Substitute the artificially generated car attributes with a real data set (such as this example) and adapt the example code to work with some of those attributes instead. Add some kind of a label in the visualization to identify each car (possibly by model and year or by listing number)
Modify the example code to look for good options of houses to buy. Either generate artificial data on houses (like we did with the cars in the example code) or use a real-world dataset of your choice. Consider carefully which attributes to maximize and which to minimize. Add some kind of a label in the visualization to identify each house (possibly by address or by listing number).
Read about Pareto fronts. Summarize the concept of Pareto optimality in your own words and discuss its relationship with the example code for this session. Try to find out what diversity means in the context of Pareto fronts.
Remember to clearly cite your sources and share good ones with your classmates.
Read about other options to handling decision-maker preferences in multi-objective optimization other than assigning weights to the individual objective functions (like we did with emissions, milage, and miles per gallon in the example code). Summarize your findings and discuss the applicability of the alternatives you found to diverse real-world situations you can think of that would benefit from them.
Remember again to clearly cite your sources and share good ones with your classmates.