Stack Five Models for a Kaggle-Style Forecasting Bake-Off
Overview
What this challenge is about.
Stack Five Models for a Kaggle-Style Forecasting Bake-Off. Intermediate challenge in code. Writing production code that solves real engineering problems, ear...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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