You guys have obviously put a lot of effort into this. The report is well written, save a few grammatical errors, and you provide many helpful graphs and tables. It is great to see that you have already gotten results from multiple models and have plans to extract the best features and make multiple more models. Your explanations and initial tables clearly show that you have a firm grasp of your problem and the data/features that you have.
Looking at your MSE table, it seems like the errors are really high; you might want to look at that. If you think you have a fitting issue, I think you should explain that more and give some quantitative results for it. You might also consider giving quantitative results/reasons (e.g. variance of predictors) for why you chose the features you did.
I think your project is very promising, and you seem to have made a lot of headway. Pinning down the right features to use and the type of regression might be hard, but you are definitely on the right track.
You guys have obviously put a lot of effort into this. The report is well written, save a few grammatical errors, and you provide many helpful graphs and tables. It is great to see that you have already gotten results from multiple models and have plans to extract the best features and make multiple more models. Your explanations and initial tables clearly show that you have a firm grasp of your problem and the data/features that you have.
Looking at your MSE table, it seems like the errors are really high; you might want to look at that. If you think you have a fitting issue, I think you should explain that more and give some quantitative results for it. You might also consider giving quantitative results/reasons (e.g. variance of predictors) for why you chose the features you did.
I think your project is very promising, and you seem to have made a lot of headway. Pinning down the right features to use and the type of regression might be hard, but you are definitely on the right track.