKLukKSpvAB4LfC9iLiu9P0FMJPjbYNq5bTHA58tL2nW9fX1sXjxYi655BKWLFmCFTBqp+0mFTLz1O3sT+DNMxaR9BgMDAxw8cUXb21b6qVOlJkvmqTvPuBlk/RPOeZm5oXAhTMeoLQdK1eu5LWvfa1VCtqlZebXgJhi94yMt5k5CDx7J8KUpq3RaDA0NGSVgtqurRM1SjOt0WjwuMc9DoDHPe5xDqKSVIP58+ezfv16qxQkqUZ9fX2sWrXKKgW1nUkFdbSxUq+IsNRLkiRJXWt4eJilS5fiiiJqN5MK6niNRoMFCxZYpSBJkqSu1Ww2GRwcpNls1h2KuoxJBXU8S70kSZLUzYaHh1m9ejWZyac//WmrFdRWJhUkSZIkqYM1m00eeeQRAB555BGrFdRWJhUkSZIkqYNdccUV49qXX355TZGoG5lUkCRJkqQO1tvbO66955571hSJupFJBUmSJEnqYA899NC49oMPPlhTJOpGJhUkSZIkqYPNmzdvXLu/v7+mSNSNTCpIkiRJUgdbuXLluPY555xTUyTqRiYVJEmSJKmDzZ8/f2u1Qn9/P0cccUTNEambmFSQJEmSpA63cuVK9tprL6sU1HZz6g5AkiRJkrRz5s+fz/r16+sOQ13ISgVJkiRJkjQtJhUkSZIkSdK0mFSQJEmSJEnTYlJBkiRJkiRNi0kFSZIkSZI0LSYVJEmSJEnStJhUkCRJkiRJ02JSQZIkSZIkTYtJBUmStFOGh4dZunQpmzZtqjsUSZLUZiYVJEnSTmk2mwwODtJsNusORZK6lgle1cWkgiRJmrbh4WHWrFlDZrJ69WovZiWpJiZ4VReTCpIkadqazSajo6MAjI6OejErSTUwwas6mVSQJEnTtm7dOkZGRgAYGRlh7dq1NUckSd2n2WyyZcsWALZs2WKCV21lUkGSJE3bwMAAvb29APT29rJw4cKaI5Kk7rNu3To2b94MwObNm03wqq1MKkiSpGlrNBr09FSXEz09PTQajZojkqTu8/KXv3xc+xWveEVNkagbmVSQpJpFxJ9FxI0RcUNEXBwRvxYRh0XENyJiKCIujYg9y7GPK+2hsv/QlvO8vfTfEhHH1fV91F36+vpYvHgxEcGSJUuYO3du3SFJU4qICyNiOCJuaOm7NCKuK68fRsR1pf/QiPivln3/1PKeoyLie2XM/VBEROl/akRcHREby8+ntP9bSlJ7mVSQpBpFxAHA/wIWZOazgT2AU4D3Ax/IzHnAA8Dp5S2nAw+U/g+U44iI+eV9zwKOB5oRsUc7v4u6V6PRYMGCBVYpqBN8nGqM3CozT87MIzPzSGA1sKZl9/fH9mXmG1v6zwPeAPSX19g5zwKuycx+4JrSlmbdF7/4xXHtq6++uqZI1I1MKkhS/eYAj4+IOcATgLuBlwKfLvsvAk4s24tKm7L/ZeUO2SLgksz8ZWb+ABgCjm5T/OpyfX19rFq1yioF7fIy86vA/ZPtK2PpScDF2zpHRDwd2Dszr83MBD7B5GN069gtzaqBgQFKwQw9PT3Ob6O2MqkgSTXKzDuBc4DbqJIJDwLrgZ9k5uZy2B3AAWX7AOD28t7N5fh9W/sneY8kafteBNybmRtb+g6LiO9ExFci4kWl7wCqMXZM63i7X2beXbbvAfab1Yil4qSTTqLKcVXL+5588sk1R6RuYlJBkmpUnrddBBwG7A88kQmlubPwmcsiYjAiBl3HWpK2OpXxVQp3Awdn5nOBM4BPRcTeO3qyUsWQk+1zHNZMu+yyy8a1L7300poiUTcyqSBJ9Xo58IPM3JSZI1TP8r4A2Kc8DgFwIHBn2b4TOAig7H8ycF9r/yTvGSczz8/MBZm5wHJ1Sdo6ni4Gtv4lVh4nu69srwe+DzyTamw9sOXtrePtveXxiLHHJIYn+zzHYc20z3zmM+PaV1xxRU2RqBuZVJCket0GHBMRTyjP874M2AB8GfiDcsxpwNjVwtrSpuz/UrkbthY4pawOcRjVxGHfbNN3kKRO93Lg5szc+lhDRMwdm/A2In6Daly9tTze8FBEHFPG7dcx+RjdOnZLs2r//fcf1z7gAJ+AVPuYVJCkGmXmN6gmXPw28D2qcfl84G3AGRExRDVnwgXlLRcA+5b+Mygzi2fmjcBlVAmJzwFvzswtbfwqkrTLi4iLga8Dh0fEHRExtrLOKTx6gsYXA9eXJSY/DbwxM8cmeWwAH6OaFPf7wGdL//uAV0TERqpExftm7ctILe66665x7TvvnLRYUZoVc7Z/iCRpNmXm2cDZE7pvZZLVGzLzF8AfTnGe9wLvnfEAJWk3kZmnTtH/+kn6VlMtMTnZ8YPAsyfpv4+q4kxqq0WLFnHxxb/Ki514oguPqH2sVJAkSZKkDvbyl798XPvYY4+tKRJ1I5MKkiRJktTB/vZv/3Zc+73vtXBR7WNSQZIkSZI62NDQ0Lj2xo0ba4pE3cikgiRJkiR1sHnz5o1r9/f31xSJupFJBUmSJEnqYCtXrhzXPuecc2qKRN3IpIIkSZIkdbD58+dvrVbo7+/niCOOqDkidROTCpIkSZLU4VauXMlee+1llYLabk7dAUiSJEmSds78+fNZv3593WGoC1mpIEmSJEmSpsWkgiRJkiR1uA0bNnDUUUdx88031x2KuoxJBUmStFO8kJWk+q1YsYKHH36YM888s+5Q1GVMKkiSpJ3ihawk1WvDhg0MDQ0BsHHjRpO8aiuTCpIkadq8kJWk+q1YsWJc2ySv2mmHkgoRcXxE3BIRQxFx1iT7D46IL0fEdyLi+oh45cyHKkmSdjVeyEpS/caSu2M2btxYUyTqRttNKkTEHsCHgROA+cCpETF/wmF/BVyWmc8FTgGaMx2oJEna9XghK0n1mzdv3rh2f39/TZGoG+1IpcLRwFBm3pqZjwCXAIsmHJPA3mX7ycBdMxeiJEnaVXkhK0n1e/vb3z6u/Zd/+Zc1RaJutCNJhQOA21vad5S+Vu8ElkbEHcBVwJ/MSHSSJGmXtnLlynHtc845p6ZIJKl7ffGLXxzX/sIXvlBTJOpGMzVR46nAxzPzQOCVwL9ExKPOHRHLImIwIgY3bdo0Qx8tSZLqMn/+/K3VCv39/RxxxBE1RyRJ3WfdunXj2mvXrq0pEnWjHUkq3Akc1NI+sPS1Oh24DCAzvw78GvC0iSfKzPMzc0FmLpg7d+70IpYkSbuUlStXstdee1mlIEk1GRgYoLe3F4De3l4WLlxYc0TqJjuSVPgW0B8Rh0XEnlQTMU5Mfd0GvAwgIn6TKqlgKYIkSV1g/vz5rF+/3ioFSapJo9Ggp6f6066np4dGo1FzROom200qZOZm4C3A54GbqFZ5uDEi3h0RYymw5cAbIuK7wMXA6zMzZytoSZIkSVKlr6+PxYsXExEsWbIEq8LVTnN25KDMvIpqAsbWvne0bG8AXjCzoUmSJEmSdkSj0WBoaMgqBbXdDiUVJEmSJEm7rr6+PlatWlV3GOpCM7X6gyRJkiRJ6jImFSRJ0k4ZHh5m6dKluFy0JEndx6SCJEnaKc1mk8HBQZrNZt2hSJKkNjOpIEmSpm14eJg1a9aQmaxevdpqBUmSuoxJBUmSNG3NZpPR0VEARkdHrVaQJKnLmFSQpBpFxOERcV3L66GIeGtEPDUiro6IjeXnU8rxEREfioihiLg+Ip7Xcq7TyvEbI+K0+r6Vusm6desYGRkBYGRkhLVr19YckTS1iLgwIoYj4oaWvndGxJ0t4/ArW/a9vYy3t0TEcS39x5e+oYg4q6X/sIj4Rum/NCL2bN+3U7f71Kc+xeGHH86ll15adyjqMiYVJKlGmXlLZh6ZmUcCRwE/By4HzgKuycx+4JrSBjgB6C+vZcB5ABHxVOBs4LeBo4GzxxIR0mwaGBigt7cXgN7eXhYuXFhzRNI2fRw4fpL+D4yNxZl5FUBEzAdOAZ5V3tOMiD0iYg/gw1Tj8Xzg1HIswPvLueYBDwCnz+q3kVq8+93vBuCd73xnvYGo65hUkKRdx8uA72fmj4BFwEWl/yLgxLK9CPhEVq4F9omIpwPHAVdn5v2Z+QBwNZNfOEszqtFo0NNTXU709PTQaDRqjkiaWmZ+Fbh/Bw9fBFySmb/MzB8AQ1RJ26OBocy8NTMfAS4BFkVEAC8FPl3e3zp2S7PqU5/6FJkJVI+iWa2gdjKpIEm7jlOAi8v2fpl5d9m+B9ivbB8A3N7ynjtK31T90qzq6+tj8eLFRARLlixh7ty5dYckTcdbyiNlF7ZUeT3W8XZf4CeZuXlCvzTrxqoUxlitoHYyqSBJu4Dy3O1C4P9O3JfVrYecwc9aFhGDETHoTP2aCY1GgwULFliloE51HvAM4EjgbuDc2f5Ax2HNtLEqhTFjE+hK7WBSQZJ2DScA387Me0v73vJYA+XncOm/Ezio5X0Hlr6p+h8lM8/PzAWZucC7ypoJfX19rFq1yioFdaTMvDczt2TmKPBRqscb4LGPt/dRPZI2Z0L/ZJ/pOKwZVT198ytjj6VJ7eB/bZK0aziVXz36ALAWGFvB4TTgMy39ryurQBwDPFgek/g8cGxEPKWU7h5b+iRJ2zCWwC3+OzC2MsRa4JSIeFxEHEY1Qe43gW8B/WWlhz2pHl1bW6rKvgz8QXl/69gtzap3vOMd49o+/qB2MqkgSTWLiCcCrwDWtHS/D3hFRGwEXl7aAFcBt1JNGPZRoAGQmfcD76G62P0W8O7SJ0kqIuJi4OvA4RFxR0ScDvxdRHwvIq4Hfg/4M4DMvBG4DNgAfA54c6lo2Ay8hSpxexNwWTkW4G3AGRExRDXHwgVt/HrqYq9+9au3Viv09PRw8skn1xyRusmc7R8iSZpNmfkzqovP1r77qFaDmHhsAm+e4jwXAhfORoyStDvIzFMn6Z7yD//MfC/w3kn6r6JK8k7sv5VfPT4htdU73vEO3vWud1mloLYzqSBJkiRJHe7Vr341r371q+sOQ13Ixx8kSZIkSdK0mFSQJEmSJEnTYlJBkiRJkiRNi0kFSZIkSZI0LSYVJEmSJEnStJhUkCRJkqQONzw8zNKlS9m0aVPdoajLmFSQJEmSpA7XbDYZHByk2WzWHYq6jEkFSZIkSepgw8PDrFmzhsxk9erVViuorUwqSJIkSVIHazabjI6OAjA6Omq1gtrKpIIkSdopPscrSfVat24dIyMjAIyMjLB27dqaI1I3MakgSZJ2is/xSlK9BgYG6O3tBaC3t5eFCxfWHJG6iUkFSZI0bT7HK0n1azQabNmyBYAtW7bQaDRqjkjdxKSCJEmaNp/jlaRdQ+tYLLWTSQVJkjRtPscrSfU799xzt9mWZpNJBUmSNG0+xytJ9bvyyiu32ZZmk0kFSZI0bY1Gg56e6nKip6fH53glaReQmXWHoC5iUkGSJE1bX18fixcvJiJYsmQJc+fOrTskSeo6r3rVq8a1BwYGaorSOpJ7AAAgAElEQVRE3cikgiRJ2imNRoMFCxZYpSBJNVm+fPm4qrHly5fXHJG6iUkFSZK0U/r6+li1apVVCpJUk76+vq1z2ixatMjxWG01p+4AJEmSJEk7Z/ny5dx5551WKajtrFSQJEk7ZXh4mKVLl7Jp06a6Q5GkrmXVmOpiUkGSJO2UZrPJ4OAgzWaz7lAkSVKbmVSQJEnTNjw8zJo1a8hMVq9ebbWCJEldxqSCJEmatmazyejoKACjo6NWK0iS1GVMKkiSpGlbt24dIyMjAIyMjLB27dqaI5IkSe1kUkGSJE3bwMAAvb29APT29m5d0kySJHUHkwqSJGnaGo0GPT3V5URPTw+NRqPmiCRJUjuZVJCkmkXEPhHx6Yi4OSJuiojfiYinRsTVEbGx/HxKOTYi4kMRMRQR10fE81rOc1o5fmNEnFbfN1I36evr44QTTgDgla98pUuZaZcWERdGxHBE3NDSt7KMv9dHxOURsU/pPzQi/isiriuvf2p5z1ER8b0yFn8oIqL0Tzp2S+1w5ZVXcvjhh/PZz3627lDUZUwqSFL9/gH4XGYeAfwWcBNwFnBNZvYD15Q2wAlAf3ktA86D6kIWOBv4beBo4GwvZiXpUT4OHD+h72rg2Zn5HOA/gbe37Pt+Zh5ZXm9s6T8PeAO/Go/HzjnV2C3NurPOqv5z+/M///OaI1G3MakgSTWKiCcDLwYuAMjMRzLzJ8Ai4KJy2EXAiWV7EfCJrFwL7BMRTweOA67OzPsz8wGqi+SJF87SjBseHt56V+yqq65ySUnt0jLzq8D9E/q+kJmbS/Na4MBtnaOMuXtn5rWZmcAnGD9GTzZ2S7Pqyiuv3Dpp7iOPPGK1gtrKpIIk1eswYBPwzxHxnYj4WEQ8EdgvM+8ux9wD7Fe2DwBub3n/HaVvqn5pVjWbTbZs2QLAli1bXFJSne5/Aq1/jR1WxuavRMSLSt8BVGPsmNbxdqqxW5pVY1UKY6xWUDuZVJCkes0Bngecl5nPBX7GhHLZcicsZ+oDI2JZRAxGxKB3lbWz1q1bx+bN1U3ezZs3u6SkOlZE/CWwGfhk6bobOLiMzWcAn4qIvXf0fNsaux2HNdPGqhTGPPLIIzVFom5kUkGS6nUHcEdmfqO0P02VZLi3lNiOldoOl/13Age1vP/A0jdV/6Nk5vmZuSAzFzipnnbWC1/4wnHtF73oRVMcKe26IuL1wKuA15RkAJn5y8y8r2yvB74PPJNqbG19RKJ1vJ1q7B7HcVgzbWxp3zF77rlnTZGoG5lUkKQaZeY9wO0RcXjpehmwAVgLjK3gcBrwmbK9FnhdWQXiGODBUmr7eeDYiHhKmaDx2NInzaqbb755m21pVxcRxwN/DizMzJ+39M+NiD3K9m9QTch4axlzH4qIY8qqD69j/Bg92dgtzar3ve9949p/93d/V1Mk6kYmFSSpfn8CfDIirgeOBP4GeB/wiojYCLy8tAGuAm4FhoCPAg2AzLwfeA/wrfJ6d+mTZtUPf/jDce0f/OAH9QQi7YCIuBj4OnB4RNwREacD/wg8Cbh6wtKRLwauj4jrqKrI3tgyrjaAj1GNxd/nV/MwTDV2S7PqVa96FXPmzAGqqoWxpX6ldpizIweVDO4/AHsAH8vMRw2QEXES8E6qZ8e+m5mvnsE4JWm3lZnXAQsm2fWySY5N4M1TnOdC4MKZjU7atnnz5jE0NLS13d/fX2M00rZl5qmTdF8wxbGrgdVT7BsEnj1J/31MMnZL7fD85z+fr3/96xx99NF1h6Ius91KhVL29WGqtdHnA6dGxPwJx/RTren7gsx8FvDWWYhVkiTtYlauXDmufc4559QUiSR1r+HhYb797W8DMDg46PK+aqsdefzhaGAoM2/NzEeAS6jW4G31BuDDZW10MnPSSWkkSdLuZf78+cybNw+oqhSOOOKImiOSpO7TbDYZHR0FYHR01OV91VY7klTYkbXPnwk8MyL+X0RcWx6XkCRJXWDlypXstddeVilIUk3WrVu3dVnJkZERl/dVW83URI1zqGbEfQlwKvDRiNhn4kGuyStJ0u5n/vz5rF+/3ioFSarJwMDA1mUle3t7WbhwYc0RqZvsSFJhR9Y+vwNYm5kjmfkD4D+pkgzjuCavJEmSJM2sRqOxzbY0m3YkqfAtoD8iDouIPYFTqNbgbXUFVZUCEfE0qschbp3BOCVJkiRJk+jr6+Pxj388AE94whPwBq7aabtJhczcDLwF+DxwE3BZZt4YEe+OiLG6ms8D90XEBuDLwIqypI4kSZIkaRZt2LCBhx56CIAHH3yQm2++ueaI1E3m7MhBmXkVcNWEvne0bCdwRnlJkiRJktpkxYoV49pnnnkmV155ZU3RqNvM1ESNkiRJkqQaDA0NjWtv3LixpkjUjUwqSJIkSVIHG1v5Ycyee+5ZUyTqRiYVJEmSJKmDjYyMjGs/8sgjNUWibmRSQZIkSZI62Lx588a1+/v7a4pE3cikgiRJkiR1sJUrV45rn3POOTVFom5kUkGSJEmSOtj8+fM58MADATjooIM44ogjao5I3cSkgiRJkiR1uIgY91NqF5MKkiRJktTBNmzYwO233w7Abbfdxs0331xzROomJhUkSZIkqYOtWLFiXPvMM8+sKRJ1I5MKkiRJktTBhoaGxrU3btxYUyTqRiYVJEmSJKmD7b333uPaT37yk2uKRN3IpIIkSZIkdbCRkZFx7UceeaSmSNSNTCpIkiRJUgc77rjjxrWPP/74miJRNzKpIEmSJEmSpsWkgiRJkiR1sM9//vPj2p/73OdqikTdyKSCJEmSJHWwPfbYY1x7zpw5NUWibmRSQZIkSZI62MMPPzyu/dOf/rSmSNSNTCpIkiRJUgeLiG22pdlkUkGSJEmSOtgxxxwzrv27v/u7NUWibmRSQZIkSZI62B133DGuffvtt9cUibqRSQVJqllE/DAivhcR10XEYOl7akRcHREby8+nlP6IiA9FxFBEXB8Rz2s5z2nl+I0RcVpd30fd59xzz+Xwww/ngx/8YN2hSNsUERdGxHBE3NDSN2PjbUQcVcbzofJea9DVFhOTCLfddltNkagbmVSQpF3D72XmkZm5oLTPAq7JzH7gmtIGOAHoL69lwHlQXRQDZwO/DRwNnD12YSzNtvPPPx+A8847r+ZIpO36OHD8hL6ZHG/PA97Q8r6JnyVJux2TCpK0a1oEXFS2LwJObOn/RFauBfaJiKcDxwFXZ+b9mfkAcDVezKoNzj333HFtqxW0K8vMrwL3T+iekfG27Ns7M6/NzAQ+0XIuaVYddNBB49oHH3xwTZGoG5lUkKT6JfCFiFgfEctK336ZeXfZvgfYr2wfALTWON5R+qbql2bVWJXCGKsV1IFmarw9oGxP7Jdm3X333Teu/eMf/7imSNSNTCpIUv1emJnPoyq1fXNEvLh1Z7njlTP1YRGxLCIGI2Jw06ZNM3VaSep4Mz3eTsVxWDNt0aJFW5eRjAhOPNEiGbWPSQVJqllm3ll+DgOXUz2je28ppaX8HC6H3wm01jgeWPqm6p/s887PzAWZuWDu3Lkz+VUkqRPN1Hh7Z9me2P8ojsOaaY1Gg97eXgB6e3tpNBo1R6RuYlJBkmoUEU+MiCeNbQPHAjcAa4GxGcVPAz5TttcCryuzkh8DPFjKdj8PHBsRTykThh1b+qRZtWzZsnHtN73pTTVFIk3bjIy3Zd9DEXFMWfXhdS3nkmZVX1/f1nkUDjnkEExWqZ1MKkhSvfYDvhYR3wW+CfxrZn4OeB/wiojYCLy8tAGuAm4FhoCPAg2AzLwfeA/wrfJ6d+mTZtXy5cvHtd/61rfWFIm0fRFxMfB14PCIuCMiTmdmx9sG8LHynu8Dn23H95KGh4e3LiP5ox/9CB+rUTvNqTsASepmmXkr8FuT9N8HvGyS/gTePMW5LgQunOkYpe1ZtmwZ559/vlUK2uVl5qlT7JqR8TYzB4Fn70yM0nQ0m02q/2QhM2k2m5x99tk1R6VuYaWCJEnaKcuXL+eWW26xSkGSarJu3TpGRkYAGBkZYe3atTVHpG5iUkGSJEmSOtjAwMC4iRoXLlxYc0TqJiYVJEmSJKmDNRoNenqqP+16enpc/UFtZVJBkiRJkjpYX18fixcvJiJYsmSJqz+orZyoUZIkSZI6XKPRYGhoyCoFtZ1JBUmSJEnqcH19faxataruMNSFfPxBkiRJkjrcRz7yEQ4//HA+9rGP1R2KuoxJBUmSJEnqcH//938PwMqVK2uORN3GpIIkSZIkdbCPfOQj49pWK6idTCpIkiRJUgcbq1IYY7WC2smkgiRJ2inDw8MsXbqUTZs21R2KJElqM5MKkiRppzSbTQYHB2k2m3WHIkmS2sykgiRJmrbh4WHWrFlDZrJ69WqrFSSpBmeccca49ooVK2qKRN3IpIIkSZq2ZrPJ6OgoAKOjo1YrSFIN/viP/3hc+4/+6I9qikTdyKSCJEmatnXr1jEyMgLAyMgIa9eurTkiSepOz3nOcwA48sgja45E3cakgiRJmraBgQF6e3sB6O3tZeHChTVHJEndZ3h4mJtvvhmAm266yUfR1FYmFSRJ0rQ1Gg16eqrLiZ6eHhqNRs0RSVL3aTabbN68GaiqxnwUTe1kUkGSJE1bX18fixcvJiJYsmQJc+fOrTskSeo6n/nMZ8bNb3PFFVfUHJG6iUkFSZK0UxqNBgsWLLBKQZJqsv/++49rH3DAATVFom40p+4AJElSZ+vr62PVqlV1hyFJXevOO+8c177jjjtqikTdyEoFSZIkSepgEysTDjzwwJoiUTfaoaRCRBwfEbdExFBEnLWN45ZEREbEgpkLUZIkSZI0lbvuumtce2LlgjSbtptUiIg9gA8DJwDzgVMjYv4kxz0J+FPgGzMdpCRJkiRpcosWLRrXPvHEE2uKRN1oRyoVjgaGMvPWzHwEuARYNMlx7wHeD/xiBuOTJEmSJG3DSSedNK598skn1xSJutGOJBUOAG5vad9R+raKiOcBB2Xmv85gbJIkSZKk7bjsssuICAAigksvvbTmiNRNdnqixojoAf4eWL4Dxy6LiMGIGNy0adPOfrQkSZIkdb1169aRmQBkJmvXrq05InWTHUkq3Akc1NI+sPSNeRLwbODfIuKHwDHA2skma8zM8zNzQWYumDt37vSjliRJkiQBMDAwQG9vLwC9vb0sXLiw5ojUTXYkqfAtoD8iDouIPYFTgK2pr8x8MDOflpmHZuahwLXAwswcnJWIJUmSJElbNRoNenqqP+16enpoNBo1R6Rust2kQmZuBt4CfB64CbgsM2+MiHdHhCkwSZoBEbFHRHwnIq4s7cMi4htlKd9LS1KXiHhcaQ+V/Ye2nOPtpf+WiDiunm+ibjQ8PMzSpUvx0UZ1qog4PCKua3k9FBFvjYh3RsSdLf2vbHnPpGPuji7FLs2kvr4+fuM3fgOAefPmYVW42mmH5lTIzKsy85mZ+YzMfG/pe0dmPuphncx8iVUKkvSY/SlV4nbM+4EPZOY84AHg9NJ/OvBA6f9AOY6y1O8pwLOA44FmWRJYmnXNZpPBwUGazWbdoUjTkpm3ZOaRmXkkcBTwc+DysvsDY/sy8yqYeszd0aXYpdlw003VZcSNN95YcyTqNjs9UaMkaedExIHA7wMfK+0AXgp8uhxyETC24PSi0qbsf1k5fhFwSWb+MjN/AAxRLQkszarh4WHWrFlDZrJ69WqrFbQ7eBnw/cz80TaOmWrM3dGl2KUZde65545rf/CDH6wpEnUjkwqSVL8PAn8OjJb2vsBPyuNnMH4p363L/Jb9D5bjt7v8rzQbms0mo6PVf7qjo6NWK2h3cApwcUv7LRFxfURcGBFPKX1TjbmOxarF+eefP6593nnn1RSJupFJBUmqUUS8ChjOzPVt/EyX99WMWbduHSMjIwCMjIy4jJk6Wpm/ZiHwf0vXecAzgCOBu4Fzp3jrY/0cx2FJuw2TCpJUrxcAC8uSvJdQPfbwD8A+ETGnHNO6lO/WZX7L/icD97H95X+3cnlfzSSXMdNu5gTg25l5L0Bm3puZWzJzFPgov3qsbKoxd4fGYsdhSbsTkwqSVKPMfHtmHliW5D0F+FJmvgb4MvAH5bDTgM+U7bWlTdn/pczM0n9KWR3iMKAf+Gabvoa6mMuYaTdzKi2PPkTE01v2/XfghrI91Zi7zaXYpdmybNmyce03velNNUWibmRSQZJ2TW8DzoiIIao5Ey4o/RcA+5b+M4CzADLzRuAyYAPwOeDNmbml7VGr6/T19bF48WIigiVLlriMmTpWRDwReAWwpqX77yLiexFxPfB7wJ/B1GPuVEuxt/FrqEstX758XPutb31rTZGoG83Z/iGSpHbIzH8D/q1s38okqzdk5i+AP5zi/e8F3jt7EUqTazQaDA0NWaWgjpaZP6NK4rb2vXYbx0865pZlJ6+a8QCl7Vi2bBnnn3++VQpqO5MKkiRpp/T19bFq1aq6w5Ckrvba176W73znO7zmNa+pOxR1GR9/kCRJkqQO12w2GRwcdGlftZ1JBUmStFOGh4dZunQpLo0nSfUYHh5mzZo1ZCarV692PFZbmVSQJEk7xbtjklSvZrPJ6OgoAKOjo47HaiuTCpIkadq8OyZJ9Vu3bh0jIyMAjIyMsHatK5mqfUwqSJKkafPumCTVb2BggN7eXgB6e3tZuHBhzRGpm5hUUMfzWV5Jqo93xySpfo1Gg4gAICJc4ldtZVJBHc9neSWpPt4dk6T69fX1cfDBBwNwyCGHMHfu3JojUjcxqaCO5rO8klSvRqNBT091OdHT0+PdMUmqwfDwMLfddhsAt912m9fEaiuTCupoPssrSfXq6+tj8eLFRARLlizx7pgk1aDZbJKZgNfEaj+TCupoPssrSfVrNBosWLDAKgVJqonXxKqTSQV1NJ/llaT69fX1sWrVKqsUJKkmAwMD4x5F85pY7WRSQR3NZ3klSZLU7U466aRxjwSffPLJNUekbmJSQR3NZ3klSZLU7S677LJx7UsvvbSmSNSNTCqo4/ksryRJkrrZunXrxrWdU0HtZFJBHc9neSVJktTNfv3Xf31ce//9968pEnUjkwqSJEmS1MGGhobGtf/zP/+zpkjUjUwqSJIkSZKkaTGpIEmSJEmSpsWkgiRJkiRJmhaTCpIkSZIkaVpMKqjjDQ8Ps3TpUjZt2lR3KJIkSVLbHXrooePahx12WD2BqCuZVFDHazabDA4O0mw26w5FkiRJaru//uu/Htc+++yza4pE3cikgjra8PAwa9asITNZvXq11QqSJEnqOl/84hfHtb/whS/UFIm6kUkFdbRms8no6CgAo6OjVitIkiSp61xxxRXj2pdffnlNkagbmVRQR1u3bh0jIyMAjIyMsHbt2pojkiRJktqrt7d3XHvPPfesKRJ1I5MK6mgDAwNbB9He3l4WLlxYc0SSJElSez300EPj2g8++GBNkagbmVRQR2s0GvT0VP8Z9/T00Gg0ao5IkiRJaq958+aNa/f399cUibqRSQV1tL6+PhYvXkxEsGTJEubOnVt3SNJjEhG/FhHfjIjvRsSNEfGu0n9YRHwjIoYi4tKI2LP0P660h8r+Q1vO9fbSf0tEHFfPN1I3cmlf7Q4i4ocR8b2IuC4iBkvfUyPi6ojYWH4+pfRHRHyojLnXR8TzWs5zWjl+Y0ScVtf3UXdZuXLluPY555xTUyTqRiYV1PEajQYLFiywSkGd6pfASzPzt4AjgeMj4hjg/cAHMnMe8ABwejn+dOCB0v+BchwRMR84BXgWcDzQjIg92vpN1LVc2le7kd/LzCMzc0FpnwVck5n9wDWlDXAC0F9ey4DzoEpCAGcDvw0cDZw9loiQZtP8+fO3Viv09/dzxBFH1ByRuolJBXW8vr4+Vq1aZZWCOlJWHi7N3vJK4KXAp0v/RcCJZXtRaVP2vywiovRfkpm/zMwfAENUF7TSrHJpX+3mWsfciWPxJ8oYfi2wT0Q8HTgOuDoz78/MB4CrqRK90qxbuXIle+21l1UKajuTCpJUs4jYIyKuA4apLkC/D/wkMzeXQ+4ADijbBwC3A5T9DwL7tvZP8h5p1ri0r3YjCXwhItZHxLLSt19m3l227wH2K9tTjbmOxarN/PnzWb9+vVUKajuTCpJUs8zckplHAgdSVRfM6tVARCyLiMGIGPSusnaWS/tqN/LCzHwe1aMNb46IF7fuzMykSjzsNMdhSbsTkwqStIvIzJ8AXwZ+h6qUdk7ZdSBwZ9m+EzgIoOx/MnBfa/8k75n4Oedn5oLMXOBjQ9pZAwMD41bhcWlfdarMvLP8HAYup0ry3lsea6D8HC6HTzXm7tBY7Dis2bBhwwaOOuoobr755rpDUZcxqSBJNYqIuRGxT9l+PPAK4Caq5MIflMNOAz5TtteWNmX/l8rds7XAKWV1iMOoJg/7Znu+hbrZSSedNO7xh5NPPrnmiKTHLiKeGBFPGtsGjgVuYPyYO3Esfl1ZBeIY4MHymMTngWMj4illgsZjS58061asWMHDDz/MmWeeWXco6jImFSSpXk8HvhwR1wPfoprg60rgbcAZETFENWfCBeX4C4B9S/8ZlJnIM/NG4DJgA/A54M2ZuaWt30Rd6bLLLqOaKxQigksvvbTmiKRp2Q/4WkR8lyoh+6+Z+TngfcArImIj8PLSBrgKuJVqUtyPAg2AzLwfeA/VeP4t4N2lT5pVGzZsYGhoCICNGzdaraC2iuoGV/stWLAgBwcHa/lsSZpKRKxvWUpst+dYrJ111FFH8fDDD29t77XXXqxfv77GiLQ76Kax2HFYM+H3f//3tyYVoFpW8sorr6wxInW6xzIOW6kgSZKmbWBggN7eXgB6e3udU0GSatCaUICqWkFqF5MKkiRp2hqNxriJGhuNRs0RSVL3OfTQQ8e1DzvssHoCUVcyqSBJkqatr6+PxYsXExEsWbIEZ7KXpPYzqaA6zdn+IZIkSVNrNBoMDQ1ZpSBJNfna1742rv3v//7vNUWibmRSQZIk7ZS+vj5WrVpVdxiSpKKuyfjVnXz8QZIkSZI62Kte9apx7YGBgZoiUTfaoaRCRBwfEbdExFBEnDXJ/jMiYkNEXB8R10TEITMfqiRJkiRpouXLl4+bNHf58uU1R6T/v737j62rvO84/v46dUEq0tqUBLGErtVsOcqmiQ43VOo/bUcpoDlZpAnTNCmbkJhkQEyDaARPIU3nlS1dp02tq9EVDehSyJpAbRQaCBRNSLDgdmgb+SFbQAqI4nT8CkWhTvzsj3sc3ZvYiXNzcx/fnPdLuvJ5zj3X93Nx+Or46+c8p0xO2lSIiHnAt4ErgaXAlyJi6TGH/RfQnVL6PeCHwN81OqgkSZIk6XgLFy48ekvfFStWuGiummo2MxWWAWMppRdSSr8G7gdWVB+QUvpJSum9YvgMsLixMSVJkiRJM7nlllv41Kc+5SwFNd1sFmpcBLxcNX4FuPQEx18HPHI6oSRJkiRJs+eiucqloQs1RsRqoBvYNMPz10fESESMHDhwoJFvLUmSJEmlNT4+zurVq/H3LDXbbJoKrwIXVY0XF/tqRMRlQD+wPKX0/nTfKKV0V0qpO6XU7XU+kiRJktQYg4ODjIyMMDg4mDuKSmY2TYVngc6I+EREfBC4BhiqPiAiPgn8M5WGwnjjY0qSJEmSpjM+Ps62bdtIKbF161ZnK6ipTtpUSCkdBm4EdgB7gC0ppecjYmNELC8O2wScB/x7RDwXEUMzfDtJkiRJUgMNDg4yOTkJwOTkpLMV1FSzWaiRlNJ2YPsx+9ZXbV/W4FySJEmSpFkYHh5mYmICgImJCYaGhrjjjjsyp1JZNHShRkmSJElSc/X09NSMly9fPsORUuPZVJAkSZKkFnbZZbUTxy+//PJMSVRGNhUkSZIkqYV9/etfrxkPDAxkSqIysqkgSZIkSS1sbGysZjw6OpopicrIpoIkSZIktbCOjo6acWdnZ6YkKiObCpIkSZLUwtatW1cz7u/vz5REZWRTQZIkSZJa2M6dO2vGjz76aKYkKiObCpIkSZLUwoaHh2vGQ0NDmZKojGwqSJIkSVIL6+npob29HYD29naWL1+eOZHKxKaCWt74+DirV6/mwIEDuaNIkiRJTdfX10dKCYCUEn19fZkTqUxsKqjlDQ4OMjIywuDgYO4okiRJUtMtXLjw6HZEsGDBgoxpVDY2FdTSxsfH2bZtGykltm7d6mwFSZIklc5TTz3F4cOHAZiYmODpp5/OnEhlYlNBLW1wcJDJyUkAJicnna0gSZKk0rn55ptrxjfddFOmJCojmwpqacPDw0xMTACVrqwr3arVRMRFEfGTiNgdEc9HxM3F/vkR8VhEjBZfP1Lsj4j4p4gYi4j/jojfr/pe1xbHj0bEtbk+kyS1mhPU4g0R8WpEPFc8rqp6zbqiFu+LiC9W7b+i2DcWEbfl+Dwqn3fffbdmfPDgwUxJVEY2FdTSenp6iAigcv2YK92qBR0GbkkpLQU+DdwQEUuB24DHU0qdwOPFGOBKoLN4XA98BypNCOAO4FJgGXDHVCNCOtO6urqOPqQWNVMtBviHlNLFxWM7QPHcNcDvAFcAgxExLyLmAd+mUquXAl+q+j6SdFayqaCW5kq3anUppddSSj8rtg8Ce4BFwArgnuKwe4A/KrZXAPemimeAD0fEhcAXgcdSSm+klN4EHqNyoitJOokT1OKZrADuTym9n1J6ERij0tBdBoyllF5IKf0auL84VjqjLrzwwprxokUn+ucrNdYHcgeQTsfOnTtrxk888QS9vb2Z0kinJyI+DnwS+E/ggpTSa8VTvwAuKLYXAS9XveyVYt9M+3UaBgYGePDBB3PHqHHo0KGja8nMBUeOHKkZd3V1MW/evExparW1tXHuuefmjlFj5cqV9Pf3546hEzimFn8GuDEivgKMUJnN8CaV+vpM1cuqa+6xtfjSMxxZ4q/XYDAAAAoBSURBVK233qoZv/HGG5mSqIycqaCWtnHjxprxhg0b8gSRTlNEnAdsBf48pfRO9XOpMh0nNfC9ro+IkYgY8Y4prefIkSNz6jHXM0qnYppa/B3gt4GLgdeAv2/Q+1iH1VDnn39+zdhbSqqZnKmgljZ16cOUufTXO2m2IqKdyknsv6WUthW7X4+IC1NKrxWXN4wX+18FLqp6+eJi36vAZ4/Z/+R075dSugu4C6C7u7thzYqzUX9//5z7q/LAwAB79+7NHeOoXbt2Hbdv2bJlGZIcb8mSJXPu56e5a7panFJ6ver57wIPF8OZajEn2H+UdViN9vLLL9eMf/7zn2dKojKyqaCWFhE1jYW2NiffqLVEZaXR7wF7UkrfrHpqCLgWuLP4+qOq/TdGxP1UptS+XTQedgB/U7U44+XAumZ8BjXXXPslebrFGe+7774MSaT6zVSLp5q7xXAl8L/F9hCwOSK+CfwmlcVzdwEBdEbEJ6g0E64BVjXnU0hSHjYV1NK+/OUv8/3vf//oeM2aNRnTSHX5DLAG+J+IeK7YdzuVZsKWiLgO2A9cXTy3HbiKyqJg7wF/CpBSeiMivgY8Wxy3MaXkBZWSNDsz1eIvRcTFVC5Bewn4M4CU0vMRsQXYTeXOETeklI4ARMSNwA5gHnB3Sun5Zn4QSWo2mwpqaUNDQzXjhx56iNtvvz1TGunUpZSeovKXren8wTTHJ+CGGb7X3cDdjUsnSeVwglq8/QSvGQAGptm//USvk5ph6pbrUjM4V1wt7Z13ataz4+23386URJIkSZobjl13TDqTbCqopZ1zzjk147l26zBJOtsde2/0xYsXZ0oiSZJysKmglvb+++/XjA8dOpQpiSSV05NPPlkzfvzxx/MEkSRJWdhUkCRJp2VqtoKzFCRJKh8XalRL85aSkpTfsbMVJElSefgbmFra+vXra8YbNmzIE0SSJEmSSsimglraqlWrjt4yp62tjd7e3syJJKl8du/ezSWXXMLevXtzR5EkSU1mU0Etb2q2grMUJCmPtWvX8u6773LrrbfmjiJJkprMpoJa3qpVq9i3b5+zFCQpg927dzM2NgbA6OiosxUkSSoZmwqSJKlua9eurRk7W0GSpHKxqSBJkuo2NUthyujoaKYkkiQpB5sKkiSpbh0dHTXjzs7OTEkkSVIONhUkSVLd1q1bVzPu7+/PlESSJOVgU0GSJNVt586dNeNHH300UxJJkpSDTQVJklS34eHhmvHQ0FCmJJIkKQebCpIkqW49PT1EBAARwfLlyzMnkiRJzWRTQZIk1e3qq68mpQRASone3t7MiSRJUjPZVJAkSXXbsmULbW2V04m2tjYeeOCBzIkkSVIz2VSQJEl1Gx4eZnJyEoDJyUnXVJAkqWRsKkiSpLr19PTQ3t4OQHt7u2sqSJJUMjYVJElS3fr6+mouf+jr68ucSJIkNZNNBUmSVLeFCxdy5ZVXAnDVVVexYMGCzIkkSVIz2VSQJEmSJEl1sakgSZLqNj4+ziOPPALA9u3bOXDgQOZEkiSpmWwqSJKkug0ODtbc/WFwcDBzIkmS1Ew2FdTyNm/eTFdXl/dGl6QMhoeHmZiYAGBiYsJbSkqSVDI2FdTyvvrVrwKwfv36zEkkqXy8paQkSeVmU0EtbfPmzTVjZytIUnN5S0lJksrtA7kDSKdjapbClPXr19Pb25spjSSVz8KFC/nc5z7Hj3/8Yz7/+c97S0lJpTAwMMDevXtzxzihNWvW5I4AwJIlS+jv788dQ2fQrJoKEXEF8I/APOBfUkp3HvP8OcC9wCXA/wG9KaWXGhtVks5OEXE38IfAeErpd4t984EHgI8DLwFXp5TejIigUo+vAt4D/iSl9LPiNdcCf1V8279OKd3TzM+h8po6sZ7rJ9hSM5zsvFmnbi7+Ar9//37ee++93DFOaM+ePbkjAJX/VnPt52ejo7FO2lSIiHnAt4EvAK8Az0bEUEppd9Vh1wFvppQ6IuIa4G8B/1wsSbPzr8C3qDRnp9wGPJ5SujMibivGfwlcCXQWj0uB7wCXFk2IO4BuIAE/LWr1m037FCql3bt389JLLwHw4osvsnfvXpYsWZI3lJTJLM+bdYp27NjB66+/njtGyzl48GDuCEAlx1z7+e3fv9+mQgPNZqbCMmAspfQCQETcD6wAqovjCmBDsf1D4FsRESml1MCspTMwMMCDDz6YO0aNX/3qV0dvHTZXdXV15Y4AVK4t/tCHPpQ7Ro2VK1daQOeglNJ/RMTHj9m9AvhssX0P8CSVpsIK4N6ivj4TER+OiAuLYx9LKb0BEBGPAVcAPzjD8VVya9eurRnfeuutPPzww5nSSNnN5rxZp2j+/PlzblbAoUOH5tQ58ZEjR47bN2/evAxJjtfW1sa5556bO0aN+fPn545wVplNU2ER8HLV+BUqfx2b9piU0uGIeBv4KPDL6oMi4nrgeoCPfexjdUaWpFK4IKX0WrH9C+CCYnu6mrzoBPuPYy1WI42NjdWMR0dHMyWR5oTZnDdbh0/RQw89lDvCcebiJRm7du06ur1s2bKMSWp5qcHZr6kLNaaU7gLuAuju7nYWw0n09/f7P+BJTDcrYd++fRmSSGdOSilFRMNqprVYjdTR0VHTWOjs7MyYRmoN1uHWNxfP0avPi++7776MSVQ2s7ml5KvARVXjxcW+aY+JiA8Av0FlwUZJUn1eLy5roPg6XuyfqSbPplZLDbdp06aa8Te+8Y1MSaQ5wVqsbPbt23f0ITXTbJoKzwKdEfGJiPggcA0wdMwxQ8C1xfYfA0+4noKa4diiaRHVWaS6rl4L/Khq/1ei4tPA28VlEjuAyyPiIxHxEeDyYp90Ri1dupSOjg6gMkvBRRpVcrM5b5aks8pJmwoppcPAjVROTvcAW1JKz0fExohYXhz2PeCjETEG/AWVVcolSbMQET8Anga6IuKViLgOuBP4QkSMApcVY4DtwAvAGPBdoA+gWKDxa1ROaJ8FNk4t2iidaZs2beK8885zloJKb6bz5rypJOnMilwTCrq7u9PIyEiW95akmUTET1NK3blzNIu1WNJcVKZabB2WNBedSh2ezeUPkiRJkiRJx7GpIEmSJEmS6mJTQZIkSZIk1cWmgiRJkiRJqotNBUmSJEmSVBebCpIkSZIkqS42FSRJkiRJUl1sKkiSJEmSpLrYVJAkSZIkSXWJlFKeN444AOzP8uY6G50P/DJ3CJ0VfiultCB3iGaxFquBrMNqpNLUYuuwGsxarEaZdR3O1lSQGikiRlJK3blzSFJZWYclKT9rsXLw8gdJkiRJklQXmwqSJEmSJKkuNhV0trgrdwBJKjnrsCTlZy1W07mmgiRJkiRJqoszFSRJkiRJUl1sKkiSJEmSpLrYVJAkSZIkSXWxqSBJkiRJkupiU0GSJEmSJNXl/wHjTFLYTKEC1AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feats = ['price', 'yearOfRegistration', 'powerPS']\n",
    "fig, axes = plt.subplots(ncols=len(feats), nrows=1, figsize=(18,6))\n",
    "\n",
    "for i, feat in enumerate(feats):\n",
    "    sns.boxplot(df[feat], ax=axes[i], orient='v', width=0.5, color='g');\n",
    "    axes[i].set_ylabel('')\n",
    "    axes[i].set_title(feat)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из-за выбросов ничего не разобрать. Начнем с цен, определим пороги отсечения выбросов. Просмотр объявлений о продаже показали, что старая машина (15-20 лет) может стоить в районе 100 евро. В качестве верхней границы возьмем 110000. Именно столько стоят Porsche, которых не так уж мало в наборе.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['price'] >= 100) & (df['price'] <= 150000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на год первой регистрации. Это важный признак, т.к. возраст автомобиля один из ключевых факторов, влияющих на его цену. Очевидно, что год регистрации не может быть позднее, чем год размешения объявлений. Объявления размещены в марте и апреле 2016, поэтому 2016-й год также не будем рассматривать. 17141 автомобилей зарегистрированы в 2016 и позднее. Удалим эти строки и машины старее 1976 года, то есть оставляем период в 40 лет."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['yearOfRegistration'] >= 1976) & (df['yearOfRegistration'] < 2016)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В выборке есть достаточно автомобилей марки Porsche, мощность двигателей которых может превышать 500 л.с. Также есть авто марки Fiat с мощность движка не превышающим 30 лошадок. Возьмем ннтервал допустимых значений (20, 600). И построим ящики с усами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[(df['powerPS'] > 20) & (df['powerPS'] < 600)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "feats = ['price', 'yearOfRegistration', 'powerPS']\n",
    "\n",
    "fig, axis = plt.subplots(ncols=3, figsize=(18, 6))\n",
    "for i, feat in enumerate(feats):\n",
    "    sns.boxplot(np.log(df[feat]), ax=axis[i], orient='v', width=0.5, color='g');\n",
    "    y_formatter = FuncFormatter(lambda x, pos: ('%i')%(np.exp(x)))\n",
    "    axis[i].yaxis.set_major_formatter(y_formatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на распределение количественных и бинарных признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Доля машин с пробегом 150000 км:  0.6358391369657245\n"
     ]
    }
   ],
   "source": [
    "print('Доля машин с пробегом 150000 км: ', df[df['kilometer'] == 150000].shape[0] / df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обратим внимание на пробег. Медиана и максимум = 150000. 64% машин имеют пробег 150000 км и мы имеем сильное скошенное распределение. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изобразим матрицу корреляции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pal = sns.light_palette(\"green\", as_cmap=True)\n",
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.heatmap(df.corr(), cmap=\"RdBu_r\", annot=True, fmt = '.2f', ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Price** положительно коррелирует с **yearOfRegistration** и **powerPS** и отрицательно - с **kilometer**. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на некоторые категориальные признаки по отдельности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feats = ['seller', 'vehicleType', 'gearbox', 'fuelType']\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(14,12))\n",
    "idx = 0\n",
    "for i in range(2): \n",
    "    for j in range(2):\n",
    "        sns.countplot(df[feats[idx]], ax=axes[i][j]); \n",
    "        idx += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оказывается у нас только одна запись с **seller** == 'gewerblich', так что можно удалить этот признак. fuelType отличный от diesel и benzin объединим в общую группу andere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6G1nYJLk7yS1JbkqyqtX2SLIiyV3tdfdWT5Izk4wluTnJQUP7Wdza35Vk8VD94Lb/sdY3U3+UkiQY/ZnNL1bVvKqa396fAlxVVXOBq9p7gCOBuW1ZApwFg3ACTgV+Bng9cOp4QLU27x3qt7D/4UiSJjLqsNnYIuC8tn4ecPRQ/fwaWAnslmRv4M3Aiqp6qKoeBlYAC9u2XatqZVUVcP7QviRJU2yUYVPAl5PckGRJq+1VVfe19fuBvdr6PsC9Q33XtNrm6msmqG8gyZIkq5KsWrdu3XM9HknSJuw0ws/++apam+THgBVJvj28saoqSfUcQFWdDZwNMH/+/K6fJUk7spGd2VTV2vb6IPBFBvdcHmiXwGivD7bma4F9h7rParXN1WdNUJckjcBIwibJLkleOr4OHAHcCiwDxmeULQYub+vLgOPbrLQFwCPtctuVwBFJdm8TA44ArmzbHk2yoM1CO35oX5KkKTaqy2h7AV9ss5F3Av6qqr6U5Hrg4iQnAPcAb2vtlwNHAWPAY8C7AarqoSR/AFzf2n20qh5q6+8HzgVeAlzRFknSCIwkbKpqNfAfJ6ivBw6boF7ASZvY11Jg6QT1VcCBz3mwkqTn7Pk29VmSNA0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3ho0kqTvDRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbSVJ3Ux42SfZNcnWS25PcluQ3W/0jSdYmuaktRw31+XCSsSR3JnnzUH1hq40lOWWovl+Sb7T6RUl2ntqjlCQNG8WZzZPAf6+q/YEFwElJ9m/bPllV89qyHKBtOxY4AFgI/GmSGUlmAJ8GjgT2B44b2s8ZbV+vBh4GTpiqg5MkPdOUh01V3VdV32zr3wfuAPbZTJdFwIVV9XhV/T0wBry+LWNVtbqqngAuBBYlCfBG4JLW/zzg6D5HI0majJHes0kyB/hp4ButdHKSm5MsTbJ7q+0D3DvUbU2rbar+cuB7VfXkRnVJ0oiMLGyS/ChwKfBbVfUocBbwKmAecB/wiSkYw5Ikq5KsWrduXe+Pk6Qd1kjCJskLGQTN56vqCwBV9UBVPVVVTwOfZXCZDGAtsO9Q91mttqn6emC3JDttVH+Gqjq7quZX1fyZM2dum4OTJD3DKGajBTgHuKOq/vdQfe+hZm8Fbm3ry4Bjk7woyX7AXOA64Hpgbpt5tjODSQTLqqqAq4FjWv/FwOU9j0mStHk7bbnJNncI8E7gliQ3tdrvMJhNNg8o4G7gfQBVdVuSi4HbGcxkO6mqngJIcjJwJTADWFpVt7X9fQi4MMnHgBsZhJskaUSmPGyq6qtAJti0fDN9TgNOm6C+fKJ+VbWaH16GkySNmE8QkCR1Z9hIkrozbCRJ3Rk2kqTuDBtJUnejmPosSc/KIZ86ZNRD2CF87QNf2+b79MxGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3Rk2kqTuDBtJUneGjSSpO8NGktSdYSNJ6s6wkSR1Z9hIkrozbCRJ3U3bsEmyMMmdScaSnDLq8UjSjmxahk2SGcCngSOB/YHjkuw/2lFJ0o5rWoYN8HpgrKpWV9UTwIXAohGPSZJ2WNM1bPYB7h16v6bVJEkjsEP/UmeSJcCS9vYHSe4c5Xg62xP4p1EPYmvk44tHPYTni+3uu+PUjHoEzyfb3feX39iq7+8Vk2k0XcNmLbDv0PtZrbaBqjobOHuqBjVKSVZV1fxRj0Nbz+9u++b3NzBdL6NdD8xNsl+SnYFjgWUjHpMk7bCm5ZlNVT2Z5GTgSmAGsLSqbhvxsCRphzUtwwagqpYDy0c9jueRHeJy4TTld7d98/sDUlWjHoMkaZqbrvdsJEnPI4bNdi7JR5J8MMlHkxy+Dfd7aJK/2Vb729ElmZPk1k77/vEkl/TYt56bJD8Y9RieL6btPZsdTVX9/qjHoNGoqn8Ejhn1OPTcJAmDWxtPj3osPXhmsx1K8rtJvpPkq8BrW+3cJMe09YOTfCXJDUmuTLJ3q/9GktuT3JzkwlbbJcnSJNcluTGJj/XpZ6ckn09yR5JLkvzIZr6ra5Kc0b6X7yR5Q6v/eZKb2rIuyanDZ01J3pXkC0m+lOSuJH80ygOeDpJc1r6f29ofgpPkB0lOS/KtJCuT7NXq+yX5epJbknxso/38dpLr2//+/merzWkPDD4fuBXYN8kRbR/fTPLXSX50qo+5i6py2Y4W4GDgFuBHgF2BMeCDwLkM/nX7QuDvgJmt/a8xmPoN8I/Ai9r6bu31D4F3jNeA7wC7AIcCfzPq450uCzAHKOCQ9n4p8Nub+a6uAT7R1o8C/naj/b0CuKO9zgFubfV3AauBlwEvBu4B9h318W/PC7BHe30Jg0B4efsuf6nV/wj4vba+DDi+rZ8E/KCtH8FgVloY/CP/b4BfaN/d08CC1m5P4Fpgl/b+Q8Dvj/o/g22xeBlt+/MG4ItV9RhAko3/WPW1wIHAisFZOTOA+9q2m4HPJ7kMuKzVjgDekuSD7f2Lgdn9hr9Du7eqvtbW/xL4HTb9XQF8ob3ewOD/lABI8mLgr4EPVNU9Seawoauq6pHW9nYGgXQverZ+I8lb2/q+wFzgCQaBAYPv501t/RDgV9r654Az2voRbbmxvf/Rtp9/AO6pqpWtvoDBk+q/1v47sTPw9W18PCNh2Ew/AW6rqp+dYNt/YfCvqV8CfjfJ61r7X6mqDZ4LN35ZQNvUxn9n8H02/V0BPN5en2LD/61+BvhCVf3tFvpN1FdbIcmhwOHAz1bVY0muYfAPsn+rdurBM/8znujvSQL8r6r6s432Pwf4543araiq47bF+J9PvGez/bkWODrJS5K8lEFwDLsTmJnkZwGSvDDJAUlewOByytUMTs1fxuBfV1cCH2g3J0ny01N1IDug2ePfC/DrwEom+K42t4MkJwEvrarT+w5VzcuAh1vQ/ASDM4/N+RqDx2MBvH2ofiXwnvH7L0n2SfJjE/RfCRyS5NWt3S5JXvOcjuB5wrDZzlTVN4GLgG8BVzB4Dtzw9icY3Ls5I8m3gJuAn2NwieYvk9zC4FT+zKr6HvAHDO7z3JzktvZefdwJnJTkDmB34FNM/F1tzgeB1w1NEjix64j1JQYTO+4ATmcQBpvzmwy+41sY+lmTqvoy8FfA19u2S4CXbty5qtYxuO92QZKbGVxC+4ltcBwj5xMEJEndeWYjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7gwbaRtrz6C7I8nnn0Xfu5PsNTS1+f4ka4fe79xjzFJvTn2WtrEk3wYOr6o1z6Lv3cD8qvqn9v4jDJ6v9fFtOkhpinlmI21DST4DvBK4IskjQ8+cI8mt488xS/KO9kTnm5L8WZIZk9z/HyY5eej9GUlOSnJ4kquTXNGeIvzpoadCHDn0FOGLkuyyLY9ZmgzDRtqGqupEBk/X/kXgkxO1SfKTDJ7wfEhVzWPwbK23T9R2AkuBxW0/M4BfZfCX6QA/A/w3Bg9y/ElgUXskyinAYVV1EIOHsf7m1h+Z9Nz4gD5p6h3G4Kcirm8nHy8BHpxMx6oaS/L99hDVVwDXVdXDbT8rq+pugAx+r+jnW7f9gb8beorwV7fdoUiTY9hI/TzJhlcPXtxeA5xXVR9+lvs9h8Hzs+YAw08R3vgGbLXP+lJVvfNZfpa0TXgZTernbuAggCQHAfu1+lXAMeNP/U2yR5JXbMV+L2XwtO95wPDPDCxIMrtdXnsbgzOYvwP+c5JXts/aJcncZ39I0rPjmY3Uz6XA8e1p2t9g8CuoVNXtSX4P+HL76Yd/Y/CrjvdMZqdV9a9JrgXurw1/r/46Br918yoGIbSsqirJCcBFQ9Omfwe467kfnjR5Tn2WtjMtoG4Cjq6q1a12OHByVR090sFJm+BlNGk70iYGfJfBfZjVox6PNFme2UiSuvPMRpLUnWEjSerOsJEkdWfYSJK6M2wkSd0ZNpKk7v4/h6OM61ThxCAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop('seller', axis=1, inplace=True)\n",
