Your question is Real Estate Price Prediction Take-Home. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Take-home: a real estate investment firm gives train.csv/test.csv of house listings (80 feature columns + sale price). Do EDA and visualize relationships between the listed metrics, build a model on the training data explaining which features drive sale price and how, predict prices for test.csv, then review the model (assumptions, surprising results, future improvements) and give recommendations in a PPT plus notebook.
Asked in the Take-home case stage. Explain how you would make the analysis reproducible, prevent leakage, validate generalization, and communicate model limitations.