At a glance
Task: classification | Headline: accuracy = 0.9996
Summary
Project
Config-driven data science pipeline for high-fidelity industrial domains.
What was investigated
The engagement modelled the target 'Class' as a classification problem in the Generic / Auto-detect (any tabular dataset) domain, using 284807 records across 31 columns (data source: creditcard.csv). The pipeline audited data quality, engineered features, split the data honestly into train/test, and compared several models by cross-validation.
Outcome
The selected model was RandomForest, with f1_macro = 0.9401 on the held-out test set. Supporting metrics: accuracy=1.000, balanced_accuracy=0.913, precision_weighted=1.000, recall_weighted=1.000, f1_weighted=1.000, precision_macro=0.971, recall_macro=0.913, cohen_kappa=0.880, matthews_. Full results, the deployment gates and the audit trail are in the run's report.
Figures
Artifacts
Data source: creditcard.csv
Download report (.docx)Audit SHA-256: 22df805a5309162805d53129d433c2660f1cf39b81117c0bf51d571b915ed644