Random_Forest_Kaggle.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyO21CVPiEUlx5he/hx17r1K",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iurymk/Random_Forest/blob/main/Random_Forest_Kaggle.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest\n",
        "\n",
        "Para introduzir o modelo Random Forest (Floresta aleatória) é interessante introduzir o conceito de Decision Tree Regression."
      ],
      "metadata": {
        "id": "eZpXjoSJCwmK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Captura de tela 2024-06-04 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)"
      ],
      "metadata": {
        "id": "Vtehg_j-Ckgb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A imagem mostra uma árvore de decisão com o nome \"Tree-1\". Cada nó na árvore representa uma decisão com base em um recurso dos dados. As linhas que conectam os nós representam os diferentes caminhos que os dados podem seguir na árvore. Abaixo, inicio utilizando o modelo Decision Tree Regressor para introdução dos conceitos e comparação com o Random Forest, que basicamente, utiliza diversas árvores e pega um valor médio do resultado de cada árvore como resposta final, como na imagem abaixo.\n"
      ],
      "metadata": {
        "id": "VprJhsLLDLgs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "-DWmQ3gxD1-O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Essa célula importa o que precisa do ambiente Kaggle. É obtida localmente : open in colab ()\n",
        "# Também é possível simplesmente fazer dowload do dados do Kaggle e subir para o Colab\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'home-data-for-ml-course:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-competitions-data%2Fkaggle-v2%2F10211%2F111096%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240603%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240603T162624Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,mobile-price-classification:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F11167%2F15520%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240603%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240603T162624Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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,melbourne-housing-snapshot:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F2709%2F38454%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240603%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240603T162624Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dl5DhOJC7Sqh",
        "outputId": "86c9153a-7559-4679-d69c-354aa179befb"
      },
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading home-data-for-ml-course, 395336 bytes compressed\n",
            "[==================================================] 395336 bytes downloaded\n",
            "Downloaded and uncompressed: home-data-for-ml-course\n",
            "Downloading mobile-price-classification, 72340 bytes compressed\n",
            "[==================================================] 72340 bytes downloaded\n",
            "Downloaded and uncompressed: mobile-price-classification\n",
            "Downloading melbourne-housing-snapshot, 461423 bytes compressed\n",
            "[==================================================] 461423 bytes downloaded\n",
            "Downloaded and uncompressed: melbourne-housing-snapshot\n",
            "Data source import complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dados sobre preços de casas em Melbourne, Austrália.\n",
        "\n",
        "Objetivo: predizer preços das casas com base em 5 características ('Quartos', 'Banheiro', 'Tamanho do terreno', 'Latitude', 'Longitude')"
      ],
      "metadata": {
        "id": "etnodJg8GiE_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "\n",
        "melbourne_data = pd.read_csv('../input/melbourne-housing-snapshot/melb_data.csv')\n",
        "\n",
        "melbourne_data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "X7vDP3NW7WDb",
        "outputId": "804f2694-4e48-492c-f6fb-67630d01a9c1"
      },
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Suburb             Address  Rooms Type      Price Method SellerG  \\\n",
              "0  Abbotsford        85 Turner St      2    h  1480000.0      S  Biggin   \n",
              "1  Abbotsford     25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
              "2  Abbotsford        5 Charles St      3    h  1465000.0     SP  Biggin   \n",
              "3  Abbotsford    40 Federation La      3    h   850000.0     PI  Biggin   \n",
              "4  Abbotsford         55a Park St      4    h  1600000.0     VB  Nelson   \n",
              "5  Abbotsford      129 Charles St      2    h   941000.0      S  Jellis   \n",
              "6  Abbotsford        124 Yarra St      3    h  1876000.0      S  Nelson   \n",
              "7  Abbotsford       98 Charles St      2    h  1636000.0      S  Nelson   \n",
              "8  Abbotsford  6/241 Nicholson St      1    u   300000.0      S  Biggin   \n",
              "9  Abbotsford       10 Valiant St      2    h  1097000.0      S  Biggin   \n",
              "\n",
              "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
              "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
              "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
              "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
              "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
              "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
              "5  7/05/2016       2.5    3067.0  ...       1.0  0.0     181.0           NaN   \n",
              "6  7/05/2016       2.5    3067.0  ...       2.0  0.0     245.0         210.0   \n",
              "7  8/10/2016       2.5    3067.0  ...       1.0  2.0     256.0         107.0   \n",
              "8  8/10/2016       2.5    3067.0  ...       1.0  1.0       0.0           NaN   \n",
