notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Setting-up-libraries-and-loading-data\" data-toc-modified-id=\"Setting-up-libraries-and-loading-data-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Setting up libraries and loading data</a></span></li><li><span><a href=\"#Data-preprocessing\" data-toc-modified-id=\"Data-preprocessing-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Data preprocessing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Features-renaming\" data-toc-modified-id=\"Features-renaming-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Features renaming</a></span></li><li><span><a href=\"#Duplicates\" data-toc-modified-id=\"Duplicates-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Duplicates</a></span></li><li><span><a href=\"#Outliers-and-erroneous-data\" data-toc-modified-id=\"Outliers-and-erroneous-data-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Outliers and erroneous data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Quantitative-features\" data-toc-modified-id=\"Quantitative-features-2.3.1\"><span class=\"toc-item-num\">2.3.1&nbsp;&nbsp;</span>Quantitative features</a></span></li><li><span><a href=\"#Categorial-features\" data-toc-modified-id=\"Categorial-features-2.3.2\"><span class=\"toc-item-num\">2.3.2&nbsp;&nbsp;</span>Categorial features</a></span></li></ul></li><li><span><a href=\"#Gaps\" data-toc-modified-id=\"Gaps-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Gaps</a></span></li><li><span><a href=\"#Feature-engineering\" data-toc-modified-id=\"Feature-engineering-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Feature engineering</a></span></li><li><span><a href=\"#Data-formats\" data-toc-modified-id=\"Data-formats-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Data formats</a></span></li></ul></li><li><span><a href=\"#Machine-learning\" data-toc-modified-id=\"Machine-learning-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Machine learning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-preparation-for-machine-learning-models\" data-toc-modified-id=\"Data-preparation-for-machine-learning-models-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Data preparation for machine learning models</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data-preparation-for-linear-model-(Ridge)\" data-toc-modified-id=\"Data-preparation-for-linear-model-(Ridge)-3.1.1\"><span class=\"toc-item-num\">3.1.1&nbsp;&nbsp;</span>Data preparation for linear model (Ridge)</a></span></li><li><span><a href=\"#LightGBM-data-preparation\" data-toc-modified-id=\"LightGBM-data-preparation-3.1.2\"><span class=\"toc-item-num\">3.1.2&nbsp;&nbsp;</span>LightGBM data preparation</a></span></li><li><span><a href=\"#CatBoost-data-preparation\" data-toc-modified-id=\"CatBoost-data-preparation-3.1.3\"><span class=\"toc-item-num\">3.1.3&nbsp;&nbsp;</span>CatBoost data preparation</a></span></li><li><span><a href=\"#Task-metrics\" data-toc-modified-id=\"Task-metrics-3.1.4\"><span class=\"toc-item-num\">3.1.4&nbsp;&nbsp;</span>Task metrics</a></span></li></ul></li><li><span><a href=\"#Ridge\" data-toc-modified-id=\"Ridge-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Ridge</a></span></li><li><span><a href=\"#LightGBM\" data-toc-modified-id=\"LightGBM-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>LightGBM</a></span></li><li><span><a href=\"#CatBoost\" data-toc-modified-id=\"CatBoost-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>CatBoost</a></span></li></ul></li><li><span><a href=\"#Model-comparison\" data-toc-modified-id=\"Model-comparison-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Model comparison</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Used cars price prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook uses prettified Kaggle [used cars dataset](https://www.kaggle.com/datasets/thedevastator/uncovering-factors-that-affect-used-car-prices). Dataset contains car ads from eBay-Kleinanzeigen.\n",
    "\n",
    "The task is to predict car price with minimum error and minimum model working time. Task metric is RMSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up libraries and loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q --user --no-deps matplotlib==3.4.0\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import re\n",
    "from datetime import datetime\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt_params = {'axes.titlesize': '16',\n",
    "              'axes.labelsize': '14',\n",
    "              'axes.spines.left': True,\n",
    "              'axes.spines.bottom': True,\n",
    "              'axes.spines.right': False,\n",
    "              'axes.spines.top': False,\n",
    "              'xtick.labelsize': '12',\n",
    "              'ytick.labelsize': '12',\n",
    "              'legend.fontsize': '12',\n",
    "              'lines.linewidth': '2.0'}\n",
    "\n",
    "plt.rcParams.update(plt_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q --user lightgbm\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q --user catboost\n",
    "from catboost import CatBoostRegressor, Pool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fix catboost compatibility issues:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q -U ipywidgets==7.6.5  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q --user scikit-learn==1.2.2\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, OrdinalEncoder\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q --user category-encoders\n",
    "from category_encoders.target_encoder import TargetEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    'https://code.s3.yandex.net/datasets/autos.csv',\n",
    "    parse_dates=['LastSeen', 'DateCreated', 'DateCrawled']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "General dataset description:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def describe_df(df):\n",
    "    display(df.head())\n",
    "    print('-----------------------------------------------------------------------------------------------')\n",
    "    display(df.info())\n",
    "    print('-----------------------------------------------------------------------------------------------')\n",
    "    display(df.describe())\n",
    "    print('-----------------------------------------------------------------------------------------------')\n",
    "    display(f'Number of duplicates: {df.duplicated().sum()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Repaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:17</td>\n",
       "      <td>480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1993</td>\n",
       "      <td>manual</td>\n",
       "      <td>0</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>petrol</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>18300</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>audi</td>\n",
       "      <td>yes</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>9800</td>\n",
       "      <td>suv</td>\n",
       "      <td>2004</td>\n",
       "      <td>auto</td>\n",
       "      <td>163</td>\n",
       "      <td>grand</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>gasoline</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>1500</td>\n",
       "      <td>small</td>\n",
       "      <td>2001</td>\n",
       "      <td>manual</td>\n",
       "      <td>75</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</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>3600</td>\n",
       "      <td>small</td>\n",
       "      <td>2008</td>\n",
       "      <td>manual</td>\n",
       "      <td>69</td>\n",
       "      <td>fabia</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>skoda</td>\n",
       "      <td>no</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  Price VehicleType  RegistrationYear Gearbox  Power  \\\n",
       "0 2016-03-24 11:52:17    480         NaN              1993  manual      0   \n",
       "1 2016-03-24 10:58:45  18300       coupe              2011  manual    190   \n",
       "2 2016-03-14 12:52:21   9800         suv              2004    auto    163   \n",
       "3 2016-03-17 16:54:04   1500       small              2001  manual     75   \n",
       "4 2016-03-31 17:25:20   3600       small              2008  manual     69   \n",
       "\n",
       "   Model  Kilometer  RegistrationMonth  FuelType       Brand Repaired  \\\n",
       "0   golf     150000                  0    petrol  volkswagen      NaN   \n",
       "1    NaN     125000                  5  gasoline        audi      yes   \n",
       "2  grand     125000                  8  gasoline        jeep      NaN   \n",
       "3   golf     150000                  6    petrol  volkswagen       no   \n",
       "4  fabia      90000                  7  gasoline       skoda       no   \n",
       "\n",
       "  DateCreated  NumberOfPictures  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  "
      ]
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     "text": [
      "-----------------------------------------------------------------------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   DateCrawled        354369 non-null  datetime64[ns]\n",
      " 1   Price              354369 non-null  int64         \n",
      " 2   VehicleType        316879 non-null  object        \n",
      " 3   RegistrationYear   354369 non-null  int64         \n",
      " 4   Gearbox            334536 non-null  object        \n",
      " 5   Power              354369 non-null  int64         \n",
      " 6   Model              334664 non-null  object        \n",
      " 7   Kilometer          354369 non-null  int64         \n",
      " 8   RegistrationMonth  354369 non-null  int64         \n",
      " 9   FuelType           321474 non-null  object        \n",
      " 10  Brand              354369 non-null  object        \n",
      " 11  Repaired           283215 non-null  object        \n",
      " 12  DateCreated        354369 non-null  datetime64[ns]\n",
      " 13  NumberOfPictures   354369 non-null  int64         \n",
      " 14  PostalCode         354369 non-null  int64         \n",
      " 15  LastSeen           354369 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(7), object(6)\n",
      "memory usage: 43.3+ MB\n"
     ]
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       "None"
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     "output_type": "display_data"
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     "text": [
      "-----------------------------------------------------------------------------------------------\n"
     ]
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     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Power</th>\n",
       "      <th>Kilometer</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.000000</td>\n",
       "      <td>354369.0</td>\n",
       "      <td>354369.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4416.656776</td>\n",
       "      <td>2004.234448</td>\n",
       "      <td>110.094337</td>\n",
       "      <td>128211.172535</td>\n",
       "      <td>5.714645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50508.689087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4514.158514</td>\n",
       "      <td>90.227958</td>\n",
       "      <td>189.850405</td>\n",
       "      <td>37905.341530</td>\n",
       "      <td>3.726421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25783.096248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1067.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050.000000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>125000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2700.000000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6400.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>143.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71083.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>9999.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99998.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price  RegistrationYear          Power      Kilometer  \\\n",
       "count  354369.000000     354369.000000  354369.000000  354369.000000   \n",
       "mean     4416.656776       2004.234448     110.094337  128211.172535   \n",
       "std      4514.158514         90.227958     189.850405   37905.341530   \n",
       "min         0.000000       1000.000000       0.000000    5000.000000   \n",
       "25%      1050.000000       1999.000000      69.000000  125000.000000   \n",
       "50%      2700.000000       2003.000000     105.000000  150000.000000   \n",
       "75%      6400.000000       2008.000000     143.000000  150000.000000   \n",
       "max     20000.000000       9999.000000   20000.000000  150000.000000   \n",
       "\n",
       "       RegistrationMonth  NumberOfPictures     PostalCode  \n",
       "count      354369.000000          354369.0  354369.000000  \n",
       "mean            5.714645               0.0   50508.689087  \n",
       "std             3.726421               0.0   25783.096248  \n",
       "min             0.000000               0.0    1067.000000  \n",
       "25%             3.000000               0.0   30165.000000  \n",
       "50%             6.000000               0.0   49413.000000  \n",
       "75%             9.000000               0.0   71083.000000  \n",
       "max            12.000000               0.0   99998.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Number of duplicates: 4'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "describe_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15,15), xrot=30)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 354368 objects and 16 features in the dataset. Three features have a date format, 7 features are quantitative and 6 other are categorical. There are gaps and illogical values in a number of columns (for example, `RegistrationYear` = 400). There are 4 complete duplicates in the dataset. According to the histograms, there are also outliers in some of the columns (for example, in the `Power` feature).\n",
    "\n",
    "The task is to select the best machine learning model. To get the data ready, the following will will be completed: \n",
    "* Columns renaming\n",
    "* Duplicates and outliers removal\n",
    "* Imputation\n",
    "* Feature engineering\n",
    "* Converting the data into appropriate formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features renaming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the columns according to snake_case for convenience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_column(col_name):\n",
    "    split = re.findall(r'[A-Z][^A-Z]*', col_name)\n",
    "    return '_'.join(map(lambda x: x.lower(), split))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date_crawled', 'price', 'vehicle_type', 'registration_year', 'gearbox',\n",
       "       'power', 'model', 'kilometer', 'registration_month', 'fuel_type',\n",
       "       'brand', 'repaired', 'date_created', 'number_of_pictures',\n",
       "       'postal_code', 'last_seen'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename and check if renamed correctly\n",
    "df.rename(columns={col: rename_column(col) for col in df.columns}, inplace=True)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping explicit duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There may be implicit duplicates in the data as well, e.g. the same ads published at different times. Since **last_seen** is almost a unique user ID, and **brand**, **price**, and **registration_year** all point almost unambiguously to the same car, we'll remove the values that match those criteria."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# implicit duplicates count\n",
    "df[['price', 'last_seen', 'registration_year', 'brand']].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping implicit duplicates\n",
    "index_to_drop = df[df[['price', 'last_seen', 'registration_year', 'brand']].duplicated()].index\n",
    "df.drop(index_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers and erroneous data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantitative and categorical features will be investigated separately."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantitative features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'registration_year', 'power', 'kilometer',\n",
       "       'registration_month', 'number_of_pictures', 'postal_code'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('int64').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to histograms and statistical description, following features definitely have outliers: **registration_year, power, date_created, number_of_pictures**.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **registration_year**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some very old cars are present:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df['registration_year'] < 1950])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All observations with date > 2016 seem erroneous as well (the dataset is from 2016):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    10441\n",
       "2018     3958\n",
       "9999       26\n",
       "2019       25\n",
       "5000       17\n",
       "Name: registration_year, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['registration_year'] > 2016]['registration_year'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['registration_year'].isin([2017, 2018])]['kilometer'].hist(figsize=(8,3))\n",
    "plt.title('2017-2018 registered cars mileage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very big mileages for new cars. These observations should be deleted. \n",
    "\n",
    "If we take a closer look at the cars registered in 2010-2014, we will see that the price distribution is very different in comparison with that for 2015 and later registered cars. New cars should be expensive, which is true for 2010-2014. As 2015+ registered cars don't follow the same pattern, it's safer to remove all 'cheap' (price < 5000) cars with registration year 2015 and 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.registration_year > 2010) & (df.registration_year < 2015)]['price'].plot(kind='kde')\n",
    "df[df.registration_year > 2014]['price'].plot(kind='kde', figsize=(8,4))\n",
    "plt.title('kdeplots for price of 2010-2014 and 2015 registered cars')\n",
    "plt.legend(('2010-2014', '2015+'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23583"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding illogical and ambiguous values to drop list\n",
    "index_to_drop = df[(df['registration_year'] > 2016) | (df['registration_year'] < 1950)].index\n",
    "index_to_drop = index_to_drop.union(df[(df['registration_year'] > 2014) & (df['price'] < 5000)].index)\n",
    "len(index_to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **power**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum car price in the dataset is 20000 euro. That means no supercars should be present and observations with horsepower > 500 should be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>date_crawled</th>\n",
