1 - Preparación, Transformación y Exploración de los Datos.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "eff6371b",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center;\">\n",
    "    <h1>Regression of Used Car Prices</h1>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1179d193",
   "metadata": {},
   "source": [
    "## Índice\n",
    "\n",
    "- [1 - Preparación de Datos](#preparaciondedatos)\n",
    "- [1.1 - Carga de Datos](#cargadedatos)\n",
    "- [2 - Transformación de Datos](#transformaciondedatos)\n",
    "- [3 - Exploración de los Datos](#exploraciondedatos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e797d9",
   "metadata": {},
   "source": [
    "### 1 - Preparación de Datos <a name=\"preparaciondedatos\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0315858a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/dask/dataframe/__init__.py:31: FutureWarning: \n",
      "Dask dataframe query planning is disabled because dask-expr is not installed.\n",
      "\n",
      "You can install it with `pip install dask[dataframe]` or `conda install dask`.\n",
      "This will raise in a future version.\n",
      "\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder, MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, KFold\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from lightgbm import LGBMRegressor\n",
    "from catboost import CatBoostRegressor\n",
    "from xgboost import XGBRegressor\n",
    "import xgboost as xgb\n",
    "import lightgbm as lgb\n",
    "import optuna\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "31194007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se define función que cuenta la cantidad de valores nulos en cada columna de un DataFrame y se calcula\n",
    "# el porcentaje de valores nulos por columna.\n",
    "\n",
    "def contar_valores_nulos_con_porcentaje(dataframe):\n",
    "\n",
    "    nulos_por_columna = dataframe.isnull().sum()\n",
    "    porcentaje_nulos_por_columna = (nulos_por_columna / len(dataframe)) * 100\n",
    "\n",
    "    resultados = pd.DataFrame({\n",
    "        'Cantidad de Nulos': nulos_por_columna,\n",
    "        'Porcentaje de Nulos (%)': porcentaje_nulos_por_columna\n",
    "    })\n",
    "\n",
    "    return resultados\n",
    "\n",
    "#Se define función que elimina los valores atípicos de una columna específica en un DataFrame.\n",
    "#La misma utiliza el método de rango intercuartílico para identificar valores atípicos, que son aquellos puntos de datos\n",
    "#que caen fuera del rango definido por el primer cuartil (Q1) y el tercer cuartil (Q3).\n",
    "\n",
    "def remove_outliers(df, column_name):\n",
    "    Q1 = df[column_name].quantile(0.25)\n",
    "    Q3 = df[column_name].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "    df_filtered = df[(df[column_name] >= lower_bound) & (df[column_name] <= upper_bound)]\n",
    "\n",
    "    return df_filtered\n",
    "\n",
    "#Se define función que elimina iterativamente los valores atípicos de una columna específica en un DataFrame hasta que\n",
    "#no se encuentren más valores atípicos según el criterio del rango intercuartílico definido en la función \"remove_outliers\".\n",
    "\n",
    "def remove_outliers_iteratively(df, column_name):\n",
    "    df_clean = df.copy()\n",
    "    while True:\n",
    "        initial_len = len(df_clean)\n",
    "        df_clean = remove_outliers(df_clean, column_name)\n",
    "        final_len = len(df_clean)\n",
    "        if initial_len == final_len:\n",
    "            break\n",
    "    return df_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "391ae744",
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_outliers(df, column):\n",
    "    \"\"\"\n",
    "    Cuenta el número de valores atípicos en una columna específica de un DataFrame.\n",
    "\n",
    "    Args:\n",
    "    df (pandas.DataFrame): El DataFrame que contiene los datos.\n",
    "    column (str): El nombre de la columna en la que se contarán los valores atípicos.\n",
    "\n",
    "    Returns:\n",
    "    int: El número de valores atípicos en la columna especificada.\n",
    "    \"\"\"\n",
    "    # Calcular Q1 (primer cuartil), Q3 (tercer cuartil) y el IQR (rango intercuartílico)\n",
    "    Q1 = df[column].quantile(0.25)\n",
    "    Q3 = df[column].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "\n",
    "    # Definir los límites para los valores atípicos\n",
    "    lower_bound = Q1 - 1.5 * IQR\n",
    "    upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "    # Contar los valores atípicos\n",
    "    outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "    num_outliers = outliers.shape[0]\n",
    "\n",
    "    return num_outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "427c37dd",
   "metadata": {},
   "source": [
    "#### 1.1 - Carga de Datos <a name=\"cargadedatos\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d499aabe",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train.csv') \n",
    "test_df = pd.read_csv('test.csv')  \n",
    "sample_submission = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b943200",
   "metadata": {},
   "source": [
    "### Preparación de Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1aaa1582",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>model_year</th>\n",
       "      <th>milage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>engine</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ext_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>accident</th>\n",
       "      <th>clean_title</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MINI</td>\n",
       "      <td>Cooper S Base</td>\n",
       "      <td>2007</td>\n",
       "      <td>213000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>172.0HP 1.6L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Gray</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>LS V8</td>\n",
       "      <td>2002</td>\n",
       "      <td>143250</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>252.0HP 3.9L 8 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Beige</td>\n",
       "      <td>At least 1 accident or damage reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Chevrolet</td>\n",
       "      <td>Silverado 2500 LT</td>\n",
       "      <td>2002</td>\n",
       "      <td>136731</td>\n",
       "      <td>E85 Flex Fuel</td>\n",
       "      <td>320.0HP 5.3L 8 Cylinder Engine Flex Fuel Capab...</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Blue</td>\n",
       "      <td>Gray</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Genesis</td>\n",
       "      <td>G90 5.0 Ultimate</td>\n",
       "      <td>2017</td>\n",
       "      <td>19500</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>420.0HP 5.0L 8 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>Transmission w/Dual Shift Mode</td>\n",
       "      <td>Black</td>\n",
       "      <td>Black</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Mercedes-Benz</td>\n",
       "      <td>Metris Base</td>\n",
       "      <td>2021</td>\n",
       "      <td>7388</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>208.0HP 2.0L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>7-Speed A/T</td>\n",
       "      <td>Black</td>\n",
       "      <td>Beige</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "      <td>97500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id          brand              model  model_year  milage      fuel_type  \\\n",
       "0   0           MINI      Cooper S Base        2007  213000       Gasoline   \n",
       "1   1        Lincoln              LS V8        2002  143250       Gasoline   \n",
       "2   2      Chevrolet  Silverado 2500 LT        2002  136731  E85 Flex Fuel   \n",
       "3   3        Genesis   G90 5.0 Ultimate        2017   19500       Gasoline   \n",
       "4   4  Mercedes-Benz        Metris Base        2021    7388       Gasoline   \n",
       "\n",
       "                                              engine  \\\n",
       "0       172.0HP 1.6L 4 Cylinder Engine Gasoline Fuel   \n",
       "1       252.0HP 3.9L 8 Cylinder Engine Gasoline Fuel   \n",
       "2  320.0HP 5.3L 8 Cylinder Engine Flex Fuel Capab...   \n",
       "3       420.0HP 5.0L 8 Cylinder Engine Gasoline Fuel   \n",
       "4       208.0HP 2.0L 4 Cylinder Engine Gasoline Fuel   \n",
       "\n",
       "                     transmission ext_col int_col  \\\n",
       "0                             A/T  Yellow    Gray   \n",
       "1                             A/T  Silver   Beige   \n",
       "2                             A/T    Blue    Gray   \n",
       "3  Transmission w/Dual Shift Mode   Black   Black   \n",
       "4                     7-Speed A/T   Black   Beige   \n",
       "\n",
       "                                 accident clean_title  price  \n",
       "0                           None reported         Yes   4200  \n",
       "1  At least 1 accident or damage reported         Yes   4999  \n",
       "2                           None reported         Yes  13900  \n",
       "3                           None reported         Yes  45000  \n",
       "4                           None reported         Yes  97500  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d943d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188533 entries, 0 to 188532\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            188533 non-null  int64 \n",
      " 1   brand         188533 non-null  object\n",
      " 2   model         188533 non-null  object\n",
      " 3   model_year    188533 non-null  int64 \n",
      " 4   milage        188533 non-null  int64 \n",
      " 5   fuel_type     183450 non-null  object\n",
      " 6   engine        188533 non-null  object\n",
