Freight Rate Prediction
An end-to-end logistics ML pipeline for freight-rate prediction using chronological validation, CatBoost, reproducible training, release checks, automated testing, and production-style inference artifacts.
Data & Machine Learning Solutions
Data analysis, prediction pipelines and usable machine-learning tools by Alaa Madi for teams working with leads, orders, bookings and operational history.
By Alaa Madi · Updated · Remote collaboration
Who this is for
Teams with historical operational data and a specific decision to improve, such as prioritizing leads, estimating demand or understanding customer groups.
When this approach makes sense
A report or dashboard can be enough when the question is what happened. Prediction requires relevant historical examples and an evaluation that reflects future use. Start by checking data quality and a simple baseline; a model is useful only if it improves a real decision.
What the scope can include
- Help a sales team prioritize leads
- Understand meaningful customer groups
- Spot customers who may be at risk of leaving
- Estimate future demand or sales
- Build relevant recommendation experiences
- Turn predictions and insights into usable internal tools
How the work happens
- Define the decision, available data and what a useful result would look like.
- Check missing values, permissions, target definitions and potential information leakage.
- Compare a simple baseline with candidate models using a validation split appropriate to the task.
- Package the result into a report, API or interface and document limitations, monitoring and retraining needs.
Related work you can inspect
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Questions before starting
Is my business data ready for machine learning?
Useful data connects historical inputs to the outcome you want to predict. Volume alone is not enough: inconsistent labels, missing dates or changing processes can make evaluation misleading. A data review can determine whether cleaning, reporting or prediction is the sensible next step.
How do you evaluate a prediction model?
Choose a baseline and a metric that reflects the business decision, then test on examples the model did not learn from. For time-dependent tasks, chronological validation helps represent future use. The freight-rate project in my portfolio demonstrates this approach; its results should not be assumed to transfer to another dataset.
Can a model become a tool my team can actually use?
Yes. Depending on the workflow, delivery can include a dashboard, an API or a small internal application. Input validation, understandable outputs and a fallback when data is missing matter as much as the model itself.
Discuss your workflow
Share the problem, your current tools and the outcome you need. We can define a useful first scope together.
Tell me about your project