3_PREDICCION_PRECIOS_VEHICULOS_USADOS.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA**"
      ],
      "metadata": {
        "id": "DrY-45lOJXk6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**IMPORTA DATOS - EXPLORACION INICIAL**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "2SZrPt22zMP2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define la ruta de tu archivo ZIP en Google Drive\n",
        "zip_file_path = '/content/drive/MyDrive/CODING_DOJO/Data_set/vehicles.zip'\n",
        "\n",
        "# Abre y extrae el contenido del archivo ZIP\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    # Listar los archivos contenidos en el archivo ZIP\n",
        "    zip_ref.extractall()  # Extrae todo en el directorio actual\n",
        "\n",
        "# Verificar qué archivos fueron extraídos\n",
        "import os\n",
        "print(os.listdir(\".\"))  # Esto te mostrará los archivos extraídos\n",
        "\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "3b872cf6-cd13-448d-f54e-d0ef4d11e409"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'drive', 'vehicles.csv', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Asume que el archivo Excel se llama 'archivo_excel.xlsx'\n",
        "csv_file = 'vehicles.csv'\n",
        "\n",
        "# Cargar el archivo Excel como un DataFrame\n",
        "df = pd.read_csv(csv_file)\n",
        "\n",
        "# Mostrar las primeras filas del DataFrame\n",
        "df.head()"
      ],
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        "outputId": "22812d91-ec33-48a1-ecbb-0dac844f9cb6"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           id                                                url  \\\n",
              "0  7222695916  https://prescott.craigslist.org/cto/d/prescott...   \n",
              "1  7218891961  https://fayar.craigslist.org/ctd/d/bentonville...   \n",
              "2  7221797935  https://keys.craigslist.org/cto/d/summerland-k...   \n",
              "3  7222270760  https://worcester.craigslist.org/cto/d/west-br...   \n",
              "4  7210384030  https://greensboro.craigslist.org/cto/d/trinit...   \n",
              "\n",
              "                   region                         region_url  price  year  \\\n",
              "0                prescott    https://prescott.craigslist.org   6000   NaN   \n",
              "1            fayetteville       https://fayar.craigslist.org  11900   NaN   \n",
              "2            florida keys        https://keys.craigslist.org  21000   NaN   \n",
              "3  worcester / central MA   https://worcester.craigslist.org   1500   NaN   \n",
              "4              greensboro  https://greensboro.craigslist.org   4900   NaN   \n",
              "\n",
              "  manufacturer model condition cylinders  ... size  type paint_color  \\\n",
              "0          NaN   NaN       NaN       NaN  ...  NaN   NaN         NaN   \n",
              "1          NaN   NaN       NaN       NaN  ...  NaN   NaN         NaN   \n",
              "2          NaN   NaN       NaN       NaN  ...  NaN   NaN         NaN   \n",
              "3          NaN   NaN       NaN       NaN  ...  NaN   NaN         NaN   \n",
              "4          NaN   NaN       NaN       NaN  ...  NaN   NaN         NaN   \n",
              "\n",
              "  image_url description county state lat long posting_date  \n",
              "0       NaN         NaN    NaN    az NaN  NaN          NaN  \n",
              "1       NaN         NaN    NaN    ar NaN  NaN          NaN  \n",
              "2       NaN         NaN    NaN    fl NaN  NaN          NaN  \n",
              "3       NaN         NaN    NaN    ma NaN  NaN          NaN  \n",
              "4       NaN         NaN    NaN    nc NaN  NaN          NaN  \n",
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              "      <th>cylinders</th>\n",
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              "      <th>0</th>\n",
              "      <td>7222695916</td>\n",
              "      <td>https://prescott.craigslist.org/cto/d/prescott...</td>\n",
              "      <td>prescott</td>\n",
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              "      <td>6000</td>\n",
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              "      <td>7218891961</td>\n",
              "      <td>https://fayar.craigslist.org/ctd/d/bentonville...</td>\n",
              "      <td>fayetteville</td>\n",
              "      <td>https://fayar.craigslist.org</td>\n",
              "      <td>11900</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7221797935</td>\n",
              "      <td>https://keys.craigslist.org/cto/d/summerland-k...</td>\n",
              "      <td>florida keys</td>\n",
              "      <td>https://keys.craigslist.org</td>\n",
              "      <td>21000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7222270760</td>\n",
              "      <td>https://worcester.craigslist.org/cto/d/west-br...</td>\n",
              "      <td>worcester / central MA</td>\n",
              "      <td>https://worcester.craigslist.org</td>\n",
              "      <td>1500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>7210384030</td>\n",
              "      <td>https://greensboro.craigslist.org/cto/d/trinit...</td>\n",
              "      <td>greensboro</td>\n",
              "      <td>https://greensboro.craigslist.org</td>\n",
              "      <td>4900</td>\n",
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              "      <td>NaN</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 426880 entries, 0 to 426879\n",
            "Data columns (total 26 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
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            " 6   manufacturer  409234 non-null  object \n",
            " 7   model         421603 non-null  object \n",
            " 8   condition     252776 non-null  object \n",
            " 9   cylinders     249202 non-null  object \n",
            " 10  fuel          423867 non-null  object \n",
            " 11  odometer      422480 non-null  float64\n",
            " 12  title_status  418638 non-null  object \n",
            " 13  transmission  424324 non-null  object \n",
            " 14  VIN           265838 non-null  object \n",
            " 15  drive         296313 non-null  object \n",
            " 16  size          120519 non-null  object \n",
            " 17  type          334022 non-null  object \n",
            " 18  paint_color   296677 non-null  object \n",
            " 19  image_url     426812 non-null  object \n",
            " 20  description   426810 non-null  object \n",
            " 21  county        0 non-null       float64\n",
            " 22  state         426880 non-null  object \n",
            " 23  lat           420331 non-null  float64\n",
            " 24  long          420331 non-null  float64\n",
            " 25  posting_date  426812 non-null  object \n",
            "dtypes: float64(5), int64(2), object(19)\n",
            "memory usage: 84.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATOS INUSUALES**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "LNfrkgL9y4Iz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para reunir los datos se uso un raspador(web scraping) una herramienta que se usa para extraer datos de una manera automatizada.\n",
        "Eso quiere decir que los datos ingresados muy dificilmnete se hayan ejecutado de forma manual, mas probable que se utilizo alguna ubicacion gps para recolectar esta informacion.\n",
        "Despues de lo analizado resulta incoherente conservar datos donde la latitud y longitud sea fuera del territorio de EE.UU\n"
      ],
      "metadata": {
        "id": "aU6xHBe1hvQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_latitud = 24.51  # Southernmost point in Hawaii (approx)\n",
        "max_latitud = 49.38  # Northernmost point in Alaska (approx)\n",
        "min_longitud = -171.83  # Westernmost point in Alaska (approx)\n",
        "max_longitud = -66.95  # Easternmost point in Maine (approx)"
      ],
      "metadata": {
        "id": "509q8zlZhtw5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#NUEVO DF CON DATOS SOLO DE USA\n",
        "data = df[(df['lat'] >= min_latitud) & (df['lat'] <= max_latitud) &\n",
        "                 (df['long'] >= min_longitud) & (df['long'] <= max_longitud)]\n",
        "\n",
        "#ELIMINAR COLUMNAS QUE NO APORTAN NADA A MI MODELO ('description', 'image_url',county)\n",
        "data = data.drop(['description', 'image_url','county','id','url', 'region_url'], axis=1)\n",
        "data = data.reset_index(drop=True)\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kcGdMv3th3H",
        "outputId": "063e13f7-4a40-4e8f-ca34-af58fd326ffc"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 413819 entries, 0 to 413818\n",
            "Data columns (total 20 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   region        413819 non-null  object \n",
            " 1   price         413819 non-null  int64  \n",
            " 2   year          412722 non-null  float64\n",
            " 3   manufacturer  396649 non-null  object \n",
            " 4   model         408791 non-null  object \n",
            " 5   condition     249462 non-null  object \n",
            " 6   cylinders     239678 non-null  object \n",
            " 7   fuel          411059 non-null  object \n",
            " 8   odometer      409506 non-null  float64\n",
            " 9   title_status  405736 non-null  object \n",
            " 10  transmission  411433 non-null  object \n",
            " 11  VIN           258929 non-null  object \n",
            " 12  drive         286184 non-null  object \n",
            " 13  size          114499 non-null  object \n",
            " 14  type          322519 non-null  object \n",
            " 15  paint_color   285754 non-null  object \n",
            " 16  state         413819 non-null  object \n",
            " 17  lat           413819 non-null  float64\n",
            " 18  long          413819 non-null  float64\n",
            " 19  posting_date  413819 non-null  object \n",
            "dtypes: float64(4), int64(1), object(15)\n",
            "memory usage: 63.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PRECIO\n",
        "pd.set_option('display.float_format', '{:.2f}'.format)\n",
        "data.describe().round(2)\n",
        "#Me resulta dificil de entender por que existe una valor que supera al millon de dolares en precio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "upW8N_Wnn2T-",
        "outputId": "a7fc042f-3358-45c0-e42b-2cebe4d3d91a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              price      year    odometer       lat      long\n",
              "count     413819.00 412722.00   409506.00 413819.00 413819.00\n",
              "mean       77058.68   2011.17    98827.06     38.44    -93.89\n",
              "std     12373033.53      9.51   216354.26      5.24     16.86\n",
              "min            0.00   1900.00        0.00     24.56   -131.92\n",
              "25%         5950.00   2008.00    38004.50     34.74   -110.98\n",
              "50%        13900.00   2013.00    86387.50     39.15    -88.07\n",
              "75%        26499.00   2017.00   134600.00     42.35    -80.73\n",
              "max   3736928711.00   2022.00 10000000.00     49.25    -67.14"
            ],
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              "      <th>price</th>\n",
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              "      <td>413819.00</td>\n",
              "      <td>413819.00</td>\n",
              "    </tr>\n",
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              "      <th>mean</th>\n",
              "      <td>77058.68</td>\n",
              "      <td>2011.17</td>\n",
              "      <td>98827.06</td>\n",
              "      <td>38.44</td>\n",
              "      <td>-93.89</td>\n",
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              "      <th>std</th>\n",
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              "      <td>-131.92</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>2008.00</td>\n",
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              "      <td>34.74</td>\n",
              "      <td>-110.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13900.00</td>\n",
              "      <td>2013.00</td>\n",
              "      <td>86387.50</td>\n",
              "      <td>39.15</td>\n",
              "      <td>-88.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>26499.00</td>\n",
              "      <td>2017.00</td>\n",
              "      <td>134600.00</td>\n",
              "      <td>42.35</td>\n",
              "      <td>-80.73</td>\n",
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              "    <tr>\n",
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              "      <td>2022.00</td>\n",
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              "      <td>49.25</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#Me resulta dificil de entender por que existe una valor que supera al millon de dolares en precio\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1320558752.0017648,\n        \"min\": 0.0,\n        \"max\": 3736928711.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          77058.68,\n          13900.0,\n          413819.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145315.8134081526,\n        \"min\": 9.51,\n        \"max\": 412722.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2011.17,\n          2013.0,\n          412722.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"odometer\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3488166.607960742,\n        \"min\": 0.0,\n        \"max\": 10000000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          98827.06,\n          86387.5,\n          409506.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146295.3060107258,\n        \"min\": 5.24,\n        \"max\": 413819.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.44,\n          39.15,\n          413819.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146335.19278614194,\n        \"min\": -131.92,\n        \"max\": 413819.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -93.89,\n          -88.07,\n          413819.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(data['price'])\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "print('\\nSe observa valores atipicos de los precios a partir de los 600 000 doalres, procedere a elimiar mis datos desde ese numero')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "eJAG--TXn5NQ",
        "outputId": "f1eca1aa-a124-47b1-962b-37001ca6121c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Se observa valores atipicos de los precios a partir de los 600 000 doalres, procedere a elimiar mis datos desde ese numero\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p = data[data['price']>600000]\n",
        "p"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "id": "g_PVWTrRuGvl",
        "outputId": "b3063879-c0cc-43f1-eee5-81423e1d92d8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            region       price    year manufacturer  \\\n",
              "252                     birmingham   987654321 1960.00    chevrolet   \n",
              "1799                        dothan    99999999 1993.00         ford   \n",
              "22552              fresno / madera     1111111 2014.00    chevrolet   \n",
              "25856              humboldt county  1111111111 1999.00         ford   \n",
              "33872                      modesto  1111111111 2020.00         jeep   \n",
              "...                            ...         ...     ...          ...   \n",
              "365687  killeen / temple / ft hood     1234567 1955.00    chevrolet   \n",
              "367305          mcallen / edinburg    25003000 1991.00    chevrolet   \n",
              "373027                        waco     1234567 1955.00    chevrolet   \n",
              "373053                        waco     1234567 1955.00    chevrolet   \n",
              "403136                  eau claire      999999 2006.00         ford   \n",
              "\n",
              "                         model condition    cylinders    fuel  odometer  \\\n",
