There are several types of predictive models, each suited for different applications:
1. Regression Models: Used for predicting continuous outcomes. Linear regression is a common example. 2. Classification Models: Used for categorizing data into distinct classes. Examples include logistic regression and support vector machines. 3. Time Series Models: Used for forecasting data over time. ARIMA (AutoRegressive Integrated Moving Average) is widely used. 4. Clustering Models: Used for grouping similar data points together. K-means clustering is a popular technique. 5. Anomaly Detection Models: Used for identifying unusual patterns that do not conform to expected behavior.