      " 7   transmission  188533 non-null  object\n",
      " 8   ext_col       188533 non-null  object\n",
      " 9   int_col       188533 non-null  object\n",
      " 10  accident      186081 non-null  object\n",
      " 11  clean_title   167114 non-null  object\n",
      " 12  price         188533 non-null  int64 \n",
      "dtypes: int64(4), object(9)\n",
      "memory usage: 18.7+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c38f392",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>brand</th>\n",
       "      <th>model</th>\n",
       "      <th>model_year</th>\n",
       "      <th>milage</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>engine</th>\n",
       "      <th>transmission</th>\n",
       "      <th>ext_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>accident</th>\n",
       "      <th>clean_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>188533</td>\n",
       "      <td>Land</td>\n",
       "      <td>Rover LR2 Base</td>\n",
       "      <td>2015</td>\n",
       "      <td>98000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>240.0HP 2.0L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>6-Speed A/T</td>\n",
       "      <td>White</td>\n",
       "      <td>Beige</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>188534</td>\n",
       "      <td>Land</td>\n",
       "      <td>Rover Defender SE</td>\n",
       "      <td>2020</td>\n",
       "      <td>9142</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>395.0HP 3.0L Straight 6 Cylinder Engine Gasoli...</td>\n",
       "      <td>8-Speed A/T</td>\n",
       "      <td>Silver</td>\n",
       "      <td>Black</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>188535</td>\n",
       "      <td>Ford</td>\n",
       "      <td>Expedition Limited</td>\n",
       "      <td>2022</td>\n",
       "      <td>28121</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>3.5L V6 24V PDI DOHC Twin Turbo</td>\n",
       "      <td>10-Speed Automatic</td>\n",
       "      <td>White</td>\n",
       "      <td>Ebony</td>\n",
       "      <td>None reported</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188536</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A6 2.0T Sport</td>\n",
       "      <td>2016</td>\n",
       "      <td>61258</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>2.0 Liter TFSI</td>\n",
       "      <td>Automatic</td>\n",
       "      <td>Silician Yellow</td>\n",
       "      <td>Black</td>\n",
       "      <td>None reported</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188537</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A6 2.0T Premium Plus</td>\n",
       "      <td>2018</td>\n",
       "      <td>59000</td>\n",
       "      <td>Gasoline</td>\n",
       "      <td>252.0HP 2.0L 4 Cylinder Engine Gasoline Fuel</td>\n",
       "      <td>A/T</td>\n",
       "      <td>Gray</td>\n",
       "      <td>Black</td>\n",
       "      <td>None reported</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id brand                 model  model_year  milage fuel_type  \\\n",
       "0  188533  Land        Rover LR2 Base        2015   98000  Gasoline   \n",
       "1  188534  Land     Rover Defender SE        2020    9142    Hybrid   \n",
       "2  188535  Ford    Expedition Limited        2022   28121  Gasoline   \n",
       "3  188536  Audi         A6 2.0T Sport        2016   61258  Gasoline   \n",
       "4  188537  Audi  A6 2.0T Premium Plus        2018   59000  Gasoline   \n",
       "\n",
       "                                              engine        transmission  \\\n",
       "0       240.0HP 2.0L 4 Cylinder Engine Gasoline Fuel         6-Speed A/T   \n",
       "1  395.0HP 3.0L Straight 6 Cylinder Engine Gasoli...         8-Speed A/T   \n",
       "2                    3.5L V6 24V PDI DOHC Twin Turbo  10-Speed Automatic   \n",
       "3                                     2.0 Liter TFSI           Automatic   \n",
       "4       252.0HP 2.0L 4 Cylinder Engine Gasoline Fuel                 A/T   \n",
       "\n",
       "           ext_col int_col       accident clean_title  \n",
       "0            White   Beige  None reported         Yes  \n",
       "1           Silver   Black  None reported         Yes  \n",
       "2            White   Ebony  None reported         NaN  \n",
       "3  Silician Yellow   Black  None reported         NaN  \n",
       "4             Gray   Black  None reported         Yes  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65102a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 125690 entries, 0 to 125689\n",
      "Data columns (total 12 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   id            125690 non-null  int64 \n",
      " 1   brand         125690 non-null  object\n",
      " 2   model         125690 non-null  object\n",
      " 3   model_year    125690 non-null  int64 \n",
      " 4   milage        125690 non-null  int64 \n",
      " 5   fuel_type     122307 non-null  object\n",
      " 6   engine        125690 non-null  object\n",
      " 7   transmission  125690 non-null  object\n",
      " 8   ext_col       125690 non-null  object\n",
      " 9   int_col       125690 non-null  object\n",
      " 10  accident      124058 non-null  object\n",
      " 11  clean_title   111451 non-null  object\n",
      "dtypes: int64(3), object(9)\n",
      "memory usage: 11.5+ MB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a68e60",
   "metadata": {},
   "source": [
    "### 2 - Transformación de Datos <a name=\"transformaciondedatos\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "199fa809",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Selecciona todas las columnas del DataFrame 'train_df' que son de tipo 'object'\n",
    "object_cols = train_df.select_dtypes(include=['object']).columns\n",
    "\n",
    "# Crea un diccionario vacío que almacenará los LabelEncoders para cada columna.\n",
    "label_encoders = {}\n",
    "\n",
    "# Itera sobre cada columna categórica seleccionada\n",
    "for col in object_cols:\n",
    "    # Crea una instancia de LabelEncoder, que convierte las categorías en números.\n",
    "    le = LabelEncoder()\n",
    "    # Aplica LabelEncoder a la columna correspondiente, luego, guarda los valores transformados en una nueva columna en 'train_df' con el sufijo '_encoded' para distinguirla de la columna original.\n",
    "    train_df[col + '_encoded'] = le.fit_transform(train_df[col].astype(str))\n",
    "    # Guarda el LabelEncoder utilizado para esta columna en el diccionario 'label_encoders', con la clave siendo el nombre de la columna original.\n",
    "    label_encoders[col] = le  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7484aba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecciona todas las columnas del DataFrame 'test_df' que son de tipo 'object'\n",
    "object_cols = test_df.select_dtypes(include=['object']).columns\n",
    "\n",
    "# Crea un diccionario vacío que almacenará los LabelEncoders para cada columna.\n",
    "label_encoders = {}\n",
    "\n",
    "# Itera sobre cada columna categórica seleccionada\n",
    "for col in object_cols:\n",
    "    # Crea una instancia de LabelEncoder, que convierte las categorías en números.\n",
    "    le = LabelEncoder()\n",
    "    # Aplica LabelEncoder a la columna correspondiente, luego, guarda los valores transformados en una nueva columna en 'test_df' con el sufijo '_encoded' para distinguirla de la columna original.\n",
    "    test_df[col + '_encoded'] = le.fit_transform(test_df[col].astype(str))\n",
    "    # Guarda el LabelEncoder utilizado para esta columna en el diccionario 'label_encoders', con la clave siendo el nombre de la columna original.\n",
    "    label_encoders[col] = le  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4887736",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cantidad de Nulos</th>\n",
       "      <th>Porcentaje de Nulos (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milage</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>5083</td>\n",
       "      <td>2.696080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_col</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_col</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accident</th>\n",
       "      <td>2452</td>\n",
       "      <td>1.300568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_title</th>\n",
       "      <td>21419</td>\n",
       "      <td>11.360876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_col_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_col_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accident_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_title_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Cantidad de Nulos  Porcentaje de Nulos (%)\n",
       "id                                    0                 0.000000\n",
       "brand                                 0                 0.000000\n",
       "model                                 0                 0.000000\n",
       "model_year                            0                 0.000000\n",
       "milage                                0                 0.000000\n",
       "fuel_type                          5083                 2.696080\n",
       "engine                                0                 0.000000\n",
       "transmission                          0                 0.000000\n",
       "ext_col                               0                 0.000000\n",
       "int_col                               0                 0.000000\n",
       "accident                           2452                 1.300568\n",
       "clean_title                       21419                11.360876\n",
       "price                                 0                 0.000000\n",
       "brand_encoded                         0                 0.000000\n",
