Machine Learning Model for Bio-Oil Yield Prediction from Biomass Feedstocks

I want to develop a machine learning models to accurately predict bio-oil yield obtained from biomass pyrolysis using the physical and chemical characteristics of biomass feedstocks. The objective is to build a regression model capable of estimating the conversion efficiency of different biomass materials such as agricultural residues, wood waste, and other renewable feedstocks. The project includes: Data preprocessing and cleaning Feature engineering Exploratory Data Analysis (EDA) Regression model development Model comparison (Random Forest, XGBoost, LightGBM, CatBoost, Neural Networks) Hyperparameter optimization Performance evaluation (RMSE, MAE, R²) Feature importance analysis using SHAP Prediction for new biomass samples Clear documentation and visualization of results The final deliverables include a trained machine learning model, reproducible Python code, evaluation report, and prediction interface if required.