              "9  8/10/2016       2.5    3067.0  ...       1.0  2.0     220.0          75.0   \n",
              "\n",
              "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
              "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
              "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
              "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
              "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
              "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
              "5        NaN        Yarra  -37.8041    144.9953  Northern Metropolitan   \n",
              "6     1910.0        Yarra  -37.8024    144.9993  Northern Metropolitan   \n",
              "7     1890.0        Yarra  -37.8060    144.9954  Northern Metropolitan   \n",
              "8        NaN        Yarra  -37.8008    144.9973  Northern Metropolitan   \n",
              "9     1900.0        Yarra  -37.8010    144.9989  Northern Metropolitan   \n",
              "\n",
              "  Propertycount  \n",
              "0        4019.0  \n",
              "1        4019.0  \n",
              "2        4019.0  \n",
              "3        4019.0  \n",
              "4        4019.0  \n",
              "5        4019.0  \n",
              "6        4019.0  \n",
              "7        4019.0  \n",
              "8        4019.0  \n",
              "9        4019.0  \n",
              "\n",
              "[10 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb56177d-1157-45d2-bc8b-7967e826a20b\" 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>Suburb</th>\n",
              "      <th>Address</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Method</th>\n",
              "      <th>SellerG</th>\n",
              "      <th>Date</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>...</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>CouncilArea</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Regionname</th>\n",
              "      <th>Propertycount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>85 Turner St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1480000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>3/12/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.7996</td>\n",
              "      <td>144.9984</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/02/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8079</td>\n",
              "      <td>144.9934</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8093</td>\n",
              "      <td>144.9944</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>40 Federation La</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.7969</td>\n",
              "      <td>144.9969</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>55a Park St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>4/06/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8072</td>\n",
              "      <td>144.9941</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>129 Charles St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>941000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Jellis</td>\n",
              "      <td>7/05/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8041</td>\n",
              "      <td>144.9953</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>124 Yarra St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1876000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>7/05/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>245.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>1910.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8024</td>\n",
              "      <td>144.9993</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>98 Charles St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1636000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>8/10/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>1890.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8060</td>\n",
              "      <td>144.9954</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>6/241 Nicholson St</td>\n",
              "      <td>1</td>\n",
              "      <td>u</td>\n",
              "      <td>300000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>8/10/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8008</td>\n",
              "      <td>144.9973</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>10 Valiant St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1097000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>8/10/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8010</td>\n",
              "      <td>144.9989</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 21 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-fb56177d-1157-45d2-bc8b-7967e826a20b')\"\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb56177d-1157-45d2-bc8b-7967e826a20b 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-fb56177d-1157-45d2-bc8b-7967e826a20b');\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-6ed9d8f9-6d4b-441d-94d5-4371b45cf6fd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6ed9d8f9-6d4b-441d-94d5-4371b45cf6fd')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-6ed9d8f9-6d4b-441d-94d5-4371b45cf6fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "melbourne_data"
            }
          },
          "metadata": {},
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Descrição dos dados**"
      ],
      "metadata": {
        "id": "ZPGu3vS5jEgs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.shape\n",
        "#(linhas, colunas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47oEH6Wojxck",
        "outputId": "3c234196-0055-4d4a-e9bd-f62003492792"
      },
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13580, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8aMapaCsra0",
        "outputId": "c88432df-7c83-4948-85e9-ff23385ba422"
      },
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
              "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
              "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
              "       'Longtitude', 'Regionname', 'Propertycount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "98NMqB62jTwj",
        "outputId": "0ac40fc2-0f13-45b6-ab55-763a33243c2e"
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
              "count  13580.000000  1.358000e+04  13580.000000  13580.000000  13580.000000   \n",
              "mean       2.937997  1.075684e+06     10.137776   3105.301915      2.914728   \n",
              "std        0.955748  6.393107e+05      5.868725     90.676964      0.965921   \n",
              "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
              "25%        2.000000  6.500000e+05      6.100000   3044.000000      2.000000   \n",
              "50%        3.000000  9.030000e+05      9.200000   3084.000000      3.000000   \n",
              "75%        3.000000  1.330000e+06     13.000000   3148.000000      3.000000   \n",
              "max       10.000000  9.000000e+06     48.100000   3977.000000     20.000000   \n",
              "\n",
              "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
              "count  13580.000000  13518.000000   13580.000000   7130.000000  8205.000000   \n",
              "mean       1.534242      1.610075     558.416127    151.967650  1964.684217   \n",
              "std        0.691712      0.962634    3990.669241    541.014538    37.273762   \n",
              "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
              "25%        1.000000      1.000000     177.000000     93.000000  1940.000000   \n",
              "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
              "75%        2.000000      2.000000     651.000000    174.000000  1999.000000   \n",
              "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
              "\n",
              "          Lattitude    Longtitude  Propertycount  \n",
              "count  13580.000000  13580.000000   13580.000000  \n",
              "mean     -37.809203    144.995216    7454.417378  \n",
              "std        0.079260      0.103916    4378.581772  \n",
              "min      -38.182550    144.431810     249.000000  \n",
              "25%      -37.856822    144.929600    4380.000000  \n",
              "50%      -37.802355    145.000100    6555.000000  \n",
              "75%      -37.756400    145.058305   10331.000000  \n",
              "max      -37.408530    145.526350   21650.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5cbdedd5-0c1c-495f-a8f3-b53f84c2a277\" 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>Rooms</th>\n",
              "      <th>Price</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Propertycount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13580.000000</td>\n",
              "      <td>1.358000e+04</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13518.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>7130.000000</td>\n",
              "      <td>8205.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.937997</td>\n",
              "      <td>1.075684e+06</td>\n",
              "      <td>10.137776</td>\n",
              "      <td>3105.301915</td>\n",
              "      <td>2.914728</td>\n",
              "      <td>1.534242</td>\n",
              "      <td>1.610075</td>\n",
              "      <td>558.416127</td>\n",
              "      <td>151.967650</td>\n",
              "      <td>1964.684217</td>\n",
              "      <td>-37.809203</td>\n",
              "      <td>144.995216</td>\n",
              "      <td>7454.417378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.955748</td>\n",
              "      <td>6.393107e+05</td>\n",
              "      <td>5.868725</td>\n",
              "      <td>90.676964</td>\n",
              "      <td>0.965921</td>\n",
              "      <td>0.691712</td>\n",
              "      <td>0.962634</td>\n",
              "      <td>3990.669241</td>\n",
              "      <td>541.014538</td>\n",
              "      <td>37.273762</td>\n",
              "      <td>0.079260</td>\n",
              "      <td>0.103916</td>\n",
              "      <td>4378.581772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1196.000000</td>\n",
              "      <td>-38.182550</td>\n",
              "      <td>144.431810</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.500000e+05</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>3044.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>1940.000000</td>\n",
              "      <td>-37.856822</td>\n",
              "      <td>144.929600</td>\n",
              "      <td>4380.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.030000e+05</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>3084.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>1970.000000</td>\n",
              "      <td>-37.802355</td>\n",
              "      <td>145.000100</td>\n",
              "      <td>6555.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.330000e+06</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>651.000000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>-37.756400</td>\n",
              "      <td>145.058305</td>\n",
              "      <td>10331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000e+06</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>3977.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>433014.000000</td>\n",
              "      <td>44515.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>-37.408530</td>\n",
              "      <td>145.526350</td>\n",
              "      <td>21650.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
