Midterm report peer review by jz552 #7
Description
I like this project about using data on target company and acquirer to predict whether a merger will succeed. It is original and the result of this study can be used to predicting stock market price of the target company as shown in section 1. The data the team has collected are from SCD Platinum, a database for corporate finance and there are many methods that can be used to learn about the data. I think the team has a good method for preprocessing the data with simplifying the status column and removing the data with no ‘Price Per Share’. I also like that the team has chosen a benchmark for comparing the models and discard the models that can’t beat the benchmark. Another good point made in this report is the showing which feature is important in the random forest classifier. I think finding the most important features will help in developing more robust models.
The team could improve on dealing with NaN’s. For example, if ‘Percent of Shares Acq.’ is not an important feature, it could be dropped from the X matrix resulting in 28 features, I think this is a good way to deal with having 1600 data points with NaN for ‘Percent of Shares Acq.’. Another place for improvement is the feature importance graph, I think it would be great to see what these features are. Currently, I only know what is the most important feature, but I think the team can include more information on the second through the fifth most important features as they are also interesting to known. Finally, I think the team could explain more in the future steps section. It would be great to known if the team is going to try other models like linear models, cubic models, or neural networks.