Stack Five Models for a Kaggle-Style Forecasting Bake-Off
Overview
What this challenge is about.
Stack five models on logistics data, apply time-series cross-validation, train a stacking meta-learner, and report MAPE. Earn your verifiable certificate.
The scenario
The startup (Series A, around 60 staff, regional Asia-Pacific freight forwarder) currently uses a naive 4-week-moving-average and any meaningful MAPE improvement would let dispatch reduce buffer inventory at the major regional hubs.
The Brief
What you'll do, and what you'll demonstrate.
Beat a moving-average baseline on next-day shipment forecasting across heterogeneous lanes and horizons, using a 5-model stacked ensemble.
Earning criteria — what you'll demonstrate
- Apply ensemble methods (stacking) to a real forecasting problem
- Engineer time-series cross-validation correctly (no future leakage)
- Reason about the heterogeneity of forecast error across segments
- Communicate forecasting trade-offs in a post-mortem format
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Advanced Machine Learning
Master · Machine Learning
Strong alignment
This challenge maps to Advanced Machine Learning at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Ensemble Methods
Apply ensemble methods to solve real industry problems and demonstrate production-level capability.
- Time Series Forecasting
Apply time series forecasting to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Cross Validation
Apply cross validation to solve real industry problems and demonstrate production-level capability.
- Scikit Learn
Apply scikit learn to solve real industry problems and demonstrate production-level capability.
- Lightgbm
Apply lightgbm to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
A clean ensemble forecasting project with honest per-segment reporting is the portfolio piece that gets a junior data scientist past the screen at most industry teams.
This challenge sharpens
- ensemble-methods
- time-series-forecasting
- cross-validation
Machine Learning Engineer
Productionizing a stacked ensemble with reproducible code and clear evaluation is the day-one MLE shape.
This challenge sharpens
- ensemble-methods
- lightgbm
- scikit-learn