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              "      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-ee2de79a-fdaf-4c9b-b5dd-9ec057d78fe1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"melbourne_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4800.099593692747,\n        \"min\": 0.9557479384215551,\n        \"max\": 13580.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          13580.0,\n          2.9379970544919,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2979225.99459393,\n        \"min\": 13580.0,\n        \"max\": 9000000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1075684.079455081,\n          903000.0,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4796.610473405383,\n        \"min\": 0.0,\n        \"max\": 13580.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10.13777614138439,\n          9.2,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3985.809947651435,\n        \"min\": 90.67696408711389,\n        \"max\": 13580.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3105.301914580265,\n          3084.0,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bedroom2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4799.649088060891,\n        \"min\": 0.0,\n        \"max\": 13580.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          13580.0,\n          2.9147275405007362,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4800.537178629762,\n        \"min\": 0.0,\n        \"max\": 13580.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          13580.0,\n          1.5342415316642122,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Car\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4778.448200723975,\n        \"min\": 0.0,\n        \"max\": 13518.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          13518.0,\n          1.6100754549489569,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 152183.13818043025,\n        \"min\": 0.0,\n        \"max\": 433014.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          558.4161266568483,\n          440.0,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BuildingArea\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15515.997074858136,\n        \"min\": 0.0,\n        \"max\": 44515.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          151.96764988779805,\n          126.0,\n          7130.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearBuilt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2438.4049227521114,\n        \"min\": 37.273762223960674,\n        \"max\": 8205.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1964.6842169408897,\n          1970.0,\n          8205.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lattitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4812.725232173229,\n        \"min\": -38.18255,\n        \"max\": 13580.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -37.80920273343152,\n          -37.802355,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longtitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4757.58128051343,\n        \"min\": 0.10391556140737544,\n        \"max\": 13580.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          144.99521618777615,\n          145.0001,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Propertycount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6643.530234671347,\n        \"min\": 249.0,\n        \"max\": 21650.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7454.417378497791,\n          6555.0,\n          13580.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os resultados mostram 8 linhas para cada coluna no seu conjunto de dados original.\n",
        "\n",
        "A primeira linha, a contagem **count**, mostra quantas linhas têm valores não ausentes. Valores ausentes surgem por muitos motivos. Por exemplo, o tamanho do 2º quarto não seria coletado ao pesquisar uma casa de 1 quarto.\n",
        "\n",
        " Abaixo, o valor médio **mean** e std, que é o desvio padrão, que mede o quão numericamente espalhados os valores estão.\n",
        "\n",
        "Para interpretar os valores mínimo, 25%, 50%, 75% e máximo, imagine classificar cada coluna do menor para o maior valor. O primeiro (menor) valor é o mínimo. Se você percorrer um quarto da lista, encontrará um número maior que 25% dos valores e menor que 75% dos valores. Esse é o valor de 25% (pronuncia-se \"25º percentil\"). Os percentis 50 e 75 são definidos analogamente, e o máximo é o maior número."
      ],
      "metadata": {
        "id": "a6fEv1dIkb7l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste caso, será usada a opção mais simples para lidar com **dados ausentes**"
      ],
      "metadata": {
        "id": "gQAxAQE6mykW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dropna remove valores ausentes (pense em na como \"não disponível\")\n",
        "melbourne_data = melbourne_data.dropna(axis = 0)"
      ],
      "metadata": {
        "id": "G_tRhdVZjwDd"
      },
      "execution_count": 215,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Passo 1: Especificar o y (Prediction Target)\n",
        "\n",
        "Você pode extrair uma variável com notação de ponto. Essa única coluna é armazenada como um DataFrame com apenas uma única coluna de dados.\n",
        "\n",
        "Abaixo é selecionada a coluna que queremos prever, que é chamada de Prediction Target. Portanto, o código que precisamos para salvar os preços das casas nos dados de Melbourne é"
      ],
      "metadata": {
        "id": "QzGQRy6JnWhA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Por convenção estes dados são chamados de y\n",
        "y = melbourne_data.Price\n",
        "print(y.head())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_R_fL1Phnhaj",
        "outputId": "83c360ef-6f7a-4f32-9c93-80db35a72526"
      },
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    1035000.0\n",
            "2    1465000.0\n",
            "4    1600000.0\n",
            "6    1876000.0\n",
            "7    1636000.0\n",