       "      <th>price</th>\n",
       "      <th>vehicle_type</th>\n",
       "      <th>registration_year</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>brand</th>\n",
       "      <th>repaired</th>\n",
       "      <th>date_created</th>\n",
       "      <th>number_of_pictures</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>last_seen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1816</th>\n",
       "      <td>2016-03-22 20:52:00</td>\n",
       "      <td>3200</td>\n",
       "      <td>small</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>1398</td>\n",
       "      <td>corolla</td>\n",
       "      <td>5000</td>\n",
       "      <td>6</td>\n",
       "      <td>petrol</td>\n",
       "      <td>toyota</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-22</td>\n",
       "      <td>0</td>\n",
       "      <td>22043</td>\n",
       "      <td>2016-03-22 21:43:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2102</th>\n",
       "      <td>2016-03-21 11:55:22</td>\n",
       "      <td>0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1799</td>\n",
       "      <td>vectra</td>\n",
       "      <td>150000</td>\n",
       "      <td>1</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>yes</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>1723</td>\n",
       "      <td>2016-04-04 04:49:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>2016-03-21 14:48:31</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017</td>\n",
       "      <td>manual</td>\n",
       "      <td>750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>smart</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>49356</td>\n",
       "      <td>2016-03-24 03:44:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4060</th>\n",
       "      <td>2016-04-03 20:31:00</td>\n",
       "      <td>3100</td>\n",
       "      <td>sedan</td>\n",
       "      <td>2005</td>\n",
       "      <td>manual</td>\n",
       "      <td>953</td>\n",
       "      <td>colt</td>\n",
       "      <td>150000</td>\n",
       "      <td>4</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>no</td>\n",
       "      <td>2016-04-03</td>\n",
       "      <td>0</td>\n",
       "      <td>60326</td>\n",
       "      <td>2016-04-07 14:56:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5328</th>\n",
       "      <td>2016-03-29 19:44:48</td>\n",
       "      <td>500</td>\n",
       "      <td>wagon</td>\n",
       "      <td>1999</td>\n",
       "      <td>manual</td>\n",
       "      <td>1001</td>\n",
       "      <td>astra</td>\n",
       "      <td>150000</td>\n",
       "      <td>7</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-03-29</td>\n",
       "      <td>0</td>\n",
       "      <td>33154</td>\n",
       "      <td>2016-04-06 05:44:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date_crawled  price vehicle_type  registration_year gearbox  \\\n",
       "1816 2016-03-22 20:52:00   3200        small               2004  manual   \n",
       "2102 2016-03-21 11:55:22      0        sedan               1999     NaN   \n",
       "3746 2016-03-21 14:48:31      0          NaN               2017  manual   \n",
       "4060 2016-04-03 20:31:00   3100        sedan               2005  manual   \n",
       "5328 2016-03-29 19:44:48    500        wagon               1999  manual   \n",
       "\n",
       "      power    model  kilometer  registration_month fuel_type       brand  \\\n",
       "1816   1398  corolla       5000                   6    petrol      toyota   \n",
       "2102   1799   vectra     150000                   1    petrol        opel   \n",
       "3746    750      NaN     150000                   8    petrol       smart   \n",
       "4060    953     colt     150000                   4  gasoline  mitsubishi   \n",
       "5328   1001    astra     150000                   7    petrol        opel   \n",
       "\n",
       "     repaired date_created  number_of_pictures  postal_code  \\\n",
       "1816       no   2016-03-22                   0        22043   \n",
       "2102      yes   2016-03-21                   0         1723   \n",
       "3746       no   2016-03-21                   0        49356   \n",
       "4060       no   2016-04-03                   0        60326   \n",
       "5328      NaN   2016-03-29                   0        33154   \n",
       "\n",
       "               last_seen  \n",
       "1816 2016-03-22 21:43:26  \n",
       "2102 2016-04-04 04:49:06  \n",
       "3746 2016-03-24 03:44:59  \n",
       "4060 2016-04-07 14:56:46  \n",
       "5328 2016-04-06 05:44:36  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['power'] > 500)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Toyota Corolla or Opel Astra are unlikely to have more than 500 hp."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature also contains 40 thousand zero values. Perhaps the zeros serve as stubs while filling out the questionnaire when the amount of hp is unknown. Anyway, it's easier to delete such observations along with those where power < 20 (such cars are not very common)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      40214\n",
       "75     24010\n",
       "60     15884\n",
       "150    14581\n",
       "101    13290\n",
       "Name: power, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.power.value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40653"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.power <= 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "459"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[df.power > 500])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_drop = index_to_drop.union(df[(df['power'] > 500) | (df['power'] <= 20)].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **date_created**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the histogram, almost all date values are from 2016. 2015 values are outliers and may be deleted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count of observations from 2015\n",
    "len(df[df['date_created'] <= '2015-12-31'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding to the drop list\n",
    "index_to_drop = index_to_drop.union(df[df['date_created'] <= '2015-12-31'].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **number_of_pictures**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This feature only has 1 value for all observations and should be removed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    354161\n",
       "Name: number_of_pictures, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['number_of_pictures'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping the feature\n",
    "df.drop('number_of_pictures', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **price, kilometer, registration_month, postal_code**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical description of the remaining features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>price</th>\n",
       "      <th>kilometer</th>\n",
       "      <th>registration_month</th>\n",
       "      <th>postal_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>354161</td>\n",
       "      <td>354161</td>\n",
       "      <td>354161</td>\n",
       "      <td>354161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4416</td>\n",
       "      <td>128213</td>\n",
       "      <td>6</td>\n",
       "      <td>50507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4514</td>\n",
       "      <td>37905</td>\n",
       "      <td>4</td>\n",
       "      <td>25784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>0</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1050</td>\n",
       "      <td>125000</td>\n",
       "      <td>3</td>\n",
       "      <td>30165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2700</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>49406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6400</td>\n",
       "      <td>150000</td>\n",
       "      <td>9</td>\n",
       "      <td>71083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20000</td>\n",
       "      <td>150000</td>\n",
       "      <td>12</td>\n",
       "      <td>99998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price  kilometer  registration_month  postal_code\n",
       "count 354161     354161              354161       354161\n",
       "mean    4416     128213                   6        50507\n",
       "std     4514      37905                   4        25784\n",
       "min        0       5000                   0         1067\n",
       "25%     1050     125000                   3        30165\n",
       "50%     2700     150000                   6        49406\n",
       "75%     6400     150000                   9        71083\n",
       "max    20000     150000                  12        99998"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.0f' % x)\n",
    "display(df[['price', 'kilometer', 'registration_month', 'postal_code']].describe())\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a lot of 150000 in the **kilometer** feature. Apparently, this is the maximum value that the site allows you to enter.\n",
    "\n",
    "There are zeros in the **price** and **registration_month** columns.\n",
    "Since **price** is the target variable, it's better to remove observations that match price < 100. Cars are unlikely to be this cheap, so it's very possible these are erroneuous values.   \n",
    "**registration_month** will be left as is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37344"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of ads with registration month == 0\n",
    "len(df[df.registration_month == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10767"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of ads with price == 0\n",
    "len(df[df.price == 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_drop = index_to_drop.union(df[df.price < 100].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Categorial features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['vehicle_type', 'gearbox', 'model', 'fuel_type', 'brand', 'repaired'], dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **vehicle_type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.vehicle_type.value_counts(dropna=False).plot(kind='bar', rot=0, figsize=(8,4))\n",
    "plt.title('vehicle count by vehicle type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks ok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **gearbox**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x48YwMTFBdnY2lixZgsaNG1da3+Li4qBQKEqdVWjUqBGMjIyQm5uLhw8fagxgyj2dpk2bqpQrj5G/ePFCY9vK2Y3qRuzqloyMDGHW5tVDSo0aNcLw4cPx448/YteuXbC3t8e9e/fg4OCA3r17a/X4TZs2xcOHD/H48WM4ODioLX/8+LHG9aysrHD37l2MGzeuzHvQmZmZOHnyJOrUqYM9e/ao/U1lZmZCLpdr1X9NtHlNleNIOaOmaVZBbMwBL8+9iYiIQOvWrdGgQQNER0djwYIF+Prrryv8PDQp77Yv6Soq5TYuPs6srKxw//59fPLJJ+jUqVPlPYEarvruTtBrM2bMGNSpUwcHDx5EVlaWcBji1cMOAITL4I4dO1Zl09Jl0b9/fwDAyZMnKzQdr6enhz59+gB4eT12ZejUqRMaN26MlJQUHD58uNT6+vr6wmWHrx4CUtqxYwcA4D//+Y9KufJNXFNgksvliImJ0arvpVEGk5Kuyy+L8PBwjYeZlPesaNGihcZzT6ZMmQLg5Ylt69evBwBMmjRJ68d3dXUFAOzbt0/jcrFy5fjXZqw8f/4chYWFeOONNzTeD2TPnj2VsqevzWtqZWUlHFrQNOYUCoVQ/uqYu3DhApYsWYK6deti69atwvkK3333neil1mK0GU/l2fa3bt3CzZs31crv3LmD69evo06dOsJYAMq3fWsDBgWClZUV+vbti+fPn2PRokW4ffs2rK2tNZ6t36FDBwwbNgwJCQkYN26cxsSemZmJPXv24J9//qmyPjs5OcHd3R3Z2dkYM2aM2p5zQUEBjh49Wqa2vvjiCxgaGmL+/PkICwvTOF1/+/ZtHDp0qEzt6evrY+bMmQCATz75RDhxrrirV6+qnG390UcfAXh5GdzZs2dV6oaGhuLYsWMwMDAQzgBXUoacoKAglRPhUlJSMHXqVNGZhvJSftAoj9OW199//425c+eqfEDExsZi2bJlAF5ePaNJu3bt0KtXL8TGxuLYsWMwNTXFqFGjtH78yZMno06dOti/f7/ah9uhQ4dEt7Wvry9sbGxw8OBBLFiwABkZGWp15HK5ypUC5ubmkEqlSE9PVztp+NKlS/jqq6+07r8m2r6m06ZNAwAsX74cN27cEMoVCoVQ1qBBA/j4+AjLlPdJKCwsxPLly9GmTRtYW1vj+++/h0QiwYcffqjVvVDMzMxgaGgIuVyO1NTUEuuWZ9srFArMnDlT5W8jPT0dM2bMgEKhwLBhw1TOb5g+fToaNGiAtWvXIiQkBHl5eWptPnz4sEI3YauJeOiBAEC4j4Hy8jvlLIMma9euRXp6Ok6ePIkuXbqgffv2ePPNN6FQKPD48WPcvHkTeXl5iI6Ohrm5eZX1+bvvvsPw4cNx6dIluLi4CHdRk8vluH37dolnkRfXsWNHrF+/HgEBAQgICEBgYCBat24NMzMzpKam4vbt20hMTMS7776rdvmkGH9/f8TFxWHz5s3w8PCAk5MTWrZsiYyMDNy9excPHz7Ezz//LBzKGThwID777DOsWLECXl5e6N69O6ytrXH37l1cv34denp6WLlypdo9FCZNmoRt27bh+vXr6NKlC7p06YKsrCxcvXoV1tbW8PDw0HovryRdunRB06ZNERMTg169eqFt27YwMDBAy5YtMX369DK3M3HiRGzfvh0RERHo3Lkz0tLScO7cOeTl5eHtt98WTl7TZMqUKfjtt98AAKNGjUL9+vW1fh4dO3bE3LlzsWjRIowdOxZdunSBra0t/vrrL1y5cgUfffQR1qxZo3L5I/DyxkB79uzByJEjsXr1amzduhXt2rWDlZUVsrKycP/+fcTGxqJJkybCB6yenh5mzZqFL7/8ElOnTsXGjRtha2uLhIQEREVFYcSIEbhw4UKFDxNp+5pOmDABUVFR2L17N/r27YuePXuiSZMmuH79Ou7duwcTExNs3LhRuIdCUVERJk+ejKSkJIwePVplxnHw4MH48MMPsWbNGkycOBHHjx8v0/kKBgYGGDJkiHCPie7du8PExATA/y7fLE7bbT9kyBDcuXMHHTp0wH/+8x9IJBL8/vvvSE1NhZ2dndrhUysrK4SFhWH8+PGYN28egoOD0aZNG1hYWCA9PR13797FgwcP0LlzZ+ES4tqAMwoEAPDw8BCuNVaetyDmjTfewIEDB7Bx40b06dMHCQkJOHz4MH777Tfk5ORg+PDh2LFjhzC1WVWUt5ZeuXIlXFxccOPGDYSHh+P+/ftwdHQUvXmNJl5eXoiMjERAQAAaNGiAqKgoHDp0CH/++SeaN2+OhQsXYt68eWVuTyKRYOXKldi3bx/c3d2RnJyMQ4cO4erVq2jcuLFwQltxc+fOxb59+zBw4EDExsbiwIEDSE5OhpeXFyIiIoRLJF99DU6cOCHsWZ06dQoPHjyAr68vIiIiRM9oLy9DQ0Ps378fQ4YMQVJSEvbs2YPt27eX+eZWSi4uLjhx4gTatGmDX3/9Fb///jvs7OwQGBiIrVu3lnheR+/evaGnpweJRFKhE9lmzJiB7du3o3v37rh9+zaOHz8OQ0NDhIaGCvcs0HTOSps2bXD+/HksWrQIrVu3xq1bt3Dw4EFcuXIFdevWxbRp04RDRUoBAQHCOUD37t3D8ePHkZubixUrVlTavTG0fU0lEgl++OEHbNy4Ed27d8cff/yB8PBwYZbut99+w8CBA4X63377LU6fPg0HBwd8++23ao+/YMECdOnSBVeuXBGufCqLoKAg4UZQ4eHh2L59u+hdSLXd9lKpFKdOncK7776La9eu4cSJE6hXrx6mTJmCU6dOaTwhtWfPnrh48SI+//xzWFpa4tq1azh48CBu3rwJc3NzfPHFF1i9enWZn9+/gSQtLa323DWCiGo85Z0q+/XrV6bvFCiP//u//8PSpUsxefJkYeqedK+s2z40NBQffvghRo8eXaE7iNJLnFEgohojMzNT+IIg5Xkd5XX//n2Nh6aOHj2KVatWlTqzRq9XZW570g7PUSCiai84OBi3b9/GxYsX8fDhQwwYMEDrb4p81Z49e7By5Uo4OTnBysoK+fn5iIuLw7179wAA//3vf9GxY8dK6D1VRFVse9IOgwIRVXsnTpzA+fPn0bhxY4wZM6ZSrtcfMGAA/vrrL1y6dAl3795FTk4OGjVqhMGDB2PSpElafyMmVY2q2PakHZ6jQERERKJ4jgIRERGJYlAgIiIiUQwKREREJIpBgSpdQUEBnj59ioKCAl13hWoojiGqKI6hysOgQJWuoKAAz5494x8olRvHEFUUx1DlYVAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkSl/XHaDKId2SqOsuvKIugKe67oSKtAlWuu4CEVGNwxkFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISpVVQuHr1Kj7//HN0794dlpaWaN++PXx9fREXF6dSz9/fH1KpVO1fly5d1NosKirC6tWr4eTkBJlMBldXV+zbt0/j48fGxsLb2xtWVlawtbXF5MmTkZKS8lraJCIiqo20uoVzUFAQoqKi4Onpifbt20Mul2PDhg3o3bs3Tp48ibZt2wp1jYyMEBwcrLK+qampWpuLFy/GqlWr4OPjA2dnZxw9ehSTJk2CRCKBt7e3UC8xMRHu7u4wNTXFvHnzkJmZiZCQENy+fRunT5+GoaFhlbZJRERUG0nS0tIUZa0cFRWFTp06qXyA3r9/H66urvD09MT69esBvJxROHToEBITS/7+gaSkJHTo0AG+vr5Yvnw5AEChUMDd3R2PHz9GTEwM9PT0AAAzZ85EWFgYoqOjYWNjAwA4c+YMvLy8EBQUBF9f3yprsyaoft/1UP3wux5qjpycHMTHx8PGxgbGxsa67g7VQBxDlUerQw/dunVT28u2s7ODg4MDYmNj1eoXFhbi+fPnou0dPXoU+fn58PPzE8okEgn8/PyQmJiI6OhoofzQoUNwc3MTPtABoE+fPrC3t8eBAweqtE0iIqLaqsLfHqlQKPDkyRM4ODiolGdlZcHGxgZZWVmQSqV47733sHDhQtSvX1+oExMTg3r16qF169Yq67q4uAjLe/TogaSkJDx58gSdOnVSe3xnZ2ecPHmyStssSU5OTpnqke5xW9UceXl5Kj+JtMUxVDZlmW2pcFDYs2cPkpKSMHv2bKHMwsICH3/8MTp06ICioiKcOnUKGzduxM2bN3H48GHo6798WLlcjiZNmkAikai0KZPJAADJyclCveLlxVlYWCA1NRW5ubkwMjKqkjZLkpSUhMLCwhLrvB51dd2Bai8+Pl7XXSAtKf9OicqLY6hkLVu2LLVOhYLC3bt38fnnn6Nr164YM2aMUL5gwQKVet7e3rC3t8fixYsRHh4unFCYnZ2t8YNYmXCys7NVfmqqqyxTtlUVbZbE0tKyxOWvz1Ndd6DaK36Iiaq3vLw8yOVyyGQynlRM5cIxVHnKHRTkcjlGjBgBU1NTbNu2TThBUExAQAACAwNx5swZISiYmJggNzdXra5yitjExETlp6a6yrLidSu7zZLwJJmag9uq5jE0NOR2owrhGKq4ct1wKT09He+99x7S09Oxb98+NG3atNR1TExM0KhRI6SmpgplMpkM//zzDxQK1QsvlFNFFhYWQr3i5cUlJyejYcOGwp5/VbRJRERUW2kdFHJycjBq1Cjcv38fu3fvVjuJUUxGRgaePn0KMzMzoczR0RFZWVlqV0xcvnxZWA68nN43MzPDtWvX1Nq9evWqUK+q2iQiIqqttAoKhYWFmDBhAi5duoStW7eia9euanVycnKQkZGhVr58+XIoFAr0799fKHN3d4eBgQE2bdoklCkUCmzevBmWlpbo1q2bUD5s2DCcOHECCQkJQtnZs2cRFxcHLy+vKm2TiIiottLqHIU5c+bg2LFjGDx4MFJTU7F7926V5SNHjoRcLkevXr3w3nvvCWdTnj59GhERERgwYAA8PDyE+lZWVvD390dwcDDy8/Ph7OyMI0eOIDIyEhs2bFA572HGjBk4ePAghg4diqlTpyIzMxPBwcFo27Ytxo4dW6VtEhER1VZa3ZnRw8MD58+fF12elpaGtLQ0zJo1C5cvX0ZycjIKCwvRokULDB8+HNOmTYOBgYHKOkVFRQgKCsKWLVsgl8thZ2eHTz/9FCNGjFBr/86dO5gzZw4uXrwIAwMDuLm5YcmSJTA3N6/yNqs73pmxdLwzY83Bu+pRRXEMVR6tggJVXwwKpWNQqDn4Jk8VxTFUefg100RERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkikGBiIiIRDEoEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKK0CgpXr17F559/ju7du8PS0hLt27eHr68v4uLi1OrGxsbC29sbVlZWsLW1xeTJk5GSkqJWr6ioCKtXr4aTkxNkMhlcXV2xb98+jY+vyzaJiIhqI31tKgcFBSEqKgqenp5o37495HI5NmzYgN69e+PkyZNo27YtACAxMRHu7u4wNTXFvHnzkJmZiZCQENy+fRunT5+GoaGh0ObixYuxatUq+Pj4wNnZGUePHsWkSZMgkUjg7e0t1NN1m0RERLWRJC0tTVHWylFRUejUqZPKB+j9+/fh6uoKT09PrF+/HgAwc+ZMhIWFITo6GjY2NgCAM2fOwMvLC0FBQfD19QUAJCUloUOHDvD19cXy5csBAAqFAu7u7nj8+DFiYmKgp6en8zZrAumWRF13odpLm2Cl6y5QGeXk5CA+Ph42NjYwNjbWdXeoBuIYqjxaHXro1q2b2l62nZ0dHBwcEBsbK5QdOnQIbm5uwocvAPTp0wf29vY4cOCAUHb06FHk5+fDz89PKJNIJPDz80NiYiKio6OrRZtERES1lVaHHjRRKBR48uQJHBwcALzco3/y5Ak6deqkVtfZ2RknT54Ufo+JiUG9evXQunVrlXouLi7C8h49eui8zZLk5OSUqR7pHrdVzZGXl6fyk0hbHENlU5bZlgoHhT179iApKQmzZ88GAMjlcgCATCZTq2thYYHU1FTk5ubCyMgIcrkcTZo0gUQiUamnXDc5OblatFmSpKQkFBYWlljn9air6w5Ue/Hx8bruAmlJ+XdKVF4cQyVr2bJlqXUqFBTu3r2Lzz//HF27dsWYMWMAANnZ2QCg8QNWWZadnQ0jIyPh56uUCUfZlq7bLImlpWWJy1+fp7ruQLVX/BATVW95eXmQy+WQyWQ8qZjKhWOo8pQ7KMjlcowYMQKmpqbYtm2bcIKgiYkJACA3N1dtHWWZso6JiYnGesop4uL1dNlmSXiSTM3BbVXzGBoacrtRhXAMVVy5briUnp6O9957D+np6di3bx+aNm0qLFNO5Wua7klOTkbDhg2FvXSZTIZ//vkHCoXqhRfKdS0sLKpFm0RERLWV1kEhJycHo0aNwv3797F7927hJEYlS0tLmJmZ4dq1a2rrXr16FY6OjsLvjo6OyMrKUrliAgAuX74sLK8ObRIREdVWWgWFwsJCTJgwAZcuXcLWrVvRtWtXjfWGDRuGEydOICEhQSg7e/Ys4uLi4OXlJZS5u7vDwMAAmzZtEsoUCgU2b94MS0tLdOvWrVq0SUREVFtpdY7CnDlzcOzYMQwePBipqanYvXu3yvKRI0cCAGbMmIGDBw9i6NChmDp1KjIzMxEcHIy2bdti7NixQn0rKyv4+/sjODgY+fn5cHZ2xpEjRxAZGYkNGzYI5z3ouk0iIqLaSqs7M3p4eOD8+fOiy9PS0oT/37lzB3PmzMHFixdhYGAANzc3LFmyBObm5irrFBUVISgoCFu2bIFcLoednR0+/fRTjBgxQq19XbZZ3fHOjKXjnRlrDt5VjyqKY6jyaBUUqPpiUCgdg0LNwTd5qiiOocrDr5kmIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkikGBiIiIRDEoEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISpXVQePHiBb7++mt4e3vD1tYWUqkUoaGhavX8/f0hlUrV/nXp0kWtblFREVavXg0nJyfIZDK4urpi3759Gh8/NjYW3t7esLKygq2tLSZPnoyUlJTX0iYREVFto6/tCk+fPsWyZctgbW2N9u3b4/fffxeta2RkhODgYJUyU1NTtXqLFy/GqlWr4OPjA2dnZxw9ehSTJk2CRCKBt7e3UC8xMRHu7u4wNTXFvHnzkJmZiZCQENy+fRunT5+GoaFhlbZJRERU22gdFCwsLBAbGwuZTIZr166hb9++4o3r62PkyJEltpeUlIQ1a9bggw8+wPLlywEA48ePh7u7O+bPnw8vLy/o6ekBAFauXImsrCycOXMGNjY2AAAXFxd4eXkhLCwMvr6+VdYmERFRbaT1oQcjIyPIZLIy1y8sLMTz589Flx89ehT5+fnw8/MTyiQSCfz8/JCYmIjo6Gih/NChQ3BzcxM+0AGgT58+sLe3x4EDB6q0TSIiotqoSk9mzMrKgo2NDZo1awZbW1t89tlnePHihUqdmJgY1KtXD61bt1Ypd3FxEZYDL2cJnjx5gk6dOqk9jrOzs1CvqtokIiKqjbQ+9FBWFhYW+Pjjj9GhQwcUFRXh1KlT2LhxI27evInDhw9DX//lQ8vlcjRp0gQSiURlfeWsRXJyslCvePmrj5Wamorc3FwYGRlVSZticnJySn8xqFrgtqo58vLyVH4SaYtjqGyMjY1LrVNlQWHBggUqv3t7e8Pe3h6LFy9GeHi4cEJhdna2xg9iZeezs7NVfmqqqyxTtlUVbYpJSkpCYWGh6PLXp66uO1DtxcfH67oLpCVlmCcqL46hkrVs2bLUOlUWFDQJCAhAYGAgzpw5IwQFExMT5ObmqtVV7v2ZmJio/NRUV1lWvG5ltynG0tKyxOWvz1Ndd6DaK34eClVveXl5kMvlkMlkvPKIyoVjqPK81qBgYmKCRo0aITU1VSiTyWQ4d+4cFAqFyqECZQq0sLAQ6hUvLy45ORkNGzYU9vyrok0xZZm2oeqB26rmMTQ05HajCuEYqrjXemfGjIwMPH36FGZmZkKZo6MjsrKyEBsbq1L38uXLwnLg5Z67mZkZrl27ptbu1atXhXpV1SYREVFtVCVBIScnBxkZGWrly5cvh0KhQP/+/YUyd3d3GBgYYNOmTUKZQqHA5s2bYWlpiW7dugnlw4YNw4kTJ5CQkCCUnT17FnFxcfDy8qrSNomIiGqjch16WL9+PdLT04WrB44fP46kpCQAwOTJk5GWloZevXrhvffeE06UOH36NCIiIjBgwAB4eHgIbVlZWcHf3x/BwcHIz8+Hs7Mzjhw5gsjISGzYsEG4MRIAzJgxAwcPHsTQoUMxdepUZGZmIjg4GG3btsXYsWOrtE0iIqLaSJKWlqbQdiVHR0fRM8ivX7+OBg0aYNasWbh8+TKSk5NRWFiIFi1aYPjw4Zg2bRoMDAxU1ikqKkJQUBC2bNkCuVwOOzs7fPrppxgxYoRa+3fu3MGcOXNw8eJFGBgYwM3NDUuWLIG5uXmVt1mdSbck6roL1V7aBCtdd4HKKCcnB/Hx8bCxseHxZSoXjqHKU66gQNUPg0LpGBRqDr7JU0VxDFUefs00ERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkikGBiIiIRDEoEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKK2DwosXL/D111/D29sbtra2kEqlCA0N1Vg3NjYW3t7esLKygq2tLSZPnoyUlBS1ekVFRVi9ejWcnJwgk8ng6uqKffv2Vbs2iYiIaht9bVd4+vQpli1bBmtra7Rv3x6///67xnqJiYlwd3eHqakp5s2bh8zMTISEhOD27ds4ffo0DA0NhbqLFy/GqlWr4OPjA2dnZxw9ehSTJk2CRCKBt7d3tWmTiIiottE6KFhYWCA2NhYymQzXrl1D3759NdZbuXIlsrKycObMGdjY2AAAXFxc4OXlhbCwMPj6+gIAkpKSsGbNGnzwwQdYvnw5AGD8+PFwd3fH/Pnz4eXlBT09PZ23SUREVBtpfejByMgIMpms1HqHDh2Cm5ub8OELAH369IG9vT0OHDgglB09ehT5+fnw8/MTyiQSCfz8/JCYmIjo6Ohq0SYREVFtVCUnMyYlJeHJkyfo1KmT2jJnZ2fExMQIv8fExKBevXpo3bq1Sj0XFxdheXVok4iIqDbS+tBDWcjlcgDQOPNgYWGB1NRU5ObmwsjICHK5HE2aNIFEIlGpp1w3OTm5WrQpJicnR3QZVS/cVjVHXl6eyk8ibXEMlY2xsXGpdaokKGRnZwOAxg9YZVl2djaMjIyEn69Sdl7Zlq7bFJOUlITCwkLR5a9PXV13oNqLj4/XdRdIS8owT1ReHEMla9myZal1qiQomJiYAAByc3PVlinLlHVMTEw01lPu/RWvp8s2xVhaWpa4/PV5qusOVHvFz0Oh6i0vLw9yuRwymYxXHlG5cAxVnioJCsqpfE1JLjk5GQ0bNhT20mUyGc6dOweFQqFyqEC5roWFRbVoU0xZpm2oeuC2qnkMDQ253ahCOIYqrkpOZrS0tISZmRmuXbumtuzq1atwdHQUfnd0dERWVhZiY2NV6l2+fFlYXh3aJCIiqo2q7BbOw4YNw4kTJ5CQkCCUnT17FnFxcfDy8hLK3N3dYWBggE2bNgllCoUCmzdvhqWlJbp161Yt2iQiIqqNynXoYf369UhPTxeuHjh+/DiSkpIAAJMnT0aDBg0wY8YMHDx4EEOHDsXUqVORmZmJ4OBgtG3bFmPHjhXasrKygr+/P4KDg5Gfnw9nZ2ccOXIEkZGR2LBhg3BjJAA6bZOIiKg2kqSlpSm0XcnR0VH0DPLr16/jzTffBADcuXMHc+bMwcWLF2FgYAA3NzcsWbIE5ubmKusUFRUhKCgIW7ZsgVwuh52dHT799FOMGDFCrX1dtlmdSbck6roL1V7aBCtdd4HKKCcnB/Hx8bCxseHxZSoXjqHKU66gQNUPg0LpGBRqDr7JU0VxDFUefs00ERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUQwKREREJIpBgYiIiEQxKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkikGBiIiIRDEoEBERkSh9XXeAiKoH6ZZEXXfhFXUBPNV1J1SkTbDSdReIXjvOKBAREZEoBgUiIiISxaBAREREohgUiIiISBSDAhEREYliUCAiIiJRDApEREQkqsqCwrlz5yCVSjX+u3TpkkrdqKgoDB48GE2bNkWrVq0wa9YsvHjxQq3N3NxcLFiwAA4ODrCwsED//v3x66+/anz8qmiTiIiotqnyGy5NmTIFzs7OKmUtWrQQ/h8TEwNPT0+0atUKgYGBSEpKQkhICP766y/s27dPZb2AgACEh4fD398fdnZ2CAsLw/Dhw/Hzzz+jR48eVdomERFRbVTlQcHV1RWenp6iyxcvXgypVIrDhw/D1NQUANCsWTNMnz4dp0+fRr9+/QAAV65cwf79+7F48WJMmzYNADBq1Cj06NEDCxYsQERERJW2SUREVBu9lnMUMjIyUFBQoFb+/Plz/PrrrxgxYoTwgQ68/LCuX78+Dhw4IJSFh4dDT08PPj4+QpmxsTHGjRuH6OhoJCQkVFmbREREtVWVB4UPP/wQNjY2kMlkePvtt3Ht2jVh2e3bt1FQUIBOnTqprGNoaAhHR0fExMQIZTExMbC3t1f58AcAFxcXAMCNGzeqrE0iIqLaqsoOPRgaGmLYsGEYOHAgGjdujNjYWISEhGDIkCE4ceIEOnToALlcDgCQyWRq68tkMkRGRgq/y+Vy0XoAkJycLNSr7DZLkpOTU2odqh64raiiOIZqjry8PJWfpJmxsXGpdaosKHTr1g3dunUTfnd3d4enpyd69uyJRYsWYf/+/cjOzgYAGBkZqa1vZGQkLAeA7OxsjfWUT1JZtyraLElSUhIKCwtLrVf16uq6A9VefHy8rrtQzXEMlYZjqOZR7jySZi1btiy1zmv9mukWLVrA3d0dP//8MwoLC2FiYgLg5SWKr8rNzRWWA4CJiYnGesqEr6xbFW2WxNLSstQ6r0f1+jre6sjGxkbXXajmOIZKwzFUc+Tl5QmzxoaGhrruTo32WoMCAFhZWSEvLw+ZmZnCFL+mxCeXy2FhYSH8LpPJ8Pfff2usB0CoWxVtlqQs0zZUPXBbUUVxDNU8hoaG3G4V9NrvzPjw4UMYGxujfv36aNOmDfT19VVOcAReJsEbN27A0dFRKHN0dERcXByeP3+uUvfy5cvCcgBV0iYREVFtVWVBISUlRa3sxo0bOHbsGPr27Ys6deqgQYMG6NOnD/bs2YOMjAyh3q5du/DixQt4eXkJZZ6enigsLMS2bduEstzcXISGhqJz586wtrYGgCppk4iIqLaqskMPEyZMgImJCbp27YomTZrgzz//xLZt22BiYoKFCxcK9ebOnQs3Nzd4eHjA19cXSUlJWLNmDfr164cBAwYI9Tp37gwvLy989dVXePLkCVq0aIGdO3fi8ePHCAkJUXnsqmiTiIioNpKkpaUpqqLhdevWYe/evfjrr7+QkZEBMzMz9OrVC//9739VbuEMAJGRkVi4cCGuX7+O+vXr45133sH8+fPxxhtvqNTLyclBYGAg9uzZg7S0NLRr1w5z5sxB//791R6/KtqszqRbEnXdhWovbYKVrrtQrXEMlY5jqObIyclBfHw8bGxseI5CBVVZUKDXi2/ypeObfMk4hkrHMVRzMChUHn7NNBEREYliUCAiIiJRDApEREQkikGBiIiIRDEoEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJYlAgIiIiUfq67gAREf07SLck6roLr6gL4KmuO6EibYKVrrugNc4oEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgACA3NxcLFiyAg4MDLCws0L9/f/z666+67hYREZHOMSgACAgIwNq1azF8+HB888030NPTw/DhwxEZGanrrhEREelUrf9SqCtXrmD//v1YvHgxpk2bBgAYNWoUevTogQULFiAiIkLHPSQiItKdWh8UwsPDoaenBx8fH6HM2NgY48aNw6JFi5CQkABra2sd9rBsGhtxcogqhmOIKopj6N+p1geFmJgY2Nvbw9TUVKXcxcUFAHDjxo0aERTuj2mq6y5QDccxRBXFMfTvVOvjn1wuh0wmUytXliUnJ7/uLhEREVUbtT4oZGdnw8jISK3c2NhYWE5ERFRb1fqgYGJigtzcXLXynJwcYTkREVFtVeuDgkwmg1wuVytXlllYWLzuLhEREVUbtT4oODo6Ii4uDs+fP1cpv3z5srCciIiotqr1QcHT0xOFhYXYtm2bUJabm4vQ0FB07ty5RlzxQEREVFVq/eWRnTt3hpeXF7766is8efIELVq0wM6dO/H48WOEhITountURo6Ojnjrrbfw/fff67orRET/KrU+KADAunXrEBgYiN27dyMtLQ3t2rXD7t270bNnT113jYj+v40bN8LExARjx47VdVeIahVJWlqaQtedIKoozij8+/Xo0QONGjXCkSNHdN0Volql1p+jQEREROIYFGqBpUuXQiqVIi4uDpMnT0azZs1gZ2eHJUuWQKFQICEhAaNHj4aNjQ1atWqlcm5GXl4eAgMD0bt3bzRr1gyWlpYYMmQIfvvtN5XHePToEaRSKUJCQrB161Z07NgR5ubm6Nu3L65evapS18PDAx4eHmr99Pf3V7vKJCQkBIMGDULz5s1hYWGB3r17Izw8vBJfHaoKjx8/xsyZM9G5c2dYWFigefPm8PHxwaNHj1TqKcfmq0JDQyGVSoX6jo6OuHPnDs6fPw+pVAqpVKoyhh4+fAgfHx/Y2tqiadOmGDBgAE6cOFGlz5FeH+U4+euvv+Dv749mzZqhWbNmCAgIQFZWllBvx44dGDp0KOzt7WFubo5u3bph06ZNau05Ojpi5MiRiIyMRL9+/SCTydChQwfs3LnzdT6tGoPnKNQiEyZMQOvWrYVvxVyxYgUaNmyIrVu3olevXli4cCH27t2LefPmwdnZGT179kRGRga2b98Ob29v+Pj44MWLF8Lvv/zyC5ycnFQeY+/evXjx4gUmTJgAiUSC1atXY9y4cfjjjz9gYGCgdZ/XrVuHIUOGYPjw4cjLy8NPP/0EHx8f7N69G25ubpX10lAlu3btGqKiovDuu+/CysoKjx8/xqZNm/D2228jKioKdevW1aq9pUuX4osvvkC9evUwc+ZMAIC5uTkA4J9//sGgQYOQnZ2NKVOmoGHDhti5cydGjx6Nbdu2YejQoZX+/Eg3fH198eabb2LBggW4fv06fvzxRzRp0gRfffUVAGDz5s1wcHDAkCFDoK+vj+PHj2PmzJkoKirCBx98oNLWX3/9BR8fH4wbNw6jR4/Gjh07EBAQgI4dO6JNmza6eHrVFoNCLeLi4oKgoCAAL//gnJycMHfuXCxYsACffPIJAMDb2xtt2rTBjh070LNnT0ilUsTExMDQ0FBoZ/z48ejatSvWr1+PNWvWqDxGQkICrl69Kuwl2tvbY8yYMfjll18wePBgrft8+fJllbtjTp48Gb1798batWsZFKqxQYMGwdPTU6Vs8ODBGDhwIA4dOoRRo0Zp1d7bb7+NwMBANGrUCCNHjlRZtmrVKvzzzz84duwYevToAQDw8fFBz549MWfOHHh4eKBOHU6e/hs4OTmpvOc8e/YM27dvF4LCkSNH1N4vvL29sXbtWrWgcO/ePRw9ehSurq4AgHfeeQft2rVDaGgolixZ8hqeTc3Bv55aZPz48cL/9fT00LFjRygUCowbN04ol0qlsLe3x8OHD4V6ypBQVFSE1NRUFBYWolOnTrh+/braY7z77rsqU8nKP0Jle9oq/keflpaG58+fo0ePHhofm6qP4tstPz8fz549Q4sWLdCgQYNK33YnT56Ei4uLEBIAoH79+vD19cXjx4/x559/Vurjke5MnDhR5fcePXrg2bNnwg3zio+79PR0PH36FD179sTDhw+Rnp6usq6Dg4Pw/gQAZmZmKu999D+cUahFXr15lKmpKYyNjdG4cWO18tTUVOH3sLAwrFmzBvfu3UN+fr5Q/uabb5b6GMrQkJaWVq4+Hz9+HCtWrMCNGzdUvpNDIpGUqz16PbKzs7Fy5UqEhYUhKSkJCsX/Lq569S6oFRUfHy98LXxxrVq1Epa3bdu2Uh+TdKOk9xdTU1NcvHgRS5cuxaVLl1TOXQBejrsGDRqItqVsr7zvVf9mDAq1iJ6eXpnKAAhv7Lt370ZAQAA8PDwwffp0NGnSBHp6eli5ciUePHigdXvAyw/54r8rFRYWqvx+4cIFjB49Gq6urlixYgUsLCxgYGCA0NBQ7N27V/yJks7NmjULoaGh8Pf3R9euXWFqagqJRIKJEyeiqKhIqCcW+F4dC0RAye8vDx48gKenJ1q2bInAwEBYWVnB0NAQERER+O6771TGXWltkSoGBSpReHg4bG1tsWPHDpU39aVLl5a7TalUqnF6Lz4+XuX3Q4cOwdjYGD/99JPKV4GHhoaW+7Hp9QgPD8fo0aMRGBgolOXk5KhN/xbfIyx+yOrVsQCIhwobGxvExcWpld+7d09YTv9+x44dQ25uLnbu3Kmyzc+dO6fDXv078BwFKpEydRdP2ZcvX0Z0dHS522zevDnu3buHlJQUoezGjRuIiopSe2yJRKKyd/no0SPecKcG0NPTU9szW79+vdpMQfPmzQG8nD1SyszM1HiZWt26ddWCBgAMHDgQV65cURmTmZmZ2Lp1K5o1awYHB4cKPReqGTS9V6Wnp3PHohJwRoFK5Obmhp9//hljx46Fm5sbHj16JFyC9OLFi3K1+f7772Pt2rV49913MW7cODx58gRbtmyBg4MDMjIyhHqDBg3C2rVr4e3tjeHDh+PJkyfYuHEjmjdvjlu3blXWU6Qq4Obmht27d8PU1BQODg6Ijo7G2bNn0ahRI5V6/fr1g7W1NaZNm4Z79+6hTp06CA0NhZmZGRISElTqduzYEZs2bcLy5cvRokULmJmZoXfv3vj000+xf/9+vPfeeyqXRz569Ag//vgjr3ioJfr16wdDQ0OMGjUKEyZMQGZmJrZt2wYzMzMkJyfruns1Gv+CqERjx47F/PnzcfPmTXzxxRf45ZdfsH79enTs2LHcbbZu3Rrr1q3D8+fPMWfOHBw7dgw//PADOnTooFKvd+/eCAkJwT///IPZs2dj//79WLhwId5+++0KPiuqat988w1GjRqFvXv3Yu7cuZDL5Th48CDq1aunUs/AwAA7duxA8+bNERgYiB9++AHjxo1Tu5QNeHnew6BBgxAcHAw/Pz8sW7YMwMv7KURERKBv375Yv349Fi1aBENDQ+zatYv3UKhFWrZsiW3btkEikWDevHnYvHkzfH19MXXqVF13rcbjdz0QERGRKM4oEBERkSgGBSIiIhLFoEBERESiGBSIiIhIFIMCERERiWJQICIiIlEMCkRERCSKQYGIiIhEMSgQERGRKAYFIiIiEsWgQERERKIYFIiIiEgUgwIRERGJ+n9mTFhQlURqvwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.gearbox.value_counts(dropna=False).plot(kind='bar', rot=0, figsize=(5,3))\n",
    "plt.title('vehicle count by gearbox type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks ok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **fuel_type**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.fuel_type.value_counts(dropna=False).plot(kind='bar', rot=0, figsize=(8,4))\n",
    "plt.title('vehicle count by fuel type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a mistake in the dataset, as 'petrol' and 'gasoline' is the same fuel type. However, renaming gasoline to petrol leads to drop in metrics. Probably the most reasonable explanation is that 'gasoline' should actually be 'diesel' and there was a translation mistake. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fuel_type'].where(df['fuel_type'] != 'gasoline', 'diesel', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "petrol    216233\n",
       "diesel     98651\n",
       "Name: fuel_type, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fuel_type.value_counts().head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **repaired**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.repaired.value_counts(dropna=False).plot(kind='bar', rot=0, figsize=(5,3))\n",
    "plt.title('Repaired vehicle count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks ok."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **brand**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alfa_romeo',\n",
       " 'audi',\n",
       " 'bmw',\n",
       " 'chevrolet',\n",
       " 'chrysler',\n",
       " 'citroen',\n",
       " 'dacia',\n",
       " 'daihatsu',\n",
       " 'daewoo',\n",
       " 'fiat',\n",
       " 'ford',\n",
       " 'honda',\n",
       " 'hyundai',\n",
       " 'jaguar',\n",
       " 'jeep',\n",
       " 'kia',\n",
       " 'lancia',\n",
       " 'land_rover',\n",
       " 'lada',\n",
       " 'mazda',\n",
       " 'mercedes_benz',\n",
       " 'mini',\n",
       " 'mitsubishi',\n",
       " 'nissan',\n",
       " 'opel',\n",
       " 'peugeot',\n",
       " 'porsche',\n",
       " 'renault',\n",
       " 'rover',\n",
       " 'saab',\n",
       " 'seat',\n",
       " 'skoda',\n",
       " 'smart',\n",
       " 'sonstige_autos',\n",
       " 'subaru',\n",
       " 'suzuki',\n",
       " 'toyota',\n",
       " 'trabant',\n",
       " 'volkswagen',\n",
       " 'volvo']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "brand_list = list(df.brand.unique())\n",
    "brand_list.sort(key=lambda x: x[:2])\n",
    "brand_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.brand.value_counts(dropna=False).head(10).plot(kind='bar', rot=20, figsize=(10,4))\n",
    "plt.title('top 10 popular brands')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates or errors, and the brand names are real."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['100',\n",
       " '147',\n",
       " '145',\n",
       " '156',\n",
       " '159',\n",
       " '1_reihe',\n",
       " '1er',\n",
       " '200',\n",
       " '2_reihe',\n",
       " '300c',\n",
       " '3_reihe',\n",
       " '3er',\n",
       " '4_reihe',\n",
       " '500',\n",
       " '5_reihe',\n",
       " '5er',\n",
       " '601',\n",
       " '6_reihe',\n",
       " '6er',\n",
       " '7er',\n",
       " '80',\n",
       " '850',\n",
       " '90',\n",
       " '900',\n",
       " '9000',\n",
       " '911',\n",
       " 'a1',\n",
       " 'a2',\n",
       " 'a3',\n",
       " 'a4',\n",
       " 'a5',\n",
       " 'a6',\n",
       " 'a8',\n",
       " 'a_klasse',\n",
       " 'accord',\n",
       " 'agila',\n",
       " 'almera',\n",
       " 'alhambra',\n",
       " 'altea',\n",
       " 'amarok',\n",
       " 'antara',\n",
       " 'arosa',\n",
       " 'astra',\n",
       " 'auris',\n",
       " 'avensis',\n",
       " 'aveo',\n",
       " 'aygo',\n",
       " 'b_klasse',\n",
       " 'b_max',\n",
       " 'berlingo',\n",
       " 'beetle',\n",
       " 'boxster',\n",
       " 'bora',\n",
       " 'bravo',\n",
       " 'c1',\n",
       " 'c2',\n",
       " 'c3',\n",
       " 'c4',\n",
       " 'c5',\n",
       " 'c_max',\n",
       " 'c_klasse',\n",
       " 'c_reihe',\n",
       " 'caddy',\n",
       " 'carisma',\n",
       " 'cayenne',\n",
       " 'carnival',\n",
       " 'calibra',\n",
       " 'captiva',\n",
       " 'cc',\n",
       " 'ceed',\n",
       " 'cherokee',\n",
       " 'charade',\n",
       " 'civic',\n",
       " 'citigo',\n",
       " 'clio',\n",
       " 'clk',\n",
       " 'cl',\n",
       " 'clubman',\n",
       " 'corsa',\n",
       " 'combo',\n",
       " 'cordoba',\n",
       " 'colt',\n",
       " 'cooper',\n",
       " 'corolla',\n",
       " 'crossfire',\n",
       " 'cr_reihe',\n",
       " 'croma',\n",
       " 'cuore',\n",
       " 'cx_reihe',\n",
       " 'defender',\n",
       " 'delta',\n",
       " 'discovery',\n",
       " 'doblo',\n",
       " 'duster',\n",
       " 'ducato',\n",
       " 'e_klasse',\n",
       " 'elefantino',\n",
       " 'eos',\n",
       " 'escort',\n",
       " 'espace',\n",
       " 'exeo',\n",
       " 'fabia',\n",
       " 'fiesta',\n",
       " 'fortwo',\n",
       " 'forester',\n",
       " 'focus',\n",
       " 'fox',\n",
       " 'forfour',\n",
       " 'freelander',\n",
       " 'fusion',\n",
       " 'g_klasse',\n",
       " 'galaxy',\n",
       " 'galant',\n",
       " 'getz',\n",
       " 'glk',\n",
       " 'gl',\n",
       " 'golf',\n",
       " 'grand',\n",
       " 'i3',\n",
       " 'i_reihe',\n",
       " 'ibiza',\n",
       " 'impreza',\n",
       " 'insignia',\n",
       " 'jazz',\n",
       " 'jetta',\n",
       " 'jimny',\n",
       " 'juke',\n",
       " 'justy',\n",
       " 'ka',\n",
       " 'kadett',\n",
       " 'kangoo',\n",
       " 'kaefer',\n",
       " 'kalos',\n",
       " 'kalina',\n",
       " 'kappa',\n",
       " 'kuga',\n",
       " 'laguna',\n",
       " 'lanos',\n",
       " 'lancer',\n",
       " 'leon',\n",
       " 'legacy',\n",
       " 'logan',\n",
       " 'lodgy',\n",
       " 'lupo',\n",
       " 'lybra',\n",
       " 'm_klasse',\n",
       " 'm_reihe',\n",
       " 'matiz',\n",
       " 'materia',\n",
       " 'meriva',\n",
       " 'megane',\n",
       " 'micra',\n",
       " 'mii',\n",
       " 'mondeo',\n",
       " 'modus',\n",
       " 'move',\n",
       " 'mustang',\n",
       " 'musa',\n",
       " 'mx_reihe',\n",
       " 'navara',\n",
       " 'niva',\n",
       " 'note',\n",
       " 'nubira',\n",
       " 'octavia',\n",
       " 'omega',\n",
       " 'one',\n",
       " 'other',\n",
       " 'outlander',\n",
       " 'passat',\n",
       " 'panda',\n",
       " 'pajero',\n",
       " 'phaeton',\n",
       " 'picanto',\n",
       " 'polo',\n",
       " 'primera',\n",
       " 'ptcruiser',\n",
       " 'punto',\n",
       " 'q3',\n",
       " 'q5',\n",
       " 'q7',\n",
       " 'qashqai',\n",
       " 'r19',\n",
       " 'rav',\n",
       " 'range_rover',\n",
       " 'range_rover_sport',\n",
       " 'rangerover',\n",
       " 'range_rover_evoque',\n",
       " 'rio',\n",
       " 'roomster',\n",
       " 'roadster',\n",
       " 'rx_reihe',\n",
       " 's60',\n",
       " 's_max',\n",
       " 's_klasse',\n",
       " 's_type',\n",
       " 'santa',\n",
       " 'sandero',\n",
       " 'samara',\n",
       " 'scirocco',\n",
       " 'scenic',\n",
       " 'seicento',\n",
       " 'serie_2',\n",
       " 'serie_3',\n",
       " 'serie_1',\n",
       " 'sharan',\n",
       " 'signum',\n",
       " 'sirion',\n",
       " 'slk',\n",
       " 'sl',\n",
       " 'sorento',\n",
       " 'sprinter',\n",
       " 'sportage',\n",
       " 'spider',\n",
       " 'spark',\n",
       " 'stilo',\n",
       " 'superb',\n",
       " 'swift',\n",
       " 'terios',\n",
       " 'tiguan',\n",
       " 'tigra',\n",
       " 'touran',\n",
       " 'touareg',\n",
       " 'toledo',\n",
       " 'transporter',\n",
       " 'transit',\n",
       " 'tt',\n",
       " 'tucson',\n",
       " 'twingo',\n",
       " 'up',\n",
       " 'v40',\n",
       " 'v50',\n",
       " 'v60',\n",
       " 'v70',\n",
       " 'v_klasse',\n",
       " 'vectra',\n",
       " 'verso',\n",
       " 'vito',\n",
       " 'viano',\n",
       " 'vivaro',\n",
       " 'voyager',\n",
       " 'wrangler',\n",
       " 'x_reihe',\n",
       " 'x_type',\n",
       " 'x_trail',\n",
       " 'xc_reihe',\n",
       " 'yaris',\n",
       " 'yeti',\n",
       " 'ypsilon',\n",
       " 'z_reihe',\n",
       " 'zafira']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_list = list(filter(lambda x: x is not np.nan, list(df.model.unique())))\n",
    "model_list.sort(key=lambda x: x[:2])\n",
    "model_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.model.value_counts(dropna=False).head(10).plot(kind='bar', rot=0, figsize=(8,4))\n",
    "plt.title('top 10 popular models')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only mistake here is that there are two different versions of the range rover."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.model.where(df['model'] != 'rangerover', 'range_rover', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "All the features have been covered.   \n",
    "Now all the observations from the `index_to_drop` list should be removed. A little less than 20% of the dataset is to be dropped, which is quite acceptable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Following data share will be removed: 18.33%\n"
     ]
    }
   ],
   "source": [
    "print(f'Following data share will be removed: {len(index_to_drop) / len(df):.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(index_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_crawled              0\n",
       "price                     0\n",
       "vehicle_type           5459\n",
       "registration_year         0\n",
       "gearbox                4793\n",
       "power                     0\n",
       "model                 10174\n",
       "kilometer                 0\n",
       "registration_month        0\n",
       "fuel_type             13043\n",
       "brand                     0\n",
       "repaired              40001\n",
       "date_created              0\n",
       "postal_code               0\n",
       "last_seen                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of gaps in features\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **vehicle_type, gearbox, fuel_type, repaired**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In these columns, simply fill in the gaps with a stub."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['vehicle_type', 'gearbox', 'fuel_type', 'repaired']].fillna('missing', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### **model**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An general idea is to fill in the gaps in this column based on the kNN algorithm, but it takes a very long time. Therefore, we sort by brand, vehicle type and power and impute with an impromptu \"nearest neighbor\" using ffill. Then the data is shuffled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10174"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.model.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by=['brand', 'vehicle_type', 'power'])\n",
    "df.fillna(method='ffill', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = shuffle(df, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "New features improving the quality of models can be derived from columns with dates. Following new features are proposed:\n",
    "1. The age of the car at the time of posting the ad.\n",
    "2. The period between the date of the ad and the date the user was last seen on the site (user activity). For example, if the user has not appeared for a long time, then he did not adjust the cost of the car, which may cause it to be overpriced.\n",
    "3. Car age category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_zeros(x):\n",
    "    if x == 0:\n",
