Regression Model
is a machine learning model used to predict continuous numerical values based on one or more input variables. It estimates relationships between variables to produce predictions such as prices, temperatures, or demand.
What is a Regression Model?
A regression model learns patterns between input features and a continuous target variable using training data. Common approaches include linear regression, polynomial regression, and more complex machine learning methods. The model's predictions can be evaluated using metrics such as mean absolute error and mean squared error.
Why is Regression Model Important?
Regression models help organizations understand relationships between variables and make predictions about numerical outcomes. They are useful for forecasting, planning, and decision-making where the target is a measurable quantity rather than a category.
Common use cases
Regression models are commonly used for sales forecasting, price prediction, demand estimation, risk modeling, financial analysis, and time-series prediction.