At a glance
Task: classification | Headline: accuracy = 0.8515
Summary
Project
Config-driven data science pipeline for high-fidelity industrial domains.
What was investigated
The engagement modelled the target 'calibration_drift' as a classification problem in the ISM + Mechatronics: Industrial Robotic Arm Kinematics domain, using 2000 records across 34 columns (data source: Synthetic sandbox). 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 LogisticRegression, with f1_macro = 0.7962 on the held-out test set. Supporting metrics: accuracy=0.852, balanced_accuracy=0.784, precision_weighted=0.851, recall_weighted=0.852, f1_weighted=0.851, precision_macro=0.810, recall_macro=0.784, cohen_kappa=0.726, matthews_. Full results, the deployment gates and the audit trail are in the run's report.
Figures
Artifacts
Data source: Synthetic sandbox
Download report (.docx)Audit SHA-256: 0a594b57dad180c37173e11c7dceb22259a1e2ea7d1207e5c0ffe5a9b69c2b60