Regressão_NãoLinear_TEMPSeatle.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
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        "features = pd.read_excel(\"temps.xlsx\")\n",
        "features.head()"
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              "   year  month  day  week  temp_2  temp_1  average  actual\n",
              "0  2016      1    1   Fri      45      45     45.6      45\n",
              "1  2016      1    2   Sat      44      45     45.7      44\n",
              "2  2016      1    3   Sun      45      44     45.8      41\n",
              "3  2016      1    4   Mon      44      41     45.9      40\n",
              "4  2016      1    5  Tues      41      40     46.0      44"
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              "         year       month         day      temp_2      temp_1     average  \\\n",
              "count   348.0  348.000000  348.000000  348.000000  348.000000  348.000000   \n",
              "mean   2016.0    6.477011   15.514368   62.652299   62.701149   59.760632   \n",
              "std       0.0    3.498380    8.772982   12.165398   12.120542   10.527306   \n",
              "min    2016.0    1.000000    1.000000   35.000000   35.000000   45.100000   \n",
              "25%    2016.0    3.000000    8.000000   54.000000   54.000000   49.975000   \n",
              "50%    2016.0    6.000000   15.000000   62.500000   62.500000   58.200000   \n",
              "75%    2016.0   10.000000   23.000000   71.000000   71.000000   69.025000   \n",
              "max    2016.0   12.000000   31.000000  117.000000  117.000000   77.400000   \n",
              "\n",
              "           actual  \n",
              "count  348.000000  \n",
              "mean    62.543103  \n",
              "std     11.794146  \n",
              "min     35.000000  \n",
              "25%     54.000000  \n",
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              "      <td>117.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>77.400000</td>\n",
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              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5aca346c-52b8-44e8-8809-b40ed11dfe64 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-79649655-0878-43f6-8b5a-bb4c41c29e91 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-79649655-0878-43f6-8b5a-bb4c41c29e91');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgAN6HqOsaCW",
        "outputId": "6110bec1-595a-458f-caf6-a12d181f017f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 348 entries, 0 to 347\n",
            "Data columns (total 8 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   year     348 non-null    int64  \n",
            " 1   month    348 non-null    int64  \n",
            " 2   day      348 non-null    int64  \n",
            " 3   week     348 non-null    object \n",
            " 4   temp_2   348 non-null    int64  \n",
            " 5   temp_1   348 non-null    int64  \n",
            " 6   average  348 non-null    float64\n",
            " 7   actual   348 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(1)\n",
            "memory usage: 21.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Como a variavel Week estava como objeto (Categorica), aplicaremos um Dummie para transforma-la em lógica\n",
        "features = pd.get_dummies(features)\n",
        "features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "qZcC4Pd6sitE",
        "outputId": "bed78022-824c-467e-99a7-e6028e08de5f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  month  day  temp_2  temp_1  average  actual  week_Fri  week_Mon  \\\n",
              "0  2016      1    1      45      45     45.6      45         1         0   \n",
              "1  2016      1    2      44      45     45.7      44         0         0   \n",
              "2  2016      1    3      45      44     45.8      41         0         0   \n",
              "3  2016      1    4      44      41     45.9      40         0         1   \n",
              "4  2016      1    5      41      40     46.0      44         0         0   \n",
              "\n",
              "   week_Sat  week_Sun  week_Thurs  week_Tues  week_Wed  \n",
              "0         0         0           0          0         0  \n",
              "1         1         0           0          0         0  \n",
              "2         0         1           0          0         0  \n",
              "3         0         0           0          0         0  \n",
              "4         0         0           0          1         0  "
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            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9c648b30-233f-48e3-9ab4-dbb2d2920451\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th></th>\n",
              "      <th>year</th>\n",
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              "      <th>day</th>\n",
              "      <th>temp_2</th>\n",
              "      <th>temp_1</th>\n",
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              "      <th>0</th>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4e091cef-aac4-43a2-a065-218f39221463')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4e091cef-aac4-43a2-a065-218f39221463 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9c648b30-233f-48e3-9ab4-dbb2d2920451 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-9c648b30-233f-48e3-9ab4-dbb2d2920451');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionando a variavel referência/target\n",
        "labels = np.array(features[\"actual\"])\n",
        "\n",
        "# Quais serão as variaveis explicativas, todas exceto a actual que já havia sido escolhida como referência\n",
        "features = features.drop(\"actual\", axis = 1)\n",
        "feature_list = list(features.columns)\n",
        "features = np.array(features)\n"
      ],
      "metadata": {
        "id": "5YZS79Zrs89z"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size = 0.25, random_state=42)"
      ],
      "metadata": {
        "id": "wKCEOM94uFaG"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos analisar o modelo em relação a média, para verificarmos como esta a resposta\n",
        "\n",
        "baseline_preds = test_features[:, feature_list.index(\"average\")]\n",
        "baseline_error = abs(baseline_preds - test_labels)\n",
        "print(\"Baseline error average:\", round(np.mean(baseline_error),2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LL_ntSTTu6ln",
        "outputId": "8168536f-3acd-4368-8318-894603fe8c1a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Baseline error average: 5.06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos importar as bibliotecas necessárias para treinar e medir o modelo\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn import metrics\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor"
      ],
      "metadata": {
        "id": "OpfKFhvvxtJ0"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinando o modelo RandomForestRegressor\n",
        "rf = RandomForestRegressor(n_estimators= 1000, random_state= 42)\n",
        "rf.fit(train_features, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "dizCvsSBy5uo",
        "outputId": "189f3766-5d2c-443c-e940-dc50a50aa963"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(n_estimators=1000, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_estimators=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_estimators=1000, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_rf = rf.predict(test_features)\n",
        "erro_rf = abs(prediction_rf - test_labels)\n",
        "\n",
        "r_sq = rf.score(features,labels)\n",
        "print(\"R^2:\", r_sq)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(test_labels, prediction_rf))\n",
        "print(\"MSE:\", metrics.mean_squared_error(test_labels, prediction_rf))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGxIoj094KFr",
        "outputId": "e8888d40-ac11-47ea-ca71-39043be241c4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2: 0.932094797587982\n",
            "MAE: 3.932057471264368\n",
            "MSE: 26.68358100000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conluimos que o erro absoluto médio (MAE) é menor do que a média, com isso construimos bom um modelo.\n",
        "\n",
        "Média 5.06 > 3.93 MAE"
      ],
      "metadata": {
        "id": "vJwEJTD05Net"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinando o modelo AdaBoostRegressor\n",
        "ada = AdaBoostRegressor(n_estimators= 100)\n",
        "ada.fit(train_features, train_labels)\n",
        "\n",
        "ada_pred = ada.predict(test_features)"
      ],
      "metadata": {
        "id": "YdqDP8Kg5pL1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_ada = abs(ada_pred - test_labels)\n",
        "\n",
        "r_sq = ada.score(features,labels)\n",
        "print(\"R^2:\", r_sq)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(test_labels, ada_pred))\n",
        "print(\"MSE:\", metrics.mean_squared_error(test_labels, ada_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2hep-jL61d2",
        "outputId": "ca40ef30-be89-4817-d70e-6aa8fc1ac58d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2: 0.8778327546366982\n",
            "MAE: 3.609523277823419\n",
            "MSE: 22.51080533569887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "O MOdelo AdaBoostRegressor teve uma performace ainda melhor que o Modelo RandomForestRegressor"
      ],
      "metadata": {
        "id": "5vGuEd4j7XHp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Treinando o modelo GradientBoostingRegressor\n",
        "gbr = GradientBoostingRegressor(n_estimators=100)\n",
        "gbr.fit(train_features, train_labels)\n",
        "gbr_pred = gbr.predict(test_features)"
      ],
      "metadata": {
        "id": "J_gqwToV7WOl"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error_gbr = abs(gbr_pred - test_labels)\n",
        "\n",
        "r_sq = gbr.score(features,labels)\n",
        "print(\"R^2:\", r_sq)\n",
        "print(\"MAE:\", metrics.mean_absolute_error(test_labels, gbr_pred))\n",
        "print(\"MSE:\", metrics.mean_squared_error(test_labels, gbr_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHhG_Yu48xmf",
        "outputId": "0a1dc0a1-74c5-4351-f952-90a21eb6d9da"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R^2: 0.9175596555480513\n",
            "MAE: 4.079084397115504\n",
            "MSE: 28.527137469473054\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ao avaliarmos a performace dos 3 MOdelos criamos, concluímos que o AdaBoostRegressor é o Modelo que obteve o melhor desempenho ( Menor MAE )"
      ],
      "metadata": {
        "id": "mz4Yf4cg9Ejc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos criar as Árvore de decisão do modelo\n",
        "\n",
        "rf = RandomForestRegressor(max_depth=3)\n",
        "rf.fit(train_features, train_labels)\n",
        "\n",
        "tree = rf.estimators_[5]\n",
        "\n",
        "tree"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "O2ncND8O9cLe",
        "outputId": "2dc38c2e-8cc7-4715-a31f-a7d83e565883"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(max_depth=3, max_features=1.0, random_state=1405772481)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=3, max_features=1.0, random_state=1405772481)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(max_depth=3, max_features=1.0, random_state=1405772481)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos preparar para exportar a imagem da árvore\n",
        "\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydot\n",
        "export_graphviz(tree, out_file=\"tree.dot\", feature_names = feature_list, rounded = True, precision=1)\n",
        "(graph, ) = pydot.graph_from_dot_file(\"tree.dot\")\n",
        "graph.write_png(\"tree.png\")"
      ],
      "metadata": {
        "id": "pmQHsxRm-RBN"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "1vPRsB3WAc1K"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Carregar a imagem PNG usando Matplotlib\n",
        "imagem = plt.imread(\"/content/tree.png\")\n",
        "plt.figure(figsize=(13, 13))\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.imshow(imagem)\n",
        "# Remover eixos\n",
        "plt.axis('off')\n",
        "# Exibindo a Imagem\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 307
        },
        "id": "cy8doNhDAGCc",
        "outputId": "c009afb2-d735-40cb-bf49-e9b8850ab3f6"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos avaliar quais as várias possuem mais importancia para o impacto da temperatura, no modelo RandomForestRegressor\n",
        "\n",
        "importances = list(rf.feature_importances_)\n",
        "\n",
        "feature_importance = [(feature, round(importance,2)) for feature, importance in zip(feature_list, importances)]\n",
        "\n",
        "feature_importance = sorted(feature_importance, key=lambda x: x[1], reverse = True)\n",
        "\n",
        "[print(\"Feature: {:20} Importance {}\".format(*pair)) for pair in feature_importance]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbkJFlG0Cnnr",
        "outputId": "abe22bb0-5466-461d-f189-d61da6f478a6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: temp_1               Importance 0.77\n",
            "Feature: average              Importance 0.21\n",
            "Feature: temp_2               Importance 0.02\n",
            "Feature: year                 Importance 0.0\n",
            "Feature: month                Importance 0.0\n",
            "Feature: day                  Importance 0.0\n",
            "Feature: week_Fri             Importance 0.0\n",
            "Feature: week_Mon             Importance 0.0\n",
            "Feature: week_Sat             Importance 0.0\n",
            "Feature: week_Sun             Importance 0.0\n",
            "Feature: week_Thurs           Importance 0.0\n",
            "Feature: week_Tues            Importance 0.0\n",
            "Feature: week_Wed             Importance 0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos avaliar quais as várias possuem mais importancia para o impacto da temperatura, no modelo AdaBoostRegressor\n",
        "importances = list(ada.feature_importances_)\n",
        "\n",
        "feature_importance = [(feature, round(importance,2)) for feature, importance in zip(feature_list, importances)]\n",
        "\n",
        "feature_importance = sorted(feature_importance, key=lambda x: x[1], reverse = True)\n",
        "\n",
        "[print(\"Feature: {:20} Importance {}\".format(*pair)) for pair in feature_importance]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WuKBZOyEHfh",
        "outputId": "4fc9df8f-270c-4909-d82b-7e3c8d0597e7"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: temp_1               Importance 0.52\n",
            "Feature: average              Importance 0.25\n",
            "Feature: temp_2               Importance 0.09\n",
            "Feature: month                Importance 0.05\n",
            "Feature: day                  Importance 0.03\n",
            "Feature: week_Mon             Importance 0.03\n",
            "Feature: week_Fri             Importance 0.01\n",
            "Feature: week_Sun             Importance 0.01\n",
            "Feature: year                 Importance 0.0\n",
            "Feature: week_Sat             Importance 0.0\n",
            "Feature: week_Thurs           Importance 0.0\n",
            "Feature: week_Tues            Importance 0.0\n",
            "Feature: week_Wed             Importance 0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Vamos avaliar quais as várias possuem mais importancia para o impacto da temperatura, no modelo GradientBoostingRegressor\n",
        "importances = list(gbr.feature_importances_)\n",
        "\n",
        "feature_importance = [(feature, round(importance,2)) for feature, importance in zip(feature_list, importances)]\n",
        "\n",
        "feature_importance = sorted(feature_importance, key=lambda x: x[1], reverse = True)\n",
        "\n",
        "[print(\"Feature: {:20} Importance {}\".format(*pair)) for pair in feature_importance]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P1fHVsUaEYJB",
        "outputId": "c50bb8c9-62ef-4805-89a1-bcba3bf55e57"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: temp_1               Importance 0.63\n",
            "Feature: average              Importance 0.3\n",
            "Feature: day                  Importance 0.02\n",
            "Feature: month                Importance 0.01\n",
            "Feature: temp_2               Importance 0.01\n",
            "Feature: week_Fri             Importance 0.01\n",
            "Feature: year                 Importance 0.0\n",
            "Feature: week_Mon             Importance 0.0\n",
            "Feature: week_Sat             Importance 0.0\n",
            "Feature: week_Sun             Importance 0.0\n",
            "Feature: week_Thurs           Importance 0.0\n",
            "Feature: week_Tues            Importance 0.0\n",
            "Feature: week_Wed             Importance 0.0\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, None, None, None, None, None, None, None, None, None, None, None, None]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    }
  ]
}