            "Name: Price, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Passo 2: Criando o X (features)\n",
        "\n",
        "As colunas que são inseridas no modelo são chamas de \"features\".\n",
        "Neste caso, seleciono apenas algumas colunas para determinar o preço das casas.\n"
      ],
      "metadata": {
        "id": "QtQxsU4GooAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']\n",
        "#Por convenção estes dados são chamados de X\n",
        "X = melbourne_data[melbourne_features]"
      ],
      "metadata": {
        "id": "CPxmdaPRpcq7"
      },
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Revisando dados:"
      ],
      "metadata": {
        "id": "4OE8Wh15tcSG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "E56k_jsEpsnn",
        "outputId": "c3afa02a-5c25-427c-ed66-4c9a403830f5"
      },
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Rooms     Bathroom      Landsize    Lattitude   Longtitude\n",
              "count  6196.000000  6196.000000   6196.000000  6196.000000  6196.000000\n",
              "mean      2.931407     1.576340    471.006940   -37.807904   144.990201\n",
              "std       0.971079     0.711362    897.449881     0.075850     0.099165\n",
              "min       1.000000     1.000000      0.000000   -38.164920   144.542370\n",
              "25%       2.000000     1.000000    152.000000   -37.855438   144.926198\n",
              "50%       3.000000     1.000000    373.000000   -37.802250   144.995800\n",
              "75%       4.000000     2.000000    628.000000   -37.758200   145.052700\n",
              "max       8.000000     8.000000  37000.000000   -37.457090   145.526350"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66be3680-ca52-4ece-ab39-4b2517779b06\" 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>Rooms</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6196.000000</td>\n",
              "      <td>6196.000000</td>\n",
              "      <td>6196.000000</td>\n",
              "      <td>6196.000000</td>\n",
              "      <td>6196.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.931407</td>\n",
              "      <td>1.576340</td>\n",
              "      <td>471.006940</td>\n",
              "      <td>-37.807904</td>\n",
              "      <td>144.990201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.971079</td>\n",
              "      <td>0.711362</td>\n",
              "      <td>897.449881</td>\n",
              "      <td>0.075850</td>\n",
              "      <td>0.099165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-38.164920</td>\n",
              "      <td>144.542370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>-37.855438</td>\n",
              "      <td>144.926198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>373.000000</td>\n",
              "      <td>-37.802250</td>\n",
              "      <td>144.995800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>628.000000</td>\n",
              "      <td>-37.758200</td>\n",
              "      <td>145.052700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>37000.000000</td>\n",
              "      <td>-37.457090</td>\n",
              "      <td>145.526350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66be3680-ca52-4ece-ab39-4b2517779b06')\"\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-66be3680-ca52-4ece-ab39-4b2517779b06 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-66be3680-ca52-4ece-ab39-4b2517779b06');\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-4a26ccc4-fe12-4445-900a-cf0e6befb852\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a26ccc4-fe12-4445-900a-cf0e6befb852')\"\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",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-4a26ccc4-fe12-4445-900a-cf0e6befb852 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2189.5117074756204,\n        \"min\": 0.9710788063154113,\n        \"max\": 6196.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.93140735958683,\n          3.0,\n          6196.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2189.845986987959,\n        \"min\": 0.7113618878731119,\n        \"max\": 6196.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          6196.0,\n          1.5763395739186572,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12804.650743040538,\n        \"min\": 0.0,\n        \"max\": 37000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          471.00693996126535,\n          373.0,\n          6196.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lattitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2202.110330119351,\n        \"min\": -38.16492,\n        \"max\": 6196.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -37.807904175274366,\n          -37.80225,\n          6196.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longtitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2147.2672729419037,\n        \"min\": 0.0991646697501218,\n        \"max\": 6196.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          144.9902011362169,\n          144.9958,\n          6196.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Passo 3: Especificar e treinar (Fit) o modelo\n",
        "\n",
        "Scikit-learn é a biblioteca mais popular para modelar dados normalmente armazenados em DataFrames.\n",
        "\n",
        "As etapas para construir um modelo são:\n",
        "\n",
        "**Definir:** Que tipo de modelo será? Uma árvore de decisão? Algum outro tipo de modelo? Alguns outros parâmetros do tipo de modelo também são especificados.\n",
        "\n",
        "**Fit (treinar):** Capture padrões de dados fornecidos. Este é o coração da modelagem.\n",
        "\n",
        "**Predizer:** Exatamente o que parece\n",
        "\n",
        "**Avaliar:** determine o quão precisas são as previsões do modelo."
      ],
      "metadata": {
