I have a CSV file that contains categorical information on concrete mix ingredients and curing ages, and I need a regression model that can predict compressive strength with solid accuracy. You will start with exploratory data analysis, handle any necessary feature engineering to make the categorical inputs usable, train and validate several algorithms, and finally settle on the one that delivers the best generalisation performance. Please work in Python and share fully commented notebooks or scripts so I can reproduce every step, from loading the raw CSV to exporting the final model. Key metrics such as MAE, RMSE, and R² on a held-out test set should be reported, and the final model object saved for later deployment. Deliverables • Cleaned and engineered dataset (CSV or pickle) • Reproducible code/notebook with clear explanations • Performance report with plots and metrics • Serialized final regression model ready for inference