Data and feasibility review
Check what data is available, how it was collected, and what a useful baseline looks like. Identify gaps before committing to a model.
WORK WITH ME
Machine learning & data systems
Use your data to support a specific decision. I work on prediction, classification, and data pipelines, starting with the question you need answered and how success can be measured.
Discuss your projectIs this what
you need?
- Estimating delivery times or another operational outcome
- Classifying a growing stream of text or records
- Moving a promising model experiment into an application
THE SCOPE
How I can help.
An evaluated approach
Prepare data and compare suitable methods on an appropriate held-out set. Choose metrics that reflect the business decision, including the cost of errors.
Production integration
Plan how predictions reach the application, how inputs are validated, and what needs monitoring. Document model limits and the maintenance requirements.
RELATED CLIENT WORK
Logistics
I built a delivery package ETA prediction model to give customers more accurate delivery windows.
Read the case studyPLANNING YOUR PROJECT
A few useful answers.
How much data do I need?
There is no single minimum for every project. The task, data quality, class balance, and model approach all matter. A review of representative data is the first step.
Can you guarantee a model accuracy before the project?
A reliable estimate needs data and a clear evaluation setup. We define useful metrics and review feasibility before making performance commitments.
Do models need maintenance after deployment?
They can. Input data and real-world behavior change over time. Monitoring and any retraining or review process should be considered during planning.
LET’S TALK ABOUT YOUR PROJECT
A useful place to start.
- The decision or prediction you want to improve
- A representative dataset and its collection history
- A current baseline and the cost of different types of error
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