I need a self-contained Python solution that lets a user upload two or more Excel files, pick the join style—inner, left, right, or full outer—and instantly merge customer tables before running a predictive-analytics routine on the unified data. Here is how I picture the flow: • Upload: drag-and-drop or file-picker for multiple .xlsx files, with automatic sheet detection. • Join engine: the user chooses inner, left, right or outer; your code validates keys, highlights mismatched columns, and produces the merged dataframe. • Predictive step: improve on transformer model , generate accuracy metrics • Benchmark: I will supply target metric so you can tune the pipeline and demonstrate improvement over a baseline I’ll share. I value clean, well-commented code (pandas, numpy, scikit-learn, matplotlib/seaborn), a small README that shows how to run it from the command line or Jupyter, and a clear separation between data prep, join logic, and modeling so I can swap parts later. Deliverables 1. Python source and requirements.txt 2. The notebook or CLI script that reproduces results on my benchmark files 3. A brief report (Markdown is fine) summarising model choice, evaluation scores, and any assumptions made If something in the spec can be improved, feel free to propose it up front—otherwise I’m ready to move quickly once I see your approach.