14_Feature_Engineering.ipynb

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      "source": [
        "# Feature Engineering\n",
        "- Definição: Feature Engineering é o processo de criar, transformar e selecionar variáveis (ou features) relevantes a partir de dados brutos para melhorar o desempenho de modelos de machine learning.\n",
        "-Ele é fundamental para extrair insights e informações úteis dos dados, aumentando a eficácia da modelagem."
      ],
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      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Montar Drive Google\n"
      ],
      "metadata": {
        "id": "PAYdSHy2aK8Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8aVoL77sZoJ_",
        "outputId": "584e16d3-e7bb-4988-de06-ddf0043ab997"
      },
      "execution_count": 28,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset : Titanic\n",
        "- Normalizacao dos dados\n",
        "\n",
        "\n",
        "```\n",
        "# Isto está formatado como código\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "y1XNoZ47aTfh"
      }
    },
    {
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/.Dataset/test.csv', sep=',', encoding='UTF-8', header=0)\n",
        "df"
      ],
      "cell_type": "code",
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        "outputId": "0fe2d1ba-7d81-47d8-ecaa-a27ba94a742d"
      },
      "execution_count": 29,
      "outputs": [
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(df)"
      ],
      "metadata": {
        "id": "h_Ye5QZ3aurp"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vTy5xNCYazw3",
        "outputId": "c3b6d2eb-a952-4bb8-fc73-cb51e1069284"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId      int64\n",
            "Pclass           int64\n",
            "Name            object\n",
            "Sex             object\n",
            "Age            float64\n",
            "SibSp            int64\n",
            "Parch            int64\n",
            "Ticket          object\n",
            "Fare           float64\n",
            "Cabin           object\n",
            "Embarked        object\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizando a tabela\n",
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "uALpFx90erl9",
        "outputId": "57e40a92-6697-49b0-c7ae-59add3b86543"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass                              Name     Sex   Age  SibSp  \\\n",
              "0          892       3                  Kelly, Mr. James    male  34.5      0   \n",
              "1          893       3  Wilkes, Mrs. James (Ellen Needs)  female  47.0      1   \n",
              "\n",
              "   Parch  Ticket    Fare Cabin Embarked  \n",
              "0      0  330911  7.8292   NaN        Q  \n",
              "1      0  363272  7.0000   NaN        S  "
            ],
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1213,\n          1216,\n          1280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Krekorian, Mr. Neshan\",\n          \"Kreuchen, Miss. Emilie\",\n          \"Canavan, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.18120923562442,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          10.0,\n          34.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2673\",\n          \"W./C. 6607\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.90757617997383,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          41.5792,\n          57.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"A21\",\n          \"E45\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Q\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Importando o DecisionTreeClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "4TR27skVet84"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "MGLMVGUNbTTq"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()"
      ],
      "metadata": {
        "id": "Z6jNo9XibVsC"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    if df[column].dtype == 'object':  # Check if column is of type object (string)\n",
        "        df[column] = encoder.fit_transform(df[column])  # Encode the column"
      ],
      "metadata": {
        "id": "8Now5s--a3Ok"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()\n",
        "df_normalized = pd.DataFrame(scaler.fit_transform(df), columns=df.columns) # Pass 'columns' to preserve column names\n",
        "print(df_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCTBrGVUa-H9",
        "outputId": "682e02a1-df99-45a7-ed7b-737a6810c949"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass      Name  Sex       Age  SibSp     Parch    Ticket  \\\n",
            "0       0.000000     1.0  0.494005  1.0  0.452723  0.000  0.000000  0.419890   \n",