       "model_encoded                         0                 0.000000\n",
       "fuel_type_encoded                     0                 0.000000\n",
       "engine_encoded                        0                 0.000000\n",
       "transmission_encoded                  0                 0.000000\n",
       "ext_col_encoded                       0                 0.000000\n",
       "int_col_encoded                       0                 0.000000\n",
       "accident_encoded                      0                 0.000000\n",
       "clean_title_encoded                   0                 0.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se visualizan la cantidad de valores nulos por columna para luego evaluar los pasos a seguir.\n",
    "contar_valores_nulos_con_porcentaje(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c265c484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clean_title\n",
       "Yes    167114\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['clean_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5bc68b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'clean_title'\n",
    "most_frequent = train_df['clean_title'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos con el valor más frecuente\n",
    "train_df['clean_title'] = train_df['clean_title'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c174e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accident\n",
       "None reported                             144514\n",
       "At least 1 accident or damage reported     41567\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['accident'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed70935d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'accident'\n",
    "most_frequent = train_df['accident'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos con el valor más frecuente\n",
    "train_df['accident'] = train_df['accident'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc487405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel_type\n",
       "Gasoline          165940\n",
       "Hybrid              6832\n",
       "E85 Flex Fuel       5406\n",
       "Diesel              3955\n",
       "–                    781\n",
       "Plug-In Hybrid       521\n",
       "not supported         15\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['fuel_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2a6bd671",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'fuel_type'\n",
    "most_frequent = train_df['fuel_type'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos con el valor más frecuente\n",
    "train_df['fuel_type'] = train_df['fuel_type'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73a50e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cantidad de Nulos</th>\n",
       "      <th>Porcentaje de Nulos (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_year</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>milage</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type</th>\n",
       "      <td>3383</td>\n",
       "      <td>2.691543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_col</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_col</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accident</th>\n",
       "      <td>1632</td>\n",
       "      <td>1.298433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_title</th>\n",
       "      <td>14239</td>\n",
       "      <td>11.328666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brand_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel_type_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>engine_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transmission_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ext_col_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_col_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accident_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_title_encoded</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Cantidad de Nulos  Porcentaje de Nulos (%)\n",
       "id                                    0                 0.000000\n",
       "brand                                 0                 0.000000\n",
       "model                                 0                 0.000000\n",
       "model_year                            0                 0.000000\n",
       "milage                                0                 0.000000\n",
       "fuel_type                          3383                 2.691543\n",
       "engine                                0                 0.000000\n",
       "transmission                          0                 0.000000\n",
       "ext_col                               0                 0.000000\n",
       "int_col                               0                 0.000000\n",
       "accident                           1632                 1.298433\n",
       "clean_title                       14239                11.328666\n",
       "brand_encoded                         0                 0.000000\n",
       "model_encoded                         0                 0.000000\n",
       "fuel_type_encoded                     0                 0.000000\n",
       "engine_encoded                        0                 0.000000\n",
       "transmission_encoded                  0                 0.000000\n",
       "ext_col_encoded                       0                 0.000000\n",
       "int_col_encoded                       0                 0.000000\n",
       "accident_encoded                      0                 0.000000\n",
       "clean_title_encoded                   0                 0.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se visualizan la cantidad de valores nulos por columna para luego evaluar los pasos a seguir.\n",
    "contar_valores_nulos_con_porcentaje(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "747f44bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clean_title\n",
       "Yes    111451\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['clean_title'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a8cc7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'clean_title'\n",
    "most_frequent = test_df['clean_title'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos (NaN) con el valor más frecuente\n",
    "test_df['clean_title'] = test_df['clean_title'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ddb5898a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accident\n",
       "None reported                             96263\n",
       "At least 1 accident or damage reported    27795\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['accident'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6c05c43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'accident'\n",
    "most_frequent = test_df['accident'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos con el valor más frecuente\n",
    "test_df['accident'] = test_df['accident'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b162849b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel_type\n",
       "Gasoline          110533\n",
       "Hybrid              4676\n",
       "E85 Flex Fuel       3523\n",
       "Diesel              2686\n",
       "–                    538\n",
       "Plug-In Hybrid       337\n",
       "not supported         14\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['fuel_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3fe828cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encontrar el valor más frecuente en la columna 'fuel_type'\n",
    "most_frequent = test_df['fuel_type'].value_counts().idxmax()\n",
    "\n",
    "# Reemplazar los valores nulos con el valor más frecuente\n",
    "test_df['fuel_type'] = test_df['fuel_type'].fillna(most_frequent)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4aa51f",
   "metadata": {},
   "source": [
    "### 3 - Exploración de los Datos <a name=\"exploraciondedatos\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "242dc6e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 188533 entries, 0 to 188532\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count   Dtype \n",
      "---  ------                --------------   ----- \n",
      " 0   id                    188533 non-null  int64 \n",
      " 1   brand                 188533 non-null  object\n",
      " 2   model                 188533 non-null  object\n",
      " 3   model_year            188533 non-null  int64 \n",
      " 4   milage                188533 non-null  int64 \n",
      " 5   fuel_type             188533 non-null  object\n",
      " 6   engine                188533 non-null  object\n",
      " 7   transmission          188533 non-null  object\n",
      " 8   ext_col               188533 non-null  object\n",
      " 9   int_col               188533 non-null  object\n",
      " 10  accident              188533 non-null  object\n",
      " 11  clean_title           188533 non-null  object\n",
      " 12  price                 188533 non-null  int64 \n",
      " 13  brand_encoded         188533 non-null  int64 \n",
      " 14  model_encoded         188533 non-null  int64 \n",
      " 15  fuel_type_encoded     188533 non-null  int64 \n",
      " 16  engine_encoded        188533 non-null  int64 \n",
      " 17  transmission_encoded  188533 non-null  int64 \n",
      " 18  ext_col_encoded       188533 non-null  int64 \n",
      " 19  int_col_encoded       188533 non-null  int64 \n",
      " 20  accident_encoded      188533 non-null  int64 \n",
      " 21  clean_title_encoded   188533 non-null  int64 \n",
      "dtypes: int64(13), object(9)\n",
      "memory usage: 31.6+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3fd11c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# En funcion del precio, se visualizan outliers.\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(y='price', data=train_df)\n",
