Project_Rusty_Bargain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando as bibliotecas\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "from sklearn.metrics import r2_score, mean_squared_error\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import lightgbm as lgb\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from scipy.stats import shapiro, anderson, kstest\n",
    "from scipy.stats import boxcox\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregando os dados\n",
    "df = pd.read_csv(\"/datasets/car_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateCrawled</th>\n",
       "      <th>Price</th>\n",
       "      <th>VehicleType</th>\n",
       "      <th>RegistrationYear</th>\n",
       "      <th>Gearbox</th>\n",
       "      <th>Power</th>\n",
       "      <th>Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>RegistrationMonth</th>\n",
       "      <th>FuelType</th>\n",
       "      <th>Brand</th>\n",
       "      <th>NotRepaired</th>\n",
       "      <th>DateCreated</th>\n",
       "      <th>NumberOfPictures</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>LastSeen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129269</th>\n",
       "      <td>18/03/2016 19:47</td>\n",
       "      <td>6800</td>\n",
       "      <td>small</td>\n",
       "      <td>2011</td>\n",
       "      <td>auto</td>\n",
       "      <td>71</td>\n",
       "      <td>fortwo</td>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>petrol</td>\n",
       "      <td>smart</td>\n",
       "      <td>no</td>\n",
       "      <td>18/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>84100</td>\n",
       "      <td>05/04/2016 22:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23369</th>\n",
       "      <td>05/04/2016 02:36</td>\n",
       "      <td>2450</td>\n",
       "      <td>small</td>\n",
       "      <td>2006</td>\n",
       "      <td>manual</td>\n",
       "      <td>60</td>\n",
       "      <td>corsa</td>\n",
       "      <td>80000</td>\n",
       "      <td>4</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>no</td>\n",
       "      <td>05/04/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>53797</td>\n",
       "      <td>05/04/2016 08:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226076</th>\n",
       "      <td>16/03/2016 14:48</td>\n",
       "      <td>4899</td>\n",
       "      <td>small</td>\n",
       "      <td>2007</td>\n",
       "      <td>manual</td>\n",
       "      <td>125</td>\n",
       "      <td>corsa</td>\n",
       "      <td>150000</td>\n",
       "      <td>9</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>opel</td>\n",
       "      <td>no</td>\n",
       "      <td>16/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>31789</td>\n",
       "      <td>17/03/2016 12:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61875</th>\n",
       "      <td>14/03/2016 21:56</td>\n",
       "      <td>4750</td>\n",
       "      <td>bus</td>\n",
       "      <td>2002</td>\n",
       "      <td>auto</td>\n",
       "      <td>116</td>\n",
       "      <td>galaxy</td>\n",
       "      <td>150000</td>\n",
       "      <td>4</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70191</td>\n",
       "      <td>28/03/2016 13:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64183</th>\n",
       "      <td>25/03/2016 14:44</td>\n",
       "      <td>400</td>\n",
       "      <td>wagon</td>\n",
       "      <td>1996</td>\n",
       "      <td>manual</td>\n",
       "      <td>140</td>\n",
       "      <td>v40</td>\n",
       "      <td>150000</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volvo</td>\n",
       "      <td>yes</td>\n",
       "      <td>25/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>92533</td>\n",
       "      <td>06/04/2016 17:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229496</th>\n",
       "      <td>05/03/2016 17:57</td>\n",
       "      <td>5890</td>\n",
       "      <td>bus</td>\n",
       "      <td>2006</td>\n",
       "      <td>manual</td>\n",
       "      <td>150</td>\n",
       "      <td>zafira</td>\n",
       "      <td>150000</td>\n",
       "      <td>11</td>\n",
       "      <td>petrol</td>\n",
       "      <td>opel</td>\n",
       "      <td>no</td>\n",
       "      <td>05/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>79232</td>\n",
       "      <td>22/03/2016 00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301580</th>\n",
       "      <td>22/03/2016 13:48</td>\n",
       "      <td>1150</td>\n",
       "      <td>small</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>84</td>\n",
       "      <td>corsa</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>opel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>59379</td>\n",
       "      <td>23/03/2016 21:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248088</th>\n",
       "      <td>10/03/2016 17:46</td>\n",
       "      <td>1200</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1997</td>\n",
       "      <td>manual</td>\n",
       "      <td>150</td>\n",
       "      <td>passat</td>\n",
       "      <td>150000</td>\n",
       "      <td>12</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>10/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>24238</td>\n",
       "      <td>05/04/2016 19:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148587</th>\n",
       "      <td>08/03/2016 21:47</td>\n",
       "      <td>3990</td>\n",
       "      <td>bus</td>\n",
       "      <td>2005</td>\n",
       "      <td>manual</td>\n",
       "      <td>109</td>\n",
       "      <td>c_max</td>\n",
       "      <td>150000</td>\n",
       "      <td>5</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>ford</td>\n",
       "      <td>no</td>\n",
       "      <td>08/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>32756</td>\n",
       "      <td>17/03/2016 08:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241047</th>\n",
       "      <td>23/03/2016 21:47</td>\n",
       "      <td>13500</td>\n",
       "      <td>wagon</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>140</td>\n",
       "      <td>passat</td>\n",
       "      <td>60000</td>\n",
       "      <td>11</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>23/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>83539</td>\n",
       "      <td>06/04/2016 03:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             DateCrawled  Price VehicleType  RegistrationYear Gearbox  Power  \\\n",
       "129269  18/03/2016 19:47   6800       small              2011    auto     71   \n",
       "23369   05/04/2016 02:36   2450       small              2006  manual     60   \n",
       "226076  16/03/2016 14:48   4899       small              2007  manual    125   \n",
       "61875   14/03/2016 21:56   4750         bus              2002    auto    116   \n",
       "64183   25/03/2016 14:44    400       wagon              1996  manual    140   \n",
       "229496  05/03/2016 17:57   5890         bus              2006  manual    150   \n",
       "301580  22/03/2016 13:48   1150       small              2004  manual     84   \n",
       "248088  10/03/2016 17:46   1200       sedan              1997  manual    150   \n",
       "148587  08/03/2016 21:47   3990         bus              2005  manual    109   \n",
       "241047  23/03/2016 21:47  13500       wagon              2011  manual    140   \n",
       "\n",
       "         Model  Mileage  RegistrationMonth  FuelType       Brand NotRepaired  \\\n",
       "129269  fortwo    50000                  2    petrol       smart          no   \n",
       "23369    corsa    80000                  4    petrol        opel          no   \n",
       "226076   corsa   150000                  9  gasoline        opel          no   \n",
       "61875   galaxy   150000                  4  gasoline        ford         NaN   \n",
       "64183      v40   150000                 10       NaN       volvo         yes   \n",
       "229496  zafira   150000                 11    petrol        opel          no   \n",
       "301580   corsa   150000                  0  gasoline        opel         NaN   \n",
       "248088  passat   150000                 12    petrol  volkswagen          no   \n",
       "148587   c_max   150000                  5  gasoline        ford          no   \n",
       "241047  passat    60000                 11  gasoline  volkswagen          no   \n",
       "\n",
       "             DateCreated  NumberOfPictures  PostalCode          LastSeen  \n",
       "129269  18/03/2016 00:00                 0       84100  05/04/2016 22:47  \n",
       "23369   05/04/2016 00:00                 0       53797  05/04/2016 08:39  \n",
       "226076  16/03/2016 00:00                 0       31789  17/03/2016 12:46  \n",
       "61875   14/03/2016 00:00                 0       70191  28/03/2016 13:17  \n",
       "64183   25/03/2016 00:00                 0       92533  06/04/2016 17:17  \n",
       "229496  05/03/2016 00:00                 0       79232  22/03/2016 00:17  \n",
       "301580  22/03/2016 00:00                 0       59379  23/03/2016 21:18  \n",
       "248088  10/03/2016 00:00                 0       24238  05/04/2016 19:18  \n",
       "148587  08/03/2016 00:00                 0       32756  17/03/2016 08:44  \n",
       "241047  23/03/2016 00:00                 0       83539  06/04/2016 03:16  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os dados\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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  object\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   Mileage            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  NotRepaired        283215 non-null  object\n",
      " 12  DateCreated        354369 non-null  object\n",
      " 13  NumberOfPictures   354369 non-null  int64 \n",
      " 14  PostalCode         354369 non-null  int64 \n",
      " 15  LastSeen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# visualizando os tipos de dados\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DateCrawled              0\n",
       "Price                    0\n",
       "VehicleType          37490\n",
       "RegistrationYear         0\n",
       "Gearbox              19833\n",
       "Power                    0\n",
       "Model                19705\n",
       "Mileage                  0\n",
       "RegistrationMonth        0\n",
       "FuelType             32895\n",
       "Brand                    0\n",
       "NotRepaired          71154\n",
       "DateCreated              0\n",
       "NumberOfPictures         0\n",
       "PostalCode               0\n",
       "LastSeen                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contando valores nulos\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sedan          91457\n",
      "small          79831\n",
      "wagon          65166\n",
      "bus            28775\n",
      "convertible    20203\n",
      "coupe          16163\n",
      "suv            11996\n",
      "other           3288\n",
      "Name: VehicleType, dtype: int64\n",
      "\n",
      "manual    268251\n",
      "auto       66285\n",
      "Name: Gearbox, dtype: int64\n",
      "\n",
      "golf                  29232\n",
      "other                 24421\n",
      "3er                   19761\n",
      "polo                  13066\n",
      "corsa                 12570\n",
      "                      ...  \n",
      "i3                        8\n",
      "serie_3                   4\n",
      "rangerover                4\n",
      "serie_1                   2\n",
      "range_rover_evoque        2\n",
      "Name: Model, Length: 250, dtype: int64\n",
      "\n",
      "petrol      216352\n",
      "gasoline     98720\n",
      "lpg           5310\n",
      "cng            565\n",
      "hybrid         233\n",
      "other          204\n",
      "electric        90\n",
      "Name: FuelType, dtype: int64\n",
      "\n",
      "no     247161\n",
      "yes     36054\n",
      "Name: NotRepaired, dtype: int64\n",
      "\n",
      "volkswagen        77013\n",
      "opel              39931\n",
      "bmw               36914\n",
      "mercedes_benz     32046\n",
      "audi              29456\n",
      "ford              25179\n",
      "renault           17927\n",
      "peugeot           10998\n",
      "fiat               9643\n",
      "seat               6907\n",
      "mazda              5615\n",
      "skoda              5500\n",
      "smart              5246\n",
      "citroen            5148\n",
      "nissan             4941\n",
      "toyota             4606\n",
      "hyundai            3587\n",
      "sonstige_autos     3374\n",
      "volvo              3210\n",
      "mini               3202\n",
      "mitsubishi         3022\n",
      "honda              2817\n",
      "kia                2465\n",
      "suzuki             2323\n",
      "alfa_romeo         2314\n",
      "chevrolet          1754\n",
      "chrysler           1439\n",
      "dacia               900\n",
      "daihatsu            806\n",
      "subaru              762\n",
      "porsche             758\n",
      "jeep                677\n",
      "trabant             589\n",
      "land_rover          545\n",
      "daewoo              542\n",
      "saab                526\n",
      "jaguar              505\n",
      "rover               486\n",
      "lancia              471\n",
      "lada                225\n",
      "Name: Brand, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# contando valores em alguns campos\n",