              "252                        NaN       NaN          NaN     gas 999999.00   \n",
              "1799                    ranger       NaN          NaN     gas  99999.00   \n",
              "22552                      NaN       NaN          NaN   other     11.00   \n",
              "25856   f350 super duty lariat      good  8 cylinders  diesel 149000.00   \n",
              "33872                      NaN       NaN          NaN     gas   1000.00   \n",
              "...                        ...       ...          ...     ...       ...   \n",
              "365687                     NaN       NaN          NaN   other  99999.00   \n",
              "367305                  camaro      fair  6 cylinders     gas 200000.00   \n",
              "373027                     NaN      good          NaN   other  99999.00   \n",
              "373053                     NaN      good          NaN   other  99999.00   \n",
              "403136                   f-350       NaN  8 cylinders  diesel 204123.00   \n",
              "\n",
              "       title_status transmission  VIN drive       size    type paint_color  \\\n",
              "252           clean       manual  NaN   NaN        NaN     NaN         NaN   \n",
              "1799          clean    automatic  NaN   NaN        NaN     NaN         NaN   \n",
              "22552         clean    automatic  NaN   NaN        NaN     NaN         NaN   \n",
              "25856         clean    automatic  NaN   rwd  full-size  pickup       white   \n",
              "33872         clean    automatic  NaN   4wd        NaN     NaN         NaN   \n",
              "...             ...          ...  ...   ...        ...     ...         ...   \n",
              "365687      missing        other  NaN   NaN        NaN     NaN         NaN   \n",
              "367305        clean    automatic  NaN   NaN        NaN     NaN         NaN   \n",
              "373027      missing        other  NaN   NaN        NaN     NaN         NaN   \n",
              "373053      missing        other  NaN   NaN        NaN     NaN         NaN   \n",
              "403136        clean    automatic  NaN   NaN        NaN     NaN         NaN   \n",
              "\n",
              "       state   lat    long              posting_date  \n",
              "252       al 33.63  -86.60  2021-05-02T13:24:09-0500  \n",
              "1799      al 31.05  -85.83  2021-05-02T17:18:25-0500  \n",
              "22552     ca 37.67 -121.01  2021-04-30T12:20:06-0700  \n",
              "25856     ca 40.95 -124.08  2021-04-11T11:40:08-0700  \n",
              "33872     ca 37.64 -120.97  2021-04-28T14:29:17-0700  \n",
              "...      ...   ...     ...                       ...  \n",
              "365687    tx 31.05  -97.50  2021-04-04T17:28:43-0500  \n",
              "367305    tx 26.24  -97.68  2021-04-12T12:36:37-0500  \n",
              "373027    tx 31.05  -97.50  2021-04-09T06:00:14-0500  \n",
              "373053    tx 31.05  -97.50  2021-04-07T17:21:24-0500  \n",
              "403136    wi 45.26  -91.79  2021-05-04T20:57:43-0500  \n",
              "\n",
              "[65 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-87b45572-296e-4db0-9c7a-7c695d300536\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>region</th>\n",
              "      <th>price</th>\n",
              "      <th>year</th>\n",
              "      <th>manufacturer</th>\n",
              "      <th>model</th>\n",
              "      <th>condition</th>\n",
              "      <th>cylinders</th>\n",
              "      <th>fuel</th>\n",
              "      <th>odometer</th>\n",
              "      <th>title_status</th>\n",
              "      <th>transmission</th>\n",
              "      <th>VIN</th>\n",
              "      <th>drive</th>\n",
              "      <th>size</th>\n",
              "      <th>type</th>\n",
              "      <th>paint_color</th>\n",
              "      <th>state</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>posting_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>birmingham</td>\n",
              "      <td>987654321</td>\n",
              "      <td>1960.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>999999.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>manual</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>al</td>\n",
              "      <td>33.63</td>\n",
              "      <td>-86.60</td>\n",
              "      <td>2021-05-02T13:24:09-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1799</th>\n",
              "      <td>dothan</td>\n",
              "      <td>99999999</td>\n",
              "      <td>1993.00</td>\n",
              "      <td>ford</td>\n",
              "      <td>ranger</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>99999.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>al</td>\n",
              "      <td>31.05</td>\n",
              "      <td>-85.83</td>\n",
              "      <td>2021-05-02T17:18:25-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22552</th>\n",
              "      <td>fresno / madera</td>\n",
              "      <td>1111111</td>\n",
              "      <td>2014.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>11.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ca</td>\n",
              "      <td>37.67</td>\n",
              "      <td>-121.01</td>\n",
              "      <td>2021-04-30T12:20:06-0700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25856</th>\n",
              "      <td>humboldt county</td>\n",
              "      <td>1111111111</td>\n",
              "      <td>1999.00</td>\n",
              "      <td>ford</td>\n",
              "      <td>f350 super duty lariat</td>\n",
              "      <td>good</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>149000.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>rwd</td>\n",
              "      <td>full-size</td>\n",
              "      <td>pickup</td>\n",
              "      <td>white</td>\n",
              "      <td>ca</td>\n",
              "      <td>40.95</td>\n",
              "      <td>-124.08</td>\n",
              "      <td>2021-04-11T11:40:08-0700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33872</th>\n",
              "      <td>modesto</td>\n",
              "      <td>1111111111</td>\n",
              "      <td>2020.00</td>\n",
              "      <td>jeep</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>gas</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4wd</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ca</td>\n",
              "      <td>37.64</td>\n",
              "      <td>-120.97</td>\n",
              "      <td>2021-04-28T14:29:17-0700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>365687</th>\n",
              "      <td>killeen / temple / ft hood</td>\n",
              "      <td>1234567</td>\n",
              "      <td>1955.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>99999.00</td>\n",
              "      <td>missing</td>\n",
              "      <td>other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tx</td>\n",
              "      <td>31.05</td>\n",
              "      <td>-97.50</td>\n",
              "      <td>2021-04-04T17:28:43-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>367305</th>\n",
              "      <td>mcallen / edinburg</td>\n",
              "      <td>25003000</td>\n",
              "      <td>1991.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>camaro</td>\n",
              "      <td>fair</td>\n",
              "      <td>6 cylinders</td>\n",
              "      <td>gas</td>\n",
              "      <td>200000.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tx</td>\n",
              "      <td>26.24</td>\n",
              "      <td>-97.68</td>\n",
              "      <td>2021-04-12T12:36:37-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373027</th>\n",
              "      <td>waco</td>\n",
              "      <td>1234567</td>\n",
              "      <td>1955.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>good</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>99999.00</td>\n",
              "      <td>missing</td>\n",
              "      <td>other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tx</td>\n",
              "      <td>31.05</td>\n",
              "      <td>-97.50</td>\n",
              "      <td>2021-04-09T06:00:14-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373053</th>\n",
              "      <td>waco</td>\n",
              "      <td>1234567</td>\n",
              "      <td>1955.00</td>\n",
              "      <td>chevrolet</td>\n",
              "      <td>NaN</td>\n",
              "      <td>good</td>\n",
              "      <td>NaN</td>\n",
              "      <td>other</td>\n",
              "      <td>99999.00</td>\n",
              "      <td>missing</td>\n",
              "      <td>other</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tx</td>\n",
              "      <td>31.05</td>\n",
              "      <td>-97.50</td>\n",
              "      <td>2021-04-07T17:21:24-0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>403136</th>\n",
              "      <td>eau claire</td>\n",
              "      <td>999999</td>\n",
              "      <td>2006.00</td>\n",
              "      <td>ford</td>\n",
              "      <td>f-350</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8 cylinders</td>\n",
              "      <td>diesel</td>\n",
              "      <td>204123.00</td>\n",
              "      <td>clean</td>\n",
              "      <td>automatic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>wi</td>\n",
              "      <td>45.26</td>\n",
              "      <td>-91.79</td>\n",
              "      <td>2021-05-04T20:57:43-0500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65 rows × 20 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87b45572-296e-4db0-9c7a-7c695d300536')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-87b45572-296e-4db0-9c7a-7c695d300536 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-87b45572-296e-4db0-9c7a-7c695d300536');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3baa76ce-3bdc-4ac3-b874-035583e69d38\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3baa76ce-3bdc-4ac3-b874-035583e69d38')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3baa76ce-3bdc-4ac3-b874-035583e69d38 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ba85c05c-5154-4567-891e-aee12edec4d8\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('p')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ba85c05c-5154-4567-891e-aee12edec4d8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('p');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "p",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# se eliminaran datos incronguentes que no aportan nada al modelo, ya que un precio que supere esasa cantidades es absurdo\n",
        "\n",
        "data = data[(data['price'] >= 10) & (data['price'] < 600000)]  #FILTRE LOS DATOS EN M IDF QUE SOLO CUMPLEN LAS CONDICIONES"
      ],
      "metadata": {
        "id": "IKpkaAJdqJEe"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.boxplot(data['price'])\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "print('Igual se oserva bastante valores atipicos pero una dispersiona tan extensa como la anteriror')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "-9uBKPD1vJlx",
        "outputId": "ec8cd74f-c947-4d52-ab5b-af4fd81fea0e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Igual se oserva bastante valores atipicos pero una dispersiona tan extensa como la anteriror\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATOS NULOS**\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "JUGnaIatzAOq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "IAO2_kRmwOP5",
        "outputId": "329f9139-548c-4990-9e4f-bc9709db6662"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "region               0\n",
              "price                0\n",
              "year              1080\n",
              "manufacturer     15593\n",
              "model             4482\n",
              "condition       144180\n",
              "cylinders       157066\n",
              "fuel              2362\n",
              "odometer          2206\n",
              "title_status      7581\n",
              "transmission      1667\n",
              "VIN             146870\n",
              "drive           117280\n",
              "size            275843\n",
              "type             83617\n",
              "paint_color     114371\n",
              "state                0\n",
              "lat                  0\n",
              "long                 0\n",
              "posting_date         0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>1080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>15593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>4482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>144180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>157066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>2362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>2206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>7581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>1667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIN</th>\n",
              "      <td>146870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>117280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>275843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>83617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>114371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lat</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>long</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posting_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NULOS CON PORCENTAJE MENOR AL 2%**"
      ],
      "metadata": {
        "id": "e8iaOaVv2Clj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figura = plt.figure(figsize=(18,12))\n",
        "\n",
        "plt.subplot(3,2,1)\n",
        "plt.title('HISTOGRAMA DE EDADES') #1097 NA = 0.2%\n",
        "plt.hist(data['year'])\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "data['transmission'].hist() #2386 NA = 0.5%\n",
        "plt.title('HIST TRANSMISION')\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "data['fuel'].hist() #2760 =  0.6%\n",
        "plt.title('HIST COMBUSTIBLE')\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "data['title_status'].hist() #8083 = 1.9%\n",
        "plt.title('HIST CILINDRADAS')\n",
        "\n",
        "plt.subplot(3,2,(5,6))\n",
        "plt.title('HISTOGRAMA DE FABRICANTES')\n",
        "data['manufacturer'].value_counts().plot(kind='bar') #17170 NA = 4%\n",
        "\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
        "plt.suptitle('HISTOGRAMAS CON MENOR PORCENTAJE', fontsize=16)\n",
        "plt.show()\n",
        "plt.close()\n",
        "print('\\nAqui estan todas las columnas donde los valores nulos no superan ni el 5% de los datos')\n",
        "print('En la mayoria de datos existe una preferencia de los datos, un valor prevalece sobre los demas')\n",
        "print('Por este motivo utlizare la moda para reemplazar todos estos valores')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "id": "zooViShwrsbw",
        "outputId": "c6c3eb97-20a8-4623-da55-581a82454afc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 5 Axes>"
            ],
