At a glance
Task: classification | Headline: accuracy = 0.6102
Summary
Project
Config-driven data science pipeline for high-fidelity industrial domains.
What was investigated
The engagement modelled the target 'Would_Recommend' as a classification problem in the Generic / Auto-detect (any tabular dataset) domain, using 300 records across 19 columns (data source: AI_Usage_and_Impact_on_Students_and_Professionals.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 XGBoost, with f1_macro = 0.5247 on the held-out test set. Supporting metrics: accuracy=0.610, balanced_accuracy=0.514, precision_weighted=0.601, recall_weighted=0.610, f1_weighted=0.603, precision_macro=0.542, recall_macro=0.514, cohen_kappa=0.272, matthews_. Full results, the deployment gates and the audit trail are in the run's report.
Figures
Artifacts
Data source: AI_Usage_and_Impact_on_Students_and_Professionals.csv
Download report (.docx)Audit SHA-256: f44a8c14dcc8ac0db8b1712ec46e421c88edca23e3873538c22c80b0b8e317ce