    "print(df[\"VehicleType\"].value_counts())\n",
    "print()\n",
    "print(df[\"Gearbox\"].value_counts())\n",
    "print()\n",
    "print(df[\"Model\"].value_counts())\n",
    "print()\n",
    "print(df[\"FuelType\"].value_counts())\n",
    "print()\n",
    "print(df[\"NotRepaired\"].value_counts())\n",
    "print()\n",
    "print(df[\"Brand\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "- We need to convert column headers to lowercase.\n",
    "- We need to convert data types:\n",
    "  - `DateCrawled`, `DateCreated`, and `LastSeen` should be of type `datetime`.\n",
    "- We identified and need to handle missing values in the following columns:\n",
    "  - `VehicleType`\n",
    "  - `Gearbox`\n",
    "  - `Model`\n",
    "  - `FuelType`\n",
    "  - `NotRepaired`\n",
    "- We need to encode certain categorical fields using One-Hot Encoding (OHE):\n",
    "  - `Gearbox`\n",
    "  - `NotRepaired`\n",
    "  - `FuelType`\n",
    "  - `VehicleType`\n",
    "- We need to encode some categorical fields using label encoding:\n",
    "  - `Model`\n",
    "  - `Brand`\n",
    "- We need to scale the data, as the features vary significantly in magnitude.\n",
    "- Since we need to predict the price, this is a regression problem, so we will use Decision Tree, Random Forest, and Linear Regression as models.\n",
    "- We will use gradient descent to minimize the MSE loss function and apply gradient boosting. We will implement regularization by adjusting tree depth and the number of elements in each node to reduce overfitting in gradient boosting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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  object\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   mileage            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  notrepaired        283215 non-null  object\n",
      " 12  datecreated        354369 non-null  object\n",
      " 13  numberofpictures   354369 non-null  int64 \n",
      " 14  postalcode         354369 non-null  int64 \n",
      " 15  lastseen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# colocando headers em minúsculas\n",
    "df.columns = df.columns.str.lower()\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datecrawled</th>\n",
       "      <th>vehicletype</th>\n",
       "      <th>registrationyear</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registrationmonth</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>brand</th>\n",
       "      <th>notrepaired</th>\n",
       "      <th>datecreated</th>\n",
       "      <th>numberofpictures</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>lastseen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2016 11:52</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>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>07/04/2016 03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2016 10:58</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>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>07/04/2016 01:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14/03/2016 12:52</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>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>05/04/2016 12:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/03/2016 16:54</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>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>17/03/2016 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31/03/2016 17:25</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>31/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>06/04/2016 10:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>04/04/2016 17:36</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1995</td>\n",
       "      <td>manual</td>\n",
       "      <td>102</td>\n",
       "      <td>3er</td>\n",
       "      <td>150000</td>\n",
       "      <td>10</td>\n",
       "      <td>petrol</td>\n",
       "      <td>bmw</td>\n",
       "      <td>yes</td>\n",
       "      <td>04/04/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>33775</td>\n",
       "      <td>06/04/2016 19:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01/04/2016 20:48</td>\n",
       "      <td>convertible</td>\n",
       "      <td>2004</td>\n",
       "      <td>manual</td>\n",
       "      <td>109</td>\n",
       "      <td>2_reihe</td>\n",
       "      <td>150000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>peugeot</td>\n",
       "      <td>no</td>\n",
       "      <td>01/04/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>67112</td>\n",
       "      <td>05/04/2016 18:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21/03/2016 18:54</td>\n",
       "      <td>sedan</td>\n",
       "      <td>1980</td>\n",
       "      <td>manual</td>\n",
       "      <td>50</td>\n",
       "      <td>other</td>\n",
       "      <td>40000</td>\n",
       "      <td>7</td>\n",
       "      <td>petrol</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>no</td>\n",
       "      <td>21/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>19348</td>\n",
       "      <td>25/03/2016 16:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>04/04/2016 23:42</td>\n",
       "      <td>bus</td>\n",
       "      <td>2014</td>\n",
       "      <td>manual</td>\n",
       "      <td>125</td>\n",
       "      <td>c_max</td>\n",
       "      <td>30000</td>\n",
       "      <td>8</td>\n",
       "      <td>petrol</td>\n",
       "      <td>ford</td>\n",
       "      <td>NaN</td>\n",
       "      <td>04/04/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>94505</td>\n",
       "      <td>04/04/2016 23:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17/03/2016 10:53</td>\n",
       "      <td>small</td>\n",
       "      <td>1998</td>\n",
       "      <td>manual</td>\n",
       "      <td>101</td>\n",
       "      <td>golf</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>27472</td>\n",
       "      <td>31/03/2016 17:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        datecrawled  vehicletype  registrationyear gearbox  power    model  \\\n",
       "0  24/03/2016 11:52          NaN              1993  manual      0     golf   \n",
       "1  24/03/2016 10:58        coupe              2011  manual    190      NaN   \n",
       "2  14/03/2016 12:52          suv              2004    auto    163    grand   \n",
       "3  17/03/2016 16:54        small              2001  manual     75     golf   \n",
       "4  31/03/2016 17:25        small              2008  manual     69    fabia   \n",
       "5  04/04/2016 17:36        sedan              1995  manual    102      3er   \n",
       "6  01/04/2016 20:48  convertible              2004  manual    109  2_reihe   \n",
       "7  21/03/2016 18:54        sedan              1980  manual     50    other   \n",
       "8  04/04/2016 23:42          bus              2014  manual    125    c_max   \n",
       "9  17/03/2016 10:53        small              1998  manual    101     golf   \n",
       "\n",
       "   mileage  registrationmonth  fueltype       brand notrepaired  \\\n",
       "0   150000                  0    petrol  volkswagen         NaN   \n",
       "1   125000                  5  gasoline        audi         yes   \n",
       "2   125000                  8  gasoline        jeep         NaN   \n",
       "3   150000                  6    petrol  volkswagen          no   \n",
       "4    90000                  7  gasoline       skoda          no   \n",
       "5   150000                 10    petrol         bmw         yes   \n",
       "6   150000                  8    petrol     peugeot          no   \n",
       "7    40000                  7    petrol  volkswagen          no   \n",
       "8    30000                  8    petrol        ford         NaN   \n",
       "9   150000                  0       NaN  volkswagen         NaN   \n",
       "\n",
       "        datecreated  numberofpictures  postalcode          lastseen  \n",
       "0  24/03/2016 00:00                 0       70435  07/04/2016 03:16  \n",
       "1  24/03/2016 00:00                 0       66954  07/04/2016 01:46  \n",
       "2  14/03/2016 00:00                 0       90480  05/04/2016 12:47  \n",
       "3  17/03/2016 00:00                 0       91074  17/03/2016 17:40  \n",
       "4  31/03/2016 00:00                 0       60437  06/04/2016 10:17  \n",
       "5  04/04/2016 00:00                 0       33775  06/04/2016 19:17  \n",
       "6  01/04/2016 00:00                 0       67112  05/04/2016 18:18  \n",
       "7  21/03/2016 00:00                 0       19348  25/03/2016 16:47  \n",
       "8  04/04/2016 00:00                 0       94505  04/04/2016 23:42  \n",
       "9  17/03/2016 00:00                 0       27472  31/03/2016 17:17  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separando os dados em features e target, removendo campos não codificados e datas pois elas não deveriam impactar os modelos\n",
    "features = df.drop(columns=['price'])\n",
    "target = df[\"price\"]\n",
    "features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 354369 entries, 0 to 354368\n",
      "Data columns (total 15 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   datecrawled        354369 non-null  datetime64[ns]\n",
      " 1   vehicletype        316879 non-null  object        \n",
      " 2   registrationyear   354369 non-null  int64         \n",
      " 3   gearbox            334536 non-null  object        \n",
      " 4   power              354369 non-null  int64         \n",
      " 5   model              334664 non-null  object        \n",
      " 6   mileage            354369 non-null  int64         \n",
      " 7   registrationmonth  354369 non-null  int64         \n",
      " 8   fueltype           321474 non-null  object        \n",
      " 9   brand              354369 non-null  object        \n",
      " 10  notrepaired        283215 non-null  object        \n",
      " 11  datecreated        354369 non-null  datetime64[ns]\n",
      " 12  numberofpictures   354369 non-null  int64         \n",
      " 13  postalcode         354369 non-null  int64         \n",
      " 14  lastseen           354369 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](3), int64(6), object(6)\n",
      "memory usage: 40.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# convertendo tipo de dados para datetime\n",
    "features['datecrawled'] = pd.to_datetime(features['datecrawled'],format='%d/%m/%Y %H:%M')\n",
    "features['datecreated'] = pd.to_datetime(features['datecreated'],format='%d/%m/%Y %H:%M')\n",
    "features['lastseen'] = pd.to_datetime(features['lastseen'],format='%d/%m/%Y %H:%M')\n",
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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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>vehicletype</th>\n",
       "      <th>registrationyear</th>\n",
       "      <th>gearbox</th>\n",
       "      <th>power</th>\n",
       "      <th>model</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registrationmonth</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>brand</th>\n",
       "      <th>notrepaired</th>\n",
       "      <th>datecreated</th>\n",
       "      <th>numberofpictures</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>lastseen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:00</td>\n",
       "      <td>unknown</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>unknown</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:00</td>\n",
       "      <td>coupe</td>\n",
       "      <td>2011</td>\n",
       "      <td>manual</td>\n",
       "      <td>190</td>\n",
       "      <td>unknown</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 01:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:00</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>unknown</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:00</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 17:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:00</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 10:17:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          datecrawled vehicletype  registrationyear gearbox  power    model  \\\n",
       "0 2016-03-24 11:52:00     unknown              1993  manual      0     golf   \n",
       "1 2016-03-24 10:58:00       coupe              2011  manual    190  unknown   \n",
       "2 2016-03-14 12:52:00         suv              2004    auto    163    grand   \n",
       "3 2016-03-17 16:54:00       small              2001  manual     75     golf   \n",
       "4 2016-03-31 17:25:00       small              2008  manual     69    fabia   \n",
       "\n",
       "   mileage  registrationmonth  fueltype       brand notrepaired datecreated  \\\n",
       "0   150000                  0    petrol  volkswagen     unknown  2016-03-24   \n",
       "1   125000                  5  gasoline        audi         yes  2016-03-24   \n",
       "2   125000                  8  gasoline        jeep     unknown  2016-03-14   \n",
       "3   150000                  6    petrol  volkswagen          no  2016-03-17   \n",
       "4    90000                  7  gasoline       skoda          no  2016-03-31   \n",
       "\n",
       "   numberofpictures  postalcode            lastseen  \n",
       "0                 0       70435 2016-04-07 03:16:00  \n",
       "1                 0       66954 2016-04-07 01:46:00  \n",
       "2                 0       90480 2016-04-05 12:47:00  \n",
       "3                 0       91074 2016-03-17 17:40:00  \n",