            "image/png": 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gLi4u8Djt/sK1GQoKCoROGT569IgTf+eqqip+DlVVVTx9+hRGRkbQ1tbGw4cPqeWqOwfbunVriImJUcvyJVweN4iJicHQ0BCFhYUwNDSkHadBo0eP5le34aouXbogKysLBgYGtKPUq7i4GEeOHEF2djb8/f2hrKyMxMRENG/eHC1btqQdD0BNn8ru3bvTjsFKdH5vI0aMgJubGwIDA/mlrOLj4+Hv7//FGvSNRVtbG1evXsX27duRk5ODw4cPo2XLltizZw90dXXRo0cPqvm6du2Ka9euoV27dhg0aBB8fX1x7949HDt2jL9gRVtpaSm/VraSkhJevXqFNm3awMzMjHpd4NTUVH4N9OzsbAA1gxxVVVWB3iq0F3hFYSC7ePFiBAQEIDg4mJOlAKytrXHjxg2sWbMGhw4dgpSUFMzNzbFr1y7ODHxatmyJ9+/fw8bGBjY2NpgzZw7Mzc2p//+rNXv2bEyfPh0fPnwAIQS3bt3C/v37sXLlSgQFBdGOB0Dwtbpy5UqYmJhg8uTJAn02uYBr/ZJqfdofkBCCZ8+e4cyZM5g0aRKlVIICAwORnZ2N5s2bQ0dHR+j9kPbnClDznj1ixAisWbMGkyZNgoWFBQDg5MmT/PEObVy8ua9bIq2wsBDLli1D//790a1bNwA1i5KRkZFYsGABlXx1icJnip6eHn+jWtu2bXHo0CF07twZp06dgqKiIu14fOXl5cjNzYW+vr7QIhANDd2DqKmpQVFRET/99BP/Gu3+wkDN2Lp2Aahly5ZITU2FmZkZiouLUVZWRjkdYGtri2fPngn1zXn79i1sbW05sSjA5XH2kCFD8NNPP2HXrl0C98tTp06l0iu1IVzuUwPUjG9mzJiBV69e8XsKR0dHIzAwkDPlRV+8eAE/Pz9+n5pPn0faz+Gni7Zc8/jxY5w8ebLesTXNjbp19evXDxs2bMCOHTsA1Lw2SkpKsGjRIgwaNIhyuo8qKioQERGB4OBgXLhwAVZWVti8eTPGjx+PV69eYf78+Rg9ejTS0tIaPVufPn0AgL+5qhbhUNnnsWPHwsPDA2vXruVPbMfFxcHf358T96SmpqZITk6Grq4uunTpgtWrV0NCQgI7duzgxIaH2o0/ZWVl9b6eudC7l8vjBgBYtWoV/P39sXXrVpiamlLN0hADAwMsWLAAN2/erPc55EJpZS8vL/j6+uL58+f1ZqT5fzElJQV9+vSBgoIC8vLyMHnyZCgrK+PYsWMoKChAWFgYtWx1eXp6Ijw8nPr9O498OqpivqmysjL4+fkhODiY35uoadOm8PDwwJo1a4R6cdBw9OhRTJw4Ec7OztizZw/S0tKgp6eHzZs34+zZszh79izVfDk5OSgpKYG5uTlKS0vh6+uL69evw9DQEOvWrePErthOnTrxJ+kcHBygqKiIlStXYtOmTfzdBszn1e5e+nShh0sD2ffv32PEiBGIi4vj7KQ717Vv3x7p6eno0KEDf5GvR48enDqltG/fPixevJj/um3RogUCAgLg4eFBOVkNMTExft+fWjdu3MCIESPw6tUrTrxWvtQvqbZ+Pg22trYCP4uJiUFNTQ12dnZwd3fnxOT7l06jcKV3W1VVFd69e8fvMQzU9IiUlpbmRIP4nj17wtfXF8OHD4eTkxPevHmD+fPnY8eOHbhz547AJhcaRo4cCVtbW6HTFps3b8bFixdx4sQJOsFEyPr169GkSRPMnDkTFy9exNChQ0EIQUVFBdatWwdvb2+q+crKyuDl5YXQ0FAANSfY9fT04OXlhZYtW2Lu3LlUcrm5uX3179J8v67l5OQEKysrzJ49G0uXLsUff/yBYcOG4cKFC+jQoQP1RUgxMTG8ePECampqAtczMjJgZWWFd+/eUUr2EZfH2cXFxZg0aRJOnTrFH1tXVlbCwcEBISEhnOnb1r59e7Rp0wYBAQH19ljhQs6tW7di+fLlePr0KQBAR0cHixcvhouLC+VkNQYOHIiCggLMmDGj3udw2LBhlJIJ+/DhAyQlJWnH4IuOjoaDgwP09PSQnp4OU1NT5OXlgRCCDh064NKlS7QjAqhZhOzfvz8IIcjMzISVlRUyMzOhqqqKK1eucGJ86OXlhf3794MQgokTJ8LT01NogeD58+do0aIFlUXfy5cvf/bx2lNgNJWXl8Pf3x/btm3jV0ARFxfHtGnTsGrVKjRr1oxqvsjISJSWlsLR0RFZWVkYMmQIMjIyoKKigoMHD/I3QdDy6tUruLm54dy5c/U+zoX7eS6PG4CazV9lZWWorKyEhISE0AZ82pVQgJrqUA3h8XjIyclpxDT1q+8UKVf6SPfp0wcdOnTA6tWrIScnh+TkZOjp6eH69etwcnJCXl4etWx1eXt7IywsDObm5jA3NxeaJ26sDThsge87qqqqQlxcHMzMzCAhIcGfLNbX1+fEwl4tS0tL+Pj4wMXFReBFk5SUhIEDB+L58+e0I3Le3r17UVlZCVdXV9y5cwcDBgxAUVERJCQkEBISgrFjx9KOiKysLGRnZ6NXr16QkpLiv2FzhSgMZMeMGYOYmBiMGjUKzZs3F3r+uDLp/vLly3pLB3FhJxhQM5Fz5coVXL58GZcvX0ZaWhrat28PW1tbLF++nHY8vrKyMpSUlHDiRvRrvHjxAunp6Zx4rZibm2PKlCmYPn06/3NFV1cXU6ZMgaamJrXm4YQQPHr0iPMNuZlvg+s397Kysrh7965QSZSsrCy0b98eJSUllJIJ+/Dhg9DuYto7d+uTn5+PO3fuwMDAgBOfed7e3oiLi8OGDRswYMAApKSkQE9PDxEREVi8eDGVMrGiqKioCB8+fOBPtq5evZq/2W/+/PkCmwwaU+1JyIiICAwYMEBgQrOqqgopKSkwMjLC+fPnqeSrSxTG2ZmZmUhPTwcAtGvXjnPlomRkZJCcnMy5XPV59eoVpKSkICsrSzuKADk5OVy9ehXt27enHaVeVVVVWLFiBbZt24YXL17wN2UsWLAAOjo6VDf7de7cGQMHDkRAQAB/bK2urg5nZ2cMGDCAE2Wpa1VWVuLAgQNISUlBSUkJOnToAGdnZ86Mve3t7eHp6QlHR8cGF6IqKysRFxfX6O+NFRUVGDBgALZt28aZagmfU1ZWJjDPyaVNu58qKir6R6XyvydnZ2fk5+djw4YNsLGxwfHjx/HixQssW7YMgYGB1Fv8ANwfN9RunmsIVyrzcF1+fv5nH6d5oEZBQQGJiYnQ19cXWKvIz8+HkZERPnz4QC1bXZ9uIK+Lx+M13gacxm3599/TrFkzkpOTQzvGZ0lJSZHc3FxCiGAj5OzsbE405NbV1SWvX78Wuv7mzRuiq6tLIdGXlZaWkjt37pBXr17RjkJev35N7OzsCI/HI2JiYvy/r5ubG5k9ezbldKJFWlqa0w2vb9++TUxMTIiYmBjh8XgCX2JiYrTjCXn9+jU5cuQImThxImnatCknMzL/jrS0NP9zRVlZmaSkpBBCCElLSyMaGhrUcolKQ25Caj7jdu7cSebOnUsKCwsJIYTcuXOHPH78mHKyGjo6OkRXV7fBL64qLCwk1dXVtGMQQgjR0tIia9euFbq+du1aoqWlRSGRoNLSUjJ9+nSipqZGxMTEhL5oKy8vJ3Z2dpx+PWtpaZEbN24QQgTH2JmZmUROTo5mNL6cnJx6n8OMjAz++zhTP1dXV+Lq6kp4PB4ZO3Ys/2dXV1fy008/kRUrVnDiXoDruDy2rsvW1pacO3eOdgyR1q5dO5KYmEg7RoMCAgKInp4e2bt3L5GSkuK/Zx84cIB07dqVajZZWVmSlZVFCCFEUVGRpKamEkIIuXv3LtHW1qaYTLSUl5cTNzc3Ts/RqaqqcnpsU1dmZiY5f/48KSsrI4QQzoyxuU5DQ4PEx8cTQgiRk5MjDx8+JIQQEhERQaytrWlGYxjOUFNT448Z6t5HRUVFkVatWtGMxkn061D94ExNTZGTk/PZo7m0aWhoICsrCzo6OgLXr127xon61Hl5efUeC/7777/x5MkTCom+TFpamt/3jjYfHx+Ii4ujoKAA7dq1418fO3YsZs+ejcDAQIrphHG5Dnnr1q05eWKhlru7O9q0aYNdu3bVe8KQC44dO4bY2FjExsYiLS0NysrK6NGjBwIDA6nvAgNqTjTX97zxeDxISkrCwMAArq6un92l871xvX8JwN1+SaLSkFsU6s3PmjVL4OeKigokJSXh/Pnz8Pf3pxPqKygrK9OOwBcQEABPT0/ExsaiS5cuAGr6Tp0/fx47d+6knA7w9/dHTEwMtm7diokTJ+LPP//EkydPsH37dqxatYp2PIiLiyMlJYV2jM969epVvafAS0tLOfMZ7erqCnd3d6H3xPj4eAQFBSE2NpZKrn9S1pLW2Ky2fKmOjg78/Pw4VaEFwD96fdAcZ9vZ2aFly5YYP348JkyYAGNjY2pZPlX3OeRyn5paR44cabD/MRdaCWzYsAFz587F9u3bheYeuCAsLAw7duyAvb09pk6dyr9uYWHBP11Ki4yMDP9vqqmpiezsbJiYmAAAXr9+TTOakMzMTMTExNRbUWbhwoWUUtUQFxfH0aNHqfdK+pwJEyZg165dnBhrNaSwsJBf3YjH4yEzMxN6enrw8PCAkpISlTmmhvoL14d2ae/S0lL++FBJSQmvXr1CmzZtYGZmxon36lrFxcXYtWsXHjx4AAAwMTGBu7s7J0pS18XVSiPu7u6ffTw4OLiRkgg6efIkBg4cCHFxcZw8efKzv0uzF7KDgwOWLFmCQ4cOAaiZkysoKMCcOXMwcuRIarm4ii3wfWfLli2Dn58fli5dio4dOwrd+HHhTWfy5Mnw9vZGcHAweDwenj59ihs3bsDPz4/qwKfuG01kZKTAh0hVVRWio6Op3hjMnj37q3+XZtPrqKgoREZGolWrVgLXDQ0Nv3gcuzGJQh3ywMBA/PLLL9i2bRsnb0pzcnJw9OhRTpcOmjp1Knr16oWffvoJvXv3hpmZGe1IAgYMGICtW7fCzMwMnTt3BgAkJCQgJSUFrq6uSEtLQ58+fXDs2DFqfUJcXV1RUFCABQsW1Nu/hAt69eqFCxcuwMzMDKNHj4a3tzcuXbqECxcuCDWMb2yi0JB79uzZcHV15debrzVo0CA4OTlRTPZRQ73N/vzzT9y+fbuR03zk6OiIkJAQyMvLf/FGn/bNvaurK9q1a4dNmzbxs7Rr1w7Xrl3jL/jRdOrUKYSFhcHGxgZubm7o2bMnDAwMoK2tjX379sHZ2Zl2RM5PgllZWeHMmTPw8vIC8LGPSVBQELp160YzGl9SUhKsra2Frnft2lWoP2RjUlRU/OrPN9pjRK6UaP9U+/bt+X1UPod2j5WnT5/iwIED2L9/P1atWgVzc3M4Oztj/PjxQvcvja2+57DuhB1X+tQAwKZNm/Dbb7/B1dUVERERcHNzQ3Z2NhISEjB9+nSq2WqNHTsWZWVl/FJ+ny6S0u6Z9OTJk3rvo6qrq1FRUUEh0Uddu3bFtWvX0K5dOwwaNAi+vr64d+8ejh07hq5du1LNVtfOnTsxbdo0qKqqQkNDQ+B9nMfjUV/gA4Dhw4fjxIkT8PHxoR2lXpWVlQgODsbFixfrnUOkObdUi4ubyLm26PQ5RkZGePjwIXR0dGBhYcHf9LBt2zZoamrSjgcAuH37Nvr37w8pKSn+vMi6deuwfPlyREVFUT/QUFpaijlz5uDQoUMoLCwUepz2ZzIAvHnzRuDniooKpKamori4mGqriOHDh+P58+dQV1fH8OHDG/w92mObwMBAjBo1Curq6nj//j169+6N58+fo1u3bpxq7QPUvF4a2mDVWHMObIHvOxs0aBCAmpXnuoMbrtwIAMDcuXNRXV0Ne3t7lJWVoVevXmjWrBn8/Pz4ExI01L7R8Hg8ofrJ4uLi0NHRoXr67NO+KYmJiaisrISRkREAICMjA02aNEHHjh1pxOMrLS2ttxZ6UVER9ebHdc2aNQvFxcWIj4+vtw45F0yYMIHTN6X29vac7w3y8uVL2hE+6/Xr1/D19RXa3LBs2TLk5+cjKioKixYtwtKlS6kt8F27do3T/UsAYPPmzfya6L/99hvExcVx/fp1jBw5EvPnz6eazcXFBWVlZbCwsOBsQ+6EhARs375d6HrLli053xd34MCBmDdvHv9kS2NTUFDgj7c+d6PPlYXxLl26YN++fbRj1KuoqIhfyUFeXp7/2ujRowdnev1wfRJsxYoVGDhwINLS0lBZWYmNGzciLS0N169f/2Jvk8bC4/H4J67revv2LdX7lJiYGP73eXl5mDt3LlxdXfkLozdu3EBoaChWrlxJJV+HDh0QHR0NJSWlBk//16K1Gz83N5fKv/tPqaqqYsaMGZgxYwZyc3MRHh6O0NBQzJs3D7169Wq83iX1EJXnEAC2bNmCHTt2YPz48QgJCcEvv/wCPT09LFy4kBNjG6DmBB+XGRsb4+rVq0I9h44cOQJLS0tKqWqsW7eO35s3ICAAJSUlOHjwIAwNDal/1tW1bNkyLF++HHPmzKEdpUGGhoZYsmQJ4uLi6h07zJw5k1KyGqmpqfzFk4yMDIHHuDJ+5eImclr3Hv+Gt7c3nj17BqBmk9CAAQOwb98+SEhIICQkhG64/+fj4wMHBwfs3LkTTZvWLB1UVlbC09MTs2bNwpUrV6jm++WXXzhdaQQAjh8/LnSturoa06ZNg76+PoVEHzPU9z3XKCgo4MKFC7h27ZpAT9c+ffrQjibgwIEDcHFxQf/+/REVFYV+/fohIyMDL168wIgRIxotB498aUsf8z/hemPSusrLy5GVlYWSkhIYGxtzpim3rq4uEhISoKqqSjtKg9atW4fY2FiEhoZCSUkJQM1ujdod776+vtSyDRo0CB07dsTSpUshJyeHlJQUaGtrY9y4caiursaRI0eoZatLU1MTERER6Ny5M+Tl5XH79m20adMGJ0+exOrVq3Ht2jXaETnfyPf169eYNGkSOnfuDFNTU6EFSJrH6z/18uXLesu20C5vpKCggDt37ggtkmZlZaFjx454+/Yt0tPT0alTp3onRBuDsbEx9u3bR32iQVRx/XUMAOrq6oiMjISlpaVAQ+kLFy7A3d0djx49oh2xQatXr8aWLVuQl5dHOwr279+P8ePH1/uYv78/1qxZ08iJBBUUFHz2cS0trUZKUj9zc3P88ccf6N27N/r06YP27dtj7dq12LRpE1avXo3Hjx9TzQdwqKn5Z2RnZ2PVqlVITk7m35jOmTOHMyfYhw4dCikpKezfvx9NmjQBULPreezYsSgtLW2wskJjsre3h6enp9DrOTw8HDt27KBSRjQgIAD+/v6QlpZGQEDAZ3+Xqyf8uKqqqgrnzp3DggULkJKSwokNsaJAWloaDx48gLa2NtTV1XHhwgVYWFggMzMTXbt2rfeEAyMoIiICkyZNwrx587BkyRIEBATg4cOHCAsLw+nTp9G3b1/aETlPXl4ed+/e5USrl4Z8rn0Oj8dDTk5OI6YRTXJyckhMTIShoaHAvUrtqS/a7zfv378HIYS/0T0/Px/Hjx+HsbEx+vXrRzVbfcrKypCeng4tLS3OzHtKSUkhKSkJbdu2FbielpYGKysrqm03gJr7pNpKI/Ly8khMTISBgQH27NmD/fv34+zZs1Tzfc7Dhw9hY2PDX+SlKSwsDGPHjhU6/FFeXs5fuKLl0aNHaN26NbV//2uZm5tjypQpmD59Ov/9UFdXF1OmTIGmpuYX7xO+GXrt/xjmx9GiRQt+o+u67t27RzQ1NSkkEsygrq5OBgwYQCQkJMioUaNIu3btSPPmzfmNurlATk6O5ObmEkII0dLSIteuXSOEEJKTk0OkpKQoJhMdJ0+eJAoKCoTH4wl9iYmJ0Y5HCCHk9u3bxMTEhIiJiQlk40pGdXV1EhoaKnQ9NDSUqKurE0IIuX//PlFVVW3saHyRkZGkX79+/NcLV7x9+/arv5jP8/DwIMOHDyfl5eVEVlaW5OTkkPz8fGJpaUm8vb1pxyOEENK+fXtiaWnJ/2rfvj3R0NAgTZo0Idu3b6cdjxBCiIKCAjl79qzQdR8fH6KhoUEhkaDa972Gvmhbt24d2bhxIyGEkAsXLhBJSUnSrFkzIiYmRjZs2EA5HfOt3L9/n6ioqBB9fX3i6upKXF1dib6+PlFTUyP37t2jHY8QQoiUlBTJyMgQuv7w4UM2RvwHsrKyyIwZM4i9vT2xt7cnXl5enLoXuHbtGpk2bRpRU1MjcnJyZMKECeTcuXO0Y/GFhISQ06dP83/29/cnCgoKpFu3biQvL49ishq6urokMTGREEJIx44dybZt2wghNeNGJSUlarnqjvtEYXx45coV0qdPH6KmpkakpKSItbU1iYyMpB1LZLi7u5OtW7fSjsF8ZwMHDiTz588nhBD+vUpVVRUZPXo0GTlyJOV0hPTt25f///DNmzdEXV2dtGrVikhKSpItW7ZQTica1NXV633vO3/+PH9ehCYZGRmSn59PCCGkZcuWJD4+nhBSM38oIyNDM9oXnTlzhup8Ul1iYmLkxYsXQtdfv35N/X5UTEyM9OrVi+zYsYMUFRVRzfI50tLS/Lk5ZWVlkpKSQgghJC0trVHnHFiJzkbAxcakotSAFqg5Cbl27Vr+c2hsbAx/f3/07NmTcrIa7969w6tXr4Suv3r1itopn1qmpqbIyMjAH3/8ATk5OZSUlMDR0RHTp0/nTH1vQDTqkNfFxUa+Xl5emDBhAhYsWIDmzZtTzdIQd3d3tGnTBrt27ULz5s05U2aklpeXF6ZOnYo7d+6gU6dOAGrKJQYFBeHXX38FUNMTlGZ5TK72LxGlfkm1uPg6BkSj3vyn9frFxMSgpqYGGxsboZ2etOzbtw/jx4/H6dOn0aNHDwA1r/GjR48KlP+j5dNS3xUVFUhKSuL3t6Ctbm+aPn36ID09nX/CmfZpay579+7dV/8uF95vjI2NkZKSgs2bNyM5ORlSUlJwcXHBjBkzoKysTDseAKB169bYuXMnVq9eLXA9KCiIUzt7y8vL661OQPs0LlAzdnFwcED79u35PRfj4uJgYmKCU6dOUT2ZNG/ePBw4cABPnz5F3759sXHjRgwbNqzeFgM0rVixAlu3bgVQUyJ28+bN2LBhA06fPg0fHx/q98x2dnY4efIkLC0t4ebmBh8fHxw5cgS3b9/+R/f+35qSkhKePXsGdXX1BseKhEPtS3r27IkLFy7QjiFETEzss+Nsms/dpk2b+N8bGBhgwYIFuHnzJszMzITuU2iXvxQFtra2n/1bc6E6werVq2Fvb4/bt2+jvLwcv/zyC+7fv4+ioiLExcXRjofExESsX78eQE2JXQ0NDSQlJeHo0aNYuHAhlVLzs2fPxtKlSyEjI4PZs2d/9ne5UHZ37Nix8PDwwNq1a9G9e3cANeMGf3//BiukNCY9PT3k5uZCS0sLbdu2xaFDh9C5c2ecOnUKioqKtOMBgNDfmRCCZ8+e4cyZM5yoGgR8/Pz91OPHj6n3tbx9+zbCw8OxZMkSeHl5YcCAAZgwYQKGDh3KqXZTSkpK/Hn/li1bIjU1FWZmZiguLm7Uk66sROd3Vl9j0oSEBLx//55qY1I3N7ev/l3ataz37t0LNzc3ODo6CtyQHj9+HCEhIXBycqKaD6jp6XT16lUEBgby/87x8fH8RcgvlYRjav7OlZWVcHV1xZ07dzBgwAAUFRXx65CPHTuWdkTON/KVk5PD3bt3qdbz/hI5OTkkJSVxuk/gvn37sHnzZjx8+BBAzeKzl5cX/73m/fv34PF4kJSUpJKPqyUm65ak/lK/JJoDWq6/juvier15URAeHo4ZM2bgwoUL2LVrFyIiIhATE4M2bdrQjtagM2fOYM2aNVTKDooaLk6CfWkSFuDWZLYoOHv2LEaOHAkDAwN06dIFAHDr1i1kZmbi6NGj/J7ntGRkZMDDwwPXr18XuM6lv7OlpSX69+8v1Jdm7ty5iIqKotYnEACsra3h7OyMMWPGcKY0WX2kpaX5JdTmzJmDZ8+eISwsDPfv34eNjU29mz0bU3V1Naqrq/m9kg4cOIDr16/D0NAQU6ZMgYSEBJVcly9fhrW1NZo2bcr59iWenp6YMGECbGxsqOaoT0REhMDPtZuCQkNDERAQAA8PD0rJPl/ysi6ulL90d3f/7OPBwcGNlKR+dTdYATV/67t37yI1NRWTJk3Cxo0bKSUT9PbtW/7moNp7Fa5sIq/7fj1mzBiYmJhg0aJFePToEYyMjKiUl1RWVkZGRgZUVVVFosR8eXk5/P39sW3bNlRWVgIAxMXFMW3aNKxatYr6Asv69evRpEkTzJw5ExcvXsTQoUNBCEFFRQXWrVsHb29vqvkA4VYCtRti7ezs4O7uzv+8pqG2f3RycjJMTEwEslRVVSE3NxcDBgzAoUOHqGWsRQhBbGwswsPDcfToUVRXV8PR0ZH6e3UtJycnWFlZ8Rfx//jjDwwbNgwXLlxAhw4dGm0DGFvg+8569uwJAwODehuT5uTkUG9MKgratWuHn376SWigs27dOuzcuZN/qo+msrIy+Pn5ITg4GBUVFQCApk2bwsPDA2vWrBFq3NzYrl69iu3btyMnJweHDx9Gy5YtsWfPHujq6vJPNXANF+uQT58+HTExMVi6dGm9jXydnZ2p5ps0aRJ69uwJT09Pqjk+Z/jw4Zg4cSJGjhxJOwrzHXGxX1Itrr+ORUVISAhcXV2FrldWVmLBggVYuXJl44dqwJYtWzB79myoqakhJiaG0xsMgJqenxYWFigtLW30f7vuLvwv4cIufC5Ogn1pArsuWpPZKSkpMDU1hZiYGFJSUj77u1w5rfno0SNs27aNP+5v164dpk6dyokTfLWLF3PnzoWmpqbQAq+FhQWlZB9JSkri3r17MDQ0FLiekZEBc3NzfPjwgVIy0VG3P66lpSVmz56NiRMnIjs7GxYWFigpKaEdUSR8+PABKSkp9Z52pd0vfNiwYYiMjISamhrGjRsHZ2dnqlU7vkZ4eDgOHjwotADINGzEiBECP1dUVCA1NRXFxcWws7Ojfhq3IYsXL0ZJSQnWrl1LOwrnmZubw9PTEyNGjICpqSnOnz+Pbt264c6dOxg8eDCeP3/e6JnExMTw/PlzqKurQ09PDwkJCVBRUWn0HP9UWVkZsrOzAYBfQYiL8vPzWaWRf6C2L1xAQAB8fX0hKyvLf0xCQgI6OjoYOXIktc1BDUlMTISHhwenejQXFRXhw4cPaNGiBaqrq7F69Wr+Bqv58+dDSUmpUXKwBb7vjOuNSet69eqVwIkVNTU1yolqNGvWDPfv3xeakMvKyoKpqSmnbkhLS0sFPvxoL+wBwNGjRzFx4kQ4Oztjz549SEtLg56eHjZv3oyzZ89ytvlsVVUV7t27B21t7UZ7Q/wSrjfyXb58OTZs2IDBgwdztiTK69evMWnSJHTu3BmmpqZCGWnf2IuK7Oxs7N69G9nZ2di4cSPU1dVx7tw5aGlpwcTEhHY8SEtLIzk5ud6JxPbt21P97OP66xhoeJGl9uSogYEBevXqhSZNmjRyso/k5eXRv39/7Nixg/8e/fDhQzg5OaGwsBB5eXlUcjVU8ubw4cPo0KGDwAln2uVvPi3lWFu2ZfHixUhPT8fdu3cbPZOo7cJvCJsE+7y6k0y1Jw7ruyXkyukzrpORkcGdO3c4U564Pq1bt8a6deswevRogeuHDh2Cn58fCgoKGjXPyZMnv/p3uTI2dHZ2Rnp6OiwtLbF//34UFBRARUUFJ0+exK+//orU1NRGzyRqi/Xnz5+Hi4sLXr9+LfQYV95v3rx5g8OHDyM8PBxXr15F27Zt4ezsDCcnJ+jo6NCOJyQnJwfm5uZsgfl/VF1djWnTpkFfXx+//PIL7Tj1ysrKQufOnam1Y/gUlxfrjxw5AicnJ1RVVcHOzo5fdnflypW4cuUKzp071+iZVFRUcPbsWXTp0gViYmJ48eIFZ+Zcv8a7d+9w6dIlGBkZoV27dlSzVFRUYMCAAdi2bZvQfAOXvH//HoQQ/qJofn4+jh8/DmNjY/Tr149yupo5171796Jfv36cOHnbkMePHyM8PBzh4eFITU1Ft27d4OzsjKlTp9KOxi2N1u3vP4rrjUkJIaSkpIS4ubmRJk2aEB6PR3g8HmnatClxd3cnpaWltOMRfX19fpPwurZu3UoMDAwoJBIt7du3J6GhoYSQmgbI2dnZhBBCEhMTSfPmzWlGE+Dt7U2CgoIIIYRUVlaS7t27Ex6PR2RkZEhMTAzdcP+P6418dXR0GvzS1dWlHY8QQsjJkyeJgoIC/72m7hetJr6KiopESUnpq764IDY2lkhJSZE+ffoQCQkJ/mt65cqVnGhqTgghbdq0If7+/kLX/f39SZs2bSgk+ojrr2NCal7LMjIyhMfjEWVlZaKsrMx/P2zevDnh8XhEX1+fFBQUUMuYlZVFunbtSlq2bEmioqLI5s2bibS0NHFyciLFxcXUctnY2HzVl62tLbWMtWrf9+p+8Xg8oqWlRa5fv047nkjLzMzkxHv2uXPnyNWrV/k/b968mVhYWJDx48dTbRafl5dHqqur+d9/7osLdHV1iaurK/nw4YPA9VevXnFifGNlZSXwd+aigIAAoqioSFatWkWuXLlCrly5QlauXEkUFRXJkiVLGj1PfeNALo0N6/PmzRsyffp04uDgQM6dO8e/vnDhQrJs2TIqmXg8Hnnx4gX/+9rPEa4+jwYGBuTnn38mz58/px3lqzx69IisXr2atG3bljRp0oR2HCFlZWXE29ub+ti6LkdHR7Jq1Sqh67///jsZNWoUhURfLz09nWhoaNCO0aCwsDCiqalJOwYhpGZ8o6amxun3m2fPnpHExERSVVXFvxYfH08ePHhAJc/kyZNJs2bNiI6ODhETEyNaWlpEV1e33i8uGD16NPnjjz8IITXvNYaGhkRcXJw0bdqUHDlyhHI6QlRVVUlGRgbtGJ/Vt29fsnXrVkJIzRhCXV2dtGrVikhKSpItW7ZQTlejWbNmJCcnh3aMem3bto306tWLNGnShJiYmJAVK1Zw5t6krrdv39b79e7dO/L33383Wg62wPedeXl5kVatWpEDBw6QgoICUlBQQPbv309atWpFvL29accjhBDy008/ET09PXL27Fn+f8QzZ84QfX19MnXqVNrxyJYtW4iEhASZOnUqCQsLI2FhYWTKlCmkWbNm9S780VBSUkLmz59PunXrRvT19Tn1AS0lJUVyc3MJIYILfNnZ2aRZs2YUkwlq2bIlSUhIIIQQcvz4cdKiRQvy8OFDMn/+fNK9e3fK6WqYmZmR2NhYQggh9vb2xNfXlxBCyMaNG0nLli1pRhMZ2traZPr06Zy6sQ8JCeF/BQYGEiUlJTJu3DiyceNGsnHjRjJu3DiipKRE1q1bRzsqIYSQrl27ksDAQEKI4Gs6Pj6eM/8Pz5w5QyQlJYmpqSnx8PAgHh4exMzMjEhKSpIzZ85QzSYKr+Pw8HBiY2NDsrKy+NcyMzOJnZ0dOXDgAHn06BGxtramvqBbVVVFvLy8iJiYGBEXFyfh4eFU84ia2NhYga8rV66QBw8ekIqKCtrRRB5XJsFMTU3573kpKSlEQkKCzJs3j3Tt2pW4urpSTic6eDweMTQ0JJ06dSLPnj3jX3/+/DknJhKjo6NJt27dSExMDHn9+rXQDT4XVFdXk3Xr1pGWLVvyJ2FbtmxJNmzYwF/sZUSPqC3Wy8nJCYxtuKy8vJwcP36cjBw5kkhKSpIWLVpQzfPphkRFRUXSpEkTIicnRyIiIqhmq0tVVZWkpKQIXU9JSeHMBveGnDlzhqiqqtKOQUaMGCHwNXz4cNKlSxfSpEkTsnjxYtrxCCGis1ifmZlJzp8/T8rKygghhPrn3blz58gff/xBeDweWbp0KdmwYUO9X1zQvHlzcvfuXUIIIfv27SMGBgaktLSUbNmyhbRv355yOkJmzZpF5syZQzvGZ6moqJDU1FRCCCE7d+4k5ubmpKqqihw6dIi0bduWcroaHTt2JBcvXqQdo16tWrUi/v7+/P+HXFXfpt26X1paWmThwoUCmw2+Sw5CWInOb61uqQyuNyYFAFVVVRw5ckSomXRMTAzGjBlDvWE4ABw/fhyBgYECfTf8/f0xbNgwyslqjB8/HpcvX8bEiRPr7b1Bs8Grnp4eduzYgT59+kBOTg7JycnQ09NDWFgYVq1ahbS0NGrZ6pKUlERWVhZatWqFn376CdLS0tiwYQNyc3NhYWEhVMqMBq438m2oNF3dsn7Dhg2DsrJyIyf7SE5ODnfv3hUok8clI0eOhK2tLWbMmCFwffPmzbh48SJOnDhBJ1gdsrKyuHfvHnR1dQVe03l5eWjbti1nyhY/fvwYW7ZsQXp6OgDu9Evi+usYqCnxfPToUaG+L0lJSRg5ciRycnJw/fp1jBw5Es+ePaMTEsCpU6fg4eGBNm3a8Ps4hYWFoUWLFtQyiaK0tDQUFBSgvLxc4DqN8kYNfY7Uh3aJUwBwdHQU+Jn8f5nT27dvY8GCBVi0aBGlZDVkZWWRmpoKHR0dLF68GKmpqThy5AgSExMxaNAgKj1gPhUWFvbZx11cXBopScOaNGmCzMxM+Pn5IT4+HidOnECnTp3w4sULtGjRgnpZPzExMQAQGv8TQjhTdrCuv/76C0DNmIz5PFEqgXnlyhV0794dTZs2FbheWVmJ69evo1evXpSSfeTu7g5ra2t4eHjQjtKgmJgYhIeH4+jRo6iuroajoyOcnZ1hZ2cn9BpvTCEhIQL/vpiYGNTU1NClSxfOtLMAalrU3L17F0ZGRgLXa8vbvn//nlKyjz4d69SOHc6cOYNJkyZh8+bNlJLVcHV1rfdvbWdnx4myfkBNqf6kpCTO3tMXFhZizJgxiImJAY/HQ2ZmJvT09ODu7g4lJSUEBgZSzefm5oZNmzZx+nNYSkoKGRkZaN26NVxcXNCiRQusWrUKBQUFMDY2pl4W2MvLC2FhYTA0NETHjh2F2iNx4T5FWloa6enp0NLSwpgxY2BiYoJFixbh0aNHMDIy4kTLrvPnz2PevHlYunRpvc+jvLw8pWQfx9FcFxYWht9++w2urq7o3LkzAODWrVsIDQ3F/Pnz8erVK6xduxb+/v749ddfv1uOpl/+FeafsrS0xLNnz6Curo62bdsiISEBK1eu5Gxj0rKyMjRv3lzourq6OifecICaRsifNkPmknPnzuHMmTOwtramHUXI5MmT4e3tjeDgYPB4PDx9+hQ3btyAn58fFixYQDseX/PmzZGWlgZNTU2cP38eW7duBVDz/5Nmn6laFRUVOH36NLZt2wYA6NOnD9LT0znVyDcpKQmJiYmoqqri31RlZGSgSZMmaNu2LbZs2QJfX19cu3YNxsbGVDI6OjoiJiaGszcDkZGR+P3334WuDxgwAHPnzqWQSJiioiKePXsm1CsrKSkJLVu2pJRKWKtWrbBixQraMYT4+Pjwv+/Tpw8ePHjA78PHhdcxADx79oy/KaiuyspK/oJAixYt+JO0NEyZMgWhoaFYvnw5Zs+ejRcvXsDd3R1mZmbYunUrxowZQy2bqMjJyYGjoyNSUlIEeqDV3sjQWBRISkr6qt/jys2WgoKCwM9iYmIwMjLCkiVLODEJJiEhwR9LX7x4kb9YpqyszImNS4DwJrSKigqUlZVBQkIC0tLSnFjgI4RAVlYWx44dw7x589C7d2/s2LEDffv2pR0NQM2CANfl5uaisrIShoaGAhOKmZmZEBcXp95b7PLly1i7di1/M6exsTH8/f3Rs2dPqrnat2/P71fZvn17oX6VtT9zYSHX1taWPwdR19u3b2Fra0s9H1CzYW706NG4evUqJ/uFt2zZEkVFRRgwYAB27NiBoUOHcmJDNlCz6CMKzMzMcPDgQSxcuFDg+oEDB6jdf37q07FO7QJaYGAg3N3dKaX6KCQkhHaELxo1ahRiY2M5e0/v4+MDcXFxFBQUCPSLGzt2LGbPnk19gW/37t1U//2v0bp1a9y4cQPKyso4f/48Dhw4AKCmT6mkpCTldEBqaio6dOgAoGbOqy6u3KcYGBjgxIkTGDFiBCIjI/nzEC9fvqS6cFbXoEGDANRsLK37vHFhbMPj8VBcXIxdu3YJjA89PDyE7gFpCg0NRWBgoMD8x9ChQ2FmZobt27cjOjoaWlpaWL58OVvgEzWKiorIzc2Furo68vLyUF1dDWlpaZiZmdGOVq9u3bph0aJFCAsL479Rv3//HgEBAejWrRvldMCkSZPg4eHBiV2HDVFSUqJ6Kupz5s6di+rqatjb26OsrAy9evVCs2bN4OfnBy8vL9rx+Nzc3DBmzBj+Ccg+ffoAAOLj49G2bVvK6WpO3n66c1dbWxva2tqUEgmrPZ23e/du/oDh7du38PT0RI8ePTB58mQ4OTnBx8cHkZGRVDK2adMG8+bNw7Vr1zh5Y6+iooKIiAj4+voKXI+IiICKigqlVILGjRuHOXPm4PDhw+DxeKiurkZcXBz8/Pw4MRFbq7i4GLdu3aq38TqXcuro6FCf2PyUra0tpkyZgqCgIFhaWgKomYyYNm0a7OzsAIB/ipOWuLg4xMfHw8LCAgCgoaGBs2fP4s8//4S7uztb4PsK3t7e0NHRwcWLF6Grq4v4+HgUFRXB19cXa9eupZJJFBYq6uL6BEmPHj0we/ZsWFtb49atWzh48CCAmomIVq1aUU5X482bN0LXMjMzMW3aNPj7+1NIJKzuhMPKlSthYmKCyZMnY/z48RRTfdS7d2/aEb7I1dUV7u7uMDQ0FLgeHx+PoKAgxMbG0gkGYO/evXBzc4OjoyN/HBgXFwd7e3uEhITAycmJWrbc3Fyoqanxv+eyhna6FxYWCu3Ip2X//v2IioqCpKQkYmNjBfLyeDzq9wGLFy/G6NGjoaioSDVHfRo6QVpbqUVLS4sTi5ELFiyAo6MjsrOz+WPW6Oho7N+/H4cPH6acrgbXxzp6enpISEgQuvcsLi5Ghw4dkJOTQynZR1xfrI+KikJkZKTQWMvQ0BD5+fmUUomWWbNmwdnZGbKystDW1uZXfLty5Qr1ue2qqioEBATAzMyMUyeYP7Vw4UL+/Ju9vT1/fj0qKop/j08bl98Pb9++jf79+0NKSop/Mm79+vVYsWIFoqKi+Au8tF2/fp1/EKQuS0tL3LhxA0DNPWFBQcH3DfJdC4D+R4la89SUlBTSokULoqKiQuzs7IidnR1RUVEhLVu25NcLpmnYsGFEXFycGBgYkOXLl5PHjx/TjiRkz549ZNSoUaS0tJR2lAb9/fff5P79+yQ+Pp789ddftOPU6/Dhw2TdunXk0aNH/GshISHkxIkTFFN9xPU63y1atCD3798Xup6amsrvG3Hnzh2ioqLS2NH4dHR0Gvziwnvi7t27SZMmTciQIUPI0qVLydKlS8mQIUNI06ZNye7du2nHI4TUvJY9PT1J06ZNCY/HI+Li4kRMTIxMmDCBVFZW0o5HCCHk5MmTRE5OjvB4PKKgoEAUFRX5X0pKSrTjkYsXL5LBgwcTPT09oqenRwYPHkwuXLhAOxbfs2fPSJ8+fQiPxyMSEhJEQkKCiImJkb59+/J7XVy6dIlERkZSy/jhw4cGH0tPT2/EJKJLRUWFJCcnE0IIkZeX5z9v0dHRnOhtIQpu3bpFbt68KXT95s2b/L6+NOXn55PBgwcTc3NzEhQUxL8+a9Ys4uXlRTHZlyUkJBAjIyPaMQghNb0tXrx4IXDt+vXrpHnz5tR68CUnJ/N7aSQnJ3/2iwvk5ORIZmam0PXMzEyioKDQ+IHqaNu2bb19jgMDAznTo4bLavt0iYmJkUGDBgn07nJwcCA6Ojqkf//+tGMSQmp6Oi1fvvy796H5Fh49eiRwT0pb3R4/tX006/b4adasGXFxcSHv37+nHZWcPn2adO/enUhLSxMVFRVia2vL73/NBTk5OSQjI0PoekZGBsnNzW38QJ+o7zOPkJq+sxISEhQSCQsKCiJNmzYlsrKyRFtbm3P39LKysvy/cd2e9QkJCURZWZlmNJGSkJBAjh07JjB/ePr0aXLt2jWKqWo0a9aM5OTk0I7xRc+ePSOJiYkCn3vx8fHsfvkr9OjRg7i6ugr0p6+oqCCTJk0iPXv2pJhMkKGhYb3zxHPmzCFt2rQhhNS8lr53L1/Wg+87OX/+PLKysjBz5kwsWbKkwdrKXOj1A9SUQdy3b59AryRnZ2dISUlRTlbj1atX2LNnD0JDQ5GWloY+ffrAw8MDw4YNE9otRIOlpSWys7NBCIGOjo5QpsTExEbN4+joiJCQEMjLywv1p/mUrKwsTExMMHXqVE4dcwZqdqlxaQcl1+t8y8rK4vTp00L9NGNjYzF06FD89ddfyMnJQfv27TlRGox8Uo6OK+Lj47Fp0yaBnp8zZ85Ely5dKCcTVFBQgNTUVJSUlMDS0lJoVz5Nbdq0waBBg7BixQpOlaQGgC1btsDb2xujRo3i76K7efMmjhw5gvXr12P69OmUE36Unp7OLzliZGQk1M+EtuzsbOzevRvZ2dnYuHEj1NXVce7cOWhpacHExIR2PM5TUlJCYmIidHV1oa+vj6CgINja2iI7OxtmZmZUyqT/k/HDsWPHGilVwzp37oxffvkFo0aNErh+7Ngx/P7774iPj6eUTPTdvXsXvXr14sR4oSEvXrxAeno6lRN0YmJi/NKNYmJiQqUba9Eub1RLQUEBsbGxQjvG79y5AxsbG6oln5s1a4b79+/DwMBA4HpWVhZMTU0501sYAB4+fIg//vhDYIzo5eVF9fPZzc0NQE2JqDFjxgjcv0tISEBHRweTJ0+GqqoqrYh8ysrKSEhI4GxZv+rqaixbtgyBgYH8/lJycnLw9fXFb7/9xu+3SUNERATmzJkDf39/gR4/gYGBWLRoESorKzF37lyMHTuWWhUAUdG7d2+4u7tj0qRJAtf37t1L9UTzyZMnAQDDhw9HaGiowNxMVVUVoqOjceHCBTx8+JBKvro0NDQwc+ZMzJ07l+rroiGDBg1Cx44dsXTpUsjJySElJQXa2toYN24cqqurceTIEdoRmf+RlZUVfv/9d9jb29OO0iB3d3ds3LhRaD2gtLQUXl5eCA4OppRMWFlZWb094Wm2MJGSkkJSUpJQRbe0tDRYWVlxpqXYyZMnMXr0aLRt2xadOnUCUHP6MD09HUeOHMGQIUOwdetWZGZmftc5Y7bA951xvXlqRUUF2rZti9OnTwvUpuayxMRE7N69G0FBQZCVlcWECRPw888/U53cDggI+OzjixYtaqQkNer+v6u96WvI33//jRs3bsDMzIw/qKTh999/h46ODsaOHQsAGDNmDI4ePQpNTU2cPXuWE72xbG1tG3yMx+Ph0qVLjZhGmLOzM27cuIHAwED+B0tCQgL8/PzQvXt37NmzBwcOHMDatWtx+/Ztajl37dqF9evXIzMzE0BNqYxZs2bB09OTWiZRxdVFUhkZGdy7dw96enq0owhp1aoV5s6dixkzZghc//PPP7FixQo8efKEUjLRcvnyZQwcOBDW1ta4cuUKHjx4AD09PaxatQq3b99mN85foWfPnvD19cXw4cPh5OSEN2/eYP78+dixYwfu3LmD1NTURs/0T8YPXCiPKSsri5SUFKH3mtzcXJibm1NdtABqJhE9PDwwevRozmya+9SnYz9CCJ49e4bNmzejdevWOHfuHKVk3Jafnw8tLS3weLwvlvviQjn3oUOHQkpKCvv37+f3tq6qqsLYsWNRWlpK9e9sYGAAf39/TJkyReD6tm3bEBgYyB8v0nb06FGMGzcOVlZWAhuEEhIScODAAYwcOZJqvoCAAPj5+XGmHGd9fHx8oKam9l170Pwv5s2bh127diEgIADW1tYAgGvXrmHx4sWYPHkyli9fTi1b586dsXTpUvTv31/gemRkJBYsWIBbt27hxIkT8PX1RXZ2NqWUwKNHj8Dj8filEW/duoXw8HAYGxvjp59+oparLnl5eX7/7bqysrJgZWWF4uJiKrlqF8rq2zBS2ys1MDAQQ4YMoRFPANcX61NTU2Fvb48OHTrg0qVLcHBwwP3791FUVIS4uDjO5qZt9uzZX/27tDe3nz9/HvPmzcPSpUvr3XzPhR53TZo0qbc37uvXr6GhoYHKykpKyT569eoV3NzcGhwH0tyk1rx5c+zZs0eor3pkZCRcXFzw4sULSsmE5eXlYfv27fwNGEZGRpgyZUrjtoL5rucDGZHQokULkpaWRjvGV3n69ClZtWoVMTIyIjIyMsTFxYXY29uTpk2b1lvahfk69+/fJ9LS0lQz6OjokLi4OEIIIVFRUURRUZFERkYSDw8P0rdvX6rZRMVff/1FPD09+eX8xMTEiISEBJk8eTIpKSkhhBCSlJREkpKSqGVcsGABkZGRIXPnziUREREkIiKCzJ07l8jKypIFCxZQy1VXZWUlOXLkCL9E57FjxzhT+rJWUFAQMTEx4ZdvNDExITt37qQdi2/EiBHk4MGDtGPUS0ZGpt4yZRkZGURGRoZCovo9evSI/Pnnn2TOnDnEx8dH4IsLunbtSgIDAwkhgqVv4uPjScuWLWlGExnnz58nR48eJYTUlMkzMjIiPB6PqKqqkujoaMrpRIOysjK5fv260PW4uDiiqKhIIZEgb29voqamRuTl5Ymnpye5ceMG7UhCaku91S351rx5czJ+/Hjy9OlT2vH4Dh8+TEaPHk26dOlCLC0tBb5osLS0JEVFRYQQQgICAjhdpp+QmrG+iooK0dfXJ66ursTV1ZXo6+sTNTU1cu/eParZtmzZQiQkJMjUqVNJWFgYCQsLI1OmTCHNmjUj27Zto5qtLj09vXrHqgsXLiR6enoUEokeLy8voqCgQHr16kVmzJjBufGNpqYmiYiIELp+4sSJ715a60skJSXJgwcPhK4/ePCASEpKEkIIyc3NJVJSUo0dTUCPHj1IWFgYIaSmNJ2cnBzp1q0bUVVVJQEBAVSz1ZKXlyeJiYlC12/fvk1kZWUpJBKko6NDXr16RTvGZ82aNYssX76cdowG5efnk8LCQrJs2TIyevRoMnDgQPLbb7+Rp0+fkvz8fNrxOMvGxkbgS15enkhLS/PHWzIyMkReXp7Y2trSjio0dq1bwphW+fZab9++JcXFxYTH45GsrCzy9u1b/ldRUREJDQ0lmpqaVDPWcnJyItbW1iQhIYHIyMiQqKgosmfPHmJkZEROnz5NNZuXlxdp1aoVOXDgACkoKCAFBQVk//79pFWrVsTb25tqtlrl5eXEzs6u3rLPjY2d4GOwYsUKZGRkICgoCE2bNqUdR0hFRQVOnjyJ3bt3IyoqCubm5vD09ISTkxN/V8bx48fh7u6ON2/eUM16584dfskWExMTzjRO/ZKqqiqkpqbCwsKCWgYpKSlkZGSgdevW8Pb2xocPH7B9+3ZkZGSgS5cu1P+2oqSkpITffFtPTw+ysrKUE32kpqaGTZs2Yfz48QLX9+/fDy8vL7x+/ZpSshpZWVkYPHgwHj9+zC+39PDhQ7Ru3RpnzpzhxG6/hQsXYt26dfDy8uLvIL9x4wY2b94MHx8fLFmyhHLCmlOaS5YsgZubW72N1x0cHCglA5ycnGBpaQl/f3+B67UnWw8cOEAp2UfR0dFwcHCAnp4e0tPTYWpqiry8PBBC+DtRaZOVlcW9e/egq6sLOTk5JCcnQ09PD3l5eWjbti2nSqqJkqKiIigpKXHuVC5XjR8/Hs+ePUNERAS/lFVxcTGGDx8OdXV1HDp0iHJCoLKyEidPnkRoaCjOnTsHAwMDuLu7Y+LEiWjevDnteAKqq6sBgHPltjZt2oTffvsNrq6u2LFjB9zc3JCdnY2EhARMnz6dyqkaKSkpZGZmolWrVg3u0Oaap0+fYvPmzUhOToaUlBTMzc0xY8YMKCsr046G48ePIzAwUKD0pb+/P4YNG0Y52UfS0tJISUkROvWTmZkJCwsL6qWiXrx4AT8/P0RHR+Ply5dCJ4C4UCqW69VQJCUlkZKSgjZt2ghcf/jwIdq3b4/3799TSlbTEsTCwgI7duyAhIQEgJp5ksmTJyM5ORlJSUmIi4vDhAkTkJubSy2nkpISbt68CSMjI2zatAkHDx5EXFwcoqKiMHXqVP49Kk1cPtEsKmbOnImwsDBYWFjA3Nxc6F6P9umuhj6XCwsLoa6uzon3Q65bt24dYmNjERoaCiUlJQDAmzdv4Obmxq9CQtPly5c/+ziN8u21aku3N4TH4yEgIAC//fZbI6aqn6amJiIiItC5c2fIy8vj9u3baNOmDU6ePInVq1fj2rVr1LKVl5fD398f27Zt4592FBcXx7Rp07Bq1So0a9aMWra61NTUcP36deotc9gCH4MRI0YgOjoasrKyMDMzEzraTLvHiqqqKqqrqzF+/HhMnjwZ7du3F/qd4uJiWFpaUhvMvnz5EuPGjUNsbCy/Z1xxcTFsbW1x4MABqKmpUcklSlq0aIEjR46ge/fuMDIywrJlyzB69Gg8fPgQnTp14nQPGObrKSoqIiEhQejDLyMjA507d6ZWEqXWoEGDQAjBvn37+BNehYWFmDBhAsTExHDmzBmq+QDuL5ICn58cptGPaNOmTfzv3717h7Vr18La2lqgxFZcXBx8fX0xf/78Rs1Wn86dO2PgwIEICAjgL56pq6vD2dkZAwYMwLRp02hHRKtWrXDo0CF0795dYIHv+PHj8PPzo1oeivn3LC0tv3pxsbH7C9fnyZMn6NWrFwoLC/mbqu7evYvmzZvjwoULaN26NeWEgl6+fIkdO3Zg+fLlqKqqwqBBgzBz5kzY2dlRzcX10tlt27bFokWLMH78eIH3m4ULF6KoqAibN29u9EzdunWDrKwsevTowS+N2NCGqoULFzZyOtHi6emJCRMmCPWQ5ppBgwZh9OjRQuWLd+/ejQMHDiAyMpJSshoDBw5EQUEBZsyYAU1NTaH3ci4tlnJVly5d0KVLF4FxI1DThz0hIQE3b96klAy4fv06HBwcICYmxm9dce/ePVRVVeH06dPo2rUr9uzZg+fPnwttYmtMsrKySE1NhY6ODhwcHGBtbY05c+agoKAARkZGVBdJa92/fx+9e/eGoqIievbsCQC4evUq3r17h0uXLsHU1JRywpoeXZcvX663J9bMmTMppfqI64v1dfvk1pWfnw9jY2OUlpZSSiY6WrZsiaioKKG+6qmpqejXrx+ePn1KKRn3Xb58GYQQ2NnZ4ejRowIbqSQkJKCtrY0WLVpQTPiRvLw8UlJSoKOjA21tbYSHh8Pa2hq5ubkwMTGhtnmpqqoKcXFxMDMzQ7NmzfhzC/r6+pCWlqaSqSE+Pj5o1qwZVq1aRTUHW+BjON9jZc+ePRg9ejQkJSWp5vicsWPHIicnB2FhYfxehmlpaZg0aRIMDAywf/9+ygm5b8aMGTh9+jQMDQ2RlJSEvLw8yMrK4sCBA1i9ejUnJhKZ/52XlxfExcWFdvX5+fnh/fv3+PPPPyklqyEjI4ObN2/CzMxM4HpycjKsra1RUlJCKdlHXF8k5SJdXd2v+j0ej8eJncVycnK4e/cu9PX1oaSkhGvXrsHExATJyckYNmwY8vLyaEeEn58f4uPjcfjwYbRp0waJiYl48eIFXFxc4OLi0ui9Z5lvo25P4Q8fPmDLli0wNjYWWAy/f/8+fv75Z6xcuZJWTAGlpaXYt2+fwKmk8ePHC+0mp+3WrVv8hQB5eXm4urriyZMnCA8Px88//4y1a9dSySUKp8KlpaXx4MEDaGtrQ11dHRcuXICFhQUyMzPRtWtXFBYWNnqmhw8fYtGiRcjOzkZiYiKMjY3rrYTC4/E4M4YtLi7GrVu38PLlS/5pzVouLi6UUtUsPEVGRkJNTQ3jx4+Hs7Mz1aoiddXtUfn06VMsXLgQY8aMQdeuXQHUvCcePnwYAQEBmDp1Kq2YAGrGDlevXq13Myzzda5cuYJBgwZBS0tL4P3w0aNHOHv2LH8xiJa//voL+/btQ0ZGBoCaHj9OTk6Qk5OjmquuLl26wNbWFoMHD0a/fv1w8+ZNWFhY4ObNmxg1ahQeP35MOyIAbp9oTkpKwqBBg1BWVobS0lIoKyvj9evXkJaWhrq6OifuVbiqtofcxo0bMXnyZIGFgKqqKsTHx6NJkyaIi4ujFVFkyMnJ4dSpU0Kbb2JiYuDg4EC9zzVQszC/fft25OTk4PDhw2jZsiX27NkDXV1d9OjRg3Y85Ofno3Xr1pyrjFFXp06dsGzZMvTv3x8ODg5QVFTEypUrsWnTJhw5coTqpl1JSUk8ePDgq+dyaPHy8kJYWBgMDQ3r7QfZWCea2QIfI1JqB4S1TZu5QkFBARcvXkSnTp0Ert+6dQv9+vVjE+5foaKiAhs3bsSjR4/g6urK34m/fv16yMnJcWYXOfPP1W3WXFlZiZCQEGhpafEnR+Lj41FQUAAXFxf88ccftGICqGkYfvr0aXTv3l3gelxcHIYOHYqioiJKyT7i+iIp87/T0NBATEwM2rVrB2NjY6xatQoODg6cWmguLy/H9OnTERISgqqqKjRt2hSVlZVwdnZGSEgIv+QRI7o8PT2hqamJpUuXClxftGgRHj16hODgYErJhKWlpdW7y51mOWCg5sTenj17sHv3bmRmZmLo0KHw9PRE//79+adrrl27hgEDBlB7XYvCqXA9PT0cPXoUlpaWsLKywuTJkzFlyhRERUVh3Lhx1D+bGzopwCWnTp2Cs7MzSkpKIC8vL3C6i8fjUX8O37x5g8OHDyM8PBxXr15F27Zt4ezsDCcnJ+jo6FDL9bWTcjSqE3zK2NgY+/btE5kWEVxTUVGBAQMGYNGiRYiKihIoF/vzzz9z5rQF18XGxmLEiBF49+4dJk2axB8r/Prrr0hPT6deHar277xt2zbq5dQaYmNjgzZt2mDbtm1QUFBAcnIyxMXFMWHCBHh7e8PR0ZF2RAFcmqOrPVl4+fJldOvWjV/OFqg5OaWjowM/Pz/O/u25xMXFBVevXkVgYCA6d+4MoGbext/fHz179kRoaCjVfEePHsXEiRPh7OyMPXv2IC0tDXp6eti8eTPOnj2Ls2fPUs1XV1lZWb33KbWnsWnau3cvKisr4erqijt37mDAgAEoKiqChIQEQkJCMHbsWGrZrKys8Pvvv8Pe3p5ahq/BlRPNbIGPwbJly+Ds7MzZVfHq6mosW7YMgYGB/MkPOTk5+Pr64rfffuPEboiGdkwmJSWhd+/erLwk85/2uQ+8urhQzsPFxQWJiYnYtWuXwEB28uTJ6NixI0JCQqjkEqVFUgBfPPHBypV93vDhwzF48GBMnjwZfn5+iIiIgKurK44dOwYlJSVcvHiRdkS+R48e4d69eygpKYGlpSW7Yf6BKCgo4Pbt20J/08zMTFhZWeHt27eUkn2Uk5ODESNG4N69e+DxeCCECCxc0J5wl5CQgL6+Ptzd3eHq6lpvyfZ3795h2LBhiImJoZBQNE6Fe3p6onXr1li0aBH+/PNP+Pv7w9raGrdv34ajoyN27dpFOyLntWnTBoMGDcKKFSs4V9roU48fP8b+/fsRHByMzMxMft8V5vOioqIQGBiI7du3U10UFWVc6aPzOVzd0AIAhBA8evQISkpKqKys5PftAoC8vDz+CTTauP53VlRURHx8PIyMjKCoqIgbN26gXbt2iI+Px6RJk5Cenk47Iufn6Nzc3LBx40bIy8tTzSHKysrK4Ofnh+DgYFRUVAAAmjZtCg8PD6xZs0bolFJjs7S0hI+PD1xcXATKtyclJWHgwIF4/vw51XwA8OrVK7i5uTXY15P2fUp9ysrKkJ6eDi0tLaiqqlLNcv78ecybNw9Lly6t92Qce30LYgt8DCwsLJCamoouXbpgwoQJGDNmDPUXcl3z5s3Drl27EBAQAGtrawA1u50XL16MyZMnY/ny5ZQT1pSWKS4uxv79+/m7+548eQJnZ2coKSnh+PHjlBNy08mTJzFw4ECIi4sLlMCpDxduWpgfX3FxMSZNmoRTp07xy7tVVFRg2LBh2L17N7/HZmMTpUVSAEK7xysqKpCbm4umTZtCX1+farkyd3f3zz7OhVNJOTk5KCkpgbm5OUpLS+Hr68ufiFi3bh20tbWp5Kq70PwljVWKgvl+NDQ0sGrVKri6ugpcDwkJwZw5c/DixQs6weoYOnQomjRpgqCgIOjq6iI+Ph5FRUXw9fXF2rVrqZZTI4Tg2rVrsLKygpSUFLUcXyIKp8Krq6tRXV3NL4F58OBBxMXFwdDQEFOnTuVEOdbMzEzExMTUW/6SC5taZGRkcO/ePejp6dGO8lkVFRU4c+YM9u7dizNnzkBZWRlPnjyhHUskKCkpoaysDJWVlZCWlhZ6XdA+pSkKuNJHpz71bWgBwN/UwoWJ4urqakhKSuL+/fucXTwDuP13BgQXINu0aYM//vgD/fv3R3p6Ojp27MiJ/nGiMEfHfBulpaUC/c9oL+zVkpaWRlpaGnR0dAQW+HJycmBsbIwPHz7QjghnZ2fk5+djw4YNsLGxwfHjx/HixQv+4vjgwYNpR+S0uhsF6m7grN3QyYXPvU/RPNEs3CiA+c9JTk7G/fv3sW/fPqxduxazZs1C37594ezsjOHDh1Pf5RkaGoqgoCCBBR5zc3O0bNkSP//8MycGD5s3b4aDgwN0dHTQunVrAEBBQQHMzMywd+9eyum4a/jw4fySRsOHD2/w97j65s38eBQVFREREYGsrCyB0jwGBgZUc9E62fFvJSUlCV179+4dXF1dMWLECAqJPnrz5o3AzxUVFUhNTUVxcTHs7OwopRJUdwJWRkYG27Zto5jmo/r+rvWpOwBnRNesWbMwbdo0JCYmCpxoDg4OxoIFCyinq3Hjxg1cunQJqqqqEBMTQ5MmTdCjRw+sXLkSM2fO/Or/s98DIQT29vacnOisu1jP4/EQFBSEqKioek+Fc4GYmBjKy8uRmJiIly9fQkpKCn369AFQs7t36NChVPPt3LkT06ZNg6qqKjQ0NITKX3Jhga9///64ffs2Zxf4YmJiEB4ejqNHj6K6uhqOjo44ffo0Zz6XAe5XJ9iwYQPVf/9HUFlZieDgYFy8eJFqH536eHt7Q1dXF9HR0dDV1cWtW7dQWFjI39DCBWJiYjA0NERhYSHnPvfq4vLfGajZKFl7sr53795YuHAhXr9+jT179sDU1JRqtlqiMEfHfBsyMjKcKCX5KQ0NDWRlZQmdWL927RpnxjqXLl1CREQErKysICYmBm1tbfTt2xfy8vJYuXIlJxb4CCE4cuRIg5vUaJZVFpU5MK6caGYn+BghcXFxCA8Px+HDh/Hhwwfq5SUlJSWRkpKCNm3aCFx/+PAh2rdvj/fv31NKJogQgujoaIFFgdrJB4ZhuEuUTyXt378fDg4OnNlJ9yX37t3D0KFDkZeXRzuKgOrqakybNg36+vr45ZdfaMfBwoULYWtri27dukFSUpJ2HOY/7NChQ9i4caPA2Mbb2xtjxoyhnKyGkpISEhMToaurC319fQQFBcHW1hbZ2dkwMzNDWVkZ1XwmJibYtWsXf+GMK0TtVPj58+cxceJEFBYWCj3GhU1g2tra+PnnnzFnzhyqOT5n165dWLJkCdzc3GBmZiZ0uotmpYyWLVuiqKgIAwYMgLOzM4YOHYpmzZpRy9MQLlcnYL4NrvTRqY+qqiouXboEc3NzKCgo4NatWzAyMsKlS5fg6+tLdUNLXadOncLq1auxdetWzixGfYrLf2cAuH37Nv766y/Y2tri5cuXcHFxwfXr19GmTRsEBQUJtYWhQVTm6Jh/xtHRESEhIZCXl/9ir0fa/TRXrlyJvXv3Ijg4GH379sXZs2eRn58PHx8fLFiwAF5eXlTzATUlJFNSUqCjowNtbW2Eh4fD2toaubm5MDExoX6fAtRsHtm+fTtsbW3RvHlzoY26u3fvppRMdHDlRDNb4GOE3L17F3v37sWBAwdQWFhI/cO5S5cu6NKlCzZt2iRw3cvLCwkJCbh58yalZIKio6MRHR1d764HLpR8EwUNPYc8Ho/1V2G+m09v8hITE1FZWQkjIyMANX2ImjRpgo4dO1K/4fuUvLw87t69y5ldal9y7do1DB06VOgUHRc8fPgQNjY2ePbsGe0o6Nu3L27cuIHKykp06tQJvXv3ho2NDaytrTld6o9hGlvPnj3h6+uL4cOHw8nJCW/evMH8+fOxY8cO3LlzB6mpqVTzicJEpygwNDREv379sHDhQjRv3px2HCGi8Fn8ud3DtBdJd+7cidGjR1Mrg/6/qFudYOLEibTjoKqqCidOnOBvyjAxMYGDgwOaNGlCORnzv+L6hpZadUvFSkhICI1bWanYL3v//j0IIfxKWnl5eTh+/DiMjY3Rv39/yulqiMocHfPPuLm5YdOmTZCTk4Obm9tnf5fGwk9KSgpMTU35Y5rly5dj5cqV/Pe/Zs2awc/PD0uXLm30bPXp1KkTli1bhv79+8PBwQGKiopYuXIlNm3ahCNHjvBLn9KkrKyMvXv3YtCgQbSjiKwWLVpg27ZtQpvlIiIi8PPPPzdaqXm2wMcAAHJzcxEeHo7w8HA8fPgQvXv3hpOTE0aNGgUFBQWq2S5fvozBgwdDS0sL3bp1A1BTkunRo0c4e/Ys1f4qtQICArBkyRJYWVlBU1NTaNcD68H3Zew5ZLhg3bp1iI2NRWhoKL8x/Js3b+Dm5safSOaSuvXmueTTmz1CCJ49e4Y9e/agd+/eCA8Pp5SsYWfPnsWkSZPw6tUr2lEA1JQPio+Px5UrV3D58mVcv34df//9Nzp16oRr167RjscwnBAZGYnS0lI4OjoiKysLQ4YMQUZGBlRUVHDw4EHq5f3YROe3IS8vj6SkJOjr69OOUi8PDw906tQJU6dOpR2FoYAr1QmysrIwaNAgPHnyhL9J7eHDh2jdujXOnDnD2dcP83W4vqGlVmho6GcfnzRpUiMl+bKsrCxkZ2ejV69ekJKS4vd1oq1fv35wdHTE1KlTUVxcjLZt20JcXByvX7/GunXrMG3aNNoRRWKOjvnxNGnSBM+ePYO6ujr09PSQkJAAOTk5ZGVloaSkBMbGxpCVlaUdk2/v3r2orKyEq6sr7ty5gwEDBqCwsBASEhIIDQ3F2LFjaUeErq4uzp07h7Zt29KOIrK4cqKZLfAx6Nq1KxISEmBubg5nZ2eMHz8eLVu2pB2Lr6CgAE2bNsWff/6J9PR0ADUlon7++WdUVlZCS0uLckJAU1MTq1ev5sTOTVHFnkOGC1q2bImoqCiYmJgIXE9NTUW/fv3w9OlTSsnqx9UFPl1dXYGfxcTEoKamBjs7O8ybNw9ycnKUkgmXZK1dfDxz5gwmTZqEzZs3U0pWv4yMDMTExODixYs4ceIEFBQU8Pr1a9qxmP+AqqoqrF+/HocOHUJBQQHKy8sFHufq4lRRURGUlJQ4MUknShOdXObu7g5ra2t4eHjQjlKvlStXYt26dRg8eHC95S9nzpxJKRnTGLhSnWDQoEEghGDfvn1QVlYGABQWFmLChAkQExPDmTNnqOZj/jd1N7RkZmZi6NCh/A0tBw4cgL29Pe2IIqOwsBBjxoxBTEwMeDweMjMzoaenB3d3dygpKSEwMJBqPlVVVVy+fBkmJiYICgrCH3/8gaSkJBw9ehQLFy7kn9Cl7enTp/XO0bVo0YJyMuZbWLZsGZydnYXu62lSUVHB2bNn0aVLF4iJieHFixdQU1OjHeurlZWVIT09HVpaWlBVVaUdB0DNvcr58+cRHBzMKgX9S1w50cwW+Bj89ttvcHZ2hrGxMe0o9aq7S6OuwsJCqKurU++7AdR80Ny6dYvtjPwfsOeQ4QI5OTmcOnUKNjY2AtdjYmLg4OCAv/76i06wBly7dg2dOnXiZK8arvq0JGvdxUd3d3c0bdqUUrKPduzYgdjYWFy+fBl///03evbsCRsbG9jY2MDc3JwTCxfMj2/hwoUICgqCr68v5s+fj99++w15eXk4ceIEFi5cyBYtmEZTVlaG0aNHQ01NjZMLaJ+b/OLxeMjJyWnENB9t2rQJP/30EyQlJYUmHT5F+zkUBVyvTiAjI4ObN2/CzMxM4HpycjKsra1RUlJCKRnzvXBpQ0tdXC8V6+LigpcvXyIoKAjt2rXjb5aMjIzE7Nmzcf/+far5pKWl+YsAY8aMgYmJCRYtWoRHjx7ByMiIejnWiooKDBgwANu2bYOhoSHVLMz3Y2FhgdTUVHTp0gUTJkzAmDFjqC9K/fTTTwgLC4OmpiYKCgrQqlWrBt9XaI29Pt1M/Dnr1q37jkm+zvv37zFixAjExcVBR0dHaIzN+gt/WUMnmgsKCnDu3LlGO9HMFvgYvvLycuTm5kJfX58TE5y1xMTE8Pz5c6EFvvz8fBgbG6O0tJRSso/mzJkDWVlZLFiwgHYUkcWeQ4YLXFxccPXqVQQGBqJz584AgPj4ePj7+6Nnz55fPI3RGD7ty5Cfn8/vy9CvXz/K6Wrs3r0b48aN4+QusLKyMhBCICMjAwD8BYt27dpxpq9F7aKjr68vfv75Z06VGmH+O/T19bFp0yYMHjwYcnJyuHv3Lv/azZs3qU9mi4rq6mpkZWXV26O5V69elFKJll27dmHq1KmQlJSEioqKwGQ2zQU0rtPV1cXt27ehoqLC2UVIUcLl6gRATR+d06dPo3v37gLX4+LiMHToUM6eumYa5ujoiJCQEMjLy8PR0fGzvysrKwsTExNMnTqVaosVUSgVq6GhgcjISFhYWAhUQ8nJyYG5uTn1xXBzc3N4enpixIgRMDU1xfnz59GtWzfcuXMHgwcPxvPnz6nmAwA1NTVcv36dLfD94O7fv499+/bhwIEDePz4Mfr27QtnZ2cMHz6cPxfR2M6fP4+srCzMnDkTS5YsafCz19vbu5GT1fh0M3FiYiIqKyv574cZGRlo0qQJOnbsiEuXLtGIKKD2NPOoUaPQvHlzoQ0jixYtopRMtDx58gRbt27lb2yhcaKZLfAxeP/+PWbMmMGfuM7IyICenh68vLzQsmVLzJ07l0qu2p0PGzduxOTJkwU+QKqqqhAfH48mTZogLi6Oaj6gZvImNDQU5ubmMDc3F9r1wIWdGVzn7e2NsLAw9hwyVJWVlcHPzw/BwcGoqKgAADRt2hQeHh5Ys2YNf1GIJlHoy9C8eXO8f/8eo0ePhoeHh9BkE02i8PydOHECV65cQWxsLB48eABLS0v+Cb4ePXpQu6Fi/ltkZGTw4MEDaGlpQVNTE2fOnEGHDh2Qk5MDS0tLvH37lnZEzrt58yacnJyQn5+PT2+5eDweJ6pQiAINDQ3MnDkTc+fOhZiYGO04AGruA5YuXQoZGZnP7tbm8XjUy70x/w0uLi5ITEzErl27BDapTZ48GR07dkRISAjdgMw/5ubmhk2bNkFOTg5ubm6f/d2///4bN27cgJmZGU6ePNlICYWJQqlYOTk5JCYmwtDQUGCB7/bt2+jfvz8KCwup5jty5AicnJxQVVUFe3t7REVFAagpB33lyhWcO3eOaj4A8PHxQbNmzbBq1SraUZhGEhcXh/DwcBw+fBgfPnzAu3fvqOap+/7IVevWrUNsbCxCQ0OhpKQEAHjz5g3c3Nz4fVVpk5GRQWRkJHr06EE7CgDA0tLyq0+lc+l04YcPH5CSklLvhk4HB4dGycCdY1oMNXPnzkVycjJiY2MxYMAA/vU+ffpg8eLF1Bb4kpKSANSUP7l37x4kJCT4j0lISMDCwgJ+fn5UsgEf89Vq3749AAg1uOZayQyuSklJYc8hQ520tDS2bNmCNWvWIDs7G0DNKRYuLOzVSkxMxPr16wHU3AA2b95coC8DFxaonjx5glOnTiEkJAQ2NjbQ09ODm5sbJk2aBA0NDarZROH5Gz58OIYPHw4AePv2La5evYrDhw9jyJAhEBMTw4cPH+gGZP4TWrVqhWfPnkFLSwv6+vqIiopChw4dkJCQwMoCf6WpU6fCysoKZ86cgaamJhvP/Evl5eUYO3YsZxb3gJr7gNqNQJ/eE9RF82/+tWWi2CLkv/Pu3TtcunQJRkZGaNeuHe042LRpEyZNmoRu3brxN0pWVFRg2LBh2LhxI+V0zL+xe/fuer9vSFpaGjp16vQ9I33R5cuXcfPmTf7iHlDTimPVqlWwtrammOyjnj17IiwsDEuXLgVQ8x5YXV2N1atXC52+oWHUqFHo0aMHnj17BgsLC/51e3t7jBgxgmKyjyorKxEcHIyLFy+iY8eOQvfKbHP2j0dGRgZSUlKQkJDgRNuSr3lPpC0wMBBRUVH8xT0AUFJSwrJly9CvXz9OLPC1bt0a8vLytGPw1c6BADWLZlu2bIGxsTG/9OXNmzdx//59/Pzzz5QSCjt//jxcXFxQWFhIdUMnO8HHQFtbGwcPHkTXrl0FdjBlZWWhQ4cOnNiZsXHjRk696TAMw9DC9b4Mn3rx4gX27t2L0NBQpKenY8CAAfDw8MDQoUOpTNaKyvNXWFiIy5cvIzY2FrGxsbh//z6UlJTQs2dPHD9+nHY85j9g7ty5kJeXx6+//oqDBw9iwoQJ0NHRQUFBAXx8fNiu7a8gIyOD5ORkGBgY0I4i0nx8fKCmpoZff/2VdhSR8rUT1TwejxNlorhuzJgx6NWrF2bMmIH379/DwsICeXl5IITgwIEDGDlyJO2IAGpKJKalpQEAjI2N2fvPf0hVVRVSU1MFFoUamyiUik1NTYW9vT06dOiAS5cuwcHBAffv30dRURHi4uI4UUaU6z73+cI+U34cubm5CA8PR3h4OB4+fIjevXvDyckJo0aNoloKWFTIycnh1KlTsLGxEbgeExMDBwcHTiyUnjlzBn/88Qe2bdsGHR0d2nEEeHp6QlNTk78Zo1bt3E1wcDClZIIMDQ3Rr18/LFy4EM2bN6eWgy3wMZCWlkZqair09PQEFviSk5PRq1cvVoKJYRiGQ0ShL8On4uPjERwcjNDQUGhqauLNmzdQUlLC7t27hQa835soPH9mZmZ48OABlJSU0KtXL9jY2KB3794wNzenHY35D7t58ya/38rQoUNpxxEJdnZ2+OWXXwQqZDD/3MyZMxEWFgYLCwtWxp2hpm7frvDwcCxatAjJyckIDQ3Fjh07PnuSs7Hs2rUL69evR2ZmJoCaSadZs2bB09OTcjLmv0JUSsUWFxfjzz//RHJyMkpKStChQwdMnz4dmpqatKMxDCd07doVCQkJMDc3h7OzM8aPH4+WLVvSjiVSXFxccPXqVQQGBgq8H/r7+6Nnz578Nlk0KSkpoaysDJWVlZCWlhYaY9PclKGgoIDbt28L9frMzMyElZUVZ9Yq5OXlkZSURH1zCCvRyfBLB3l5eQH4WEomKCiIfwyWYRiG4YaFCxfCyckJPj4+sLOz479PR0VFwdLSknK6j168eIE9e/Zg9+7dyMnJwfDhw3H69Gn06dMHpaWlWLJkCSZNmoT8/PxGzVX3+bO3t+fk8zd16lT07t0bpqamtKMw/2ErV65E8+bN4e7uDqDmRr9r164IDg7G77//jjlz5lBOyE0pKSn87728vODr64vnz5/DzMxM6KaZLdp/nXv37vHfn1kZd4aWt2/f8ssOnj9/HiNHjoS0tDQGDx4Mf39/yulqxjfr1q2Dl5cXf2xz48YN+Pj4oKCgAEuWLKGckPkvEJVSsZKSkujbty8sLCz4/ZISEhIANF6/pB/F/v374eDgwKmWFsz/zt7eHsHBwTA2NqYdRWRt27YNfn5+cHJy4pd1b9q0KTw8PLBmzRrK6Wps2LCBdoQGSUlJIS4uTmiBLy4uDpKSkpRSCRs1ahRiY2OpL/CxE3wMrl27hoEDB2LChAkICQnBlClTkJaWhuvXr+Py5cvo2LEj7YgMwzBMHc+fP+f3Zagtc3nr1i3Iy8ujbdu2lNMBQ4cORWRkJNq0aQNPT0+4uLgI9OIAgJcvX0JDQ0OoCXFj4Prz96mqqircu3cP2traAjX8GeZ70tHRQXh4uFCZrfj4eIwbNw65ubmUknGbmJgYeDyeUA+GWrWPNWZPBoZh/ndt2rTBsmXLMHjwYOjq6uLAgQOws7NDcnIy7O3t8fr1a6r51NTUsGnTJowfP17g+v79++Hl5UU9H/PfwuVSsefPn8fEiRNRVFREtV/Sj0JeXh53796Fnp4e7SgMw0mlpaXIzs4GAOjr67PF8K+0atUqBAQEYPLkyQInIIODg7FgwQLMnTuXcsIaZWVlGD16NNTU1Ord0Dlz5sxGycEW+BgAQHZ2NlatWiVQomDOnDkwMzOjHY1hGIapR1ZWFrKzs9GrVy9ISUnxJ4y5wMPDA56enp89BU4IQUFBAbS1tRsxmWiYNWsWzMzM4OHhgaqqKvTq1Qs3btyAtLQ0Tp8+3ehlTZn/JklJSTx48AC6uroC13NycmBsbIwPHz5QSsZt/+RUMnv/YxjRsWXLFnh7e0NWVhba2tpITEyEmJgY/vjjDxw7dgwxMTFU8ykqKiIhIUFop3tGRgY6d+6M4uJiOsGY/xyul4rlSr+kH0XdNj/Mj+Xx48c4efIkCgoKUF5eLvAYK4/+4xk8eDCCgoI4Var40KFD2LhxIx48eAAAaNeuHby9vTFmzBjKyT7atWsXpk6dCklJSaioqAjMyfF4POTk5DRKDrbAxzAMwzAipLCwEGPGjEFMTAx4PB4yMzOhp6cHd3d3KCkpITAwkHZEAEB0dDSio6Px8uVLoVN6XGmIzFWtWrXCiRMnYGVlhRMnTmD69OmIiYnBnj17cOnSJcTFxdGOyPwHGBoaYtGiRZgwYYLA9T179mDRokWNdrMiyj4tc1orODgYr169YmVOGUbE3LlzBwUFBejbty9kZWUBAGfOnIGSkpLQaefG5uXlBXFxcaFJVz8/P7x//x5//vknpWTMf0lDpWI3b94MHx8fTpSK5Uq/pB8FW+D7MUVHR8PBwQF6enpIT0+Hqakp8vLyQAhBhw4dcOnSJdoRmW+MvZb/HQ0NDcycORNz587lV4eigfXgYwAA1dXVyMrKqncitlevXpRSMQzDMJ/y8fGBuLg4CgoK0K5dO/71sWPHYvbs2ZxY4FuyZAkCAgJgZWUFTU1NzpwsFBWvX7+GhoYGAODs2bMYPXo02rRpA3d3d071L2F+bJMnT8asWbNQUVEBOzs7ADU3+7/88gt8fX0ppxMN27dvR3h4uNB1ExMTjBs3ji3wMYwIWbJkCfz8/ITaV9jZ2WHNmjXUF/iAml3kUVFR6Nq1K4CaUlYFBQVwcXHB7Nmz+b/HTl4w38vWrVuxc+dOgVKxDg4OMDc3h5eXFycW+LjSL+lHce7cObRo0YJ2DOYbmzdvHvz8/BAQEAA5OTkcPXoU6urqcHZ2xoABA2jHY/5DysvL612r0NLSopRIUHl5OcaOHUt1cQ9gJ/gYADdv3oSTkxPy8/NZDXKGYRiO09DQQGRkJCwsLAR2WeXk5MDc3BwlJSW0I0JTUxOrV6/GxIkTaUcRSdra2ti5cyfs7e2hq6uLrVu3YvDgwbh//z569OiBN2/e0I7I/AcQQjB37lxs2rSJX5ZHUlISc+bMwcKFCymnEw2szCnD/DiaNGmCZ8+eQV1dXeB6YWEh1NXVqd8z29raftXv8Xg8dvKC+W5EoVQsV/olMQyXycnJ4e7du9DX14eSkhKuXbsGExMTJCcnY9iwYcjLy6MdkfnGTE1Nce7cObRu3Zp2FABAZmYm3N3dcf36dYHrXOtl7uPjAzU1Nfz6669Uc7ATfAymTp0KKysrnDlzhp20YBiG4bjS0lJIS0sLXS8qKkKzZs0oJBJWXl7OiZ3sosrNzQ1jxozhfyb36dMHQM1O/LZt21JOx/xX8Hg8/P7771iwYAEePHgAKSkpGBoacuZ9RhS0bt0acXFxQgt8cXFxbLc7w4iYhnodJycnQ1lZmUIiQbR7ADIMAEycOBFbt24VOiW6Y8cOODs7U0olaP/+/YiKioKkpCRiY2OF+iWxBb6vc+TIERw6dKje/myJiYmUUjHfioyMDP/vqqmpiezsbJiYmACoqTbD/HhSU1NpRxDg6uqKpk2b4vTp05xeq6iqqsLq1asRGRkJc3NzoU0jjVU1gS3wMcjMzMSRI0dgYGBAOwrDMAzzBT179kRYWBiWLl0KoOZGtLq6GqtXr/7q3dvfm6enJ8LDw7FgwQLaUUTS4sWLYWpqikePHmH06NH8BZUmTZpg7ty5lNMx/zWysrLo1KkT7RgiiZU5ZRjRp6SkBB6PBx6PhzZt2ghMMFVVVaGkpARTp06lmJBhuIXrpWJ/++03BAQEUO+XJMo2bdqE3377Da6uroiIiICbmxuys7ORkJCA6dOn047HfANdu3bFtWvX0K5dOwwaNAi+vr64d+8ejh07xn9tMz+G4uJi3Lp1q94ymC4uLpRSAXfv3sWdO3c4v8H53r17sLS0BCC8SNqYi5KsRCcDOzs7/PLLL6yOMsMwjAhITU2Fvb09v7m1g4MD7t+/j6KiIsTFxVHrJ1H3hr26uhqhoaEwNzenuotJVD1+/BitWrWq97GbN2+ymyqGERGszCnDiL7Q0FAQQuDu7o4NGzZAQUGB/5iEhAR0dHTQrVs3igkZhjtEoVSssrIyEhISWA++/0Hbtm2xaNEijB8/XqBlxMKFC1FUVITNmzfTjsj8j3JyclBSUgJzc3OUlpbC19cX169fh6GhIdatWwdtbW3aEZlv4NSpU3B2dkZJSQnk5eWFTjQXFRVRy9apUyesX78ePXr0oJZBlLAFvv+olJQU/vfZ2dmYP38+/P39661Bbm5u3tjxGIZhmM94+/YtNm/ejOTkZJSUlKBDhw6YPn06NDU1qWUShRt6UWFsbIxr164JlfyKi4vD4MGDOdG/hGGYr1dSUsLKnDKMiLt8+TK6d+8udK/MMIxo4Uq/JFEmLS2NBw8eQFtbG+rq6rhw4QIsLCyQmZmJrl27orCwkHZEhmG+Qps2bTBo0CCsWLGi3jYwNF26dAnz58/HihUr6l2rkJeXp5SMm9gC33+UmJgYeDweGvrz1z7GpcaVDMMwDPNf4O7ujpSUFMTExEBOTg4AcOXKFQwZMgQBAQHw8fGhnJBhGIZhfnzv3r3jTyC9e/fus7/LJpoYRjTMnDkTYWFhsLCwYJVG/iU9PT0cPXoUlpaWsLKywuTJkzFlyhRERUVh3LhxVE/9MAzz9WRkZHDv3j3o6enRjiKktoTyp2Uu2VpF/VgPvv+o3Nxc2hEYhmGYr1T31PWXsFPXoi8oKAijRo3C0KFDERkZievXr8PBwQHLli2Dt7c37XgMwzAM85+gpKSEZ8+eQV1dHYqKivX2UmETTQwjWrjSL0mU2dnZ4eTJk7C0tISbmxt8fHxw5MgR3L59G46OjrTjMf9Sbc/Zr8EWcX8M/fv3x+3btzm5wBcTE0M7gkhhJ/gYrFy5Es2bN4e7u7vA9eDgYLx69Qpz5syhlIxhGIYBBE9d1x10136E173GJph+DOXl5Rg8eDDKysqQkpKClStXYsaMGbRjMQzDMMx/xuXLl2FtbY2mTZvi8uXLn/3d3r17N1IqhmEYuqqrq1FdXY2mTWvOjBw4cIDfn23KlCmQkJCgnJD5N0JDQ/nfFxYWYtmyZejfvz+/z+yNGzcQGRmJBQsWsIoyP4hdu3ZhyZIlcHNzq7cMpoODA6VkzD/FFvgY6OjoIDw8HN27dxe4Hh8fj3HjxrHTfgzDMJTl5+fzv09KSoKfnx/8/f0FBtuBgYFYvXo1hg8fTikl87+o75TmX3/9hfHjx2Pw4MGYNm0a/zo7pckwDMMwje/Dhw9ISUnBy5cvUV1dLfAYmwRjGOZH5ujoiJCQEMjLyyMsLAxjx45lPYV/YCNHjoStra3QBtPNmzfj4sWLOHHiBJ1gzDdVWwazPlypTlBWVoaCggKUl5cLXGdzIoLYAh8DSUlJPHjwALq6ugLXc3JyYGxsjA8fPlBKxjAMw3yqc+fOWLx4MQYNGiRw/ezZs1iwYAHu3LlDKRnzv6ivN27dn1lvXIZhGIah5/z583BxccHr16+FHmOfzQzD/OgkJCSQn58PTU1NNGnShF++mPkxycrK4u7duzAwMBC4npWVhfbt26OkpIRSMua/4tWrV3Bzc8O5c+fqfZyNuwSxHnwMWrdujbi4OKEFvri4OLRo0YJSKoZhGKY+9+7dE3q/BgBdXV2kpaVRSMR8C+y0PMMwDMNwl5eXF0aPHo2FCxeiefPmtOMwDMM0qrZt22LevHmwtbUFIQSHDh2CvLx8vb/r4uLSyOmYb01FRQURERHw9fUVuB4REQEVFRVKqZj/klmzZqG4uBjx8fGwsbHB8ePH8eLFCyxbtgyBgYG043EOO8HHYPXq1Vi9ejXWrFkDOzs7AEB0dDR++eUX+Pr6Yt68eZQTMgzDMLU6dOgAU1NTBAUF8fsblJeXw9PTE6mpqUhMTKSckGEYhmEY5sciLy+PpKQk6Ovr047CMAzT6K5fv47Zs2cjOzsbRUVFkJOTE+gDX4vH46GoqIhCQuZbCgkJgaenJwYOHIguXboAqGnjdP78eezcuROurq50AzLfzOXLl7F27Vo8ePAAAGBsbAx/f3/07NmTai5NTU1ERESgc+fOkJeXx+3bt9GmTRucPHkSq1evxrVr16jm4xq2wMeAEIK5c+di06ZN/Jq2kpKSmDNnDhYuXEg5HcMwDFPXrVu3MHToUBBC+HXHU1JSwOPxcOrUKXTu3JlyQubfOHny5Ff/LuvzwzAMwzCNy93dHdbW1vDw8KAdhWEYhioxMTE8f/6clej8wcXHx2PTpk38hZ927dph5syZ/AU/RvTt3bsXbm5ucHR0hLW1NYCaan7Hjx9HSEgInJycqGWTl5dHSkoKdHR0oK2tjfDwcFhbWyM3NxcmJiYoKyujlo2L2AIfw1dSUoIHDx5ASkoKhoaGrGEuwzAMR5WWlmLfvn1IT08HUDPYdnJygoyMDOVkzL/1uQbXdbE+PwzDMAzT+MrKyjB69GioqanBzMwM4uLiAo/PnDmTUjKGYZjGlZ+fDy0trXpP8DEMIzratWuHn376CT4+PgLX161bh507d/IXd2no1KkTli1bhv79+8PBwQGKiopYuXIlNm3ahCNHjiA7O5taNi5iC3wMwzAMwzAMwzAMwzAN2LVrF6ZOnQpJSUmoqKgITGzzeDzk5ORQTMcwDPN9paSkfPXv1laZYX4MgwcPRlBQEDQ1NWlHYb6xZs2a4f79+zAwMBC4npWVBVNTU3z48IFSsprThZWVlXB1dcWdO3cwYMAAFBUVQUJCAiEhIRg7diy1bFzEFvgYhmEYRsTs2bMH27dvR05ODm7cuAFtbW2sX78eenp6GDZsGO14DMMwDMMwPxQNDQ3MnDkTc+fO/epT9wzDMD8KMTEx8Hg8fGkKmVUb+fHIyckhOTkZenp6tKMw35iBgQH8/f0xZcoUgevbtm1DYGAgMjMzKSUTVlZWhvT0dGhpaUFVVZV2HM5pSjsAwzAMwzBfb+vWrVi4cCFmzZqFZcuW8W+glJSUsGHDBrbA94PgarNrhmEYhvkvKi8vx9ixY9niHsMw/0m5ubm0IzAM8435+vpi5syZuHv3Lrp37w6gpgdfSEgINm7cSDmdIGlpaXTo0IF2DM5iJ/gYhmEYRoQYGxtjxYoVGD58uMBuutTUVNjY2OD169e0IzL/Iy43u2YYhmGY/yIfHx+oqanh119/pR2FYRiGE9LS0lBQUIDy8nL+NR6Ph6FDh1JMxXxrpqamOHfuHFq3bk07CvMdHD9+HIGBgfyNxe3atYO/vz+VjeOzZ8/+6t9dt27dd0wietgCH8MwDMOIECkpKaSnp0NbW1tggS8zMxPm5uZ4//497YjM/4jLza4ZhmEY5r9o5syZCAsLg4WFBczNzSEuLi7wOJtoYhjmvyInJwcjRozAvXv3BMp21vYmZSU6RV9OTg4ryck0Oltb26/6PR6Ph0uXLn3nNKKFlehkGIZhGBGiq6uLu3fvQltbW+D6+fPn0a5dO0qpmG8pJyen3p2vDg4O7OQAwzAMw1Bw7949WFpaAgBSU1MFHqud1GYYhvkv8Pb2hq6uLqKjo6Grq4v4+HgUFRXB19cXa9eupR2P+QYMDAzQu3dveHh4YNSoUZCUlKQdifkO9PT0kJCQABUVFYHrxcXF6NChA3Jycho1T0xMTKP+ez8StsDHMAzDMCJk9uzZmD59Oj58+ABCCG7duoX9+/dj5cqVCAoKoh2P+QZat26N6OhoGBgYCFy/ePEiK43CMAzDMBSwSSeGYZgaN27cwKVLl6CqqgoxMTE0adIEPXr0wMqVKzFz5kwkJSXRjsj8jxITE7F7927Mnj0bM2bMwNixY+Hh4YHOnTvTjsZ8Q3l5efWeuP3777/x5MkTComYf4st8DEMwzCMCPH09ISUlBTmz5+PsrIyODk5oUWLFti4cSPGjRtHOx7zDYhSs2uGYRiGYRiGYf47qqqqICcnBwBQVVXF06dPYWRkBG1tbTx8+JByOuZbaN++PTZu3IjAwECcPHkSISEh6NGjB9q0aQN3d3dMnDgRampqtGMy/9LJkyf530dGRkJBQYH/c1VVFaKjo6Gjo0MhGfNvsR58DMMwDCOiysrKUFJSAnV1ddpRmG+MS82uGYZhGIZhGIZhAKBnz57w9fXF8OHD4eTkhDdv3mD+/PnYsWMH7ty5I1TGmBF9f//9N7Zs2YJ58+ahvLwcEhISGDNmDH7//XdoamrSjsf8Q2JiYgAg0EOzlri4OHR0dBAYGIghQ4bQiMf8C2yBj2EYhmFESG5uLiorK2FoaChwPTMzkz8YY0Sbp6cnJkyYABsbG9pRGIZhGIZhGIZh+CIjI1FaWgpHR0dkZWVhyJAhyMjIgIqKCg4ePAg7OzvaEZlv5Pbt2wgODsaBAwcgIyODSZMmwcPDA48fP0ZAQADevXuHW7du0Y7J/Eu6urpISEiAqqoq7SjM/4gt8DEMwzCMCOnduzfc3d0xadIkget79+5FUFAQYmNj6QRjvplhw4YhMjISampqGD9+PJydnWFhYUE7FsMwDMMwDMMwjJCioiIoKSmBx+PRjsJ8A+vWrcPu3bvx8OFDDBo0CJ6enhg0aBD/5BcAPH78GDo6OqisrKSYlPnWiouLoaioSDsG8w+JfflXGIZhGIbhiqSkJFhbWwtd79q1K+7evdv4gZhvLiIiAs+ePcOCBQtw69YtdOjQASYmJlixYgXy8vJox2MYhmEYhmEYhuFTVlZmi3s/kK1bt8LJyQn5+fk4ceIEhgwZIrC4BwDq6urYtWsXpYTMt/D777/j4MGD/J9Hjx4NZWVltGzZEsnJyRSTMf8UO8HHMAzDMCJEQUEBsbGxsLS0FLh+584d2NjY4K+//qKUjPleHj9+jP379yM4OBiZmZlslyTDMAzDMAzDMAzDMP+arq4u9u3bh+7du+PChQsYM2YMDh48iEOHDqGgoABRUVG0IzJfiZ3gYxiGYRgR0qtXL6xcuRJVVVX8a1VVVVi5ciV69OhBMRnzPVRUVOD27duIj49HXl4emjdvTjsSwzAMwzAMwzAM8wO7evUqJkyYgG7duuHJkycAgD179uDatWuUkzHfyvPnz9G6dWsAwOnTpzFmzBj069cPv/zyCxISEiinY/4JtsDHMAzDMCJk1apVuHTpEoyMjODm5gY3NzcYGRnhypUrWLNmDe14zDcSExODyZMno3nz5nB1dYW8vDxOnz6Nx48f047GMAzDMAzDMAzD/KCOHj2K/v37Q0pKCklJSfj7778BAG/fvsWKFSsop2O+FSUlJTx69AgAcP78efTp0wcAQAgR2FDOcB9b4GMYhmEYEWJiYoKUlBSMGTMGL1++xF9//QUXFxekp6fD1NSUdjzmG2jZsiUGDRqE169fY8eOHXjx4gWCg4Nhb2/PelswDMMwDMMwDMMw382yZcuwbds27Ny5E+Li4vzr1tbWSExMpJiM+ZYcHR3h5OSEvn37orCwEAMHDgQAJCUlwcDAgHI65p9oSjsAwzAMwzBfp6KiAgMGDMC2bdvYzrkf2OLFizF69GgoKirSjsIwDMMwDMMwDMP8hzx8+BC9evUSuq6goIDi4uLGD8R8F+vXr4eOjg4ePXqE1atXQ1ZWFgDw7Nkz/Pzzz5TTMf8EW+BjGIZhGBEhLi6OlJQU2jGY72zy5Mm0IzAMwzAMwzAMwzD/QRoaGsjKyoKOjo7A9WvXrkFPT49OKOabExcXh5+fH9LS0lBQUICTJ08CAPT19SknY/4ptsDHMAzDMCJkwoQJ2LVrF1atWkU7CsMwDMMwDMMwDMMwP5DJkyfD29sbwcHB4PF4ePr0KW7cuAE/Pz8sWLCAdjzmG8nJyYGjoyPu3bsHoKb3HgB+WxDWh090sAU+hmEYhhEhlZWVCA4OxsWLF9GxY0fIyMgIPL5u3TpKyRiGYRiGYRiGYRiGEWVz585FdXU17O3tUVZWhl69eqFZs2bw8/ODl5cX7XjMN+Lt7Q0dHR1cvHgRurq6uHXrFgoLC+Hr64u1a9fSjsf8AzxSuzzLMAzDMAzn2draNvgYj8fDpUuXGjENwzAMwzAMwzAMwzA/mvLycmRlZaGkpATGxsb8Hm3Mj0FVVRWXLl2Cubk5FBQUcOvWLRgZGeHSpUvw9fVFUlIS7YjMV2In+BiGYRhGhMTExNCOwDAMwzAMwzAMwzDMD0xCQgLGxsa0YzDfSVVVFeTk5ADULPY9ffoURkZG0NbWxsOHDymnY/4JtsDHMAzDMCIoKysL2dnZ6NWrF6SkpEAI4ddKZxiGYRiGYRiGYRiG+RqOjo5f/bvHjh37jkmYxmJqaork5GTo6uqiS5cuWL16NSQkJLBjxw7o6enRjsf8A2yBj2EYhmFESGFhIcaMGYOYmBjweDxkZmZCT08PHh4eUFJSQmBgIO2IDMMwDMMwDMMwDMOICAUFBdoRmEY2f/58lJaWAgCWLFmCIUOGoGfPnlBRUcHBgwcpp2P+CdaDj2EYhmFEiIuLC16+fImgoCC0a9cOycnJ0NPTQ2RkJGbPno379+/TjsgwDMMwDMMwDMMwjIghhODRo0dQU1ODlJQU7ThMIysqKoKSkhKrDiVixGgHYBiGYRjm60VFReH3339Hq1atBK4bGhoiPz+fUiqGYRiGYRiGYRiGYUQZIQQGBgZ4/Pgx7SgMBcrKymxxTwSxBT6GYRiGESGlpaWQlpYWul5UVIRmzZpRSMQwDMMwDMMwDMMwjKgTExODoaEhCgsLaUdhGOYrsQU+hmEYhhEhPXv2RFhYGP9nHo+H6upqrF69Gra2thSTMQzDMAzDMAzDMAwjylatWgV/f3+kpqbSjsIwzFdgPfgYhmEYRoSkpqbC3t4eHTp0wKVLl+Dg4ID79++jqKgIcXFx0NfXpx2RYRiGYRiGYRiGYRgRpKSkhLKyMlRWVkJCQkKoF19RURGlZAzD1Kcp7QAMwzAMw3w9U1NTPHz4EH/++Sfk5ORQUlICR0dHTJ8+HZqamrTjMQzDMAzDMAzDMAwjojZs2EA7AsMw/wA7wccwDMMwIubDhw9ISUnBy5cvUV1dLfCYg4MDpVQMwzAMwzAMwzAMwzAMwzQWdoKPYRiGYUTI+fPnMXHiRBQVFeHTPTo8Hg9VVVWUkjEMwzAMwzAMwzAM86P48OEDysvLBa7Jy8tTSsMwTH3EaAdgGIZhGObreXl5YcyYMXj69Cmqq6sFvtjiHsMwDMMwDMMwDMMw/1ZpaSlmzJgBdXV1yMjIQElJSeCLYRhuYQt8DMMwDCNCXrx4gdmzZ6N58+a0ozAMwzAMwzAMwzAM8wP55ZdfcOnSJWzduhXNmjVDUFAQAgIC0KJFC4SFhdGOxzDMJ1gPPoZhGIYRIe7u7rC2toaHhwftKAzDMAzDMAzDMAzD/EC0tLQQFhYGGxsbyMvLIzExEQYGBtizZw/279+Ps2fP0o7IMEwdbIGPYRiGYURIWVkZRo8eDTU1NZiZmUFcXFzg8ZkzZ1JKxjAMwzAMwzAMwzCMKJOVlUVaWhq0tLTQqlUrHDt2DJ07d0Zubi7MzMxQUlJCOyLDMHU0pR2AYRiGYZivt3//fkRFRUFSUhKxsbHg8Xj8x3g8HlvgYxiGYRiGYRiGYRjmX9HT00Nubi60tLTQtm1bHDp0CJ07/197dxZidf33Afx9nBS3KXXGdMJS0zIRR2k1KVAvNIoyhSwSIbFo1bKGFssVhLJc0bpQTKHFBSqyEKlRUVQaNS3BshpSKwyrOzPRHJ+Lh//wP1ppPT2NY6/X1fl9t/P5nds3n/O9NqtWrUqbNm0aujzgJDr4AKAR6dixY8aNG5enn346TZq4ShcAAAD4e8yePTslJSUZN25cPvzww9x66605ceJEjh07llmzZuXRRx9t6BKB/yLgA4BGpF27dtm6dWu6devW0KUAAAAA57B9+/Zl+/bt6d69eyorKxu6HOAkAj4AaETGjx+f9u3bZ8KECQ1dCgAAAHCOqa6uTnV1dQ4ePJi6urqiucWLFzdQVcBvcQcfADQix48fz4wZM7JmzZpUVlamadOmRfOzZs1qoMoAAACAxmzq1KmZNm1arr766lRUVKRQKDR0ScAf0MEHAI3IwIEDf3euUChk7dq1/2A1AAAAwLmioqIiM2bMyKhRoxq6FOAMCPgAAAAAAOBfrqysLDU1NenWrVtDlwKcgSYNXQAAAAAAANCw7r333rzxxhsNXQZwhnTwAQAAAADAv9Djjz9e/7muri5Lly5NZWVlKisr07Rp06K1s2bN+qfLA/6AgA8AAAAAAP6FBg4ceEbrCoVC1q5d+/9cDfBnCPgAAAAAAACgEXEHHwAAAAAAADQiAj4AAAAAAABoRAR8AAAAAAAA0IgI+AAAAAAAAKAREfABAADwl3z++efp169fmjdvnr59+zZ0OQAAAP8aAj4AAAD+ksmTJ6dVq1bZs2dPqqur/5Yzu3Tpkjlz5vwtZwEAAJyrBHwAAAD8JbW1tbnhhhvSuXPnlJWVNXQ5RY4ePfqP7gMAAPgnCfgAAADOYgMGDMjYsWPz2GOPpW3btunQoUMWLly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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Aqui estan todas las columnas donde los valores nulos no superan ni el 5% de los datos\n",
            "En la mayoria de datos existe una preferencia de los datos, un valor prevalece sobre los demas\n",
            "Por este motivo utlizare la moda para reemplazar todos estos valores\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SUSTITUCION CON LA MODA DE TODOS LAS COLUMNAS CON NULOS MENORES DEL 5%\n",
        "for column in ['year', 'manufacturer', 'fuel', 'title_status', 'transmission']:\n",
        "    data[column] = data[column].fillna(data[column].mode()[0])"
      ],
      "metadata": {
        "id": "zKMQaFdsxkWN"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[['year', 'manufacturer', 'fuel', 'title_status', 'transmission']].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "7iJFyA7hxsPv",
        "outputId": "db1eab21-9fc3-4234-fbae-bf0834111c51"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year            0\n",
              "manufacturer    0\n",
              "fuel            0\n",
              "title_status    0\n",
              "transmission    0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**NULOS CON PORCENTAJE MAYOR AL 20%**"
      ],
      "metadata": {
        "id": "R-ViTs02ybNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(18,12))\n",
        "\n",
        "plt.subplot(3,2,1)\n",
        "data['condition'].hist() #164357 NA =39%\n",
        "plt.title('HIST CONDICION')\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "data['cylinders'].hist() #174141 = 42%\n",
        "plt.title('HIST CILINDRADAS')\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "data['drive'].hist() #127635 NA = 31%\n",
        "plt.title('HIST CONDUCCION')\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "data['size'].hist() #299320 NA = 72%\n",
        "plt.title('HIST TAMAÑO')\n",
        "\n",
        "plt.subplot(3,2,5)\n",
        "data['type'].hist() #91300 NA = 22%\n",
        "plt.title('HIST TIPO')\n",
        "\n",
        "plt.subplot(3,2,6)\n",
        "data['paint_color'].hist() #128065 = 31%\n",
        "plt.title('HIST COLOR DE PINTURA')\n",
        "\n",
        "plt.tight_layout(rect=[0, 0, 1, 0.95])\n",
        "plt.suptitle('HISTOGRAMAS CON MAYOR PORCENTAJE', fontsize=16)\n",
        "plt.show()\n",
        "plt.close()\n",
        "print('\\nEn los 6 graficos tiene un % de NA muy alto')\n",
        "print('Aunque se observa una preferencia por algunos datos, considero que seria imprudente llenarlo con algun valor estadistic(moda, media)')\n",
        "print('Si opto por un valor estadistico, mi grafico solo le dara mas valor a las preferencias, reduciendo el valor de la competencia')\n",
        "print('Por ese motivo lo sustituire por el valor de \"DESCONOCIDO\"')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "EaDf-j8-ERXL",
        "outputId": "6f29299d-d9b8-4389-ec5b-6a5ffadd0030"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1200 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "En los 6 graficos tiene un % de NA muy alto\n",
            "Aunque se observa una preferencia por algunos datos, considero que seria imprudente llenarlo con algun valor estadistic(moda, media)\n",
            "Si opto por un valor estadistico, mi grafico solo le dara mas valor a las preferencias, reduciendo el valor de la competencia\n",
            "Por ese motivo lo sustituire por el valor de \"DESCONOCIDO\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for column in ['condition','drive','size','type','paint_color']:\n",
        "  data[column].fillna('DESCONOCIDO', inplace=True)\n",
        "\n",
        "data[column].isna().sum()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8jhdMIl7IrT7",
        "outputId": "f9c6da6f-78d0-438f-c96d-47ef8d9d36c1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['cylinders'].fillna('other', inplace = True) # lo sustituyo por este valor\n",
        "data['cylinders'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YovkfNIzOiPi",
        "outputId": "089a2cae-9667-4bcc-ac78-19c4a994ce49"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**COLUMNAS ATIPICAS**"
      ],
      "metadata": {
        "id": "SIduyERoAx8W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#ODOMETER(KM RECORRIDO) , 4313 NA = 1%\n",
        "#==================================================\n",
        "# Lo completare por el promedio de km promedio\n",
        "data['odometer'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "XV7hpmsvA2Z6",
        "outputId": "a943ea5e-e9df-4442-e606-39dbe4cd908c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['odometer'].fillna(data['odometer'].mean(), inplace=True)\n",
        "#Comprobar que lso valores fueron sustituidos\n",
        "data['odometer'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dStuNaLMBH1G",
        "outputId": "0a05882d-103d-42de-9588-564b1e047268"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#VIN 154890 = 37%\n",
        "#===================================\n",
        "#es como su numero de placa, ya que representa un numero alto de valores (37%) reemplzare los NA con \"desconocido\"\n",
        "data['VIN'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssoCfh2FC7EE",
        "outputId": "41ca77f2-05f7-4a30-c015-90daf7155a70"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['3GTP1VEC4EG551563', '1GCSCSE06AZ123805', '3GCPWCED5LG130317', ...,\n",