    "df['fuelType'].replace(['lpg', 'hybrid', 'cng', 'elektro'], 'andere', inplace=True)\n",
    "sns.countplot(df['fuelType']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посмотрим на взаимодействие категориальных признаков и цены."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
    "sns.boxplot(x='fuelType', y='price', data=df, ax=axis[0]);\n",
    "sns.boxplot(x='gearbox', y='price', data=df, ax=axis[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Автомобили с дизельными двигателями дороже бензиновых и прочих, а машины с автоматической коробкой дороже, чем с ручной. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axis = plt.subplots(figsize=(16, 8), )\n",
    "sns.boxplot(x='brand', y='price', data=df);\n",
    "axis.set_xticklabels(df['brand'].unique(), rotation=80);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из этой картинки следует, что Porsche существенно дороже остальных марок."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Предобработка данных "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log1p(df['price'])\n",
    "X = df.drop(['price'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разделим данные на тренировочную и тестовую части и применим dummy-кодирование к категориальным признакам. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = \\\n",
    "            train_test_split(X, y, test_size=0.3, shuffle=True, stratify=X['brand'], random_state=RANDOM_SEED)\n",
    "split_idx = X_train.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for cat_feature in X.columns[X.dtypes == 'object']:\n",
    "    X_train[cat_feature] = X_train[cat_feature].astype('category')  \n",
    "    X_train[cat_feature].cat.set_categories(X_train[cat_feature].unique(), inplace=True)\n",
    "    X_test[cat_feature] = X_test[cat_feature].astype('category')\n",
    "    X_test[cat_feature].cat.set_categories(X_train[cat_feature].unique(), inplace=True)\n",
    "    \n",
    "# В тестовом наборе оказались модели, которых не было в тренировочном, появившиеся NaN замняем \"andere\" (\"другое\").\n",
    "X_test['model'].fillna(value='andere', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = pd.get_dummies(X_train, columns=X_train.columns[X_train.dtypes == 'category'])\n",
    "X_test = pd.get_dummies(X_test, columns=X_test.columns[X_test.dtypes == 'category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((205044, 764), (87876, 764))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Построение базовых моделей. Выбор метрик качества. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте построим и сравним линейную модель и случайный лес. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Удаляем даты\n",
    "X_train_base = X_train.drop(['dateCrawled','dateCreated', 'lastSeen'], axis=1)\n",
    "X_test_base = X_test.drop(['dateCrawled','dateCreated', 'lastSeen'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Базовая модель линейной регрессии"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отмасштабируем признаки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train_base)\n",
    "X_train_scaled = scaler.transform(X_train_base)\n",
    "X_test_scaled = scaler.transform(X_test_base)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 49s, sys: 29.9 s, total: 2min 18s\n",
      "Wall time: 23 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В качестве метрик качества линейной регрессии выберем MAE за его интерпретируемость. Также посмотрим на коэффициент детерминации, или коэффициент $R^2$. Данная мера качества — это нормированная среднеквадратичная ошибка. Чем она ближе к единице, тем лучше модель объясняет данные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression:\n",
      "\tMAE:  0.3700205712558758\n",
      "\tR2:  0.7929934947817355\n"
     ]
    }
   ],
   "source": [
    "y_preds_lr = lr.predict(X_test_scaled)\n",
    "print('LinearRegression:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds_lr))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Базовая модель случайного леса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 16s, sys: 676 ms, total: 1min 17s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf = RandomForestRegressor(random_state=RANDOM_SEED)\n",
    "rf.fit(X_train_base, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor:\n",
      "\tMAE:  0.2810948947341652\n",
      "\tR2:  0.8688083341019125\n"
     ]
    }
   ],
   "source": [
    "y_preds = rf.predict(X_test_base)\n",
    "print('RandomForestRegressor:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target range: (4.62, 11.92) \n"
     ]
    }
   ],
   "source": [
    "print('Target range: (%.2f, %.2f) ' % (y.min(), y.max()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Учитывая, что целевая переменная принимает значения в диапазоне (4.62, 11.51), то ошибка выглядит допустимой."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Создание новых признаков и описание этого процесса"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Новые признаки, которые предположительно могут коррелировать с целевым: \n",
    "- **adUpDays** - сколько дней висело объявление   \n",
    "- **kilPerYear** - среднегодовой пробег."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feats_train = pd.DataFrame(index=X_train.index)\n",
    "new_feats_test = pd.DataFrame(index=X_test.index)\n",
    "\n",
    "new_feats_train['adUpDays'] = (X_train['lastSeen'] - X_train['dateCrawled']).dt.days + 1\n",
    "new_feats_test['adUpDays'] = (X_test['lastSeen'] -  X_test['dateCrawled']).dt.days + 1\n",
    "\n",
    "new_feats_train['age'] = X_train['dateCrawled'].apply(lambda x: x.year) - X_train['yearOfRegistration']\n",
    "new_feats_test['age'] = X_test['dateCrawled'].apply(lambda x: x.year) - X_test['yearOfRegistration']\n",
    "\n",
    "new_feats_train['kilPerYear'] = X_train['kilometer'] / new_feats_train['age']\n",
    "new_feats_test['kilPerYear'] = X_test['kilometer'] / new_feats_test['age']\n",
    "\n",
    "X_train.drop(['dateCrawled','dateCreated', 'lastSeen'], axis=1, inplace=True)\n",
    "X_test.drop(['dateCrawled','dateCreated', 'lastSeen'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_feats_train['adUpDays'].hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_feats_train['kilPerYear'].hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применим к признакам со смещенным распределением логарифмирование."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feats_train['kilPerYear_log'] = np.log1p(new_feats_train['kilPerYear'])\n",
    "new_feats_test['kilPerYear_log'] = np.log1p(new_feats_test['kilPerYear'])\n",
    "\n",
    "new_feats_train['powerPS_log'] = np.log1p(X_train['powerPS'])\n",
    "new_feats_test['powerPS_log'] = np.log1p(X_test['powerPS'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Добавление новых признаков и полиномов второй степени улучшило качество линейной модели, но оно всё же хуже, чем у базового случайного леса, поэтому сосредоточимся на последнем. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X_train[['kilometer', 'yearOfRegistration']])\n",
    "features_scaled_train = scaler.transform(X_train[['kilometer', 'yearOfRegistration']])\n",
    "features_scaled_test = scaler.transform(X_test[['kilometer', 'yearOfRegistration']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(2)\n",
    "X_train_poly = poly.fit_transform(np.concatenate([new_feats_train[['powerPS_log', 'kilPerYear_log', 'adUpDays']],\n",
    "                                                features_scaled_train], axis=1))\n",
    "X_test_poly = poly.transform(np.concatenate([new_feats_test[['powerPS_log', 'kilPerYear_log', 'adUpDays']],\n",
    "                                                features_scaled_test], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new = np.concatenate([X_train_poly, X_train.drop(['powerPS', 'kilometer',  \\\n",
    "                                                           'yearOfRegistration'], axis=1)], axis=1)\n",
    "X_test_new = np.concatenate([X_test_poly, X_test.drop(['powerPS', 'kilometer',  \\\n",
    "                                                       'yearOfRegistration'], axis=1)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 53s, sys: 25.7 s, total: 2min 18s\n",
      "Wall time: 25.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression:\n",
      "\tMAE:  0.3164375496390628\n",
      "\tR2:  0.8529288462807411\n"
     ]
    }
   ],
   "source": [
    "y_preds_lr = lr.predict(X_test_new) \n",
    "print('LinearRegression:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds_lr))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds_lr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Модель случайного леса немного улучшилась при добавлении **adUpDays**. Добавление **kilPerYear** никак не повлияло на качество."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new = pd.concat([X_train, new_feats_train[['kilPerYear']]], axis=1)\n",
