README.md

metadata

dataset_info:
  features:
    - name: age
      dtype: int64
    - name: job
      dtype:
        class_label:
          names:
            '0': admin.
            '1': blue-collar
            '2': entrepreneur
            '3': housemaid
            '4': management
            '5': retired
            '6': self-employed
            '7': services
            '8': student
            '9': technician
            '10': unemployed
            '11': unknown
    - name: marital
      dtype:
        class_label:
          names:
            '0': divorced
            '1': married
            '2': single
            '3': unknown
    - name: education
      dtype:
        class_label:
          names:
            '0': primary
            '1': secondary
            '2': tertiary
            '3': unknown
            '4': basic.4y
            '5': basic.6y
            '6': basic.9y
            '7': high.school
            '8': illiterate
            '9': professional.course
            '10': university.degree
    - name: default
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
            '2': unknown
    - name: housing
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
            '2': unknown
    - name: loan
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
            '2': unknown
    - name: contact
      dtype:
        class_label:
          names:
            '0': cellular
            '1': telephone
            '2': unknown
    - name: month
      dtype:
        class_label:
          names:
            '0': jan
            '1': feb
            '2': mar
            '3': apr
            '4': may
            '5': jun
            '6': jul
            '7': aug
            '8': sep
            '9': oct
            '10': nov
            '11': dec
    - name: day_of_week
      dtype:
        class_label:
          names:
            '0': mon
            '1': tue
            '2': wed
            '3': thu
            '4': fri
    - name: duration
      dtype: int64
    - name: campaign
      dtype: int64
    - name: pdays
      dtype: int64
    - name: previous
      dtype: int64
    - name: poutcome
      dtype:
        class_label:
          names:
            '0': failure
            '1': other
            '2': success
            '3': unknown
            '4': nonexistent
    - name: emp.var.rate
      dtype: float32
    - name: cons.price.idx
      dtype: float32
    - name: cons.conf.idx
      dtype: float32
    - name: euribor3m
      dtype: float32
    - name: nr.employed
      dtype: float32
    - name: 'y'
      dtype:
        class_label:
          names:
            '0': 'no'
            '1': 'yes'
  splits:
    - name: train
      num_bytes: 6095824
      num_examples: 41188
  download_size: 469576
  dataset_size: 6095824
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

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
})