ML Data Audit — Is Your Dataset Ready for Machine Learning?

I will audit your dataset and deliver a detailed ML readiness report covering completeness, class balance, feature quality, missing value patterns, outlier distribution, and a specific remediation plan for every data quality issue preventing your dataset from being ML-ready. Data quality is the single biggest determinant of ML model performance — a well-designed model trained on poor data will consistently underperform a simple model trained on clean, well-prepared data, making data audit the highest-value step in any ML project.

The audit covers field-level completeness, class distribution analysis, feature correlation matrix, outlier characterisation, data type validation, leakage risk identification, and a prioritised data remediation plan with effort estimates for each issue.