    "X_test_new = pd.concat([X_test, new_feats_test[['kilPerYear']]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 19s, sys: 648 ms, total: 1min 20s\n",
      "Wall time: 1min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf = RandomForestRegressor(random_state=RANDOM_SEED)\n",
    "rf.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor:\n",
      "\tMAE:  0.2807144067771242\n",
      "\tR2:  0.8692330143706801\n"
     ]
    }
   ],
   "source": [
    "y_preds = rf.predict(X_test_new)\n",
    "print('RandomForestRegressor:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_new = pd.concat([X_train, new_feats_train[['adUpDays']]], axis=1)\n",
    "X_test_new = pd.concat([X_test, new_feats_test[['adUpDays']]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 19s, sys: 191 ms, total: 1min 19s\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rf = RandomForestRegressor(random_state=RANDOM_SEED)\n",
    "rf.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor:\n",
      "\tMAE:  0.2784478399766084\n",
      "\tR2:  0.8723696499008463\n"
     ]
    }
   ],
   "source": [
    "y_preds = rf.predict(X_test_new)\n",
    "print('RandomForestRegressor:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какие же признаки оказались наиболее важными для модели случайного леса. Как видно из графика ниже, наиболее важным признаком оказался год регистрации, второй по важности признак - мощность двигателя, третий - **notRepairedDamage**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = X_train_new.columns\n",
    "importances = rf.feature_importances_\n",
    "num_to_plot = 10\n",
    "indices = np.argsort(importances)[::-1][:num_to_plot]\n",
    " \n",
    "plt.figure(figsize=(14,6))\n",
    "plt.title('Feature impotances for a RandomForestRegressor model', size=15)\n",
    "bars = plt.bar(range(num_to_plot), importances[indices], align=\"center\")\n",
    "ticks = plt.xticks(range(num_to_plot), features.values[indices[:]], rotation=70, size=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Построение кривых валидации"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем улучшить результат случайного леса. Для начала посмотрим как ведут себя кривые валидации при изменении основных параметров.\n",
    "\n",
    "Начнем с количества деревьев:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def valid_curves_plot(hyperparam, param_range):\n",
    "    param_range = param_range\n",
    "    train_scores, test_scores = validation_curve(RandomForestRegressor(random_state=RANDOM_SEED), \n",
    "                                X_train_new, y_train, param_name=hyperparam, param_range=param_range, \n",
    "                                cv=kf, n_jobs=-1, scoring='r2')\n",
    "\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.figure(figsize=(9,6))\n",
    "    plt.title('Validation curves for a RandomForestRegressor model')\n",
    "    plt.xlabel(hyperparam)\n",
    "    plt.ylabel('R2')\n",
    "    plt.ylim(0.8, 1.0)\n",
    "    plt.fill_between(param_range, train_scores_mean - train_scores_std, \\\n",
    "                     train_scores_mean + train_scores_std, alpha=0.2, color=\"r\")\n",
    "    plt.plot(param_range, train_scores_mean, label='Training error', color=\"r\")\n",
    "    plt.fill_between(param_range, test_scores_mean - test_scores_std, \\\n",
    "                     test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\n",
    "    plt.plot(param_range, test_scores_mean, label='Validation error', color=\"g\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    plt.xticks(param_range)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e1NfXs3r1ampra7u6KdJBpaWlDBs2jOLi4g5tr5AjIiKJtHr1anr37s2IESPQl+N3P+7Oxo0bWb16NSNHjuxQHV098FhERCQvamtrGThwoAJON2VmDBw4cJ964hRyREQksRRwurd9ffwUckRERPJg48aNjB8/nvHjxzNkyBCGDh2ama+rq2tXHddeey3Lli1rtcw999zDww8/3BlNThyNyREREcmDgQMHsnDhQgBuv/12ysvL+fKXv9ykjLvj7hQU5O5zePDBB9vcz+c///l9b+xeSKVSFBUVtTjf3u32B/XkiIiI7EfLly+noqKCK6+8krFjx7Ju3Tquv/56KisrGTt2LLNmzcqUPf3001m4cCGpVIp+/fpx2223ccIJJ3Dqqaeyfv16AL7xjW9w1113ZcrfdtttTJo0idGjR/OXv/wFgJ07d/KJT3yCiooKLr30UiorKzMBLG7+/PlMnTqVE088kQsuuIAPPvggU+8tt9xCZWUl//Ef/8FVV13FDTfcwKRJk/j617/Ohg0bmDZtGuPGjeO0007j9ddfz7TtmmuuYfLkycycOTOfd2tO6skREZHk++IXIcdJfZ+MHw8hXOytN954g4ceeojKyuirXr73ve8xYMAAUqkUZ511FpdeeikVFRVNttm6dStTp07le9/7Hl/60pd44IEHuO2225rV7e68/PLLzJ07l1mzZvHb3/6WH/7whwwZMoQnnniCV199lYkTJzbbbvfu3dx8883MnTuXQYMG8fDDD/PNb36T++67D4CGhgbS3zl31VVXsW7dOl566SUKCgq44YYbOPnkk5k7dy6/+93vmDlzZqbsG2+8wfPPP09paWmH7qt9oZAjIiKynx111FGZgAPw6KOP8rOf/YxUKsXatWtZsmRJs5DTs2dPLrjgAgBOPPFE/vd//zdn3ZdcckmmzMqVKwF44YUX+OpXvwrACSecwNixY5ttt3TpUhYvXsyHP/xhIAo1w4bt+bnIT33qU03Kf/KTn8xcZnvhhRf4zW9+A8B5553HzJkz2blzJwDTp0/vkoADCjkiInIw6GCPS76UlZVlpt966y3+/d//nZdffpl+/fpx1VVX5fzYdElJSWa6sLCQVCqVs+4ePXq0WSYXd2fcuHEthqd4m3PNt6S95fJBY3JERES60LZt2+jduzd9+vRh3bp1PPvss52+j8mTJ/P4448D8Nprr7FkyZJmZSoqKlizZg0vv/wyAHV1dSxevLhd9U+ZMiXzCa8//OEPDB06tEvDTZp6ckRERLrQxIkTqaio4Nhjj2X48OFMnjy50/fxhS98gWuuuYaKiorMrW/fvk3K9OjRg1/96lfcdNNNbNu2jYaGBm699dacl7ayzZo1i8985jOMGzeO8vLydn0qbH/QD3SKiEgiLV26lDFjxnR1Mw4IqVSKVCpFaWkpb731Fueddx5vvfXWfv9Id0fkehz1A50iIiICwI4dOzjnnHNIpVK4Oz/5yU+6RcDZV8k/QhERkYNcv379WLBgQVc3Y7/TwGMRERFJJIUcERERSSSFHBEREUkkhRwRERFJJIUcERGRPDjrrLOafbHfXXfdxQ033NDqduXl5QCsXbuWSy+9NGeZM888k7a+GuWuu+6ipqYmM3/hhReyZcuW9jQ9MRRyRERE8mDGjBnMmTOnybI5c+YwY8aMdm1/+OGH86tf/arD+88OOU8//TT9+vXrcH17I/vnJNr78xJ78zMU7aGQIyIikgeXXnopv/nNb6irqwNg5cqVrF27lilTpmS+t2bixIkcf/zxPPXUU822X7lyJccddxwAu3bt4vLLL2fMmDFcfPHF7Nq1K1PuhhtuoLKykrFjx/Ltb38bgLvvvpu1a9dy1llncdZZZwEwYsQINmzYAMAPfvADjjvuOI477jjuCr/rtXLlSsaMGcN1113H2LFjOe+885rsJ626uppPfOITnHTSSZx00kn8+c9/BuD222/n6quvZvLkyVx99dXMnj2badOmcfbZZ3POOefg7nzlK1/huOOO4/jjj+exxx4D4LnnnmPKlClMmzat2Y+S7it9T46IiCTeF3/7RRa+v7BT6xw/ZDx3nd/yD38OGDCASZMm8cwzzzB9+nTmzJnDZZddhplRWlrKk08+SZ8+fdiwYQOnnHIK06ZNw8xy1nXvvffSq1cvli5dyqJFi5g4cWJm3R133MGAAQNoaGjgnHPOYdGiRdx000384Ac/YN68eQwaNKhJXQsWLODBBx/kr3/9K+7OySefzNSpU+nfvz9vvfUWjz76KPfffz+XXXYZTzzxBFdddVWT7W+++WZuueUWTj/9dN577z0+8pGPsHTpUgCWLFnCCy+8QM+ePZk9ezavvPIKixYtYsCAATzxxBMsXLiQV199lQ0bNnDSSSdxxhlnAPDKK6/w+uuvM3LkyA49Fi1RT46IiEiexC9ZxS9VuTtf//rXGTduHB/+8IdZs2YNH3zwQYv1PP/885mwMW7cOMaNG5dZ9/jjjzNx4kQmTJjA4sWLc/74ZtwLL7zAxRdfTFlZGeXl5VxyySWZXx4fOXIk48ePB+DEE09k5cqVzbb/wx/+wI033sj48eOZNm0a27ZtY8eOHQBMmzaNnj17Zsqee+65DBgwILPfGTNmUFhYyKGHHsrUqVOZP38+AJMmTer0gAPqyRERkYNAaz0u+TR9+nRuueUWXnnlFWpqajjxxBMBePjhh6murmbBggUUFxczYsQIamtr97r+d955hzvvvJP58+fTv39/Zs6c2aF60nr06JGZLiwszHm5qrGxkZdeeonS0tJm67J/eby9v0Ser18sV0+OiIhInpSXl3PWWWfxmc98psmA461bt3LIIYdQXFzMvHnzePfdd1ut54wzzuCRRx4B4PXXX2fRokUAbNu2jbKyMvr27csHH3zAM888k9mmd+/ebN++vVldU6ZM4de//jU1NTXs3LmTJ598kilTprT7mM477zx++MMfZuYXLmzfZcApU6bw2GOP0dDQQHV1Nc8//zyTJk1q9347QiFHREQkj2bMmMGrr77aJORceeWVVFVVcfzxx/PQQw9x7LHHtlrHDTfcwI4dOxgzZgzf+ta3Mj1CJ5xwAhMmTODYY4/liiuuYPLkyZltrr/+es4///zMwOO0iRMnMnPmTCZNmsTJJ5/M3/3d3zFhwoR2H8/dd99NVVUV48aNo6Kigh//+Mft2u7iiy9m3LhxnHDCCZx99tn867/+K0OGDGn3fjvC3D2vOzgQVFZWelvfJyAiIsmydOlSxowZ09XNkH2U63E0swXuXtnWturJERERkURSyBEREZFEUsgRERGRRFLIERGRxDoYxp0m2b4+fnkNOWZ2vpktM7PlZnZbjvXDzeyPZrbIzJ4zs2Fh+VlmtjB2qzWzi8K62Wb2Tmzd+Hweg4iIdE+lpaVs3LhRQaebcnc2btyY8/t42itvXwZoZoXAPcC5wGpgvpnNdff4VzHeCTzk7j83s7OB7wJXu/s8YHyoZwCwHPhdbLuvuHvHf7VMREQSb9iwYaxevZrq6uqubop0UGlpKcOGDevw9vn8xuNJwHJ3XwFgZnOA6UA85FQAXwrT84Bf56jnUuAZd6/JsU5ERCSn4uLivPxUgHQf+bxcNRRYFZtfHZbFvQpcEqYvBnqb2cCsMpcDj2YtuyNc4vo3M+uBiIiISJauHnj8ZWCqmf0NmAqsARrSK83sMOB44NnYNl8DjgVOAgYAX81VsZldb2ZVZlalrkoREZGDTz5DzhrgiNj8sLAsw93Xuvsl7j4B+MewbEusyGXAk+5eH9tmnUd2Aw8SXRZrxt3vc/dKd68cPHhw5xyRiIiIdBv5DDnzgVFmNtLMSoguO82NFzCzQWaWbsPXgAey6phB1qWq0LuDmRlwEfB6HtouIiIi3VzeQo67p4AbiS41LQUed/fFZjbLzKaFYmcCy8zsTeBQ4I709mY2gqgn6E9ZVT9sZq8BrwGDgO/k6xhERESk+9IPdIqIiEi3oh/oFBERkYOaQo6IiIgkkkKOiIiIJJJCjoiIiCRSPn/WQURk77lDQ8Oem3t0a2xsfbqt9druwNwu/eGX9PL0dHz53k53Rh1tTafbmmuf8XJtbXegHE9HyrZVprwcNm4EM7qKQo5IPqXfuBsaIJXac+Lu7Ol81t3QAPX1e7c813RjY8tl4rf4SUDyz2zfbwUFe+pKTxcU7Dm55ZpOz2dPZ7drX5a3tD67THuXp48v+6Td0vF0dD/5PIZ92efeLu/RI3qPKCmhqyjkSPfW2Ai1tbBrV+f8ranZM79rV/QCzT4xtxVasm8HssLC6E27sHDPdHq+o8tLSqC0dN/rSS/r6Ek2fkLKVT77hBUv29pJ2yxqX/Y+cv1NT6fLFxXtqSt+S5eJb5ddT7od6fsm177iZdJ1Zi/Lrr+1sNGevx0pG1+eS2vr2rO+M+roLvuQVinkSOdyj8LB5s2wZQvs3Nk5YSO9PH3btQt274a6un1rb0lJ9L+N+N/4rago+pt9Qs4+Me/r8vSy9HxRUe51hYXRuuxbcfGe6XS708sLC5uujy/PPrHnmm7phNae6dbWi4jkmUKONJdKRQFly5Y9YSX9Nz69aVM0HV+3dWvU+7E3zHKHjHj4KC+HgQNbXh//W1oaTffoEU2XlUGvXtHf8vI987167TnpZ/coxP+HnG5jaydyERE54CjkHCwaG+H99+Gdd5reqqubh5idO1uvq7AQeveOAkN5eTR9xBEwZsye+fTfXr2ioJEOGz17RsvLyvaEjZ499/QsxMNGdpd/ri52ERGRFijkJMnmzVFwWbFiz9/09HvvRZd34gYMgL59o9DRpw8MHbonuJSXR8v69InKDBgAgwfDoYdCv35RaMnuBSkq2tPzISIi0sUUcrqTmhpYubJpT0w6yKxcCdu2NS1fXg5DhkS3E06I/g4bBsOHQ0VFFFji4zfiPSkiIiLdnELOgeqdd+CRR2Dx4j0h5oMPmpYpKdkTYs48M/p7+OHRpaOKiijMpMenpMOMiIjIQUJnvQNJXR089RTcdx/84Q/RuJNDD43Cy4QJ0d/DDot6Y445BkaPjsa1pAfdFhd39RGIiIgcMBRyDgRvvgk//Sk8+CBs2BCNfbnmGpg+HU4/PRqYm/5EkQbcioiItItCTleprYX/+q+o1+ZPf4oG7J5yCnzpS3DJJXsuNYmIiEiHKOTsb0uWwP33w89/Hn0a6rDDYObMqNfmjDOiTy7pE0oiIiL7TCFnf6ipgV/+En7yE3jxxWgA8Kmnwsc+BhdfHA0UVq+NiIhIp1LIyaeFC6PLUQ8/HH28e+hQ+Mxn4KKL4LTToH9/9dqIiIjkiUJOZ9u+HebMiXptFiyIPvE0eXLUazN9ehR0evbs6laKiIgknkJOZ3CHqqqo1+bRR6OfRRg+HK67LrocddJJ0TcGq9dGRERkv1