        "id": "54oKkEKFqrPI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "# Define o modelo. Especifica o random_state (estado aleatório) para garantir os mesmos resultados em cada execução\n",
        "melbourne_model = DecisionTreeRegressor(random_state=1)\n",
        "\n",
        "# Fit model\n",
        "melbourne_model.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "NMAnUFNbrNSd",
        "outputId": "d9b9ed71-1176-438d-b293-64a2b4aa6c6a"
      },
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=1)"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#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>DecisionTreeRegressor(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(random_state=1)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Agora tenho um modelo ajustado que podemos usar para fazer previsões.\n",
        "\n",
        "Na prática, queremos fazer previsões para novas casas que chegam ao mercado, em vez das casas para as quais já temos preços. Mas faremos previsões para as primeiras linhas dos dados de treinamento para ver como a função de previsão funciona.\n",
        "\n"
      ],
      "metadata": {
        "id": "Pm4eLVgZr5ra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Etapa 4: Fazer previsões\n",
        "\n"
      ],
      "metadata": {
        "id": "GgHZKH5CZn-W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Passando as features (X) para as predições dos preços\n",
        "\n",
        "#Retomando: passaei as features para a variável X, informei o target y e treinei o modelo.\n",
        "#A variável val_predict armazena os valores preditos, após passado as features (neste caso os mesmo valores de treino)\n",
        "val_predict = melbourne_model.predict(X)"
      ],
      "metadata": {
        "id": "2lm0OzFsr-93"
      },
      "execution_count": 220,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A função display deixa os dados organizados numa tabela mais legal\n",
        "print(\"Features das 5 primeiras casas:\")\n",
        "display(X.head(5))\n",
        "print(\"As previsões são:\")\n",
        "X_predict = melbourne_model.predict(X)\n",
        "print(X_predict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "lY4FTny7uPob",
        "outputId": "d0ff56a6-b233-4235-83f1-1eb360f9ffaf"
      },
      "execution_count": 221,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features das 5 primeiras casas:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Rooms  Bathroom  Landsize  Lattitude  Longtitude\n",
              "1      2       1.0     156.0   -37.8079    144.9934\n",
              "2      3       2.0     134.0   -37.8093    144.9944\n",
              "4      4       1.0     120.0   -37.8072    144.9941\n",
              "6      3       2.0     245.0   -37.8024    144.9993\n",
              "7      2       1.0     256.0   -37.8060    144.9954"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"print(X_predict)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bathroom\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5477225575051661,\n        \"min\": 1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Landsize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 63.774603095589704,\n        \"min\": 120.0,\n        \"max\": 256.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          134.0,\n          256.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lattitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0026140007651112474,\n        \"min\": -37.8093,\n        \"max\": -37.8024,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -37.8093,\n          -37.806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longtitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002338161671055716,\n        \"min\": 144.9934,\n        \"max\": 144.9993,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          144.9944,\n          144.9954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As previsões são:\n",
            "[1035000. 1465000. 1600000. ...  385000.  560000. 2450000.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Validation\n",
        "\n",
        "Uma métrica para validação é o MAE (Erro Absoluto Médio).  "
      ],
      "metadata": {
        "id": "PxwGhRw_hL6n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "erro = y - val_predict # preços reais - preços preditos\n",
        "erro_abs = erro.abs()\n",
        "MAE = erro_abs.mean()\n",
        "print(MAE)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jv6uwq7H1ddy",
        "outputId": "d54792f4-3fa7-4fa1-f665-c0a3c0287635"
      },
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1115.7467183128902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O sktlearn oferece uma biblioteca que calcula isso automaticamente:"
      ],
      "metadata": {
        "id": "94kcI3tn4cMd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "val_mae = mean_absolute_error(y, val_predict)\n",
        "\n",
        "print(val_mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tIa2D3F4hHo",
        "outputId": "99a8523a-3351-4ecf-b7e8-b5ecbb874ae5"
      },
      "execution_count": 223,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1115.7467183128902\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neste exemplo, os dados não foram dividios em dados e teste e validação. Os dados que treinam o modelo foram utilizados para fazer as previsões. Por este motivo, é óbvio que o modelo irá acertar, mas pode não gerar bons resultados para dados que ele não conhece. Esse problema é chamado de \"In-Sample\" Scores."
      ],
      "metadata": {
        "id": "iUG2-b234uwn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Divisão em dados de treino e validação\n",
        "A biblioteca scikit-learn tem uma função train_test_split para dividir os dados em duas partes. Usaremos alguns desses dados como dados de treinamento para ajustar o modelo, e usaremos os outros dados como dados de validação para calcular mean_absolute_error."
