supersam7/simulated_bank_data_2012_2026

Simulated Bank Marketing Dataset (2012-2026)

Description

Simulated extension of UCI Bank Marketing dataset for predicting term deposit subscriptions. Original data from 2008-2010; simulated for 2012-2026 with similar distributions.

Data Source

• —Based on UCI ML Repository: https://archive.ics.uci.edu/dataset/222/bank+marketing
• —41,188 instances, 21 features. Quinlan, J. (1987). Credit Approval [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5FS30.

Features

• —age: Numeric, client's age.
• —job: Categorical, type of job (e.g., admin, blue-collar).
• —marital: Categorical, marital status (e.g., married, single).
• —education: Categorical, education level (e.g., university.degree, high.school).
• —default: Categorical, has credit in default? (yes/no/unknown).
• —housing: Categorical, has housing loan? (yes/no/unknown).
• —loan: Categorical, has personal loan? (yes/no/unknown).
• —contact: Categorical, contact type (cellular/telephone).
• —month: Categorical, last contact month.
• —day_of_week: Categorical, last contact day of week.
• —duration: Numeric, last contact duration (seconds).
• —campaign: Numeric, contacts during this campaign.
• —pdays: Numeric, days since last contact (999 if none).
• —previous: Numeric, contacts before this campaign.
• —poutcome: Categorical, previous campaign outcome (success/failure/nonexistent).
• —emp.var.rate: Numeric, employment variation rate.
• —cons.price.idx: Numeric, consumer price index.
• —cons.conf.idx: Numeric, consumer confidence index.
• —euribor3m: Numeric, Euribor 3-month rate.
• —nr.employed: Numeric, number of employees.
• —y: Categorical, subscribed to term deposit? (yes/no).

Usage

Load with pandas or R: df = pd.read_csv('simulated_bank_data_2012_2026.csv')

Citation

Original: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014