HI2Rdbt0aXon78Y3jttei7aiZPhmnT4KMfjb6Yr1evrm6liIjIQUkhp6OWLYu+oG/XLvjQh+Bzn4s++j1hQjTWRj+NICIi0qUUcjrqmGPghhvg6KPh3HOjj4KXlXV1q0RERCRQyOkoM/j+96GxUWNtREREDkA6O+8rBRwREZEDks7QIiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSHkNOWZ2vpktM7PlZnZbjvXDzeyPZrbIzJ4zs2GxdQ1mtjDc5saWjzSzv4Y6HzOzknweg4iIiHRPeQs5ZlYI3ANcAFQAM8ysIqvYncBD7j4OmAV8N7Zul7uPD7dpseX/Avybux8NbAY+m69jEBERke4rnz05k4Dl7r7C3euAOcD0rDIVwP+E6Xk51jdhZgacDfwqLPo5cFGntVhEREQSI58hZyiwKja/OiyLexW4JExfDPQ2s4FhvtTMqszsJTNLB5mBwBZ3T7VSJwBmdn3Yvqq6unpfj0VERES6ma4eePxlYKqZ/Q2YCqwBGsK64e5eCVwB3GVmR+1Nxe5+n7tXunvl4MGDO7XRIiIicuDL5w90rgGOiM0PC8sy3H0toSfHzMqBT7j7lrBuTfi7wsyeAyYATwD9zKwo9OY0q1NEREQE8tuTMx8YFT4NVQJcDsyNFzCzQWaWbsPXgAd1z/bHAAAgAElEQVTC8v5m1iNdBpgMLHF3Jxq7c2nY5tPAU3k8BhEREemm8hZyQk/LjcCzwFLgcXdfbGazzCz9aakzgWVm9iZwKHBHWD4GqDKzV4lCzffcfUlY91XgS2a2nGiMzs/ydQwiIiLSfVnUOZJslZWVXlVV1dXNEBERkU5gZgvCuN1WdfXAYxEREZG8UMgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRFLIERERkURSyBEREZFEUsgRERGRRMpryDGz881smZktN7PbcqwfbmZ/NLNFZvacmQ0Ly8eb2Ytmtjis+1Rsm9lm9o6ZLQy38fk8BhEREeme8hZyzKwQuAe4AKgAZphZRVaxO4GH3H0cMAv4blheA1zj7mOB84G7zKxfbLuvuPv4cFuYr2MQERGR7iufPTmTgOXuvsLd64A5wPSsMhXA/4Tpeen17v6mu78VptcC64HBeWyriIiIJEw+Q85QYFVsfnVYFvcqcEmYvhjobWYD4wXMbBJQArwdW3xHuIz1b2bWI9fOzex6M6sys6rq6up9OQ4RERHphrp64PGXgalm9jdgKrAGaEivNLPDgF8A17p7Y1j8NeBY4CRgAPDVXBW7+33uXunulYMHqxNIRETkYFOUx7rXAEfE5oeFZRnhUtQlAGZWDnzC3beE+T7Ab4B/dPeXYtusC5O7zexBoqAkIiIi0kQ+e3LmA6PMbKSZlQCXA3PjBcxskJml2/A14IGwvAR4kmhQ8q+ytjks/DXgIuD1PB6DiIiIdFN5CznungJuBJ4FlgKPu/tiM5tlZtNCsTOBZWb2JnAocEdYfhlwBjAzx0fFHzaz14DXgEHAd/J1DCIiItJ9mbt3dRvyrrKy0quqqrq6GSIiItIJzGyBu1e2Va6rBx6LiIiI5IVCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJFKbIcfM+pjZUTmWj2vHtueb2TIzW25mt+VYP9zM/mhmi8zsOTMbFlv3aTN7K9w+HVt+opm9Fuq828ysrXaIiIjIwafVkGNmlwFvAE+Y2WIzOym2enYb2xYC9wAXABXADDOryCp2J/CQu48DZgHfDdsOAL4NnAxMAr5tZv3DNvcC1wGjwu38No5RREREDkJt9eR8HTjR3ccD1wK/MLOLw7q2elAmAcvdfYW71wFzgOlZZSqA/wnT82LrPwL83t03uftm4PfA+WZ2GNDH3V9ydwceAi5qox0iIiJyEGor5BS6+zoAd38ZOAv4hpndBHgb2w4FVsXmV4dlca8Cl4Tpi4HeZjawlW2HhunW6hQRERFpM+Rsj4/HCYHnTKIel7GdsP8vA1PN7G/AVGAN0NAJ9WJm15tZlZlVVVdXd0aVIiIi0o20FXJuyC7j7tuJxsF8po1t1wBHxOaHhWXxuta6+yXuPgH4x7BsSyvbrgnTLdYZq/s+d69098rBgwe30VQRERFJmlZDjru/6u5v5VjVnt6W+cAoMxtpZiXA5cDceAEzG2Rm6TZ8DXggTD8LnGdm/cOA4/OAZ0NP0jYzOyV8quoa4Kl2tEVEREQOMm19uqqPmX3NzP7DzM6zyBeAFcBlrW3r7ingRqLAshR43N0Xm9ksM5sWip0JLDOzN4FDgTvCtpuA/0sUlOYDs8IygH8AfgosB94GntnbgxYREZHks+hDSi2sNHsK2Ay8CJwDHEL0qaqb3X3hfmlhJ6isrPSqqqquboaIiIh0AjNb4O6VbZUramP9h9z9+FDhT4F1wJHuXtsJbRQRERHJm7YGHtenJ9y9AVitgCMiIiLdQVs9OSeY2bYwbUDPMG+Au3ufvLZOREREpINaDTnuXri/GiIiIiLSmfQr5CIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJICjkiIiKSSAo5IiIikkgKOSIiIpJIeQ05Zna+mS0zs+VmdluO9Uea2Twz+5uZLTKzC8PyK81sYezWaGbjw7rnQp3pdYfk8xhERESkeyrKV8VmVgjcA5wLrAbmm9lcd18SK/YN4HF3v9fMKoCngRHu/jDwcKjneODX7r4wtt2V7l6Vr7aLiIhI95fPnpxJwHJ3X+HudcAcYHpWGQf6hOm+wNoc9cwI24qIiIi0Wz5DzlBgVWx+dVgWdztwlZmtJurF+UKOej4FPJq17MFwqeqbZmad1F4RERFJkK4eeDwDmO3uw4ALgV+YWaZNZnYyUOPur8e2udLdjwemhNvVuSo2s+vNrMrMqqqrq/N3BCIiInJAymfIWQMcEZsfFpbFfRZ4HMDdXwRKgUGx9ZeT1Yvj7mvC3+3AI0SXxZpx9/vcvdLdKwcPHrwPhyEiIiLdUT5DznxglJmNNLMSosAyN6vMe8A5AGY2hijkVIf5AuAyYuNxzKzIzAaF6WLgY8DriIiIiGTJ26er3D1lZjcCzwKFwAPuvtjMZgFV7j4XuBW438xuIRqEPNPdPVRxBrDK3VfEqu0BPBsCTiHwB+D+fB2DiIiIdF+2J1MkV2VlpVdV6RPnIiJycMs+5zve6vr2lMleny5jZpQUlnS0qa0yswXuXtlWubz15IiIHKziJ4H0CaCrlsWX789l7h7dwr+2lsXbnV1vSyfRlso43vxE3EYZx8neTc627I+2trCsve3I3mf29mFBE2bWpIxhzQNP9meZ26ij0Ao5/cjT6Vnck66ikCMiLcqckPL4t9EbW7xlysZODK0ui52oMqfS2AmhybIcJ41c9TU7mbRQX7xMmACy3vjbuyww2resI9u0uCx9LNb+Zen7JP24pe+HRm/MLAea/M1+HFsKC+5OI417HtdY+fQxZPbpTR+/TH1ZZTL78z2PV6M3NplvqUyucJHreZ3zWHKUyRn6sp7P2c+zJsdrNK+zrddI7Fha3G87yuTapslrm0YmHzmZrqSQI9LJ9kcwcJzGxkYaaWz+N3ZiaRIYwptOa+Xi9eb8n6DT/H9z7bpTwnY5/jpOqjFFY2MjKY/+NngD9Y31zd4w8diJkqbtz5xoyTqBZi3Lvi/SJ5BmdXjT5fE3/AZvyBnUmuyDpm1rdj9nrWu2jNYfxybH4o3NlqXbmP1Yt7asyXZZbW1tWfoEJwcuwzCznH+BFtd1uAxGSVF+LlXtDYUcSbRGb6ShsaHJSaLB98zH16UaU9Q31pNqTJFqiKYbGhv2Ohg0CQPZf/dWC/Vk3lCwJsdV31ifCQmpxhQN3kBDY5hubMjMp48tU66xgZRHx53yVGab9C1zvzSmqG/YM53yPfPpetvcJqvedGhJCsMosALMwt8wn15WaIUtrisgtl34m17WZLusdfG6iwuKmy3LrjvX/uLtyd5f9rrWluU8rlaW7dXJNEzHyxSEr1bblzLp+2FfysSPJ/08iLe/s8rk2qY9ZbpCdU3Xf0edQo50uXhoaCmAxNfVN9RHJ01vfmKNh5MGb2jxf5jpbu74fFp9Qz27G3ZT11BHfUN9pi0pT+0JBCFApPebXt/ecNCkzfHy6TLecjDI3n5/hITigmKKCoooKiiiuDBMWxFFhUV7lsfKlBWXZaabbJN1y1lvbHlhQWGrQSB7WbMTcztP7nt7IjesSduyT9oHk1yX6uKXTVoqH1+X3WvY0rat7bul7fJZd65y7e3Vam27eE9ke9rQVt25LoXmrMayZ9sxLidHXen9HQj/gVHIkU7h7tQ3RuGjrqGO+sZ66lJ11KZqMyfk+sb6Zj0Fmf/Jp18klmOsQGzSzMChvjEEkVQdtQ211DXUsTu1m90Nu9md2s2u1C5qU7XUpmoz0zX1NexK7WJX/a7M8pr6Gmrro+l0uc5mWKsn8SYn+jBdWlBKcWnbwaCleptt00KZlkJG/JYODvnW2nX+9Hpo34kz5xiZrLK5yrdWd/xyUVt15xpzkz45NDlxpO/WVsblpKVfF81eH521rj1tynEsmbqyejfivQxN1sd7bLJCYZPplspgTfabqTvrOdqsnJF7u+xlOdrUVpnstrdYpoNtb9fx5SiXvT5XO3OVa0+ZXOVy1dOzqOsGHYNCjrTB3TNhJN2zUddQx676KCDUpGrYVb+Luoa6ZsEk1ZjKhI+6xjpq62vZ3bA7CiUhnNTW11LbUMuu+j0hY1f9LmpSTcNHen06oOzNGIBCK6S0qJSexT3pVdSL0uJSehb1pLy4nMG9BlNaVEqv4l70LOpJz+KeUdkwXVJY0uFwEA8y+1NHBhBml0n3KmWHDloYV5N+s2vtcUm/AbZUjxH1xBQUZF3ayHHSzFx+yHFSLUh/x6lldePHTjotnZAzdbZywo4vi59o4svS89nHnn3iz7WsPdvls+693U7kQKaQcxBr9EZ2p3Y3CTDp3o2aupomPRvxk1djYyM19TVs2b2Fzbs2s2nXJjbWbKS6ppr1Neup3llNdU01W2q3tLstBVZAz6KezcJIr+JeDOw5MBM+0mEkXa5nUU96FfdqMp8OKOm/6TEKHb2Pmn3SIzbQsqVPF6R7qlosQ8vjdjJhoD2BIUc96RCQfaknPtaiwAqaXdrJdUuHjgIKMuGjtfEU8b9tlUm3J9f/eEVEOoNCzkEifQmnpm5PONlRtwPYc6I0M+oa6thauzUKL7Wb2LRrUxRedu4JL9U11ZkTeJphDOg5gMFlgxlSPoRxh45jUK9B9C7p3a4wUlJY0qETXTx0NDQ2ZK4Dp8NJTX1NkzJNuuab9GTnvkyQOdFnj90IJ/Fc63IFinToyA4LuU727QkErQUKERGJKOQkTKoxlbm0s612G1t2b2HLri2kPIQSh427NvLulnd5Z8s7vL/jfdbXrGfDzg2sr1mfCT5xZcVlDOo1iEPKDmHCkAkMLhvM4F6DOaTsEAb3GszgssEM6jWIooLcT6d46Ih/PDUdSHaldrGzfmemTEuXPjJBJLa4oKAgM24kfWmotKB0z3iSgsJogGy4rFRYUNi8pyJXD0YoJyIi3ZdCTjfV6I2Z8Ss763eyeddmttRuYVf9rsylC3fn/Z3v887md1i+eTnLNixj2cZl7KzfCUS9FOmQMrzfcCoPr4yCS9lgDul1SCbMlJWU5WxDfPDwjrodpBpTzXpIAIoshI34QNvCpmNW4mNYWgocuS6lqOdCRERaopDTDcQH+m6p3cLm2uhSU/rjeYbR0NjAe1vf4+3Nb7Ns4zLe3PgmKzavoL6xHoDSolKOGXAMFxx9AaMHjeaYgcdwVP+jKC0qbba/dI9L+iPMm3dtjnqCsr7rpciK6FXSiz49+lBWUkavol70KOpBcWExJYUlmdCiICIiIl1BIecA1dDYwKqtq1ixZQV1qbrMWJLigmJ21O9g5ZaVmTCzbOMyVm9bndm2f2l/Rg8czRXHX8ExA49h9MDRHNHniCaf8kk1pqhN1bKhZkNmUGz8ExU9inrQq6gXfUv7UlZSlhk3Ew8w+/tTQyIiIntDIecA4+5s3LWR19e/zo7dO6ipr2H55uVRmNmwjDc3vcmmXZsy5Yf1GcbogaP5+DEfZ/TA0YweOJpBvQY16T1JNaaiT0o11Ga+YKpHUQ8G9hxI/5796VHYo1mAUe+LiIh0dwo5B5AddTt4Y8MbrNu2jnnvzuPBhQ9mPoZdVFDEUf2PYvIRkzNhZtTAUZSXlDepI9WYYmf9zkygASgpLGFA6QA+1OtDlJeU06s4uqwkIiKSZAo5B4D6hnre2fwOb29+m8XrF3PvgntZsXkFJx1+EheOupDRA0czst9IiguLm2yXakyxo24HtalaGr0RM6OksIT+pf0Z2WskvUt6K9CIiMhBSyGnCzV6Ix/s+IDF1YtZvXU1s1+dzZ/e/RNDew/lznPvZOrwqZnLRvFAkx5D06OoRxRo+o+kvKScsuIyBRoREZFAIaeLbKndwpLqJazbvo6nlj3FnNfnUFRQxOdP+jxXHHcFPYp6sKNuBzWpmsyA4/49+zOi3wh69+id+WI9ERERyU0hZz+rTdWyfNNyVm5eyV9W/4X7X7mfDTUb+Oioj3LjSTcyuGwwu1O7Wb9zPf1K+zH2kLGUl5Qr0IiIiOwlhZz9pKGxgdXbVvPGhjdYtnEZ9y24j8XVixk7eCx3nnsnxx1yHA2NDWyo2UBxQTEThkzg0PJD9SknERGRDlLI2Q821Gxg8frFrNq6ikdef4Rnlj/DoF6D+Kcz/4kLjr4Aw9hSu4X6xnpGDRjF8H7DW/yJBBEREWkfnUnzaGfdTpZuWMqqrav47fLf8otFvyDVmOLa8dcy84SZlJWUsaNuBzvrdzKs9zBGDRxFz+KeXd1sERGRRFDIyYP6hnpWblnJmxvfZMHaBdz3yn2s2b6Gs0acxc0n38ywPsOoa6hj/c719C3ty2lDTqNfab+ubraIiEiiKOR0Infn/R3vs6R6CW9tfIuf/e1nVK2r4qj+R/GjC3/EpKGTmoy7GT9kPEPKh2jcjYiISB4o5HSSrbVbWVK9hHe3vssvF/+SXy/7Nb1LevPVyV/l4mMvptAKo3E3DfWMGjiKI/se2ezL/URERKTzKOTso9pULW9vepu3N73NH9/5I7Nfnc3Oup18suKTXD/xevqW9mVn3U521O/g8N6Hc8zAY+hV3Kurmy0iIpJ4Cjn7YGfdTv6y+i+8svYVfrLgJ7yz5R0mDZ3ErafcylEDjsqMu+lT2odTDz2V/j37d3WTRUREDhoKOfvgjQ1v8O153+bF1S82+SmGRm9k466NFFoh44eM59DyQymwgq5uroiIyEFFIaeDllYv5bQHTqPQCrnxpBu54vgrKC4oZuvurdQ31HPUgKMY0W+Ext2IiIh0EYWcDjp20LF87fSvcczAYxg9cDQ19TVs3rWZw/to3I2IiMiBQNdQOsjMuOnkm+hT0of1O9dTYAWcesSpjB8yXgFHRETkAKCenH1QYAWUlZRxzMBjOKz3YRp3IyIicgDJ61nZzM43s2VmttzMbsux/kgzm2dmfzOzRWZ2YVg+wsx2mdnCcPtxbJsTzey1UOfd1oXfpNe3R1/OGH4GQ/sMVcARERE5wOTtzGxmhcA9wAVABTDDzCqyin0DeNzdJwCXAz+KrXvb3ceH2+diy+8FrgNGhdv5+TqGtpgZhQWFXbV7ERERaUU+ux8mAcvdfYW71wFzgOlZZRzoE6b7Amtbq9DMDgP6uPtL7u7AQ8BFndtsERERSYJ8hpyhwKrY/OqwLO524CozWw08DXwhtm5kuIz1JzObEqtzdRt1AmBm15tZlZlVVVdX78NhiIiISHfU1QNJZgCz3X0YcCHwCzMrANYBR4bLWF8CHjGzPq3U04y73+fule5eOXjw4E5vuIiIiBzY8vnpqjXAEbH5YWFZ3GcJY2rc/UUzKwUGuft6YHdYvsDM3gaOCdsPa6NOERERkbz25MwHRpnZSDMrIRpYPDerzHvAOQBmNgYoBarNbHAYuIyZfYhogPEKd18HbDOzU8Knqq4BnsrjMYiIiEg3lbeeHHdPmdmNwLNAIfCAuy82s1lAlbvPBW4F7jezW4gGIc90dzezM4BZZlYPNAKfc/dNoep/AGYDPYFnwk1ERESkCYs+pJRslZWVXlVV1dXNEBERkU5gZgvcvbKtcl098FhEREQkLxRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJEUckRERCSRFHJEREQkkRRyREREJJHyGnLM7HwzW2Zmy83sthzrjzSzeWb2NzNbZGYXhuXnmtkCM3st/D07ts1zoc6F4XZIPo9BREREuqeifFVsZoXAPcC5wGpgvpnNdfclsWLfAB5393vNrAJ4GhgBbAA+7u5rzew44FlgaGy7K929Kl9tFxERke4vnz05k4Dl7r7C3euAOcD0rDIO9AnTfYG1AO7+N3dfG5YvBnqaWY88tlVEREQSJp8hZyiwKja/mqa9MQC3A1eZ2WqiXpwv5KjnE8Ar7r47tuzBcKnqm2ZmuXZuZtebWZWZVVVXV3f4IERERKR76uqBxzOA2e4+DLgQ+IWZZdpkZmOBfwH+PrbNle5+PDAl3K7OVbG73+fule5eOXjw4LwdgIiIiByY8hly1gBHxOaHhWVxnwUeB3D3F4FSYBCAmQ0DngSucfe30xu4+5rwdzvwCNFlMREREZEm8hly5gOjzGykmZUAlwNzs8q8B5wDYGZjiEJOtZn1A34D3Obuf04XNrMiM0uHoGLgY8DreTwGERER6abyFnLcPQXcSPTJqKVEn6JabGazzGxaKHYrcJ2ZvQo8Csx0dw/bHQ18K+uj4j2AZ81sEbCQqGfo/nwdg4iIiHRfFmWKZKusrPSqKn3iXEREJAnMbIG7V7ZVrqsHHouIiIjkhUKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkkkKOiIiIJJJCjoiIiCSSQo6IiIgkUl5Djpmdb2bLzGy5md2WY/2RZjbPzP5mZovM7MLYuq+F7ZaZ2UfaW6eIiIgI5DHkmFkhcA9wAVABzDCziqxi3wAed/cJwOXAj8K2FWF+LHA+8CMzK2xnnSIiIiJ57cmZBCx39xXuXgfMAaZnlXGgT5juC6wN09OBOe6+293fAZaH+tpTp4iIiEheQ85QYFVsfnVYFnc7cJWZrQaeBr7QxrbtqVNERESEoi7e/wxgtrt/38xOBX5hZsd1RsVmdj1wfZjdYWbLOqPeHAYBG/JUt+rvet29/d2d7v9k0+ObbPl8fIe3p1A+Q84a4IjY/LCwLO6zRGNucPcXzayU6E5pbdu26iTUdx9wX0cb315mVuXulaq/a+rPt+7e/u5O93+y6fFNtgPh8c3n5ar5wCgzG2lmJUQDiedmlXkPOAfAzMYApUB1KHe5mfUws5HAKODldtYpIiIikr+eHHdPmdmNwLNAIfCAuy82s1lAlbvPBW4F7jezW4gGIc90dwcWm9njwBIgBXze3RsActWZr2MQERGR7iuvY3Lc/WmiAcXxZd+KTS8BJrew7R3AHe2ps4vl+5KY6u9a3b393Z3u/2TT45tsXf74WtRxIiIiIpIs+lkHERERSSSFnA4ys5Vm9pqZLTSzqk6o7wEzW29mr8eWDTCz35vZW+Fv/06u/3YzWxOOYWH8ZzU6UP8R4Sc6lpjZYjO7ubOPIZ/MrNTMXjazV0P7/yksH2lmfw0/I/JYGPAueZDrNdVdnj/SOjMbHXufWWhm28zsi535HiT7396ctyxyd3gvXWRmE/dHGxVy9s1Z7j6+kz4iN5vwcfqY24A/uvso4I9hvjPrB/i3cAzjw3injkoBt7p7BXAK8PnwkxudeQz5tBs4291PAMYD55vZKcC/EN1HRwObib72QPIn+zXVXZ4/0gp3X5Z+nwFOBGqAJ8PqznoPkv1vNu0/b11A9EnpUUTfYXfv/migQs4Bwt2fBzZlLZ4O/DxM/xy4qJPr7zTuvs7dXwnT24GlRN9G3WnHkE8e2RFmi8PNgbOBX4XlB2z7E6xbPH9kr5wDvO3u73Z1Q2Tf7OV5azrwUHivfQnoZ2aH5buNCjkd58DvzGxB+HblfDjU3deF6feBQ/OwjxtD1+EDnXUpwMxGABOAv7J/jqFThB+BXQisB34PvA1scfdUKKKfEcmvXK+pbvP8kXa7HHg0Nt/p70HSpVp6zXbJzzIp5HTc6e4+kagL7vNmdkY+dxa+P6izPwp3L3AU0eWZdcD397VCMysHngC+6O7b4uvydAydxt0bQnf6MKIfgz22i5t0sGn1NXWgP3+kbWFM2zTgl2FRp78HyYHjQHjNKuR0kLuvCX/XE11bnpSH3XyQ7s4Lf9d3ZuXu/kE4sTcC97OPx2BmxUQB52F3/6+wOK/HkA/uvgWYB5xK1KWa/j6pFn9GRPZdC6+pbvf8kVZdALzi7h9A578HyQGhpddse37qqdMp5HSAmZWZWe/0NHAe8HrrW4amuzYAAARqSURBVHXIXODTYfrTwFOdWXnW9dD/v717C7GqiuM4/v01BhZZYkUgFKYEUpCC2Z0yI+gCQpGKFGFE5INIRQ8RUeSLRRcoDKmQBLvIRKBGIYJMJYVdzJkmMyjSjAIx0OmGgfrrYa+hwzCmZ+acZmb3+zzts/c+a619Zu8z/7PW2vt/K8M4BkkCVgO7bD/XsKmtx9Aqks6WNLEsnwLcQDWvqAu4vew2ats/1v3LNTUmzp84YYtoGKpq5XdQjBrHumY3AneVu6wuB/oahrXaJg8DHAJJU/nnzoBxwBvlCc3DKfNNYA5VgtJ9wOPAeqATOA/4AVhge0iTh49R/hyqbmIDe4D7hnrSSboa2Ar0AkfL6keo5uW05BjaSdLFVJPkOqiC/07by8vfeh0wCdgB3Gn7r5FraT0d65qSdCZj4PyJ4yvB615gqu2+sm4tLfoOiv9eM/+3yg/hlVR3Y/0J3G172I9fOW4bE+REREREHWW4KiIiImopQU5ERETUUoKciIiIqKUEOREREVFLCXIiIiKilhLkRERERC0lyImIMUHSTEk3N7yeJ6klWckl3S/p1FaUFRGjR56TExFjgqTFwCW2l7ah7D2l7F+aeE+H7SOtbktEtE56ciKipSRNkbRL0iuSdkraXFJlDLbvNEmbSubxrZKml/XzJX0lqUfShyWx43JgoaRuSQslLZa0suy/RtIqSdskfS9pTslqvUvSmob6Vkn6vLTribJuGTAZ6JLUVdYtktRb2vBUw/t/l/SspB7gCklPSvq6ZNF+pj2faEQMVXpyIqKlJE0BvqPqGemW1AlstP3aIPtuAZbY/lbSZcAK23Ml9QI32v5J0kTbBwf25DS+LoHMeKrcSPOAtcBVwE7gM+Ce0pZJ5RHzHcAWYJntLxt7ciRNBrYBs4ADwGbgBdvrJRlYaLuzpJz4GJhu2/3tbPkHGhFDlp6ciGiH3ba7y/J2YMrAHSSdBlwJvCWpG3gJ6E/Y+BGwRtK9VPnETsQ7rn619QL7bPeW7NY7G+pfIOkLqjxkFwEXDlLObOB92/ttHwZeB64p244Ab5flPuAQsFrSbVT5eCJiFBk30g2IiFpqTGJ6BBhsuOok4KDtmQM32F5SenZuAbZLmtVEnUcH1H8UGCfpfOAhYLbtAw29P8041D8Px/ZhSZcC11Nlql8KzG2yvIhoo/TkRMSIsP0rsFvSfABVZpTlabY/sf0YsB84F/gNmDCMKk8H/gD6JJ0D3NSwrbHsT4FrJZ1VhrUWAR8MLKz0RJ1h+z3gAWDGMNoWEW2QnpyIGEl3AKskPQqcDKwDeoCnJV0AiGruTA+wF3i4DG2taLYi2z2SdgDfAD9SDYn1exnYJOln29eVW9O7Sv3v2t4wSJETgA2Sxpf9Hmy2TRHRXpl4HBEREbWU4aqIiIiopQxXRUTbSXqR6pbuRs/bfnUk2hMR/w8ZroqIiIhaynBVRERE1FKCnIiIiKilBDkRERFRSwlyIiIiopYS5EREREQt/Q0KKS1/t3r/UwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 19.5 s, sys: 2.33 s, total: 21.8 s\n",
      "Wall time: 42min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_curves_plot(hyperparam='n_estimators', param_range=[5, 10, 15, 20, 30, 50, 75, 100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видно при достижении 30 деревьев точность модели на тесте выходит на асимптоту. \n",
    "\n",
    "Давайте посмотрим какие параметры регуляризации добавить в модель, чтобы недопустить переобучение.\n",
    "Посмотрим как ведет себя модель в зависимости от параметра максимальной глубины – `max_depth`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 23.6 s, sys: 1.98 s, total: 25.6 s\n",
      "Wall time: 10min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_curves_plot(hyperparam='max_depth', param_range=[3, 5, 7, 9, 11, 13, 15, 17, 20, 22, 24])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим, строить деревья глубиной более 22 смысла не имеет, качество на тесте выходит на ассиптоту.<br> \n",
    "<br>\n",
    "Построим кривые валидации для параметра `min_samples_leaf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.3 s, sys: 1.53 s, total: 16.9 s\n",
      "Wall time: 8min 4s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_curves_plot(hyperparam='min_samples_leaf', param_range=[1, 3, 5, 7, 9, 11, 13])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим на тесте максимальное качество достигается, если минимальном числе объектов в листе 3. <br>\n",
    "<br>\n",
    "Параметр `max_features` определяет количество случайных признаков из `n`  исходных. Для задач регрессии рекомендуется использовать $\\frac{n}{3}$. Давайте определим оптимальный параметр для нашего случая."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 17.8 s, sys: 1.5 s, total: 19.3 s\n",
      "Wall time: 7min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_curves_plot(hyperparam='max_features', param_range=[50, 100, 200, 300, 400, 500, 600, 700])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**max_features** = 200 - оптимальный вариант."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Кросс-валидация, подбор параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=KFold(n_splits=5, random_state=17, shuffle=True),\n",
       "       error_score='raise',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=-1,\n",
       "           oob_score=False, random_state=17, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'min_samples_leaf': [1, 3, 5, 7], 'max_features': [100, 200, 300, 400, 500], 'max_depth': [13, 15, 17, 20, 22, 24]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Сделаем инициализацию параметров, по которым хотим сделать полный перебор\n",
    "parameters = {'max_features': [100,200, 300, 400, 500], \n",
    "              'min_samples_leaf': [1, 3, 5, 7], \n",
    "              'max_depth': [13, 15, 17, 20, 22, 24]}\n",
    "rfc = RandomForestRegressor(n_estimators=30, random_state=RANDOM_SEED, n_jobs=-1)\n",
    "gcv = GridSearchCV(rfc, parameters, n_jobs=-1, cv=kf)\n",
    "gcv.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=24,\n",
       "            max_features=200, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=-1,\n",
       "            oob_score=False, random_state=17, verbose=0, warm_start=False),\n",
       " 0.8801176354012498)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcv.best_estimator_, gcv.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Прогноз для тестовой выборки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучим случайный лес с оптимальными гиперпараметрами и получим прогноз для тестовой выборки. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 57s, sys: 873 ms, total: 2min 58s\n",
      "Wall time: 25.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=24,\n",
       "           max_features=200, max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=30, n_jobs=-1,\n",
       "           oob_score=False, random_state=17, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "gcv.best_estimator_.fit(X_train_new, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor:\n",
      "\tMAE:  0.2689071527534981\n",
      "\tR2:  0.8836972391517763\n"
     ]
    }
   ],
   "source": [
    "y_preds = gcv.best_estimator_.predict(X_test_new)\n",
    "print('RandomForestRegressor:')\n",
    "print('\\tMAE: ', mean_absolute_error(y_test, y_preds))\n",
    "print('\\tR2: ', r2_score(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нам удалось выиграть \"аж\" 0.01 на обоих метриках."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Оценка модели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Переведем целевой признак обратно в километры и посмотрим насколько хорошо наша модель отработала на тестовом наборе. Для начала сравним реальные и предсказанные цены по нескольким примерам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>331865</th>\n",
       "      <th>281816</th>\n",
       "      <th>105039</th>\n",
       "      <th>365553</th>\n",
       "      <th>18253</th>\n",
       "      <th>289220</th>\n",
       "      <th>367269</th>\n",
       "      <th>326988</th>\n",
       "      <th>212097</th>\n",
       "      <th>279259</th>\n",
       "      <th>38123</th>\n",
       "      <th>286722</th>\n",
       "      <th>359805</th>\n",
       "      <th>363031</th>\n",
       "      <th>263592</th>\n",
       "      <th>35037</th>\n",
       "      <th>89607</th>\n",
       "      <th>287819</th>\n",
       "      <th>103711</th>\n",
       "      <th>328275</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>y_preds</th>\n",
       "      <td>1552.11</td>\n",
       "      <td>10049.54</td>\n",
       "      <td>7987.47</td>\n",
       "      <td>12343.62</td>\n",
       "      <td>5357.12</td>\n",
       "      <td>1138.47</td>\n",
       "      <td>18530.25</td>\n",
       "      <td>2285.7</td>\n",
       "      <td>856.01</td>\n",
       "      <td>1962.55</td>\n",
       "      <td>3639.48</td>\n",
       "      <td>1892.26</td>\n",
       "      <td>5794.73</td>\n",
       "      <td>9094.17</td>\n",
       "      <td>7662.2</td>\n",
       "      <td>647.19</td>\n",
       "      <td>9106.47</td>\n",
