Many real-world modeling, prediction, and forecasting problems are best framed and solved as regression problems. Regression has a rich history predating the advent of machine learning and has long been a part of the standard statistician’s toolkit.
Regression techniques have been developed and widely applied in many areas. Here are just a few examples:
- Weather forecasting—To predict the precipitation tomorrow using data from today, including temperature, humidity, cloud cover, wind, and more
- Insurance analytics—To predict the number of automobile insurance claims over a period of time, given various vehicle and driver attributes
- Financial forecasting—To predict stock prices using historical stock data and trends
- Demand forecasting—To predict the residential energy load for the next three months using historical, demographic, and weather data