Predictive modeling

Predictive modeling refers to techniques for predicting future outcomes using historical data. It involves developing mathematical models that capture relationships between variables to generate accurate predictions.

Key aspects:

Statistical and machine learning models uncover patterns in data.
Models are trained on labeled examples then tested on unseen data.
Predictions generated can be numeric values, categories, probabilities.
Used for forecasting, risk assessment, trend analysis.

Common models include:

Linear/logistic regression for statistical modeling.
Decision trees, random forests for nonlinear relationships.
Neural networks which learn complex mappings.
Ensemble models combining multiple techniques.

Applications span business, sciences, medicine, economics. Steps involve data preprocessing, model training/tuning, evaluation, and deployment.

Predictive modeling is crucial for leveraging data to guide real-world decision making under uncertainty. It enables estimating future risks, demands, outcomes for better planning.