Data Exploration: In-depth analysis using statistical summaries, histograms, correlation matrices, and scatter plots. Visualization of feature distributions and relationships.
Data Preprocessing: Handling missing values through appropriate techniques. Outlier detection and treatment.
Hyperparameter Tuning: Exploration of different hyperparameter values for potential performance improvements.
Model Building: Linear regression models for each dataset. Model training and evaluation using appropriate metrics.
Model Evaluation: Comprehensive evaluation using metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared, Mean Absolute Error (MAE).