Module: config.py

Configuration Settings

The config.py module contains configuration settings for the tabular data classification process. These settings include model type, dataset split ratio, random state, target column, and feature columns.

Configuration Variables

• MODEL_TYPE: Specifies the type of model to be used. Options could include "RandomForestClassifier", "LogisticRegression", "SVM", etc.

  • Example: "RandomForestClassifier"

• TEST_SIZE: The proportion of the dataset to include in the test split.

  • Example: 0.2 (20% of the dataset will be used for testing)

• RANDOM_STATE: Controls the shuffling applied to the data before applying the split. This ensures reproducibility of the split.

  • Example: 42

• TARGET_COLUMN: The name of the column in the dataset that contains the target variable (the variable to be predicted).

  • Example: "target"

• FEATURE_COLUMNS: A list of column names to be used as features in the model. These columns are used for training the model.

  • Example:

    [
        "feature1",
        "feature2",
        "feature3"
    ]

These settings will be used throughout the classification process to ensure consistency and reproducibility.