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Predictive analytics, forecasting and machine learning
Forecasts and risk scores are only worth having if you know how far to trust them. I build predictive models around a specific business decision and report how they were validated, including the uncertainty, so you can plan with them instead of guessing.
What this covers
Forecasting
Time-series models that project demand, revenue and risk.
Predictive analytics
Forecasts and risk scores that estimate what happens next — and how confident we should be.
Machine learning
Classification, regression, clustering and anomaly detection — built around a business decision.
Honest evaluation
Pipelines, cross-validation and honest model evaluation.
Questions this work answers
- How much demand should we plan for next quarter, and how wide is the range?
- Which customers are likely to churn or to be worth the most?
- Which transactions or machines should be looked at first?
- Which leads or deals deserve the sales team’s time?
Tools
- Python
- Scikit-learn
- LightGBM
- SHAP
- SQL
- Forecasting
Related case studies
These are public projects with code on GitHub. Seven of my nine case studies use synthetic data generated for the project, so they show method and engineering rather than client results. Each README states its data source and limitations.
- Predictive Customer Lifetime Value & Revenue Forecasting (built on synthetic data generated for the project). LightGBM forecasts next-12-month revenue for every customer, with SHAP drivers, conformal prediction intervals and leakage-safe temporal validation. Tools: Python, LightGBM, SHAP, DuckDB, Streamlit.
- Industrial AI Predictive Maintenance (built on synthetic data generated for the project). Calibrated failure-risk forecasts for a 5,000-machine fleet, explained with SHAP and turned into a capacity-constrained weekly maintenance plan. RUL is validated on NASA C-MAPSS. Tools: Python, LightGBM, SHAP, Streamlit, Docker.
- Retail Demand Forecasting & Inventory Optimization (built on synthetic data generated for the project). ML demand forecasting with hidden-demand estimation for stock-outs, replenishment analytics and Power BI-ready reporting. Tools: Python, SQL, ML, Power BI.
- Financial Fraud Detection & Transaction Risk (built on synthetic data generated for the project). Classifiers plus anomaly detection, explained with XAI, and investigation-queue optimisation on transaction data. Tools: Python, SQL, Anomaly Detection, XAI.
See all repositories on GitHub
How a project usually runs
- Agree the question. We settle what decision the work should support and what a useful answer looks like.
- Look at the data. I review what you have, check its quality, and tell you early about any limits.
- Build and check. I do the analysis or build the model, and test it before presenting it.
- Hand over. You receive the results in a form your team can use, with the reasoning written down.
Common questions
How accurate will the model be?
That depends on your data. I will not promise a number up front. I report validation results and uncertainty ranges, and I tell you early if the data cannot support a reliable model.
How much data do I need?
It varies with the problem. A short conversation about what you have is usually enough for me to say whether a model is realistic.
Will I be able to understand the results?
Yes. I use explainability methods so you can see which factors drive a forecast or score, and I write the results for decision makers, not only for data scientists.
How much does a predictive project cost?
It depends on scope and data. Send me the details and I will reply with a clear plan and quote.
Related services: Data analysis · Dashboards
Further reading
Tell me what you are trying to decide
Send a short description of your data and the question behind it. I will reply personally with whether I can help and what the next step would be.
Email hello@zubairlabs.com Use the project form Hire on Upwork