    "plt.title('Price Distribution by Fuel Type')\n",
    "plt.xlabel('Fuel Type')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9f556604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10880"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Se encuentran 10.880 outliers sobre 188.533 valores.\n",
    "count_outliers(train_df, 'price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad2e4904",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_df = remove_outliers_iteratively(train_df, 'price')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6098c6ad",
   "metadata": {},
   "source": [
    "El histograma muestra una distribución que se asemeja a una campana, lo que sugiere que la mayoría de los precios están concentrados alrededor de un rango central. La distribución parece tener una ligera asimetría hacia la derecha, lo que indica que hay algunos precios relativamente altos que son menos frecuentes.\n",
    "\n",
    "Aunque la mayoría de los precios se agrupan en el rango mencionado, se observan algunos picos en la parte superior del gráfico (valores logarítmicos por encima de 12), lo que indica que hay un número considerable de precios más altos que podrían considerarse outliers. Estos valores pueden afectar el análisis general y la interpretación de los precios.\n",
    "\n",
    "Al aplicar el logaritmo a los precios, se reduce la influencia de los valores atípicos y se hace más fácil visualizar la distribución de precios. La transformación logarítmica es útil cuando los datos originales tienen una distribución sesgada, como es común en los precios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "74986ea5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkCFDAJg0aRJpaWnU1dWxefNmxowZ06gcYN26daSkpAAwYcIEPv30U+rq6trydkREREQ6lDbdR+7EXrLs7GyCgoK47777OHjwIMnJycycOZOdO3cSERHRUC8iIoL8/HxKSkqw2+1YrdZG5QAFBQUNr7FardjtdoqLi4mKimrLWxIRERHpMNp1Q2CXy8Xnn3/OW2+9RWxsLLNmzeKVV17h0ksvbVLXZDLh8XiaLT+Z5s4gO5mMjIwW1/Um6enpRofQ4ahNmqd2aUpt0jy1S1Nqk+apXZpq6zZp10Sue/fuDB48mJ49ewIwbtw4/v3vfzNp0iSKiooa6hUWFhIZGUlYWBgOhwOXy4XFYmkoB4iMjKSoqIjo6GicTicOh4PQ0NAWx5KYmNjpdqBOT08nKSnJ6DA6FLVJ89QuTalNmqd2aUpt0jy1S1Ot0SY1NTWn7Hxq1+1HLrvsMr799lvy8vIA+OSTTxg4cCBxcXH4+/s3ZK3Lly9n5MiR2Gw2kpOTSU1NbVQOMGrUKJYvXw5AamoqycnJJz2HTERERKQzatceuZiYGJ588knuvvtuampqOO+883jkkUcAWLBgAbNnz6aiooLzzz+fW265BYC5c+cyc+ZMXnrpJWJiYnj22WcBmD59OjNnzmT8+PGEhISwYMGC9rwVEREREcO1SyK3du3ahq+vuOIKrrjiiiZ1EhISGrYi+aG4uDgWL17cpDw0NJSXX365VeMUERER8SY62UFETovL5W6TuiIicvradWhVRLyfxWJmyZrMFtWdOiahjaMREfFt6pETERER8VJK5ERERES8lBI5ERERES+lRE5ERETESymRExEREfFSSuREREREvJQSOREREREvpURORERExEspkRMRERHxUkrkRERERLyUEjkRERERL6VETkRERMRLKZETERER8VJK5ERERES8lBI5ERERES+lRE5ERETESymRExEREfFSVqMDEBHflrGviDc/2g1AbISd3tFdSD4viqiwIIMjExHp+JTIiYghisuqeWX5DjZsy6V71wDCuwby6deHqajKAqBvbBcGnxtBfK9unNcnjO6hgcYGLCLSASmRE5GzVlXjpLyilohugZhMph+tX+d089tXN3Ew38HUMQlcf0U/AvyseDwe8o5W8GXGEb789ggfbDjA8vX7ALhkUAw/uzqefj1C2/huRES8hxI5ETljB3JLycwu4Vh5DQBJCZHE9+rW8LzL5cZiaToVd/HqXXx3qJTHbruYERfE4HK5ATCZTMR2t3P9Ff25/or+1DndZOWVsinjCKs+38/G7Xlcf0V/fpkysH1uUESkg1MiJyJnpM7pZvPOfOyBNgaf25384kq+3lNIRLdAuoUEAGCxmFmyJrPR6/KKKli39RD9e3TlQG4pB3JLmTomodn3sFnNnNuzG+f27MakK/rz9xUZvLfuO4bER3DhgMg2v0cRkY5Oq1ZF5IzkFjlwuT0knx/F+X3DGXFBDP42Mxu35+H8voftRNU1TjZl5NEl2I+hp5mIBQfauGfyIHpE2nlh6TdUVNW1xm2IiHg1JXIickZy8ssJ8LM0LEII8LMyPDGGsopa0jML8Hg8jep7PB6+/PYItU43lw6KxdrMkOuP8bNZePCmoRSXVvGPld+2yn2IiHgzJXIictqcTje5hRX0jArB/IPFDdHhwQw8J5z9h0vZfbCk0Wv2HDxGblEFQ+MjCA3xP+P3HtA7jOuv6M+HX2bzXc6xM76OiEhnoERORE5bblEFLreHnlEhTZ67oF84PSLtfL27kM07j+Bye8grquCbPYXERdg5t2foWb//DVfHYzGb+Hzb4bO+loiIN1MiJyKnLaegHH+bhYhuTfd2M5lMjEiMoVuIP0+/tpllH+9l3dZD+PtZGDYwqkXbk/wYe6CNQf27s3FHXpMhXBERX6JVqyJyWmrqXOQWOugT06XRsOoPWa1mRg6No6i0mqPHqgjvGkhUWBD+fpZWi2PEoFgWLdvGwSPl9I7p0mrXFRHxJuqRE5HT8vXuApyu5odVfygowMbMWy5i6IBIekWHtEoS5/rBatjhA6MxmWDjjrxT1hMR6czatEfO4XBw00038fLLL9OjR4+G8tdff520tDQWL14MQG5uLjNmzODo0aP07duXBQsWEBwcTFlZGQ899BA5OTmEhYWxcOFCIiIiqK2tZdasWWRkZBAQEMCCBQvo169fW96KiHxvd3YJZhNEdGv/s1BP3JcuvGsgqRsPNBlePdm+dCIinU2b9cht27aNKVOmkJWV1aj8u+++469//Wujsnnz5jF16lTS0tJITExk0aJFACxcuJDk5GRWr17NjTfeyPz58wFYvHgxgYGBrF69mscee4yZM2e21W2IyAmy8sroEuyPxXz2c93OVs9IO8fKa3BU1hodioiIIdoskVu6dClz584lMvK/m37W1tby+OOPM3369Iayuro6Nm/ezJgxYwCYNGkSaWlpAKxbt46UlBQAJkyYwKeffkpdXR3r1q3jpz/9KQAXXXQRJSUl5ObmttWtiMgPZOWWntX2Ia2pR6QdgJwCh8GRiIgYo80Sufnz55OcnNyo7E9/+hOTJ09uNMxaUlKC3W7Haq0f5Y2IiCA/Px+AgoICIiIiALBardjtdoqLixuVH3/NkSNH2upWROR75ZW1FJVWE2rvGImcPciP0BB/DhcqkRMR39Ruq1Y3bNhAXl4ejz76KF9++WVDeXNbB5xqewKzufnc82TlJ5ORkXFa9b1Fenq60SF0OGqT5p1JuxzIrwbAVesgN7e6Ba9IaGFveUKL4klKSmpyvWA/N3nFNRw+fPgHnx0tu96J9L3SPLVLU2qT5qldmmrrNmm3RG7VqlXs3buXiRMnUllZSVFREQ8++CB//OMfcTgcuFwuLBYLhYWFDcOxkZGRFBUVER0djdPpxOFwEBoaSmRkJIWFhfTu3Rug0WtaKjExEX//jtGr0FrS09NJSkoyOowORW3SvDNtl8Of7gOK6NcnlkD/ln18xMbGtqheS+M58Xo1lHL46BGCu3RvNOR7uven75XmqV2aUps0T+3SVGu0SU1NzSk7n9pt+5Gnn36a1atXs2LFCp566ikSExNZuHAhNpuN5ORkUlNTAVi+fDkjR44EYNSoUSxfvhyA1NRUkpOTsdlsjBo1ihUrVgCwZcsW/P39W/zDQkTOXFZeGaF2/xYnce0hrEsAAMVlLekhFBHpXDrEPnJz585l6dKlXHvttWzZsoUHH3wQgOnTp/PNN98wfvx4lixZwuOPPw7AzTffTG1tLePHj2f+/Pn84Q9/MDB6Ed9xILeUPh1s892QYD+sFpMSORHxSW3+a/XatWublA0bNoxhw4Y1PI6Li2vYU+6HQkNDefnll5uU+/v788wzz7RuoCJySi6Xm4NHyrn20r5Gh9KI2WSiW5cAJXIi4pM6RI+ciHR8uUUV1Drd9I3tWD1yUD+8eqy8Brdb566KiG9RIiciLZKVWwZAn5iuBkfSVFiXAFxuD6UVNUaHIiLSrpTIiUiLHMgrxWI20TPKbnQoTfx3wYMSORHxLUrkRKRFDuSW0SPSjs1qafVrn+0h9yFBNmxWM8WlmicnIr6l4+whICIdWlZeGYnnhLfJtS0WM0vWZP5ovaljEpotN5lMdAvRggcR8T3qkRORH1Vd46ToWBU9OuCw6nFhXfw55qjBpQUPIuJDlMiJyI/KO1oBQGz3DpzIdQ3A7fZQ6tA8ORHxHUrkRORH5RYdT+SCDY7k5MJC6hc8HCtXIicivkOJnIj8qNxCBwAxHTiRCw60YTZBWUWt0aGIiLQbJXIi8qPyiioIDfEnKMBmdCgnZTabsAf5UV6pRE5EfIcSORH5UblFFcSEd9zeuOO6BPupR05EfIoSORH5UXlFFcRGdPxELiTYD0dl7VnvSyci4i2UyInIKVXXOCkuq+7Q8+OO6xLkh9sD+cWVRociItIulMiJyCl5w9Yjx3UJ9gPg0PeLM0REOjslciJySse3HvGGHrmQ7xO5wwVK5ETENyiRE5FTyvOCPeSO87dZ8LdZOKweORHxEUrkROSUcgsdhNo79tYjP9Ql2I9D6pETER+hRE5ETinvaIVXDKseFxLsp6FVEfEZSuRE5JRyC70rkesS5McxRw2OqjqjQxERaXNK5ETkpI5vPeINe8gd998FD+UGRyIi0vaUyInISTVsPRLe8bceOa5hCxINr4qID1AiJyIndXzFaowX9cjZA21YzCatXBURn6BETkROKteLth45zmw2ER0erB45EfEJSuRE5KTyiiq8auuR43pE2pXIiYhPUCInIieVW+TwqhWrx8V0Dyb/aAVut8foUERE2pQSOREBwOVyNynLK/KurUeOiw4PptbppqS82uhQRETalNXoAESkY7BYzCxZk9nw2Olyc7S0msJjVY3Kp45JMCK80xIdHgTAkaOVhHcNNDgaEZG2ox45EWmWo7J+Q92QIO+aHwf1PXIA+cUVBkciItK2lMiJSLPKK2sBCAnyMziS0xfZLRCTqb5HTkSkM1MiJyLNOp7I2QO9r0fOZrUQ3jWQI0fVIycinVubJ3IOh4MJEyZw6NAhAN566y0mTJhASkoKjz76KLW19T8sdu3axeTJkxkzZgyzZs3C6XQCkJuby7Rp0xg7diz33HMPFRX1H8xlZWXceeedjBs3jmnTplFYWNjWtyLiUxyVdfjbLPjZLEaHckaiw4PUIycinV6bJnLbtm1jypQpZGVlAXDgwAFeffVV3nzzTd5//33cbjdLliwBYMaMGcyZM4c1a9bg8XhYunQpAPPmzWPq1KmkpaWRmJjIokWLAFi4cCHJycmsXr2aG2+8kfnz57flrYj4nPLKWkKCva837rjosGDNkRORTq9NE7mlS5cyd+5cIiMjAfDz8+OJJ57AbrdjMpmIj48nNzeXw4cPU11dzZAhQwCYNGkSaWlp1NXVsXnzZsaMGdOoHGDdunWkpKQAMGHCBD799FPq6ura8nZEfEp5ZR32QO+bH3dcdHgQxWU1VNc6jQ5FRKTNtGkiN3/+fJKTkxsex8XFcckllwBQXFzM66+/ztVXX01BQQEREREN9SIiIsjPz6ekpAS73Y7Vam1UDjR6jdVqxW63U1xc3Ja3I+IznC43VTVOQoK9N5GL+n7lakGxhldFpPMyZB+5/Px87rjjDiZPnsywYcPYunVrkzomkwmPp+mu7CaT6aTXNZtbnpdmZGS0uK43SU9PNzqEDkdt0rwT2yUpKYnc3FwAHNUuAJzVDnJza054ZUJDvR/X0rqtX+9Y4UEAPv9qOwPiWraXnL5Xmqd2aUpt0jy1S1Nt3Sbtnsjt27eP//3f/+UXv/gFv/zlLwGIioqiqKiooU5hYSGRkZGEhYXhcDhwuVxYLJaGcoDIyEiKioqIjo7G6XTicDgIDQ1tcRyJiYn4+/u36r0ZLT09naSkJKPD6FDUJs07WbvExsYCkJNfDlTSq0cUYV0CTlqvJVpat7XrXXFJEq9+mIa9WwxJSf1+tL6+V5qndmlKbdI8tUtTrdEmNTU1p+x8atftRxwOB7fffjvTp09vSOKgfsjV39+/IWtdvnw5I0eOxGazkZycTGpqaqNygFGjRrF8+XIAUlNTSU5Oxmbz3onZIh2JN289clxXux8BfhbytXJVRDqxdk3kli1bRlFREf/4xz+YOHEiEydO5PnnnwdgwYIFPP3004wbN46qqipuueUWAObOncvSpUu59tpr2bJlCw8++CAA06dP55tvvmH8+PEsWbKExx9/vD1vRaRT8/atR6B+GkZ0eLC2IBGRTq1dhlbXrl0LwG233cZtt93WbJ2EhASWLVvWpDwuLo7Fixc3KQ8NDeXll19u1ThFpF55Za1XHs11oqiwIPK0KbCIdGI62UFEmnBU1mH3wqO5TnS8R665hVMiIp2BEjkRacTpclNZ4+wUPXLR4UHU1rk4Vn7iylsRkc5BiZyINFJWUb/QoUuw96/qjv5+LznNkxORzkqJnIg0cjyR62r3/qHVqLAgAI7oqC4R6aSUyIlII6WOGkwmOsUcuYZETj1yItJJKZETkUbKKmoJCfLDYj75KSrews9mIbxrAEe0clVEOiklciLSSKmjhq5efMbqiaLDg8nXeasi0kkpkRORBi63G0dlHV3s3r/Q4biosCD1yIlIp6VETkQalFfU4QGv75FzudwNX0eHB3O0tJraOtcp64mIeKN2OdlBRLxDaUX9fmtdvHzFqsViZsmaTAAO5JYC8I+VGU22VJk6JqHdYxMRaU3qkRORBmWOWkxAl06wYvW446tvHZV1BkciItL6lMiJSIPSihrsQTYsls7z0WAPrD+hwlGlRE5EOp/O82ktImet1FFL10600AEgwM+CxWxSIicinZISOREBoM7ppryyli5evtDhRCaTCXugTUOrItIpKZETEQDyihx4PHS6HjkAe5ANR1Wt0WGIiLQ6JXIiAkBOvgOg0/XIAQQH+lFRVYfH4zE6FBGRVqVETkQAOJhfDnTORM4eaMPp8lBT23QvORERb6ZETsRLtXQz25bW23foGCFBNqydaMXqcfYgrVwVkc5JGwKLeKkfbnp7Ki3Z9Nbt9vDt/qNEhgW1Rmgdzg+3IOkeGmhwNCIirafz/eotIqct+0gZjqo6Irt1ziQnWHvJiUgnpURORMjYdxSAyG6ds0fOajET6G/BUamVqyLSuSiRExEy9hcR2S2woeeqMzq+clVEpDNRIifi4zye+vlxA88JNzqUNmUPtGloVUQ6HSVyIj7uUIGDUkctif26Gx1Km7IH2qisduJyt2wVr4iIN1AiJ+LjMvbXz49L7Ow9ct9vQVJR5TQ4EhGR1qPtR0Q6gcrqOiqq6qiscRIWEkDIaWzqm7GviLAu/sR0D27DCI33wy1IOuOmxyLim5TIiXgxl8vNVzvzycoraygzmaB/j1AS+4UT4Hfqf+Iej4eMfUdJPKc7JpOprcM1VHBgffJWUVkLdO6kVUR8hxI5ES9VUlbNx1tyOFpaTUKfbkSFBRPgZ2HfoVK+O3SMrLwyLh8Sd8prfHfoGMVl1Qzs17mHVQEC/S1YzCYteBCRTkWJnIgXOlZew0N//pRj5TVcNjiWnlEhDc+FnR9AfK9QPt+Wy7qth7h0UCwXD4xucg2Px8O/Vu0kJMiPkUN7tGf4hjCZTARr5aqIdDJa7CDiZVwuN3/89xaOlddw9UU9GyVxx3W1+/OTi3oRavdn/r++Yu2Wg03qbN6Zz/bvipg6ZkDD/LHOTluQiEhno0ROxMssXr2L7d8Vce8NgwnvevIjtfz9LFyV3JPEc8J57o2v+deqb3G5PQC43B7+sfJb4iLsjB3Rp50iN549yIajsg6Px2N0KCIiraLNEzmHw8GECRM4dOgQABs3biQlJYXRo0fz3HPPNdTbtWsXkydPZsyYMcyaNQuns36LgNzcXKZNm8bYsWO55557qKioAKCsrIw777yTcePGMW3aNAoLC9v6VkQMt2F7Lu988h3jRvTh6ot6/Wh9m9XMvDtHMG5EH9755Due+NsXLF69i3c3FnO40MH/TDgfq8V3fp8LDrThdLmprXMZHYqISKto00/wbdu2MWXKFLKysgCorq7mscceY9GiRaSmppKRkcH69esBmDFjBnPmzGHNmjV4PB6WLl0KwLx585g6dSppaWkkJiayaNEiABYuXEhycjKrV6/mxhtvZP78+W15KyKGy8kv5/k3tzKgVzf+97rEFr/OajFz7w2DuWfyIHYeKGbZx3vYc7iaK5J6NDt3rjOzf79yVcOrItJZtGkit3TpUubOnUtkZCQA27dvp3fv3vTs2ROr1UpKSgppaWkcPnyY6upqhgwZAsCkSZNIS0ujrq6OzZs3M2bMmEblAOvWrSMlJQWACRMm8Omnn1JXpw9n6Zwqq+t4+rWv8LNZmHnrRdisltO+xrWX9OXt341nxYKJzPp5HP9valKn33LkRA17yVXqs0JEOoc2XbV6Yi9ZQUEBERERDY8jIyPJz89vUh4REUF+fj4lJSXY7XasVmuj8hOvZbVasdvtFBcXExUV1aLYMjIyzureOqr09HSjQ+hwvL1NPB4Pb28o5lBBFTdf2Z3sfTvJBpKSksjNzW3BFRKabYMTy07nei2rdzp126fe8TmCuflHsXkcnNg23v690lbULk2pTZqndmmqrdukXbcfaW6CsclkOu3ykzGbW97BmJiYiL+/f4vre4P09HSSkpKMDqND6Qxtsm7rIXYePMwt157HjVfHN3ouNja2Rdc4sQ1O1i4tvV5L67XFNc+2XsB332G2BRIbWz+sfLwdOsP3SltQuzSlNmme2qWp1miTmpqaU3Y+tess56ioKIqKihoeFxQUEBkZ2aS8sLCQyMhIwsLCcDgcuFyuRuVQ35t3/DVOpxOHw0FoaGj73YxIOyivrOXvK3YQ3yuUSVeea3Q4nYI90KahVRHpNNo1kRs8eDAHDhwgOzsbl8vFqlWrGDlyJHFxcfj7+zd0Py5fvpyRI0dis9lITk4mNTW1UTnAqFGjWL58OQCpqakkJydjs/nGXljiO/658lvKK+v41Y1DsJh9az5bWwkOslFRVWt0GCIiraJdh1b9/f35/e9/z/33309NTQ2jRo1i7NixACxYsIDZs2dTUVHB+eefzy233ALA3LlzmTlzJi+99BIxMTE8++yzAEyfPp2ZM2cyfvx4QkJCWLBgQXveikib27GviI++OsjkK/vTN7ar0eF0GvZAPw7mlTfMlxMR8WbtksitXbu24esRI0bw/vvvN6mTkJDAsmXLmpTHxcWxePHiJuWhoaG8/PLLrRuoSAfy5oe76R4ayE2jBxgdSqdiD7ThoX4lsIiIt/OdnUBFvEhBSSU79hUxZnhvAvx0JHJr0hYkItKZtOgnRHFxMe+//z4VFRV4PB7cbjfZ2dn86U9/auv4RHyOy+Vm/dZDeDxwxYVnf5i9y+XGcsLpDb68sswe9H0ip02BRaQTaFEi9+CDDxIQEMB3333HJZdcwsaNG336B4FIWzKbTSxfv4+I0EDWbsk5ab2pYxJadD2LxcySNZmNynJzc5tsz9HS63m7QH8rFrOJ8koteBAR79eiodXc3FxeeeUVRo4cyS9+8QveeOMNDh482Naxifik7w4do6yilj6xXYwOpVMymUyEhvhTXFptdCgiImetRYlc9+7dAejTpw979uwhKiqq4VB7EWldn6Qfwmw20SsqxOhQOq3wrgEUl1XjcrmNDkVE5Ky0KJELDw/n73//O4mJibzzzjusXbsWh8PR1rGJ+Byny82nXx8iLsKOn+30z1OVlgnvGojL7SGnQJ9jIuLdWpTIPfnkk/j5+ZGcnExiYiJ//vOfeeihh9o6NhGfk5lVTKmjlt7R6o1rS2FdAgDYc7DE4EhERM5Oi3vkjm/QO2PGDJYvX84111zTpoGJ+KLd2fWJRWS3QIMj6dxCgmzYrGb25hwzOhQRkbNyylWrU6ZM4Y033mDo0KHNHla/devWNgtMxBdlZhcT0z0Yf+0d16ZMJhPhXQPUIyciXu+UPy2ef/55AFatWtUuwYj4Mo/Hw+7sEobERxgdik8I6xJAZnYJNXUu/DUfUUS81CmHViMjIwEoLy9n3rx5xMXF4XA4uPfee6mpqWmXAEV8RWFJFSXlNQzoHWZ0KD4hvGsAbreHA4dLjQ5FROSMtWiO3BNPPMGNN94IwIABA7j//vuZO3dumwYm4muOz48b0LubwZH4hvCu9fMQNbwqIt6sRYlcVVVVo8UNP/nJT7T9iEgry8wuxs9moU+MNgJuD4H+VsK7BmjBg4h4tRYlciaTid27dzc83rdvH2Zzi14qIi20O7uEc3uGYrXo31Z7ie/VTT1yIuLVWrQ0bvr06fziF78gPj4egP3797NgwYI2DUzEl9Q5Xew7XMrEkecYHYpPObdnKF/syKPoWJXRoYiInJEWJXJXXnklaWlpbN26FYvFwuDBgwkPD2/r2ER8xr7DpThdbs2Pa2eXD4nj36t3serz/VwQY3Q0IiKnr8VjOPv27QPA6XSSnp7Ohx9+2GZBifiazKz64b34Xkrk2lN0eDCXDIol7Yssaup07qqIeJ8W9cg9/PDDbNq0id69ezeUmUwmRo8e3WaBifiS3dnFRHQLbFhJKe1n0pX9+XxbLlv3VXDJcKOjERE5PS1K5LZs2cLq1asJDg5u63hEfNKB3FL69wg1OgyfdG7PbiT2C2dT5jHudrm12EREvEqLPrFiY2OVxIm0kZo6F3lFFfSO1rYjRrn+iv6UVrr4ePNBo0MRETktLeqRu/DCC/n1r3/NlVdeSUBAQEO5hlZFzl7OkXLcHrR/nIGSE6KICbPx4tvbSM8s4LYJ5xPb3W50WCIiP6pFidzXX38NwNtvv91QpjlyIq0jK68MgN4xIQZH4rvMZhP/85MIDpZ3YdnHe/nq2yNcPiSO60b1o5+GvEWkA2tRIrd48eK2jkPEZ2UfKcNmNROjHiBD+VnN/PwnAxh9cW+WfbKXj77MZt3WQ1x7SR/umTzY6PBERJrVojlyhYWF3HnnnYwZM4ajR49y++23U1hY2NaxifiE7LwyekaFYDGbjA5FgG5dAvjfiRfwjzljGDuiD6kbs9i884jRYYmINKtFidy8efP4yU9+gr+/P126dCEhIYFZs2a1dWwiPiH7SJnmxxnE5frv3nFJSUmNnrMH2rjzukR6RoWw6J1tVFbXtXd4IiI/qkVDq4cPH+ZnP/sZS5YswWazMWPGDFJSUto6NpFOr6yiluKyGq1YNYjFYmbJmkwAcnNziY2NbVInoXc3/rP5IItTd3HXpEHtHaKIyCm1qEfOZDLhdv/3N1eHw9HosYicmewjWujQ0XUPDWT8pX35YOMBDuSWGh2OiEgjLUrkRo8ezUMPPUR5eTlvvvkmt956K+PGjWvr2EQ6vezvV6xqaLVjmzYmAbPJxLr0Q0aHIiLSSIsSubvvvpuRI0dywQUXsHHjRn7+859z3333tXVsIp1eVl4Z9kAbYV0CfryyGMYe5Mfg+Ag2bM/F4/EYHY6ISIMWzZEDuO6667juuuvaMBQR33PwSDm9Y7pgMmnFakd3yQWxvPj2N+w/XKq95USkw2hRIneyhQ0rV648ozddsWIFr7zyCgAjR47kkUceYdeuXcyePRuHw0FycjLz5s3DarWSm5vLjBkzOHr0KH379mXBggUEBwdTVlbGQw89RE5ODmFhYSxcuJCIiIgzikfECB6Ph+wjZVxxYQ+jQ5EWGJ4YzaJ3TGzckadETkQ6jBYNrc6ZM6fhz8yZM0lOTubaa689ozesqqpi/vz5LF68mBUrVrBlyxY2btzIjBkzmDNnDmvWrMHj8bB06VKgfuuTqVOnkpaWRmJiIosWLQJg4cKFJCcns3r1am688Ubmz59/RvGIGKWwpIrKaqfmx3mJrnZ/Es8JZ8M2Da+KSMfRokTu4osvbvhz6aWX8vjjj7Nu3bozekOXy4Xb7aaqqgqn04nT6cRqtVJdXc2QIUMAmDRpEmlpadTV1bF582bGjBnTqBxg3bp1DT2FEyZM4NNPP6WuTvs8iff479FcSuS8xaWDYzlc6OBgfrnRoYiIAC1M5E5UUlJCQUHBGb2h3W5n+vTpjBs3jpEjRxIXF4fNZms0LBoREUF+fj4lJSXY7XasVmujcoCCgoKG11itVux2O8XFxWcUk4gR9h0uxWTSilVvMiIxBpMJNm7LNToUERHgDOfI5ebm8vOf//yM3jAzM5N33nmHTz75hJCQEB566CE2bNjQpJ7JZGp2+OJUk8LN5pbnpRkZGS2u603S09ONDqHD6ahtkv5tEWEhVnZ9u71ReVJSErm5LUkUEs6qXtOys7ve2dXtGPVO/pqEhu+jnt39WLt5P/HdK1pw/c6ho/4bMpLapHlql6bauk1alMjNmTOn4WuTyURYWBj9+vU7ozf8/PPPGTFiBOHh4UD9cOmrr75KUVFRQ53CwkIiIyMJCwvD4XDgcrmwWCwN5QCRkZEUFRURHR2N0+nE4XAQGhra4jgSExPx9/c/o3voqNLT05scM+TrOnKbvJj6IYn9opuNr7kTBppzpvVOdopBa79vW1yzreqdrE2OO/73tOfobt74MJMB5w/CHmhr0Xt4s478b8goapPmqV2aao02qampOWXnU4u6sHr16kVqaioXX3wx4eHh/OlPf2qUeJ2OhIQENm7cSGVlJR6Ph7Vr13LxxRfj7+/fkLUuX76ckSNHYrPZSE5OJjU1tVE5wKhRo1i+fDkAqampJCcnY7N1/g9V6RxKHTUUHauiX4+uRociLfDDM1kHnhOGxwO7Dhw9ZT0RkfbQoh65mTNnctVVVwEQFxfHxRdfzKOPPsrf/va3037Dyy67jJ07dzJp0iRsNhsXXHABd955J9dccw2zZ8+moqKC888/n1tuuQWAuXPnMnPmTF566SViYmJ49tlnAZg+fTozZ85k/PjxhISEsGDBgtOORcQo+w7VH/WkRM47/PBMVqfLjdkEb6/dy96cY43qTR2TYEB0IuLLWpTIlZSUNCRW/v7+3HbbbQ29YWfizjvv5M4772xUlpCQwLJly5rUjYuLY/HixU3KQ0NDefnll884BhEj7Tt8DIBz4kINjUNOn9VipluXAApLqowORUSkZUOrLperYbUoQFFRkfZREjkL+w6VEhMe7BNzrDqjiG6BFJdWayhVRAzXoh652267jeuuu47LL78ck8nExo0befjhh9s6NpFOa9/hYzodwItFhgaRmVXC0dJqIsOCjA5HRHxYixK5G264gcTERDZt2oTFYuH2228nPj6+rWMT6ZQclbUcOVrJ6GG9jQ5FzlD3boEAFB6rUiInIoZq8cZrtbW1BAQE8POf/5yKCt/ZP0mkte07fHyhQ6ixgcgZ87dZ6Gr3o6Ck0uhQRMTHtSiRe/fdd3n00Uf5+9//Tnl5Offee2/DWagicnoaVqzGacWqN4sIDaToWDVuzRcWEQO1KJFbvHgxb731Fna7nfDwcN59911ee+21to5NpFPad/gYEd0C6WrvXBtS+5qIbkE4XW6OldcYHYqI+LAWJXJmsxm73d7wOCYmBovF0mZBiXRWHo+HzKxi+mtY1etFfD9PruiYtiEREeO0KJELDQ1l165dDeecvv/++3TtqmEhkdP13aFjFJRUcdF5UUaHImcpOMBGoL9ViZyIGKpFq1Yfe+wxpk+fzsGDB7nsssvw9/dn0aJFbR2bSKezYVsuFrOJ4RfEGB2KtILuoQEUHas2OgwR8WEtSuSqq6tZsWIFWVlZuFwu+vbtq3NNRU6T0+ni8225DI6PICTIz+hwpBV07xpITr6Dqhongf4t+jgVEWlVLfrkeeihh1i9ejX9+vVr63hEOq0DeWXkF1fSJ6ZLw7mdzdF5nd4jPLR+ntzR0ip6RIYYHI2I+KIWzZEbMGAAK1euJDc3l2PHjjX8EZGW27AtF5MJ4iLtP15ZvEJYiD9mExpeFRHDtKhH7uOPPyYtLa1RmclkYteuXW0SlEhn4/F4+HxbLtFhwfjbtOK7s7BYzHTrEqAFDyJimBYlcjt27GjrOEQ6td3ZJeQXVzJsYLTRoUgrC+8ayL5Dx3C7tTGwiLS/Uw6tzpkzp+Hr4uLiNg9GpKNwudytVjcnv5ynX/uKrnY/emhYtdPpHhqAy+3hmEMbA4tI+ztlj1xGRkbD17fffjvvvfdemwck0hFYLOZTLkj4oVMtTsjOK2P2yxvBBPPvuZQN23JbK0TpILp31cbAImKcUyZynh+cIejReYIiLXKsvIY1m7JIzyxg98ESQu3+zL/nEnpEhiiR64SCAqzaGFhEDNPijY+On+ogIieXfaSMeX/fRGFJFf17hnLDVecyZnhvIrsFGR2atBGTyUR41wCKSrVyVUTa3ykTObfbTWlpKR6PB5fL1fD1caGhoW0dn4jX+GZPAU+/thl/m4XnHhxF/56hRock7SSiWyCHChwcLa0i/PuhVhGR9nDKRG7Pnj0MHz68IXkbNmxYw3PafkTkvw4XOpj39y/pEWlnzu3D1APnY47/fX+7/ygjh/YwOBoR8SWnTOQyM1s22VvEl3k8Hv62fAc2q5kn7xxBty4BRock7SzU7o/VYiZjnxI5EWlfLTrZQURObvPOfNIzC5g6ZoCSOB9lNpuICA0kY/9Ro0MRER+jRE7kLLhcbv62Ygc9o+xMuOwco8MRA0WGBZKTX06p9pMTkXakRE7kLGRml3DkaCV3XncBVov+OfmyiO/nye08oF45EWk/Ld5+REQaq6l1sSurmGEDo9l5oJidB059+smpNg4W7xfWJQA/q5mM/UcZcUGs0eGIiI9QF4LIGdqZdZQ6p5ubx51ndCjSAVjMJhL6hJGxTz1yItJ+lMiJnIHK6jr2HjxGn5gu9I7pYnQ40kEknhPOgdxSKqrqjA5FRHyEEjmRM5Cx7ygej4cL+nc3OhTpQAb2C8fjgV1Zpx5mFxFpLUrkRE5TSVk1+3NL6d8zFHugzehwpAOJ79UNq8XMtr2FRociIj5CiZzIaXC53HyxI48APwuJ56g3ThoL8LNyQb9wtuzKNzoUEfERhiRya9euZdKkSYwdO5annnoKgI0bN5KSksLo0aN57rnnGuru2rWLyZMnM2bMGGbNmoXT6QQgNzeXadOmMXbsWO655x4qKiqMuBXxMdu/K6K0opaLB8bg72cxOhzpgJLPj+JQgYMjR/WZJCJtr90TuZycHObOncuiRYtYuXIlO3fuZP369Tz22GMsWrSI1NRUMjIyWL9+PQAzZsxgzpw5rFmzBo/Hw9KlSwGYN28eU6dOJS0tjcTERBYtWtTetyI+Jr+4kszsEvr3CCW2e7DR4UgHlXxeFFB/4oeISFtr90Tuo48+4tprryU6OhqbzcZzzz1HYGAgvXv3pmfPnlitVlJSUkhLS+Pw4cNUV1czZMgQACZNmkRaWhp1dXVs3ryZMWPGNCoXaStlFbVs2JZLSJCNofERRocjHVhsdztxEcFsyVQiJyJtr903BM7OzsZms3H77bdTWFjIlVdeybnnnktExH9/OEZGRpKfn09BQUGj8oiICPLz8ykpKcFut2O1WhuVi7SFyuo6PknPAROMGtoDq1VTS+XUks+LJnXjAaprnAT4a991EWk77f4J43K52LJlC4sXLyYoKIh7772XwMDAJvVMJhMej+e0yk9HRkbGadX3Funp6UaH0OGcSZskJSWRm5uL0+Xh6/2V1NS5Gdw3iPLSIspLT6ydQG5ubguu2rHqNS1r7fdti2u2bb2Tv6bl10tPT6eLtZo6p5t312xiQFzTzzdvo8+VptQmzVO7NNXWbdLuiVz37t0ZMWIEYWFhAFx99dWkpaVhsfx34nhBQQGRkZFERUVRVFTUUF5YWEhkZCRhYWE4HA5cLhcWi6Wh/HQkJibi7+/fOjfVQaSnp5OUlGR0GB3K2bRJTEwMG3fkUVXr5sqknkSFBZ20bmxsy45k6ij1cnNzm31ta79vW1yzreqdrE1O93pJSUkMcrpZtiGVY7UhJCUNbtHrOip9rjSlNmme2qWp1miTmpqaU3Y+tfsY0ZVXXsnnn39OWVkZLpeLzz77jLFjx3LgwAGys7NxuVysWrWKkSNHEhcXh7+/f0M2u3z5ckaOHInNZiM5OZnU1NRG5SKt6UBuGQePlHNBv+6nTOJETmSzmhkSH8nmXfnNjiCIiLSWdu+RGzx4MHfccQdTp06lrq6OSy+9lClTpnDOOedw//33U1NTw6hRoxg7diwACxYsYPbs2VRUVHD++edzyy23ADB37lxmzpzJSy+9RExMDM8++2x734p0YrlFDtIz84nsFsh5fcOMDke80MXnR/HFjjy+O3SMc3t2MzocEemkDJmFe8MNN3DDDTc0KhsxYgTvv/9+k7oJCQksW7asSXlcXByLFy9usxjFt724dBtmk4nhF8RgPs35lyIAwxNj+MuybXz2Ta4SORFpM1p+J3KCzKxiduwrIrFfd4IDdASXnBl7kB9D4iP5fNthDa+KSJtRIidygnfXfUdIkI1+cV2NDkW83OVD4igsqWL3wRKjQxGRTkqJnMgPHCooZ1NGHtde2lf7xclZGzYwGqvFzGffHDY6FBHppPSTSuQHlq/fh81iZsKl5xgdinQCwYE2khIi2bAtF7dbw6si0vqUyIl8r6Ssmo8353D1xb0IDelcewyKcS4fEsfR0mp2ZRUbHYqIdEJK5ES+95/NB3G63Fw3sp/RoUgncvHAaPysGl4VkbahRE4E8Hg8rNt6iPP6hBEbYTc6HOlEAv2tJJ8fxYbtubg0vCoirUyJnAiQlVd/isMVST2MDkU6ocuHxHGsvIZv9xf9eGURkdOgRE4EWJd+CIvZxKWDWn6GqEhLJZ8XRYCfhc++yTU6FBHpZJTIic9zuT2s//oQFyZE0tWuRQ7S+gL8rFx8fjQbt+ficrmNDkdEOhElcuLzvt1fxNHSaq68sKfRoYiXO1WSdtmQWMoqatn2XdGP1hURaSlDzloV6UjWpR8i0N/CRQOjjA5FvJzFYmbJmsxmn3O53FgtZl77YCeZWcVMHZPQztGJSGekHjnxabV1LjZsz2XEBbEE+On3Gmk7FouZHpF2DuWXa/WqiLQaJXLi0zbvzKey2skVF2q1qrS9XtEh1Drd5B+tMDoUEekklMiJT1u3NYduIf4MOjfC6FDEB0SHB2Gzmsk+Um50KCLSSSiRE59VXlnLll0FjBzaA4vZZHQ44gMs5vrh1cOFDuqcLqPDEZFOQImc+KwN23JxutwaVpV21Su6C3VON1szC4wORUQ6ASVy4rPWbT1Ej0g7/Xp0NToU8SHRYUH42czaHFhEWoUSOfFJBcWVfLv/KFdc2AOTScOq0n7MZhM9I0P4amceNXUaXhWRs6NETnzS+q8PATBKw6pigF7RIVTVuNiyK9/oUETEyymRE5/j8Xj4eHMO5/cNIzo82OhwxAdFdguiq92Pjds0vCoiZ0eJnPicPQdLOFzo4OqLehkdivgos9lE8nlRbN1doKO6ROSsKJETn/Pxlhz8bBYuGxxrdCjiw5ISonBU1bHn4DGjQxERL6ZETnxKbZ2Lz74+zIjEGIICbEaHIz5sSHwEZhOk79Y8ORE5c0rkxKd8tfMIjqo6rrqop9GhiI8LCfJjQO8w0rWfnIicBSVy4lM+3pxDeNcAButILukAkhIi+S7nGMfKa4wORUS8lBI58RlHS6vYuruAKy7UkVzSMVyYEAnA1t3qlRORM6NETnzGmk3ZuN0exgzvY3QoIgD0iwsl1O6v47pE5IwpkROf4HS5WbMpmwsHRBLTXXvHScdgNpsYOiCifhsSt8focETECymRk07hZHtxJSUlAfDVt0coLqtm9PDe7RmWyI+6MCGK8spa9h06ZnQoIuKFrEYHINIaLBYzS9ZkNinPzc0lNjaWT9JzCPK3MnxgdLP1TjR1TEJbhCnSxJDvF95s21tIfK9uBkcjIt7G0B65Z555hpkzZwKwa9cuJk+ezJgxY5g1axZOpxOo/0E8bdo0xo4dyz333ENFRQUAZWVl3HnnnYwbN45p06ZRWFho2H1Ix1ZeUcuRo5X069EVi0Wd0NIxHO9FDg3xp09MF7bvLTplPRGR5hj2U+2LL77gvffea3g8Y8YM5syZw5o1a/B4PCxduhSAefPmMXXqVNLS0khMTGTRokUALFy4kOTkZFavXs2NN97I/PnzDbkP6fj25JRgMkG/HqFGhyLS4Hgv8pI1mfjZLOzYV8Ti1J0NZcf/6JcPETkVQz4hjh07xnPPPcfdd98NwOHDh6murmbIkCEATJo0ibS0NOrq6ti8eTNjxoxpVA6wbt06UlJSAJgwYQKffvopdXV17X8z0qHV1LnZd6iUPjFdCPTXTALpmKLDgnC5PRSVVhsdioh4GUMSuccff5xf//rXdOnSBYCCggIiIv67QWtERAT5+fmUlJRgt9uxWq2Nyk98jdVqxW63U1xc3M53Ih1dTlEtbo+HgeeEGx2KyElFdAvEBOQXVxodioh4mXbvonj77beJiYlhxIgRvPvuuwB4PE2X3ZtMppOWn4zZ3PK8NCMjo8V1vUl6errRIRgiKSmJ3NzcRmW1dW7yiuuI7Gql/FgR5ccAEprUa15L67XFNdunXtMytc3JX9P2bWMPNJOTV0L3oNom9Yz+d230+3dEapPmqV2aaus2afdELjU1lcLCQiZOnEhpaSmVlZWYTCaKiv470bewsJDIyEjCwsJwOBy4XC4sFktDOUBkZCRFRUVER0fjdDpxOByEhoa2OI7ExET8/f1b+/YMlZ6e3rDdhi+KjY1t9Pjr3QW4PRVcNLAnIcF+J63X0uu1Rt2OUu/4at62ft+2uGZb1TtZm7TV+55Yt7CikF1ZxURERmOzNv6l1Mh/177+udIctUnz1C5NtUab1NTUnLLzqd2HVv/5z3+yatUqVqxYwQMPPMBVV13F008/jb+/f0PWunz5ckaOHInNZiM5OZnU1NRG5QCjRo1i+fLlQH1ymJycjM1ma+/bkQ7KUVnL3pxjRIVaGyVxIh1VVFgQHg8UlGh4VURarsMsh1qwYAFPP/0048aNo6qqiltuuQWAuXPnsnTpUq699lq2bNnCgw8+CMD06dP55ptvGD9+PEuWLOHxxx83MHrpSNweD5syjmA2m+gT2bl6XaXz6h4aiNls0jw5ETkthi7jmzRpEpMmTQIgISGBZcuWNakTFxfH4sWLm5SHhoby8ssvt3mM4n0ys4opPFbF8MRo/KkwOhyRFrFazHTvGqhETkROS4fpkRNpDcVl1ez4roieUSH0ielidDgipyU6PIhj5TVU1zqNDkVEvIQSOek08osr+WRLDv5+Vi46P+qUK5xFOqKosCAACoqrDI5ERLyFEjnxeh6PhzWbsvkkPYcAfytXX9QTf5vF6LBETltYlwCsFjP5xZoSICIto63uxWvVOV2s33qYFZ/uIyuvjOjwIC4dFIufkjjxUmazichumicnIi2nRE68TqmjhtVfZPHBhgMcK6+hV3QI9/9sCAXFlZjNGk4V7xYVHkRuUQUV1XUEB2hLJRE5NSVy0qG5XO6GQ8M9Hg+pG7P4x8pvqa1zkZQQycSR/RgSH4HJZGLJmkyDoxU5e8fnyeUfreScuK4GRyMiHZ0SOenQLBYzS9ZkUlvn4qudR8jJdxDTPZih8RF0tfuzK6uYXVnFTB2TYHSoIq0i1O6Pv81CfrESORH5cUrkpMOrc7r5eHMOpRU1DDk3goQ+3bQiVTotk8lEVFgQ+cWVzZ43LSLyQ1q1Kh2ax+NhU0YepY4aRg7twXl9w5TESacXFRZEVY2T8so6o0MRkQ5OiZx0aEs/3sOhAgeD4yOI7R5sdDgi7SIqvH6eXF6RtiERkVNTIicd1ra9hbyelknv6BASenczOhyRdhMS5EdIkI3cIofRoYhIB6dETjokp8vNX9/bTnRYMBcPjNZwqvic2O52CkqqqK7RcV0icnJK5KRD+mDDAXLyHdwxMRGrRd+m4ntiIoJxuz1s31dkdCgi0oHpJ6R0OMfKa1iyJpMLEyK56Pwoo8MRMURkt0CsFhNbduYbHYqIdGBK5KTD+b/UndTUuvjfiYkaUhWfZTGbiQoLZktmvrYhEZGTUiInHUpukYOPNx9kwmXn0CMyxOhwRAwVGxFMYUkVB/PLjQ5FRDooJXLSobz7yXdYLGYmXdnf6FBEDHd8yx0Nr4rIySiRkw6juKyajzfncPVFvQjrEmB0OCKGCwqw0SemC5t3KZETkeYpkZMOY8X6fbjdbiZdod44keOGJ8aw88BRjpZWGR2KiHRASuSkQ3BU1rL6iwNcNiSOGJ3gINLgiqQeeDzw6deHjQ5FRDogJXLSIaz+IouqGhc3XHWu0aGIdChxEXbie4WyLv2Q0aGISAekRE4M53Z7SNuUzaD+3ekb29XocEQ6nCsu7Mn+3FKy88qMDkVEOhglcmK4r/cUUFBcybhL+hgdikiHdPmQOMxmE+u2qldORBpTIieGW70xi9AQf4YNjDE6FJEOKTTEn6HxEazbegi3W5sDi8h/KZETQxWWVLF55xGuubgXNqu+HUVO5oqknhQdq+LbA0eNDkVEOhD95BRDffhlNh5gzPA+Roci0qENHxiNPdDG+5/uMzoUEelAlMiJYZwuNx9+mUVSQhRRYUFGhyPSoQX4W5lw2TlsyjiiRQ8i0kCJnBhm884jFJfVMG5EH6NDEemwXC53w9cpl59DgJ+FZWv3nrKeiPgOq9EBiO9avTGL7l0DSEqINDoUkQ7LYjGzZE1mw+M+MV1Yv/UQ9iAbIUF+DeVTxyQYEZ6IGEw9cmKIvKIKvt5TyOjhfbBY9G0o0lIJfcIwmU3syio2OhQR6QAM+Qn64osvMn78eMaPH88f/vAHADZu3EhKSgqjR4/mueeea6i7a9cuJk+ezJgxY5g1axZOpxOA3Nxcpk2bxtixY7nnnnuoqKgw4lbkDK3ZlIXZbGL0sF5GhyLiVQL9rfSL68r+w6UUl1UbHY6IGKzdE7mNGzfy+eef895777F8+XK+/fZbVq1axWOPPcaiRYtITU0lIyOD9evXAzBjxgzmzJnDmjVr8Hg8LF26FIB58+YxdepU0tLSSExMZNGiRe19K3KG6pwuPvrqIMMGRhPeNdDocES8zgX9uxPgZ2HTjjzNjRPxce0+Ry4iIoKZM2fi51c/t6Nfv35kZWXRu3dvevbsCUBKSgppaWn079+f6upqhgwZAsCkSZP485//zI033sjmzZv5y1/+0lD+i1/8ghkzZrT37cgZ2Lg9j7KKWvxtlkZzf5qjeT8iTfnbLFw8MIb1Ww+x/bsihg7QPFMRX9XuPXLnnntuQ2KWlZVFamoqJpOJiIiIhjqRkZHk5+dTUFDQqDwiIoL8/HxKSkqw2+1YrdZG5dLxeTwe3v3kO2K7BxMdri1HRM5UbPdg+vcIJTO7hLwiTS0R8VWGrVrdu3cvd911F4888ghWq5UDBw40et5kMuHxND2K5lTlpyMjI+P0AvYS6enpRodwSrtyqtifW8qvpwxl647vWvCKBHJzc8+qXuPys7/emdftWPWalqltTv6ajtk20V085PqbWf/1IS46P4pgd9v8QtvRP1eMoDZpntqlqbZuE0MSufT0dB544AEee+wxxo8fz1dffUVRUVHD8wUFBURGRhIVFdWovLCwkMjISMLCwnA4HLhcLiwWS0P56UhMTMTf37/V7qkjSE9PJykpyegwTsrj8fB/69YT0z2YUUN7cORoZYteFxsbe8b1cnNzm5SfzfXOtm5Hqddcu7TF+7bFNduq3snapK3et7WuGRnl4rNvDvPHf6dz87jzuP6K/q163F1H/1wxgtqkeWqXplqjTWpqak7Z+dTuQ6t5eXncd999LFiwgPHjxwMwePBgDhw4QHZ2Ni6Xi1WrVjFy5Eji4uLw9/dvyGaXL1/OyJEjsdlsJCcnk5qa2qhcOrYvvz3C/txSfv6TeG05ItJK/G0WrkzqwcghcSxevYs7f/cRy9fvo7K6zujQRKQdtHuP3KuvvkpNTQ2///3vG8puuukmfv/733P//fdTU1PDqFGjGDt2LAALFixg9uzZVFRUcP7553PLLbcAMHfuXGbOnMlLL71ETEwMzz77bHvfipwGt9vDGx/uJiY8mCsu7GF0OCKdisVs5qFfJHFlck/e+WQvr76fwetpu7h8SBxjR/Qhvlc3o0MUkTbS7onc7NmzmT17drPPvf/++03KEhISWLZsWZPyuLg4Fi9e3OrxSdtY+vEe9h8u5TdTL1RvnEgbMJlMJJ8XRfJ5Uew5WELaF1l89s1hPvrqIJcNjuWOiYna7kekE9IRXdKqXC53k0Rty658lqzJ5MqkHuqNE2kjP/y3F9+rG/G9unHHxETe/2w/S/+zh/TMAv53YiJXJffUL1MinYgSOWlVJ54LWV5Zy5pN2XS1+9M9NJA3PtwNaH84kdZ24r+9HxozvDebd+bz56XfkF9SybQxCae90l9EOib9WiZt5sjRCv7z1UFMwOWDY7GqF0DEECFBflxxYQ/OievKWx/t4YWl3+hECJFOQj1y0urcbg/f7j9Kxv6jdAn249JBsdiD/IwOS8Snmc0mLj4/iovOj+Ktj/bg8cADPx+injkRL6dETlrVtj2FrP4ii7KKWvrGdiEpIapV97QSkTNnMpmYNiYBs8nEGx/uxh5k45cpA5XMiXgxJXLSKg4VlPN/qbv4Ykce9kAbI4fEERdpNzosEWnGlNEDKK+oZfn6fXQJ9uPGq+ONDklEzpASOTkr2XllfLDhAGu+zMbfZuYXYxOorXNpVZxIB2Yymfjf6y7AUVXH/6Xuwh7kx7gRfYwOS0TOgBI5aRGPx0NxWTU5+eUcOVrJkaMVbNmVT/aRcixmE+NG9OHn18TTLSTgpCvnRKTjMJtNTL9pKI6qOl56Zxv2ABuXD40zOiwROU1K5OSUcgsd/OuDnWTsO0p5ZW1DucVsIr5XN+66/gIuHRxLt5AAA6MUkTNhtZh55JZknvjbJp59Ix0/m5lhiTFGhyUip0GJnDSrzuli2drvePvjPdisZi4bHEff2C70ju5CVHgQ4V0DsZg1QVrE25y4aXeAn5U5vxzGnL9u5HevbeY3Uy5k1IU9mt3cW0Q6HiVy0oTb7eEPi7ewKeMIlw+J446JiXQN9tOHukgncLKNgwefG0Gpo4YFr6fzSXoOc+8YbkB0InK6lMhJE/9O28WmjCPc/tNErhvVr6G8JXPfdGKDiHeyWc2MurAHG7blkp5ZwLy/b+LeGwYbHZaI/AglctLIuvQc3v54L6OH9WbiyHOMDkdE2pHVYubyoXHsPXiMjP1H+dUf1zKoTyBBYcUM6N0Ns6ZTiHQ4SuSkQU5+OS8s/YbEfuHcPWmQNgkV8UFmk4kBvbtxz+RB/GvVTjZl5PLl7s/oavejT0wXekaF0Cu6C72iQugdHaJTW0QMpkROAHC5PTz/5tf4+1l4+BfJOo1BxMdFhwcz89aL2PDFZmr9otm2t5Cc/HI+3nyQqhpXQ70ekXbO6xPGeX3CSOgTRo9Ie7O/BGrxhEjbUCLnw374wbpi/XfsPljCQ9OS6NZFW4mISL0APzOXJvXkyqSeQP1iqKJjVRzML2f/4VIys4tZ//UhPvrqIAB+NgvduwYQ0S2Q7l0D6dYlAJvVrPmzIm1EiZwPO756rdRRQ9qmbHpE2jlUUN7sogZ9CIv4luO/6CUlJTUqN5tNRIYFERkWRPJ5UQC8nraL8spaCkuqKCqtprCkityiCgBMQJdgPwpLqojvFUpCnzD6xHTR1A2RVqJEzse5PR6+/PYIVouZ5POi9OEqIsB/f9HLzc0lNjb2pPWmjknAZDLRJdifLsH+9OtRX15T6+RoaTVHy6opLq3mq51H+M/m+l67sC7+DB0QSdKAKIYMiCBE8+xEzpgSOR+3O6uEo6XVXHJBDIH++nYQkdbh72clNsJObIQdgJ9dfS5FpdVk7DvK1t0FfJlxhI8352A2wbm9upE0IJKhCZH07xGKCTSfTqSF9JPbh+Xkl7N9XxE9Iu30ig4xOhwR6cSsVgtrt+QA9QskYiOCKS6tJq+ogryjFSz5cDdLPtyN2WSib1z9KTLR4cFEhQXSN7YrvaO7aPsTkWYokfNRLpeb59/8WkOqImIIs8lE99BAuocGckH/7tTUusgvrqC4rAZ/Pwvb9hbySXoOHk99/eBAGxf0C2fyVeeS0DsM0EpYEVAi55M8Hg9/WbaN3QdLuGSQhlRFxHj+fpb6/emi6+fdLVmTicvtpqLKydHSKgpLqvh6TyGbMo7QI9LOkPgI7rp+kNFhixhOP8F90Jsf7eGjrw7y82viMasnTkQ6KIvZTJdgP7oE+9E3titDnW52ZxezK6uYNZsqSUqIalg5K+Kr1CftYz74fD9L1mRyVXJPpmlLERHxIjarmcR+3bn2kr7YA208+eom3v1kL57j468iPkg9cj6iusbJS+9uZ+2WHJLPi+L+nw3RvDgR8UrBgTZ+cnEv8ooq+OeqneQWVXDPpEGaLyc+SYlcJ1fndLFxex5vfrSbw4UOpowewM+vGYBFq79ExItZLWYevjmZ2IhMlv5nD0dLq3n45mTN+RWfo+/4Tqasopb9h49xqMDBgdwyNmXkUVZRS0x4ML+98xIGx0cYHaKISKswmUzcPO48IkIDeemdbTzy4mc8cstFxH2/d52IL1Ai5+UOFzrYmlnA1t0FZGYV4qg61PCcn9VMZFgQFyZEEh0WxLcHjvLtgaMNz+vYLRHpDMaO6ENEt0D+9PpWHnx2HfdMHsRVyb3a7P0GJJzPkaMVlFfWNmyP0iPSTlCArVE9bY8i7UGJnBdxudyYzSYO5pezcXsen31zmJz8cgBiugdz8cA4ekd3oU9MF7buLiDQ36J5cCLiE5ISonjhoStY8Ho6z73xNR9vzuEXY8/jvL5hZ3XdOqebA7mlZGYXszurhL2HjpFfXInb3XSBRajdn6jwIOJ7hmIP8tMvy9IulMh1IB6Ph7KKWvKLKyl11FBb56amzklZRR1lFTUcKqjvfaupcwEQ0S2QpIRIYroHExLkR25uLtW19T1wmdnFBt+NiEjbOrHHK7xrIE/dfSmpGw6w9OM9PPziZww+tzsjh8RxyaBY7C040/VoaRWZ2SVkZhWzO7uEfYeOUet0A9C9awDxvbtx+ZA4svLKCLBZMJnqz6wuKa+h6FgVew+WsCe7hB5RIQwbGE2/HqFtdfsigJcncitXruSll16irq6O2267jWnTphkd0mlxuT0cyC1l+94itn9XyM4DxVTVOJutazGbiOgWSEz3YCK///+J3fgiIr7EYjGzZE1ms89dc1Ev9uSU8N2hUrbtLeKld7fTJ7Yr/eK60iMyhKAAKwF+Fiqq6igqreZwoYPd2cUUHasG6hdT9O/RlWsv7UtC7zAG9O5G99DAhuuf+L49IuuPOaysdta/b84xHnxuPSMuiOGmawZwTlzXNmoF8XVem8jl5+fz3HPP8e677+Ln58dNN93EsGHD6N+/v9GhNcvj8VBR7eRAbil7skvIzC4mY99RHFV1QP38iisu7EGPKDtR3YIIDfHH38+Kn7V+Q8zgQBsmk+mkH1oiIvJfVquZ8/uGc16fMC46P5ovduSx52AJn2/LpeL7z93jzOb648ISeodREVVH966BdOvij8Vc39uXfaSM7CNlDfVPNWQaFGBlyLkRnN8nDH+bheWf7uOLHXmcE9uVURf2YOA5YUSGBRFq9wfA6fLgcrlxutzUudy4XJ76r531ZS63B5vFjM1qxma14Ger/zrQ36qpMwJ4cSK3ceNGhg8fTmhoKABjxowhLS2NX/3qV8YG9r3PvjlM+q58jpZWU1RaxdHSKqpqXA3PR4cHMeKCGAadG8EF/cJZsykbAEdlHY7K0mavqfkWIiKnx2QyEd+rG/G9ugH1v1Q7quqoqnFSXeMkONBGaEhAw5ZMrfXLsp/NwpQxCaRcfg5rt+Sw/utD/HPVtw3Pm03QzDS7FrNazISG+NMtxP/7/wcQ2S2QqPBgYsKDiAgNpFuXAK9J9lwuN+WVdZRX1uKorMODB6vF/P0fE1aLGfP3f0dmkwlM9f93ezy43fV/XN//sVpM2AP9sAfZsLbyYhOPx0NVjZOyilpq6lzU1rmI7W4nONC4ETKvTeQKCgqIiPjvVhqRkZFs3779R193fAfw2traNosNYPO3h9l/uBSTCUICrER07UqgvwV7kF99b5vNAkDB0TLsAyOwmt0/es2amppT1guwmbCa3T9ar6XXO5O6Ha3e8TZpq/dti2u2R70T26Ut3rctrtmW9Zprk7Z637a4ZlvVO1W7tMX7tsU1KyurGs2l87OAX5CFrkH1n8POulqOT2pp7Ta0WWDMsB6MGdaDwpJKcosqOFpaxbHyGixmMxarCavJzM7so1hMJsxm0/eJigmL2VQ/B88Nbrcbt8dD4jnhfL2nkJrv51DX1DjJL6rhYG5Jw/zp4/z9rITa/bAH2ggKtBEcYMMeaMPPZsZkqn8fk6m+R9JsNmHC9H3i5zn+Hx7qf2Z6PN9//X358TI839fwQH5BGd8VfVv/uu9/1v7wa7cbqmudVFQ5qaiqo7KmjspqJ46qOqpPMq3obPn7WQkOtBIUYCXQ30aQv5UAfwtB/jb8/czft0F9O5hMJvBATZ2r/k+ti1qni+oaJ+WVdTgqaymvrMPpavx3P/CccH495cKTxlBTU3NW93A8XznZCSYmj5eebfLyyy9TVVXFr3/9awDefvttduzYwZNPPnnK15WXl7Nnz572CFFERESkVcTHxxMSEtKk3Gt75KKiotiyZUvD44KCAiIjI3/0dcHBwcTHx2Oz2bymy1lERER8k8fjoa6ujuDg4Gaf99pE7pJLLuGFF16guLiYwMBAPvzwQ37729/+6OvMZnOzGa2IiIhIRxQQEHDS57w2kYuKiuLXv/41t9xyC3V1ddxwww0MGjTI6LBERERE2o3XzpETERER8XU6BE5ERETESymRExEREfFSSuREREREvJQSOREREREvpURORERExEspkesEVqxYwfjx4xk/fjzPPPOM0eF0CK+88gpjxowhJSWFl156yehwDOdwOJgwYQKHDh0C6s8qTklJYfTo0Tz33HMGR2ecE9sF4JFHHuHdd981MCpjndgmb731FhMmTCAlJYVHH320zY837IhObJMlS5Ywfvx4rr32Wp555pmTHp3U2TX37wfg9ddf5+abbzYoKmOd2CaPPvooo0ePZuLEiUycOJGPPvqo1d9TiZyXq6qqYv78+SxevJgVK1awZcsWNm7caHRYhtq4cSMrV67knXfeYfny5Wzbto0PP/zQ6LAMs23bNqZMmUJWVhYA1dXVPPbYYyxatIjU1FQyMjJYv369sUEa4MR2yc/P5+677yYtLc3YwAx0YpscOHCAV199lTfffJP3338ft9vNkiVLjA2ynZ3YJjk5OfzrX//i7bffZuXKlXz99dds2LDB2CANcGK7HPfdd9/x17/+1ZigDNZcm2RkZPDvf/+bFStWsGLFCq655ppWf18lcl7O5XLhdrupqqrC6XTidDrx9/c3OixD7dy5k8suuwy73Y7FYuHyyy/nP//5j9FhGWbp0qXMnTu34Qi77du307t3b3r27InVaiUlJcUnk5cT22XlypVcffXVjBs3zuDIjHNim/j5+fHEE09gt9sxmUzEx8eTm5trcJTt68Q26dmzJx988AFBQUGUlZXhcDjo0qWLwVG2vxPbBeoPd3/88ceZPn26gZEZ58Q2qaysJDc3lzlz5pCSksKf//xn3G53q7+v157sIPXsdjvTp09n3LhxBAQEcPHFF3PhhRcaHZahBg4cyO9+9zvuuusuAgMDWbt2rc8OfQDMnz+/0eOCggIiIiIaHkdGRpKfn9/eYRnuxHa54447AEhPTzcinA7hxDaJi4sjLi4OgOLiYl5//XWefvppI0IzzIltAmCz2Vi6dCnPPPMMgwYNIiEhwYDIjNVcu/zpT39i8uTJ9OjRw4CIjHdimxw9epThw4fz5JNPEhQUxF133cWyZcv42c9+1qrvqx45L5eZmck777zDJ598wueff47ZbObVV181OixDjRgxgkmTJnHzzTdzxx13kJSUhM1mMzqsDqO5pNZkMhkQiXiL/Px8br31ViZPnsywYcOMDqdD+NnPfsaXX35J9+7defHFF40Ox3AbNmwgLy+PyZMnGx1Kh9GzZ0/+8pe/EB4eTmBgIDfffHObTGNRIuflPv/8c0aMGEF4eDh+fn5MmjSJr776yuiwDOVwOLjmmmtYuXIlixcvJjAwkJ49exodVocRFRVFUVFRw+OCgoJGwyMiP7Rv3z6mTJnC