I will share a raw, multi-sensor dataset captured from three different tractor models, along with any background information you need. The study is grounded in agricultural machinery, and the single, clear objective is to understand equipment efficiency through predictive modeling. Everything else—from framing the research questions and reviewing literature, to designing the methodology, cleaning and validating data, building and evaluating models, visualizing the results, interpreting the findings, and writing a complete, publication-ready paper—rests with you. The dataset contains detailed operating metrics such as fuel flow, engine load, GPS paths, soil conditions, and maintenance history. Feel free to work in Python, R, MATLAB, or any other environment you prefer, as long as the code is reproducible and well-commented. Deliverables • A full research paper/report (journal style, ready for submission) • Documented methodology, including model selection rationale and hyper-parameter settings • Cleaned and annotated dataset plus any feature-engineered versions used • Executable scripts/notebooks and requirements file for easy replication • High-resolution charts and tables that illustrate key insights and model performance Acceptance Criteria • Predictive model(s) demonstrate reliable accuracy on hold-out data • All figures/tables are clearly labeled and referenced in the text • Paper meets standard academic formatting and citation guidelines • Code runs end-to-end with a single command and recreates every result in the paper Ask any clarifying questions early so we can keep momentum. Once you deliver, I plan to submit the work to a peer-reviewed journal focused on precision agriculture, so rigor and clarity are critical.