    "        x = np.random.randint(1, 12)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_month = df['registration_month'].apply(replace_zeros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a complete date of registration (day and sometimes month is randomly generated)\n",
    "date_of_reg = pd.to_datetime({\n",
    "    'year': df['registration_year'],\n",
    "    'month': real_month,\n",
    "    'day': [np.random.randint(1, 28) for i in range(len(df))]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new feature - age of vehicle in days\n",
    "df['age'] = df['date_created'] - date_of_reg\n",
    "df['age'] = df['age'].dt.days\n",
    "df.drop(df[df['age'] < 0].index, inplace=True)  # remove negative values from data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### User activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new feature describing user activity\n",
    "df['afk'] = df['last_seen'] - df['date_created']\n",
    "df['afk'] = df['afk'].dt.days\n",
    "df.drop(df[df['afk'] < 0].index, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Age category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_type = {\n",
    "    'registration_year < 1985': 'retro',\n",
    "    'registration_year < 2000 & registration_year >= 1985': '85-00',\n",
    "    'registration_year < 2005 & registration_year >= 2000': '00-05',\n",
    "    'registration_year < 2010 & registration_year >= 2005': '05-10',\n",
    "    'registration_year < 2015 & registration_year >= 2010': '10-15',\n",
    "    'registration_year >= 2015': '>=15'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating category: retro\n",
      "Creating category: 85-00\n",
      "Creating category: 00-05\n",
      "Creating category: 05-10\n",
      "Creating category: 10-15\n",
      "Creating category: >=15\n"
     ]
    }
   ],
   "source": [
    "# adding new feature - car age category\n",
    "# this improves LinearRegression score A LOT\n",
    "for condition, category in age_type.items():\n",
    "    print('Creating category:', category)\n",
    "    df.loc[df.query(condition).index, 'age_type'] = category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the convenience of working with machine learning models, the datetime type will be cast to int."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in ['date_crawled', 'last_seen', 'date_created']:\n",
    "    df[column] = (df[column].view('int64') // 10e9).astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking erroneous dates count\n",
    "display(len(df[df['last_seen'] < df['date_created']]))\n",
    "display(len(df[df['date_crawled'] < df['date_created']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 288503 entries, 320845 to 198135\n",
      "Data columns (total 18 columns):\n",
      " #   Column              Non-Null Count   Dtype \n",
      "---  ------              --------------   ----- \n",
      " 0   date_crawled        288503 non-null  int64 \n",
      " 1   price               288503 non-null  int64 \n",
      " 2   vehicle_type        288503 non-null  object\n",
      " 3   registration_year   288503 non-null  int64 \n",
      " 4   gearbox             288503 non-null  object\n",
      " 5   power               288503 non-null  int64 \n",
      " 6   model               288503 non-null  object\n",
      " 7   kilometer           288503 non-null  int64 \n",
      " 8   registration_month  288503 non-null  int64 \n",
      " 9   fuel_type           288503 non-null  object\n",
      " 10  brand               288503 non-null  object\n",
      " 11  repaired            288503 non-null  object\n",
      " 12  date_created        288503 non-null  int64 \n",
      " 13  postal_code         288503 non-null  int64 \n",
      " 14  last_seen           288503 non-null  int64 \n",
      " 15  age                 288503 non-null  int64 \n",
      " 16  afk                 288503 non-null  int64 \n",
      " 17  age_type            288503 non-null  object\n",
      "dtypes: int64(11), object(7)\n",
      "memory usage: 49.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# checking the final dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three models will be considered:\n",
    "1. Ridge (fast);\n",
    "2. LightGBM (fast and efficient);\n",
    "3. CatBoost (not as fast but also efficient)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All models require separate sampling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation for machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save for future reuse\n",
    "cat_cols = df.select_dtypes('object').columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[cat_cols] = df[cat_cols].astype('category')  # saves memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['price'], axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data preparation for linear model (Ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pipeline includes following steps:\n",
    "1. One-hot encoding (other encoders are unsuitable);\n",
    "2. MinMax scaling;\n",
    "3. Feature selection (in this notebook LGBM was used, implementing Lasso may be more justified, but it's slower). We'll have more than 300 features after OHE, so it's reasonable to reduce the count to half."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "OHE = OneHotEncoder(drop='first', sparse_output=False, handle_unknown='ignore')\n",
    "encoder_ohe = make_column_transformer((OHE, cat_cols), remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = lgb.LGBMRegressor(metric='rmse')\n",
    "rfe = RFE(gbm, step=40)  # remove 40 features at one step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create pipeline\n",
    "pipe_ohe = Pipeline([\n",
    "    ('encode', encoder_ohe),\n",
    "    ('scale', scaler),\n",
    "    ('select features', rfe)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform train\n",
    "X_train_ohe = pd.DataFrame(\n",
    "    pipe_ohe.fit_transform(X_train, y_train), \n",
    "    columns=pipe_ohe[2].get_feature_names_out(input_features=pipe_ohe[0].get_feature_names_out()),\n",
    "    index=X_train.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform test\n",
    "X_test_ohe = pd.DataFrame(\n",
    "    pipe_ohe.transform(X_test), \n",
    "    columns=X_train_ohe.columns,\n",
    "    index=X_test.index\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All encoded columns have the float64 data type. All \"former\" categorical variables containing only 0 and 1 will be cast to the least resource-intensive type `int8` for more efficient work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_cols = X_train_ohe.filter(regex=\"^onehot.*\")\n",
    "for col in selected_cols:\n",
    "    X_train_ohe[col] = X_train_ohe[col].astype('int8')\n",
    "    X_test_ohe[col] = X_test_ohe[col].astype('int8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data after transformation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_ohe.shape = (216377, 158)\n",
      "X_test_ohe.shape = (72126, 158)\n"
     ]
    }
   ],
   "source": [
    "print(f'{X_train_ohe.shape = }')\n",
    "print(f'{X_test_ohe.shape = }')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>onehotencoder__vehicle_type_convertible</th>\n",
       "      <th>onehotencoder__vehicle_type_coupe</th>\n",
       "      <th>onehotencoder__vehicle_type_other</th>\n",
       "      <th>onehotencoder__vehicle_type_sedan</th>\n",
       "      <th>onehotencoder__vehicle_type_small</th>\n",
       "      <th>onehotencoder__vehicle_type_suv</th>\n",
       "      <th>onehotencoder__vehicle_type_wagon</th>\n",
       "      <th>onehotencoder__gearbox_manual</th>\n",
       "      <th>onehotencoder__model_145</th>\n",
       "      <th>onehotencoder__model_147</th>\n",
       "      <th>...</th>\n",
       "      <th>remainder__date_crawled</th>\n",
       "      <th>remainder__registration_year</th>\n",
       "      <th>remainder__power</th>\n",
       "      <th>remainder__kilometer</th>\n",
       "      <th>remainder__registration_month</th>\n",
       "      <th>remainder__date_created</th>\n",
       "      <th>remainder__postal_code</th>\n",
       "      <th>remainder__last_seen</th>\n",
       "      <th>remainder__age</th>\n",
       "      <th>remainder__afk</th>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.25</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.19</td>\n",
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       "      <td>0.18</td>\n",
       "      <td>0.86</td>\n",
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       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.28</td>\n",
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       "      <td>0.42</td>\n",
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       "      <td>0.18</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 158 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        onehotencoder__vehicle_type_convertible  \\\n",
       "230610                                        1   \n",
       "155206                                        1   \n",
       "30285                                         0   \n",
       "191081                                        0   \n",
       "39839                                         0   \n",
       "\n",
       "        onehotencoder__vehicle_type_coupe  onehotencoder__vehicle_type_other  \\\n",
       "230610                                  0                                  0   \n",
       "155206                                  0                                  0   \n",
       "30285                                   0                                  0   \n",
       "191081                                  0                                  0   \n",
       "39839                                   0                                  0   \n",
       "\n",
       "        onehotencoder__vehicle_type_sedan  onehotencoder__vehicle_type_small  \\\n",
       "230610                                  0                                  0   \n",
       "155206                                  0                                  0   \n",
       "30285                                   0                                  0   \n",
       "191081                                  0                                  0   \n",
       "39839                                   0                                  1   \n",
       "\n",
       "        onehotencoder__vehicle_type_suv  onehotencoder__vehicle_type_wagon  \\\n",
       "230610                                0                                  0   \n",
       "155206                                0                                  0   \n",
       "30285                                 0                                  1   \n",
       "191081                                0                                  0   \n",
       "39839                                 0                                  0   \n",
       "\n",
       "        onehotencoder__gearbox_manual  onehotencoder__model_145  \\\n",
       "230610                              0                         0   \n",
       "155206                              1                         0   \n",
       "30285                               1                         0   \n",
       "191081                              1                         0   \n",
       "39839                               1                         0   \n",
       "\n",
       "        onehotencoder__model_147  ...  remainder__date_crawled  \\\n",
       "230610                         0  ...                     0.76   \n",
       "155206                         0  ...                     0.34   \n",
       "30285                          0  ...                     0.18   \n",
       "191081                         0  ...                     0.49   \n",
       "39839                          0  ...                     0.22   \n",
       "\n",
       "        remainder__registration_year  remainder__power  remainder__kilometer  \\\n",
       "230610                          0.83              0.48                  0.24   \n",
       "155206                          0.89              0.20                  1.00   \n",
       "30285                           0.86              0.28                  1.00   \n",
       "191081                          0.82              0.22                  1.00   \n",
       "39839                           0.82              0.16                  0.59   \n",
       "\n",
       "        remainder__registration_month  remainder__date_created  \\\n",
       "230610                           0.75                     0.92   \n",
       "155206                           0.17                     0.77   \n",
       "30285                            0.25                     0.72   \n",
       "191081                           0.58                     0.82   \n",
       "39839                            0.42                     0.73   \n",
       "\n",
       "        remainder__postal_code  remainder__last_seen  remainder__age  \\\n",
       "230610                    0.25                  1.00            0.16   \n",
       "155206                    0.45                  0.87            0.11   \n",
       "30285                     0.93                  0.99            0.14   \n",
       "191081                    0.35                  0.98            0.18   \n",
       "39839                     0.63                  0.30            0.18   \n",
       "\n",
       "        remainder__afk  \n",
       "230610            0.08  \n",
       "155206            0.19  \n",
       "30285             0.28  \n",
       "191081            0.18  \n",
       "39839             0.03  \n",
       "\n",
       "[5 rows x 158 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_ohe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### LightGBM data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For LightGBM, ordinal encoding is used. The built-in LGBM encoding will be applied as it's fast and efficient, but it works only after the categorical features were encoded with OrdinalEncoder. No scaling is necessary (in fact, it even makes the results worse)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "OE = OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_oe = X_train.copy()\n",
    "X_test_oe = X_test.copy()\n",
    "\n",
    "X_train_oe[cat_cols] = OE.fit_transform(X_train[cat_cols])\n",
    "X_test_oe[cat_cols] = OE.transform(X_test[cat_cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_oe.shape = (216377, 17)\n",
      "X_test_oe.shape = (72126, 17)\n"
     ]
    }
   ],
   "source": [
    "print(f'{X_train_oe.shape = }')\n",
    "print(f'{X_test_oe.shape = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CatBoost data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target encoder will be used for catboost data encoding, as it provides the best results. Built-in CatBoost encoder may secure even better metrics, but it's very slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoder_te = TargetEncoder(cols=cat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make pipeline\n",
    "pipe_te = Pipeline([\n",
    "    ('encode', encoder_te),\n",
    "    ('scale', scaler),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_te = pd.DataFrame(\n",
    "    pipe_te.fit_transform(X_train, y_train), \n",
    "    columns=pipe_te[0].get_feature_names_out(),\n",
    "    index=X_train.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_te = pd.DataFrame(\n",
    "    pipe_te.transform(X_test), \n",
    "    columns=X_train_te.columns,\n",
    "    index=X_test.index\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_te.shape = (216377, 17)\n",
      "X_test_te.shape = (72126, 17)\n"
     ]
    }
   ],
   "source": [
    "print(f'{X_train_te.shape = }')\n",
    "print(f'{X_test_te.shape = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Task metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dict for metrics storage (fit time, RMSE):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {\n",
    "    'CV fit time, seconds': {},\n",
    "    'best model train dataset fit time, seconds': {},\n",
    "    'best CV score (RMSE), euro': {},\n",
    "}\n",
    "\n",
    "\n",
    "def metrics_append(model_name, val1, val2, val3):\n",
    "    metrics['CV fit time, seconds'][model_name] = val1  # adding this out of scientific interest\n",
    "    metrics['best model train dataset fit time, seconds'][model_name] = val2\n",
    "    metrics['best CV score (RMSE), euro'][model_name] = val3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create decorator for time counting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import wraps\n",
    "\n",
    "\n",
    "def timeit(func) -> tuple or int:\n",
    "    @wraps(func)\n",
    "    def inner(*args, **kwargs):\n",
    "        start = datetime.now()\n",
    "        result = func(*args, **kwargs)\n",
    "        end = datetime.now()\n",
    "\n",
    "        print(f'Execution time: {(end - start).seconds:.2f} seconds') \n",
    "        if result is not None:\n",
    "            return (end - start).seconds, result\n",
    "        else:\n",
    "            return (end - start).seconds\n",
    "    return inner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create function for model fitting and counting fit time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "@timeit\n",
    "def model_fit(model, X_train, y_train=None, **kwargs) -> None:\n",
    "    if y_train is not None:\n",
    "        model.fit(X_train, y_train, **kwargs)\n",
    "    else:\n",
    "        model.fit(X_train, **kwargs)  # for CatBoost\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ridge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since no correlation check was performed, and there are also some highly correlated features that were added during feature engineering step, the Ridge regression will be used as the simplest model. The best linear model parameters are chosen via cross-validation (GridSearchCV)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_lin = Ridge(solver='cholesky')  # fastest solver giving best results\n",
    "params_lin = {'alpha': [0.001, 0.01, 0.1, 1]}\n",
    "grid_lin = GridSearchCV(model_lin, params_lin, cv=5, n_jobs=-1, scoring='neg_root_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 13.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# CV time\n",
    "ridge_CV_time = model_fit(grid_lin, X_train_ohe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.01, solver=&#x27;cholesky&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.01, solver=&#x27;cholesky&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.01, solver='cholesky')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_lin.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 0.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# fit time\n",
    "ridge_fit_time = model_fit(grid_lin.best_estimator_, X_train_ohe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2151.677061057322"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best CV score\n",