       "4                 0       60437 2016-04-06 10:17:00  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tratando valores vazios para poder rodar o label encoder, já que ele não lida bem com NaN\n",
    "\n",
    "features['vehicletype'] = features[\"vehicletype\"].fillna('unknown')\n",
    "features['gearbox'] = features[\"gearbox\"].fillna('unknown')\n",
    "features['model'] = features[\"model\"].fillna('unknown')\n",
    "features['fueltype'] = features[\"fueltype\"].fillna('unknown')\n",
    "features['notrepaired'] = features[\"notrepaired\"].fillna('unknown')\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<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  object\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   mileage            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  notrepaired        283215 non-null  object\n",
      " 12  datecreated        354369 non-null  object\n",
      " 13  numberofpictures   354369 non-null  int64 \n",
      " 14  postalcode         354369 non-null  int64 \n",
      " 15  lastseen           354369 non-null  object\n",
      "dtypes: int64(7), object(9)\n",
      "memory usage: 43.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>mileage</th>\n",
       "      <th>registrationmonth</th>\n",
       "      <th>fueltype</th>\n",
       "      <th>brand</th>\n",
       "      <th>notrepaired</th>\n",
       "      <th>datecreated</th>\n",
       "      <th>numberofpictures</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>lastseen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24/03/2016 11:52</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>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>07/04/2016 03:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24/03/2016 10:58</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>24/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>07/04/2016 01:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14/03/2016 12:52</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>14/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>05/04/2016 12:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17/03/2016 16:54</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>17/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>17/03/2016 17:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31/03/2016 17:25</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>31/03/2016 00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>06/04/2016 10:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        datecrawled  price vehicletype  registrationyear gearbox  power  \\\n",
       "0  24/03/2016 11:52    480         NaN              1993  manual      0   \n",
       "1  24/03/2016 10:58  18300       coupe              2011  manual    190   \n",
       "2  14/03/2016 12:52   9800         suv              2004    auto    163   \n",
       "3  17/03/2016 16:54   1500       small              2001  manual     75   \n",
       "4  31/03/2016 17:25   3600       small              2008  manual     69   \n",
       "\n",
       "   model  mileage  registrationmonth  fueltype       brand notrepaired  \\\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  24/03/2016 00:00                 0       70435  07/04/2016 03:16  \n",
       "1  24/03/2016 00:00                 0       66954  07/04/2016 01:46  \n",
       "2  14/03/2016 00:00                 0       90480  05/04/2016 12:47  \n",
       "3  17/03/2016 00:00                 0       91074  17/03/2016 17:40  \n",
       "4  31/03/2016 00:00                 0       60437  06/04/2016 10:17  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# gerando os boxplots para analisar outliers\n",
    "\n",
    "numerical_cols = features.select_dtypes(include=['int64', 'float64']).columns\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=features[numerical_cols])\n",
    "plt.title(\"Boxplots of Numerical Columns\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column: registrationyear\n",
      "Number of outliers: 6309\n",
      "Percentage of data that would be lost: 1.78%\n",
      "\n",
      "Column: power\n",
      "Number of outliers: 7340\n",
      "Percentage of data that would be lost: 2.07%\n",
      "\n",
      "Column: mileage\n",
      "Number of outliers: 53257\n",
      "Percentage of data that would be lost: 15.03%\n",
      "\n",
      "Column: registrationmonth\n",
      "Number of outliers: 0\n",
      "Percentage of data that would be lost: 0.00%\n",
      "\n",
      "Column: numberofpictures\n",
      "Number of outliers: 0\n",
      "Percentage of data that would be lost: 0.00%\n",
      "\n",
      "Column: postalcode\n",
      "Number of outliers: 0\n",
      "Percentage of data that would be lost: 0.00%\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# analisando a perda caso sejam descartados os outliers\n",
    "\n",
    "# threshold para outliers (1.5 * IQR)\n",
    "threshold = 1.5\n",
    "\n",
    "outlier_stats = {}\n",
    "\n",
    "# calculando quartis 25 e 75 para cada coluna numérica\n",
    "for col in features.select_dtypes(include=['int64', 'float64']).columns:\n",
    "    \n",
    "    q1 = features[col].quantile(0.25)\n",
    "    q3 = features[col].quantile(0.75)\n",
    "    \n",
    "    # Calculando IQR\n",
    "    iqr = q3 - q1\n",
    "    \n",
    "    # Definindo limites para outliers\n",
    "    lower_bound = q1 - (threshold * iqr)\n",
    "    upper_bound = q3 + (threshold * iqr)\n",
    "    \n",
    "    # identificando outliers\n",
    "    outliers = features[(features[col] < lower_bound) | (features[col] > upper_bound)]\n",
    "    \n",
    "    # calculando número de outliers\n",
    "    num_outliers = len(outliers)\n",
    "    \n",
    "    # calculando % de perda de dados\n",
    "    pct_lost = (num_outliers / len(features)) * 100\n",
    "    \n",
    "    # atualizando dicionario\n",
    "    outlier_stats[col] = {'num_outliers': num_outliers, 'pct_lost': pct_lost}\n",
    "\n",
    "\n",
    "for col, stats in outlier_stats.items():\n",
    "    print(f\"Column: {col}\")\n",
    "    print(f\"Number of outliers: {stats['num_outliers']}\")\n",
    "    print(f\"Percentage of data that would be lost: {stats['pct_lost']:.2f}%\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    354369.000000\n",
       "mean     128211.172535\n",
       "std       37905.341530\n",
       "min        5000.000000\n",
       "25%      125000.000000\n",
       "50%      150000.000000\n",
       "75%      150000.000000\n",
       "max      150000.000000\n",
       "Name: mileage, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[\"mileage\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "We can discard outliers from the `registration year` and `power` columns since the loss of data is minimal. However, we will need to handle outliers in the `mileage` column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson-Darling test: statistic=56653.12, critical values=[0.576 0.656 0.787 0.918 1.092], significance level=[15.  10.   5.   2.5  1. ]\n",
      "Shapiro-Wilk test: statistic=0.63, p-value=0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python3/lib/python3.9/site-packages/scipy/stats/_morestats.py:1816: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "# checando a normalidade da distribuição de mileage\n",
    "\n",
    "# Anderson-Darling test\n",
    "result = anderson(features['mileage'])\n",
    "print(f\"Anderson-Darling test: statistic={result.statistic:.2f}, critical values={result.critical_values}, significance level={result.significance_level}\")\n",
    "\n",
    "# Shapiro-Wilk test\n",
    "stat, p = shapiro(features['mileage'])\n",
    "print(f\"Shapiro-Wilk test: statistic={stat:.2f}, p-value={p:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: Mileage is not normally distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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atctaImHiRLDZ4K23YNEiKFHC3ZW5lFtD1bJly+jduzdr164lKiqK5ORkmjZtyoULFxza9ejRg5iYGPs2atQo+7mUlBSaN29OUlISq1ev5ttvvyUyMpIhQ4bY2xw8eJDmzZvz8MMPs3XrVvr168fzzz/P77//bm8zffp0+vfvz9ChQ9m8eTPVq1cnPDycEydO2Nu8+uqrzJ07lxkzZrBs2TKOHTtG69atM/AnJCIiks1NmgR16sCff0JwMCxYAG+/DXly4Awkk4WcOHHCAGbZsmX2Yw899JDp27fvDV/z22+/GQ8PDxMbG2s/Nn78eOPv728SExONMcYMGjTIVKlSxeF1bdu2NeHh4fb9unXrmt69e9v3U1JSTPHixc2IESOMMcbExcWZvHnzmhkzZtjb7Nq1ywBmzZo16fr+4uPjDWDi4+PT1V5ERCTbOn/emK5djQFre+QRY2Ji3F3VbUnv53eWmlMVHx8PQKFChRyOT5kyhaCgIKpWrcrgwYO5ePGi/dyaNWuoVq0awcHB9mPh4eEkJCSwY8cOe5uwsDCHa4aHh7NmzRoAkpKS2LRpk0MbDw8PwsLC7G02bdpEcnKyQ5vKlStTunRpexsRERHB6pW6/35rzSkPD6tnasECCAlxd2UZKsv0vaWmptKvXz/q169P1apV7cc7dOhAmTJlKF68ONu3b+f1119nz549zJo1C4DY2FiHQAXY92NjY2/aJiEhgUuXLnH27FlSUlKu22b37t32a3h5eREYGJimzdX3+afExEQSExPt+wkJCen9cYiIiGQ/xljP6nv5ZWtierFi8P330LixuyvLFFkmVPXu3Zs///yTlStXOhzv2bOn/etq1apRrFgxmjRpwv79+6lQoUJml+mUESNGMHz4cHeXISIikvHOnYMXX4QpU6z9pk2t5RKKFnVvXZkoSwz/9enTh19++YUlS5ZQsmTJm7atV68eAPv27QMgJCQkzR14V/dD/r+b8UZt/P39yZcvH0FBQXh6el63zbXXSEpKIi4u7oZt/mnw4MHEx8fbt8OHD9/0exMREcmWtm2zJqNPmQKenjBiBMybl6sCFbg5VBlj6NOnD7Nnz2bx4sWUK1fulq/ZunUrAMWKFQMgNDSUP/74w+EuvaioKPz9/bn33nvtbRYtWuRwnaioKEJDQwHw8vKidu3aDm1SU1NZtGiRvU3t2rXJmzevQ5s9e/Zw6NAhe5t/8vb2xt/f32ETERHJMYyBL76AevXgf/+DkiWtldHfeMOaS5XbZM68+et78cUXTUBAgFm6dKmJiYmxbxcvXjTGGLNv3z7z9ttvm40bN5qDBw+aOXPmmPLly5tGjRrZr3HlyhVTtWpV07RpU7N161Yzf/58U6RIETN48GB7mwMHDhhfX18zcOBAs2vXLjNu3Djj6elp5s+fb28zbdo04+3tbSIjI83OnTtNz549TWBgoMNdhS+88IIpXbq0Wbx4sdm4caMJDQ01oaGh6f5+dfefiIjkGHFxxrRp8/fdfc2bG3PypLuryhDp/fx2a6gCrrt98803xhhjDh06ZBo1amQKFSpkvL29TcWKFc3AgQPTfFPR0dGmWbNmJl++fCYoKMgMGDDAJCcnO7RZsmSJqVGjhvHy8jLly5e3v8e1xo4da0qXLm28vLxM3bp1zdq1ax3OX7p0ybz00kumYMGCxtfX17Rq1crEOHF7qEKViIjkCBs3GlOhghWm8uQxZvRoY1JS3F1Vhknv57fNGGPc1UuW2yQkJBAQEEB8fLyGAkVEJPsxBj77DF57DZKSoEwZmDYNHnjA3ZVlqPR+fmeZu/9EREQkCzt7Frp3h9mzrf2WLa3lEwoWdGtZWUkunEUmIiIiTlm/HmrVsgJV3rzwyScwa5YC1T8oVImIiMj1GQNjxkD9+hAdDeXLw+rV8Mor1oORxYGG/0RERCStM2ega1eYO9faf/ppmDgRAgLcWlZWpp4qERERcbR6NdSoYQUqb2/4/HP44QcFqltQqBIRERFLaiqMHAmNGsHhw1CpEqxdaz1+RsN9t6ThPxEREYGTJ6FzZ5g/39pv3x4mTIACBdxbVzainioREZHcbvlya7hv/nzw8YEvv7Se46dA5RSFKhERkdwqJQXeeQcefhiOHYPKla3lE3r00HDfbdDwn4iISG50/Dh06gQLF1r7nTvDuHHg5+feurIxhSoREZHcZvFi6NDBCla+vlaY6trV3VVlexr+ExERyS1SUmDoUAgLswJVlSqwYYMClYuop0pERCQ3OHYMOnaEpUut/e7d4dNPrZ4qcQmFKhERkZxuwQJr/tTJk5A/v7VUQseO7q4qx9Hwn4iISE515Qr8618QHm4FqurVYfNmBaoMop4qERGRnOjIEWsBz5Urrf0XXrAejpwvn3vrysEUqkRERHKaX3+FLl3g9GlrAc+JE6FNG3dXleNp+E9ERCSnSE6GgQPh8cetQFWrljXcp0CVKdRTJSIikhP89Re0a2c9ABng5Zdh9Gjw9nZvXbmIQpWIiEh2N2eOtdZUXBwEBMDXX0Pr1u6uKtfR8J+IiEh2lZQE/fpBy5ZWoKpbF7ZsUaByE4UqERGR7OjAAahfHz75xNrv3x9WrIBy5dxbVy6m4T8REZHsZuZMa0X0hAQoWBC+/RaeeMLdVeV66qkSERHJLi5fht694ZlnrED14IOwdasCVRahUCUiIpId7N1rhajPP7f2X3/deo5f6dJuLUv+puE/ERGRrG7aNOjRA86fh6AgmDQJmjVzd1XyD+qpEhERyaouXYJevazHzZw/Dw0bWsN9ClRZkkKViIhIVrR7N9SrB19+CTYb/PvfsHgxlCjh7srkBjT8JyIiktVMmgQvvggXL0LRojBlCoSFubsquQX1VImIiGQVFy5At27Ww5AvXoRHHrGG+xSosgWFKhERkaxgxw5rRfTISPDwgOHDYcECKFbM3ZVJOmn4T0RExJ2MgW++gT59rInpxYrB999D48burkycpFAlIiLiLufPwwsvWHOmAJo2hcmTrXlUku1o+E9ERMQdtm2D2rWtQOXpCe+9B/PmKVBlY+qpEhERyUzGWMsk9O0LiYnWEgnTpkGDBu6uTO6QQpWIiEhmSUiAnj1h+nRr/7HHrIchBwW5ty5xCQ3/iYiIZIbNm6FWLStQ5ckDo0fD3LkKVDmIeqpEREQykjEwbhwMGABJSdYDkKdPhwcecHdl4mIKVSIiIhklLg66d4dZs6z9Fi3g66+hUCG3liUZQ8N/IiIiGWH9eqhZ0wpUefPCxx/D7NkKVDmYQpWIiIgrGQMffWTdzRcdDeXKwapV1t1+Npu7q5MMpOE/ERERVzlzBrp2tSagAzz1FEycCIGB7qxKMol6qkRERFxh9WqoUcMKVF5e1uT0GTMUqHIRhSoREZE7kZoKo0ZBo0Zw+DBUrAhr18JLL2m4L5fR8J+IiMjtOnkSunSxHi8D0K4dTJgA/v7urUvcQqFKRETkdqxYYYWoY8fAxwc+/RSef169U7mYhv9ERESckZoK774LjRtbgeruu2HdOujRQ4Eql1NPlYiISHodPw7PPgtRUdb+s8/C55+Dn59765IsQaFKREQkPRYvho4dITYW8uWzwlTXru6uSrIQtw7/jRgxgvvvv58CBQpQtGhRWrZsyZ49exzaXL58md69e1O4cGH8/Px46qmnOH78uEObQ4cO0bx5c3x9fSlatCgDBw7kypUrDm2WLl1KrVq18Pb2pmLFikRGRqapZ9y4cZQtWxYfHx/q1avH+vXrna5FRERymJQUGDYMwsKsQFWlCmzcqEAlabg1VC1btozevXuzdu1aoqKiSE5OpmnTply4cMHe5tVXX2Xu3LnMmDGDZcuWcezYMVq3bm0/n5KSQvPmzUlKSmL16tV8++23REZGMmTIEHubgwcP0rx5cx5++GG2bt1Kv379eP755/n999/tbaZPn07//v0ZOnQomzdvpnr16oSHh3PixIl01yIiIjlMTIwVpoYPt1ZKf+456/Ez997r7sokKzJZyIkTJwxgli1bZowxJi4uzuTNm9fMmDHD3mbXrl0GMGvWrDHGGPPbb78ZDw8PExsba28zfvx44+/vbxITE40xxgwaNMhUqVLF4b3atm1rwsPD7ft169Y1vXv3tu+npKSY4sWLmxEjRqS7lluJj483gImPj09XexERcaPffzemSBFjwJj8+Y2ZPNndFYmbpPfzO0vd/RcfHw9Aof9/2OSmTZtITk4mLCzM3qZy5cqULl2aNWvWALBmzRqqVatGcHCwvU14eDgJCQns2LHD3ubaa1xtc/UaSUlJbNq0yaGNh4cHYWFh9jbpqUVERHKAK1fgzTchIsJah+q++2DTJujUyd2VSRaXZSaqp6am0q9fP+rXr0/VqlUBiI2NxcvLi8B/LPEfHBxMbGysvc21gerq+avnbtYmISGBS5cucfbsWVJSUq7bZvfu3emu5Z8SExNJTEy07yckJNzqxyAiIu505Ah06GCtQQXQq5f1cOR8+dxbl2QLWaanqnfv3vz5559MmzbN3aW4zIgRIwgICLBvpUqVcndJIiJyI7/9Zj27b8UKKFAApk2DL75QoJJ0yxKhqk+fPvzyyy8sWbKEkiVL2o+HhISQlJREXFycQ/vjx48TEhJib/PPO/Cu7t+qjb+/P/ny5SMoKAhPT8/rtrn2Greq5Z8GDx5MfHy8fTt8+HA6fhoiIpKpkpNh0CBo3hxOn4ZatWDzZmjb1t2VSTbj1lBljKFPnz7Mnj2bxYsXU65cOYfztWvXJm/evCxatMh+bM+ePRw6dIjQ0FAAQkND+eOPPxzu0ouKisLf3597///ujNDQUIdrXG1z9RpeXl7Url3boU1qaiqLFi2yt0lPLf/k7e2Nv7+/wyYiIlnIoUPw0EMwerS1//LLsHq19VBkEWdlzrz563vxxRdNQECAWbp0qYmJibFvFy9etLd54YUXTOnSpc3ixYvNxo0bTWhoqAkNDbWfv3Lliqlatapp2rSp2bp1q5k/f74pUqSIGTx4sL3NgQMHjK+vrxk4cKDZtWuXGTdunPH09DTz58+3t5k2bZrx9vY2kZGRZufOnaZnz54mMDDQ4a7CW9VyK7r7T0QkC5kzx5iCBa27+wICjPnxR3dXJFlUej+/3RqqgOtu33zzjb3NpUuXzEsvvWQKFixofH19TatWrUxMTIzDdaKjo02zZs1Mvnz5TFBQkBkwYIBJTk52aLNkyRJTo0YN4+XlZcqXL+/wHleNHTvWlC5d2nh5eZm6deuatWvXOpxPTy03o1AlIpIFJCYa06+fFabAmPvvN+bAAXdXJVlYej+/bcYY465estwmISGBgIAA4uPjNRQoIuIOBw9ac6U2bLD2X30V3n8fvLzcW5dkaen9/M4ySyqIiIhkqB9/hO7dIT4eChaEyEh48kl3VyU5SJa4+09ERCTDXL4MffrA009bgSo0FLZuVaASl1OoEhGRnGvfPnjwQRg3ztofNAiWLYPSpd1bl+RIGv4TEZGcado06NkTzp2DoCCYNAmaNXN3VZKDqadKRERylkuXrMfLtG9vBaqGDa3hPgUqyWAKVSIiknPs2QMPPABffgk2G/z737B4MZQo4e7KJBfQ8J+IiOQM330HL7wAFy5A0aLW/qOPursqyUXUUyUiItnbxYvw3HPw7LNWoHr4YWu4T4FKMplClYiIZF87dsD998M331jDfcOGQVQUFCvm7sokF9Lwn4iIZD/GWIt39u5tTUwPCYHvv7d6qUTcRKFKRESyl/Pn4cUXrTlTYA3zffedNY9KxI00/CciItnH9u1Qp44Vojw84N13Yf58BSrJEtRTJSIiWZ8x8NVX8MorkJhoLZEwdaq1BpVIFqFQJSIiWVtCgrWY57Rp1v5jj8G331qrpItkIRr+ExGRrGvLFqhd2wpUefLAqFEwd64ClWRJ6qkSEZGsxxj4/HPo3x+SkqwHIE+bBqGh7q5M5IYUqkREJGuJi4Pnn4cff7T2n3zSWoeqUCG3liVyKxr+ExGRrGPDBqhVywpUefPCRx/BTz8pUEm2oJ4qERFxP2Pgk09g0CBIToZy5WD6dGu1dJFsQqFKRETc68wZ6NYNfv7Z2n/qKZg4EQID3VqWiLM0/CciIu6zZg3UrGkFKi8v+OwzmDFDgUqyJYUqERHJfKmpMHo0NGoEhw5BxYqwdq31LD+bzd3VidwWDf+JiEjmOnUKunSB336z9tu1gwkTwN/fvXWJ3CH1VImISOZZsQJq1LAClY+PFaa+/16BSnIEhSoREcl4qanw3nvw8MNw9CjcfTesWwc9e2q4T3IMDf+JiEjGOnECOnWCqChr/9lnrdXS/fzcW5eIiylUiYhIxlmyBDp0gNhYyJcPxo2Drl3VOyU5kob/RETE9VJSYPhwCAuzAtW991qrpXfrpkAlOZbToerw4cMcOXLEvr9+/Xr69evHl19+6dLCREQkm4qJgaZNYdgway7Vc89ZgapKFXdXJpKhnA5VHTp0YMmSJQDExsby6KOPsn79et58803efvttlxcoIiLZSFSUdXff4sWQPz9MmgT//S/4+rq7MpEM53So+vPPP6lbty4AP/zwA1WrVmX16tVMmTKFyMhIV9cnIiLZwZUr8O9/Q3i4NTG9WjXYuNGalC6SSzg9UT05ORlvb28AFi5cyJNPPglA5cqViYmJcW11IiKS9R05Yk1GX7HC2u/VCz76yJqYLpKLON1TVaVKFb744gtWrFhBVFQUERERABw7dozChQu7vEAREcnC5s2zhvtWrIACBWDqVPjiCwUqyZWcDlUjR45kwoQJNG7cmPbt21O9enUAfv75Z/uwoIiI5HDJyfD66/DYY3D6tPVQ5M2brUfOiORSNmOMcfZFKSkpJCQkULBgQfux6OhofH19KVq0qEsLzEkSEhIICAggPj4efz2SQUSyq0OHrPC0Zo2136eP9XBkHx/31iWSQdL7+X1b61QZY9i0aRMTJkzg3LlzAHh5eeGruztERHK2n3+2hvvWrIGAAJg5E8aOVaAS4TYmqv/1119ERERw6NAhEhMTefTRRylQoAAjR44kMTGRL774IiPqFBERd0pKgjfesCagA9x/P0ybBuXLu7cukSzE6Z6qvn37UqdOHc6ePUu+ayYitmrVikWLFrm0OBERyQIOHoSGDf8OVP36wcqVClQi/+B0T9WKFStYvXo1Xl5eDsfLli3L0aNHXVaYiIhkAbNmWSuix8dDwYIQGQn/v5SOiDhyuqcqNTWVlJSUNMePHDlCgQIFXFKUiIi4WWIivPwyPPWUFageeAC2bFGgErkJp0NV06ZN+fjjj+37NpuN8+fPM3ToUB577DFX1iYiIu6wbx88+CB89pm1P2gQLF8OZcq4ty6RLM7pJRWOHDlCeHg4xhj27t1LnTp12Lt3L0FBQSxfvlxLKtyEllQQkSxv+nTo0QPOnYPCha1n9+k/mCWXS+/n922tU3XlyhWmTZvG9u3bOX/+PLVq1aJjx44OE9clLYUqEcmyLl2CV1+FCROs/QYNrNXRS5Z0b10iWUB6P7+dnqgOkCdPHjp16nTbxYmISBayZw+0aQPbt4PNBv/6FwwbBnlu6yNCJNdy+l/MpEmTbnq+c+fOt12MiIhksu++gxdegAsXoEgRmDIFHn3U3VWJZEtOD/9d+2gagOTkZC5evGhfUf3MmTMuLTAn0fCfiGQZFy9ad/d9/bW1//DDVqAqVsy9dYlkQRn2mJqzZ886bOfPn2fPnj00aNCAqVOn3lHRIiKSCXbuhLp1rUBls8HQoRAVpUAlcodu69l//1SpUiXef/99+vbt64rLiYhIRomMhDp1YMcOCAmBhQut+VOenu6uTCTbc0moAmvy+rFjx1x1ORERcaXz56FLF+jWzbrT79FHYetWeOQRd1cmkmM4PVH9559/dtg3xhATE8Nnn31G/fr1nbrW8uXLGT16NJs2bSImJobZs2fTsmVL+/muXbvy7bffOrwmPDyc+fPn2/fPnDnDyy+/zNy5c/Hw8OCpp57ik08+wc/Pz95m+/bt9O7dmw0bNlCkSBFefvllBg0a5HDdGTNm8NZbbxEdHU2lSpUYOXKkw2KmxhiGDh3KV199RVxcHPXr12f8+PFUqlTJqe9ZRNzHZnN3Be5RlT/4gTbcw25S8GAIbzMiajAmxGX/XS05jIeH9e8lTx7w8bH2U1Ot/fz5rScWJSdbbb29rTa+vta9Dtf+O/PwgFKloFAhiIuzjhUqBEWLwunTVvuQEOv4iRPW8atfFytmPXISYMUKiIn5+9g/O1ZTUm7dJlMYJ9lsNofNw8PDBAcHm/bt25tjx445da3ffvvNvPnmm2bWrFkGMLNnz3Y436VLFxMREWFiYmLs25kzZxzaREREmOrVq5u1a9eaFStWmIoVK5r27dvbz8fHx5vg4GDTsWNH8+eff5qpU6eafPnymQkTJtjbrFq1ynh6eppRo0aZnTt3mn//+98mb9685o8//rC3ef/9901AQID56aefzLZt28yTTz5pypUrZy5dupTu7zc+Pt4AJj4+3qmfk4jcOciNW6p5ni/NRXyMAXOYEqYBy7NAXdq0pW8rXNjarj1WsqQxP/7497/tH3+0jt2szZ1K7+c3rnvLO3OjUNWiRYsbvmbnzp0GMBs2bLAfmzdvnrHZbObo0aPGGGM+//xzU7BgQZOYmGhv8/rrr5u7777bvt+mTRvTvHlzh2vXq1fP9OrVyxhjTGpqqgkJCTGjR4+2n4+LizPe3t5m6tSp6f4eFapE3MPdHwzu2AoQb76nnf3ArzQzhTnp9rq0abvTzWazth9/tDab7eZtXCG9n99Zvu936dKlFC1alLvvvpsXX3yR06dP28+tWbOGwMBA6tSpYz8WFhaGh4cH69ats7dp1KgRXl5e9jbh4eHs2bOHs2fP2tuEhYU5vG94eDhr1qwB4ODBg8TGxjq0CQgIoF69evY2IpI15cYhvxpsYRO1ac80ruDJQEbxOL9wmiB3lyZyx4yx/rdvX2u7un+9Nv36WUODmSVdc6r69++f7guOGTPmtov5p4iICFq3bk25cuXYv38///rXv2jWrBlr1qzB09OT2NjYNM8azJMnD4UKFSI2NhaA2NhYypUr59AmODjYfq5gwYLExsbaj13b5tprXPu667W5nsTERBITE+37CQkJznz7IiJOMrzIeD7iVbxJ4hClaMt01hLq7sJEXMoYOHLk1m0OH7bmWjVunCllpS9UbdmyJV0Xs7n4PwnbtWtn/7patWrcd999VKhQgaVLl9KkSROXvldGGDFiBMOHD3d3GSKSC/gTz0Se5xlmAjCHJ+nGN5ylkJsrE3GvmJjMe690haolS5ZkdB3pUr58eYKCgti3bx9NmjQhJCSEEydOOLS5cuUKZ86cIeT/bycICQnh+PHjDm2u7t+qzbXnrx4rds3ieMePH6dGjRo3rHfw4MEOvXwJCQmUKlXKmW9ZROSW6rCB6bSlPAdJIi+vM5KP6QfkwrFPkX/IzDVts/ycqmsdOXKE06dP24NNaGgocXFxbNq0yd5m8eLFpKamUq9ePXub5cuXk3z13k8gKiqKu+++2/7IndDQUBYtWuTwXlFRUYSGWl3m5cqVIyQkxKFNQkIC69ats7e5Hm9vb/z9/R02ERHXMbzCJ6yiPuU5yEHK0oCVfMyrKFBJTmazQcmS1najQTKbzVrO4eqyDJnidmbBb9iwwQwcONC0bdvWtGrVymFzxrlz58yWLVvMli1bDGDGjBljtmzZYv766y9z7tw589prr5k1a9aYgwcPmoULF5patWqZSpUqmcuXL9uvERERYWrWrGnWrVtnVq5caSpVquSwpEJcXJwJDg42zz77rPnzzz/NtGnTjK+vb5olFfLkyWM++OADs2vXLjN06NDrLqkQGBho5syZY7Zv325atGihJRVEsgl3362UEVtBTpvZtLAfmElrE8BZt9elTVtGb9e7+++fdwC66+4/nL3w1KlTTd68ec3jjz9uvLy8zOOPP27uuusuExAQYLp27erUtZYsWWKANFuXLl3MxYsXTdOmTU2RIkVM3rx5TZkyZUyPHj1MbGyswzVOnz5t2rdvb/z8/Iy/v7/p1q2bOXfunEObbdu2mQYNGhhvb29TokQJ8/7776ep5YcffjB33XWX8fLyMlWqVDG//vqrw/nU1FTz1ltvmeDgYOPt7W2aNGli9uzZ49T3q1Al4j7u/iBw5VaPNSaa0saAuYyX6c1YA6lur0ubNldv11unqlSpW69T9c82dyq9n982649N+t1333306tWL3r17U6BAAbZt20a5cuXo1asXxYoV08Tsm0jvU65FJGNk9+UVbKTSnzGMYDB5ucI+KtCGH9hCLXeXJjmMVlR3lN7Pb6dDVf78+dmxYwdly5alcOHCLF26lGrVqrFr1y4eeeQRYjJzmn02o1AlIrft1Cno2hV+/dXab9sWvvwS9LdEJMOl9/Pb6YnqBQsW5Ny5cwCUKFGCP//8E4C4uDguXrx4m+WKiMgNrVwJNWtagcrbGyZMgKlTFahEshinH6jcqFEjoqKiqFatGs888wx9+/Zl8eLFREVFZYu1o0REso3UVBg5Et56yxrfuOsumDED7rvP3ZWJyHWkO1T9+eefVK1alc8++4zLly8D8Oabb5I3b15Wr17NU089xb///e8MK1REJFc5cQKefRYWLLD2O3WC8ePBz8+9dYnIDaV7TpWHhwf3338/zz//PO3ataNAgQIZXVuOozlVIpIuS5dChw7WrNt8+eCzz6Bbt+w/014km3L5nKply5ZRpUoVBgwYQLFixejSpQsrVqxwSbEiIoI1xPf229CkiRWo7r0XNmyA555ToBLJBtIdqho2bMjXX39NTEwMY8eOJTo6moceeoi77rqLkSNH3vTBwiIicguxsdC0KQwdas2l6tYN1q+HKlXcXZmIpJPTd//lz5+fbt26sWzZMv73v//xzDPPMG7cOEqXLs2TTz6ZETWKiORsCxdC9eqweLG1CNCkSfD119bXIpJtOL1O1T9duHCBKVOmMHjwYOLi4khJSXFVbTmO5lSJiIMrV2D4cHj3XWsh6GrV4IcfoHJld1cmItdI7+e300sqXLV8+XK+/vprfvzxRzw8PGjTpg3du3e/3cuJiOQuR49ak9GXL7f2e/aEjz+2JqaLSLbkVKg6duwYkZGRREZGsm/fPh588EE+/fRT2rRpQ351U4uIpM/8+dZyCadOWUskfPUVtGvn7qpE5A6lO1Q1a9aMhQsXEhQUROfOnXnuuee4++67M7I2EZGcJTnZWshz5Ehrv2ZNmD4dKlVyb10i4hLpDlV58+Zl5syZPP7443i68imFIiK5weHDVm/U6tXWfu/e8MEH1pNoRSRHSHeo+vnnnzOyDhGRnGvuXOthyGfOWM/r++9/4emn3V2ViLiY00sqiIhIOiUlwYAB8OSTVqCqUwe2bFGgEsmhbvvuPxERuYmDB63hvvXrrf1+/ay5VF5ebi1LRDKOQpWIiKvNnm2tiB4fD4GBEBkJLVq4uyoRyWAa/hMRcZXERHjlFWjd2gpUDzwAW7cqUInkEunqqXJmkroeVSMiudL+/dC2LWzaZO0PHGitlJ43r3vrEpFMk65Q1bJlS4d9m83GtU+3sV3z9HQ9pkZEMtI1f25c4s4e1PX/fvgBnn8ezp2DwoXh22+heXMXXFhEspN0Df+lpqbatwULFlCjRg3mzZtHXFwccXFx/Pbbb9SqVYv58+dndL0ikou5OlDd8TUvX4YXX7R6qM6dgwYNrOE+BSqRXMnpier9+vXjiy++oEGDBvZj4eHh+Pr60rNnT3bt2uXSAkVEIGMC1bXXdrrH6n//gzZtYNs26wKDB1sPR86j+39Eciun//Xv37+fwMDANMcDAgKIjo52QUkiIo4yMlBd+x7pDlZTpkCvXnDhAhQpAt99B02bZmh9IpL1OX333/3330///v05fvy4/djx48cZOHAgdevWdWlxIiJZysWL1typTp2sQNW4sTXcp0AlItxGqPr666+JiYmhdOnSVKxYkYoVK1K6dGmOHj3Kf//734yoUUTE/XbuhLp1rUfM2GwwdCgsXAjFi7u7MhHJIpwe/qtYsSLbt28nKiqK3bt3A3DPPfcQFhbmcBegiEiOERlpPQD54kUICbGG/x55xN1ViUgWYzPm9m8ovnz5Mt7e3gpT6ZSQkEBAQADx8fH4+/u7uxyRbCOz/sSk+Wt4/rwVpiZNsvbDwqz5U8HBmVOQiGQJ6f38dnr4LzU1lf/85z+UKFECPz8/Dh48CMBbb72l4T8RyRAuWUvK2ff44w+4/34rUHl4wDvvwO+/K1CJyA05HareeecdIiMjGTVqFF7XPBi0atWqTJw40aXFiYhclZHByuHaxsDEidb8qd27rTlTS5bAm29a4UpE5Aac/gsxadIkvvzySzp27Iinp6f9ePXq1e1zrEREMkJGBCuHa547Bx07Qo8e1sKeERHW3X2NGrn+jUUkx3F6ovrRo0epWLFimuOpqakkJye7pCgRkRvJsB6rrVutxTz37gVPT3jvPXjtNfVOiUi6Of3X4t5772XFihVpjs+cOZOaNWu6pCgRkUxjDIwfDw88YAWqUqVg+XIYNEiBSkSc4nRP1ZAhQ+jSpQtHjx4lNTWVWbNmsWfPHiZNmsQvv/ySETWKiGSM+HhrqG/GDGv/iSfgm2+shyKLiDjJ6f8Ma9GiBXPnzmXhwoXkz5+fIUOGsGvXLubOncujjz6aETWKiLjexo1Qq5YVqPLkgTFjYM4cBSoRuW1O9VRduXKF9957j+eee46oqKiMqklEJOMYA59+CgMHQnIylC0L06dbd/uJiNwBp3qq8uTJw6hRo7hy5UpG1SMiknHOnoXWraFfPytQtW4NW7YoUImISzg9/NekSROWLVuWEbWIiGScdeugZk346Sfw8oKxY2HmTAgMdHdlIpJDOD1RvVmzZrzxxhv88ccf1K5dm/z58zucf/LJJ11WnIjIHUtNhY8+gjfegCtXoEIFa7ivdm13VyYiOYzTz/7zuMktxjabjZSUlDsuKqfSs/9EMtnp09ClC/z6q7Xfpg189RXo35+IOCG9n99O91SlpqbeUWEiIpli1Spo1w6OHAFvb/jkE+jZM/Oeziwiuc4drWx3+fJlV9UhIuIaqanw/vvw0ENWoLrrLms+Va9eClQikqGcDlUpKSn85z//oUSJEvj5+XHgwAEA3nrrLf773/+6vEARkXQ7cQIeewwGD4aUFOs5fhs3QvXq7q5MRHIBp0PVu+++S2RkJKNGjcLLy8t+vGrVqkycONGlxYmIpNuyZVCjBvz+O+TLB//9L0yeDAUKuLsyEcklnA5VkyZN4ssvv6Rjx454enraj1evXp3du3e7tDgRkVtKSYH//AceeQRiYuCee2D9enjuOQ33iUimcnqi+tGjR6lYsWKa46mpqSQnJ7ukKBGRdImNhU6dYNEia79bN2v9qX8s9SIikhmc7qm69957WbFiRZrjM2fOpGbNmi4pSkTklhYtsob7Fi0CX1+YNAm+/lqBSkTcxumeqiFDhtClSxeOHj1Kamoqs2bNYs+ePUyaNIlffvklI2oUEfnblSvw9tvwzjvWc/yqVYMffoDKld1dmYjkck73VLVo0YK5c+eycOFC8ufPz5AhQ9i1axdz587l0UcfzYgaRUQsx45BkybWHCpjoEcPa7kEBSoRyQKcXlFdbp9WVBe5A/Pnw7PPwqlT4OcHX34J7du7uyoRyQXS+/l9R4t/iohkBJvt7y2P7QojbIOhWTM4dYot1KDS+c3YOrS3txERyQrSFaoKFixIoUKF0rU5Y/ny5TzxxBMUL14cm83GTz/95HDeGMOQIUMoVqwY+fLlIywsjL179zq0OXPmDB07dsTf35/AwEC6d+/O+fPnHdps376dhg0b4uPjQ6lSpRg1alSaWmbMmEHlypXx8fGhWrVq/Pbbb07XIiJ37tqQVJLDLKUxg3kfgHG8RChr2EelG75GRMRd0hWqPv74Yz766CM++ugj/v3vfwMQHh7OsGHDGDZsGOHh4YC1qrozLly4QPXq1Rk3btx1z48aNYpPP/2UL774gnXr1pE/f37Cw8MdHo/TsWNHduzYQVRUFL/88gvLly+nZ8+e9vMJCQk0bdqUMmXKsGnTJkaPHs2wYcP48ssv7W1Wr15N+/bt6d69O1u2bKFly5a0bNmSP//806laROTOXBuOmvMLW6lBA1YRjz9PM4M+jCMRn1u+VkTELYyTWrdubcaOHZvm+NixY02LFi2cvZwdYGbPnm3fT01NNSEhIWb06NH2Y3Fxccbb29tMnTrVGGPMzp07DWA2bNhgbzNv3jxjs9nM0aNHjTHGfP7556ZgwYImMTHR3ub11183d999t32/TZs2pnnz5g711KtXz/Tq1SvdtaRHfHy8AUx8fHy6XyOSW1gzz43JS6IZzQD7gfXUMeXYbz9/q01ExNXS+/nt9Jyq33//nYiIiDTHIyIiWLhw4R2HvKsOHjxIbGwsYWFh9mMBAQHUq1ePNWvWALBmzRoCAwOpU6eOvU1YWBgeHh6sW7fO3qZRo0YOj9QJDw9nz549nD171t7m2ve52ubq+6SnlutJTEwkISHBYRORGytDNMtpxGt8CMDH9KUBKzlIeTdXJiJya06HqsKFCzNnzpw0x+fMmUPhwoVdUhRAbGwsAMHBwQ7Hg4OD7ediY2MpWrSow/k8efJQqFAhhzbXu8a173GjNteev1Ut1zNixAgCAgLsW6lSpW7xXYvkXi34iS3U5AHWcZZAWjKbV/mYJLzdXZqISLo4vfjn8OHDef7551m6dCn16tUDYN26dcyfP5+vvvrK5QVmZ4MHD6Z///72/YSEBAUrkX9KTIRBg/iJTwFYwwO0YxqHKOPmwkREnON0T1XXrl1ZtWoV/v7+zJo1i1mzZuHv78/KlSvp2rWrywoLCQkB4Pjx4w7Hjx8/bj8XEhLCiRMnHM5fuXKFM2fOOLS53jWufY8btbn2/K1quR5vb2/8/f0dNhG5xv79UL8+fGoFqtG8RiOWK1CJSLbkVKhKTk7mueeeo2jRokyZMoXNmzezefNmpkyZYu+1cpVy5coREhLCoqsPSsXq6Vm3bh2hoaEAhIaGEhcXx6ZNm+xtFi9eTGpqqr2e0NBQli9f7vCw56ioKO6++24KFixob3Pt+1xtc/V90lOLiDhpxgyoVQs2bYLCheGXXxjEaK6Q97YvqaWMRcStnJ0B7+/vbw4cOHDbM+ivde7cObNlyxazZcsWA5gxY8aYLVu2mL/++ssYY8z7779vAgMDzZw5c8z27dtNixYtTLly5cylS5fs14iIiDA1a9Y069atMytXrjSVKlUy7du3t5+Pi4szwcHB5tlnnzV//vmnmTZtmvH19TUTJkywt1m1apXJkyeP+eCDD8yuXbvM0KFDTd68ec0ff/xhb5OeWm5Fd/+JGGMuXTLmxRf/vl2vfn1jDh+2n07vXX66609EMkt6P7+d/lPUuXNnM2bMmNsu7FpLliwxQJqtS5cuxhhrKYO33nrLBAcHG29vb9OkSROzZ88eh2ucPn3atG/f3vj5+Rl/f3/TrVs3c+7cOYc227ZtMw0aNDDe3t6mRIkS5v33309Tyw8//GDuuusu4+XlZapUqWJ+/fVXh/PpqeVWFKok19uzx5jq1f9OQoMHG5OcnKaZApWIZCXp/fx2+tl/77zzDh9++CFNmjShdu3a5M+f3+H8K6+84oL+s5xJz/6TXO3776FXLzh/HooUgcmT4f8XDhYRycrS+/ntdKgqV67cjS9ms3HgwAFnLperKFRJrnTxIvTtCxMnWvuNG8OUKdhKFHfqMpovJSLukt7Pb6eXVDh48OAdFSYiuciuXdCmDfz5p/UcmbfegiFDsOXxdPpSNpuClYhkbU6HqqtOnToFQFBQkMuKEZEc5Ntv4aWXrJ6q4GCYMgWaNLmjZ/QpWIlIVubUkgpxcXH07t2boKAggoODCQ4OJigoiD59+hAXF5dBJYpItnLhAnTtam0XL0JYGGzbdseB6io9OFlEsqp091SdOXOG0NBQjh49SseOHbnnnnsA2LlzJ5GRkSxatIjVq1fb134SkVzozz+t4b5du8DDA4YPh8GDwdP54T4Rkewm3aHq7bffxsvLi/3796d5Bt7bb79N06ZNefvtt/noo49cXqSIZHHGwH//Cy+/DJcvQ/Hi1t1+Dz3k7spERDJNuof/fvrpJz744IM0gQqsx7iMGjWK2bNnu7Q4EckGzp2DTp2gRw8rUEVEwNatClQikuukO1TFxMRQpUqVG56vWrUqsbGxLilKRLKJrVuhTh2rV8rTE95/H3791VqHSkQkl0l3qAoKCiI6OvqG5w8ePEihQoVcUZOIZHXGwPjx8MAD8L//QcmSsGwZvP66NZfqJi9zxVuLiGRF6Q5V4eHhvPnmmyQlJaU5l5iYyFtvvUVERIRLixORLCg+Htq1s5ZLSEyExx+3eqzq10/Xy+8kFClQiUhWlu4V1Y8cOUKdOnXw9vamd+/eVK5cGWMMu3bt4vPPPycxMZGNGzdSqlSpjK4529KK6pLtbdpk3d134ADkyQMjR8Krr97WOgfOvkSBSkTcxeUrqpcsWZI1a9bw0ksvMXjwYK5mMZvNxqOPPspnn32mQCWSUxkDn30Gr70GSUlQpgxMnw716t3RJUVEchKnVlQvV64c8+bN4+zZs+zduxeAihUrai6VSE529ix07w5X7+5t1cpaPiGda9I50yOloCUi2dltPaamYMGC1K1b19W1iEhWs26dNX8qOhq8vOCDD6BPn3QnJWeH+PQYGhHJzpx6TI2I5BLGwIcfQoMGVqAqXx5Wr7YW98ygQHWnrxMRcbfbfqCyiORQp09bz+375Rdrv00b+PJLCAhI9yXuNBipx0pEsiP1VInI31atgpo1rUDl7W2tRTVtmlOBSkQkt1KoEhFITbVWQ3/oITh8GCpVgrVr4YUXNB4nIpJOGv4Tye1OnoTOnWH+fGu/Qwf44gsoUMC9dYmIZDMKVSK52fLl0L49HDsGPj7WWlTPPafeKRGR26DhP5HcKCUF3nkHHn7YClT33AMbNljrUbkgUN3pJHNNUheR7Eg9VSK5zfHj0LEjLFpk7XftavVQ5c/v0rcx5vbymQKViGRXClUiucmiRVagOn4cfH2tu/s6d86wt3M2WClQiUh2puE/kdwgJQWGDoVHH7UCVdWqsHFjhgaqq4xJ/yYikp2pp0okpzt2zOqdWrrU2u/RAz75BPLlc+nbqEdKRHI7hSqRnOz33+HZZ61lE/z8YMIEa8kEF9Mz/kRENPwnkjNduQKDB0NEhBWoqleHTZuyRKC609eJiGRV6qkSyWkOH7bWnlq1ytp/6SXr4cg+Pi5/Kz3jT0TkbwpVIjnJr79ak8/PnAF/f5g4EZ55xt1ViYjkChr+E8kJkpNh4EB4/HErUNWuDZs3K1CJiGQi9VSJZHd//QVt28K6ddZ+374wciR4e7u3LhGRXEahSiQ7++kn6NYN4uIgMBC++QZatnRvTSIiuZSG/0Syo6Qk6NcPWrWyAlW9erBlS6YHKj3jT0TkbwpVItnNgQNQv761gCfAgAGwfDmULeuWcm43GClQiUhOo+E/kexk5kzo3h0SEqBQIfj2W2tyupvpGX8iIgpVItnD5ctWj9Tnn1v79evD1KlQqpR767qGgpKI5HYKVSJZ3d690KYNbN1q7Q8eDMOHQ968mV6Ks4t9KmiJSG6iUCWSlU2dCj17wvnzEBQE330H4eFuKeV2Vk/XiukikptoorpIVnTpkhWmOnSwAtVDD8G2bdkqULnitSIi2YlClUhWs3s31K0LX31lJZIhQ2DhQihe3C3luCIUKViJSG6g4T+RrGTSJHjxRbh4EYKDYcoUaNLE3VWJiEg6qKdKJCu4cMFaGb1LFytQNWliTUxXoBIRyTYUqkTcbccOa7gvMhI8PODtt+H33yEkxN2ViYiIEzT8J+IuxsDXX8PLL1sT04sXh++/tyali4hItqNQJeIO585Zc6emTLH2w8Nh8mQoUsS9dV2Hs6ul3+gaIiI5nYb/RDLbtm1Qp44VqDw9YcQI+O23LBmorrqTUKRAJSK5hXqqRDKLMfDll9C3LyQmQsmSMG2a9ciZbOB2eqwUqEQkN1GoEskMCQnQowf88IO1//jj1sT0woXdWpazFJJERG5MoUoko23aBG3bwv79kCcPjBwJr76apVfEdKY0BS0REUuWnlM1bNgwbDabw1a5cmX7+cuXL9O7d28KFy6Mn58fTz31FMePH3e4xqFDh2jevDm+vr4ULVqUgQMHcuXKFYc2S5cupVatWnh7e1OxYkUiIyPT1DJu3DjKli2Lj48P9erVY/369RnyPUsOYgyMHQsPPmgFqjJlYOVK6N8/xwSq22kvIpJTZelQBVClShViYmLs28qVK+3nXn31VebOncuMGTNYtmwZx44do3Xr1vbzKSkpNG/enKSkJFavXs23335LZGQkQ4YMsbc5ePAgzZs35+GHH2br1q3069eP559/nt9//93eZvr06fTv35+hQ4eyefNmqlevTnh4OCdOnMicH4JkP3Fx8PTT8MorkJQELVvCli1Qr567K7up2w1IClYiIoDJwoYOHWqqV69+3XNxcXEmb968ZsaMGfZju3btMoBZs2aNMcaY3377zXh4eJjY2Fh7m/Hjxxt/f3+TmJhojDFm0KBBpkqVKg7Xbtu2rQkPD7fv161b1/Tu3du+n5KSYooXL25GjBjh1PcTHx9vABMfH+/U6ySbWbfOmLJljQFj8uY15pNPjElNdXdVt2R1rd3ZJiKSE6X38zvL91Tt3buX4sWLU758eTp27MihQ4cA2LRpE8nJyYSFhdnbVq5cmdKlS7NmzRoA1qxZQ7Vq1QgODra3CQ8PJyEhgR07dtjbXHuNq22uXiMpKYlNmzY5tPHw8CAsLMze5kYSExNJSEhw2CQHMwbGjLHu5ouOhvLlYfVqq7dKXTkiIjlelg5V9erVIzIykvnz5zN+/HgOHjxIw4YNOXfuHLGxsXh5eREYGOjwmuDgYGJjYwGIjY11CFRXz189d7M2CQkJXLp0iVOnTpGSknLdNlevcSMjRowgICDAvpUqVcrpn4FkE2fOQIsWMGAAXLkCzzwDmzdb61GJiEiukKXv/mvWrJn96/vuu4969epRpkwZfvjhB/Lly+fGytJn8ODB9O/f376fkJCgYJUTrV4N7drB4cPg7Q0ffQQvvKDeKRGRXCZL91T9U2BgIHfddRf79u0jJCSEpKQk4uLiHNocP36ckP9/EG1ISEiauwGv7t+qjb+/P/ny5SMoKAhPT8/rtgm5xQNvvb298ff3d9gkB0lNhVGjoFEjK1BVqgRr11qPn1GgEhHJdbJVqDp//jz79++nWLFi1K5dm7x587