Real-World Predictive Data Analysis

I am preparing an academic-style report that demonstrates the full predictive analytics pipeline on a genuine dataset. The scope begins with sourcing or recommending a high-quality public dataset, continues through cleaning, feature engineering, model selection, and evaluation, and ends with a polished narrative that translates numbers into clear, actionable insights.

The analysis must centre on predictive techniques—think regression, classification, or time-series forecasting—whichever best suits the chosen data. Python with Pandas, NumPy, scikit-learn, and Seaborn/Matplotlib is welcome, but I am equally comfortable reviewing an R workflow if that is your forte; the key is reproducibility and clarity.

Deliverables
• A structured report in the required academic format (abstract, methodology, results, discussion, conclusion).
• Well-designed visualisations embedded in the document and supplied separately as image files.
• Interpreted findings and realistic recommendations backed by the model’s performance metrics.
• The complete, executable code or notebook with comments explaining each step.

Acceptance criteria
• Report adheres to the prescribed structure and citation style.
• All assumptions and preprocessing decisions are stated and justified.
• Visuals are clearly labelled and referenced.
• Predictive performance is evaluated with suitable metrics (accuracy, RMSE, AUC, etc.) and the choice of metric is explained.
• Code runs end-to-end in a standard environment without manual tweaks.

Once the dataset is confirmed, I will share page limits and any additional formatting notes so we can focus on producing insights that read as well as they predict.