            "1       0.002398     1.0  0.966427  0.0  0.617566  0.125  0.000000  0.610497   \n",
            "2       0.004796     0.5  0.645084  1.0  0.815377  0.000  0.000000  0.201657   \n",
            "3       0.007194     1.0  0.978417  1.0  0.353818  0.000  0.000000  0.406077   \n",
            "4       0.009592     1.0  0.426859  0.0  0.287881  0.125  0.111111  0.381215   \n",
            "..           ...     ...       ...  ...       ...    ...       ...       ...   \n",
            "413     0.990408     1.0  0.846523  1.0       NaN  0.000  0.000000  0.737569   \n",
            "414     0.992806     0.0  0.678657  0.0  0.512066  0.000  0.000000  0.895028   \n",
            "415     0.995204     1.0  0.796163  1.0  0.505473  0.000  0.000000  0.955801   \n",
            "416     0.997602     1.0  0.920863  1.0       NaN  0.000  0.000000  0.607735   \n",
            "417     1.000000     1.0  0.724221  1.0       NaN  0.125  0.111111  0.290055   \n",
            "\n",
            "         Fare     Cabin  Embarked  \n",
            "0    0.015282  1.000000       0.5  \n",
            "1    0.013663  1.000000       1.0  \n",
            "2    0.018909  1.000000       0.5  \n",
            "3    0.016908  1.000000       1.0  \n",
            "4    0.023984  1.000000       1.0  \n",
            "..        ...       ...       ...  \n",
            "413  0.015713  1.000000       1.0  \n",
            "414  0.212559  0.289474       0.0  \n",
            "415  0.014151  1.000000       1.0  \n",
            "416  0.015713  1.000000       1.0  \n",
            "417  0.043640  1.000000       0.0  \n",
            "\n",
            "[418 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_normalized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "27ekE0YVbk_k",
        "outputId": "260b0471-54e9-42e4-a4cd-a66b2a59bf6f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     PassengerId  Pclass      Name  Sex       Age  SibSp     Parch    Ticket  \\\n",
            "0       0.000000     1.0  0.494005  1.0  0.452723  0.000  0.000000  0.419890   \n",
            "1       0.002398     1.0  0.966427  0.0  0.617566  0.125  0.000000  0.610497   \n",
            "2       0.004796     0.5  0.645084  1.0  0.815377  0.000  0.000000  0.201657   \n",
            "3       0.007194     1.0  0.978417  1.0  0.353818  0.000  0.000000  0.406077   \n",
            "4       0.009592     1.0  0.426859  0.0  0.287881  0.125  0.111111  0.381215   \n",
            "..           ...     ...       ...  ...       ...    ...       ...       ...   \n",
            "413     0.990408     1.0  0.846523  1.0       NaN  0.000  0.000000  0.737569   \n",
            "414     0.992806     0.0  0.678657  0.0  0.512066  0.000  0.000000  0.895028   \n",
            "415     0.995204     1.0  0.796163  1.0  0.505473  0.000  0.000000  0.955801   \n",
            "416     0.997602     1.0  0.920863  1.0       NaN  0.000  0.000000  0.607735   \n",
            "417     1.000000     1.0  0.724221  1.0       NaN  0.125  0.111111  0.290055   \n",
            "\n",
            "         Fare     Cabin  Embarked  \n",
            "0    0.015282  1.000000       0.5  \n",
            "1    0.013663  1.000000       1.0  \n",
            "2    0.018909  1.000000       0.5  \n",
            "3    0.016908  1.000000       1.0  \n",
            "4    0.023984  1.000000       1.0  \n",
            "..        ...       ...       ...  \n",
            "413  0.015713  1.000000       1.0  \n",
            "414  0.212559  0.289474       0.0  \n",
            "415  0.014151  1.000000       1.0  \n",
            "416  0.015713  1.000000       1.0  \n",
            "417  0.043640  1.000000       0.0  \n",
            "\n",
            "[418 rows x 11 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Verificando o tipo de dados"
      ],
      "metadata": {
        "id": "mzMbBFJxc1Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_normalized\n",
        "    .dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tQ6Phr5cfBM",
        "outputId": "93b95ba9-a5d1-46a5-ff78-a5c34bba44ab"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PassengerId    float64\n",
            "Pclass         float64\n",
            "Name           float64\n",
            "Sex            float64\n",
            "Age            float64\n",
            "SibSp          float64\n",
            "Parch          float64\n",
            "Ticket         float64\n",
            "Fare           float64\n",
            "Cabin          float64\n",
            "Embarked       float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hshwtmV9czYK"
      },
      "execution_count": 39,
      "outputs": []
    }
  ]
}