Gold Digger Job Applicant Classification Case Study with 20,000 Observations

Case-Study-Applicants-for-a-Gold-Digger-position is a synthetic dataset from OpenML containing 20,000 fictional job applications. It includes applicant characteristics such as age, diploma, salary expectation, and exam score, with a binary hiring outcome. The dataset is intended as a playground for data science skill development and interview preparation.

Use Cases

• Predict hiring decisions based on applicant demographics and qualifications.
• Analyze the relationship between salary expectations and hiring outcomes.
• Evaluate the impact of experience and exam grades on candidate selection.
• Build binary classification models using features like diploma, availability, and hair color.

Strengths

• Contains 20,000 observations, providing a substantial sample for modeling.
• Includes 11 distinct features covering demographic, educational, and professional attributes.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data is fictional and may reflect synthetic biases inherent to its construction.

Provenance

Source

OpenML