At a glance
Task: classification | Headline: accuracy = 1.0
Summary
Project
Config-driven data science pipeline for high-fidelity industrial domains.
What was investigated
The engagement modelled the target 'curtailment_risk' as a classification problem in the Renewable Energy: Smart Grid Load & Solar/Wind Forecasting domain, using 2000 records across 14 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 LightGBM, with f1_macro = 1.0000 on the held-out test set. Supporting metrics: accuracy=1.000, balanced_accuracy=1.000, precision_weighted=1.000, recall_weighted=1.000, f1_weighted=1.000, precision_macro=1.000, recall_macro=1.000, cohen_kappa=1.000, 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: 2db0f107fd9f62bce80079c4372de097ced9014f844788bebe96afc8a787df76