Mid-term Report #87
Description
This project is using horse races in Hong Kong from 2014 to 2016 to be able to predict horse's ranking at the end of the race based on the specific features. They want to know the ranking of each horse not just necessarily which horse will be in first place.
I really like the idea of being able to predict horse race outcomes. I think this is a fun dataset to play with. I would like to see the dataset itself so try to provide the link for tht somehow. I also liked the way you went about explaining your model selection. It seems this team is aware of how to move on the the next steps. It was very easy to understand where you were taking this project.
What I think could be improved is to be more specific on how you cleaned the data. What type of transformations did you do. For example, did you use one-hot encoding for your nominal features. You mentioned how different types of models would need different types of data selection/cleaning. I think is good since you are trying to get the best outcome. However, you seemed to not go in detail of what you did so i'm still left wondering how your dataset looks like. You should also do some tests to see if features you take out are actually significant or not. I agree with the other peer reviews in that taking features out because its intuitive is not the best approach. You should provide evidence on why certain things wont make an impact. I also think you could make your visuals i bit more visualizing appealing. The histogram categories are very small and I can barely read it. Also there are titles missing for the graphs.