Prediction & Forecasting

Prediction and forecasting models are among the most widely deployed ML applications in industry. They turn historical data into actionable insights — predicting customer churn, forecasting demand, classifying risks, and enabling data-driven decision making.

Core techniques include classification models (logistic regression, random forests, XGBoost, LightGBM for binary and multi-class prediction), time series forecasting (ARIMA, Prophet, LSTM, temporal fusion transformers), and model interpretation (SHAP values, feature importance, partial dependence plots).

Key considerations include feature engineering for temporal data, handling class imbalance (SMOTE, threshold tuning, class weights), proper evaluation (stratified cross-validation, calibration), and deploying models that drive business action (threshold selection based on business costs, monitoring prediction drift). These models are where ML creates the most direct business value.