regression project

The project involves performing a comprehensive linear regression analysis on a dataset, with the goal of building the best possible predictive model. It requires preprocessing the data by removing irrelevant variables, modifying existing variables, and adding dummy variables and interaction terms. The project also involves selecting the most suitable model using statistical criteria like AIC or BIC and verifying the model's assumptions (e.g., normality and homoscedasticity). The final step includes improving the model through transformations and validating the results with both visual and formal statistical tests.