You should not take this dataset seriously, as it is a synthetic representation based on true trends in education and career outcomes.

About the Dataset

This dataset provides insights into how different study habits, learning styles, and external factors influence student performance. It includes 10,000 records, covering details about students' study hours, online learning participation, exam scores, and other factors impacting academic success.

Dataset Features

• Student_ID – Unique identifier for each student
• Age – Student's age (18-30 years)
• Gender – Male, Female, or Other
• Study_Hours_per_Week – Hours spent studying per week (5-50 hours)
• Preferred_Learning_Style – Visual, Auditory, Reading/Writing, Kinesthetic
• Online_Courses_Completed – Number of online courses completed (0-20)
• Participation_in_Discussions – Whether the student actively participates in discussions (Yes/No)
• Assignment_Completion_Rate (%) – Percentage of assignments completed (50%-100%)
• Exam_Score (%) – Student’s final exam score (40%-100%)
• Attendance_Rate (%) – Percentage of classes attended (50%-100%)
• Use_of_Educational_Tech – Whether the student uses educational technology (Yes/No)
• Self_Reported_Stress_Level – Student’s stress level (Low, Medium, High)
• Time_Spent_on_Social_Media (hours/week) – Weekly hours spent on social media (0-30 hours)
• Sleep_Hours_per_Night – Average sleep duration (4-10 hours)
• Final_Grade – Assigned grade based on exam score (A, B, C, D, F)

Use Cases

• Predicting Student Performance – Analyze how different factors influence exam scores.
• Educational Insights – Understand the impact of study habits, learning styles, and external activities.
• Machine Learning Applications – Train predictive models for student success.

The Bank Account Fraud (BAF) suite of datasets has been published at NeurIPS 2022 and it comprises a total of 6 different synthetic bank account fraud tabular datasets. BAF is a realistic, complete, and robust test bed to evaluate novel and existing methods in ML and fair ML, and the first of its kind!

This suite of datasets is: - Realistic, based on a present-day real-world dataset for fraud detection; - Biased, each dataset has distinct controlled types of bias; - Imbalanced, this setting presents a extremely low prevalence of positive class; - Dynamic, with temporal data and observed distribution shifts;
- Privacy preserving, to protect the identity of potential applicants we have applied differential privacy techniques (noise addition), feature encoding and trained a generative model (CTGAN).

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Each dataset is composed of: - 1 million instances; - 30 realistic features used in the fraud detection use-case; - A column of “month”, providing temporal information about the dataset; - Protected attributes, (age group, employment status and % income).

Detailed information (datasheet) on the suite: https://github.com/feedzai/bank-account-fraud/blob/main/documents/datasheet.pdf

Check out the github repository for more resources and some example notebooks: https://github.com/feedzai/bank-account-fraud

Read the NeurIPS 2022 paper here: https://arxiv.org/abs/2211.13358

Learn more about Feedzai Research here: https://research.feedzai.com/

Please, use the following citation of BAF dataset suite @article{jesusTurningTablesBiased2022, title={Turning the {{Tables}}: {{Biased}}, {{Imbalanced}}, {{Dynamic Tabular Datasets}} for {{ML Evaluation}}}, author={Jesus, S{\'e}rgio and Pombal, Jos{\'e} and Alves, Duarte and Cruz, Andr{\'e} and Saleiro, Pedro and Ribeiro, Rita P. and Gama, Jo{\~a}o and Bizarro, Pedro}, journal={Advances in Neural Information Processing Systems}, year={2022} }