cestwc/bank-marketing-additional

Dataset Card for Bank Marketing (additional)

This dataset is a precise version of UCI Bank Marketing

We first created the default bank marketing dataset, as seen here. Then we further run the following Python script to create this additional portion.

# Define feature types
continuous_columns = ["age", "duration", "campaign", "pdays", "previous",
                      "emp.var.rate", "cons.price.idx", "cons.conf.idx",
                      "euribor3m", "nr.employed"]

categorical_columns = ["job", "marital", "education", "default", "housing", "loan",
                       "contact", "month", "day_of_week", "poutcome", "y"]

# Extract category mappings from the reference dataset (bank-additional)
category_mappings_additional = {col: reference_categories[col] for col in categorical_columns}


hf_features_additional = Features({
    "age": Value("int64"),
    "job": ClassLabel(names=category_mappings_additional["job"]),
    "marital": ClassLabel(names=category_mappings_additional["marital"]),
    "education": ClassLabel(names=category_mappings_additional["education"]),
    "default": ClassLabel(names=category_mappings_additional["default"]),
    "housing": ClassLabel(names=category_mappings_additional["housing"]),
    "loan": ClassLabel(names=category_mappings_additional["loan"]),
    "contact": ClassLabel(names=category_mappings_additional["contact"]),
    "month": ClassLabel(names=category_mappings_additional["month"]),
    "day_of_week": ClassLabel(names=category_mappings_additional["day_of_week"]),
    "duration": Value("int64"),
    "campaign": Value("int64"),
    "pdays": Value("int64"),
    "previous": Value("int64"),
    "poutcome": ClassLabel(names=category_mappings_additional["poutcome"]),
    "emp.var.rate": Value("float32"),
    "cons.price.idx": Value("float32"),
    "cons.conf.idx": Value("float32"),
    "euribor3m": Value("float32"),
    "nr.employed": Value("float32"),
    "y": ClassLabel(names=category_mappings_additional["y"])  # Target column
})

# Convert pandas DataFrame to Hugging Face Dataset
hf_dataset_additional = Dataset.from_pandas(df_additional, features=hf_features_additional)

# Print dataset structure
print(hf_dataset_additional)

The printed output could look like

Dataset({
    features: ['age', 'job', 'marital', 'education', 'default', 'housing', 'loan', 'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],
    num_rows: 41188
})