I have an Excel sheet of experimental concrete mixes and their measured compressive strength. I need a complete, ready-to-run machine-learning solution that takes this structured data, explores suitable nature-inspired algorithms—genetic programming, particle-swarm optimisation, ant-colony regression or any comparable approach—and delivers a model that can accurately predict strength for unseen mixes. What I will hand over: • One Excel workbook containing all variables and target values. • Brief notes on the meaning of each column. What I expect back within 2–3 days: • Python notebook or script (pandas, scikit-learn / DEAP / PySwarms or similar) that cleans the data, performs feature engineering if beneficial, trains and tunes the model and outputs performance metrics. • The trained model saved to disk plus straightforward instructions to load and use it for new predictions. • A short summary explaining algorithm choice, hyper-parameter settings and validation results (R², RMSE, etc.). If you have a creative idea for combining classical regressors with nature-based optimisation, feel free to implement it—accuracy and reproducibility are the key acceptance criteria.