      ],
      "metadata": {
        "id": "LQ_LV2Su5ukM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Dividindo os dados\n",
        "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)\n",
        "# Define o modelo\n",
        "melbourne_model = DecisionTreeRegressor(random_state = 1)\n",
        "# Fit model\n",
        "melbourne_model.fit(train_X, train_y)\n",
        "\n",
        "# get predicted prices on validation data\n",
        "val_predictions = melbourne_model.predict(val_X)\n",
        "MAE_split = mean_absolute_error(val_y, val_predictions)\n",
        "print(MAE_split)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ae-J6PWO6e3I",
        "outputId": "26cf28e0-6afc-45f8-a5d3-d24929dcd1cd"
      },
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "273518.01872175594\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calcular o erro médio absoluto em dados de validação"
      ],
      "metadata": {
        "id": "r7zzfEUJ65Yz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"MAE dados sem validação\", MAE)\n",
        "print(\"MAE dados com validação\", MAE_split)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6iLJvEAJ7DYH",
        "outputId": "2e121805-8b61-4529-f56f-46da7627eed5"
      },
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAE dados sem validação 1115.7467183128902\n",
            "MAE dados com validação 273518.01872175594\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Underfitting and Overfitting\n",
        "\n",
        "\n",
        "**Overfitting:** ocorre quando um modelo se adapta excessivamente aos dados de treinamento, memorizando padrões específicos em vez de aprender as características gerais dos dados. Como resultado, o modelo apresenta um desempenho excepcional no conjunto de treinamento, mas falha miseravelmente quando confrontado com novos dados não vistos durante o treinamento.\n",
        "\n",
        "**Underfitting:** ocorre quando um modelo não aprende suficientemente os dados de treinamento. Isso resulta em um desempenho ruim tanto no conjunto de treinamento quanto em novos dados, pois o modelo não foi capaz de capturar as características relevantes dos dados.\n",
        "\n",
        "Os gráficos MAE x tree depths são úteis para encontrar o equilíbrio ideal entre a capacidade de aprendizado e a generalização do modelo.\n",
        "\n",
        "Ajustando o modelo para descobrir o melhor tamanho da árvore com a função max_leaf_nodes: essa função controla o crescimento da árvore e a complexidade do modelo. Ela define o número máximo de folhas que a árvore pode ter durante o processo de treinamento, sendo uma maneira (apenas umas delas) sensata de controlar o underfiting vs overfiting."
      ],
      "metadata": {
        "id": "Iov_hx1E_GRE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "#obtendo diretamente o MAE por uma função e aplicar max_leaf_nodes\n",
        "def get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
        "    model = DecisionTreeRegressor(max_leaf_nodes=max_leaf_nodes, random_state=1) #define modelo\n",
        "    model.fit(train_X, train_y) # treina modelo\n",
        "    preds_val = model.predict(val_X) # predição dos valores\n",
        "    mae = mean_absolute_error(val_y, preds_val) # calcula o erro\n",
        "    return(mae)\n",
        "\n",
        "# comparar MAE com valores diferentes de max_leaf_nodes\n",
        "for max_leaf_nodes in [5, 50, 500, 5000]:\n",
        "    my_mae = get_mae(max_leaf_nodes, train_X, val_X, train_y, val_y)\n",
        "    print(\"Max leaf nodes: %d  \\t\\t Mean Absolute Error:  %d\" %(max_leaf_nodes, my_mae))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ld0eSIjc_cS2",
        "outputId": "e96ac9a9-1da5-4a5f-856f-3a7801ee21b1"
      },
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max leaf nodes: 5  \t\t Mean Absolute Error:  385696\n",
            "Max leaf nodes: 50  \t\t Mean Absolute Error:  279794\n",
            "Max leaf nodes: 500  \t\t Mean Absolute Error:  260235\n",
            "Max leaf nodes: 5000  \t\t Mean Absolute Error:  273303\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Perceba que o erro absoluto médio começa a aumentar depois de max_leaf_nodes = 500, ou seja, os dados estão overfitando. Para max_leaf_nodes = 5, os dados não generalizam o suficiente e o erro absoluto médio é grande também, caracterizando underfiting."
      ],
      "metadata": {
        "id": "8jFH2REyFcBh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Escolhendo o melhor tamanho da árvore:"
      ],
      "metadata": {
        "id": "dNz9p_5VMDuY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "candidate_max_leaf_nodes = [5, 25, 50, 100, 250, 500, 5000]\n",
        "\n",
        "erro = 1e10 # erro bem grande só pra começar a variável de iteração\n",
        "for candidate in candidate_max_leaf_nodes:\n",
        "    erro_do_candidato = get_mae(candidate, train_X, val_X, train_y, val_y)\n",
        "    if erro_do_candidato < erro:\n",
        "        erro = erro_do_candidato\n",
        "        best_tree_size = candidate\n",
        "    print(\"Candidato: \", candidate, \"Erro do candidato: \", erro_do_candidato, \"Melhor erro geral: \", erro )\n",
        "print(\"\")\n",
        "print(\"A melhor escolha para tamanho da árvore (max_leaf_nodes) = \", best_tree_size, \"Com erro global = \", erro_do_candidato)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Tvs6cfqJlP2",
        "outputId": "2be23227-b8a0-4ad5-b4f7-1799681dd877"
      },
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Candidato:  5 Erro do candidato:  385696.54278937966 Melhor erro geral:  385696.54278937966\n",
            "Candidato:  25 Erro do candidato:  307919.7001056724 Melhor erro geral:  307919.7001056724\n",
            "Candidato:  50 Erro do candidato:  279794.61143891385 Melhor erro geral:  279794.61143891385\n",
            "Candidato:  100 Erro do candidato:  267978.30318055797 Melhor erro geral:  267978.30318055797\n",
            "Candidato:  250 Erro do candidato:  267540.4286450778 Melhor erro geral:  267540.4286450778\n",
            "Candidato:  500 Erro do candidato:  260235.05178963946 Melhor erro geral:  260235.05178963946\n",
            "Candidato:  5000 Erro do candidato:  273303.2892188509 Melhor erro geral:  260235.05178963946\n",
            "\n",
            "A melhor escolha para tamanho da árvore (max_leaf_nodes) =  500 Com erro global =  273303.2892188509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Forest\n",
        "\n",
        "Finalmente, vou utilziar o modelo Random Forest para ver se melhora os resultados. A única coisa que muda no código é na hora definir o modelo."
