Structured Data ML Model Development

I’m rolling out a set of data-driven initiatives that combine Python-based analytics, predictive modelling and Power BI visualisation. Most of my data arrives as cleanly structured tables—CSV exports and relational database pulls—so the heavy lifting centres on training robust classification and regression models that unlock fresh business insight.

Here’s what I need from you:

• Well-documented Python notebooks or scripts (pandas, scikit-learn, XGBoost or similar) that cover the full modelling pipeline—from feature engineering through hyper-parameter tuning to final evaluation.
• A concise performance report comparing candidate models with clear metrics (accuracy, AUC, RMSE, or whichever measures fit the target).
• The serialized model files plus instructions for re-training so my team can reproduce results.
• An optional Power BI dashboard that surfaces key findings and live predictions, ready for stakeholder review.

Success looks like models that outperform our current baselines, code that a mid-level analyst can pick up without hand-holding, and visuals that make it obvious where the value lies. If that’s your wheelhouse, let’s get started.