              "       '2HGES15535H620534', '1FDWF37P64EA24868', 'SAJGX2749VCOO8376'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['VIN'].fillna('DESCONOCIDO', inplace=True)\n",
        "data['VIN'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "duy2tecADL6j",
        "outputId": "333b56cc-1a05-4a2e-fd64-3ab2345fb308"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#MODEL 5028 = 1.2%\n",
        "#===========================================================\n",
        "#SE IMPUTARA EL FABRICANTE + AÑO\n",
        "data['model'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mLqEZwJwDtdT",
        "outputId": "13e553bc-e7e9-4702-ef86-99a3f914754b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sierra 1500 crew cab slt', 'silverado 1500',\n",
              "       'silverado 1500 crew', ..., 'gand wagoneer', '96 Suburban',\n",
              "       'Paige Glenbrook Touring'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['year'] = data['year'].astype(str)\n",
        "modelo = data['manufacturer'] + '' + data['year']\n",
        "data['model'].fillna(modelo, inplace=True)\n",
        "data['model'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4x8Xa9sEUKf",
        "outputId": "c5bf6076-28fe-4d24-e8be-70bbd2398c18"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['year'] = data['year'].astype(float) # una vez tenga mi columna sustituida, devolver el valor de mi columna a su valor original"
      ],
      "metadata": {
        "id": "hR71-2gCFmtH"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oaUZ6wcKB1q",
        "outputId": "5054a7fc-ba11-485f-aaab-5562d8185c14"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 382240 entries, 0 to 413818\n",
            "Data columns (total 20 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   region        382240 non-null  object \n",
            " 1   price         382240 non-null  int64  \n",
            " 2   year          382240 non-null  float64\n",
            " 3   manufacturer  382240 non-null  object \n",
            " 4   model         382240 non-null  object \n",
            " 5   condition     382240 non-null  object \n",
            " 6   cylinders     382240 non-null  object \n",
            " 7   fuel          382240 non-null  object \n",
            " 8   odometer      382240 non-null  float64\n",
            " 9   title_status  382240 non-null  object \n",
            " 10  transmission  382240 non-null  object \n",
            " 11  VIN           382240 non-null  object \n",
            " 12  drive         382240 non-null  object \n",
            " 13  size          382240 non-null  object \n",
            " 14  type          382240 non-null  object \n",
            " 15  paint_color   382240 non-null  object \n",
            " 16  state         382240 non-null  object \n",
            " 17  lat           382240 non-null  float64\n",
            " 18  long          382240 non-null  float64\n",
            " 19  posting_date  382240 non-null  object \n",
            "dtypes: float64(4), int64(1), object(15)\n",
            "memory usage: 61.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "jBWdFeSgKGES",
        "outputId": "8f1cac54-161f-4512-8fd0-c66a23d0d322"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "region          0\n",
              "price           0\n",
              "year            0\n",
              "manufacturer    0\n",
              "model           0\n",
              "condition       0\n",
              "cylinders       0\n",
              "fuel            0\n",
              "odometer        0\n",
              "title_status    0\n",
              "transmission    0\n",
              "VIN             0\n",
              "drive           0\n",
              "size            0\n",
              "type            0\n",
              "paint_color     0\n",
              "state           0\n",
              "lat             0\n",
              "long            0\n",
              "posting_date    0\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>region</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>manufacturer</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>condition</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cylinders</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fuel</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>odometer</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title_status</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>transmission</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>VIN</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>drive</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>size</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>paint_color</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>state</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lat</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>long</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>posting_date</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **PREPROCESAMIENTO**"
      ],
      "metadata": {
        "id": "qfO6SZKzJb0x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyNgI0OnKteD",
        "outputId": "2d15d18a-54fd-4212-ba3a-84767cabefde"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 382240 entries, 0 to 413818\n",
            "Data columns (total 20 columns):\n",
            " #   Column        Non-Null Count   Dtype  \n",
            "---  ------        --------------   -----  \n",
            " 0   region        382240 non-null  object \n",
            " 1   price         382240 non-null  int64  \n",
            " 2   year          382240 non-null  float64\n",
            " 3   manufacturer  382240 non-null  object \n",
            " 4   model         382240 non-null  object \n",
            " 5   condition     382240 non-null  object \n",
            " 6   cylinders     382240 non-null  object \n",
            " 7   fuel          382240 non-null  object \n",
            " 8   odometer      382240 non-null  float64\n",
            " 9   title_status  382240 non-null  object \n",
            " 10  transmission  382240 non-null  object \n",
            " 11  VIN           382240 non-null  object \n",
            " 12  drive         382240 non-null  object \n",
            " 13  size          382240 non-null  object \n",
            " 14  type          382240 non-null  object \n",
            " 15  paint_color   382240 non-null  object \n",
            " 16  state         382240 non-null  object \n",
            " 17  lat           382240 non-null  float64\n",
            " 18  long          382240 non-null  float64\n",
            " 19  posting_date  382240 non-null  object \n",
            "dtypes: float64(4), int64(1), object(15)\n",
            "memory usage: 61.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.select_dtypes(include=['int64', 'float64']).columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4kRvsq2UfdM",
        "outputId": "bf69261b-5213-4652-f6ee-4cf62b10f4d9"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['price', 'year', 'odometer', 'lat', 'long'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Jm-MY9yx4f_",
        "outputId": "7e6ea533-7c7a-4316-e47f-211aeedfcb5a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['region', 'price', 'year', 'manufacturer', 'model', 'condition',\n",
              "       'cylinders', 'fuel', 'odometer', 'title_status', 'transmission', 'VIN',\n",
              "       'drive', 'size', 'type', 'paint_color', 'state', 'lat', 'long',\n",
              "       'posting_date'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler, OrdinalEncoder\n",
        "from sklearn.compose import make_column_selector, make_column_transformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#REDUCIR EL TAMAÑO DE LAS MUESTRAS YA QUE LA DATA ES MUY PESADA\n",
        "data_sample = data.sample(frac=0.3, random_state=42)\n",
        "\n",
        "#DIVIDIR VALORE INDEPENDIENTES Y DEPENDIENTES\n",
        "X = data_sample.drop(['price', 'lat', 'long','posting_date'],axis=1)\n",
        "y = data_sample['price']\n",
        "\n",
        "#DIVIDIR NUMENAL, NOMINALES Y ORDINALES\n",
        "numeral = X.select_dtypes(include=['int64', 'float64']).columns\n",
        "nominal = ['region', 'manufacturer', 'model', 'fuel','title_status', 'transmission', 'VIN', 'drive', 'size', 'type','paint_color', 'state']\n",
        "ordinal = ['condition','cylinders']\n",
        "#ordinales\n",
        "condicion = ['new', 'like new', 'excellent', 'good', 'fair', 'salvage', 'DESCONOCIDO']\n",
        "cilindrada = ['12 cylinders', '10 cylinders', '8 cylinders','6 cylinders', '5 cylinders', '4 cylinders', '3 cylinders', 'other']\n",
        "\n",
        "#NUMERALES\n",
        "impu_num = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('scalar', StandardScaler())\n",
        "])\n",
        "#NOMINALES\n",
        "impu_nom = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy= 'most_frequent')),\n",
        "    ('one', OneHotEncoder(handle_unknown='ignore'))\n",
        "])\n",
        "#ORDINALES\n",
        "impu_ord = Pipeline(steps=[\n",
        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
        "    ('ordina', OrdinalEncoder(categories=[condicion, cilindrada]))\n",
        "])"
      ],
      "metadata": {
        "id": "FRrYW_XwKBC6"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#TRANSFORMACION\n",
        "#=======================\n",
        "preprocesamiento = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('num', impu_num, numeral),\n",
        "        ('nom', impu_nom, nominal),\n",
        "        ('ord', impu_ord, ordinal)\n",
        "    ],\n",
        "    remainder='passthrough', #Dejar columnas no transformadas\n",
        "    n_jobs=-1,              #Usar todos los nucleos disponibles para el procesamiento\n",
        "    verbose=True,           #Imprime informacion sobre el proceso, recomendado en dataset grandes\n",
        "    sparse_threshold =0.3\n",
        ")\n",
        "preprocesamiento"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 150
        },
        "id": "-mBGG0oGVzWo",
        "outputId": "80dcf772-efdf-4bfb-abee-57984d225780"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
              "                  transformers=[('num',\n",
              "                                 Pipeline(steps=[('imputer',\n",
              "                                                  SimpleImputer(strategy='most_frequent')),\n",
              "                                                 ('scalar', StandardScaler())]),\n",
              "                                 Index(['year', 'odometer'], dtype='object')),\n",
              "                                ('nom',\n",
              "                                 Pipeline(steps=[('imputer',\n",
              "                                                  SimpleImputer(strategy='most_frequent')),\n",
              "                                                 ('one',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                 ['region', 'm...\n",
              "                                  'state']),\n",
              "                                ('ord',\n",
              "                                 Pipeline(steps=[('imputer',\n",
              "                                                  SimpleImputer(strategy='most_frequent')),\n",
              "                                                 ('ordina',\n",
              "                                                  OrdinalEncoder(categories=[['new',\n",
              "                                                                              'like '\n",
              "                                                                              'new',\n",
              "                                                                              'excellent',\n",
              "                                                                              'good',\n",
              "                                                                              'fair',\n",
              "                                                                              'salvage',\n",
              "                                                                              'DESCONOCIDO'],\n",
              "                                                                             ['12 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '10 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '8 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '6 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '5 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '4 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              '3 '\n",
              "                                                                              'cylinders',\n",
              "                                                                              'other']]))]),\n",
              "                                 ['condition', 'cylinders'])],\n",
              "                  verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;manufacturer&#x27;, &#x27;model&#x27;, &#x27;fuel&#x27;, &#x27;title_status&#x27;, &#x27;transmission&#x27;, &#x27;VIN&#x27;, &#x27;drive&#x27;, &#x27;size&#x27;, &#x27;type&#x27;, &#x27;paint_color&#x27;, &#x27;state&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;condition&#x27;, &#x27;cylinders&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;new&#x27;, &#x27;like new&#x27;, &#x27;excellent&#x27;, &#x27;good&#x27;, &#x27;fair&#x27;,\n",
              "                            &#x27;salvage&#x27;, &#x27;DESCONOCIDO&#x27;],\n",
              "                           [&#x27;12 cylinders&#x27;, &#x27;10 cylinders&#x27;, &#x27;8 cylinders&#x27;,\n",
              "                            &#x27;6 cylinders&#x27;, &#x27;5 cylinders&#x27;, &#x27;4 cylinders&#x27;,\n",
              "                            &#x27;3 cylinders&#x27;, &#x27;other&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#DIVISION DEL CONJUNTO DE DATOS\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)\n"
      ],
      "metadata": {
        "id": "SoB3aTvJYGgz"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MODELOS**"
      ],
      "metadata": {
        "id": "mEzu04jqo7lK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**REGRESION LINEAL**"
      ],
      "metadata": {
        "id": "WqjuDRLYo_a1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lineal = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', LinearRegression())\n",
        "])\n",
        "lineal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "5Rm1sgdemVyX",
        "outputId": "8e8cdc42-97ea-4318-a2ba-f1071bff0e17"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('pre',\n",
              "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
              "                                   transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('scalar',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index(['year', 'odometer'], dtype='object')),\n",
              "                                                 ('nom',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('one',\n",
              "                                                                   OneHotEncoder(handle_unknown='i...\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('ordina',\n",
              "                                                                   OrdinalEncoder(categories=[['new',\n",
              "                                                                                               'like '\n",
              "                                                                                               'new',\n",
              "                                                                                               'excellent',\n",
              "                                                                                               'good',\n",
              "                                                                                               'fair',\n",
              "                                                                                               'salvage',\n",