      ],
      "metadata": {
        "id": "myhvDy2xITds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "forest_model = RandomForestRegressor(random_state=1)\n",
        "forest_model.fit(train_X, train_y)\n",
        "melb_preds = forest_model.predict(val_X)\n",
        "print(\"Erro Absoluto Médio: \", mean_absolute_error(val_y, melb_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ClpXBb-Igp9",
        "outputId": "9f2cc061-3d51-4893-ea35-c471b4f6d5c9"
      },
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Erro Absoluto Médio:  207190.6873773146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_mae_rf(max_leaf_nodes, train_X, val_X, train_y, val_y):\n",
        "    forest_model = RandomForestRegressor(max_leaf_nodes=max_leaf_nodes, random_state=1)\n",
        "    forest_model.fit(train_X, train_y)\n",
        "    preds_val = forest_model.predict(val_X) # predição dos valores\n",
        "    mae = mean_absolute_error(val_y, preds_val) # calcula o erro\n",
        "    return(mae)\n",
        "\n",
        "# comparar MAE com valores diferentes de max_leaf_nodes\n",
        "for max_leaf_nodes in [5, 50, 500, 5000, 10000]:\n",
        "    my_mae = get_mae_rf(max_leaf_nodes, train_X, val_X, train_y, val_y)\n",
        "    print(\"Max leaf nodes: %d  \\t\\t Mean Absolute Error:  %d\" %(max_leaf_nodes, my_mae))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqjcYTOTSzeZ",
        "outputId": "96c9b429-ac62-4228-cfa7-556d1716bb9e"
      },
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max leaf nodes: 5  \t\t Mean Absolute Error:  375869\n",
            "Max leaf nodes: 50  \t\t Mean Absolute Error:  252233\n",
            "Max leaf nodes: 500  \t\t Mean Absolute Error:  208267\n",
            "Max leaf nodes: 5000  \t\t Mean Absolute Error:  206811\n",
            "Max leaf nodes: 10000  \t\t Mean Absolute Error:  206811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comparando os dois modelos (Decision Tree Regressor e Random Forest):"
      ],
      "metadata": {
        "id": "sbSvAQsVSgr6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print()\n",
        "print(\"Erro Absoluto Médio Random Forest: \", mean_absolute_error(val_y, melb_preds))\n",
        "print(\"Erro Absoluto Médio Decision Tree Regressor:\", erro_do_candidato)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zxwg63g-Sm_A",
        "outputId": "7ae6ec5a-27d9-4f03-fc24-5f4ca0599113"
      },
      "execution_count": 243,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Erro Absoluto Médio Random Forest:  207190.6873773146\n",
            "Erro Absoluto Médio Decision Tree Regressor: 273303.2892188509\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusão\n",
        "\n",
        "Neste código foram alterados dois hiperparâmetros: tamanho da árvore e o modelo. A melhor escolha para tamanho da árvore (max_leaf_nodes) em Decision Tree Regressor =  500 , Com erro global (MAE) =  273303.2892188509 (apesar de parecer haver um erro no loop). Para o modelo Random Forest a melhor escolha para tamanho da árvore (max_leaf_nodes) = 5000 , Com erro global (MAE) =  206811. Portanto o modelo teve um MAE menor e pareceu se beneficiar de uma profundidade maior. O que não parece bem é que nos dois casos não se atingiu o overfiting, os modelos estabilizaram nos resultados. Possivelmente há algum erro sutil, considerando que este notebook é apenas um objeto de estudos futuros e meu primeiro modelo de ML."
      ],
      "metadata": {
        "id": "jzKRlQfObKCS"
      }
    }
  ]
}