I have a finance-sector dataset built around customer behaviour and I need a full predictive analysis carried out on it. Your job is to take the raw customer data, explore it, select or engineer the most relevant features, and train reliable models that forecast the key outcomes I will specify once we start (think churn probability, product uptake, default risk, etc.). Python (pandas, scikit-learn) or R are both fine; SQL will help when querying the source tables. I expect clean, reproducible code in a notebook or script, together with a concise write-up that explains your methodology, the performance metrics, and the business implications of your findings. Deliverables • Cleaned and documented dataset • Well-commented modelling code / notebook • Performance report with visualisations and actionable insights I will validate the work by rerunning your notebook on my side and checking that the metrics match the figures in your report, so please keep everything self-contained and version-controlled. Looking forward to seeing how you turn customer data into precise, finance-ready predictions.