Jo0SL7+T179nDo0CFCQ0MBCA0N5Y8//nC4Sy8qKgp/f3/uvfdee5trr3G1zdVreHl5Ubt2bYc2qampLFq0yN5GcqGTJ60FPF9/HVJSoEMHaz2qGjXcXdltu9P1prRelYjkepk0cf62DBgwwCxdutQcPHjQrFq1yoSFhZmgoCBz4sQJY4wxL7zwgildurRZvHix2bhxowkNDTWhoaH211+5csVUrVrVNG3a1GzdutXMnz/fFClSxAwePNje5sCBA8bX19cMHDjQ7Nq1y4wbN854enqa+fPn29tMmzbNeHt7m8jISLNz507Ts2dPExgY6HBXYXro7r8cYtkyY4oXt2538/ExZuLEbHF3X3rprj8REUfp/fzO0n8O27Zta4oVK2a8vLxMiRIlTNu2bc2+ffvs5y9dumReeuklU7BgQePr62tatWplYmJiHK4RHR1tmjVrZvLly2eCgoLMgAEDTHJyskObJUuWmBo1ahgvLy9Tvnx5880336SpZezYsaZ06dLGy8vL1K1b16xdu9bp70ehKpu7csWY//zHGA8PK0lUrmzMH3+4u6oMoUAlIvK39H5+24xRp31mSUhIICAggPj4eM2vym6OH4dOnWDhQmu/SxcYNw7y53dvXSIikuHS+/mdreZUibjF4sXWXKmFC8HX13oQcmRktg9UNlv6tyefdHe1IiJZn0KVyI2kpMDQoRAWBrGxULUqbNhg9VJlc87enDh3rm5oFBG5lSy9TpWI2xw7Bh07wtKl1v7zz8Mnn1g9VdncnYQjm013+YmI3Ih6qkT+acECa7hv6VLw84MpU+Crr3J9oLpKQ4EiItenUCVy1ZUr8OabEBFhrUNVvbq19lSHDu6uLEuZO9fdFYiIZE0a/hMBOHIE2reHlSut/RdftB6O7OPj3rpERCTbUKgS+e036NwZTp8Gf39rqK9NG3dXJSIi2YyG/yT3Sk6GQYOgeXMrUNWuDZs3K1DdwhNPuLsCEZGsST1Vkjv99Re0a2c9ABnglVeshyN7e7u3rgxmzJ1PVv/5Z9fUIiKS0yhUSe4zZw506wZnz0JgIHz9NbRq5e6qMs2dBCstpyAicmMa/pPcIykJ+vWDli2tQFW3LmzZkqsC1VXOhqMnnlCgEhG5FfVUSe5w4AC0bQsbN1r7AwbAe++Bl5d763IjhSQREddSqJKc78cf4bnnICEBChWyntuXS2ZbOzvMp6AlInL7NPwnOdfly9CnDzz9tBWoHnwQtm5VoHLxa0RExKJQJTnT3r1WiBo3ztp/4w3rsTOlSrm1rMxyp8/3ExER52n4T3KeadOgZ084dw6CgmDyZOvRM7mEK0KRHpwsIuI89VRJznHpEvTqZT1u5tw5aNTIGu7LRYFKRETcR6FKcobdu6FePfjyS6ub5a23YNEiKFHC3ZWJiEguoeE/yf4mT7YegHzhAgQHw3ffQViYu6sSEZFcRj1Vkn1duGAtldC5s/X1I49Yw30KVCIi4gYKVZI97dhhrYj+zTfg4QFvvw0LFkBIiLsrcztXTDDXJHUREedp+E+yF2OsINWnjzUxvVgx+P57aNzY3ZVlKXq+n4hI5lOokuzj/Hlr7tR331n7TZta86mKFnVvXVnU7QQrBSoRkdunUCXZw/bt8Mwz8L//gacnvPMODBpkDf3JDSkkiYhkHoUqydqMsZZJ6NsXEhOhZEmYOhUaNHB3ZW6n4T0RkaxFoUqyroQEa2X06dOt/ebNrYchBwW5tays4E4fQ6NgJSLieho7kaxp82aoXdsKVHnywAcfwM8/K1DhusfQiIiIa6mnSrIWY6yHIA8YAElJUKaM9Sy/Bx5wd2VZgivDkHqsRERcS6FKso64OHj+efjxR2u/ZUv4+msoWNCdVYmIiKSLhv8ka1i/HmrWtAJV3rzwyScwa5YClYiIZBsKVeJexsBHH1l380VHQ/nysHo1vPKKJv6IiEi2ouE/cZ8zZ6BbN2sCOsDTT8PEiRAQ4N66REREboN6qsQ91qyBGjWsQOXtDZ9/Dj/8oEB1C66cWK5J6iIirqVQJZkrNRVGjYKGDeHwYahUCdautR4/o+G+dNEDk0VEsiYN/0nmOXUKOneGefOs/fbtYcIEKFDAvXVlQ3pgsohI1qNQJZljxQorRB09Cj4+MHYsdO+u3qk7oHAkIpK1aPhPMlZqKrz7LjRubAWqypWt5ROef16B6jbYbDffRETEfdRTJRnn+HF49lmIirL2O3e2Vkv383NvXdlUekKTVkkXEXEfhSrJGEuWQIcOEBsLvr5WmOra1d1VZVvO9EIpWImIuIeG/8S1UlJg+HAIC7MCVZUqsGGDAtUduJ1hPQ0FiohkPvVUievExEDHjlYvFVgT0T/91OqpEhERyeEUqsQ1oqKgUyc4cQLy57eWSujY0d1ViYiIZBqFqmwuJcVarSAmBooVs9bU9PRMXzv4+1jRotb+iRN/fx0bCydPQuHC1v+ePm0dDwy0njBz6BDYUq7w1J/DaLHjPTww7PWtzjvVfuDwf+/i+Htw/rz1GmPg8mW4csWqJSUFkpP//lpERCS7U6jKxmbNgr594ciRv4+VLAmffAKtW9+8XeHC1v9eDUq3owRH+J4ONGIFAON5gf4Xx3B5bb7bv6iIiEg2pYnq2dSsWdbzh68NSmAtBfX009b5m7U7ffrOAlUzfmMrNWjEChIoQBum8xLjuYwCVVagu/9ERDKfQlU2lJJi9Txd74Pz6rF+/SAp6cbtblcekhnJIH6jOUGcZhO1qMkWZtDGdW8id0SBSkTEPTT8lw2tWJG25+laxljPKv7885u3c1YpDjGNdjzIGgA+5WUGMpokvF33JnJHFKhERNxHoSobiolJX7v9+133nk/wM5F0pRBniSOA5/ia2bS+9QtFRERyCQ3/ZUPFiqWvXYUKd/5eeUniQ/rzMy0oxFnWUZeabFGgyoI89K9ZRMSt9GfYSePGjaNs2bL4+PhQr1491q9fn+k1NGxo3eV3o1WzbTYoVQpeeunm7W6lLAdZSQP68xEAH9KfhqwgmnK3WblkpD/+cHcFIiK5m0KVE6ZPn07//v0ZOnQomzdvpnr16oSHh3PixIlMrcPT01o2AdIGpqv7H38MXl43bncrrZjFFmpSlw2coSBP8DOv8SHJeN1R7ZJx7r3X3RWIiORuClVOGDNmDD169KBbt27ce++9fPHFF/j6+vL1119nei2tW8PMmVCihOPxkiWt41fXqbpRu8KF/16r6lreXOZTXmYWTxFIPKt4kBps5ReeyJhvRFxCE9RFRNzPZoz+HKdHUlISvr6+zJw5k5YtW9qPd+nShbi4OObMmZPmNYmJiSQmJtr3ExISKFWqFPHx8fj7+7ukLleuqJ7v6D4qD21DoegtAOx4/HUWNf4PBYvmveGK6lf/35OaCqdOwaVL4ONjHT9+XCuqZ7QdO9RDJSKS0RISEggICLjl57fu/kunU6dOkZKSQnBwsMPx4OBgdu/efd3XjBgxguHDh2doXZ6e0Ljx7bezH5s+Hfr0gHPnICgIJk+mSkQEVVxXqoiISI6m4b8MNHjwYOLj4+3b4cOH3V1SWpcuwQsvQLt2VqBq1Ai2boWICHdXJiIikq2opyqdgoKC8PT05Pjx4w7Hjx8/TkhIyHVf4+3tjbd3Fl4Yc88eaNMGtm+3ZrK/+SYMHQp59H8LERERZ6mnKp28vLyoXbs2ixYtsh9LTU1l0aJFhIaGurGy2/Tdd1C7thWoihaFBQvgP/9RoBIREblN+gR1Qv/+/enSpQt16tShbt26fPzxx1y4cIFu3bq5u7T0u3gRXn4Zrt6x+MgjVsBK74qiIiIicl0KVU5o27YtJ0+eZMiQIcTGxlKjRg3mz5+fZvJ6lrVzJzzzjPW/Hh7WUN+bb17/dkERERFxipZUyETpvSXT5YyByEjo3duamF6sGHz/ffpuGxQREcnl0vv5rTlVOd3589ClCzz3nBWomja17u5ToBIREXEphaqcbPt2qFMHJk+2hvjeew/mzbMmpouIiIhLaU5VTmQMfPUV9O1rLWNeogRMmwYNGri7MhERkRxLoSqnSUiAXr2sEAXQvLk1nyooyK1liYiI5HQa/stJtmyx1p6aNs1ab2r0aPj5ZwUqERGRTKCeqpzAGPj8c+jfH5KSoHRp61l+Dzzg7spERERyDYWq7M4YePZZmDLF2m/RwlrYs1Ah99YlIiKSy2j4L7uz2aweqbx54eOPYfZsBSoRERE3UE9VTtC7t7X+1F13ubsSERGRXEs9VTmBzaZAJSIi4mYKVSIiIiIuoFAlIiIi4gIKVSIiIiIuoFAlIiIi4gIKVSIiIiIuoFAlIiIi4gIKVSIiIiIuoFAlIiIi4gIKVSIiIiIuoFAlIiIi4gIKVSIiIiIuoFAlIiIi4gIKVSIiIiIukMfdBeQmxhgAEhIS3FyJiIiIpNfVz+2rn+M3olCVic6dOwdAqVKl3FyJiIiIOOvcuXMEBATc8LzN3Cp2icukpqZy7NgxChQogM1mc3c5LpGQkECpUqU4fPgw/v7+7i4nV9LvIGvQ7yFr0O/B/XLi78AYw7lz5yhevDgeHjeeOaWeqkzk4eFByZIl3V1GhvD3988x/3iyK/0Osgb9HrIG/R7cL6f9Dm7WQ3WVJqqLiIiIuIBClYiIiIgLKFTJHfH29mbo0KF4e3u7u5RcS7+DrEG/h6xBvwf3y82/A01UFxEREXEB9VSJiIiIuIBClYiIiIgLKFSJiIiIuIBClYiIiIgLKFSJyyUmJlKjRg1sNhtbt251dzm5SnR0NN27d6dcuXLky5ePChUqMHToUJKSktxdWo42btw4ypYti4+PD/Xq1WP9+vXuLilXGTFiBPfffz8FChSgaNGitGzZkj179ri7rFzv/fffx2az0a9fP3eXkmkUqsTlBg0aRPHixd1dRq60e/duUlNTmTBhAjt27OCjjz7iiy++4F//+pe7S8uxpk+fTv/+/Rk6dCibN2+mevXqhIeHc+LECXeXlmssW7aM3r17s3btWqKiokhOTqZp06ZcuHDB3aXlWhs2bGDChAncd9997i4lU2lJBXGpefPm0b9/f3788UeqVKnCli1bqFGjhrvLytVGjx7N+PHjOXDggLtLyZHq1avH/fffz2effQZYz/gsVaoUL7/8Mm+88Yabq8udTp48SdGiRVm2bBmNGjVydzm5zvnz56lVqxaff/4577zzDjVq1ODjjz92d1mZQj1V4jLHjx+nR48eTJ48GV9fX3eXI/8vPj6eQoUKubuMHCkpKYlNmzYRFhZmP+bh4UFYWBhr1qxxY2W5W3x8PID+f+8mvXv3pnnz5g7/LnILPVBZXMIYQ9euXXnhhReoU6cO0dHR7i5JgH379jF27Fg++OADd5eSI506dYqUlBSCg4MdjgcHB7N79243VZW7paam0q9fP+rXr0/VqlXdXU6uM23aNDZv3syGDRvcXYpbqKdKbuqNN97AZrPddNu9ezdjx47l3LlzDB482N0l50jp/T1c6+jRo0RERPDMM8/Qo0cPN1Uukrl69+7Nn3/+ybRp09xdSq5z+PBh+vbty5QpU/Dx8XF3OW6hOVVyUydPnuT06dM3bVO+fHnatGnD3Llzsdls9uMpKSl4enrSsWNHvv3224wuNUdL7+/By8sLgGPHjtG4cWMeeOABIiMj8fDQfz9lhKSkJHx9fZk5cyYtW7a0H+/SpQtxcXHMmTPHfcXlQn369GHOnDksX76ccuXKubucXOenn36iVatWeHp62o+lpKRgs9nw8PAgMTHR4VxOpFAlLnHo0CESEhLs+8eOHSM8PJyZM2dSr149SpYs6cbqcpejR4/y8MMPU7t2bb777rsc/0fM3erVq0fdunUZO3YsYA0/lS5dmj59+miieiYxxvDyyy8ze/Zsli5dSqVKldxdUq507tw5/vrrL4dj3bp1o3Llyrz++uu5YjhWc6rEJUqXLu2w7+fnB0CFChUUqDLR0aNHady4MWXKlOGDDz7g5MmT9nMhISFurCzn6t+/P126dKFOnTrUrVuXjz/+mAsXLtCtWzd3l5Zr9O7dm++//545c+ZQoEABYmNjAQgICCBfvnxuri73KFCgQJrglD9/fgoXLpwrAhUoVInkKFFRUezbt499+/alCbPqlM4Ybdu25eTJkwwZMoTY2Fhq1KjB/Pnz00xel4wzfvx4ABo3buxw/JtvvqFr166ZX5DkWhr+ExEREXEBzV4VERERcQGFKhEREREXUKgSERERcQGFKhEREREXUKgSERERcQGFKhEREREXUKgSERERcQGFKhFxmaVLl2Kz2YiLi3N3KU6x2Wz89NNPLrte2bJl+fjjj112vcwWHR2NzWZj69atQPb9vYpkNoUqEUkXm812023YsGHuLvGWhg0bRo0aNdIcj4mJoVmzZplfUBbQtWtXh4dBA5QqVYqYmJhc82gREVfRY2pEJF1iYmLsX0+fPp0hQ4awZ88e+zE/Pz82btzojtJISkrCy8vrtl+v5yI68vT01M9E5Daop0pE0iUkJMS+BQQEYLPZHI5dfYg2wKZNm6hTpw6+vr48+OCDDuELYM6cOdSqVQsfHx/Kly/P8OHDuXLliv38oUOHaNGiBX5+fvj7+9OmTRuOHz9uP3+1x2nixImUK1cOHx8fAOLi4nj++ecpUqQI/v7+PPLII2zbtg2AyMhIhg8fzrZt2+y9a5GRkUDa4b8jR47Qvn17ChUqRP78+alTpw7r1q0DYP/+/bRo0YLg4GD8/Py4//77WbhwoVM/y5SUFPr3709gYCCFCxdm0KBBdOnSxaHH6HpDiDVq1HDoERwzZgzVqlUjf/78lCpVipdeeonz58/bz0dGRhIYGMjvv//OPffcg5+fHxEREfaAPGzYML799lvmzJlj/5ksXbo0zfDf9axcuZKGDRuSL18+SpUqxSuvvMKFCxfs5z///HMqVaqEj48PwcHBPP300079jESyI4UqEXG5N998kw8//JCNGzeSJ08ennvuOfu5FStW0LlzZ/r27cvOnTuZMGECkZGRvPvuuwCkpqbSokULzpw5w7Jly4iKiuLAgQO0bdvW4T327dvHjz/+yKxZs+wf/s888wwnTpxg3rx5bNq0iVq1atGkSRPOnDlD27ZtGTBgAFWqVCEmJoaYmJg01wQ4f/48Dz30EEePHuXnn39m27ZtDBo0iNTUVPv5xx57jEWLFrFlyxYiIiJ44oknOHToULp/Ph9++CGRkZF8/fXXrFy5kjNnzjB79mxnf8x4eHjw6aefsmPHDr799lsWL17MoEGDHNpcvHiRDz74gMmTJ7N8+XIOHTrEa6+9BsBrr71GmzZt7EErJiaGBx988Jbvu3//fiIiInjqqafYvn0706dPZ+XKlfTp0weAjRs38sorr/D222+zZ88e5s+fT6NGjZz+/kSyHSMi4qRvvvnGBAQEpDm+ZMkSA5iFCxfaj/36668GMJcuXTLGGNOkSRPz3nvvObxu8uTJplixYsYYYxYsWGA8PT3NoUOH7Od37NhhALN+/XpjjDFDhw41efPmNSdOnLC3WbFihfH39zeXL192uHaFChXMhAkT7K+rXr16mroBM3v2bGOMMRMmTDAFChQwp0+fTudPw5gqVaqYsWPH2vfLlCljPvrooxu2L1asmBk1apR9Pzk52ZQsWdK0aNHipteoXr26GTp06A2vO2PGDFO4cGH7/jfffGMAs2/fPvuxcePGmeDgYPt+ly5dHN7XGGMOHjxoALNlyxZjzN+/17NnzxpjjOnevbvp2bOnw2tWrFhhPDw8zKVLl8yPP/5o/P39TUJCwg1rFcmJNKdKRFzuvvvus39drFgxAE6cOEHp0qXZtm0bq1atsvdMgTUcdvnyZS5evMiuXbsoVaoUpUqVsp+/9957CQwMZNeuXdx///0AlClThiJFitjbbNu2jfPnz1O4cGGHWi5dusT+/fvTXfvWrVupWbMmhQoVuu758+fPM2zYMH799VdiYmK4cuUKly5dSndPVXx8PDExMdSrV89+LE+ePNSpUwdjTLrrBFi4cCEjRoxg9+7dJCQkcOXKFfvP0dfXFwBfX18qVKhgf02xYsU4ceKEU+/zT9u2bWP79u1MmTLFfswYQ2pqKgcPHuTRRx+lTJkylC9fnoiICCIiImjVqpW9JpGcSqFKRFwub9689q9tNhuAw/DZ8OHDad26dZrXXZ0blR758+d32D9//jzFihVj6dKladoGBgam+7r58uW76fnXXnuNqKgoPvjgAypWrEi+fPl4+umnSUpKSvd7pIeHh0eakJWcnGz/Ojo6mscff5wXX3yRd999l0KFCrFy5Uq6d+9OUlKSPcBc+7sA6/fhbHj7p/Pnz9OrVy9eeeWVNOdKly6Nl5cXmzdvZunSpSxYsIAhQ4YwbNgwNmzY4NTvQiS7UagSkUxVq1Yt9uzZQ8WKFa97/p577uHw4cMcPnzY3lu1c+dO4uLiuPfee2963djYWPLkyUPZsmWv28bLy4uUlJSb1nffffcxceJEzpw5c93eqlWrVtG1a1datWoFWAEjOjr6pte8VkBAAMWKFWPdunX2eUZXrlyxzwG7qkiRIg53XCYkJHDw4EH7/qZNm0hNTeXDDz/Ew8OaHvvDDz+ku46r0vMz+adatWqxc+fOG/4Owep9CwsLIywsjKFDhxIYGMjixYuvG6ZFcgpNVBeRTDVkyBAmTZrE8OHD2bFjB7t27WLatGn8+9//BiAsLIxq1arRsWNHNm/ezPr16+ncuTMPPfQQderUueF1w8LCCA0NpWXLlixYsIDo6GhWr17Nm2++aV/qoWzZshw8eJCtW7dy6tQpEhMT01ynffv2hISE0LJlS1atWsWBAwf48ccfWbNmDQCVKlWyT47ftm0bHTp0sPfCpVffvn15//33+emnn9i9ezcvvfRSmoU1H3nkESZPnsyKFSv4448/6NKlC56envbzFStWJDk5mbFjx3LgwAEmT57MF1984VQdV38m27dvZ8+ePZw6dcqhN+xGXn/9dVavXk2fPn3YunUre/fuZc6cOfaJ6r/88guffvopW7du5a+//mLSpEmkpqZy9913O12fSHaiUCUimSo8PJxffvmFBQsWcP/99/PAAw/w0UcfUaZMGcAanpozZw4FCxakUaNGhIWFUb58eaZPn37T69psNn777TcaNWpEt27duOuuu2jXrh1//fUXwcHBADz11FNERETw8MMPU6RIEaZOnZrmOl5eXixYsICiRYvy2GOPUa1aNd5//317oBkzZgwFCxbkwQcf5IknniA8PNyhhyk9BgwYwLPPPkuXLl0IDQ2lQIEC9p6vqwYPHsxDDz3E448/TvPmzWnZsqXD3Kjq1aszZswYRo4cSdWqVZkyZQojRoxwqg6AHj16cPfdd1OnTh2KFCnCqlWrbvma++67j2XLlvG///2Phg0bUrNmTYYMGULx4sUBa7h11qxZPPLII9xzzz188cUXTJ06lSpVqjhdn0h2YjN3OrguIiJ3rGvXrsTFxbn0cTkikrnUUyUiIiLiAgpVIiIiIi6g4T8RERERF1BPlYiIiIgLKFSJiIiIuIBClYiIiIgLKFSJiIiIuIBClYiIiIgLKFSJiIiIuIBClYiIiIgLKFSJiIiIuIBClYiIiIgL/B/begXmHtU3pQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando Mileage\n",
    "\n",
    "# Boxplot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=features[\"mileage\"])\n",
    "plt.title(\"Boxplot Mileage\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.show()\n",
    "\n",
    "# Histogram\n",
    "plt.hist(features['mileage'], bins=50, alpha=0.7, color='skyblue')\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mileage Distribution')\n",
    "plt.show()\n",
    "\n",
    "# Density Plot\n",
    "sns.kdeplot(features['mileage'], shade=True)\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Mileage Density Plot')\n",
    "plt.show()\n",
    "\n",
    "# Q-Q Plot\n",
    "import scipy.stats as stats\n",
    "stats.probplot(features['mileage'], dist='norm', plot=plt)\n",
    "plt.title('Mileage Q-Q Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The `mileage` column is left-skewed because the outliers are concentrated towards \"lower mileage.\" Therefore, we will transform the data to stabilize variance and improve symmetry.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson-Darling test: statistic=58207.55, critical values=[0.576 0.656 0.787 0.918 1.092], significance level=[15.  10.   5.   2.5  1. ]\n",
      "\n",
      "Shapiro-Wilk test: statistic=0.60, p-value=0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python3/lib/python3.9/site-packages/scipy/stats/_morestats.py:1816: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "# transformando dados usando square root\n",
    "sqrt_mileage = np.sqrt(df['mileage'])\n",
    "\n",
    "# testando normalidade novamente\n",
    "# Anderson-Darling test\n",
    "result2 = anderson(sqrt_mileage)\n",
    "print(f\"Anderson-Darling test: statistic={result2.statistic:.2f}, critical values={result2.critical_values}, significance level={result2.significance_level}\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# Shapiro-Wilk test\n",
    "stat2, p2 = shapiro(sqrt_mileage)\n",
    "print(f\"Shapiro-Wilk test: statistic={stat2:.2f}, p-value={p2:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando Mileage após a transformação\n",
    "\n",
    "# Boxplot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=sqrt_mileage)\n",
    "plt.title(\"Boxplot Mileage\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.show()\n",
    "\n",
    "# Histogram\n",
    "plt.hist(sqrt_mileage, bins=50, alpha=0.7, color='skyblue')\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mileage Distribution')\n",
    "plt.show()\n",
    "\n",
    "# Density Plot\n",
    "sns.kdeplot(sqrt_mileage, shade=True)\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Mileage Density Plot')\n",
    "plt.show()\n",
    "\n",
    "# Q-Q Plot\n",
    "import scipy.stats as stats\n",
    "stats.probplot(sqrt_mileage, dist='norm', plot=plt)\n",
    "plt.title('Mileage Q-Q Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion: Square root transformation did not eliminate non-normality, let's try box-cox transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson-Darling test: statistic=58243.58, critical values=[0.576 0.656 0.787 0.918 1.092], significance level=[15.  10.   5.   2.5  1. ]\n",
      "\n",
      "Shapiro-Wilk test: statistic=0.66, p-value=0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python3/lib/python3.9/site-packages/scipy/stats/_morestats.py:1816: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "# transformando com boxcox\n",
    "bc_mileage, lam = boxcox(features['mileage'])\n",
    "\n",
    "# testando normalidade novamente\n",
    "# Anderson-Darling test\n",
    "result3 = anderson(bc_mileage)\n",
    "print(f\"Anderson-Darling test: statistic={result3.statistic:.2f}, critical values={result3.critical_values}, significance level={result3.significance_level}\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# Shapiro-Wilk test\n",
    "stat3, p3 = shapiro(bc_mileage)\n",
