Enhance AI Data Accuracy
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted1 hour ago
Our Providence AI-Human platform already matches users with curated content, yet the current data-processing module still misclassifies edge-case inputs. I need a specialist to refine the underlying functionality so that accuracy rises measurably in production.
Scope of work
• Review the existing Python-based pipeline (Pandas, NumPy, scikit-learn with a light TensorFlow layer) and pinpoint where noise, bias, or schema drift is degrading results.
• Re-engineer the key steps—feature extraction, validation, and model inference—so that the system handles atypical data without manual overrides.
• Deliver automated tests and a short benchmark comparing the before/after precision, recall, and F1 scores on our held-out dataset. A lift of at least 8 % in overall accuracy is the acceptance target.
• Provide a concise hand-off note explaining any new hyper-parameters, data-quality checks, or monitoring hooks you introduce.
Helpful details to include in your bid
Past work on similar accuracy-driven upgrades, a line on the specific tools or frameworks you plan to use, and a brief outline of milestones will let me gauge fit quickly. If you have published code or a live demo that shows tangible accuracy gains, even better.
The repository and anonymised sample data will be shared once we agree on terms; NDAs are ready. I’m ready to move fast and can test improvements the same day they land.
Contact me for project details, gh repo etc
Jordan
(801)979-7005
Jordansboxing@gmail
Scope of work
• Review the existing Python-based pipeline (Pandas, NumPy, scikit-learn with a light TensorFlow layer) and pinpoint where noise, bias, or schema drift is degrading results.
• Re-engineer the key steps—feature extraction, validation, and model inference—so that the system handles atypical data without manual overrides.
• Deliver automated tests and a short benchmark comparing the before/after precision, recall, and F1 scores on our held-out dataset. A lift of at least 8 % in overall accuracy is the acceptance target.
• Provide a concise hand-off note explaining any new hyper-parameters, data-quality checks, or monitoring hooks you introduce.
Helpful details to include in your bid
Past work on similar accuracy-driven upgrades, a line on the specific tools or frameworks you plan to use, and a brief outline of milestones will let me gauge fit quickly. If you have published code or a live demo that shows tangible accuracy gains, even better.
The repository and anonymised sample data will be shared once we agree on terms; NDAs are ready. I’m ready to move fast and can test improvements the same day they land.
Contact me for project details, gh repo etc
Jordan
(801)979-7005
Jordansboxing@gmail
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