       "      <td>1517.07</td>\n",
       "      <td>1391.9</td>\n",
       "      <td>6515.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y_true</th>\n",
       "      <td>1150.00</td>\n",
       "      <td>11990.00</td>\n",
       "      <td>9900.00</td>\n",
       "      <td>10500.00</td>\n",
       "      <td>4500.00</td>\n",
       "      <td>1299.00</td>\n",
       "      <td>19500.00</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>2990.00</td>\n",
       "      <td>1250.00</td>\n",
       "      <td>4999.00</td>\n",
       "      <td>8950.00</td>\n",
       "      <td>6700.0</td>\n",
       "      <td>349.00</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>800.00</td>\n",
       "      <td>700.0</td>\n",
       "      <td>5700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diff</th>\n",
       "      <td>402.11</td>\n",
       "      <td>1940.46</td>\n",
       "      <td>1912.53</td>\n",
       "      <td>1843.62</td>\n",
       "      <td>857.12</td>\n",
       "      <td>160.53</td>\n",
       "      <td>969.75</td>\n",
       "      <td>214.3</td>\n",
       "      <td>643.99</td>\n",
       "      <td>262.55</td>\n",
       "      <td>649.48</td>\n",
       "      <td>642.26</td>\n",
       "      <td>795.73</td>\n",
       "      <td>144.17</td>\n",
       "      <td>962.2</td>\n",
       "      <td>298.19</td>\n",
       "      <td>206.47</td>\n",
       "      <td>717.07</td>\n",
       "      <td>691.9</td>\n",
       "      <td>815.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          331865    281816   105039    365553   18253    289220    367269  \\\n",
       "y_preds  1552.11  10049.54  7987.47  12343.62  5357.12  1138.47  18530.25   \n",
       "y_true   1150.00  11990.00  9900.00  10500.00  4500.00  1299.00  19500.00   \n",
       "diff      402.11   1940.46  1912.53   1843.62   857.12   160.53    969.75   \n",
       "\n",
       "         326988   212097   279259   38123    286722   359805   363031  263592  \\\n",
       "y_preds  2285.7   856.01  1962.55  3639.48  1892.26  5794.73  9094.17  7662.2   \n",
       "y_true   2500.0  1500.00  1700.00  2990.00  1250.00  4999.00  8950.00  6700.0   \n",
       "diff      214.3   643.99   262.55   649.48   642.26   795.73   144.17   962.2   \n",
       "\n",
       "         35037    89607    287819  103711   328275  \n",
       "y_preds  647.19  9106.47  1517.07  1391.9  6515.98  \n",
       "y_true   349.00  8900.00   800.00   700.0  5700.00  \n",
       "diff     298.19   206.47   717.07   691.9   815.98  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_preds = pd.DataFrame({'y_true': np.exp(y_test) - 1,\n",
    "                           'y_preds': np.exp(y_preds) - 1})\n",
    "y_test_preds['y_true'] =round(y_test_preds['y_true'], 2)\n",
    "y_test_preds['y_preds'] =round(y_test_preds['y_preds'], 2)\n",
    "y_test_preds['diff'] = np.absolute(y_test_preds['y_true'] - y_test_preds['y_preds'])\n",
    "y_test_preds.head(20).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как видим модель довольно сильно ошибается в некоторых случаях, но в целом закономерности в данных выявлены и результат выглядит неплохо. На графике ниже видим рассеяние реальной цены vs. предсказанной цены относительно линии идентичности (красная линия). Хорошо видно, что чем больше цена, тем сильнее ошибается модель и видно тенденцию к недооценке. Очевидно это связано с недостаточностью данных по дорогим автомобилям.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8732041641820)>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ggplot import *\n",
    "\n",
    "ggplot(y_test_preds, aes(x='y_true',y='y_preds')) + \\\n",
    "    geom_point(color='blue')  + \\\n",
    "    xlim(-1e03,  1e+05) + ylim(-1e03, 1e+05) + \\\n",
    "    ggtitle(\"RandomForestRegressor model\") + \\\n",
    "    xlab(\"True price\") + ylab(\"Predicted price\") + \\\n",
    "    geom_abline(intercept=0, size=2, color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Также видим в самом начале какие-то сильные выбросы. Посмотрев на данные, становится ясно, что модель предсказывает более высокую стоимость машин, выставленных за \"бесценок\". Возможно продавцы указывают низкую цену, чтобы привлечь покупателей, а может забыли указать, что машина требует ремонта. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_preds</th>\n",
       "      <th>y_true</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>257519</th>\n",
       "      <td>378.43</td>\n",
       "      <td>100.0</td>\n",
       "      <td>278.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207452</th>\n",
       "      <td>127.30</td>\n",
       "      <td>100.0</td>\n",
       "      <td>27.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162023</th>\n",
       "      <td>516.15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>416.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234667</th>\n",
       "      <td>512.03</td>\n",
       "      <td>100.0</td>\n",
       "      <td>412.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36232</th>\n",
       "      <td>465.84</td>\n",
       "      <td>100.0</td>\n",
       "      <td>365.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        y_preds  y_true    diff\n",
       "257519   378.43   100.0  278.43\n",
       "207452   127.30   100.0   27.30\n",
       "162023   516.15   100.0  416.15\n",
       "234667   512.03   100.0  412.03\n",
       "36232    465.84   100.0  365.84"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_preds.sort_values('y_true').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Построение кривых обучения"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Давайте построим кривые обучения для всего объема данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 26min 35s, sys: 1min 3s, total: 1h 27min 38s\n",
      "Wall time: 16min 18s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title(\"Learning curves for a RandomForestRegressor model\")\n",
    "plt.xlabel(\"Training examples\")\n",
    "plt.ylabel(\"MSE\")\n",
    "train_sizes, train_scores, test_scores = \\\n",
    "            learning_curve(RandomForestRegressor(max_depth=24,\n",
    "                                                max_features=200,\n",
    "                                                min_samples_leaf=1,\n",
    "                                                n_estimators=30, n_jobs=-1,\n",
    "                                                random_state=RANDOM_SEED), \n",
    "                            np.concatenate([X_train, X_test]),\n",
    "                            np.concatenate([y_train, y_test]),\n",
    "                            cv=kf,\n",
    "                            train_sizes=np.linspace(0.1, 1.0, 5),\n",
    "                            random_state=RANDOM_SEED)\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "plt.grid()\n",
    "\n",
    "plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                 train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                 color=\"r\")\n",
    "plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                 test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "         label=\"Training error\")\n",
    "plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "         label=\"Validation error\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Большое расстояние между кривыми указывает на переобучение. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сырые данные содержали много пропусков и выбросы. Нам потребовалось провести значительную обработку и фильтрацию. К категориальным признакам мы применили one-hot encoding. Целевой признак имел сильно скошенное распределение, поэтому мы применили к нему логарифмическое преобразование.  \n",
    "\n",
    "Мы сравнили две модели и пришли к выводу, что линейная модель с полиномиальными признаками дает MAE: 0.31 и $R^2$: 0.85, в то время как случайный лес \"из коробки\" сразу выдал MAE:  0.28 и $R^2$: 0.87. К сожалению, нам не удалось синтезировать признаки, улучшающие этот результат случайного леса. Настройка гиперпараметров привела к незначительному росту качества: MAE:  0.27 и $R^2$: 0.88. \n",
    "\n",
    "Учитывая, что случайный лес показал неплохие результаты, с моей стороны было бы упущением не попровать бустинг на наших данных. Я воспользовалась питоновской реализацией XGBoost, понастраивала параметры с помощью hyperopt, но значимых улучшений не получила, поэтому решила не докучать читателям и опустила выкладки. \n",
    "\n",
    "Таким образом можно заключить, что применительно к этой задаче случайный лес сработал хорошо.\n",
    "\n",
    "Исходя из кривых обучения можно сделать вывод, чтобы улучшить модель можно:\n",
    "- увеличить размер выборки;\n",
    "- понизить сложность модели. Этого можно добиться, если использовать меньший набор признаков или увеличить регуляризацию (к примеру, можно снизить глубину деревьев или увеличить минимальном число объектов в листе)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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  "widgets": {
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