    "(-grid_lin.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add metrics to dict\n",
    "metrics_append('Ridge', ridge_CV_time, ridge_fit_time, (-grid_lin.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll implement GridSearchCV to find the best parameters for LightGBM using the scikit-learn API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting categorical columns locs\n",
    "col_indices = [X_train_oe.columns.get_loc(col) for col in cat_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = lgb.LGBMRegressor(\n",
    "    application='regression',\n",
    "    metric='rmse',\n",
    "    categorical_feature=col_indices  # use built-in encoding\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "params = {\n",
    "#     'learning_rate': [0.1, 0.01, 0.001]  # has no impact\n",
    "#     'max_bin': [255],\n",
    "    'n_estimators': [300],  # leaving only best params to speed up the notebook :)\n",
    "    'num_leaves': [400],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_gbm = GridSearchCV(gbm, params, cv=3, n_jobs=-1, scoring='neg_root_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 31.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# get CV time and find best model\n",
    "gbm_CV_time = model_fit(grid_gbm, X_train_oe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(application=&#x27;regression&#x27;,\n",
       "              categorical_feature=[1, 3, 5, 8, 9, 10, 16], metric=&#x27;rmse&#x27;,\n",
       "              n_estimators=300, num_leaves=400)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(application=&#x27;regression&#x27;,\n",
       "              categorical_feature=[1, 3, 5, 8, 9, 10, 16], metric=&#x27;rmse&#x27;,\n",
       "              n_estimators=300, num_leaves=400)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(application='regression',\n",
       "              categorical_feature=[1, 3, 5, 8, 9, 10, 16], metric='rmse',\n",
       "              n_estimators=300, num_leaves=400)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best model\n",
    "grid_gbm.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 10.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# fit time\n",
    "gbm_fit_time = model_fit(grid_gbm.best_estimator_, X_train_oe, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1453.632059389359"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best CV score\n",
    "(-grid_gbm.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result is significantly better than RMSE for linear model. We can also try adjusting n_estimators in order to control overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = grid_gbm.best_estimator_.get_params()\n",
    "best_params['n_estimators'] = 600\n",
    "best_params['verbose'] = -1\n",
    "\n",
    "lgb_metrics = lgb.cv(best_params, lgb.Dataset(X_train_oe, y_train), nfold=3, metrics='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lgb_metrics['rmse-mean'])\n",
    "plt.ylim((1450, 1455))\n",
    "plt.title('LGBM RMSE on CV vs. n_iterations')\n",
    "plt.xlabel('n_iterations')\n",
    "plt.ylabel('RMSE, cross-val')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best `n_estimators` parameter range is around [200, 300]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also interesting to see which features the model considers to be the most important for predictions.   \n",
    "Top-5 includes **power, postal_code, age, last_seen, date_crawled**. The \"engineered\" attribute **age** turned out to be the third most important."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgb.plot_importance(grid_gbm.best_estimator_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding to comparison\n",
    "metrics_append('LightGBM', gbm_CV_time, gbm_fit_time, (-grid_gbm.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add CatBoost to compare it with LightGBM in terms of quality. CatBoost regressor doesn't overfit, so we can use a larger number of iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set params\n",
    "cbr_params = {\n",
    "    'learning_rate': 0.2,\n",
    "    'max_leaves': 400,\n",
    "    'thread_count': -1, \n",
    "    'loss_function': 'RMSE',  \n",
    "    'grow_policy': 'Lossguide',\n",
    "    'verbose': 200,\n",
    "    'langevin': True,\n",
    "    'early_stopping_rounds': 50,\n",
    "    'boosting_type': 'Plain',  # should increase speed\n",
    "    'per_float_feature_quantization': '14:border_count=1024',  # increase age importance\n",
    "    'leaf_estimation_iterations': 5  # provides better results on data with small # of features\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare params and function for cross-val\n",
    "from catboost import cv\n",
    "cv_cbr_params = cbr_params.copy()\n",
    "cv_cbr_params.pop('per_float_feature_quantization')\n",
    "cv_cbr_params.pop('thread_count')\n",
    "\n",
    "cv_cbr_params.pop('verbose')\n",
    "cv_cbr_params['logging_level'] = 'Silent'\n",
    "\n",
    "\n",
    "@timeit\n",
    "def cbr_cv() -> pd.core.frame.DataFrame:\n",
    "    return cv(pool=train, params=cv_cbr_params, plot=True, as_pandas=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbr = CatBoostRegressor(**cbr_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = Pool(X_train_te, y_train)\n",
    "test = Pool(X_test_te, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3743842912c2416cb6c3b83119fc76c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 161.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# perform CV\n",
    "cbr_cv_time, cbr_cv_results = cbr_cv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 3935.5677453\ttotal: 105ms\tremaining: 1m 45s\n",
      "200:\tlearn: 1338.1052191\ttotal: 19s\tremaining: 1m 15s\n",
      "400:\tlearn: 1219.4804306\ttotal: 36.9s\tremaining: 55.1s\n",
      "600:\tlearn: 1140.4175549\ttotal: 54.9s\tremaining: 36.4s\n",
      "800:\tlearn: 1082.1560131\ttotal: 1m 12s\tremaining: 18s\n",
      "999:\tlearn: 1036.3341237\ttotal: 1m 29s\tremaining: 0us\n",
      "Execution time: 90.00 seconds\n"
     ]
    }
   ],
   "source": [
    "# count fit time\n",
    "cbr_fit_time = model_fit(cbr, train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1475.2665030025446"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# best CV RMSE for CatBoost\n",
    "min(cbr_cv_results['test-RMSE-mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross-validation result is a bit worse than that for LightGBM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what features are in the CatBoost feature imortance top and compare with LGBM. **power** and **age** are also in top-5. However, unlike LGBM, CatBoost does not consider **last_seen** and even more so **postal_code** to be relevant. At the same time, **age, model, age_type** are relevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EBgZqBAAAuXLJ++70aMU38/z5c/Lly/dRy9TR0fnkm1J8zHrr6+t/lHK+FkqlksTERNlYRAghhNAiuXPnBkBPTy/DvLt37wagZMmSmSr7wYMHAGmOlkhNO3TokCotJSWF8PBwTExMcHFx4ezZsxw+fJiUlBQqVKhAnTp10NX9at93f7DP/s34+/ujUCj4559/6NGjB9bW1jRp0gSANWvW4OLiQuHChbGxsaFbt27cunVLo4z58+fzzTffULhwYerVq8fRo0c1xtSnNSfg/v379O3bFwcHB8zNzSldujQdO3YkMjISgAoVKnDp0iWOHDmCQqFAoVCoyly5ciUKhYLDhw8zbNgw7OzscHBwACAqKophw4ZRpUoVChcujK2tLV27dlWVm3p+165dAWjWrJmq/NS/zGnNCXj48CH9+/enVKlSWFhYULNmTY1eitT7nDVrFkuWLMHR0RFzc3Pq1q3LmTNnMt0uz549o2jRoowYMULj2O3btzExMSEwMFCVFhsby8iRIylXrhzm5uZUqlSJmTNnolSqr9Yza9YsGjVqhK2tLYULF8bFxYVNmzZpXEOhUDB8+HDWrl2Lk5MT5ubm/P7775muvxBCCCE+rejoaA4cOEDhwoUpV66cxvGwsDD8/f0ZO3Ys7dq1o3fv3lhZWdG7d+9MlW9qagqg9vyUKjXt6tWramlPnjzB2tqawYMH4+rqip+fH2PGjMHDwwNXV1du3779PreaI2RbT4CXlxclSpRg7NixpKSkMH36dCZNmkTLli3p0qULjx49IiQkhO+++46IiAgUCgXwZrjO8OHDcXZ2pm/fvkRFReHp6YlCoaBo0aLvvGaXLl24dOkSvXr1wsrKikePHrF//35u3bqFtbU1/v7+jBgxgnz58jFs2DAAzM3N1cr48ccfMTU1xcfHhxcvXgDw559/cuLECVq1akWxYsWIiopi4cKFuLu7c+LECQwNDalZsya9e/dm3rx5DBs2DHt7ewBKly6dZl1fvnyJu7s7169fp2fPnlhbWxMeHk7fvn2Ji4ujT58+avnXrVvHs2fP8Pb2RkdHh19//ZXOnTtz9uxZVdT+LkZGRri7uxMWFsbkyZPVIvwNGzaQkpJC27ZtAXjx4gVubm7cvXsXb29vihcvzokTJxg/fjz37t1jypQpqnPnzp1L06ZNadu2LYmJiWzcuJGuXbuyZs0aGjdurFaHiIgIwsLC6NmzJ6amplhZWWVYbyGEEEJ8PAkJCWmmJyUl0bNnT169esXo0aNJSkoiKSlJLc/69etVQ3oAvvnmG2bNmoWBgQGJiYkZXtvFxYUNGzYQGBhItWrVVKMBHj9+zOzZswGIi4tT1TH1Af/8+fNcuXKFmTNn0qRJE+Lj4wkKCmLFihV07tw5w5WFPiVtHtGQbUFA+fLlWbBgAfDmTXqlSpXw8/NTPXzDmzfmderUYeHChQwbNozExEQmTZpE5cqV2bJli2qcV7ly5ejbt+87g4DY2FhOnDjBxIkTGTBggCp96NChqp/d3d2ZNGkSJiYmtG/fPs1yChYsyObNm9Uekhs1akSLFi3U8jVp0oSGDRuyefNmOnTogI2NDTVq1GDevHm4urpSu3btd34/S5Ys4fLly4SEhNCuXTsAunXrhpubG5MmTeL7778nf/78qvy3bt3izJkzqmDJzs6OTp06sXfvXlVPS0Y6dOjA2rVr2b9/Pw0aNFClr1mzhho1aqjG2gUHB3Pz5k0iIiJUXXze3t4UKVKEoKAg+vfvT/HixQE4deqU2ri+Xr164eLiQnBwsEYQ8O+//3L06FHKlCmTqfoKIYQQ4uNKa/y+Uqlk7NixHD9+HA8PD5ycnNLMN27cOMaNG8fTp0+5fPkys2fP5rvvvmPq1KlUrVo1w2tXrVqVKlWqcPz4cWrXro2zszPJyckcOHBA1Uugo6Ojunbq8qOvX7+mV69e1KxZk6dPn6Kjo8OgQYM4e/YsZ86cYcuWLTg6On7At/L+SpUqlS3XzYxsCwK8vb1VP2/ZsgWlUknLli2Jifnfxh4WFhaULFmSQ4cOMWzYMP78808eP37MTz/9pDbRo127dowaNeqd1zMwMEBfX5/Dhw/TuXNn1cNyVnXp0kVjHNzbD7lJSUk8ffqUEiVKUKBAAc6dO5flyckAe/bswcLCgjZt2qjScufOTe/evenevTtHjhxRe7hv1aqV2j3VqFEDeLNubma5urpSpEgR1q5dqwoCLl68yN9//01QUJAqX3h4OM7OzigUCrX2cnV1ZcaMGRw9elQVuLz93cTGxvL69WucnZ1Zv369xvVr1qwpAYAQQgiRjf47uVapVDJ48GB27dpFmzZtCAoKytQ4ewcHBxo0aECNGjUYN24cx44dy9Q8yg0bNjBr1izCwsIIDw8nf/78uLm50adPH2rUqIGZmZmqjs+fP1ed16FDB426u7u789dff3H37l2aNWuWmdvPUbItCHh70sf169dJSUmhcuXKaeZNfeBPjfxsbW01jmc0dCRPnjyMGzcOPz8/SpUqRZUqVWjSpAkdOnTAwsLiveqd6uXLlwQGBrJq1Sru3LlDSkqK6lh8fHymy35bdHQ0JUqU0PiHljqM6L8ReOqb91SpAUFsbGymr6mrq0vbtm1ZtGgRL168wNDQkHXr1pE3b161no7r16/z999/pzvR5+HDh6qfd+7cyfTp07lw4QKvXr1Spevo6GicJ8umCiGEENnr7eErSqWSvn37snbtWtq0acO8efMyNSH47bKqVKnCjh07uH37NhUrVszUOX5+fvj5+amlp86hrFy5sqqOZcqUQU9Pj9evX2Nubq4x9Ca19yA5OVmrh+Vkl2wLAt5+Q6xUKtHR0WH9+vVp/uX6WCvw9O3bl6ZNm7J161b27dvHpEmTCAwMZPPmzXzzzTdZrncqHx8fVq5cSZ8+fahWrRrGxsbo6OjQrVs3jYmyn0p6/yjfDkgyo0OHDgQFBbFt2zbatGnDunXraNy4MQUKFFDlUSqV1K1bl0GDBqVZRmpwcPToUTp27EiNGjWYPn06hQsXJnfu3KxcuZJ169ZpnJfWdyuEEEKIzy81AFi9ejWtWrXKcgCQ6t69ewCZmp/4LqnPDa1bt1al5c2bl2rVqnHs2DH++ecfnJ2d1c65fPkygMwxTIdWLBFqa2tLSkoK1tbW2NnZpZsvtZvnxo0b1KlTR5WenJxMVFRUmjPV07rWgAEDGDBgANeuXaN27doEBwerdqFL6w11RjZt2kTHjh2ZNGmSKi0hIYG4uLgsl5XK0tKSv//+G6VSqdYb8O+//6qOfwoODg5UrFiRdevWUbRoUW7dusXUqVPV8tja2vL8+fMMN/7YvHkzefPmZePGjaoNRYAvZhdnIYQQIidSKpX069eP1atX4+HhQUhISLoBwNOnT7l3716aY9+XL1/On3/+iZWVldoojqSkJG7cuEHu3Lk1RnfEx8djbGyslrZp0yZWrFhB5cqVNYb1dO/enWPHjjFlyhTWrl2ret64cuUKq1atIn/+/GrzHMX/aEUQ0KxZM8aPH09AQAAhISFqD+IpKSk8efIEExMTKlWqhImJCUuXLsXT01M1TGjt2rUZDnt58eIFurq6at1Btra2GBkZqQ1TMTQ0zPLDu56ensYb95CQEF6/fq2WltqjkZnyGzZsyL59+9i4caNqXkBycjIhISEYGRlRs2bNLNUxK9q3b89PP/2Evr4+JiYmNGzYUO24h4cHU6ZMYe/evdSvX1/tWGxsLEZGRuTKlQs9PT10dHTUvofIyEi1lQOEEEIIoV0CAgIIDQ3FyMgIOzs7pk2bppHHzc2NihUr8vjxY6pVq0alSpUoVaoURYsWJTY2ljNnznDu3Dny58/PTz/9pHbunTt3qFatGpaWlly4cEHtWIMGDShWrBj29vbkzZuX06dPc/jwYWxsbFiyZIlGMNK6dWu2bNnCpk2bqFWrFvXq1SM+Pp4tW7aQkJDA3Llz33se6NdOK4IAW1tb/Pz8GD9+PFFRUbi5uWFkZERkZCRbt27Fy8uLAQMGoK+vz8iRI/Hx8aFZs2a0bNmSqKgoVq1aha2t7Tvf4l+9epUWLVrQsmVLSpcuTa5cudi6dSsPHjygVatWqnyOjo4sXLiQadOmUaJECczMzHBxcXln/Rs3bsyaNWswNjamTJkynDx5koMHD2JiYqKWr0KFCujp6fHrr78SHx9Pnjx5qFOnDoUKFdIo08vLiyVLltC3b1/Onj2LlZUVmzZt4vjx4/j7+6utDPSxtW3blp9++omtW7fSvXt3jS68gQMHsmPHDtq3b0+nTp1wdHTk+fPnXLx4kc2bN3P+/HlMTU1p1KgRwcHBtG7dmrZt2/Lw4UMWLFiAra0tf//99yervxBCCCHeX1RUFPBmD6Hp06enmcfKyoqKFStiZmbG8OHDOXz4MAcOHODx48fo6+tjZWVF37596dGjh8ZL0Xdp2bIlW7Zs4dSpUyQlJWFtbc2PP/7IwIEDNXoI4M0IjoULF1KtWjVWrFjBkiVLyJMnD9WqVWPo0KFp7nws3tCKIABgyJAhlCxZkjlz5hAQEABAsWLFqFevHk2bNlXl69WrFykpKfz222+MGTOG8uXLExoayogRI9456aN48eK0adOGgwcPsmbNGvT09LC3t2fJkiVqk159fHyIjo4mKCiIp0+fUrNmzQyDgClTpqCnp8e6det49eoV1atXJzw8XC24gDerHc2YMYPAwEAGDBjA69ev2bJlS5pBgIGBAVu3bmXcuHGEhoby9OlT7OzsCA4OxtPTM1Pf6fsyNzenXr167N69O82lUg0NDdm2bRuBgYGEh4ezevVq8ufPj52dHSNHjlT9I3VxcWHWrFnMnDkTX19frK2tGTduHFFRURIECCGEEFpqzpw5zJkzJ1N58+XL984VGhMSEjQWM7G2tk53BIevry++vr6Zriu8WSCmX79+9OvXL0vn5XQ6sbGxWZs5qoWUSiUlS5akWbNmaktZivfn6enJxYsX+fPPP7O7KukqueouMa8+z8RrIYQQIieI9S72UctLDQIsLS1lhR4tozU9AZmVkJBAnjx51Ib+hIaG8uTJE+ny+Uju3bvH7t271TZu00bXOhXJ7irkaPKLXTtIO2gPaQvtIW0hRMa+uCDgjz/+YNSoUXh4eGBiYsK5c+dYvnw5Dg4OeHh4ZHf1tFJiYiJPnjx5Zx5jY2Pu37/PiRMnWLZsGblz51bb0E0IIYQQQnw9vrggwMrKimLFijFv3jyePHlCwYIF6dChA+PGjUNfXz+7q6eVTpw4keFOecHBwQD069eP4sWLM2fOnCxtoiaEEEIIIb4cX1wQYG1tzerVq7O7Gl+UChUqEB4e/s48ZcqUoXDhwp980rEQQgghhMh+X1wQILJOoVBkuLHXl0gmBmsDQyAmuyshpB20iLTF+/jYk1GFEBnTzTiLEEIIIcSnt2bNGgYPHoyrqyvm5uYoFIp37jJ/7do1+vbtS+XKlSlcuDBly5bFw8ODXbt2Zem6SqWSkJAQ6tSpQ5EiRbC0tKRp06Zs3749U+ffvHmTYsWKoVAoGDJkSJauLUR2kSBACCGEEFrh559/ZsmSJURHR2c4L+3UqVPUqlWLtWvXUq5cOX744Qfq1avHmTNn6Nq1K/Pnz8/UNVNSUvDy8sLHx4enT5/y/fff06pVK65evUqnTp0ICQl55/lKpZI+ffpk+h6F0BYSBAghhBBCK8yaNYvz589z7do1unXr9s68AQEBvHz5kqVLl7J8+XLGjRtHcHAwhw8fxsjIiKVLl/Lq1asMr7l582Y2b96Mk5MTR48eZdq0afz6668cP34cS0tLxowZQ2RkZLrnBwcH88cffzB69Ogs368Q2UmCACGEEEJoBVdXV6ysrDKV9+bNm+jo6NCwYUO1dCsrK8qWLcurV694/vx5huVs27YNgKFDh2JgYKBKNzU1pW/fvrx69SrdIUlXrlxh0qRJDBkyhAoVKmSq3kJoCwkChFZSKpUkJCRkdzWEEEJoqbJly5KSksKePXvU0qOjo7l06RKlSpXCxMQkw3IePHgAvFl98L9S0w4dOqRx7PXr1/Tp04cSJUowfPjw97kFIbKVBAFfKX9/fxQKBVeuXMHLywtLS0tsbW0ZMWKE2sN1cnIyU6dOxdHREXNzcypUqMCECRPUulBHjRqFra0tKSkpqrThw4ejUCiYO3euKu3BgwcoFAoWLlyoSnv16hWTJ0+mUqVKmJubU65cOcaOHavRRatQKBg+fDhr167FyckJc3Nzfv/990/x1QghhPgK+Pn5YWFhQdeuXencuTPjx4+nf//+1KpVCxsbG/z9/TNVjqmpKUCaQ35S065evapxLDAwkHPnzjF79mzZp0h8kSQI+Mp5eXmRkJDA2LFjadiwIfPmzWPQoEGq4wMGDGDy5Ml88803TJ48mZo1axIYGKg2FtPZ2ZknT55w6dIlVdqxY8fQ1dXl2LFjamkANWrUAN68ze/YsSO//fYbTZo0YerUqXz33XfMnj07zd2IIyIiGDVqFC1btmTKlCmZ7hIWQgiR89jb27Nnzx4qVqzIli1bmDFjBitWrEBPT48OHTpQvHjxTJXToEEDAGbMmKH2kuzx48fMmTMHgLi4OLVzLly4wNSpUxk4cCCOjo4f54aE+Mxkn4CvnLW1NaGhoQD07NkTY2NjFixYwIABA0hJSSE0NJQuXboQFBQEQI8ePShUqBCzZs0iIiKCOnXq4OzsDLx5yHdwcCAuLo6LFy/SvHlzjh49qrrW0aNHKViwIGXKlAFg3bp1HDhwgG3btqnKAHBwcGDIkCGcOHGC6tWrq9L//fdfjh49qjpfCCFEzpDW8M/k5GQAkpKS0jx+5swZvL29KVu2LLt378bOzo6HDx+yaNEi/Pz8OHDgAIsWLcrw2s2aNWPFihUcOXIEZ2dn6tatS3JyMjt27KBQoUIA6OrqquqQmJjIDz/8gI2NDYMGDVJLhzfDhGQ46/+kfi+pf+Y0efPmze4qpEuCgK9cz5491T736tWLBQsWsGfPHtXwnn79+qnl6d+/P7NmzWL37t3UqVMHMzMz7O3tOXr0KN27d+fEiRPo6ekxcOBAwsPDuXbtGiVLluTYsWM4OTmho6MDQHh4OKVLl8be3p6YmP9tnlOnTh3gzRjLt4OAmjVrSgAghBA5UHR0tEZabGws8OaN/H+PJycn06NHD1JSUpg4cSJ58+YlJiYGXV1devToweXLl/n999/ZvXs333zzTYbXnzp1KkuXLmXnzp0sX74cIyMjXF1d+f7772ndujUKhUJVh/nz53Pp0iUWLlyomk8A8PDhQwCePXuW5v3kdPfv38/uKmSLUqVKZXcV0iVBwFeuRIkSap9tbW3R1dUlKioKePN24795LCwsKFCggNovMWdnZ9Xkq2PHjlGpUiUqVapEwYIFOXr0KIUKFeKvv/6ibdu2qnOuX7/O5cuXKVmyZJp1S/2FmSqtSVlCCCG+fpaWlhppCoUCABMTE43jly5d4s6dO7i5uaX5kOXq6sqBAwe4c+cO7u7umarDhAkTmDBhglrakSNHAPj2229VdYiKikKpVKY5rBVg48aNbNy4kSZNmrBkyZJMXftrlpiYyP3797GwsJC5E1pGgoAcJvUtfUZp/+Xk5MTSpUu5efMmx44dw9nZGR0dHZycnDh27BhFihRBqVSqDftRKpU4ODgwefLkNMssVkx9m/i3l2YTQgiRc6Q1ZCJXrjePKLlz59Y4nvr/rcePH6d5buoYfkNDww8ajrFp0yYA2rZtqyqnfv36qmFCb7t//z67d+/G3t6e6tWrU7FiRa0eCvK56evry/ehZSQI+Mpdv34dGxsbtc9KpRIrKytSUlJQKpVcu3aN0qVLq/I8ePCAuLg4tTcvqQ/3+/fv58yZMwwePBh4Mwl40aJFFClShHz58qlNkLK1teWvv/7CxcUlU4GGEEIIkRlly5bF2NiYEydOsG/fPurVq6c6duvWLZYtW4aOjo7ai6mkpCRu3LhB7ty5sbW1VSsvPj4eY2NjtbRNmzaxYsUKKleuTLNmzVTp/x1mm+rQoUPs3r2bmjVrMmPGjI9xm0J8UhIEfOXmz5+v9ssxdfvz1NUQJkyYwJw5c5g5c6YqT3BwMACNGjVSpdnY2FC0aFFmz55NUlISTk5OwJsgYMyYMWzatIkqVaqo3twAeHh4sHv3bpYuXYqXl5davV6+fIlSqSRfvnwf9X6FEEJ8uZYtW6Zaae7ixYsALF++nMOHDwNvXkh16dKFPHnyMGHCBAYPHkybNm1o3Lgx9vb23L9/n61bt/Ls2TM8PT3VhqPeuXOHatWqYWlpyYULF9Su26BBA4oVK4a9vT158+bl9OnTHD58GBsbG5YsWYKent5n+gaE+HwkCPjKRUZG0qFDBxo0aMDJkydZu3Ytbdu2Ve1s2LFjR5YsWUJcXBw1a9bk9OnThIaG4ubmpprAm8rZ2ZkNGzbg4OCgGqv5zTffkC9fPq5evUqbNm3U8nfo0IHw8HCGDBmimgT8+vVr/v33X8LCwti4cSOVKlX6LN+DEEII7Xfs2DHVinapjh8/zvHjx1Wfu3TpArxZAtva2pq5c+dy8uRJdu/eTb58+ahYsSKenp5Uq1Yt09dt2bIlW7Zs4dSpUyQlJWFtbc2PP/7IwIEDNXoIhPhaSBDwlVu8eDGTJ09m/Pjx6Onp0bNnTyZOnKg6PmvWLGxsbFi1ahVbt27FwsKCoUOHMmLECI2yUoOAt7tXc+XKRdWqVTlw4IBaOryZdLxy5Upmz57N6tWr2bp1KwYGBtjY2PDDDz+kO2FYCCFEzjRnzhzV2vyZUbduXerWrauRnpCQoLFCj7W1tWrFof/y9fXF19c3S3X9r9q1a6dbvhDaSCc2NjYl42ziS+Pv709AQADXrl1T7Yb4tSm56i4xr5TZXQ0hhBAfKNa7WMaZsiA1CLC0tJTJqNlM2kJ7SU+A+GJd61Qku6uQo8kvdu0g7aA9pC2EEF8S3eyugBBCCCGEEOLzkiBACCGEEEKIHEaCgK+Ur68vsbGxX+18ACGEEEII8f5kToD4YsnEYG1gCMRkdyW0wsee2CiEEEJ8StITIIQQH1lKSgqbN2/G3d2d0qVLU6RIEapUqcLgwYO5efPme5WZmJhIrVq1UCgUVK1aVeN4QkICo0aNomnTppQpUwYLCwvs7e1p3LgxK1asICkp6QPvSgghxNdEggDx0UVGRqJQKFi5cmWWzz106BAKhYJDhw59gpoJ8Xn4+fnRpUsXrl69ipubG7169cLa2pqlS5dSu3Zt1U6oWREQEMCNGzfSPf78+XMWLVqEjo4OjRo1ol+/fri7u3Pnzh369+9P+/btUSql50wIIcQbMhxICCE+ovv37zNnzhwsLS05fPgwBQoUUB0LDg5m9OjRBAcHExwcnOkyT58+zcyZM5kyZQrDhw9PM0/BggWJiopCX19fLT05ORkPDw/27dvHnj17aNy48fvdmBBCiK+K9AQIIcRHFBUVhVKpxMnJSS0AAGjSpAkAjx49ynR5CQkJ9OnTBycnJ3r06JFuPl1dXY0AAN7s6u3u7g7A9evXM31dIYQQXzfpCRBCiI+oZMmS6Ovrc/z4ceLj4zE2NlYd27lzJwAuLi6ZLm/ChAncunWLNWvWoKOjk+X6KJVK9u7dC4CDg0OWzxdCCPF1kp6Ar5S/vz8KhYKrV6/Sq1cvrKysKFmyJD///DMpKSncunWLjh07Ymlpib29PbNmzVI7/+HDh/Tv359SpUphYWFBzZo1WbVqlcZ1YmNj6dOnD1ZWVlhZWfHDDz8QFxeXZp2uXLlCly5dsLGxwcLCAldXV7Zv3/5J7l+I7GJiYsJPP/3ErVu3qFatGkOHDuWnn36idevWjBs3jh49etCrV69MlXXkyBHmzp3LmDFjsLW1zdQ5iYmJ+Pv7M3nyZIYPH46TkxN79uzB09MzS8GHEEKIr5v0BHzlvL29KV26ND/99BO7d+9m+vTpFCxYkCVLllCnTh3GjRvHunXrGDNmDJUrV6ZmzZq8fPkSd3d3rl+/Ts+ePbG2tiY8PJy+ffsSFxdHnz59gDcroHTq1Injx4/TrVs37O3t2bp1q+r42y5dukTjxo0pWrQoQ4YMwdDQkLCwMDw9PVm2bBnNmjX73F+NEJ9Mv379KFq0KAMHDmTRokWqdGdnZ9q0aUOuXBn/6n3+/Dn9+vWjWrVq9O7dO9PXTkxMJCAgQPVZR0eHAQMG8NNPP2XtJoQQQnzVJAj4yn377bfMnDkTAC8vLypWrIifnx8//fQTgwcPBqB169aULVuWFStWULNmTZYsWcLly5cJCQmhXbt2AHTr1g03NzcmTZrE999/T/78+dm+fTtHjx5lwoQJDBw4EIDu3burxh+/beTIkRQvXpz9+/eTJ08eAHr06EGTJk0YN26cBAHii5eQkKD6+ZdffmHmzJn4+PjQunVrChQowF9//cXYsWNxd3dn4cKFGU7Q9fX15d69e6xcuZLExES1YykpKarrpR5L/TNXrlzcu3cPpVLJvXv32L17N/7+/pw4cYKVK1eSP3/+j3nb4i3/bQuRfaQttEdOb4u8efNmdxXSJUHAV65Lly6qn/X09HB0dOT27dt07txZla5QKLCzs1OtX75nzx4sLCxo06aNKk/u3Lnp3bs33bt358iRIzRp0oQ9e/aQK1cuunXrpnaN3r17c+zYMVXakydPiIiIYNSoUTx79oxnz56pjtWrVw9/f3/u3LlD0aJFP8VXIMRnER0dDcCJEyeYNm0anTp1wsPDg9evX/P48WOKFi3KlClT8PDwwM/P753j80+fPs2yZcsYOHAg+vr6qrJTJSUlaaTdv38/zbLq169PSkoKvr6+TJw4kQEDBnzgnYqMpNcW4vOTttAeObUtSpUqld1VSJcEAV+54sWLq302NjYmb968mJqaaqQ/efIEePMwU6JECXR11aeM2Nvbq46n/lm4cGGMjIzU8tnZ2al9vn79OikpKUyaNIlJkyalWc+HDx9KECC+aJaWlgAsWLAAeLMSUGra23ns7e25cOECJiYm5MuXL82yduzYAUBQUBBBQUEaxyMjI6latSrGxsb89ddf3L9/HwsLizRXB4I3vX2+vr5cuHBBo07i40lMTMywLcTnIW2hPaQttJcEAV85PT29TKXBmyEGn0LqBkUDBgygfv36aeYpUaLEJ7m2EJ9Lapdv6t/3uLi4NLuBHz9+jK6uLkZGRqqhcf9VsWJFtd66ty1fvhxjY2NatGiBgYGB6n+q+vr66XY7R0ZGApAnTx6t7pr+WryrLcTnJW2hPaQttI8EAUKDpaUlf//9N0qlUq034N9//1UdT/3z4MGDPHv2TK034OrVq2rl2djYAG+GFLm6un7ayguRzZycnJg/fz6zZ8+mefPmansFLFq0iNu3b+Pk5KQKAGJiYoiJicHU1FTVQ+fq6pruv5Xly5djYWGhWtErdW7A5cuXKVWqFIaGhmr5X7x4wejRowFo2LDhR71XIYQQXy5ZIlRoaNiwIffv32fjxo2qtOTkZEJCQjAyMqJmzZqqfMnJyWqrn7x+/Zp58+aplVeoUCFq1arF4sWLuXfvnsb1srJxkhDazsPDgxo1anDp0iWqVKnCwIEDGTNmDM2bN2fo0KEYGBioDYsLCQmhWrVqhISEfNB1N2/eTOnSpWnbti3Dhg1j3Lhx9OrVi/Lly/P777/j7OxM3759P/T2hBBCfCWkJ0Bo8PLyYsmSJfTt25ezZ89iZWXFpk2bOH78OP7+/qrVRZo2bYqTkxPjxo0jKiqK0qVLs2XLFuLj4zXKnD59Ok2aNKFGjRp07doVGxsbHjx4wB9//MHt27c5cuTI575NIT4JPT09wsLCmD17NmFhYaxfv57ExETMzc1p164dw4YNo3Tp0h/9ug0bNuTRo0ecPHmSkydP8vz5c4yNjSlXrhytW7fm+++/z9TSpEIIIXIG+T+C0GBgYMDWrVsZN24coaGhPH36FDs7O4KDg/H09FTl09XVJTQ0lJEjR7J27VrgTWDw888/U6dOHbUyy5Qpw4EDB5gyZQqrVq3i8ePHFCpUiAoVKuDj4/NZ70+ITy1PnjwMGTKEIUOGZJjX19cXX1/fTJcdGxubZrqjoyNOTk6ZLkcIIUTOphMbG/tpZoMK8YmVXHWXmFfK7K6GEADEehfLlusmJCQQHR2NpaWlTLrLZtIW2kPaQntIW2gv6QkQX6xrnYpkdxVyNPnFLoQQQny5ZGKwEEIIIYQQOYwEAUIIIYQQQuQwEgQIIYQQQgiRw8icAPHFkonB2sAQiMnuSmRZdk3iFUIIIbSF9AQIIXKslStXolAo3vlf8+bNMyzn1