9ddfz3333Wd0OIbLy8tr6LW1Wq2MHz+e3bt3GxyV8VatWsXevXuZOHEis2fPJiMjgwcffNDosAy1e/du1qxZ0/DY4/Fgtbb+QKgSOS+XkJDAxo0bqaysxOPxsHbtWi644AKjwzLUoUOHuO+++3A6nZSXl/P222/79LynEw0ePJgDBw6QnZ2Ny+Vi1apVjBw50uiwpANyOBzcfvvtTJ8+nV/+8pdGh9MhlJeXM2PGDMrKyvB4PKxZs4akpCSjwzLc008/zerVq1mxYgVPPfUUiYmJLFy40OiwDOXxePjd735HaWkpdXV1vPXWW22y2EFz5LzcZZddxs6dO5k0aRI2m40LLriAO++80+iwDJWQkMDo0aP56U9/isvl4rbbbtMH7Q/4+/vz+9//nvvvv5+amhpGjRrF2LFjjQ5LOqBly5ZRVFTEP/7xD/7xj38AcNVVV/nsZHaA+Ph47rzzTm666SYsFgvJycn8z//8j9FhSQeUkJDAnXfeyZQpU3A6nYwePZoJEya0+vuYPL48C1xERETEi2loVURERMRLKZETERER8VJK5ERERES8lBI5ERERES+lRE5ERETESymRE5FO59ChQwwdOrRVrzljxgz27NnDl19+yaBBg5g4cSLXXXcdEydOZNKkSaxdu7bV3mvWrFls3LjxlHUyMjKYM2dOq72niHgn7SMnIvIjUlNTCQkJIT4+ni+//JJevXqxYsWKhuczMzOZMmUKH3/8MWFhYWf9fs2dY3mixMREXn/9dT755BOuvPLKs35PEfFOSuRExGeUl5czb948MjMzMZlMXH755fzmN7/BarWyfv16FixYgNls5rzzzmPjxo0sWbKEHj168MILL/D888+f9LoJCQkEBARw+PBhXn/9db755hsKCgoYMGAACxYs4KWXXuLDDz/E7XYTFxfH3LlziYqKorCwkLlz57J//37MZjM33XQTt9xyCzfffDPTpk1j7Nix/Oc//+HFF1/E5XJht9t59NFHGTRoEAA///nPeeKJJ5TIifgwDa2KiM946qmnCA0NZeXKlbzzzjvs3r2bf/zjH5SUlPDwww/zxz/+kRUrVjBs2DDy8/MB2LNnDzU1NcTHx5/0uh9++CFms5n+/fsDcPjwYd577z0WLFjA8uXL2bNnD2+//TYrVqxg1KhRzJ49G4B58+bRp08f0tLSeOutt1i6dCnZ2dkN1923bx9z587lhRdeYOXKlTzwwAPce++9OBwOAIYMGUJ+fj45OTlt1WQi0sGpR05EfMann37KG2+8gclkws/Pj5tuuonXXnuNvn370q9fPxISEgC4/vrreeqppwDYv38/vXr1anSdgwcPMnHiRACcTifR0dEsWrSIwMBAoD7BOn449ieffMKOHTuYPHkyAG63m6qqKgA2btzIjBkzAAgJCWHVqlWN3mfTpk0MHz6cnj17AjBixAjCwsLIyMhg+PDhAPTs2ZMDBw401BER36JETkR8htvtbvLY6XRisVg48bRCs9nc8H+Xy9XouRPnyJ0oKCio0XvccccdTJ06FYDa2lpKS0sBsFqtmEymhro5OTl069at4XFzJyh6PB6cTmfDY5fLhcViOWksItK5aWhVRHzGZZddxuuvv47H46G2tpalS5dyySWXcOGFF5KVlUVmZiYAa9asoaysDJPJRJ8+fTh06NBZveeyZcsahkOff/55Hn74YaC+h+2dd94B6ufv3XrrrWRlZTW8dvjw4WzYsKFh6PSLL74gLy+PwYMHA/VJ3eHDh+nbt+8Zxyci3k09ciLSKVVWVjbZguSvf/0rb731FikpKdTV1XH55Zdz99134+fnx7PPPssjjzyC2WwmMTERq9VKYGAgcXFx+Pv7s2/fPvr163facdx4443k5+fzs5/9DJPJRExMDL///e8BePzxx3niiSdISUnB4/Fw1113kZiY2PDa/v37M3fuXH71q1/hcrkICAjg5ZdfJiQkBIAdO3bQq1cvYmNjz6KlRMSbmTzN9d2LiPgQh8PBokWLuP/++wkMDOTbb7/lrrvu4rPPPsNkMrFy5UrS09N54oknjA61kZkzZzJ27FiuuOIKo0MREYOoR05EfJ7dbsdms3HDDTdgtVqxWq0sXLiwYf5aSkoKH3/8Mbt372bAgAEGR1tvx44dmEwmJXEiPk49ciIiIiJeSosdRERERLyUEjkRERERL6VETkRERMRLKZETERER8VJK5ERERES8lBI5ERERES/1/wFhC3eil2HEDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Aplicación del logaritmo y visualización de la distribución\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(np.log(train_df['price']), bins=50, kde=True)\n",
    "plt.title('Distribución Logarítmica de los Precios')\n",
    "plt.xlabel('Log(Precio)')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c44e4180",
   "metadata": {},
   "source": [
    "El gráfico ilustra la cantidad de vehículos disponibles según diferentes marcas, mostrando una clara jerarquía en la popularidad y presencia de cada fabricante en el mercado.\n",
    "\n",
    "Algunas marcas, como Ford, Mercedes-Benz, y BMW, destacan significativamente con un alto número de vehículos. Esto sugiere que estas marcas tienen una fuerte aceptación en el mercado, posiblemente debido a su reputación, calidad, y estrategias de marketing efectivas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4e759a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.countplot(data=train_df, x='brand', order=train_df['brand'].value_counts().index)\n",
    "plt.title('Cantidad de Vehiculos por Marca')\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Brand')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd3e9a9",
   "metadata": {},
   "source": [
    "El gráfico muestra la correlación entre dos variables: milage (kilometraje) y price (precio).\n",
    "\n",
    "Correlación entre milage y price: El valor de -0.28 sugiere una correlación negativa moderada. Esto significa que, en general, a medida que aumenta el kilometraje de un vehículo, su precio tiende a disminuir. Esta tendencia es común en el mercado automotriz, ya que un mayor kilometraje generalmente se asocia con un desgaste mayor del vehículo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ad807f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "corr = train_df[['milage', 'price']].corr()\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12cb3bae",
   "metadata": {},
   "source": [
    "**Price vs Milage**\n",
    "\n",
    "- La mayoría de los puntos se concentran en el rango de precios bajos (debajo de 0.5 millones) y kilometrajes por debajo de los 150,000 km. Hay varios vehículos con precios superiores a 1 millón, pero estos tienden a tener un kilometraje más bajo.\n",
    "\n",
    "- Se observan algunos puntos con precios significativamente altos (2-3 millones), incluso para vehículos con kilometrajes altos (por encima de 100,000 km). Esto podría sugerir vehículos de lujo o ediciones especiales que mantienen su valor a pesar de su uso.\n",
    "\n",
    "- En general, parece que a medida que el kilometraje aumenta, el precio tiende a reducirse. Esta es una tendencia lógica ya que los vehículos con mayor kilometraje suelen tener más desgaste y, por tanto, un valor de reventa más bajo.\n",
    "\n",
    "- Hay algunos puntos con precios muy bajos y kilometrajes extremadamente altos (más de 350,000 km). Estos podrían representar vehículos muy antiguos o en malas condiciones, lo cual podría merecer más análisis para eliminar outliers o corroborar datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "16e510aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='milage', y='price', data=train_df)\n",
    "plt.title('Scatter Plot of Price vs. Milage')\n",
    "plt.xlabel('Milage')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaa9a8eb",
   "metadata": {},
   "source": [
    "**Model Year vs Price**\n",
    "\n",
    "Existe una correlación moderada entre el año del modelo y el precio: los vehículos más nuevos (posteriores a 2010) tienden a tener precios más altos.\n",
    "Vehículos anteriores a 1990 tienden a tener precios muy bajos, lo cual es esperable debido a la antigüedad de estos modelos.\n",
    "\n",
    "- Vehículos con precios altos:\n",
    "\n",
    "Hay vehículos relativamente recientes (después del año 2000) con precios que supera 1.5. Esto podría ser indicativo de modelos de lujo, autos eléctricos de gama alta o ediciones limitadas.\n",
    "También se observa que algunos vehículos antiguos (de antes del 2000) pueden alcanzar precios superiores a los 2 millones, posiblemente debido a que se trata de autos clásicos o de colección.\n",
    "\n",
    "- Concentración de puntos:\n",
    "\n",
    "Una gran cantidad de puntos se agrupa en el rango de precios bajos (por debajo de 0.5) y en los años más recientes (posteriores a 2010), lo que podría corresponder a vehículos de gama media o baja con más disponibilidad en el mercado.\n",
    "El gráfico sugiere que hay un número considerable de vehículos nuevos que se venden a precios accesibles (debajo de 0.5).\n",
    "\n",
    "- Outliers:\n",
    "\n",
    "Hay algunos puntos dispersos que presentan precios extremadamente bajos o altos para su año de fabricación, lo cual podría ser un buen lugar para realizar un análisis más profundo de outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "041d91e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='model_year', y='price', data=train_df)\n",
    "plt.title('Scatter Plot of Price vs. Model Year')\n",
    "plt.xlabel('Model Year')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec38ec9",
   "metadata": {},
   "source": [
    "**Distribución de Precios por Tipo de Combustible**\n",
    " \n",
    "- Para todos los tipos de combustible, la mayor parte de los precios se concentra por debajo de 0.5 millones, como se observa en la parte inferior de las cajas. Sin embargo, hay outliers que superan el millón en la mayoría de las categorías.\n",
    "\n",
    "\n",
    "- El combustible más común es la gasolina, que presenta una amplia distribución de precios, con algunos outliers que alcanzan los 3 millones.\n",
    "El tipo E85 Flex Fuel y diésel también tienen una alta densidad de precios en el rango bajo, con algunos precios elevados, pero no tan extremos como los de gasolina.\n",
    "\n",
    "\n",
    "- Los vehículos híbridos y plug-in híbridos muestran una distribución más concentrada, especialmente en el caso de los plug-in híbridos, donde los precios parecen ser más bajos en comparación con otros tipos de combustible.\n",
    "Los híbridos tienen algunos outliers, pero su rango intercuartil es similar al de los vehículos de gasolina, lo que sugiere que pueden ser competitivos en precio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8570d885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='fuel_type', y='price', data=train_df)\n",
    "plt.title('Price Distribution by Fuel Type')\n",
    "plt.xlabel('Fuel Type')\n",
    "plt.ylabel('Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "291f135b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contar las marcas y seleccionar las 20 más comunes\n",
    "top_brands = train_df['brand'].value_counts().nlargest(20).index\n",
    "\n",
    "# Filtrar el DataFrame para que solo incluya las marcas seleccionadas\n",
    "filtered_df = train_df[train_df['brand'].isin(top_brands)]\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.boxplot(x='brand', y='price', data=filtered_df)\n",
    "plt.title('Boxplot por Marca')\n",
    "plt.xlabel('Brand')\n",
    "plt.ylabel('Price')\n",
    "plt.xticks(rotation=45) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "79daf8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "brand\n",
       "Ford             23088\n",
       "Mercedes-Benz    19172\n",
       "BMW              17028\n",
       "Chevrolet        16335\n",
       "Audi             10887\n",
       "Porsche          10612\n",
       "Land              9525\n",
       "Toyota            8850\n",
       "Lexus             8643\n",