              "                                                                                               'DESCONOCIDO'],\n",
              "                                                                                              ['12 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '10 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '8 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '6 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '5 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '4 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '3 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               'other']]))]),\n",
              "                                                  ['condition', 'cylinders'])],\n",
              "                                   verbose=True)),\n",
              "                ('modelo', LinearRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;manufacturer&#x27;, &#x27;model&#x27;, &#x27;fuel&#x27;, &#x27;title_status&#x27;, &#x27;transmission&#x27;, &#x27;VIN&#x27;, &#x27;drive&#x27;, &#x27;size&#x27;, &#x27;type&#x27;, &#x27;paint_color&#x27;, &#x27;state&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;condition&#x27;, &#x27;cylinders&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;new&#x27;, &#x27;like new&#x27;, &#x27;excellent&#x27;, &#x27;good&#x27;, &#x27;fair&#x27;,\n",
              "                            &#x27;salvage&#x27;, &#x27;DESCONOCIDO&#x27;],\n",
              "                           [&#x27;12 cylinders&#x27;, &#x27;10 cylinders&#x27;, &#x27;8 cylinders&#x27;,\n",
              "                            &#x27;6 cylinders&#x27;, &#x27;5 cylinders&#x27;, &#x27;4 cylinders&#x27;,\n",
              "                            &#x27;3 cylinders&#x27;, &#x27;other&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ENTRENAMIENTO\n",
        "lineal.fit(X_train,y_train)\n",
        "#PARAMETRO DE ENTRENAMIENTO\n",
        "lineal_pred_train = lineal.predict(X_train)\n",
        "r2_train_lineal = r2_score(y_train, lineal_pred_train)#evalua en entrenamiento mas no el modelo\n",
        "\n",
        "#PREDICCION\n",
        "lineal_pred = lineal.predict(X_test)\n",
        "#COMPROBAR PARAMETROS\n",
        "r2_lineal = r2_score(y_test, lineal_pred)\n",
        "mse_lineal = mean_squared_error(y_test, lineal_pred)\n",
        "msa_lineal = mean_absolute_error(y_test, lineal_pred)\n",
        "print('REGRESION LINEAL')\n",
        "print('R2 DE ENTRENAMIENTO:', r2_train_lineal)\n",
        "print('R2:', r2_lineal)\n",
        "print('MSE:', mse_lineal)\n",
        "print('MSA', msa_lineal)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w5X-Z2wgusXg",
        "outputId": "1ffecf70-53a2-40e3-f810-4e914db593a0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "REGRESION LINEAL\n",
            "R2 DE ENTRENAMIENTO: 0.9065049647902329\n",
            "R2: 0.5981113047706783\n",
            "MSE: 93168657.7314261\n",
            "MSA 5178.827623889305\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "zS62Y8JlL2mw",
        "outputId": "9242ad0d-705e-4cc5-91a3-c8872a1792a4"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count   114672.00\n",
              "mean     18925.50\n",
              "std      15271.67\n",
              "min         10.00\n",
              "25%       7295.00\n",
              "50%      15200.00\n",
              "75%      27590.00\n",
              "max     400123.00\n",
              "Name: price, dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>114672.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>18925.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15271.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7295.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15200.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>27590.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>400123.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"REGRESION LINEAL\n",
        "R2 DE ENTRENAMIENTO: 0.9998488136386875\n",
        "R2: -0.0007325034686791465\n",
        "MSE: 341195582578020.06\n",
        "MSA 314156.11917903705\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4ILjGy6qEHsY",
        "outputId": "f090de87-7ddb-412c-d87b-98e89eefcfdb"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'REGRESION LINEAL\\nR2 DE ENTRENAMIENTO: 0.9998488136386875\\nR2: -0.0007325034686791465\\nMSE: 341195582578020.06\\nMSA 314156.11917903705'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ARBOL DE DECISION**"
      ],
      "metadata": {
        "id": "kASrlyd__CZG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arbol = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', DecisionTreeRegressor(max_depth=20, random_state=42))\n",
        "])\n",
        "arbol"
      ],
      "metadata": {
        "id": "dAgcVldW_FW5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "9fff3234-3e8e-460f-aa64-13cf893a6dfe"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('pre',\n",
              "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
              "                                   transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('scalar',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index(['year', 'odometer'], dtype='object')),\n",
              "                                                 ('nom',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('one',\n",
              "                                                                   OneHotEncoder(handle_unknown='i...\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('ordina',\n",
              "                                                                   OrdinalEncoder(categories=[['new',\n",
              "                                                                                               'like '\n",
              "                                                                                               'new',\n",
              "                                                                                               'excellent',\n",
              "                                                                                               'good',\n",
              "                                                                                               'fair',\n",
              "                                                                                               'salvage',\n",
              "                                                                                               'DESCONOCIDO'],\n",
              "                                                                                              ['12 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '10 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '8 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '6 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '5 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '4 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '3 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               'other']]))]),\n",
              "                                                  ['condition', 'cylinders'])],\n",
              "                                   verbose=True)),\n",
              "                ('modelo',\n",
              "                 DecisionTreeRegressor(max_depth=20, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 DecisionTreeRegressor(max_depth=20, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 DecisionTreeRegressor(max_depth=20, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;manufacturer&#x27;, &#x27;model&#x27;, &#x27;fuel&#x27;, &#x27;title_status&#x27;, &#x27;transmission&#x27;, &#x27;VIN&#x27;, &#x27;drive&#x27;, &#x27;size&#x27;, &#x27;type&#x27;, &#x27;paint_color&#x27;, &#x27;state&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;condition&#x27;, &#x27;cylinders&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;new&#x27;, &#x27;like new&#x27;, &#x27;excellent&#x27;, &#x27;good&#x27;, &#x27;fair&#x27;,\n",
              "                            &#x27;salvage&#x27;, &#x27;DESCONOCIDO&#x27;],\n",
              "                           [&#x27;12 cylinders&#x27;, &#x27;10 cylinders&#x27;, &#x27;8 cylinders&#x27;,\n",
              "                            &#x27;6 cylinders&#x27;, &#x27;5 cylinders&#x27;, &#x27;4 cylinders&#x27;,\n",
              "                            &#x27;3 cylinders&#x27;, &#x27;other&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=20, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ENTRENAMIENTO\n",
        "arbol.fit(X_train, y_train)\n",
        "#PREDICCION\n",
        "arbol_pred = arbol.predict(X_test)\n",
        "#COMPROBAR PARAMETROS\n",
        "r2_arbol = r2_score(y_test, arbol_pred)\n",
        "mse_arbol = mean_squared_error(y_test, arbol_pred)\n",
        "msa_arbol = mean_absolute_error(y_test, arbol_pred)\n",
        "print('ARBOL')\n",
        "print('R2:', r2_arbol)\n",
        "print('MSE:', mse_arbol)\n",
        "print('MSA', msa_arbol)"
      ],
      "metadata": {
        "id": "vB2643yU_JXT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb6a5664-c71f-4ed8-9c63-ee815d2d0879"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ARBOL\n",
            "R2: 0.6835031712943087\n",
            "MSE: 73372515.96474105\n",
            "MSA 4479.83226256091\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BAGGING**"
      ],
      "metadata": {
        "id": "EHPXScBWAE2-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import BaggingRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "bag = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', BaggingRegressor(DecisionTreeRegressor(), n_estimators=30,\n",
        "                               max_samples=200, bootstrap=True, random_state=42))\n",
        "])\n",
        "bag\n",
        "#n_estimators=30 ; cuantos arboles voy a copiar\n",
        "#max_samples=200; cuantas muestras van a entrar por casa arbol maximo\n",
        "#bootstrap=True, me dice si es baggin(reemplazo) o pasting(no reeemplazo)\n",
        "#random_state=42"
      ],
      "metadata": {
        "id": "MuA9dcTNADsN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "outputId": "0332c64a-2487-4591-fefb-05fc1de3f90f"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('pre',\n",
              "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
              "                                   transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('scalar',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index(['year', 'odometer'], dtype='object')),\n",
              "                                                 ('nom',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('one',\n",
              "                                                                   OneHotEncoder(handle_unknown='i...\n",
              "                                                                   OrdinalEncoder(categories=[['new',\n",
              "                                                                                               'like '\n",
              "                                                                                               'new',\n",
              "                                                                                               'excellent',\n",
              "                                                                                               'good',\n",
              "                                                                                               'fair',\n",
              "                                                                                               'salvage',\n",
              "                                                                                               'DESCONOCIDO'],\n",
              "                                                                                              ['12 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '10 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '8 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '6 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '5 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '4 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '3 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               'other']]))]),\n",
              "                                                  ['condition', 'cylinders'])],\n",
              "                                   verbose=True)),\n",
              "                ('modelo',\n",
              "                 BaggingRegressor(estimator=DecisionTreeRegressor(),\n",
              "                                  max_samples=200, n_estimators=30,\n",
              "                                  random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 BaggingRegressor(estimator=DecisionTreeRegressor(),\n",
              "                                  max_samples=200, n_estimators=30,\n",
              "                                  random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 BaggingRegressor(estimator=DecisionTreeRegressor(),\n",
              "                                  max_samples=200, n_estimators=30,\n",
              "                                  random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;manufacturer&#x27;, &#x27;model&#x27;, &#x27;fuel&#x27;, &#x27;title_status&#x27;, &#x27;transmission&#x27;, &#x27;VIN&#x27;, &#x27;drive&#x27;, &#x27;size&#x27;, &#x27;type&#x27;, &#x27;paint_color&#x27;, &#x27;state&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;condition&#x27;, &#x27;cylinders&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;new&#x27;, &#x27;like new&#x27;, &#x27;excellent&#x27;, &#x27;good&#x27;, &#x27;fair&#x27;,\n",
              "                            &#x27;salvage&#x27;, &#x27;DESCONOCIDO&#x27;],\n",
              "                           [&#x27;12 cylinders&#x27;, &#x27;10 cylinders&#x27;, &#x27;8 cylinders&#x27;,\n",
              "                            &#x27;6 cylinders&#x27;, &#x27;5 cylinders&#x27;, &#x27;4 cylinders&#x27;,\n",
              "                            &#x27;3 cylinders&#x27;, &#x27;other&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-53\" type=\"checkbox\" ><label for=\"sk-estimator-id-53\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-54\" type=\"checkbox\" ><label for=\"sk-estimator-id-54\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">modelo: BaggingRegressor</label><div class=\"sk-toggleable__content\"><pre>BaggingRegressor(estimator=DecisionTreeRegressor(), max_samples=200,\n",
              "                 n_estimators=30, random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-55\" type=\"checkbox\" ><label for=\"sk-estimator-id-55\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-56\" type=\"checkbox\" ><label for=\"sk-estimator-id-56\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ENTRENAMIENTO\n",
        "bag.fit(X_train, y_train)\n",
        "#PREDICCION\n",
        "bag_pred = bag.predict(X_test)\n",
        "#COMPROBAR PARAMETROS\n",
        "r2_bag = r2_score(y_test, bag_pred)\n",
        "mse_bag = mean_squared_error(y_test, bag_pred)\n",
        "msa_bag = mean_absolute_error(y_test, bag_pred)\n",
        "print('BAGGING')\n",
        "print('R2:', r2_bag)\n",
        "print('MSE:', mse_bag)\n",
        "print('MSA', msa_bag)"
      ],
      "metadata": {
        "id": "e6Lio2ekAK9Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d29fa056-dd9d-44bc-b7fb-b4969bbe5d03"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "BAGGING\n",
            "R2: 0.5697247971317713\n",
            "MSE: 99749417.14515066\n",
            "MSA 5985.9491386159725\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RANDOM FOREST**"
      ],
      "metadata": {
        "id": "qcBZ-ezxxirM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', RandomForestRegressor(n_estimators=30, max_samples=200,random_state=42))\n",
        "])\n",
        "random"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "S_3juwANZMdr",
        "outputId": "3ec02881-05c6-46a2-d786-439ba36e6ce4"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('pre',\n",
              "                 ColumnTransformer(n_jobs=-1, remainder='passthrough',\n",
