Predictive / AI-Driven Analytics Practice Questions
Responsible AI: Bias Detection & Mitigation
Build models that are accurate and fair across groups. Compare common bias metrics and practical…
MLOps Basics: From Notebook to Production
Ship models with confidence by treating machine learning like software, not experiments that never…
Neural Nets for Demand Forecasting
Neural networks can capture nonlinearity and complex seasonality that trip up simple baselines.…
AutoML Platforms: Strengths and Pitfalls
AutoML can speed up experimentation and raise baselines, but it is not a silver bullet. Know when to…
Prophet vs. ARIMA: Which Fits Your Data?
Compare model assumptions and features to pick the best forecaster for your series. Know when…
Handling Seasonality and Trend in Retail Forecasts
Retail demand swings with holidays, promotions, and calendar quirks. Learn how to encode seasonality…
Ensemble Methods: Bagging, Boosting & Stacking
Boost accuracy by combining weak and strong learners the right way. Understand how bagging,…
Interpreting SHAP Values Like a Pro
Learn how SHAP breaks a single prediction into feature contributions you can trust. Understand…
Avoiding Data Leakage in Model Training
Spot the subtle ways information from the future can sneak into your training data. Master pipelines…
Hyperparameter Tuning: Grid, Random or Bayesian?
Compare popular search strategies and when to use each for faster, better models. Avoid overfitting…