    "print(f\"Shapiro-Wilk test: statistic={stat3:.2f}, p-value={p3:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The `mileage` data remains non-normal even after applying the Box-Cox transformation, indicating that it is inherently non-normal. Therefore, we will proceed with winsorization and truncation techniques.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson-Darling test: statistic=60197.75, critical values=[0.576 0.656 0.787 0.918 1.092], significance level=[15.  10.   5.   2.5  1. ]\n",
      "\n",
      "Shapiro-Wilk test: statistic=0.64, p-value=0.00\n"
     ]
    }
   ],
   "source": [
    "# calculando thresholds\n",
    "mileage = features[\"mileage\"].values\n",
    "p10 = np.percentile(mileage, 10)\n",
    "p90 = np.percentile(mileage, 90)\n",
    "\n",
    "# winsor\n",
    "mileage_winsorized = np.clip(mileage, p10, p90)\n",
    "\n",
    "# truncate\n",
    "mileage_truncated = mileage_winsorized[(mileage_winsorized > (np.mean(mileage_winsorized) - 3 * np.std(mileage_winsorized))) & \n",
    "                                       (mileage_winsorized < (np.mean(mileage_winsorized) + 3 * np.std(mileage_winsorized)))]\n",
    "\n",
    "# re testando normalidade\n",
    "# Anderson-Darling test\n",
    "result4 = anderson(mileage_truncated)\n",
    "print(f\"Anderson-Darling test: statistic={result4.statistic:.2f}, critical values={result4.critical_values}, significance level={result4.significance_level}\")\n",
    "print(\"\")\n",
    "\n",
    "\n",
    "# Shapiro-Wilk test\n",
    "stat4, p4 = shapiro(mileage_truncated)\n",
    "print(f\"Shapiro-Wilk test: statistic={stat4:.2f}, p-value={p4:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "After applying winsorization and truncation, the `mileage` data still remains non-normal. Therefore, we will use censoring and replace the outliers with the minimum mileage value from the dataset (excluding outliers).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14495.308394309206\n"
     ]
    }
   ],
   "source": [
    "# calculando threshold de outliers\n",
    "out_threshold = np.mean(mileage) - 3 * np.std(mileage)\n",
    "print(out_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the quartiles\n",
    "mileageQ1 = np.percentile(mileage, 25)\n",
    "mileageQ3 = np.percentile(mileage, 75)\n",
    "\n",
    "# Calculate the IQR\n",
    "mileageIQR = mileageQ3 - mileageQ1\n",
    "\n",
    "# Calculate the whisker values\n",
    "lower_whisker = mileageQ1 - 1.5 * mileageIQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# censurando left-skewed outliers\n",
    "mileage_censored = np.where(mileage < lower_whisker, lower_whisker, mileage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizando Mileage após a censura\n",
    "\n",
    "# Boxplot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=mileage_censored)\n",
    "plt.title(\"Boxplot Mileage\")\n",
    "plt.xlabel(\"Columns\")\n",
    "plt.ylabel(\"Values\")\n",
    "plt.show()\n",
    "\n",
    "# Histogram\n",
    "plt.hist(mileage_censored, bins=50, alpha=0.7, color='skyblue')\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Mileage Distribution')\n",
    "plt.show()\n",
    "\n",
    "# Density Plot\n",
    "sns.kdeplot(mileage_censored, shade=True)\n",
    "plt.xlabel('Mileage')\n",
    "plt.ylabel('Density')\n",
    "plt.title('Mileage Density Plot')\n",
    "plt.show()\n",
    "\n",
    "# Q-Q Plot\n",
    "import scipy.stats as stats\n",
    "stats.probplot(mileage_censored, dist='norm', plot=plt)\n",
    "plt.title('Mileage Q-Q Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "458\n"
     ]
    }
   ],
   "source": [
    "# removendo right-skewed outliers das colunas power e registration year\n",
    "\n",
    "power = features[\"power\"].values\n",
    "regyear = features[\"registrationyear\"].values\n",
    "\n",
    "# calculando thresholds para cada coluna\n",
    "threshold1 = np.mean(power) + 3 * np.std(power)\n",
    "threshold2 = np.mean(regyear) + 3 * np.std(regyear)\n",
    "\n",
    "# identificando os índices dos outliers em cada coluna\n",
    "outlier_indices1 = np.where(power > threshold1)[0]\n",
    "outlier_indices2 = np.where(regyear > threshold2)[0]\n",
    "\n",
    "# combinando os indices de outliers das duas colunas\n",
    "outlier_indices = np.union1d(outlier_indices1, outlier_indices2)\n",
    "print(len(outlier_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          datecrawled  vehicletype  registrationyear gearbox  power    model  \\\n",
      "0 2016-03-24 11:52:00      unknown              1993  manual      0     golf   \n",
      "1 2016-03-24 10:58:00        coupe              2011  manual    190  unknown   \n",
      "2 2016-03-14 12:52:00          suv              2004    auto    163    grand   \n",
      "3 2016-03-17 16:54:00        small              2001  manual     75     golf   \n",
      "4 2016-03-31 17:25:00        small              2008  manual     69    fabia   \n",
      "5 2016-04-04 17:36:00        sedan              1995  manual    102      3er   \n",
      "6 2016-04-01 20:48:00  convertible              2004  manual    109  2_reihe   \n",
      "7 2016-03-21 18:54:00        sedan              1980  manual     50    other   \n",
      "8 2016-04-04 23:42:00          bus              2014  manual    125    c_max   \n",
      "9 2016-03-17 10:53:00        small              1998  manual    101     golf   \n",
      "\n",
      "   mileage  registrationmonth  fueltype       brand notrepaired datecreated  \\\n",
      "0   150000                  0    petrol  volkswagen     unknown  2016-03-24   \n",
      "1   125000                  5  gasoline        audi         yes  2016-03-24   \n",
      "2   125000                  8  gasoline        jeep     unknown  2016-03-14   \n",
      "3   150000                  6    petrol  volkswagen          no  2016-03-17   \n",
      "4    90000                  7  gasoline       skoda          no  2016-03-31   \n",
      "5   150000                 10    petrol         bmw         yes  2016-04-04   \n",
      "6   150000                  8    petrol     peugeot          no  2016-04-01   \n",
      "7    40000                  7    petrol  volkswagen          no  2016-03-21   \n",
      "8    30000                  8    petrol        ford     unknown  2016-04-04   \n",
      "9   150000                  0   unknown  volkswagen     unknown  2016-03-17   \n",
      "\n",
      "   numberofpictures  postalcode            lastseen  mileage_censored  \n",
      "0                 0       70435 2016-04-07 03:16:00          150000.0  \n",
      "1                 0       66954 2016-04-07 01:46:00          125000.0  \n",
      "2                 0       90480 2016-04-05 12:47:00          125000.0  \n",
      "3                 0       91074 2016-03-17 17:40:00          150000.0  \n",
      "4                 0       60437 2016-04-06 10:17:00           90000.0  \n",
      "5                 0       33775 2016-04-06 19:17:00          150000.0  \n",
      "6                 0       67112 2016-04-05 18:18:00          150000.0  \n",
      "7                 0       19348 2016-03-25 16:47:00           87500.0  \n",
      "8                 0       94505 2016-04-04 23:42:00           87500.0  \n",
      "9                 0       27472 2016-03-31 17:17:00          150000.0  \n"
     ]
    }
   ],
   "source": [
    "# Juntando a coluna mileage_censored de volta para as features\n",
    "features[\"mileage_censored\"] = mileage_censored\n",
    "print(features.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removendo das fdeatures as observações correspondentes aos indices de outliers removidos de power e registration year\n",
    "features = features.drop(features.index[outlier_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 353911 entries, 0 to 354368\n",
      "Data columns (total 16 columns):\n",
      " #   Column             Non-Null Count   Dtype         \n",
      "---  ------             --------------   -----         \n",
      " 0   datecrawled        353911 non-null  datetime64[ns]\n",
      " 1   vehicletype        353911 non-null  object        \n",
      " 2   registrationyear   353911 non-null  int64         \n",
      " 3   gearbox            353911 non-null  object        \n",
      " 4   power              353911 non-null  int64         \n",
      " 5   model              353911 non-null  object        \n",
      " 6   mileage            353911 non-null  int64         \n",
      " 7   registrationmonth  353911 non-null  int64         \n",
      " 8   fueltype           353911 non-null  object        \n",
      " 9   brand              353911 non-null  object        \n",
      " 10  notrepaired        353911 non-null  object        \n",
      " 11  datecreated        353911 non-null  datetime64[ns]\n",
      " 12  numberofpictures   353911 non-null  int64         \n",
      " 13  postalcode         353911 non-null  int64         \n",
      " 14  lastseen           353911 non-null  datetime64[ns]\n",
      " 15  mileage_censored   353911 non-null  float64       \n",
      "dtypes: datetime64[ns](3), float64(1), int64(6), object(6)\n",
      "memory usage: 45.9+ MB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# codifidando variáveis categóricas com OHE,dropando a primeira coluna \n",
    "categorical = ['gearbox','fueltype','notrepaired','vehicletype']\n",
    "features = pd.get_dummies(features,columns=categorical)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# codificando variaveis categoricas com label encoder pois possuem alta cardinalidade e nao tem uma ordem lógica\n",
    "\n",
    "categorical_cols = ['brand', 'model']\n",
    "\n",
    "# applicando LabelEncoder para cada coluna\n",
    "for col in categorical_cols:\n",
    "    le = LabelEncoder()\n",
    "    features[f\"{col}_encoded\"] = le.fit_transform(features[col])\n",
    "    \n",
    "# removendo colunas desnecessarias\n",
    "features.drop(columns=['model', 'brand'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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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>registrationyear</th>\n",
       "      <th>power</th>\n",
       "      <th>mileage</th>\n",
       "      <th>registrationmonth</th>\n",
       "      <th>datecreated</th>\n",
       "      <th>numberofpictures</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>lastseen</th>\n",
       "      <th>mileage_censored</th>\n",
       "      <th>...</th>\n",
       "      <th>vehicletype_convertible</th>\n",
       "      <th>vehicletype_coupe</th>\n",
       "      <th>vehicletype_other</th>\n",
       "      <th>vehicletype_sedan</th>\n",
       "      <th>vehicletype_small</th>\n",
       "      <th>vehicletype_suv</th>\n",
       "      <th>vehicletype_unknown</th>\n",
       "      <th>vehicletype_wagon</th>\n",
       "      <th>brand_encoded</th>\n",
       "      <th>model_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-24 11:52:00</td>\n",
       "      <td>1993</td>\n",
       "      <td>0</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>70435</td>\n",
       "      <td>2016-04-07 03:16:00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-24 10:58:00</td>\n",
       "      <td>2011</td>\n",
       "      <td>190</td>\n",
       "      <td>125000</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>0</td>\n",
       "      <td>66954</td>\n",
       "      <td>2016-04-07 01:46:00</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-14 12:52:00</td>\n",
       "      <td>2004</td>\n",
       "      <td>163</td>\n",
       "      <td>125000</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-03-14</td>\n",
       "      <td>0</td>\n",
       "      <td>90480</td>\n",
       "      <td>2016-04-05 12:47:00</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-03-17 16:54:00</td>\n",
       "      <td>2001</td>\n",
       "      <td>75</td>\n",
       "      <td>150000</td>\n",
       "      <td>6</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>91074</td>\n",
       "      <td>2016-03-17 17:40:00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:00</td>\n",
       "      <td>2008</td>\n",
       "      <td>69</td>\n",
       "      <td>90000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0</td>\n",
       "      <td>60437</td>\n",
       "      <td>2016-04-06 10:17:00</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-04 17:36:00</td>\n",
       "      <td>1995</td>\n",
       "      <td>102</td>\n",
       "      <td>150000</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>0</td>\n",
       "      <td>33775</td>\n",
       "      <td>2016-04-06 19:17:00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-01 20:48:00</td>\n",
       "      <td>2004</td>\n",
       "      <td>109</td>\n",
       "      <td>150000</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>0</td>\n",
       "      <td>67112</td>\n",
       "      <td>2016-04-05 18:18:00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-03-21 18:54:00</td>\n",
       "      <td>1980</td>\n",
       "      <td>50</td>\n",
       "      <td>40000</td>\n",
       "      <td>7</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>0</td>\n",
       "      <td>19348</td>\n",
       "      <td>2016-03-25 16:47:00</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-04 23:42:00</td>\n",
       "      <td>2014</td>\n",
       "      <td>125</td>\n",
       "      <td>30000</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>0</td>\n",
       "      <td>94505</td>\n",
       "      <td>2016-04-04 23:42:00</td>\n",
       "      <td>87500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-03-17 10:53:00</td>\n",
       "      <td>1998</td>\n",
       "      <td>101</td>\n",