              "                                   transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('scalar',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index(['year', 'odometer'], dtype='object')),\n",
              "                                                 ('nom',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('one',\n",
              "                                                                   OneHotEncoder(handle_unknown='i...\n",
              "                                                                  ('ordina',\n",
              "                                                                   OrdinalEncoder(categories=[['new',\n",
              "                                                                                               'like '\n",
              "                                                                                               'new',\n",
              "                                                                                               'excellent',\n",
              "                                                                                               'good',\n",
              "                                                                                               'fair',\n",
              "                                                                                               'salvage',\n",
              "                                                                                               'DESCONOCIDO'],\n",
              "                                                                                              ['12 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '10 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '8 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '6 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '5 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '4 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               '3 '\n",
              "                                                                                               'cylinders',\n",
              "                                                                                               'other']]))]),\n",
              "                                                  ['condition', 'cylinders'])],\n",
              "                                   verbose=True)),\n",
              "                ('modelo',\n",
              "                 RandomForestRegressor(max_samples=200, n_estimators=30,\n",
              "                                       random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 RandomForestRegressor(max_samples=200, n_estimators=30,\n",
              "                                       random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-57\" type=\"checkbox\" ><label for=\"sk-estimator-id-57\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;pre&#x27;,\n",
              "                 ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                                   transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;scalar&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                                 (&#x27;nom&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;one&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;i...\n",
              "                                                                  (&#x27;ordina&#x27;,\n",
              "                                                                   OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                                               &#x27;like &#x27;\n",
              "                                                                                               &#x27;new&#x27;,\n",
              "                                                                                               &#x27;excellent&#x27;,\n",
              "                                                                                               &#x27;good&#x27;,\n",
              "                                                                                               &#x27;fair&#x27;,\n",
              "                                                                                               &#x27;salvage&#x27;,\n",
              "                                                                                               &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                                              [&#x27;12 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;10 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;8 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;6 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;5 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;4 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;3 &#x27;\n",
              "                                                                                               &#x27;cylinders&#x27;,\n",
              "                                                                                               &#x27;other&#x27;]]))]),\n",
              "                                                  [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                                   verbose=True)),\n",
              "                (&#x27;modelo&#x27;,\n",
              "                 RandomForestRegressor(max_samples=200, n_estimators=30,\n",
              "                                       random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-58\" type=\"checkbox\" ><label for=\"sk-estimator-id-58\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pre: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(n_jobs=-1, remainder=&#x27;passthrough&#x27;,\n",
              "                  transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;scalar&#x27;, StandardScaler())]),\n",
              "                                 Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)),\n",
              "                                (&#x27;nom&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;one&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;region&#x27;, &#x27;m...\n",
              "                                  &#x27;state&#x27;]),\n",
              "                                (&#x27;ord&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;ordina&#x27;,\n",
              "                                                  OrdinalEncoder(categories=[[&#x27;new&#x27;,\n",
              "                                                                              &#x27;like &#x27;\n",
              "                                                                              &#x27;new&#x27;,\n",
              "                                                                              &#x27;excellent&#x27;,\n",
              "                                                                              &#x27;good&#x27;,\n",
              "                                                                              &#x27;fair&#x27;,\n",
              "                                                                              &#x27;salvage&#x27;,\n",
              "                                                                              &#x27;DESCONOCIDO&#x27;],\n",
              "                                                                             [&#x27;12 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;10 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;8 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;6 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;5 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;4 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;3 &#x27;\n",
              "                                                                              &#x27;cylinders&#x27;,\n",
              "                                                                              &#x27;other&#x27;]]))]),\n",
              "                                 [&#x27;condition&#x27;, &#x27;cylinders&#x27;])],\n",
              "                  verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-59\" type=\"checkbox\" ><label for=\"sk-estimator-id-59\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;year&#x27;, &#x27;odometer&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-60\" type=\"checkbox\" ><label for=\"sk-estimator-id-60\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-61\" type=\"checkbox\" ><label for=\"sk-estimator-id-61\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-62\" type=\"checkbox\" ><label for=\"sk-estimator-id-62\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">nom</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;manufacturer&#x27;, &#x27;model&#x27;, &#x27;fuel&#x27;, &#x27;title_status&#x27;, &#x27;transmission&#x27;, &#x27;VIN&#x27;, &#x27;drive&#x27;, &#x27;size&#x27;, &#x27;type&#x27;, &#x27;paint_color&#x27;, &#x27;state&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-63\" type=\"checkbox\" ><label for=\"sk-estimator-id-63\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-64\" type=\"checkbox\" ><label for=\"sk-estimator-id-64\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-65\" type=\"checkbox\" ><label for=\"sk-estimator-id-65\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ord</label><div class=\"sk-toggleable__content\"><pre>[&#x27;condition&#x27;, &#x27;cylinders&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-66\" type=\"checkbox\" ><label for=\"sk-estimator-id-66\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-67\" type=\"checkbox\" ><label for=\"sk-estimator-id-67\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(categories=[[&#x27;new&#x27;, &#x27;like new&#x27;, &#x27;excellent&#x27;, &#x27;good&#x27;, &#x27;fair&#x27;,\n",
              "                            &#x27;salvage&#x27;, &#x27;DESCONOCIDO&#x27;],\n",
              "                           [&#x27;12 cylinders&#x27;, &#x27;10 cylinders&#x27;, &#x27;8 cylinders&#x27;,\n",
              "                            &#x27;6 cylinders&#x27;, &#x27;5 cylinders&#x27;, &#x27;4 cylinders&#x27;,\n",
              "                            &#x27;3 cylinders&#x27;, &#x27;other&#x27;]])</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_samples=200, n_estimators=30, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ENTRENAMIENTO\n",
        "random.fit(X_train, y_train)\n",
        "#PREDICCION\n",
        "random_pred = random.predict(X_test)\n",
        "#COMPROBAR PARAMETROS\n",
        "r2_random = r2_score(y_test, random_pred)\n",
        "mse_random = mean_squared_error(y_test, random_pred)\n",
        "msa_random = mean_absolute_error(y_test, random_pred)\n",
        "print('RANDOM')\n",
        "print('R2:', r2_random)\n",
        "print('MSE:', mse_random)\n",
        "print('MSA', msa_random)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIpdNUdvZaa7",
        "outputId": "1d30e4d1-74f6-4697-f111-d68bca14b249"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RANDOM\n",
            "R2: 0.5665636988592273\n",
            "MSE: 100482245.13087444\n",
            "MSA 5964.570052128753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **BENCHMARKING**"
      ],
      "metadata": {
        "id": "JSsdf3bXk8VR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('==============\\033[1m MODELOS DE REGRESIÓN SIN OPTIMIZAR \\033[0m==============')\n",
        "\n",
        "print('\\n\\n\\033[1mPRECISION DEL MODELO DE REGRESION LINEAL\\033[0m')\n",
        "print('R2 DE ENTRENAMIENTO:', r2_train_lineal)\n",
        "print('R2:', r2_lineal)\n",
        "print('MSE:', mse_lineal)\n",
        "print('MSA', msa_lineal)\n",
        "\n",
        "print('\\n\\033[1mPRECISION DEL MODELO DE ARBOL DE DECISION\\033[0m')\n",
        "print('R2:', r2_arbol)\n",
        "print('MSE:', mse_arbol)\n",
        "print('MSA', msa_arbol)\n",
        "\n",
        "print('\\n\\033[1mPRECISION DEL MODELO DE BAGGING\\033[0m')\n",
        "print('R2:', r2_bag)\n",
        "print('MSE:', mse_bag)\n",
        "print('MSA:', msa_bag)\n",
        "\n",
        "print('\\n\\033[1mPRECISION DEL MODELO DE RANDOM\\033[0m')\n",
        "print('R2:', r2_random)\n",
        "print('MSE:', mse_random)\n",
        "print('MSA', msa_random)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHolbU463B2D",
        "outputId": "ac09b5ff-7bda-4962-9888-34d8efdbf43d"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============\u001b[1m MODELOS DE REGRESIÓN SIN OPTIMIZAR \u001b[0m==============\n",
            "\n",
            "\n",
            "\u001b[1mPRECISION DEL MODELO DE REGRESION LINEAL\u001b[0m\n",
            "R2 DE ENTRENAMIENTO: 0.9065049647902329\n",
            "R2: 0.5981113047706783\n",
            "MSE: 93168657.7314261\n",
            "MSA 5178.827623889305\n",
            "\n",
            "\u001b[1mPRECISION DEL MODELO DE ARBOL DE DECISION\u001b[0m\n",
            "R2: 0.6835031712943087\n",
            "MSE: 73372515.96474105\n",
            "MSA 4479.83226256091\n",
            "\n",
            "\u001b[1mPRECISION DEL MODELO DE BAGGING\u001b[0m\n",
            "R2: 0.5697247971317713\n",
            "MSE: 99749417.14515066\n",
            "MSA: 5985.9491386159725\n",
            "\n",
            "\u001b[1mPRECISION DEL MODELO DE RANDOM\u001b[0m\n",
            "R2: 0.5665636988592273\n",
            "MSE: 100482245.13087444\n",
            "MSA 5964.570052128753\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CONCLUSION:**\n",
        "\n",
        "Despues de comparar 4 modelos, realizar la investigacion requerida, optare por el modelo de ARBOL DE DECISION:\n",
        "    -Tiene el r2 mas alto, lo que significa que la relacion de las variable independites es fuerte sobre mi target\n",
        "    -MSE mas bajo, eso me da entender que tiene menos errores al momento de predecir"
      ],
      "metadata": {
        "id": "RnaQrnjli4Bf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **OPTIMIZACION DEL MODELO**"
      ],
      "metadata": {
        "id": "nNrx1PRZltaf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BUSCAR MODELO**"
      ],
      "metadata": {
        "id": "zDmO4uAJnTCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "#entrenar modelo OTRA VEZ\n",
        "arbol = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', DecisionTreeRegressor(random_state=42))\n",
        "])\n",
        "\n",
        "#definir parametros\n",
        "para = {\n",
        "    'modelo__max_depth': range(1,31),\n",
        "    'modelo__min_samples_split': range(2, 21),  # Número mínimo de muestras para dividir un nodo\n",
        "    'modelo__min_samples_leaf': range(1, 11)\n",
        "}\n",
        "\n",
        "#VALIDACION CRUZADA\n",
        "grid = GridSearchCV(arbol, para, cv=2, scoring='neg_mean_squared_error', n_jobs=-1)\n",
        "grid.fit(X_train, y_train)\n",
        "\n",
        "#RESULTADOS\n",
        "mejor_arbol = grid.best_params_\n",
        "mejor_arbol"
      ],
      "metadata": {
        "id": "4Z4AylUnmHin"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**OPTIMIZAR EL MODELO**"
      ],
      "metadata": {
        "id": "UNpi6yhynYyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arbol_op = Pipeline(steps=[\n",
        "    ('pre', preprocesamiento),\n",
        "    ('modelo', DecisionTreeRegressor(max_depth= mejor_arbol['modelo__max_depth'], min_samples_split= mejor_arbol['modelo__min_samples_split'],\n",
        "                                     min_samples_leaf= mejor_arbol['modelo__min_samples_leaf'],random_state=42))\n",
        "])\n",
        "\n",
        "#Entrenar optimizado\n",
        "arbol_op.fit(X_train, y_train)\n",
        "#predicciones\n",
        "arbol_op_pred = arbol_op.predict(X_test)\n",
        "\n",
        "#METRICAS\n",
        "r2_arbol_op = r2_score(y_test, arbol_op_pred)\n",
        "mse_arbol_op = mean_squared_error(y_test, arbol_op_pred)\n",
        "msa_arbol_op = mean_absolute_error(y_test, arbol_op_pred)\n",
        "print('\\nARBOL DE DECISION  OPTIMIZADO')\n",
        "print('R2:', r2_arbol_op)\n",
        "print('Error cuadratico medio:', mse_arbol_op)\n",
        "print('Error absoluto medio:', msa_arbol_op)"
      ],
      "metadata": {
        "id": "bsB9in3bnVyi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}