I am advancing a machine-learning study and need a detail-oriented researcher to help me move faster from raw data to publishable insights. The work begins with data preprocessing: cleaning messy inputs, transforming variables into model-ready formats, and performing thoughtful feature selection. Once the dataset is in shape, I’ll hand over my existing notebooks so you can extend the model-training pipeline (primarily Python with pandas, scikit-learn, and, where helpful, PyTorch or TensorFlow). After training, I’m looking for clear, statistically sound result analysis—plots, metrics, and concise commentary that reveal whether the approach is working and where it can be improved. Finally, I’d like your support in drafting the technical sections of our report: methodology, experiments, and results, written so they can drop straight into an academic paper or internal white-paper. Deliverables • Cleaned and transformed dataset with documented preprocessing steps • Reproducible training scripts/notebooks and saved model artifacts • Analysis notebook (or report) summarizing metrics, visualizations, and interpretation • Draft text for the paper’s data, method, and results sections If you have a solid grasp of modern ML workflows and can communicate findings clearly, I’d love to collaborate and push this research forward.