# 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.

See also: