Numerical Data Analysis in Jupyter

I have a Jupyter notebook set up with a purely numerical dataset and need an experienced Python analyst to take it the rest of the way. The core objectives are clear:

• Run thorough statistical analysis: descriptive stats, distribution checks, correlations, and any relevant significance tests so I can understand patterns at a glance.
• Build and interpret regression models—starting with linear regression and extending to any more suitable techniques you identify—to explain or predict my target variable. Clear diagnostics and visualisations that justify model choice are essential.

I’ll share the notebook, the CSV, and a brief schema. Please keep all work inside the notebook, commenting each step so I can reproduce or tweak it later. The final deliverable is the updated notebook with clean, well-structured code cells, explanatory markdown, and a short takeaway section that highlights key insights and next steps. If you lean on pandas, NumPy, SciPy, scikit-learn, or seaborn/matplotlib, that’s perfect; I already have those libraries installed.