KlT/PDDDzg7O2NjY4OFhQWVKlXC29ubP//8853nbtmyBQ8PD2xtbbGwsKBixYp0796dW7dufazbFEIIITRIT4AQIseqUKECI0aMSPPY5s2buXTpEvXr18+wnGPHjnHgwAGqVKlC7dq1MTQ05ObNm+zYsYPw8HDmzJlDhw4d1M5JSUlhyJAhLFmyBFtbW1q3bo2RkRF3797lyJEjREdHU7x48Y9yn0IIIcR/yT4BQs3du3dZsmQJbm5uVKxYMbur804yHEi8r4yGAyUmJlKmTBni4+O5ePEi5ubm78yfkJCQ5jKpFy9epF69euTPn58rV66go6OjOjZnzhx8fX3p0aMHAQEB6OnpqZ2bnJycqR1+ZalW7SFtoT2kLbSHtIX2kuFAQs29e/cICAjgwoUL2V0VIbLNtm3bePz4MY0bN84wAADS/R+bg4MD9vb2PHz4kPj4eFX6y5cvCQgIwMbGhilTpmgEAECmAgAhhBDifcn/ZYQQ4j+WLVsGQJcuXT6onBs3bnD16lWKFy9OgQIFVOn79u0jNjYWT09PXr9+zfbt27l27RoFChTA1dWVEiVKfNB1hRBCiIxIT0A2iIqKYtiwYVSpUoXChQtja2tL165diYyM1Mj7119/8d1331G4cGEcHByYNm0aK1asQKFQaOTfs2cPTZs2pWjRohQvXpx27dpx6dKlTNfr0KFD1K1bF4B+/fqpJkauXLmSyZMnY2ZmxqNHjzTOGzRoEFZWViQkJABvxlm3b9+effv2UatWLSwsLKhevTqbN2/WODc2NpaRI0dSrlw5zM3NqVSpEjNnzkSplGE+IntERUVx8OBBihUrRoMGDbJ07unTp/H392fixIn06tWLOnXqABAYGKiW7+zZswDo6elRs2ZNunTpwvjx4xk6dChVqlTBz8/vo9yLEEIIkR7pCcgGf/75JydOnKBVq1YUK1aMqKgoFi5ciLu7OydOnMDQ0BCAO3fu0KxZM3R0dBgyZAiGhoYsX74cfX19jTJXr15Nnz59qF+/PuPGjePly5csXLiQJk2aEBERgbW1dYb1Kl26NKNGjWLy5Ml4eXnh7OwMQPXq1XF2dmbq1Kls3LiRXr16qc5JTExk06ZNNG/eXG1IxLVr1/D29qZbt2507NiRlStX4uXlxYYNG1SBxosXL3Bzc+Pu3bt4e3tTvHhxTpw4wfjx47l37x5Tpkz5oO9ZiPexcuVKlEolHTt2THOYzrucPn2agIAA1Wdzc3Pmzp1LvXr11PKlBtPBwcF888037Nu3D3t7e86fP8/gwYP57bffsLW1pXv37h9+Q0IIIUQaZGJwNnj58iUGBgZqaX/88QcNGzZk7ty5qlVEfHx8mD9/PgcPHlRN0n3y5AmVK1fmyZMnnDt3Dmtra549e0a5cuXw8PDg119/VZX54MEDqlSpQsuWLdXS3+XPP/+kbt26BAcH4+npqXasUaNGKJVKfv/9d1Xali1b6Ny5M1u2bKF27drAm56A6Oholi1bplpeMT4+nmrVqmFubk5ERAQA06ZNY+bMmURERFCyZElVmePHjycoKIhz5869c3UUmRgs3te9jqZppiuVSqpWrcqdO3c4fvx4poLntLx8+ZIbN24wd+5c1q9fj5+fH3379lUd//HHH1mxYgUGBgYcO3aMwoULq46lrkhkZWXF8ePHM7xWYmIi9+/fx8LCIs0XBOLzkbbQHtIW2iOnt4U2T4aWnoBs8HYAkJSUxNOnTylRogQFChTg3LlzqiBg7969VKtWTW2VnoIFC9K2bVtCQkJUafv37ycuLo7WrVsTE/O/jZv09PSoUqUKhw4d+ij17tChA0OHDuXGjRvY2toCsG7dOooXL06tWrXU8hYpUoRmzZqpPhsbG9OhQwdmzpyp+mUQHh6Os7MzCoVCrd6urq7MmDGDo0eP0q5du49SdyHeFh0dnWb68ePHuX37NlWrVkVXVzfdfJmRP39+hg8fzu3bt/n5558pW7YsdnZ2annKlClDUlKS2nWMjIwoVqwYN2/e5OLFi+TPnz9T17t///5711V8XNIW2kPaQnvk1LYoVapUdlchXRIEZIOXL18SGBjIqlWruHPnDikp/+uMeXsFkejoaKpWrapx/n8nDV6/fh0g3U2NjI2NP0a1adWqFb6+vqxdu5YRI0YQFxfHrl276Nu3r9rShwC2trYaaalv+6OiorCwsOD69ev8/fffar0Ab3v48OFHqbcQ/2VpaZlm+oQJEwDo3r17unmyqkmTJhw5coTIyEjVULhKlSqxYsUKChUqlOZ1TExMiI6OxtTUFAsLi3eWn9PfsmkTaQvtIW2hPaQttJcEAdnAx8eHlStX0qdPH6pVq4axsTE6Ojp069btvSbEpp4zb968NB8YsjquOT0KhYLGjRuzbt06RowYwaZNm3j16tV7v61XKpXUrVuXQYMGpXk8veBAiA+VVvfs48eP2bVrFwULFqRly5bkyZPno1wrdfy/oaGh6rqpcwSuXr2qUZekpCRu3rxJvnz5KFasWKaXCtXX19fqbuecRNpCe0hbaA9pC+0jQUA22LRpEx07dmTSpEmqtISEBOLi4tTyWVpacuPGDY3zU9/8p0odmlOoUCFcXV0/qG7/fXv/Xx06dKBTp06cOXOGdevWUbFiRcqWLauR78aNG6SkpKiVd+3aNQCsrKxU9X7+/PkH11mIj2H16tUkJibi7e2dbgAQExNDTEwMpqammJr+b17Bn3/+SaVKlTTynz9/nsWLF5M7d25cXFxU6ba2ttSrV499+/axbNkytaVIZ8yYQVxcHO3atZO9AoQQQnwyskRoNtDT01MbAgQQEhLC69ev1dLq1avHyZMnOX/+vCrtyZMnrFu3TiOfsbExv/zyC0lJSRrXS2tZz/Skrkz034AkVcOGDTE1NWXmzJkcOXIk3V6Au3fvsmXLFtXn+Ph4Vq9eTYUKFVS9FR4eHpw8eZK9e/dqnB8bG0tycnKm6y3Eh1qxYgXw7r0BQkJCqFatmtqcnNRzKlWqhLe3N2PGjGHEiBG0bt0aV1dXnj59ys8//6wxyfiXX36hUKFCDBw4kPbt2+Pn50fz5s2ZPHkylpaWTJw48ePfpBBCCPH/5DVTNmjcuDFr1qzB2NiYMmXKcPLkSQ4ePIiJiYlavkGDBrF27VpatmxJr169VEuEFi9enCdPnqjesqcGAL1798bFxYVWrVphZmbGrVu32LVrF05OTkybNi1TdbO1taVAgQIsXrwYIyMj8uXLx7fffouNjQ0AuXPnplWrVsyfPx89PT3atGmTZjl2dnYMGDCAP//8k0KFCrFixQoePHhAcHCwKs/AgQPZsWMH7du3p1OnTjg6OvL8+XMuXrzI5s2bOX/+vNrbViE+ldOnT3Px4kW+/fZbypUrl+Xzhw0bxs6dO/njjz/YuXMnSqUSCwsL2rRpQ69evahSpYrGOba2tuzfv5/Jkyezd+9e9u3bh4WFBT179sTHx4dChQp9jFsTQggh0iRLhGaD2NhYRo8ezc6dO3n16hXVq1dnypQptGrVilq1ajFnzhxV3vPnzzNixAjOnDmDmZkZPXr0wNDQkBEjRnDlyhXMzc1VeQ8dOsTMmTP5448/SExMpEiRIjg7O9OrVy8cHR0zXb/t27czYcIErl69SnJyssZyoWfOnKFevXrUq1ePjRs3apxfoUIFHBwc6N27N2PGjOHq1atYW1szevRoWrRooZb32bNnBAYGEh4ezq1bt8ifPz92dna4u7vzww8/kDt37nTrKUuEivcV610su6vw0SQkJBAdHY2lpaWMt81m0hbaQ9pCe0hbaC8JAr5AI0eOZMmSJdy+ffujTfrNigsXLlC7dm21PQ3elhoErFmz5pPWQ4IA8b4kCBCfgrSF9pC20B7SFtpLhgNpuf9uLPb48WPWrFmDk5NTtgQAAMuWLcPIyEhtH4DscK1TkWy9fk4nv9iFEEKIL5cEAVquYcOG1KpVi9KlS/PgwQOWL1/O06dPGT58eJbKSUxM5MmTJ+/MY2xsrLGT8dt27NjB5cuXWbJkCT179iRfvnxZqoMQQgghhNAOEgRouUaNGrFp0yaWLl2Kjo4O33zzDb/99hs1a9bMUjknTpzI8M39f8f+/5ePjw8PHz6kYcOG+Pr6Zun6QgghhBBCe0gQoOXGjh3L2LFjP7icChUqEB4e/s48ZcqUeefxCxcuZOpamc0nhBBCCCGyhwQBOYRCofjqNuWSicH/8zVNdBVCCCHEpyebhQnxlUhISGDUqFE0bdqUMmXKYGFhgb29PY0bN2bFihVpbiSXGYmJidSqVQuFQkHVqlXTzKNUKgkJCaFOnToUKVIES0tLmjZtyvbt2z/kloQQQgjxiUgQkAP4+/ujUCiIiYnJ7qpkKLWuIuueP3/OokWL0NHRoVGjRvTr1w93d3fu3LlD//79ad++PUpl1ntOAgICuHHjRrrHU1JS8PLywsfHh6dPn/L999/TqlUrrl69SqdOnTR21xVCCCFE9pPhQEJ8JQoWLEhUVBT6+vpq6cnJyXh4eLBv3z727NlD48aNM13m6dOnmTlzJlOmTEl3RaqtW7eyefNmnJycCAsLU60wNXbsWFxdXRkzZgyNGzfG2tr6/W9OCCGEEB+V9AQI8ZXQ1dXVCAAAcuXKhbu7OwDXr1/PdHkJCQn06dMHJycnevTokW6+Xbt2ATB06FC1JWZNTU3p27cvr169YuXKlZm+rhBCCCE+PekJEOlSKpUkJibKRlBfOKVSyd69ewFwcHDI9HkTJkzg1q1brFmzBh0dnXTzPXjwACDNN/2paYcOHcpKlYUQQgjxiUlPQA4SExODl5cXlpaW2NraMmLECBISElTHFQoFw4cPZ+3atTg5OWFubs7vv/8OwKxZs2jUqBG2trYULlwYFxcXNm3apHGN1DK2bt2Ks7Mz5ubmODk5qcp527Fjx6hbty4WFhY4OjqyePHiT3fzOUhiYiL+/v5MnjyZ4cOH4+TkxJ49e/D09MTFxSVTZRw5coS5c+cyZswYbG1t35nXxMQEgMjISI1jqWlXr17N4l0IIYQQ4lOSnoAcxMvLCysrK8aOHcsff/zBvHnziI2NZd68eao8ERERhIWF0bNnT0xNTbGysgJg7ty5NG3alLZt25KYmMjGjRvp2rUra9as0RhjfuzYMbZs2UL37t0xMjJi3rx5dOnShb/++kv1wPj333/TqlUrTE1NGTlyJMnJyfj7+1OoUKHP94V8pRITEwkICFB91tHRYcCAAfz000+ZOv/58+f069ePatWq0bt37wzz16tXj/DwcGbMmEGdOnVUPUePHz9mzpw5AMTFxb3HnQghhBDiU5EgIAextrYmNDQUgJ49e2JsbMyCBQsYMGAA5cuXB+Dff//l6NGjGhuHnTp1Sm28d69evXBxcSE4OFgjCLhy5QonTpxQvUGuXbs2tWrVYv369fTq1QuAyZMnk5KSwo4dO7C0tASgefPm1KhR49Pc/Ffu7R6dXLlyce/ePZRKJffu3WP37t34+/tz4sQJVq5cSf78+d9Zlq+vL/fu3WPlypUkJiaqHUtJSVFdK/WYu7s7a9as4ciRIzg7O1O3bl2Sk5PZsWOHKqjT1dVVq6P4eFLb4b9tJT4/aQvtIW2hPXJ6W2jzkGoJAnKQnj17qn3u1asXCxYsYM+ePaogoGbNmmnuHPx2ABAbG8vr169xdnZm/fr1GnldXV3VhpCUL18eY2Njbt68CcDr16/Zt28fbm5uqgAAoHTp0tSvX5/du3d/0H3mRNHR0ekeq1+/PikpKfj6+jJx4kQGDBiQbt7Tp0+zbNkyBg4ciL6+vka5SUlJGmkxMTFMnTqVpUuXsnPnTpYvX46RkRGurq58//33tG7dGoVC8c46ig93//797K6C+H/SFtpD2kJ75NS2KFWqVHZXIV0SBOQgJUqUUPtsa2uLrq4uUVFRqrT0lnHcuXMn06dP58KFC7x69UqVntaE0eLFi2ukFShQgNjYWAAePXrEy5cvNeoDYGdnJ0HAe3g7mEpL69at8fX15cKFC+/Mu2PHDgCCgoIICgrSOB4ZGUnVqlUxNjbmr7/+4v79+1hYWKCvr8+ECROYMGGCWv4jR44A8O2332ZYR/F+EhMT1dpBZB9pC+0hbaE9pC20lwQBOVhaD/Bvv/FPdfToUTp27EiNGjWYPn06hQsXJnfu3KxcuZJ169Zp5NfT00vzeikpKR9eaZGmjLobUyfo5smT5515K1asSOfOndM8tnz5coyNjWnRogUGBgaqX+b6+vrplpk6ebxt27Za3SX6NXhXO4jPS9pCe0hbaA9pC+0jQUAOcv36dWxsbNQ+K5VK1eTf9GzevJm8efOyceNG8uTJo0p/37XfzczMMDAwSHPNellF5v39888/WFlZYWhoqJb+4sULRo8eDUDDhg1V6TExMcTExGBqaoqpqSnwZiiXq6trmuUvX74cCwsLZs2aBajPQ4iPj8fY2Fgt/6ZNm1ixYgWVK1emWbNmH3x/QgghhPh4JAjIQebPn0+9evVUn0NCQgBo0KDBO8/T09NDR0eH169fq9IiIyPZtm3be9VDT0+PevXqsW3bNqKjo1XDRC5fvqxaz15kXVhYGLNnz8bJyQkrKyvy58/PnTt3+P3333n8+DHOzs707dtXlT8kJISAgABGjBiBr6/vB127QYMGFCtWDHt7e/Lmzcvp06c5fPgwNjY2LFmyJN3eISGEEEJkDwkCcpDIyEg6dOhAgwYNOHnyJGvXrqVt27ZUqFDhnec1atSI4OBgWrduTdu2bXn48CELFizA1taWv//++73q4uvry969e2natCk9evQgOTmZkJAQypQp895l5nRNmjTh3r17nDx5kpMnT/L8+XOMjY0pV64crVu35vvvvydXrk/zT75ly5Zs2bKFU6dOkZSUhLW1NT/++CMDBw7U6CEQQgghRPaTICAHWbx4MZMnT2b8+PHo6enRs2dPJk6cmOF5Li4uzJo1i5kzZ+Lr64u1tTXjxo0jKirqvR/Yy5cvz4YNGxg9ejSTJ0+maNGiqqUpJQh4P5UqVaJSpUqZzu/r65ulHoDUid0foywhhBBCZC+d2NhYma0pvkglV90l5pUyu6uhFWK9i332ayYkJKiGc8lkr+wj7aA9pC20h7SF9pC20F7SEyC+WNc6FcnuKgghhBBCfJF0s7sCQgghhBBCiM9LggAhhBBCCCFyGAkChBBCCCGEyGFkToD4Yn2NE4OzY4KvEEIIIXIe6QkQQgvduXOH2bNn07JlS8qXL0+hQoWwt7enc+fOnDp1KktlxcbGMmnSJGrUqEHx4sUpUaIEdevWJSQkRG3X37edOXOGtm3bYmVlRdGiRWnQoAFhYWEf49aEEEIIoQUkCPhM/P39USgUxMTEpJunT58+Ght3KRQK/P39P3X1hJYJCQlh1KhR3Lx5k7p169K/f3+cnJzYvn07jRo1YuPGjZkqJzY2FldXV6ZNm0aBAgXw8vKidevWxMbG4uPjQ7t27VAq1XtTIiIiaNy4McePH6dly5Z4e3tz//59vL29mTVr1qe4XSGEEEJ8ZjIcSHywBQsWYGBggKenZ3ZX5atRuXJltm7dSq1atdTSjx49SosWLRg6dChubm7kyZPnneUsXbqUmzdv0qdPH7VgMjExkcaNGxMREcGxY8eoWbMmAMnJyQwaNAhdXV22bdtGxYoVAfDx8aF+/fpMnDiRFi1aYGVl9ZHvWAghhBCfk/QEaJGgoKAsD/XQBgsXLmTVqlXZXY2vSvPmzTUCAIAaNWpQu3ZtYmNjuXjxYobl3Lx5E4BGjRqppevr61OvXj0AHj16pEqPiIjgxo0btGnTRhUAABQoUIChQ4eSmJhIaGjo+9ySEEIIIbSIBAFaJHfu3Bm+2c0pkpOTSUxMzO5qaKXcuXMDoKenl2HesmXLArB792619MTERPbv34+BgQHVqlVTpR8+fBhAFSC8rX79+gAcOXLk/SouhBBCCK0hQUA2ioqKolKlSjg7O/PgwYM05wSk5dy5c7Rp0wZLS0uKFStG8+bN+eOPP9TyrFy5EoVCwbFjx/Dx8aFkyZJYWVkxePBgEhMTiY2NpXfv3lhbW2Ntbc3YsWNJSUlRK0OpVDJ79mycnJywsLCgVKlSDB48mNjYWFWeChUqcOnSJY4cOYJCoUChUODm5qY6Hhsby8iRIylXrhzm5uZUqlSJmTNnqo1Dj4yMRKFQMGvWLGbPno2joyPm5ub8888/7/nNfr2io6M5cOAAhQsXply5chnm79y5M1WrVmXOnDk0bdqUMWPG4OPjQ/Xq1bl16xaLFi2iSJH/7bx87do1AEqWLKlRloWFBUZGRly/fv3j3ZAQQgghsoXMCcgmN27coFmzZhQsWJDw8HBMTU0zdd6lS5f47rvvyJ8/PwMHDiR37twsXrwYd3d3tm3bRpUqVdTy+/j4YGFhga+vL3/88QdLliyhQIECnDx5kuLFizN27Fh2795NUFAQZcuWpWPHjqpzBw8ezKpVq/D09KR3795ERkYyf/58zp8/z65du8idOzf+/v6MGDGCfPnyMWzYMADMzc0BePHiBW5ubty9exdvb2+KFy/OiRMnGD9+PPfu3WPKlClqdV25ciUJCQl4eXmhr69PwYIFP+Qr/uokJSXRu3dvXr16xbhx4zLVE2BgYMDmzZsZOnQooaGhHDt2DHjTi9CrVy+qV6+ulj8+Ph4AY2PjNMvLnz+/Ko8QQgghvlwSBGSDK1eu0KJFC4oUKcLGjRtRKBSZPvfnn38mKSmJnTt3YmNjA0CHDh2oWrUqY8eOZfv27Wr5zc3NWbduHTo6OvTo0YMbN24QFBSEt7c3gYGBAHh5eVGxYkVWrFihCgKOHTvGsmXLmD9/Pm3btlWVV7t2bVq3bk14eDht27bF3d2dSZMmYWJiQvv27dWuHRwczM2bN4mIiFC9Wfb29qZIkSIEBQXRv39/ihcvrsp/584dzpw5g5mZWaa/j69Nekt2KpVK+vfvz9GjR/n+++/x8PBIN+/bHj16hJeXFzExMaxcuZJq1arx8uVLdu7cybhx49i5cye7d+8mf/78qusAvHr1Ks3yU1JSSElJISEhQTVcS4ZtZS9pB+0hbaE9pC20R05vi7x582Z3FdIlQcBndvHiRbp160aJEiVYt25dum9c0/L69Wv279+Pm5ubKgAAKFy4MG3atGHp0qXEx8erldm5c2d0dHRUn7/99ltOnjxJ586dVWl6eno4Ojpy7tw5VVp4eDjGxsbUrVtXbVlTR0dHjIyMOHTokFpwkJbw8HCcnZ01lkZ1dXVlxowZHD16lHbt2qnSmzVrlqMDAHgz3Oe/lEolEyZMYNu2bTRt2pQBAwakmS8tY8aM4dSpU6xatYpSpUqphnLVrVuXe/fuERgYSGBgIN26dQMgV643vxL+/fdf1c9vi4+PJ3/+/GrXv3//flZvU3wC0g7aQ9pCe0hbaI+c2halSpXK7iqkS4KAz6xjx44UKlSIDRs2YGRklKVzHz16xIsXL7Czs9M4Zm9vj1Kp5Pbt22pBwNtv2uF/wzyKFSumkf72WP/r168THx+f5rUAHj58mGF9r1+/zt9//53m+PK0yrC2ts6wzK+dpaWl2melUsmgQYPYtm0bLVu25LfffsvUMKBUJ06coGDBgmlO9HV3dycwMJCoqCjVdcuVK8fevXt58eKFRl0ePHjAixcvqFy5MpaWliQmJnL//n0sLCzQ19d/j7sVH4O0g/aQttAe0hbaQ9pCe0kQ8Jk1a9aM0NBQ1q1bh7e39ye/XnoPjGmlvz0xWKlUUqhQIebPn5/m+ZmZw6BUKqlbty6DBg1K8/h/gwMDA4MMy/zavd1tqFQq6du3L+vWraNVq1bMnz8/SwEAvJlHkJCQgK6ursYv36dPnwJgaGiouq6LiwtBQUEcOnSIDh06qOVPXTmodu3aavXU19fX6u7OnELaQXtIW2gPaQvtIW2hfSQI+MwmTpxIrly5GDZsGEZGRhkOqXmbmZkZhoaGXL16VePYv//+i66ursYb/vdla2vLgQMHqF69eoYP528PN/pvGc+fP8fV1fWj1CknUSqV9OvXj9WrV+Ph4UFISMg7A4CYmBhiYmIwNTVVC9CqV6/O3r17mTp1Kn5+fqr0hIQEpk2bBrx5qE/l4uKCjY0N69evp3fv3qq9AuLi4ggMDERfX18jOBBCCCHEl0eCgM9MR0eHX3/9lWfPntGnTx/y5cvHd999l6lz9fT0qFu3Ltu3bycyMlI1fObBgwesX78eJyenLM0xeBcPDw8WLFjAtGnTGDt2rNqx5ORknj17pprQbGhoSFxcXJplTJkyhb1796rWmE8VGxuLkZFRmuPOBQQEBBAaGoqRkRF2dnaqB/a3ubm5qR7SQ0JCCAgIYMSIEfj6+qryjB07luPHjzN9+nQOHDhAtWrVSEhIYM+ePURHR1OtWjW1h/pcuXIRFBRE69atcXNzo1WrVhgZGbF582aio6OZOHGiDNsSQgghvgLyBJYNdHV1CQkJwdPTE29vb9auXYuLi0umzvXz8+PAgQM0bdqU7t27kytXLhYvXsyrV6+YMGHCR6tjrVq1VCsIXbhwgbp165I7d26uXbvGpk2bmDJlCi1atADeTBZeuHAh06ZNo0SJEpiZmeHi4sLAgQPZsWMH7du3p1OnTjg6OvL8+XMuXrzI5s2bOX/+fKaXRs1poqKiAHj27BnTp09PM4+VlZXarr5p+eabbzh48CC//PILhw4dYv78+eTKlYsSJUowevRo+vfvrzFMqE6dOuzcuRN/f3/CwsJISkrCwcGB8ePH06pVq49zg0IIIYTIVhIEZJPcuXOzdOlS2rRpg6enJ+Hh4Zk6r2zZsmzfvp0JEyYwY8YMlEol3377LSEhIRp7BHyoGTNm4OjoyOLFi1XDmCwtLWnXrp3a+vI+Pj5ER0cTFBTE06dPqVmzJi4uLhgaGrJt2zYCAwMJDw9n9erV5M+fHzs7O0aOHPnRei2+RnPmzGHOnDmZzu/r66vWA/A2Ozu7LJUFb1aRWr9+fZbOEUIIIcSXQyc2NjYl42xCaJ+Sq+4S80qZccYvSKz3x5nT8TkkJCQQHR2NpaWlTPbKRtIO2kPaQntIW2gPaQvtJT0B4ot1rVOR7K6CEEIIIcQXSTe7KyCEEEIIIYT4vCQIEEIIIYQQIoeRIEAIIYQQQogcRuYEiC/W1zQx+EuaECyEEEKIL5/0BAihJe7cucPs2bNp2bIl5cuXp1ChQtjb29O5c2dOnTqV5fKePn3K5MmTcXZ2pkiRIlhZWVGnTh2mTJmikTclJYXNmzfj7u5O6dKlKVKkCFWqVGHw4MHcvHnzI9ydEEIIIbSJ9AQIoSVCQkKYOXMmtra21K1bFzMzM65du8a2bdvYtm0bCxYsyPRmXdHR0TRv3pybN2/i6upKo0aNePXqFTdu3GDz5s2MHDlSLb+fnx/BwcEULlwYNzc38ufPz19//cXSpUvZsGEDu3btwsHB4VPcthBCCCGygQQBH9nKlSvp168f+/fvp1KlSu/M6+bmBsC2bduydA2FQsGIESPS3RxKfJkqV67M1q1bqVWrllr60aNHadGiBUOHDsXNzY08efK8s5zk5GS6dOnCvXv32LRpE3Xq1NE4/rb79+8zZ84cLC0tOXz4MAUKFFAdCw4OZvTo0QQHBxMcHPyBdyiEEEIIbSHDgYSaBQsWsHLlyuyuRo7UvHlzjQAAoEaNGtSuXZvY2FguXryYYTmbNm3izz//pH///hoBAECuXOqxf1RUFEqlEicnJ7UAAKBJkyYAPHr0KCu3IoQQQggtJz0B2SgsLCy7q6Bh4cKFmJiY4Onpmd1VEW/JnTs3AHp6ehnm3bhxIwAeHh7cunWL3bt3ExcXh62tLQ0aNMDIyEgtf8mSJdHX1+f48ePEx8djbGysOrZz504AXFxcPtatCCGEEEILSBCQjfT19bO7CuILEB0dzYEDByhcuDDlypXLMP+5c+eAN8OI/Pz8ePXqleqYmZkZixcvpnbt2qo0ExMTfvrpJ/z8/KhWrRrfffedak5AREQEPXr0oFevXh//xoQQQgiRbXL8cKBNmzahUCg4fPiwxrHFixejUChUQzCuXLlCly5dsLGxwcLCAldXV7Zv355mua9evWLUqFGULFmSokWL4unpqTGkws3NTTUvIFVCQgL+/v58++23WFhYULp0ab7//ntu3Ljxzvu4c+cO/fr1o1SpUpibm+Pk5MTy5cuz8lVQoUIFLl26xJEjR1AoFCgUCtzc3Lh58yYKhSLNMeEnTpxAoVCwfv16APz9/VEoFFy5cgUvLy8sLS2xtbVlxIgRJCQkaJy/Zs0aXFxcKFy4MDY2NnTr1o1bt25lqd5fs6SkJHr37s2rV68YN25cpnoCHj58CMDIkSPp06cPf//9N9euXSMgIID4+Hg8PT25d++e2jn9+vVj0aJFPH/+nEWLFvHrr7+yd+9eqlSpQps2bTSGEAkhhBDiy5bj/8/eqFEjjIyMCA8P1xiPvXHjRsqWLYuDgwOXLl2icePGFC1alCFDhmBoaEhYWBienp4sW7aMZs2aqZ3r4+OjmsAbFRXFnDlzGD58OIsXL063Lq9fv6Z9+/YcPHiQ1q1b88MPP/Ds2TP279/PxYsXsbW1TfO8Bw8e0LBhQ3R0dOjZsydmZmbs2bOHAQMG8PTpU/r27Zup78Lf358RI0aQL18+hg0bBoC5uTk2NjY4OTmxbt06+vXrp3bOunXryJ8/P999951aupeXF1ZWVowdO5Y//viDefPmERsby7x581R5pk+fzqRJk2jZsiVdunTh0aNHhISE8N133xEREYFCochUvb8GaQVISqWS/v37c/ToUb7//ns8PDzSzJfWeQANGzZUWwWoa9euREVFERwczOLFixkyZIjq2C+//MLMmTPx8fGhdevWFChQgL/++ouxY8fi7u7OwoULady4sdp1EhMT1f4U2UPaQXtIW2gPaQvtkdPbIm/evNldhXTl+CDAwMCAJk2asGnTJgICAlRvWu/fv8+RI0dUD1EjR46kePHi7N+/X7U6S48ePWjSpAnjxo3TCAJMTEwICwtDR0cHePNgNm/ePOLi4jQmX6YKDQ3l4MGDTJo0Se1he8iQIaSkpKR7DxMnTuT169ccPXoUExMTALp160b37t2ZMmUK3t7eGBgYZPhduLu7M2nSJExMTGjfvr3asQ4dOjB48GCuXLmCvb098OYtdVhYGO7u7hgaGqrlt7a2JjQ0FICePXtibGzMggULGDBgAOXLlycqKgp/f3/8/PxUAQdAs2bNqFOnDgsXLlRL/9pFR0erfVYqlUyYMIFt27bRtGlTBgwYoJEnPfny5SM2NpaqVatqnJO6YtXx48dVx06cOMG0adPo1KkTHh4evH79msePH1O0aFGmTJmCh4cHfn5+6S4Rev/+/azervgEpB20h7SF9pC20B45tS1KlSqV3VVIV44PAgBatmzJ+vXrOXz4sGoC5KZNm1AqlbRq1YonT54QERHBqFGjePbsGc+ePVOdW69ePfz9/blz5w5FixZVpXt5eakCAABnZ2dmz55NdHR0ukHAli1bMDU1pXfv3hrH3i7rbambPLVs2ZKUlBRiYmLU6rZhwwbOnTuHk5NT1r6U//Dw8GDkyJGsXbsWPz8/APbu3UtMTIxGwABvHvzf1qtXLxYsWMCePXsoX748W7ZsQalU0rJlS7U6W1hYULJkSQ4dOpSjggBLS0vVz0qlkkGDBrFt2zZatmzJb7/9lqlhQKns7e05efIkNjY2auUCPH/+HHjz9yn12IIFC4A3KwH9N7+lpSX29vZcuHABExMT8uXLpzqWmJjI/fv3sbCwkPkt2UjaQXtIW2gPaQvtIW2hvSQIABo0aICxsTEbN25UBQFhYWFUqFABOzs7Tp8+TUpKCpMmTWLSpElplvHw4UO1IKB48eJqx1OHtsTGxqZbjxs3blCqVKksjb9+9OgRcXFxLFmyhCVLlqRbtw+lUCho0qQJ69evVwUB69ato2jRomkuQ1miRAm1z7a2tujq6hIVFQXA9evXSUlJoXLlymleL6eNQU/tLlQqlfTt25d169bRqlUr5s+fn6UAAN6s5HPy5EmuX7+u0Q2ZuvuvjY2N2jUB4uLi0uy2fPz4Mbq6uhgZGaW5R4G+vr5Wd3fmFNIO2kPaQntIW2gPaQvtk7OetNKRJ08e3Nzc2Lp1K7/88gsPHjzg+PHjjB07FvjfQ9KAAQOoX79+mmX896E3vQe3dw3reR+pdWvXrh2dOnVKM09mVpTJjA4dOhAeHs6JEydwcHBgx44ddO/eHV3djOeX/7cnQ6lUoqOjw/r169P8rt5+45xTKJVK+vXrx+rVq/Hw8CAkJOSdAUBMTAwxMTGYmppiamqqSvf09CQoKIiQkBA8PT1VwenTp0/55ZdfgDe9X6mcnJyYP38+s2fPpnnz5mo9VYsWLeL27ds4OTlluEmZEEIIIb4cEgT8v1atWqnG5F++fJmUlBTVg5KNjQ3wZq12V1fXT1YHW1tbTp06RVJSkmpd+IyYmZmRP39+lErlR6lbesOO4E2PiZmZGevWrePbb7/lxYsXaQ4Fgjdv+lO/t9TPSqUSKysr4M29pqSkYG1tjZ2d3QfX+2sQEBBAaGgoRkZG2NnZMW3aNI08bm5uVKxYEYCQkBACAgI0do+2sbFhwoQJjBgxglq1auHu7k6ePHnYtWsXUVFReHt7q6377+HhwcKFCzl69ChVqlShadOmFChQgHPnzhEREYGBgUG6PWBCCCGE+DJJEPD/XF1dKViwIBs3buTKlSt8++23qofYQoUKUatWLRYvXkyvXr0oXLiw2rmPHj3CzMzsg+vQrFkzdu3aRUhIiMYqPCkpKWk+oOvp6dGsWTPWr1/PxYsXNSZvZrVuhoaGxMXFpXksV65ctG7dmvXr13P58mUcHBwoX758mnnnz59PvXr1VJ9DQkKAN4FE6r2OHz+egIAAQkJC1O4tJSWFJ0+eqCY55xSpQ6WePXvG9OnT08xjZWWlCgLepXfv3lhZWREUFMTGjRtJTk6mTJky/Pjjj3Tp0kUtr56eHmFhYcyePZuwsDDWr19PYmIi5ubmtGvXjmHDhlG6dOkPv0EhhBBCaA0JAv5f7ty5adasGRs3buT58+dMnDhR7fj06dNp0qQJNWrUoGvXrtjY2PDgwQP++OMPbt++zZEjRz64Dh07dmT16tWMHj2aM2fO4OzszIsXLzhw4ADdu3fX2FMg1bhx4zh06BANGjSgS5culClThidPnnDu3DkOHDigGgeeGY6OjixcuJBp06ZRokQJzMzM1N4ad+zYkXnz5nHo0CHGjx+fbjmRkZF06NCBBg0acPLkSdauXUvbtm2pUKEC8KYnwM/Pj/HjxxMVFYWbmxtGRkZERkaydetWvLy8GDBgQKbr/TWYM2cOc+bMyXR+X19ftR6A/2ratClNmzbNVFl58uRhyJAhasuGCiGEEOLrJUHAW1q1asWyZcvQ0dFRGzMNUKZMGQ4cOMCUKVNYtWoVjx8/plChQlSoUAEfH5+Pcn09PT3WrVvHL7/8wrp169i8eTMmJiY4OTm9c1y/ubk5+/btY+rUqWzdupWFCxdiYmJCmTJl3vmgnhYfHx+io6MJCgri6dOn1KxZUy0IcHR0pGzZsly+fJm2bdumW87ixYuZPHky48ePR09Pj549e2oEVkOGDKFkyZLMmTOHgIAAAIoVK0a9evUy/fAqhBBCCCGyTic2NvbjzlQVX73atWtTsGBBNm/erHHM39+fgIAArl27pjZZVXx9EhISiI6OxtLSUlZ8yEbSDtpD2kJ7SFtoD2kL7ZXxsi5CvOXPP//kwoULdOjQIburIoQQQggh3pMMB8ohHj16xOvXr9M9rq+vT8GCBdM9fvHiRc6ePctvv/1G4cKFadWq1aeophBCCCGE+AwkCMgh6tatS3R0dLrHa9asybZt29I9vmnTJqZOnUqpUqVYsGCBdOkJIYQQQnzBZE5ADnH8+HESEhLSPa5QKHB0dPx8FfoISq66S8wr5UcrL9a72EcrKyeQcZ7aQdpBe0hbaA9pC+0hbaG9pCcgh3BycsruKnwx1qxZw7Fjxzh79iwXL14kMTGR4OBgPD09M13Gw4cPWb58OWfPnuXs2bOqPQBiY2M/6XWFEEIIITJDggAh/uPnn38mOjoaU1NTLCws3jmMKj3//PMPEyZMQEdHh5IlS2JoaMiLFy8++XWFEEIIITJDVgf6zFauXIlCoSAyMjK7qyLSMWvWLM6fP8+1a9fo1q3be5VRunRptm3bRlRUFKdOnaJYsYyHGn2M6wohhBBCZIb0BHxFfvnlF0qXLo27u3t2V+WL5urq+sFlmJubY25u/tmvK4QQQgiRGdIT8BUJDAx85wo/QgghhBBCgAQBQgghhBBC5DgSBGSzbdu20a5dO8qUKYO5uTmOjo5MnTpVY2Ova9eu0blzZ+zt7bGwsMDBwYFu3boRFxcHvFni8/nz54SGhqJQKFAoFPTp0yfT9diwYQMuLi4UL14cS0tLatSowZw5c9TyxMbGMnLkSMqVK4e5uTmVKlVi5syZKJXqy3QqlUpmz56Nk5MTFhYWlCpVisGDB2usjFOhQgXat2/PsWPHqFevHhYWFnzzzTeEhoZm4RsUQgghhBBZJXMCstmqVavIly8f/fr1I1++fERERDB58mSePn3KxIkTAUhMTKRVq1YkJibSq1cvzM3NuXv3Lrt27SIuLo4CBQowb948Bg4cSOXKlfHy8gLA1tY2U3XYv38/3bt3x8XFhXHjxgFw+fJlTpw4oQokXrx4gZubG3fv3sXb25vixYtz4sQJxo8fz71795gyZYqqvMGDB7Nq1So8PT3p3bs3kZGRzJ8/n/Pnz7Nr1y5y586tynv9+nW6du1K586d6dixIytWrKBv3744OjpStmzZj/ANZ15a+ygkJycDkJSU9M59FjKSkpKS7jXS8rGu+yklJiaq/Smyh7SD9pC20B7SFtojp7eFNu+NIEFANluwYAEGBgaqz926dWPIkCEsXLgQPz8/8uTJwz///ENkZCRLly6lRYsWqrwjRoxQ/dy+fXuGDh2KjY0N7du3z1Iddu3ahbGxMRs3bkRPTy/NPMHBwdy8eZOIiAhKliwJgLe3N0WKFCEoKIj+/ftTvHhxjh07xrJly5g/fz5t27ZVnV+7dm1at25NeHi4Wvq///7L9u3bqVGjBgAtW7akXLlyrFy5kp9//jlL9/Gh0lqSM7X34vHjxx+0ZGdSUlK610jLx7ru53D//v3sroJA2kGbSFtoD2kL7ZFT26JUqVLZXYV0SRCQzd4OAJ4+fUpiYiLOzs4sXryYK1euUKFCBYyNjQHYu3cvDRs2xNDQ8KPWoUCBAjx//pz9+/fToEGDNPOEh4fj7OyMQqEgJiZGle7q6sqMGTM4evQo7dq1Izw8HGNjY+rWrauWz9HRESMjIw4dOqQWBJQpU0YVAACYmZlhZ2fHzZs3P+o9ZoalpaVGmkKhAMDExCTN45mV2vuR2TI+1nU/pcTERO7fv4+FhQX6+vrZXZ0cS9pBe0hbaA9pC+0hbaG9JAjIZpcuXeLnn3/m0KFDxMfHqx1L/WxjY0O/fv0IDg5m3bp1ODs707RpU9q1a0eBAgU+uA49evQgPDycNm3aULRoUerWrUvLli3VAoLr16/z999/q3oB/uvhw4eqfPHx8djZ2b0zX6rixYtr5FEoFO/cWfdTSavLLleuN/9EcufO/UFdejo6OuleIy0f67qfg76+vtbXMSeQdtAe0hbaQ9pCe0hbaB8JArJRbGwsbm5u5M+fH19fX2xtbcmbNy/nzp3jp59+UptwO2nSJDp16sT27dvZt28fI0aMYMaMGezZsydTG1G9S6FChTh06BB79+7l999/Z8+ePaxcuZIOHTowd+5c4M1k37p16zJo0KA0y0gNDpRKJYUKFWL+/Plp5jM1NVX7nN7wo9Qx9NouJiaGmJgYTE1NNe5NCCGEEEJbSRCQjQ4fPszjx49Zvnw5NWvWVKWnt5twuXLlKFeuHMOHD+fEiRM0btyYxYsX4+fnB/zvbfP70NfXp2nTpjRt2hSlUsmwYcNYvHgxPj4+lChRAltbW54/f57hhla2trYcOHCA6tWrqw11+pIsW7aMY8eOAXDx4kUAli9fzuHDhwFwdnamS5cuAISEhBAQEMCIESPw9fVVK+ft1ZlSx0K+nTZkyBDs7e3f67pCCCGEEB9CgoBslPoW/O233omJiSxYsEAtX3x8PIaGhqohIgAODg7o6ury6tUrVZqhoaFqydCsePz4MSYmJqrPurq6lCtXDkBVvoeHB1OmTGHv3r3Ur19f7fzY2FiMjIzIlSsXHh4eLFiwgGnTpjF27Fi1fMnJyTx79kw13l1bHTt2TGOZ0uPHj3P8+HHV58w8jKe11OnbaZ06dVILAj7WdYUQQgghMiJBQDaqXr26aj3/3r17o6Ojw5o1azSGwkRERODj40OLFi2ws7MjOTmZNWvWoKenR/PmzVX5HB0dOXjwIL/99htFihTB2tqaKlWqZFiPAQMG8OTJE+rUqUPRokWJjo4mJCSEChUqULp0aQAGDhzIjh07aN++PZ06dcLR0ZHnz59z8eJFNm/ezPnz5zE1NaVWrVp4e3sTGBjIhQsXqFu3Lrlz5+batWts2rSJKVOmqK1wpI3mzJmjsUdCenx9fTV6AFJldV5DVq4rhBBCCPEhJAjIRiYmJqxZswY/Pz8mTZqEQqGgXbt2uLi40KpVK1W+8uXLU69ePXbu3Mndu3cxMDCgfPnyrF+/nqpVq6ryTZo0iUGDBjFp0iRevnxJx44dMxUEtGvXjqVLl7Jw4ULi4uIwNzenVatWjBw5El3dN/vJGRoasm3bNgIDAwkPD2f16tXkz58fOzs7Ro4cqVrBCGDGjBk4OjqyePFiJk6cSK5cubC0tKRdu3ZUr179I36DQgghhBDifejExsZ+GTMwhRBaJSEhgejoaCwtLWXFh2wk7aA9pC20h7SF9pC20F662V0BIYQQQgghxOclw4G+Yq9fv+bRo0fvzJMvXz6MjIw+U42EEEIIIYQ2kCDgK3br1i2++eabd+ZJa2lLIYQQQgjxdZMg4CtmYWFBeHj4O/PY2Nh8lrp8CiVX3SXmlTLjjBmI9f6wzdaEEEIIIb40EgR8xfLmzZvh5l7if9asWcOxY8c4e/YsFy9eJDExkeDgYDw9PbNUjlKpZP78+SxdupTr16+TL18+XF1dGTNmTJpB17x58zh37hxnz57l8uXLvH79mi1btlC7du2PdGdCCCGEEOpkYrDIkEKhwN/f/7Ncy83NDTc3t89yrf/6+eefWbJkCdHR0VhYWLx3OYMHD2bEiBGkpKTQu3dv6tevz5YtW6hbty7Xrl3TyD9ixAhWrVrF48ePMTMz+5BbEEIIIYTIFAkChPh/s2bN4vz581y7do1u3bq9VxkREREsW7aMGjVqcPDgQcaPH09ISAgrV67kyZMnDB8+XOOcNWvWcPnyZf755x++++67D70NIYQQQogMyXAgkaF79+6RK9fX/1flYwydWrZsGQCjR49GX19fld6wYUNq1arFvn37VOslp2rcuPEHX1cIIYQQIiukJ+AL8Pz582y9ft68eTMMArK7jtri8OHD5MuXDycnJ41j9evXB+DIkSOfu1pCCCGEEGokCNAy/v7+KBQK/vnnH3r06IG1tTVNmjQB3gwbcXFxoXDhwtjY2NCtWzdu3bqldr6bmxvOzs6cPXuWRo0aUbhwYSpWrMiiRYvU8iUmJjJp0iRcXFywsrKiaNGiNG3alIiICI06/XdOwLvqmNl6AixZsgRHR0cKFy5MvXr1OHr06Ad9d9nt+fPn3Lt3D2tra/T09DSOlyhRAiDNeQFCCCGEEJ+TBAFaysvLixcvXjB27Fi6du3K9OnT+eGHHyhZsiSTJk2iT58+HDx4kO+++47Y2Fi1c2NjY2nbti3ffPMN48ePp1ixYgwdOpTly5er8jx9+pTly5dTq1Ytxo0bx8iRI3n06BGtW7fm/Pnz71VHINP1XLZsGYMHD8bCwoLx48dTvXp1OnbsyO3btz/4u8su8fHxABgbG6d5PDU9NZ8QQgghRHb5+gd6f6HKly/PggULAIiKiqJSpUr4+fkxbNgwVZ5mzZpRp04dFi5cqJZ+9+5dfv75Z/r37w+At7c39evXZ8KECXTo0IHcuXOjUCg4f/682rj1Ll26UK1aNUJCQvjtt9+yVMfUevr7+2dYz6SkJCZOnEiFChXYsmWLqg5lypRh0KBBFCv2edftT0hI0EhLTk4GICkpKc3jaXn16hXwZonQtM5JTEwE3uzknF6Zr1+/VuXN7HWzS+r9pP4psoe0g/aQttAe0hbaI6e3Rd68ebO7CumSIEBLeXt7q37esmULSqWSli1bEhMTo0q3sLCgZMmSHDp0SO2hO1euXGrn6+vr4+3tzdChQzl79ixVq1ZFT09PNWRFqVQSFxeHUqmkUqVKnDt3Lst1zEo9//zzTx4+fMioUaPUgpBOnToxZsyYTH5DH090dLRGWmqvxePHj9M8npaXL18CEBMTk+Y5N2/eBCAlJSXdMp89ewbAw4cPM33d7Hb//v3sroJA2kGbSFtoD2kL7ZFT26JUqVLZXYV0SRCgpaytrVU/X79+nZSUFCpXrpxm3v9O2i1cuDD58uVTSytZsiTw5m191apVAVi1ahW//fYb//77L0lJSWleO7N1zEo9Ux9uU8fIp8qdO3e27GD89ko9qRQKBQAmJiZpHk+PhYUFd+/epWjRohrzAlKHATk6OqZbppGREQCFChXK0nWzQ2JiIvfv38fCwkItmBOfl7SD9pC20B7SFtpD2kJ7SRCgpQwMDFQ/K5VKdHR0WL9+fZoTTv/7wJ8Za9asoW/fvri5uTFw4EAKFSqEnp4egYGB3LhxI8t1/FT1/BzS6qpLDVhy586dpa68WrVqsWHDBs6ePUvNmjXVjh08eBAAFxeXdMtM/d709fW1ugvxbV9SXb9m0g7aQ9pCe0hbaA9pC+0jQcAXwNbWlpSUFKytrbGzs8sw/71793j+/LnaQ3fqijRWVlYAbNq0CRsbG1asWIGOjo4q34fsDJzZeqa+4b5+/TouLi6q9KSkJCIjIylfvvx71+FziYmJISYmBlNTU0xNTVXpXbt2ZcOGDUyaNInw8HDVW489e/Zw+PBh6tWrp2oDIYQQQojsIqsDfQGaNWuGnp4eAQEBpKSkqB1LSUnh8ePHamnJycksXrxY9TkxMZHFixdjZmaGo6Mj8L83zm+Xd+rUKU6ePPnJ61mpUiXMzMxYtGiR2kShVatWERcX997X/1DLli2jT58+9OnTh/DwcACWL1+uSkvdCAwgJCRENYn6bXXq1KFLly4cPXoUFxcXfvrpJ3r37o2npycFCxZk6tSpGtedMWOG6hqHDh0CYObMmaq0rVu3frqbFkIIIUSOJD0BXwBbW1v8/PwYP348UVFRuLm5YWRkRGRkJFu3bsXLy4sBAwao8hcpUoRff/2VqKgo7OzsCAsL48KFC/z666/kzp0beLNL7ZYtW/D09KRx48ZERkayaNEiypQpo5qc+qnqmTt3bvz8/Bg8eDDNmzenZcuWREZGsnLlymyZE5Dq2LFjhIaGqqUdP36c48ePqz536dIlw3JmzpyJg4MDS5cuZe7cueTLlw93d3fGjBmDra2tRv7ff/9dYwOxvXv3qn62srLC3d09q7cjhBBCCJEuCQK+EEOGDKFkyZLMmTOHgIAAAIoVK0a9evVo2rSpWl6FQsGcOXPw8fFh2bJlFCpUiGnTpqnW8gfw9PTkwYMHLF68mH379lG6dGlCQkIIDw/n8OHDn7yeXl5evH79mqCgIMaOHYuDgwOhoaFMmjTpva/9oebMmcOcOXMyldfX1xdfX980j+nq6vLDDz/www8/ZKqsbdu2ZbqOQgghhBAfg05sbGxKxtnEl8LNzY3Hjx9z7Nix7K7KJ1dy1V1iXik/uJxY78+7L8HXIiEhgejoaCwtLWWyVzaSdtAe0hbaQ9pCe0hbaC/pCRBfrGudimR3FYQQQgghvkgyMVgIIYQQQogcRoIAIYQQQgghchgZDvSVkUmmQgghhBAiIxIEiC/Wh04MlgnBQgghhMipZDiQEMCZM2do27YtVlZWFC1alAYNGhAWFpalMu7evcuIESOoXr06RYsWpVSpUjRp0oTVq1fz+vVrjfwJCQlMnTqV6tWrY2FhgbW1NW3atFHbl0AIIYQQ4lOQngCR40VERNC6dWvy5s1Lq1atMDIyYvPmzXh7e3Pr1i21jdjSc/PmTerXr8/jx4+pX78+TZo04enTp2zbto0ffviBiIgIZs+ercqfkJBAixYtOHHiBOXKlaNbt27ExcWxZcsW3NzcWLZsGW5ubp/ytoUQQgiRg0lPwFdm5cqVKBQKIiMjs7sqag4dOoRCoeDQoUPZXRU1ycnJDBo0CF1dXbZt28avv/7KpEmTOHz4MHZ2dkycOJGoqKgMy5k1axYxMTFMnjyZ9evXM378eAIDAzlx4gTFixdn1apVauXMnz+fEydO4OHhQUREBP7+/syePZuIiAjy5cvHoEGDePr06ae8dSGEEELkYBIEaJEXL17g7++vdQ/KX7OIiAhu3LhBmzZtqFixoiq9QIECDB06lMTEREJDQzMs5+bNmwA0atRILV2hUODs7AzA48ePVenbt28HYOTIkejp6anSbW1t8fT05NGjR2zatOm970sIIYQQ4l0kCNAiL1++JCAggMOHD2d3VXKM1O+6Xr16Gsfq168PwJEjRzIsp2zZsgDs3r1bLT02Npbjx49jYWFB6dKlVen3798HwNraWqOs1LSIiIjM3IIQQgghRJbJnACRo127dg2AkiVLahyzsLDAyMiI69evZ1jOwIED2blzJ6NGjWLv3r2UK1dONSfAwMCAFStWYGBgoMpvamrK9evXiYyMpEyZMmplpQ7lSq2bEEIIIcTHJj0B7+Dv749CoeDKlSt4eXlhaWmJra0tI0aMICEhQZUvOTmZqVOn4ujoiLm5ORUqVGDChAm8evVKrbw///yTVq1aUaJECQoXLkzFihXp168f8ObBL/VBNCAgAIVCgUKhwN/fH4C//vqLPn368M0332BhYYG9vT39+vVTG2LyIe7cuUP//v0pU6YM5ubmVKxYUTUcJtXNmzfp2rUrNjY2FClShAYNGrBr1y6Nsm7fvk2nTp0oWrQodnZ2+Pr6qpXztlOnTtG6dWusrKwoUqQI33333WddHSc+Ph4AY2PjNI/nz59fleddzM3N2bNnDw0aNOD333/n119/ZdGiRcTHx9OhQwfKly+vlr9BgwYATJ06VW3loJs3b7Jy5UoA4uLi3uuehBBCCCEyIj0BmeDl5YWVlRVjx47ljz/+YN68ecTGxjJv3jwABgwYQGhoKC1atKB///6cOnWKwMBALl++rHqge/jwIS1btsTMzIwhQ4ZQoEABoqKi2LJlCwBmZmYEBgYydOhQ3N3dadasGQDlypUDYP/+/dy8eRNPT08sLCy4dOkSS5cu5Z9//uH3339HR0fnve/v7t271K9fn7i4OLp27Yq9vT137txh8+bNvHjxAn19fR48eECjRo14+fIlvXv3pmDBgoSGhtKxY0eWLl2qqu/Lly9p0aIFt27donfv3hQuXJg1a9akObTl4MGDtG3bFkdHR3x8fNDV1WXlypU0b96cHTt28O233773PWVGQkICSuWbfQZevXqlFtilSklJISUlJc1jb7tx4wZdunTB0NCQTZs2Ub58eeLi4tiwYQNTpkzh999/Z9OmTarx/927d2fDhg1s3LiRy5cvU6tWLeLi4ti2bRuWlpZcvHgRHR2dDK+bnVIDu/QCPPF5SDtoD2kL7SFtoT1yelvkzZs3u6uQLgkCMsHa2lo1ObRnz54YGxuzYMECBgwYQEpKCqGhoXTp0oWgoCAAevToQaFChZg1axYRERHUqVOHEydOEBsbS1hYGJUqVVKV7efnB0C+fPlo0aIFQ4cOpVy5crRv316tDj169NBYqrJq1ap0796dY8eOUaNGjfe+v/Hjx3P//n327t2rVrfRo0eTkpICwIwZM3jw4AE7duxQTXTt2rUrNWvWZPTo0bi5uaGrq8uSJUu4evUqS5YswcPDQ5WvVq1aatdMSUlh6NCh1K5dm/Xr16uCGG9vb5ycnPj555+zvE5/VkVHR5Mr15t/Av/++6/q57fFx8eTP39+oqOj31nWDz/8QFRUFOHh4ZiZmal6aDw8PLh+/TqhoaEsXLiQpk2bqs6ZO3cuCxYs4MCBAyxcuBATExNatmxJrVq16NWrF0ZGRhleVxukzm8Q2UvaQXtIW2gPaQvtkVPbolSpUtldhXRJEJAJPXv2VPvcq1cvFixYwJ49e1QPyanDelL179+fWbNmsXv3burUqUOBAgUA2LlzJ+XLlyd37txZqsPb48kTEhJ4/vw5VapUAeDcuXPvHQQolUq2b99OkyZN1AKAVKkP53v27OHbb79VBQAARkZGeHl5MX78eP755x8cHBzYs2cPhQsXpkWLFqp8hoaGeHl5MXbsWFXa+fPnuXbtGj/++KPGkCYXFxfWrFmDUqlEV/fTjViztLSkXLly7N27lxcvXmBpaal2/MGDB7x48YLKlStrHHvbs2fPOHfuHBUrVkzzO2zSpAmhoaHcuXNHo5zAwECN/KtXrwagWrVq77xudktMTOT+/ftYWFigr6+f3dXJsaQdtIe0hfaQttAe0hbaS4KATChRooTaZ1tbW3R1dVXrvuvq6mrksbCwoECBAqo3ubVq1aJ58+YEBAQwZ84catasiZubG23btiVPnjwZ1uHJkydMmTKFjRs38vDhQ7VjmRmznp5Hjx4RHx+Pg4PDO/NFR0enOTzH3t5eddzBwYHo6GhsbW01hifZ2dmpfU6dbNunT590rxkfH49CocjMbbyXvHnz4uLiQlBQEIcOHaJDhw5qx1NXDqpdu/Y7u/NevHgBvFkCNK18qWP78+XLl6luwfDwcADat2+v1d2IqfT19b+Ien7tpB20h7SF9pC20B7SFtpHgoD3kNb4+4zG5Ovo6LBs2TL++OMPdu7cyd69e+nfvz/BwcHs2bMHIyOjd57v5eXFyZMnGTBgABUqVMDIyAilUknr1q1V49q/JKl1njhxIhUqVEgzT758+T55PVxcXLCxsWH9+vX07t1btVdAXFwcgYGB6OvrqwUH9+7dIz4+XhXkAZiYmFCqVCn+/fdfli1bRpcuXVT5Y2Nj+e2334A3wcTb4uPjNSYkBwcHc+DAAdzd3alcufInuWchhBBCCAkCMuH69evY2NiofVYqlVhZWZGSkoJSqeTatWtq68A/ePCAuLg4jeEcVatWpWrVqowZM4Z169bRs2dPNm7cSJcuXdINJGJjYzl48CC+vr6MGDFClf4xlpA0MzPD2NiYixcvvjOfpaUlV69e1Uj/999/VcdT/7x06RIpKSlq9/Pfc21tbYE3q++4urp+yC18kFy5chEUFETr1q1xc3OjVatWGBkZsXnzZqKjo5k4caLaWv7jx48nNDSU4OBgPD09VemTJ0+mY8eODBw4kA0bNlCxYkViY2PZsWMHjx49onnz5hr36eDgQK1atShZsiQ6OjocPnyYs2fPUqlSJVXgIIQQQgjxKcgSoZkwf/58tc8hISHAm2UeGzZsCMCcOXPU8gQHBwP/20E2NjZWNX8gVeob8NSlRFPH/f93acjUcfH/Pf+/13wfurq6fPfdd+zcuZM///xT43jqNRs2bMjp06c5efKk6tjz589ZsmQJVlZWqrXuGzZsyN27d9V2u33x4gVLlixRK9fR0RFbW1tmzZrFs2fPNK776NGjD763zKpTpw47d+6kevXqhIWFsWjRIszNzVm0aJHGZOz0NGzYkN27d+Ph4cHly5eZM2cOYWFhWFlZMXXqVBYvXqxxTrt27bh27RqLFy9m8eLFKJVKJk6cyM6dOz/pMCghhBBCCOkJyITIyEg6dOhAgwYNOHnyJGvXrqVt27aqh/iOHTuyZMkS4uLiqFmzJqdPnyY0NBQ3Nzfq1KkDwKpVq1i4cCHu7u7Y2try9OlTli1bhrGxsSqQMDAwoEyZMoSFhWFnZ0fBggUpW7YsDg4O1KhRg6CgIJKTkylSpAj79u1TbSr1ocaOHcv+/ftxc3Oja9eulC5dmnv37rFp0yZ27NiBQqFgyJAhbNiwgTZt2qgtERoZGcmyZctUgUrXrl2ZP38+P/zwA2fPnsXCwoI1a9aoTWyGN8FHUFAQbdu2xcnJCU9PT4oWLcqdO3c4dOgQ+fPnZ82aNR/l/jLj22+/Zf369RnmmzNnTrrBV+XKlTWCnXdJa1KwEEIIIcTnIEFAJixevJjJkyczfvx49PT06NmzJxMnTlQdnzVrFjY2NqxatYqtW7diYWHB0KFD1Ybu1KxZkzNnzrBhwwYePnyIsbExlStXZv78+WpDjYKCgvDx8WHUqFEkJiYyYsQIHBwcWLBgAT4+PsyfP5+UlBTq1avH+vXrNXabfR9Fixbl999/Z9KkSaxbt46nT59SpEgRGjZsiKGhIfBmM6zdu3fz008/ERISwqtXryhXrhyrV6+mcePGqrJS18n38fEhJCQEAwMD2rZtS8OGDWndurXadWvXrs3u3buZNm0a8+fP5/nz55ibm1OlShW8vLw++L6EEEIIIUTadGJjY1MyzpYz+fv7ExAQwLVr1zA1Nc3u6gihVRISEoiOjsbS0lJWfMhG0g7aQ9pCe0hbaA9pC+0lcwKEEEIIIYTIYWQ40Ffs2bNnPH/+/J15zMzM0NPT+0w1EkIIIYQQ2kCCgK/YrFmzCAgIeGeec+fOqS2BKYQQQgghvn4SBLyDr68vvr6+2V2N99axY0ecnZ3fmcfCwuIz1ebjK7nqLjGvsr5RWqx3sU9QGyGEEEKIL4cEAV8xGxsbtZWHRNrOnDmDv78/J06cIDk5GQcHB/r160fLli0zXcbdu3eZOXMmBw4cIDo6mnz58lGyZEm8vLxo27at2pCrBw8eMG3aNM6cOUNUVBRxcXGYmZlhZ2dHjx49aNasWYY7UAshhBBCfAgJAkSOFhERQevWrcmbN6/absHe3t7cunUrU5uF3bx5k/r16/P48WPq169PkyZNePr0Kdu2beOHH34gIiKC2bNnq/Lfvn2b1atXU6VKFdzd3SlYsCAPHz5k586ddOnShS5duhAUFPQpb1sIIYQQOZwsESo+i2vXrvHjjz9y+vRp4uPjWbFiBRcuXPigJVg/dDhQcnIyVatW5c6dO+zZs4eKFSsCb3Zsrl+/PlFRUZw6dQorK6t3ljds2DAWLlyIv78/ffr0+d91YmOpVasWt27d4vz586pykpKS0NXV1ZiQ/fTpUxo0aMDly5c5duwYZcuWzfK9fU6y7Jt2kHbQHtIW2kPaQntIW2gvWSJUfBZ9+vTh4sWL+Pn5MW/ePCpVqpTdVSIiIoIbN27Qpk0bVQAAUKBAAYYOHUpiYiKhoaEZlnPz5k0AGjVqpJauUChUczIeP36sSs+dO3eaKzLlz5+f+vXrA3D9+vUs348QQgghRGZJECA+uZcvX3Ly5Ek6d+5Mr169aN++PcWKZf/k3MOHDwNQr149jWOpD+NHjhzJsJzUN/a7d+9WS4+NjeX48eNYWFhQunTpDMtJSEggIiICHR0dre8FEEIIIcSXTeYEiE/u0aNHwJs37Nrk2rVrAJQsWVLjmIWFBUZGRpl6Iz9w4EB27tzJqFGj2Lt3L+XKlVPNCTAwMGDFihUYGBhonPfw4UMWLFiAUqnk0aNH7Nmzh1u3bjFixAhKlCjx4TcohBBCCJEO6QkQ7y0qKophw4ZRpUoVChcujK2tLV27diUyMlKVx9/fnwoVKgAwZswYFAqF6nN6ZVaq9H/t3XlcVGX7+PEPqyggIyJDIrIIaqJGmuKO61OKBoILZqlYZqCmabmULYaIZmFhRpK49KSkoOJCrmiBa5aZpokLpPiouAIKAgLz/cMf83MakEV0Rrner5cv4Zx77nOduZw615z7vs/zdOzYkStXrjzS+LOzswGoW7duqfstLS3VbR7E1taWHTt20Lt3b3bu3MlXX33F0qVLyc7OJiAggJYtW5b6uqtXrzJv3jzmz5/PsmXLyMjIICQkhOnTp1f9pIQQQgghKkDuBIgq++OPPzh48CB+fn7Y29tz/vx5oqOj6d+/PwcPHqROnToMGDAAKysr3n//fQYNGkSfPn0wNzcvtb+0tDQGDBhAvXr1iI+Pr9Jk4YrIy8sDoLj43qTi/Px89bb7qVQqVCpVqfvul5aWxogRI6hTpw4bNmygZcuWZGVlsXbtWubOncvOnTvZsGGD1jwAFxcXLl++TFFREf/73/+Ij48nJCSE/fv3ExUVhbGxfn88CwoKNP4WuiF50B+SC/0hudAfNT0X+jwZWr+vMoRe+89//oOPj4/Gtpdeeok+ffqwceNG9bfglpaWvP/++zz33HMMHTq01L5OnTqFj48PzzzzDOvWrUOhUDyyuNPT0wHUF9mnT58u9YI7OzsbS0tLdfuyvPXWW5w/f574+HhsbGzUk4B9fX1JTU0lJiaG6Oho+vbtW2YfBgYGDBw4kNu3bxMREcHChQsZNGhQVU/xscrIyNB1CALJgz6RXOgPyYX+qKm5cHNz03UIZZIiQFTZ/ePc7969y61bt3BxccHKyoo///yTgICACvVz4sQJRo8ejYuLC7GxsWUOz6kuDg4OALi7u5OYmEhubq56W4krV66Qm5tLmzZttPbd7/bt2/z555+0bt261BWPXnrpJWJiYrh48eID+ynh4+NDREQEf//9d4Xa61JBQQEZGRkolUpMTU11HU6NJXnQH5IL/SG50B+SC/0lRYCosjt37hAeHs6qVau4ePEiKtX/f+RERcbSlxg2bBgNGjRg7dq1WFhYPIpQNZTcmvPy8iIiIoLk5GStgqVk5aCuXbs+8FZebm4ucG8J0NLaZWVlAWBubl6hW4IldxHMzMz0+hbi/UxNTZ+YWJ9mkgf9IbnQH5IL/SG50D8yMVhU2dSpU/niiy/w9fVl+fLlrF+/nvj4eKytrdXj7StiwIABpKWlERsb+wij1ebl5YWTkxNxcXEcPXpUvT0rK4vw8HBMTU01ioPLly9z6tQp9YU9gLW1NW5ubly4cIHvv/9eo//MzEy+/vpr4F4xUeLYsWPcvXtXK56bN2/y6aefAtCnT5/qOUkhhBBCiFLInQBRZRs2bGDYsGGEhoaqt+Xl5WlcJFdESEgIxsbGTJkyBQsLCwYPHlzdoZbK2NiYiIgI/P398fb2xs/PDwsLCzZu3Eh6ejohISE4Ojqq28+aNYuYmBgWLVrE8OHD1dvnzJnDsGHDePvtt1m7di2tW7cmMzOTLVu2cO3aNV5++WW6d++ubv/NN9+wbds2PD09adSoEbVr1yY9PZ3t27eTk5ODr6/vEzMfQAghhBBPJikCRJUZGRlpDAECiIqKoqioqFL9GBgY8NVXX3H79m2CgoIwNzenX79+1Rlqmbp168bWrVsJCwtj/fr13L17lxYtWjBr1iz8/Pwq1EefPn3Yvn07ERERHDhwgL1792JmZkbTpk2ZOnUqr7/+ukb7gIAAiouL+f3339mzZw937tzB2tqaTp06MWzYsAofVwghhBCiqqQIEFX24osvsnr1aurWrUvz5s359ddf+eWXX7C2tq50X4aGhkRFRTF8+HACAwNZs2YNXl5ejyBqbW3btiUuLq7cdpGRkURGRpa6r02bNixfvrxCx/Py8nps5yaEEEIIURqZEyCqbO7cuQQEBBAbG8vMmTPJyMggPj6+zOcAlMfExIQVK1bwwgsvMHz4cH777bdqjlgIIYQQQgAYZGZmqspvJoQQmvLy8khPT8fBwUFWfNAhyYP+kFzoD8mF/pBc6C+5EyCEEEIIIUQNI0WAEEIIIYQQNYwUAUIIIYQQQtQwsjqQeGI1WXWJ6/kPfihZZqD9Y4pGCCGEEOLJIXcCRI1x+PBhBg8eTOPGjWnYsCG9e/dm/fr1FX79ypUrUSgUZf5JTk4ut48vv/xS3f7QoUMPczpCCCGEEFUmdwJEjZCUlIS/vz9mZmYaTwYODAzkwoULTJgwocJ99evXj1atWmltb9y48QNfd+LECcLCwjA3NycnJ6fS5yCEEEIIUV2kCADCwsKYN28emZmZug7lidOqVSu6dOlS5kO0KuvcuXM899xzLFq0iOHDh1dLn4WFhUycOBFDQ0MSEhJo3bo1AFOnTqVXr16EhITg4+NT7kV8CW9v70rHdvfuXYKCgmjVqhUuLi6sWbOm0uchhBBCCFFdZDjQQ1iyZAkrV67UdRiiHElJSaSlpTFo0CB1AQBgZWXF5MmTKSgoICYm5pHG8Pnnn3Py5Em+/vprjIyMHumxhBBCCCHKI3cCHkJ0dDTW1tbV9o21eDT27NkDQM+ePbX29erVC4C9e/dWuL+jR49y48YNioqKaNy4Md27d8fa2rrM9keOHOGLL77g/fffp3nz5pWMXgghhBCi+kkR8JTKycnB3Nxc12HohbNnzwLQpEkTrX1KpRILCwtSU1Mr3N/ixYs1fq9duzbTpk1j0qRJWm3z8/PVw4AmTpxYucCFEEIIIR6RGjccaP/+/fTo0QOlUomHhwfLli3TavPDDz8wYMAAXF1dsbW1xdPTk+joaI02rVq14u+//2bv3r3q1V68vb3V+zMzM5k+fTru7u7Y2try/PPP8+WXX1Jc/OAlLUvz22+/MXjwYBwdHWnYsCGdOnXSGIMfFBSEvb09aWlpDB48mEaNGjFmzBgA9u3bx8iRI2nZsiW2tra4u7szY8YM7ty5o379Tz/9hEKh4K+//lJv27BhAwqFgldffVUjlvbt2xMYGPjAeCt67pmZmQQFBdG4cWMaN27MW2+9RVZWVqXfn/JkZ2cDULdu3VL3W1paqts8iKOjI5999hm///47ly5d4sSJE3z77bfUq1ePTz75RKs4AJgzZw5nz55l0aJFMgxICCGEEHqjRt0JOH78OH5+ftSvX5/p06dTWFhIWFgYDRo00Gi3dOlSmjdvTt++fTE2Nmbr1q1MmTKF4uJi9cV1WFgY06ZNw9zcnClTpgBga2sLQG5uLt7e3ly6dInAwEAaNWrEwYMHmTVrFpcvX2bu3LkVjnn37t0MHToUpVLJW2+9hVKpJCUlhW3bthEUFKRuV1hYiJ+fHx06dCAkJITatWsDEB8fz507dxg9ejTW1tb8/vvvREVFcfHiRVasWAFAx44dMTAwYN++fbRs2RK4VywZGhpy4MAB9TGuXbvGqVOn1O9BaSp67iqVildeeYUDBw4wevRomjZtyubNmzXOqTrk5eWpi4/8/Hzy8vK02qhUKlQqVan77vfCCy/wwgsvqH+3trbG19eX5s2b89JLLzF37lyGDx+OsfG9j9Vvv/3GwoULeffdd3FxcVH3X1RUBEBBQUG5x9RnBQUFGn8L3ZA86A/Jhf6QXOiPmp4LMzMzXYdQphpVBMyZMweVSsWWLVtwcHAA4OWXX6ZTp04a7RISEtQX0QBvvvkm/v7+LFq0SH0B3L9/f0JDQ7G2tmbo0KEar1+0aBH//PMPSUlJ6iEogYGBPPPMM0RERDB+/HgaNWpUbrxFRUVMmjQJpVJJcnIyCoVCvU+lUmm0zc/Px9fXl48//lhj+6xZszTOZdSoUbi4uPDpp5+Snp6Og4MD9erVo3nz5uzfv58333wTuFcEvPzyy8THx3Pq1CmaNm3K/v37gXtFQ1kqeu4//fQT+/bt49NPP+Xtt98G4PXXX6d///7lvi+VkZ6err4oP336tPrn+2VnZ2NpaUl6enqVjmFubk7r1q359ddfSU5OxtXVlcLCQoKDg3F1dWXgwIEafZcsD5qRkVHlY+qTjIwMXYcgkDzoE8mF/pBc6I+amgs3Nzddh1CmGlMEFBUVsWvXLry9vdUFAECzZs3o1asX27dvV2+7/6I5KyuLwsJCOnfuTGJiIllZWVhZWT3wWPHx8XTs2BGFQsH169fV27t3786CBQvYt28fQ4YMKTfmo0ePcu7cOebMmaNRAAAYGBhotR89erTWtvvPJScnh7y8PNq3b49KpeLo0aPq96Jjx4789NNPANy6dYu//vqLWbNmkZyczP79+9VFgJWVFS1atHjoc9+xYwfGxsYaMRsZGTF27Fh1sVEdHBwccHd3JzExkdzcXI3cA1y5coXc3FzatGmjta8yGjZsCNwbcuTg4EBWVhbnz58Hyi6aSs592bJl9O3bt8rH1pWCggIyMjJQKpWYmprqOpwaS/KgPyQX+kNyoT8kF/qrxhQB165d486dO7i4uGjtc3V11SgCDhw4QFhYGIcOHSI3N1ejbXZ2drlFQGpqKsePHy91IirA1atXKxRzWloawAMvuksYGxtjb2+vtT09PZ05c+awZcsWrecg3D8OvmPHjixdupTU1FTS0tIwMDCgXbt2dOzYUT2vYP/+/XTo0AFDw7KnklT03NPT07Gzs8PCwkJjv6ura7nnWhlmZmZ4eXkRERFBcnIyAQEBGvtLVg7q2rVrlW/ZFRUVcfToUeDe5GMzMzNUKhWvvfZaqe337dvH2bNn6du3LzY2NurXPKlMTU2f6PifFpIH/SG50B+SC/0hudA/NaYIqKi0tDR8fHxwc3MjNDQUe3t7TE1N2b59O998802FJvYWFxfTo0ePMleDKesC+WHUqlVL6+K8qKiIgQMHcvPmTSZNmoSbmxvm5uZcvHiR4OBgjXPp0KEDcG+pzJIHdpmbm9OxY0cWL17M7du3OXr0KB9++OED49DFuZfHy8sLJycn4uLiGDt2rPpZAVlZWYSHh2NqaqpRHFy+fJns7GyUSqVGwXfkyBE8PDw0+i4qKuKTTz4hNTWVrl27YmdnB9y7A7Nw4cJS4wkKCuLs2bNMnjyZdu3aVfPZCiGEEEKUr8YUATY2NtSuXbvUpSDPnDmj/nnLli3k5+cTExOjMTwkOTlZ63WlDckBcHZ2Jicnh+7duz9UzM7OzgCcOHGiSn0dP36cM2fOEBkZybBhw9Tbd+/erdXWwcGBRo0asX//fv755x/1EJbOnTvzwQcfEB8fT1FRkdb8idJirsi5Ozg48Msvv3D79m2NuwH356K6GBsbExERgb+/P97e3vj5+WFhYcHGjRtJT08nJCQER0dHdftZs2YRExOj9dTi7t274+7ujru7Ow0bNuTmzZvs3buXM2fOYG9vX+ZFvxBCCCGEvqkxS4QaGRnRs2dPEhISNCZjpqSkkJiYqNEONCfeZmVllfpk4Dp16pS6pKWvry+//vqrRr8lMjMzKSwsrFDMzz33HI6OjkRGRmoN5fn3xODSlHYuKpWKb7/9ttT2nTp1IikpicOHD6uLgFatWmFpacmXX35J7dq1tb4J/7eKnnufPn0oLCxk6dKl6v1FRUWlLrNZHbp168bWrVvx9PRk/fr1LF26FFtbW5YuXcqECRMq1Mf48eOxtLTk559/ZtGiRcTFxWFmZsa7777L3r17cXJyeiSxCyGEEEJUtxpzJwBgxowZJCYm0rdvX9544w0KCwuJioqiefPmHD9+HLj3VNmS4SGBgYHk5OSwYsUKbGxsuHz5skZ/Hh4eREdHM3/+fFxcXLCxscHLy4u3336bLVu2MHToUF555RU8PDzIycnhxIkTbNy4kaNHj1K/fv1y4zU0NCQ8PJyAgAC6du3K8OHDsbOz49SpU5w8eZJ169Y98PVNmzbF2dmZDz/8kEuXLmFpacnGjRu1CooSHTt2ZM2aNRgYGKiLACMjI9q3b09iYiJdunQpd1JPRc+9b9++dOjQgU8++YTz58/TrFkzNm3aVKH1+quqbdu2xMXFldsuMjJS4zkMJWbPnl0tcZTVvxBCCCHE41KjioCWLVuydu1aPvjgA+bMmUPDhg2ZMWMGly9fVhcBbm5urFixgtmzZ/Phhx9ia2vL66+/Tv369Rk/frxGf1OnTiU9PZ2IiAhu3bpF586d8fLyok6dOiQkJBAeHk58fDw//vgjlpaWuLq6Mn369DIfWlWaXr16sWnTJubNm8eiRYsoLi7GycmJkSNHlvtaExMTfvzxR6ZNm8aCBQuoVasW/fv3Z8yYMXTp0kWrfcmFf9OmTbG2ttbYnpiY+MClQUtU9NwNDQ2JiYlh+vTprFmzBoC+ffsye/ZsunXrVqH3RgghhBBCVI1BZmZm+eNKhBDiX/Ly8tTPmpAVH3RH8qA/JBf6Q3KhPyQX+qvGzAkQQgghhBBC3FOjhgPpk5s3bz7wEdpGRkbY2Ng8xoiEEEIIIURNIUWAjrz66qvs3bu3zP0ODg4cO3bsMUYkhBBCCCFqCikCdCQ0NLTMVXoAGTcnhBBCCCEeGSkCdKS89fZF+ZqsusT1/NKf4JwZaK/++fDhw4SFhXHw4EEKCwtp0aIF48aNY+DAgVU6bmZmJh07duTSpUv06tWLtWvXarVRKBRlvn7YsGGyRKgQQgghdEqKAPFUS0pKwt/fHzMzM40nBQcGBnLhwoUKPyjsfu+9916Fnmfg4ODAK6+8orW9VatWlT6mEEIIIUR1kiJARw4fPsz06dP566+/yM3NJSkpidatW1frMby9vQFISEio1n6fFIWFhUycOBFDQ0MSEhLU7+/UqVPp1asXISEh+Pj40Lhx4wr3uWHDBmJjY5k/fz7vvffeA9s2btyYGTNmPNQ5CCGEEEI8CrJEqA7cvXuXkSNHcvPmTebMmcPixYsrdSH6KC1ZsoSVK1fqOoxqkZSURFpaGoMGDdIosKysrJg8eTIFBQXExMRUuL9r164xZcoUhg4dyn/+859HEbIQQgghxGMhdwJ0IC0tTf2k4REjRug6HA3R0dFYW1szfPhwXYfy0Pbs2QNAz549tfb16tUL4IErNP3bO++8g5GREfPmzSMrK6vc9llZWSxfvpzr169Tr149PD09cXd3r/DxhBBCCCEeFSkCdODq1avAvW+kxaNz9uxZAJo0aaK1T6lUYmFhQWpqaoX6Wr16NZs2bWLlypUoFIoKFQF//fUXkyZN0tjWu3dvIiMjadCgQYWOK4QQQgjxKMhwoMcsKChIPVZ/5MiRKBQKvL291X9Ka//viaTFxcV88803dOjQAaVSiZubG5MmTXrgkqMV0apVK/7++2/27t2LQqFQx/bPP/+gUChYtGiR1msOHjyIQqEgLi4OgLCwMBQKBadOnWLUqFE4ODjg7OzMtGnTyMvL03r96tWr8fLyws7ODicnJ0aPHs2FCxce6jxKlEzerVu3bqn7LS0tKzTB99KlS0ybNo1BgwaVmqPSjB8/nu3bt5Oamkp6ejrbt2+nT58+7Ny5k6FDh1JUVFTxExFCCCGEqGZyJ+AxCwwMpGHDhnzxxReMHTuWNm3aYGtry+eff17hPiZNmsSqVasYPnw4Y8eO5dy5c3z33XccPXqUbdu2YWJiUqXYwsLCmDZtGubm5kyZMgUAW1tbnJyc6NChA7GxsYwbN07jNbGxsVhaWtKvXz+N7aNGjaJx48Z89NFHHDp0iMWLF5OZmcnixYvVbT7//HNCQ0MZOHAgI0aM4Nq1a0RFRdGvXz+SkpIeuMxmefLy8iguvrd8aH5+fqkFiEqlQqVSlbrvfuPGjcPY2JhZs2ap2+bn5wP3CrLSXj9z5kyN31u3bs2KFSvw9/dn//79xMfHV7ig0FclT7x+0JOvxaMnedAfkgv9IbnQHzU9F/r83CcpAh6z9u3bk5+fzxdffEGnTp3w8fEBqHARsH//fr7//nu+++47Bg8erN7etWtX/P39iY+P19heGf379yc0NBRra2uGDh2qsS8gIIBJkyZx6tQpmjZtCtyb4Lx+/Xr69+9PnTp1NNo7OjqqJ92OGTOGunXrsmTJEiZMmEDLli05f/48YWFhzJw5U11wAAwYMIBu3boRHR2tsb2y0tPTMTa+98/79OnT6p/vl52djaWlJenp6WX2s3nzZnbt2sXcuXPJzc0lNzcXuHd3AODOnTsPfP2/9e3bl/3795OYmFjtq0HpSkZGhq5DEEge9InkQn9ILvRHTc2Fm5ubrkMokxQBT5j4+Hjq1q1Ljx49uH79unq7h4cHFhYWJCcnV7kIeBBfX1+mT5/OmjVr1N9yJyYmcv36da2CAe5d+N/vzTffZMmSJezYsYOWLVuyadMmiouLGThwoMZ5KJVKmjRpQnJy8kMVAQ4ODri7u5OYmEhubi4ODg4a+69cuUJubi5t2rTR2ne/ixcvAjB9+vRS9x84cIB27dqpj1Wekv8YGBsbP/C4T4KCggIyMjJQKpWYmprqOpwaS/KgPyQX+kNyoT8kF/pLioAnTGpqKtnZ2bi6upa6v2TScXVTKBS89NJLxMXFqYuA2NhYGjZsSLdu3bTau7i4aPzu7OyMoaEh58+fB+6dh0qlok2bNqUer7Rv7ivDzMwMLy8vIiIiSE5OJiAgQGN/ycpBXbt2feCtuo4dO5Y63CcnJ4d169Zhb29Pz549adSoUYVu+R09ehS4937o8y3CyjA1NX1qzuVJJnnQH5IL/SG50B+SC/0jRYCeMDAwQKVSaW3/9wTS4uJiGjRowHfffVdqP/Xr138k8cG9IUHx8fEcPHiQFi1asGXLFl5//XUMDcufX25gYKDxe3FxMQYGBsTFxWFkZKTV3tzc/KHj9fLywsnJibi4OMaOHasefpOVlUV4eDimpqYaxcHly5fJzs5GqVSqV27y8/PDz89Pq+9z586xbt06mjdvzsKFCzX2HT9+nKZNm2rNzTh48CBfffUVJiYm+Pr6PvT5CSGEEEJUlRQBekKhUPDPP/9obf/3eHNnZ2d+/vlnPD09qV27drXH8e+L9fv17t0bGxsbYmNjadu2Lbm5uaUOBYJ73/Q7OTlp/F5cXKx+KJqzszMqlQpHR8cy72o8LGNjYyIiIvD398fb2xs/Pz8sLCzYuHEj6enphISE4OjoqG4/a9YsYmJiWLRo0UM9J+Hrr79m+/btdOjQAXt7e0xMTDh58iS7du3CwMCAzz//HGdn5+o4RSGEEEKIKpElQvWEs7Mzp0+f5tq1a+ptx44d4+DBgxrtfH19KSoqYv78+Vp9FBYWPvQyoXXq1ClzDXxjY2P8/f1Zv349q1atokWLFrRs2bLUtv++UxEVFQXcKyTg3gTgkgdv/fsOiEql4saNGw91HiW6devG1q1b8fT0ZP369SxduhRbW1uWLl3KhAkTquUY/9avXz88PT3566+/WLlyJVFRUaSkpODv78+OHTsYPXr0IzmuEEIIIURFyZ0APfHqq6+yaNEi/Pz8eO2117h69SrLli2jefPm3Lp1S92uS5cuBAYGEh4ezrFjx+jRowcmJiacPXuWDRs2MHfuXPWKQ1Xh4eFBdHQ08+fPx8XFBRsbG7y8vNT7hw0bxuLFi0lOTmbWrFll9nPu3DkCAgLo3bs3v/76K2vWrGHw4MHqZx44Ozszc+ZMZs2axfnz5/H29sbCwoJz586xefNmRo0aVW0X6W3btlU/x+BBIiMjiYyMrFCfjo6OZRZcAwYMYMCAAZUJUQghhBDisZIiQE80a9aMb7/9ljlz5vDBBx/QrFkzFi9eTGxsrHoSa4kFCxbg4eHBsmXLCAkJUa80M2TIEDw9PR8qjqlTp5Kenk5ERAS3bt2ic+fOGkWAh4cHzz77LCkpKQ9chWjZsmXMmTOHWbNmYWRkxJgxYwgJCdFo884779CkSRMiIyOZN28egHqibd++fR/qPIQQQgghRNkMMjMztWejCvEAXbt2pV69emzcuFFrX1hYGPPmzePs2bOPdJKy0L28vDzS09NxcHCQFR90SPKgPyQX+kNyoT8kF/pL5gSISvnjjz84duyY1pKbQgghhBDiySHDgWqIa9euaS03ej9TU1Pq1atX5v4TJ05w5MgRvv76a+zs7EpdNlMIIYQQQjwZpAioIXr06KG13Oj9OnfuTEJCQpn7N2zYwGeffYabmxtLliyRW3pCCCGEEE8wmRNQQxw4cKDUJ9+WUCgUeHh4PL6AxBNPxnnqB8mD/pBc6A/Jhf6QXOgvuRNQQ3To0EHXIVS7JqsucT2/WP17ZqC9+ufDhw8TFhbGwYMHKSwspEWLFowbN46BAwdWqO8dO3YQExPDsWPHyMjI4O7duzRq1AhPT08mTZpU6gPOFi9ezJ9//smRI0dISUmhqKiITZs20bVr14c/WSGEEEKIaiRFgHjqJCUl4e/vj5mZmcZTggMDA7lw4UKFnj+wfft2Dh06xAsvvEDv3r0xMTEhJSWFmJgYYmNjWbNmjcbSqQDTpk0DwM7ODhsbGzIyMh7J+QkhhBBCPCy9XR3o3LlzKBQKVq5cqetQKsTb2xtvb29dh/FEadWqFUOHDq3WPgsLC5k4cSKGhoYkJCTw1VdfERoayp49e3B1dSUkJITz58+X209ISAjHjh1j2bJlhIWF8emnn7J69WrWrVtHfn4+n3zyidZrVq9eTUpKCidPnqRfv37Vel5CCCGEENVJb4uAqlqyZMkjKxxOnjxJWFgY586deyT9P40e93uWlJREWloagwYNonXr1urtVlZWTJ48mYKCAmJiYsrtp6xxi15eXigUClJTU7X2vfjiiyiVyqoHL4QQQgjxmOjtcKDGjRtz+fJlTExMKvW66OhorK2tGT58eLXHlJKSwrx58+jSpQuOjo4a+9avX1/tx3saPOg9exRKnq7cs2dPrX29evUCYO/evVXu/9dffyUzM5OOHTtWuQ8hhBBCCF2rliIgJycHc3Pz6uhKzcDA4JHPIq/OuE1NTaulH/Fwzp49C0CTJk209imVSiwsLEr9Fr8su3bt4uDBgxQUFHD27Fm2bdtG/fr1mTNnTrXFLIQQQgjxuFV6OFBYWBgKhYKTJ0/yxhtv4OjoyEsvvQTcGxPt5eWFnZ0dTk5OjB49mgsXLmj18d133/Hcc89hZ2dHz5492bdvn9aY+tLmBGRkZBAcHEyLFi2wtbWlWbNmDBs2TD3UpFWrVvz999/s3bsXhUKBQqFQ97ly5UoUCgV79uxhypQpuLq60qJFCwDOnz/PlClTeOGFF7Czs8PZ2ZmRI0dqDGFZuXIlI0eOBGDAgAHq/pOTk4HS5wRcvXqV8ePH4+bmhlKppHPnzqxatUqjTcl5Lly4kOXLl+Ph4YGtrS09evTg8OHDlcpNcnIyCoWC9evXM3fuXJ599lkaNWrEiBEjyMrKIj8/n+nTp+Pq6oq9vT3BwcHk5+dr9FFYWMhnn32mjqNVq1Z8+umnWu1KxvPv37+fnj17olQqee655zSG2pT3npV4UB+VlZ2dDUDdunVL3W9paaluUxG7du1i3rx5LFiwgI0bN2Jvb8/atWt5/vnnqxyjEEIIIYSuVflOwKhRo3BxceGjjz5CpVLx+eefExoaysCBAxkxYgTXrl0jKiqKfv36kZSUhEKhAO4N13nvvffo2LEjwcHBnD9/nuHDh6NQKGjYsOEDjzlixAj+/vtv3nzzTRo3bsy1a9fYvXs3Fy5cwNHRkbCwMKZNm4a5uTlTpkwBwNbWVqOPd999l/r16zN16lRyc3MB+OOPPzh48CB+fn7Y29tz/vx5oqOj6d+/PwcPHqROnTp07tyZsWPHsnjxYqZMmULTpk0BaNasWamx3rlzh/79+5OamsqYMWNwdHQkPj6e4OBgsrKyCAoK0mgfGxvL7du3CQwMxMDAgK+++orXXnuNI0eOVHpIVHh4OLVr12bSpEmkpqYSFRWFiYkJhoaGZGZmMn36dA4dOsSqVatwdHRUr2oDMGHCBGJiYvDx8WH8+PH89ttvhIeHk5KSojXXIjU1lZEjR/Laa68xbNgwfvjhB4KDg/Hw8ODZZ5+t0HtWXh+VkZeXR3HxvSVD8/PzS30ugkqlQqVSPfCZCfebOXMmM2fOJCcnh5SUFMLDw3nxxRdZsGDBA5+aXPJ05oKCggof60lTUFCg8bfQDcmD/pBc6A/Jhf6o6bnQ52cjVLkIaNmyJUuWLAHufZP+/PPPM3PmTPXFN9z79rdbt25ER0czZcoUCgoKCA0NpU2bNmzatAlj43uHd3d3Jzg4+IFFQGZmJgcPHiQkJERjicfJkyerf+7fvz+hoaFYW1uXuepMvXr12LhxI0ZGRupt//nPf/Dx8dFo99JLL9GnTx82btxIQEAATk5OdOrUicWLF9O9e/dy135fvnw5KSkpREVFMWTIEABGjx6Nt7c3oaGhvPrqq1haWqrbX7hwgcOHD6uLJVdXV1555RUSExPVd1oqqqioiISEBHXxcP36ddauXUvv3r2JjY0F4I033iAtLY0ffvhBXQQcO3aMmJgYRowYQUREhLpdgwYNWLhwIUlJSXTr1k19nNOnT/PTTz/RqVMnAAYOHIi7uzsrV65k9uzZFXrPyuujMtLT09X/pk6fPq3++X7Z2dlYWlo+8OnJZWnQoAEhISGMGDFCfTepXr16pba9ffs2cO9uUFWO9SSRpVD1g+RBf0gu9IfkQn/U1Fy4ubnpOoQyVbkICAwMVP+8adMmiouLGThwINevX1dvVyqVNGnShOTkZKZMmcIff/zBjRs3+PjjjzUu0IYMGcL777//wOPVrl0bU1NT9uzZw2uvvaa+WK6sESNGaBQAJX2XuHv3Lrdu3cLFxQUrKyv+/PNPAgICKn2cHTt2oFQqGTRokHqbiYkJY8eO5fXXX2fv3r0aF/d+fn4a51RyUfzPP/9U+tgBAQEadw/atm1LXFwcr776qka7tm3bsnjxYgoLCzE2NmbHjh0AjBs3TqPd+PHjWbhwIdu3b9coApo3b66OE8DGxgZXV9dKxVwdfZRwcHDA3d2dxMREcnNzcXBw0Nh/5coVcnNzadOmjda+yujRowdRUVFcvXpVYwWi+1lYWAD3CoeHOZY+KygoICMjA6VSKXNidEjyoD8kF/pDcqE/JBf6q8pFwP0rvaSmpqJSqWjTpk3pB/l/F/wl34g6Oztr7W/cuPEDj1erVi0++eQTZs6ciZubGy+88AIvvfQSAQEBlVqWsbQVau7cuUN4eDirVq3i4sWLqFQq9b7KjB+/X3p6Oi4uLhgaak67KBkS8+9vhxs1aqTxe0lBkJmZWelj/7uvkvHx9vb2WtuLi4vJzs7G2tqa9PR0DA0NcXFx0WinVCqxsrIqN+aSuCsTc3X0UcLMzAwvLy8iIiJITk7WKt5KVg7q2rXrQ92eu3r1KgDm5uZl9lNSaJqamur1rcDqUBPO8UkgedAfkgv9IbnQH5IL/VPlIuD+b8+Li4sxMDAgLi5O61t2oNpW4AkODqZv375s3ryZXbt2ERoaSnh4OBs3buS5556rdNwlpk6dysqVKwkKCqJ9+/bUrVsXAwMDRo8erR5j/qiV9r4BGgXJw/ZV0WMYGBg81HEqE3N1njfcW8ffycmJuLg4xo4dq/6mPisri/DwcExNTTWKg8uXL5Odna0udEr88ccfpU7+TUxMZPPmzVhZWdGuXbsqxSiEEEIIoWvVskSos7MzKpUKR0dHXF1dy2xXMiwiLS1NY1hJYWEh58+fx93dvULHmjBhAhMmTODs2bN07dqVRYsWERUVBVT8AvZ+GzZsYNiwYYSGhqq35eXlkZWVVem+Sjg4OHD8+HGKi4s17gacPn1avV/fODg4UFxczNmzZzUm7165coWsrCy9jPnfjI2NiYiIwN/fH29vb/z8/LCwsGDjxo2kp6cTEhKicTdo1qxZxMTEsGjRIo1nS/To0YMWLVrg7u5Ow4YNyc3N5a+//mL//v2YmJjw9ddfaxW3CxYs4NSpUwAcOnQIgC+//FK9IpS3tzf9+/d/1G+BEEIIIUS5quWJwQMGDMDIyIh58+ZpfYOrUqm4ceMGAM8//zzW1tasWLGCwsJCdZs1a9aUO/wjNzdXa5UVZ2dnLCwsNJavrFOnTqUv3o2MjLTijoqKUq/wUqLkoq8i/ffp04eMjAzWrVun3lZYWEhUVBQWFhZ07ty5UjE+Dn369AEgMjJSY/uiRYuAexOoK6sy71l16datG1u3bsXT05P169ezdOlSbG1tWbp0qcak8gf56KOPUCqV7N27l8jISP773/9y9epVRo0axZ49exgwYIDWa3bu3ElMTAwxMTGcOXMGuHfnoGTbsWPHqvU8hRBCCCGqqtruBMycOZNZs2Zx/vx5vL29sbCw4Ny5c2zevJlRo0YxYcIETE1NmT59OlOnTmXAgAEMHDiQ8+fPs2rVKpydnR/4Lf6ZM2fw8fFh4MCBNGvWDGNjYzZv3syVK1c0lmr08PAgOjqa+fPn4+Ligo2NDV5eXg+M/8UXX2T16tXUrVuX5s2b8+uvv/LLL79gbW2t0a5Vq1YYGRnx1VdfkZ2dTa1atejWrRsNGjTQ6nPUqFEsX76c4OBgjhw5QuPGjdmwYQMHDhwgLCxMY2UgfdGqVSuGDRvG8uXLycrKonPnzvz+++/ExMTg7e2tcfemMn1W9D2rTiWTocsTGRmpVfTAvVWn7l95qiISEhIq1V4IIYQQQleqpQgAeOedd2jSpAmRkZHMmzcPuDcRtWfPnvTt21fd7s0330SlUvH111/z4Ycf0rJlS2JiYpg2bdoDJ4w0atSIQYMG8csvv7B69WqMjIxo2rQpy5cv11jec+rUqaSnpxMREcGtW7fo3LlzuUXA3LlzMTIyIjY2lvz8fDw9PYmPj9daB16pVLJgwQLCw8OZMGECRUVFbNq0qdQL2tq1a7N582Y++eQTYmJiuHXrFq6urlrDTvTNwoULcXJyYtWqVWzevBmlUsnkyZM1niVQGZV5z4QQQgghxONhkJmZWbUZmNWouLiYJk2aMGDAAPX69EII/ZaXl0d6ejoODg6y4oMOSR70h+RCf0gu9IfkQn9Vy5yAysjLy9Mafx8TE8PNmzfp0qXL4w5HCCGEEEKIGqfahgNV1KFDh3j//ffx9fXF2tqaP//8k//+97+0aNECX1/fxx3OE6GgoICbN28+sE3dunVLXf5UCCGEEEKIf3vsRUDjxo2xt7dn8eLF3Lx5k3r16hEQEMAnn3wiT5Irw8GDB0tdjeZ++j7XQAghhBBC6I/HXgQ4Ojry448/Pu7DPtFatWpFfHz8A9s0b9788QQjhBBCCCGeeI+9CBCVp1Ao6N69u67D0DtNVl3iev69JzpnBtqrtx8+fJiwsDAOHjxIYWEhLVq0YNy4cQwcOLDcPlUqFTt37mTLli0cOHCACxcucPfuXVxcXPDz82PcuHEVmtg0efJkli5dCkBKSgpKpbKKZymEEEIIUf2kCBBPlaSkJPz9/TEzM9N4WnBgYCAXLlwo92Fh+fn5DB48mFq1atGlSxd69epFXl4eu3btIiQkhISEBDZv3kydOnXK7GP37t0sXboUc3NzcnJyqvsUhRBCCCEemhQBosK8vb25ceMG+/fv13UopSosLGTixIkYGhqSkJBA69atgXvPjujVqxchISH4+PjQuHHjMvswMjJi5syZvPHGGygUCvX2u3fv8tprr7F161aWLFnC22+/Xerrs7KyGD9+PD4+Ply7do29e/dW6zkKIYQQQlSHx75EqBCPSlJSEmlpaQwaNEhdAABYWVkxefJkCgoKiImJeWAfJiYmvPvuuxoFQMn2kicIP+jCfvr06dy5c4fPP/+86icihBBCCPGIyZ0A8dTYs2cPAD179tTa16tXL+DBF/DlMTExAe7dLSjNli1biImJYcmSJfJEZCGEEELoNbkTIMr1pIxrP3v2LABNmjTR2qdUKrGwsCA1NbXK/f/www9A6UXGjRs3mDhxIt7e3gwaNKjKxxBCCCGEeBykCNBjycnJdO/eHaVSiYeHB8uWLSMsLExrqMrq1avx8vLCzs4OJycnRo8ezYULFzTa7Nu3j5EjR9KyZUtsbW1xd3dnxowZ3LlzR6NdUFAQ9vb2pKWlMXjwYBo1asSYMWM02hw5coT//Oc/2NnZ0bp1a/UqOPe7evUq48ePx83NDaVSSefOnVm1apXG/iZNmuDt7a3xBOnU1FQaNmxIYGBgpd+v7Oxs4N6D00pjaWmpblNZO3bsYNmyZTRr1ozXXntNa/+UKVMoKCggPDy8Sv0LIYQQQjxOMhxIT/35558MGjQIpVLJjBkzKCoq4rPPPqN+/foa7T7//HNCQ0MZOHAgI0aM4Nq1a0RFRdGvXz+SkpLUBUN8fDx37txh9OjRWFtb8/vvvxMVFcXFixdZsWKFRp+FhYX4+fnRoUMHQkJCNJ5EnJmZyeDBg/H19cXf35/4+HgmT56MiYmJ+uL4zp079O/fn9TUVMaMGYOjoyPx8fEEBweTlZVFUFAQDRo0IDw8nJEjR7J48WLeeustiouLCQ4OxsLCgi+++KJS71deXh7FxfeWC83PzycvL0+rjUqlQqVSlbrvQf744w8CAwOpW7cuixcv1uojPj6e9evXs3DhQqysrNT7yovnSVdQUKDxt9ANyYP+kFzoD8mF/qjpuajIsuK6IkWAngoLC8PIyIht27bxzDPPADBw4EDat2+vbnP+/HnCwsKYOXMmU6ZMUW8fMGAA3bp1Izo6Wr191qxZGhfzo0aNwsXFhU8//ZT09HQcHBzU+/Lz8/H19eXjjz/WiuvSpUvMnj2b8ePHAxAYGEivXr349NNPCQgIwMTEhOXLl5OSkkJUVBRDhgwBYPTo0Xh7exMaGsqrr76KpaUlPj4+DBo0iE8//ZQ+ffrw008/ceDAAVauXIm1tXWl3q/09HSMje/9cz59+rT65/tlZ2djaWlJenp6hfs9ceKE+ly//PJLzM3NNV6flZXFtGnT6NKlCx06dNDYl5+fD8D//vc/9c9Po4yMDF2HIJA86BPJhf6QXOiPmpoLNzc3XYdQJikC9FBRURG//PIL/fv3VxcAAC4uLvTu3ZutW7cCsGnTJoqLixk4cCDXr19Xt1MqlTRp0oTk5GR1EXB/AZCTk0NeXh7t27dHpVJx9OhRjSIA7l20l8bY2FhjqI6pqSmBgYFMnjyZI0eO0K5dO3bs2IFSqdQYG29iYsLYsWN5/fXX2bt3Ly+99BIA8+fPZ8+ePYwYMYKzZ88ydOhQvL29K/2eOTg44O7uTmJiIrm5uVrnc+XKFXJzc2nTpo3WvrIcOXJEvRTomjVraNOmjVabrKwssrKy2LNnD+3atSu1n759+wKwc+dOWrZsWZnT0msFBQVkZGSgVCoxNTXVdTg1luRBf0gu9IfkQn9ILvSXFAF66OrVq9y5cwdnZ2etfS4uLuqfU1NTUalUpV6cAhrfhqenpzNnzhy2bNlCZmamRrt/j5M3NjbG3t6e0tjZ2WFubq6xrWQi7vnz52nXrh3p6em4uLhgaKg55aRp06bqWErUq1ePefPmMXLkSGxtbZk3b16pxy2PmZkZXl5eREREkJycTEBAgMb+kpWDunbtWqFbc3/88QdDhw6luLiYtWvXatyBuZ+dnV2pcwQAtm/fTkZGBoMHD8bMzAw7Ozu9vi1YVaampk/leT1pJA/6Q3KhPyQX+kNyoX+kCHiCFRcXY2BgQFxcXKnLVpZcrBcVFTFw4EBu3rzJpEmTcHNzw9zcnIsXLxIcHKweu16iVq1aWhfwj1JiYiJwb77BxYsXtSY+V5SXlxdOTk7ExcUxduxY9bMCsrKyCA8Px9TUVKM4uHz5MtnZ2SiVSqysrNTbjxw5gq+vL0VFRcTFxZVZAAA0atSIhQsXlrrP29ubjIwMZs+ejVKprNI5CSGEEEI8ClIE6KEGDRpgZmZGWlqa1r77l7h0dnZGpVLh6OiIq6trmf0dP36cM2fOEBkZybBhw9Tbd+/eXenYLl++TE5OjsbdgJKlOUuexOvg4MDx48cpLi7WKCZOnz6t3l9i586dfP/990ycOJHY2FiCgoJITEwsdUx/eYyNjYmIiMDf3x9vb2/8/PywsLBg48aNpKenExISgqOjo7r9rFmziImJYdGiRQwfPhyAmzdv4uvrS1ZWFr1792b37t1a75OVlRXBwcGVjk8IIYQQQl/IEqF6yMjICC8vLxISErh06ZJ6e2pqKjt37lT/PmDAAIyMjJg3b57GMptwbyWcGzduqPsr2Xb//m+//bbSsRUWFrJs2TL17wUFBSxbtgwbGxs8PDwA6NOnDxkZGaxbt07jdVFRUVhYWNC5c2fg3jf/b7/9Nm3btuWjjz4iIiKCP//8s9IrA92vW7dubN26FU9PT9avX8/SpUuxtbVl6dKlTJgwodzXZ2dnq4dL7dy5k3nz5mn9iYyMrHJ8QgghhBD6QO4E6KkZM2awe/duXnzxRV5//XWKior47rvvePbZZzl27Bhw707AzJkzmTVrFufPn8fb2xsLCwvOnTvH5s2bGTVqFBMmTKBp06Y4Ozvz4YcfcunSJSwtLdm4caPW3ICKeOaZZ/jqq684f/48rq6urF+/nmPHjvHVV1+pn6g7atQoli9fTnBwMEeOHKFx48Zs2LCBAwcOEBYWhqWlJQDTp0/nxo0bxMfHY2RkRO/evRkxYgSff/45/fr1o1WrVlV679q2bUtcXFy57SIjI7Uu6B0dHav0vpQmISGhWvoRQgghhKhucidAT3l4eBAbG4tCoSA0NJT//ve/zJgxAy8vL42JNe+88w7ff/89hoaGzJs3jw8//JAtW7bQs2dP9ao0JiYm/Pjjj7Rq1YoFCxYwb948mjRpUqU7AQqFgtjYWI4cOcJHH33EhQsXmD9/PiNHjlS3qV27Nps3b2bw4MHExMQwc+ZMbt68yaJFiwgKCgLgp59+4scff2TmzJnqCcMAoaGh2NnZERQUxN27d6v69gkhhBBCiAcwyMzMVJXfTOiLV155hZMnT3L48GFdhyJquLy8PPUzJmTFB92RPOgPyYX+kFzoD8mF/pI7AXrszp07Gr+fPXuWHTt20KVLFx1FJIQQQgghngYyJ0CPeXh48Morr+Dk5ER6ejrR0dGYmpoyceJEXYcmhBBCCCGeYFIE6LFevXoRFxfHlStXqFWrFu3atePDDz9UP5xLCCGEEEKIqpAiQI998803ug5BCCGEEEI8hWROgHgqHD58mMGDB9O4cWMaNmxI7969Wb9+fYVfn5aWRlhYGAEBATz77LMoFIpylyhdvHgxwcHBdOrUifr166NQKEhOTn7YUxFCCCGEeOTkToB44iUlJeHv74+ZmZnGU4IDAwO5cOFChR4Stm/fPubNm4eRkRHNmjUjIyOj3NdMmzYNADs7O2xsbCr0GiGEEEIIfSB3Au4TFhaGQqHQdRg1nkKhICwsrEJtCwsLmThxIoaGhiQkJPDVV18RGhrKnj17cHV1JSQkhPPnz5fbT+fOndmxYwcXLlxg37596gefPcjq1atJSUnh5MmT9OvXr0LxCiGEEELoAykCqsGSJUtYuXKlrsN4rGJjY/VizkJSUhJpaWkMGjSI1q1bq7dbWVkxefJkCgoKiImJKbcfJycn2rVrR+3atSt87BdffBGlUlmluIUQQgghdEmKgGoQHR3NqlWrdB3GYxUXF0dkZKSuw2DPnj0A9OzZU2tfr169ANi7d+9jjUkIIYQQQt9JEfCUy8nJ0XUIj9TZs2cBSl02ValUYmFhQWpq6uMOSwghhBBCr9XYImD//v306NEDpVKJh4cHy5Yt02rzww8/MGDAAFxdXbG1tcXT05Po6GiNNq1ateLvv/9m7969KBQKFAoF3t7e6v2ZmZlMnz4dd3d3bG1tef755/nyyy8pLi6udMy//fYbgwcPxtHRkYYNG9KpUyeNb+ODgoKwt7cnLS2NwYMH06hRI8aMGQNAcXEx33zzDR06dECpVOLm5sakSZPIzMzUOEZCQgJDhgyhefPm2Nra4uHhwWeffUZRUZG6jbe3N9u2bSM9PV19zvevpJOfn8+cOXN4/vnnsbW1xd3dnY8++oj8/HyNY+Xn5zNjxgyaNGlCo0aNCAgI4H//+1+l3pPs7GwA6tatW+p+S0tLdRshhBBCCHFPjVwd6Pjx4/j5+VG/fn2mT59OYWEhYWFhNGjQQKPd0qVLad68OX379sXY2JitW7cyZcoUiouL1RfXYWFhTJs2DXNzc6ZMmQKAra0tALm5uXh7e3Pp0iUCAwNp1KgRBw8eZNasWVy+fJm5c+dWOObdu3czdOhQlEolb731FkqlkpSUFLZt20ZQUJC6XWFhIX5+fnTo0IGQkBD1GPdJkyaxatUqhg8fztixYzl37hzfffcdR48eZdu2beqJsKtWrcLc3Jxx48Zhbm5OUlISc+bM4datW4SEhADw7rvvkp2dzcWLF5kzZw4A5ubmwL1iY9iwYRw4cICRI0fSrFkzjh8/zjfffMOZM2c0hk1NmDCBNWvWMHjwYNq3b09SUhJDhgyp8HuSl5enLqby8/PJy8vTaqNSqVCpVKXuK09lXldSJBUUFFTpWE+igoICjb+Fbkge9IfkQn9ILvRHTc+FmZmZrkMoU40sAubMmYNKpWLLli04ODgA8PLLL9OpUyeNdgkJCRoTRd988038/f1ZtGiRugjo378/oaGhWFtbM3ToUI3XL1q0iH/++YekpCT1cJXAwECeeeYZIiIiGD9+PI0aNSo33qKiIiZNmoRSqSQ5OVljBSOVSqXRNj8/H19fXz7++GP1tv379/P999/z3XffMXjwYPX2rl274u/vT3x8vHr7kiVLNM559OjRvPPOO0RHRzNz5kxq1apFjx49aNiwIZmZmVrnHBsby88//0xCQgIdO3ZUb2/RogXvvPMOBw8exNPTk2PHjrFmzRreeOMNPv/8cwDGjBnDmDFjOH78eLnvCcDFixcxNr73T/j06dPqn++XnZ2NpaUl6enpFeqzhEqloqioqMKvu337NgBXr16t9LGedLI0qn6QPOgPyYX+kFzoj5qaCzc3N12HUKYaVwQUFRWxa9cuvL291QUAQLNmzejVqxfbt29Xb7v/YjgrK4vCwkI6d+5MYmIiWVlZWFlZPfBY8fHxdOzYEYVCwfXr19Xbu3fvzoIFC9i3b1+Fvvk+evQo586dY86cOVpLmBoYGGi1Hz16tFYcdevWpUePHhpxeHh4YGFhQXJysroIuP+cb926RUFBAR07dmTZsmWcOnWq3AdoxcfH06xZM5o2bapxrG7dugGQnJyMp6cnO3bsAGDs2LEarw8KCiI2NvaBxyjRsGFD3N3dSUxMJDc3VyOfAFeuXCE3N5c2bdpo7SuPgYEBRkZGFX6dhYUFAA0aNKj0sZ5UBQUFZGRkoFQqMTU11XU4NZbkQX9ILvSH5EJ/SC70V40rAq5du8adO3dwcXHR2ufq6qpRBBw4cICwsDAOHTpEbm6uRtvs7Oxyi4DU1FSOHz9e6qRVuPetcUWkpaUB975NL4+xsTH29vZacWRnZ+Pq6lpuHH///TezZ88mOTlZayx9RcbWp6amkpKSUu45p6enY2hoiLOzs8b+smIsjZmZGV5eXkRERJCcnExAQIDG/pKVg7p27Vql23EGBgYVfp2RkREApqamen3r71GoieesjyQP+kNyoT8kF/pDcqF/alwRUFFpaWn4+Pjg5uZGaGgo9vb2mJqasn37dr755psKTewtLi6mR48eTJw4sdT9ZV0oP4xatWphaKg537u4uJgGDRrw3Xfflfqa+vXrA/cmMXt7e2NpacmMGTNwdnbGzMyMP//8k48//rjC59yiRQv1XIF/+3eB8rC8vLxwcnIiLi6OsWPHqp8VkJWVRXh4OKamphrFweXLl8nOzkapVJZbxAkhhBBCPK1qXBFgY2ND7dq1S1028syZM+qft2zZQn5+PjExMRrDO5KTk7VeV9qQHABnZ2dycnLo3r37Q8Vc8m35iRMnqtSXs7MzP//8M56eng98GNaePXu4ceMG//3vf+ncubN6+7lz5yp1rL/++gsvL68y3xcABwcHiouLSUtL0xgvd38OKsLY2JiIiAj8/f3x9vbGz88PCwsLNm7cSHp6OiEhITg6Oqrbz5o1i5iYGBYtWsTw4cPV269fv87MmTPVv9+9e5cbN25oTLqePXu2umACWLBgAadOnQLg0KFDAHz55Zfqyc/e3t7079+/UucjhBBCCPE41LglQo2MjOjZsycJCQkaEzhTUlJITEzUaAeaE2+zsrJKfTJwnTp1yMrK0tru6+vLr7/+qtFviczMTAoLCysU83PPPYejoyORkZFaS3r+e2JwaXx9fSkqKmL+/Pla+woLC9V9lnbOBQUFLFmyROt15ubmpQ4P8vX15eLFi6xYsUJr3507d9TPLejduzcAixcv1mhTlQeQdevWja1bt+Lp6cn69etZunQptra2LF26lAkTJlSoj9u3bxMTE6P+U1xcTE5Ojsa2ksm/JXbu3KneV1K8JCYmqrcdO3as0ucihBBCCPE41Lg7AQAzZswgMTGRvn378sYbb1BYWEhUVBTNmzdXr0zTs2dP9VCSwMBAcnJyWLFiBTY2Nly+fFmjPw8PD6Kjo5k/fz4uLi7Y2Njg5eXF22+/zZYtWxg6dCivvPIKHh4e5OTkcOLECTZu3MjRo0c1vlkui6GhIeHh4QQEBNC1a1eGDx+OnZ0dp06d4uTJk6xbt+6Br+/SpQuBgYGEh4dz7NgxevTogYmJCWfPnmXDhg3MnTsXHx8fPD09USgUBAUFMXbsWAwMDFi9enWphYaHhwfr1q3j/fffp02bNpibm9O3b18CAgKIj4/nnXfeUU8CLioq4vTp06xfv55169bx/PPP07p1awYNGsSSJUvIzs6mffv2/PLLL1V+sFfbtm2Ji4srt11kZGSphYajo6NWgVWehISESrUXQgghhNAXNbIIaNmyJWvXruWDDz5gzpw5NGzYkBkzZnD58mV1EeDm5saKFSuYPXs2H374Iba2trz++uvUr1+f8ePHa/Q3depU0tPTiYiI4NatW3Tu3BkvLy/q1KlDQkIC4eHhxMfH8+OPP2JpaYmrqyvTp08v8wFXpenVqxebNm1i3rx5LFq0iOLiYpycnBg5cmSFXr9gwQL1Q9FCQkIwNjbGwcGBIUOG4OnpCYC1tTWrV69m5syZhIaGolAoGDJkCF5eXvj5+Wn09/rrr3Ps2DFWrlzJN998g4ODA3379sXQ0FC97ccff2Tz5s3Url0bJycn3nrrLY15EF9//TX169cnNjaWhIQEunbtypo1a3B3d6/w+yKEEEIIISrPIDMzs/zxJEII8S95eXmkp6fj4OAgKz7okORBf0gu9IfkQn9ILvRXjZsTIIQQQgghRE1XI4cD6ZObN28+8FHaRkZG2NjYPMaIhBBCCCHE006KAB179dVX2bt3b5n7HRwcZJUZIYQQQghRraQI0LHQ0NAHrkoj4+eEEEIIIUR1kyJAxzw8PHQdghBCCCGEqGFkYrAQQgghhBA1jBQBQgghhBBC1DBSBAghhBBCCFHDSBEghBBCCCFEDSNFgBBCCCGEEDWMFAFCCCGEEELUMFIECCGEEEIIUcNIESCEEEIIIUQNI0WAEEIIIYQQNYwUAUKIKjMyMtJ1CALJgz6RXOgPyYX+kFzoJ4PMzEyVroMQQgghhBBCPD5yJ0AIIYQQQogaRooAIYQQQgghahgpAoQQQgghhKhhpAgQQgghhBCihpEiQAghhBBCiBpGigAhhBBCCCFqGCkChBBCCCGEqGGkCBBCCCGEEKKGkSJAPBHy8/P5+OOPad68OXZ2dvTq1Yvdu3frOqwaJzk5GYVCUeqfQ4cO6Tq8p9bt27eZM2cO/v7+ODk5oVAoWLlyZaltU1JS8Pf3x97eHicnJ958802uXbv2mCN+elU0F0FBQaV+Ttq1a6eDqJ8+hw8f5r333qNDhw40bNiQli1bMmrUKM6cOaPVVj4Tj05F8yCfB/1krOsAhKiI4OBgNmzYQFBQEE2aNGHVqlUMHjyYTZs20bFjR12HV+OMHTuWNm3aaGxzcXHRUTRPv+vXr/PZZ5/RqFEjWrZsyZ49e0pt97///Y9+/fpRt25dPvzwQ3Jycli4cCEnTpxg165dmJqaPubInz4VzQVArVq1iIiI0NhWt27dRx1ijfDll19y8OBBfHx8aNmyJRkZGXz33Xd4eXmxY8cOWrRoAchn4lGraB5APg/6SIoAofd+//131q5dS0hICBMmTAAgICCAjh078vHHH7N9+3YdR1jzdOrUCR8fH12HUWPY2dmRkpKCUqnkjz/+oEePHqW2Cw8PJzc3l59//hkHBwcA2rZti6+vL6tWrWLUqFGPMeqnU0VzAWBsbMzQoUMfY3Q1x7hx41iyZInGRbyfnx+dOnXiyy+/JCoqCpDPxKNW0TyAfB70kQwHEnpvw4YNGBkZMXLkSPU2MzMzXnvtNX799VcuXLigw+hqrlu3blFYWKjrMGqEWrVqoVQqy223ceNGXnzxRfXFDkD37t1xdXVl/fr1jzLEGqOiuShRVFREdnb2I4yoZvL09NT6Fr9JkyY0b96clJQU9Tb5TDxaFc1DCfk86BcpAoTeO3r0KK6urlq3Ddu2bQvAsWPHdBFWjTZu3DgcHBxQKpX079+fP/74Q9ch1XgXL17k6tWrPP/881r72rRpw9GjR3UQVc2Wm5uLg4MDjRs3xsnJiXfffZfbt2/rOqynlkql4urVq9SvXx+Qz4Su/DsPJeTzoH9kOJDQexkZGaV+81ay7fLly487pBrL1NSUl19+mT59+lC/fn1SUlJYuHAhffv2Zdu2bTz33HO6DrHGysjIACj1s2JnZ8fNmzfJz8+nVq1ajzu0GsnOzo6JEyfy3HPPUVxczM6dO1myZAl//fUXmzdvxthY/vdb3dasWcPFixeZMWMGIJ8JXfl3HkA+D/pK3nWh9+7cuVPqf6TNzMzU+8Xj4enpiaenp/r3fv364ePjQ+fOnfn0009Zu3atDqOr2Uo+B6V9Vkq2lfVZEtXv448/1vjd398fV1dXQkJC2LBhA/7+/jqK7Ol06tQp3nvvPdq3b88rr7wCyGdCF0rLA8jnQV/JcCCh92rXrk1+fr7W9ry8PPV+oTsuLi7069eP5ORkioqKdB1OjVXyOSjts1KyTT4ruhUcHIyhoSE///yzrkN5qmRkZDBkyBDq1q3LihUrMDIyAuQz8biVlYeyyOdB9+ROgNB7SqWSS5cuaW0vudVrZ2f3uEMS/2Jvb09BQQE5OTmy5JuOlAx5KPlc3O/y5cvUq1dPvvHUsdq1a2Ntbc3Nmzd1HcpTIysri0GDBpGVlcWWLVt45pln1PvkM/H4PCgPZZHPg+7JnQCh91q1asWZM2e0VhT47bff1PuFbv3zzz+YmZlhYWGh61BqrIYNG2JjY1PqJO3Dhw/L50QP3Lp1i+vXr2NjY6PrUJ4KeXl5BAQEcPbsWVavXk3z5s019stn4vEoLw9lkc+D7kkRIPSej48PRUVFrFixQr0tPz+flStX8sILL9CoUSMdRlezlPaUzWPHjrFlyxZ69OiBoaH8J0WXXn75ZbZt26axbO4vv/zCmTNn8PX11V1gNUxeXh63bt3S2j5//nxUKhW9evXSQVRPl6KiIgIDAzl06BDLly+nffv2pbaTz8SjVZE8yOdBfxlkZmaqdB2EEOUZNWoUmzdvJjg4GBcXF2JiYvj999/ZsGEDnTt31nV4NcaAAQOoXbs27du3p0GDBpw8eZIVK1ZgbGzMjh07aNasma5DfGpFRUWRlZXF5cuXiY6OZsCAAbRu3RqAN998EysrKy5cuEC3bt2wsrLirbfeIicnh4iICBo2bMju3btl6EM1KS8XmZmZdOvWjUGDBuHm5gbArl272L59O71792bNmjVSMD+k6dOn8+233/LSSy8xcOBArf0lD6WSz8SjVZE8nDt3Tj4PekqKAPFEyMvLIzQ0lDVr1pCZmYm7uzsffPCBfIPwmH377bfExsaSmprKrVu3sLGxoVu3bkyfPh0XFxddh/dUa9WqFenp6aXu+/PPP3F0dATg77//5oMPPuDAgQOYmJjw4osvMnv2bGxtbR9nuE+18nJhZWXF1KlT+e2337h8+TJFRUW4uLgwePBgJkyYgImJyWOO+Onj7e3N3r17y9yfmZmp/lk+E49ORfKQmZkpnwc9JUWAEEIIIYQQNYzcfxFCCCGEEKKGkSJACCGEEEKIGkaKACGEEEIIIWoYKQKEEEIIIYSoYaQIEEIIIYQQooaRIkAIIYQQQogaRooAIYQQQgghahgpAoQQQgghhKhhpAgQQgghhBCihpEiQAghhBBCiBpGigAhhBBCCCFqGCkChBBCCCGEqGH+D7UmUc5+GBsZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.Series(cbr.feature_importances_, index=cbr.feature_names_).sort_values()\n",
    "ax = plt.barh(data.index, data.values)\n",
    "plt.bar_label(ax, fmt='%.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding results to comparison\n",
    "metrics_append(\n",
    "    'СatBoost', \n",
    "    cbr_cv_time,\n",
    "    cbr_fit_time,\n",
    "    min(cbr_cv_results['test-RMSE-mean'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The comparison of all models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>Ridge</th>\n",
       "      <th>LightGBM</th>\n",
       "      <th>СatBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CV fit time, seconds</th>\n",
       "      <td>13</td>\n",
       "      <td>31</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best model train dataset fit time, seconds</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>best CV score (RMSE), euro</th>\n",
       "      <td>2152</td>\n",
       "      <td>1454</td>\n",
       "      <td>1475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Ridge  LightGBM  СatBoost\n",
       "CV fit time, seconds                           13        31       161\n",
       "best model train dataset fit time, seconds      0        10        90\n",
       "best CV score (RMSE), euro                   2152      1454      1475"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.0f' % x)\n",
    "pd.DataFrame(metrics).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The linear model is predictably the fastest (less than 1 second to fit!), but the quality metric (RMSE = 2152) is significantly worse than that of gradient models.\n",
    "  \n",
    "Of the gradient models, LightGBM is the fastest (only 8 seconds to train the optimal model). It is also the most accurate model among those considered (RMSE = 1454 on cross-validation).\n",
    "\n",
    "The result of the best model on the test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 8.53 s\n",
      "Wall time: 1.32 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "gbm_pred = grid_gbm.predict(X_test_oe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBM test score: 1415\n"
     ]
    }
   ],
   "source": [
    "print(f'LGBM test score: {(mean_squared_error(y_test, gbm_pred)) ** (1/2):.0f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing with CatBoost:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CatBoost test score: 1438 \n",
      "\n",
      "CPU times: total: 5.75 s\n",
      "Wall time: 956 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print(f'CatBoost test score: {min(cbr.eval_metrics(data=test, metrics=[\"RMSE\"])[\"RMSE\"]):.0f} \\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the test sample, LGBM shows RMSE = 1415, which is even better than on cross-validation. It takes the model 6 seconds to make prediction.  \n",
    "*(Catboost predicts a little faster and provides a slightly worse score)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** of the considered models, LightGBM with parameters `max_bin=255, num_iterations=300, num_leaves=400` is the optimal one in terms of speed and metrics. It is relatively fast (6-8 seconds for training and prediction) and provides the highest quality of predictions (RMSE = 1415 on the test set)."
   ]
  }
 ],
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