       "Jeep              6474\n",
       "Cadillac          4674\n",
       "RAM               4249\n",
       "Nissan            3930\n",
       "Tesla             3738\n",
       "INFINITI          3276\n",
       "GMC               3215\n",
       "Dodge             3133\n",
       "Mazda             2719\n",
       "Kia               2497\n",
       "Lincoln           2423\n",
       "Subaru            2381\n",
       "Acura             2282\n",
       "Honda             2101\n",
       "Hyundai           2045\n",
       "Volkswagen        1765\n",
       "Jaguar            1319\n",
       "Bentley           1155\n",
       "MINI              1064\n",
       "Genesis            969\n",
       "Buick              940\n",
       "Maserati           939\n",
       "Lamborghini        809\n",
       "Chrysler           727\n",
       "Volvo              723\n",
       "Alfa               682\n",
       "Rivian             590\n",
       "Rolls-Royce        561\n",
       "Mitsubishi         551\n",
       "Pontiac            538\n",
       "Hummer             520\n",
       "Ferrari            359\n",
       "McLaren            243\n",
       "Aston              238\n",
       "Saturn             148\n",
       "Scion              120\n",
       "Lucid               69\n",
       "Mercury             57\n",
       "Lotus               52\n",
       "FIAT                44\n",
       "Saab                22\n",
       "Karma               17\n",
       "Suzuki               8\n",
       "Plymouth             7\n",
       "Bugatti              7\n",
       "Polestar             7\n",
       "smart                3\n",
       "Maybach              3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4ef4c4b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>brand</th>\n",
       "      <th>Acura</th>\n",
       "      <th>Alfa</th>\n",
       "      <th>Aston</th>\n",
       "      <th>Audi</th>\n",
       "      <th>BMW</th>\n",
       "      <th>Bentley</th>\n",
       "      <th>Bugatti</th>\n",
       "      <th>Buick</th>\n",
       "      <th>Cadillac</th>\n",
       "      <th>Chevrolet</th>\n",
       "      <th>...</th>\n",
       "      <th>Saab</th>\n",
       "      <th>Saturn</th>\n",
       "      <th>Scion</th>\n",
       "      <th>Subaru</th>\n",
       "      <th>Suzuki</th>\n",
       "      <th>Tesla</th>\n",
       "      <th>Toyota</th>\n",
       "      <th>Volkswagen</th>\n",
       "      <th>Volvo</th>\n",
       "      <th>smart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>28628.496494</td>\n",
       "      <td>51993.095308</td>\n",
       "      <td>1.123646e+05</td>\n",
       "      <td>4.100486e+04</td>\n",
       "      <td>4.365978e+04</td>\n",
       "      <td>1.292729e+05</td>\n",
       "      <td>169600.0</td>\n",
       "      <td>29617.835106</td>\n",
       "      <td>4.565909e+04</td>\n",
       "      <td>4.183676e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>28594.0</td>\n",
       "      <td>18204.236486</td>\n",
       "      <td>14652.416667</td>\n",
       "      <td>21521.107938</td>\n",
       "      <td>30124.75</td>\n",
       "      <td>5.644527e+04</td>\n",
       "      <td>2.914781e+04</td>\n",
       "      <td>26339.422663</td>\n",
       "      <td>30879.215768</td>\n",
       "      <td>34664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Min</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>3.850000e+03</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2.300000e+03</td>\n",
       "      <td>67900.0</td>\n",
       "      <td>3200.000000</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>3850.000000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>2900.000000</td>\n",
       "      <td>8900.00</td>\n",
       "      <td>5.500000e+03</td>\n",
       "      <td>2.000000e+03</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>9000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Max</th>\n",
       "      <td>489995.000000</td>\n",
       "      <td>649999.000000</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>1.950995e+06</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>259500.0</td>\n",
       "      <td>449991.000000</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>75500.0</td>\n",
       "      <td>200000.000000</td>\n",
       "      <td>42850.000000</td>\n",
       "      <td>479995.000000</td>\n",
       "      <td>101999.00</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>2.954083e+06</td>\n",
       "      <td>599995.000000</td>\n",
       "      <td>489000.000000</td>\n",
       "      <td>84997.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "brand          Acura           Alfa         Aston          Audi           BMW  \\\n",
       "Mean    28628.496494   51993.095308  1.123646e+05  4.100486e+04  4.365978e+04   \n",
       "Min      2000.000000    3000.000000  3.850000e+03  2.000000e+03  2.000000e+03   \n",
       "Max    489995.000000  649999.000000  2.954083e+06  2.954083e+06  1.950995e+06   \n",
       "\n",
       "brand       Bentley   Bugatti          Buick      Cadillac     Chevrolet  ...  \\\n",
       "Mean   1.292729e+05  169600.0   29617.835106  4.565909e+04  4.183676e+04  ...   \n",
       "Min    2.300000e+03   67900.0    3200.000000  2.000000e+03  2.000000e+03  ...   \n",
       "Max    2.954083e+06  259500.0  449991.000000  2.954083e+06  2.954083e+06  ...   \n",
       "\n",
       "brand     Saab         Saturn         Scion         Subaru     Suzuki  \\\n",
       "Mean   28594.0   18204.236486  14652.416667   21521.107938   30124.75   \n",
       "Min     2900.0    3850.000000   2900.000000    2900.000000    8900.00   \n",
       "Max    75500.0  200000.000000  42850.000000  479995.000000  101999.00   \n",
       "\n",
       "brand         Tesla        Toyota     Volkswagen          Volvo    smart  \n",
       "Mean   5.644527e+04  2.914781e+04   26339.422663   30879.215768  34664.0  \n",
       "Min    5.500000e+03  2.000000e+03    2000.000000    2000.000000   9000.0  \n",
       "Max    2.954083e+06  2.954083e+06  599995.000000  489000.000000  84997.0  \n",
       "\n",
       "[3 rows x 57 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcular las estadísticas: media, mínimo y máximo por marca\n",
    "stats = train_df.groupby('brand')['price'].agg(['mean', 'min', 'max'])\n",
    "\n",
    "# Transponer el DataFrame para que cada columna sea una marca\n",
    "stats = stats.transpose()\n",
    "\n",
    "# Establecer nombres de las filas\n",
    "stats.index = ['Mean', 'Min', 'Max']\n",
    "\n",
    "# Mostrar el cuadro de estadísticas\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2464413a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ext_col\n",
       "Black                                 48658\n",
       "White                                 43815\n",
       "Gray                                  25293\n",
       "Silver                                16995\n",
       "Blue                                  14555\n",
       "                                      ...  \n",
       "GT SILVER                                 5\n",
       "Blue Caelum                               5\n",
       "Granite Crystal Metallic Clearcoat        5\n",
       "BLUE                                      3\n",
       "Mango Tango Pearlcoat                     2\n",
       "Name: count, Length: 319, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['ext_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eb8bdc30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contar las categorías de combustible y seleccionar las 20 más comunes\n",
    "top_ext_col = train_df['ext_col'].value_counts().nlargest(20).index\n",
    "\n",
    "# Filtrar el DataFrame para que solo incluya los tipos de combustible seleccionados\n",
    "filtered_df = train_df[train_df['ext_col'].isin(top_ext_col)]\n",
    "\n",
    "# Crear el boxplot\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.boxplot(x='ext_col', y='price', data=filtered_df)\n",
    "plt.title('Price Distribution by Fuel Type')\n",
    "plt.xlabel('Ext Col')\n",
    "plt.ylabel('Price')\n",
    "plt.xticks(rotation=45)  # Rotar etiquetas del eje x para mejor visibilidad\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a24bd4da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int_col\n",
       "Black           107674\n",
       "Beige            24495\n",
       "Gray             21204\n",
       "Brown             5810\n",
       "Red               5145\n",
       "                 ...  \n",
       "Nougat Brown         7\n",
       "Classic Red          7\n",
       "Cobalt Blue          6\n",
       "WHITE                6\n",
       "ORANGE               4\n",
       "Name: count, Length: 156, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['int_col'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9b83a1ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Contar las categorías y seleccionar las 20 más comunes\n",
    "top_int_col = train_df['int_col'].value_counts().nlargest(20).index\n",
    "\n",
    "# Filtrar el DataFrame para incluir solo los tipos seleccionados\n",
    "filtered_df = train_df[train_df['int_col'].isin(top_int_col)]\n",
    "\n",
    "# Crear el boxplot\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.boxplot(x='int_col', y='price', data=filtered_df)\n",
    "plt.title('Price Distribution by Fuel Type')\n",
    "plt.xlabel('Int Col')\n",
    "plt.ylabel('Price')\n",
    "plt.xticks(rotation=45)  # Rotar etiquetas del eje x para mejor visibilidad\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "484d2677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clean_title\n",
       "Yes    188533\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['clean_title'].value_counts()"
   ]
  }
 ],
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