Clean Numerical Data, Repair Gaps

I have a numerical dataset that contains gaps I need filled correctly. The core task is to locate every missing value, decide on an appropriate strategy to handle it (imputation, interpolation, or justified removal), then supply the refreshed dataset back to me ready for analysis. Please outline the exact approach you will follow, the libraries or tools you prefer—whether that is Python (pandas, NumPy, scikit-learn), R, Excel Power Query, or another proven method—and any quality checks you run to make sure the fixes are sound.

When you respond, attach a concise yet detailed project proposal so I can see your thought process, timeline, and any assumptions up front.

Deliverables:
• The cleaned numerical dataset in its original file format
• A brief report summarizing the techniques used, percentage of values affected, and any noteworthy patterns you uncovered

If you have questions about the dataset’s size or structure, let me know and I’ll share a sample before we start.