       "      <td>150000</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0</td>\n",
       "      <td>27472</td>\n",
       "      <td>2016-03-31 17:17:00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          datecrawled  registrationyear  power  mileage  registrationmonth  \\\n",
       "0 2016-03-24 11:52:00              1993      0   150000                  0   \n",
       "1 2016-03-24 10:58:00              2011    190   125000                  5   \n",
       "2 2016-03-14 12:52:00              2004    163   125000                  8   \n",
       "3 2016-03-17 16:54:00              2001     75   150000                  6   \n",
       "4 2016-03-31 17:25:00              2008     69    90000                  7   \n",
       "5 2016-04-04 17:36:00              1995    102   150000                 10   \n",
       "6 2016-04-01 20:48:00              2004    109   150000                  8   \n",
       "7 2016-03-21 18:54:00              1980     50    40000                  7   \n",
       "8 2016-04-04 23:42:00              2014    125    30000                  8   \n",
       "9 2016-03-17 10:53:00              1998    101   150000                  0   \n",
       "\n",
       "  datecreated  numberofpictures  postalcode            lastseen  \\\n",
       "0  2016-03-24                 0       70435 2016-04-07 03:16:00   \n",
       "1  2016-03-24                 0       66954 2016-04-07 01:46:00   \n",
       "2  2016-03-14                 0       90480 2016-04-05 12:47:00   \n",
       "3  2016-03-17                 0       91074 2016-03-17 17:40:00   \n",
       "4  2016-03-31                 0       60437 2016-04-06 10:17:00   \n",
       "5  2016-04-04                 0       33775 2016-04-06 19:17:00   \n",
       "6  2016-04-01                 0       67112 2016-04-05 18:18:00   \n",
       "7  2016-03-21                 0       19348 2016-03-25 16:47:00   \n",
       "8  2016-04-04                 0       94505 2016-04-04 23:42:00   \n",
       "9  2016-03-17                 0       27472 2016-03-31 17:17:00   \n",
       "\n",
       "   mileage_censored  ...  vehicletype_convertible  vehicletype_coupe  \\\n",
       "0          150000.0  ...                        0                  0   \n",
       "1          125000.0  ...                        0                  1   \n",
       "2          125000.0  ...                        0                  0   \n",
       "3          150000.0  ...                        0                  0   \n",
       "4           90000.0  ...                        0                  0   \n",
       "5          150000.0  ...                        0                  0   \n",
       "6          150000.0  ...                        1                  0   \n",
       "7           87500.0  ...                        0                  0   \n",
       "8           87500.0  ...                        0                  0   \n",
       "9          150000.0  ...                        0                  0   \n",
       "\n",
       "   vehicletype_other  vehicletype_sedan  vehicletype_small  vehicletype_suv  \\\n",
       "0                  0                  0                  0                0   \n",
       "1                  0                  0                  0                0   \n",
       "2                  0                  0                  0                1   \n",
       "3                  0                  0                  1                0   \n",
       "4                  0                  0                  1                0   \n",
       "5                  0                  1                  0                0   \n",
       "6                  0                  0                  0                0   \n",
       "7                  0                  1                  0                0   \n",
       "8                  0                  0                  0                0   \n",
       "9                  0                  0                  1                0   \n",
       "\n",
       "   vehicletype_unknown  vehicletype_wagon  brand_encoded  model_encoded  \n",
       "0                    1                  0             38            116  \n",
       "1                    0                  0              1            228  \n",
       "2                    0                  0             14            117  \n",
       "3                    0                  0             38            116  \n",
       "4                    0                  0             31            101  \n",
       "5                    0                  0              2             11  \n",
       "6                    0                  0             25              8  \n",
       "7                    0                  0             38            166  \n",
       "8                    0                  0             10             60  \n",
       "9                    0                  0             38            116  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# escalando as variáveis numéricas\n",
    "numeric = ['registrationyear','power','mileage_censored','registrationmonth','numberofpictures','postalcode']\n",
    "# numeric = features.select_dtypes(include=['int64', 'float64']).columns\n",
    "scaler = MaxAbsScaler()\n",
    "scaler.fit(features[numeric])\n",
    "\n",
    "features[numeric] = scaler.transform(features[numeric])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-03-31 17:25:00</td>\n",
       "      <td>0.903690</td>\n",
       "      <td>0.101770</td>\n",
       "      <td>90000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604382</td>\n",
       "      <td>2016-04-06 10:17:00</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-04-04 17:36:00</td>\n",
       "      <td>0.897840</td>\n",
       "      <td>0.150442</td>\n",
       "      <td>150000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.337757</td>\n",
       "      <td>2016-04-06 19:17:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-04-01 20:48:00</td>\n",
       "      <td>0.901890</td>\n",
       "      <td>0.160767</td>\n",
       "      <td>150000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2016-04-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.671133</td>\n",
       "      <td>2016-04-05 18:18:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-03-21 18:54:00</td>\n",
       "      <td>0.891089</td>\n",
       "      <td>0.073746</td>\n",
       "      <td>40000</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>2016-03-21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.193484</td>\n",
       "      <td>2016-03-25 16:47:00</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-04-04 23:42:00</td>\n",
       "      <td>0.906391</td>\n",
       "      <td>0.184366</td>\n",
       "      <td>30000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2016-04-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.945069</td>\n",
       "      <td>2016-04-04 23:42:00</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-03-17 10:53:00</td>\n",
       "      <td>0.899190</td>\n",
       "      <td>0.148968</td>\n",
       "      <td>150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016-03-17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.274725</td>\n",
       "      <td>2016-03-31 17:17:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          datecrawled  registrationyear     power  mileage  registrationmonth  \\\n",
       "0 2016-03-24 11:52:00          0.896940  0.000000   150000           0.000000   \n",
       "1 2016-03-24 10:58:00          0.905041  0.280236   125000           0.416667   \n",
       "2 2016-03-14 12:52:00          0.901890  0.240413   125000           0.666667   \n",
       "3 2016-03-17 16:54:00          0.900540  0.110619   150000           0.500000   \n",
       "4 2016-03-31 17:25:00          0.903690  0.101770    90000           0.583333   \n",
       "5 2016-04-04 17:36:00          0.897840  0.150442   150000           0.833333   \n",
       "6 2016-04-01 20:48:00          0.901890  0.160767   150000           0.666667   \n",
       "7 2016-03-21 18:54:00          0.891089  0.073746    40000           0.583333   \n",
       "8 2016-04-04 23:42:00          0.906391  0.184366    30000           0.666667   \n",
       "9 2016-03-17 10:53:00          0.899190  0.148968   150000           0.000000   \n",
       "\n",
       "  datecreated  numberofpictures  postalcode            lastseen  \\\n",
       "0  2016-03-24               0.0    0.704364 2016-04-07 03:16:00   \n",
       "1  2016-03-24               0.0    0.669553 2016-04-07 01:46:00   \n",
       "2  2016-03-14               0.0    0.904818 2016-04-05 12:47:00   \n",
       "3  2016-03-17               0.0    0.910758 2016-03-17 17:40:00   \n",
       "4  2016-03-31               0.0    0.604382 2016-04-06 10:17:00   \n",
       "5  2016-04-04               0.0    0.337757 2016-04-06 19:17:00   \n",
       "6  2016-04-01               0.0    0.671133 2016-04-05 18:18:00   \n",
       "7  2016-03-21               0.0    0.193484 2016-03-25 16:47:00   \n",
       "8  2016-04-04               0.0    0.945069 2016-04-04 23:42:00   \n",
       "9  2016-03-17               0.0    0.274725 2016-03-31 17:17:00   \n",
       "\n",
       "   mileage_censored  ...  vehicletype_convertible  vehicletype_coupe  \\\n",
       "0          1.000000  ...                        0                  0   \n",
       "1          0.833333  ...                        0                  1   \n",
       "2          0.833333  ...                        0                  0   \n",
       "3          1.000000  ...                        0                  0   \n",
       "4          0.600000  ...                        0                  0   \n",
       "5          1.000000  ...                        0                  0   \n",
       "6          1.000000  ...                        1                  0   \n",
       "7          0.583333  ...                        0                  0   \n",
       "8          0.583333  ...                        0                  0   \n",
       "9          1.000000  ...                        0                  0   \n",
       "\n",
       "   vehicletype_other  vehicletype_sedan  vehicletype_small  vehicletype_suv  \\\n",
       "0                  0                  0                  0                0   \n",
       "1                  0                  0                  0                0   \n",
       "2                  0                  0                  0                1   \n",
       "3                  0                  0                  1                0   \n",
       "4                  0                  0                  1                0   \n",
       "5                  0                  1                  0                0   \n",
       "6                  0                  0                  0                0   \n",
       "7                  0                  1                  0                0   \n",
       "8                  0                  0                  0                0   \n",
       "9                  0                  0                  1                0   \n",
       "\n",
       "   vehicletype_unknown  vehicletype_wagon  brand_encoded  model_encoded  \n",
       "0                    1                  0             38            116  \n",
       "1                    0                  0              1            228  \n",
       "2                    0                  0             14            117  \n",
       "3                    0                  0             38            116  \n",
       "4                    0                  0             31            101  \n",
       "5                    0                  0              2             11  \n",
       "6                    0                  0             25              8  \n",
       "7                    0                  0             38            166  \n",
       "8                    0                  0             10             60  \n",
       "9                    0                  0             38            116  \n",
       "\n",
       "[10 rows x 35 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 353911 entries, 0 to 354368\n",
      "Data columns (total 35 columns):\n",
      " #   Column                   Non-Null Count   Dtype         \n",
      "---  ------                   --------------   -----         \n",
      " 0   datecrawled              353911 non-null  datetime64[ns]\n",
      " 1   registrationyear         353911 non-null  float64       \n",
      " 2   power                    353911 non-null  float64       \n",
      " 3   mileage                  353911 non-null  int64         \n",
      " 4   registrationmonth        353911 non-null  float64       \n",
      " 5   datecreated              353911 non-null  datetime64[ns]\n",
      " 6   numberofpictures         353911 non-null  float64       \n",
      " 7   postalcode               353911 non-null  float64       \n",
      " 8   lastseen                 353911 non-null  datetime64[ns]\n",
      " 9   mileage_censored         353911 non-null  float64       \n",
      " 10  gearbox_auto             353911 non-null  uint8         \n",
      " 11  gearbox_manual           353911 non-null  uint8         \n",
      " 12  gearbox_unknown          353911 non-null  uint8         \n",
      " 13  fueltype_cng             353911 non-null  uint8         \n",
      " 14  fueltype_electric        353911 non-null  uint8         \n",
      " 15  fueltype_gasoline        353911 non-null  uint8         \n",
