Question

Describe a real-world prediction problem using urban data for which interpretability of your models and results is essential, and for which it might be preferable to use decision trees rather than random forests. Argue why this is the case.

Answer 1

Real-world prediction problem using urban data for which interpretability

This research study where a number of software tools and data mining techniques are used to build models to predict financial success (box-office receipts) of Hollywood movies while they are nothing more than ideas (pre-release). Predicting box office receipts (financial success) of a particular motion picture is an interesting and challenging problem. The difficulty associated with forecasting product demand, making the movie business in Hollywood a risky endeavor

Methodology:-

The collected data from a variety of movie-related databases and consolidated into a single data set. -used a variety of data mining methods including neutral networks, decision trees, support machines, and using three types of ensembles to develop the prediction models.

Results:-

The ensemble models performed better than the individual predictions model of which the fusion algorithm performed the best. The significantly low standard deviation obtained from the ensembles compared to the individual models.

Conclusion:

Sensitivity analysis helps through this research study in analyzing effect of change of one variable at a time let’s say actor here, on the financial outcome of the cinema.

The model can be improved by addition of relevant variables & doing sensitivity analysis on them