Datasetpublic

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"]… See the full description on the dataset page: https://huggingface.co/datasets/cestwc/bank-marketing-additional.

sourceHugging Faceupdated 1y agoView on Hugging Face

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Dataset Card

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.

python

# 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
})