      " 16  fueltype_hybrid          353911 non-null  uint8         \n",
      " 17  fueltype_lpg             353911 non-null  uint8         \n",
      " 18  fueltype_other           353911 non-null  uint8         \n",
      " 19  fueltype_petrol          353911 non-null  uint8         \n",
      " 20  fueltype_unknown         353911 non-null  uint8         \n",
      " 21  notrepaired_no           353911 non-null  uint8         \n",
      " 22  notrepaired_unknown      353911 non-null  uint8         \n",
      " 23  notrepaired_yes          353911 non-null  uint8         \n",
      " 24  vehicletype_bus          353911 non-null  uint8         \n",
      " 25  vehicletype_convertible  353911 non-null  uint8         \n",
      " 26  vehicletype_coupe        353911 non-null  uint8         \n",
      " 27  vehicletype_other        353911 non-null  uint8         \n",
      " 28  vehicletype_sedan        353911 non-null  uint8         \n",
      " 29  vehicletype_small        353911 non-null  uint8         \n",
      " 30  vehicletype_suv          353911 non-null  uint8         \n",
      " 31  vehicletype_unknown      353911 non-null  uint8         \n",
      " 32  vehicletype_wagon        353911 non-null  uint8         \n",
      " 33  brand_encoded            353911 non-null  int64         \n",
      " 34  model_encoded            353911 non-null  int64         \n",
      "dtypes: datetime64[ns](3), float64(6), int64(3), uint8(23)\n",
      "memory usage: 42.9 MB\n"
     ]
    }
   ],
   "source": [
    "features.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removendo colunas desnecessárias nas features\n",
    "features.drop(columns=['datecrawled','mileage','datecreated','lastseen'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removendo observações do target\n",
    "target = target.drop(target.index[outlier_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criando os conjuntos de treinamento e validação\n",
    "features_train,features_valid,target_train,target_valid = train_test_split(features,target,test_size=.25,random_state=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((265433, 31), (88478, 31))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.shape, features_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression:\n",
      "RMSE: 3117.35\n",
      "R²: 0.52\n"
     ]
    }
   ],
   "source": [
    "# treinando regressão linear\n",
    "\n",
    "lr_reg = LinearRegression()\n",
    "lr_reg.fit(features_train, target_train)\n",
    "pred_linear = lr_reg.predict(features_valid)\n",
    "rmse_linear = np.sqrt(mean_squared_error(target_valid, pred_linear))\n",
    "r2_linear = r2_score(target_valid,pred_linear)\n",
    "print(\"Linear Regression:\")\n",
    "print(f\"RMSE: {rmse_linear:.2f}\")\n",
    "print(f\"R²: {r2_linear:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9 µs, sys: 0 ns, total: 9 µs\n",
      "Wall time: 5.96 µs\n",
      "Decision Tree Regression:\n",
      "RMSE: 2354.84\n",
      "R²: 0.73\n"
     ]
    }
   ],
   "source": [
    "# treinando decision tree\n",
    "%time\n",
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(features_train, target_train)\n",
    "pred_tree = tree_reg.predict(features_valid)\n",
    "rmse_tree = np.sqrt(mean_squared_error(target_valid, pred_tree))\n",
    "r2_tree = r2_score(target_valid, pred_tree)\n",
    "print(\"Decision Tree Regression:\")\n",
    "print(f\"RMSE: {rmse_tree:.2f}\")\n",
    "print(f\"R²: {r2_tree:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 6.2 µs\n",
      "Random Forest Regression:\n",
      "RMSE: 1818.85\n",
      "R²: 0.84\n"
     ]
    }
   ],
   "source": [
    "# treinando random forest\n",
    "%time\n",
    "rf_reg = RandomForestRegressor(n_estimators=10)\n",
    "rf_reg.fit(features_train, target_train)\n",
    "pred_rf = rf_reg.predict(features_valid)\n",
    "rmse_rf = np.sqrt(mean_squared_error(target_valid, pred_rf))\n",
    "r2_rf = r2_score(target_valid, pred_rf)\n",
    "print(\"Random Forest Regression:\")\n",
    "print(f\"RMSE: {rmse_rf:.2f}\")\n",
    "print(f\"R²: {r2_rf:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression:\n",
      "RMSE: 3117.35\n"
     ]
    }
   ],
   "source": [
    "lr_t0 = time.perf_counter()\n",
    "# otimizando Linear Regression\n",
    "\n",
    "lr_reg = LinearRegression(copy_X=True, fit_intercept=False,normalize=True)\n",
    "lr_reg.fit(features_train,target_train)\n",
    "\n",
    "# previsões\n",
    "pred_lr = lr_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_lr = np.sqrt(mean_squared_error(target_valid, pred_lr))\n",
    "\n",
    "print(\"Linear Regression:\")\n",
    "\n",
    "print(f\"RMSE: {rmse_lr:.2f}\")\n",
    "\n",
    "lr_t1 = time.perf_counter()\n",
    "lr_time = lr_t1 - lr_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Regressor:\n",
      "RMSE: 2001.72\n"
     ]
    }
   ],
   "source": [
    "dt_t0 = time.perf_counter()\n",
    "# otimizando Decision Tree Regressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(max_depth=15,min_samples_leaf=10, min_samples_split=2)\n",
    "tree_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_dt = tree_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_dt = np.sqrt(mean_squared_error(target_valid, pred_dt))\n",
    "\n",
    "print(\"Decision Tree Regressor:\")\n",
    "print(f\"RMSE: {rmse_dt:.2f}\")\n",
    "\n",
    "dt_t1 = time.perf_counter()\n",
    "dt_time = dt_t1 - dt_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Regressor:\n",
      "RMSE: 2028.79\n"
     ]
    }
   ],
   "source": [
    "rf_t0 = time.perf_counter()\n",
    "# otimizando Random Forest Regressor\n",
    "rf_reg = RandomForestRegressor(n_estimators=50, max_depth=10, min_samples_split=5)\n",
    "rf_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_rf = rf_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_rf = np.sqrt(mean_squared_error(target_valid, pred_rf))\n",
    "\n",
    "print(\"Random Forest Regressor:\")\n",
    "print(f\"RMSE: {rmse_rf:.2f}\")\n",
    "\n",
    "rf_t1 = time.perf_counter()\n",
    "rf_time = rf_t1 - rf_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM:\n",
      "RMSE: 2002.55\n"
     ]
    }
   ],
   "source": [
    "lgb_t0 = time.perf_counter()\n",
    "# treinando modelo otimizado com LGB\n",
    "\n",
    "lgb_reg = lgb.LGBMRegressor(learning_rate=0.1, max_depth=7, n_estimators=50, num_leaves=20)\n",
    "lgb_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_lgb = lgb_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_lgb = np.sqrt(mean_squared_error(target_valid, pred_lgb))\n",
    "\n",
    "print(\"LightGBM:\")\n",
    "print(f'RMSE: {rmse_lgb:.2f}')\n",
    "\n",
    "lgb_t1 = time.perf_counter()\n",
    "lgb_time = lgb_t1 - lgb_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingRegressor:\n",
      "RMSE: 1929.93\n"
     ]
    }
   ],
   "source": [
    "gb_t0 = time.perf_counter()\n",
    "# treinando modelo otimizado com GradientBoostingRegressor\n",
    "\n",
    "gb_reg = GradientBoostingRegressor(learning_rate=0.5, max_depth=7, max_features='sqrt', n_estimators=50, subsample=0.8)\n",
    "gb_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_gb = gb_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_gb = np.sqrt(mean_squared_error(target_valid, pred_gb))\n",
    "\n",
    "print(\"GradientBoostingRegressor:\")\n",
    "print(f'RMSE: {rmse_gb:.2f}')\n",
    "\n",
    "gb_t1 = time.perf_counter()\n",
    "gb_time = gb_t1 - gb_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 3242.7498281\ttotal: 116ms\tremaining: 5.67s\n",
      "1:\tlearn: 2666.3624162\ttotal: 182ms\tremaining: 4.36s\n",
      "2:\tlearn: 2398.8063770\ttotal: 244ms\tremaining: 3.83s\n",
      "3:\tlearn: 2283.2986316\ttotal: 304ms\tremaining: 3.5s\n",
      "4:\tlearn: 2194.9093136\ttotal: 367ms\tremaining: 3.31s\n",
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      "6:\tlearn: 2123.2079509\ttotal: 478ms\tremaining: 2.94s\n",
      "7:\tlearn: 2076.5930507\ttotal: 548ms\tremaining: 2.87s\n",
      "8:\tlearn: 2052.2592353\ttotal: 607ms\tremaining: 2.76s\n",
      "9:\tlearn: 2037.0943331\ttotal: 668ms\tremaining: 2.67s\n",
      "10:\tlearn: 2014.3136262\ttotal: 731ms\tremaining: 2.59s\n",
      "11:\tlearn: 2000.1718183\ttotal: 793ms\tremaining: 2.51s\n",
      "12:\tlearn: 1987.2049958\ttotal: 852ms\tremaining: 2.43s\n",
      "13:\tlearn: 1975.9366144\ttotal: 918ms\tremaining: 2.36s\n",
      "14:\tlearn: 1970.5574688\ttotal: 976ms\tremaining: 2.28s\n",
      "15:\tlearn: 1959.7188297\ttotal: 1.04s\tremaining: 2.21s\n",
      "16:\tlearn: 1949.7738213\ttotal: 1.1s\tremaining: 2.13s\n",
      "17:\tlearn: 1943.8406892\ttotal: 1.15s\tremaining: 2.04s\n",
      "18:\tlearn: 1933.5912553\ttotal: 1.21s\tremaining: 1.97s\n",
      "19:\tlearn: 1927.6321403\ttotal: 1.27s\tremaining: 1.91s\n",
      "20:\tlearn: 1923.0940894\ttotal: 1.34s\tremaining: 1.85s\n",
      "21:\tlearn: 1913.5695597\ttotal: 1.41s\tremaining: 1.79s\n",
      "22:\tlearn: 1907.0807351\ttotal: 1.46s\tremaining: 1.72s\n",
      "23:\tlearn: 1902.6464935\ttotal: 1.52s\tremaining: 1.65s\n",
      "24:\tlearn: 1896.0124893\ttotal: 1.59s\tremaining: 1.59s\n",
      "25:\tlearn: 1891.2491361\ttotal: 1.65s\tremaining: 1.52s\n",
      "26:\tlearn: 1887.9355619\ttotal: 1.71s\tremaining: 1.45s\n",
      "27:\tlearn: 1884.0999943\ttotal: 1.76s\tremaining: 1.39s\n",
      "28:\tlearn: 1880.2832832\ttotal: 1.82s\tremaining: 1.32s\n",
      "29:\tlearn: 1875.6624412\ttotal: 1.88s\tremaining: 1.26s\n",
      "30:\tlearn: 1872.3893887\ttotal: 1.94s\tremaining: 1.19s\n",
      "31:\tlearn: 1869.0941416\ttotal: 2s\tremaining: 1.12s\n",
      "32:\tlearn: 1864.6214187\ttotal: 2.07s\tremaining: 1.07s\n",
      "33:\tlearn: 1860.7862891\ttotal: 2.13s\tremaining: 1s\n",
      "34:\tlearn: 1857.9517815\ttotal: 2.17s\tremaining: 932ms\n",
      "35:\tlearn: 1852.8723652\ttotal: 2.23s\tremaining: 869ms\n",
      "36:\tlearn: 1850.2873854\ttotal: 2.28s\tremaining: 802ms\n",
      "37:\tlearn: 1846.6951910\ttotal: 2.34s\tremaining: 739ms\n",
      "38:\tlearn: 1842.7943397\ttotal: 2.4s\tremaining: 678ms\n",
      "39:\tlearn: 1838.9383760\ttotal: 2.47s\tremaining: 618ms\n",
      "40:\tlearn: 1835.7903091\ttotal: 2.52s\tremaining: 554ms\n",
      "41:\tlearn: 1832.4654059\ttotal: 2.58s\tremaining: 492ms\n",
      "42:\tlearn: 1830.5400947\ttotal: 2.64s\tremaining: 430ms\n",
      "43:\tlearn: 1827.6547711\ttotal: 2.7s\tremaining: 368ms\n",
      "44:\tlearn: 1824.2583741\ttotal: 2.77s\tremaining: 307ms\n",
      "45:\tlearn: 1821.7795079\ttotal: 2.83s\tremaining: 246ms\n",
      "46:\tlearn: 1817.2943757\ttotal: 2.9s\tremaining: 185ms\n",
      "47:\tlearn: 1814.2678641\ttotal: 2.96s\tremaining: 124ms\n",
      "48:\tlearn: 1812.3754877\ttotal: 3.02s\tremaining: 61.7ms\n",
      "49:\tlearn: 1809.6830636\ttotal: 3.08s\tremaining: 0us\n",
      "CatBoost Regressor:\n",
      "RMSE: 1867.96\n"
     ]
    }
   ],
   "source": [
    "cat_t0 = time.perf_counter()\n",
    "# treinando modelo otimizado com CatBoost\n",
    "\n",
    "cat_reg = CatBoostRegressor(depth=7, iterations=50, l2_leaf_reg=0.1, learning_rate=0.5)\n",
    "cat_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_cat = cat_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_cat = np.sqrt(mean_squared_error(target_valid, pred_cat))\n",
    "\n",
    "print(\"CatBoost Regressor:\")\n",
    "print(f'RMSE: {rmse_cat:.2f}')\n",
    "\n",
    "cat_t1 = time.perf_counter()\n",
    "cat_time = cat_t1 - cat_t0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Regressor:\n",
      "RMSE: 1818.23\n"
     ]
    }
   ],
   "source": [
    "xgb_t0 = time.perf_counter()\n",
    "# treinando modelo otimizado com XGBoost Regressor\n",
    "\n",
    "xgb_reg = XGBRegressor(gamma=0, learning_rate=0.5, max_depth=7, n_estimators=50)\n",
    "xgb_reg.fit(features_train, target_train)\n",
    "\n",
    "# previsões\n",
    "pred_xgb = xgb_reg.predict(features_valid)\n",
    "\n",
    "# calculando mean squared error\n",
    "rmse_xgb = np.sqrt(mean_squared_error(target_valid, pred_xgb))\n",
    "\n",
    "print(\"XGBoost Regressor:\")\n",
    "print(f'RMSE: {rmse_xgb:.2f}')\n",
    "\n",
    "xgb_t1 = time.perf_counter()\n",
    "xgb_time = xgb_t1 - xgb_t0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Model Name         RMSE    Time(s)\n",
      "0  Linear Regression  3117.352007   0.631018\n",
      "1      Decision Tree  2001.718420   2.303014\n",
      "2      Random Forest  2028.791328  56.139108\n",
      "3            LightGB  2002.553863   3.127458\n",
      "4   GradientBoosting  1929.929530  11.883024\n",
      "5           CatBoost  1867.959478   3.600214\n",
      "6            XGBoost  1818.232296  45.313120\n"
     ]
    }
   ],
   "source": [
    "# Criando um dicionário e dataframe para consolidar os nomes dos modelos e MSEs\n",
    "mse_dict = {\n",
    "    'Model Name': ['Linear Regression', 'Decision Tree','Random Forest','LightGB','GradientBoosting','CatBoost', 'XGBoost'],\n",
    "    'RMSE': [rmse_lr, rmse_dt, rmse_rf, rmse_lgb, rmse_gb, rmse_cat, rmse_xgb],\n",
    "    'Time(s)': [lr_time, dt_time, rf_time, lgb_time, gb_time, cat_time, xgb_time]\n",
    "}\n",
    "\n",
    "mse_df = pd.DataFrame(mse_dict)\n",
    "\n",
    "print(mse_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "The most accurate model was **XGBoost** with an **RMSE of 1818**, but it was the second slowest in terms of training time (**45 seconds**). The **CatBoost** model was the second most accurate (**RMSE of 1868**), with a significantly faster training time of just **3.6 seconds**.\n"
   ]
  }
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