👉 The regression project involves using statistical models to predict a continuous outcome variable based on one or more predictor variables. It aims to understand the relationship between these predictors and the outcome, often by fitting a model that minimizes prediction error. Common applications include forecasting sales, estimating house prices, or predicting patient outcomes based on various health indicators. The project typically involves data collection, exploratory data analysis, model selection (such as linear regression, polynomial regression, or more complex models), parameter estimation, and validation through metrics like R-squared or mean squared error to ensure the model's reliability and predictive power.
