Overview
1. Project Proposal (Due Nov. 8)
(a) Description of the problem (b) Description of the data set (c) Analysis Plan (Inference or Prediction? Regression or Classification?) The proposal should be concise and clear. Maximum 1 page. You also need to include the citation for the data of choice. 2. Presentation (Dates will be updated) (a) Description of the data and the questions that you are interested in answering (b) Review of some of the approaches that you tried or thought about trying (c) Summary of the final approach you used and why you chose that approach (d) Summary of the results (e) Conclusions Points will be allocated for clear articulations of the question of interest, the approach you used to solve it, the reason you chose that approach, and the conclusions you were able to draw.
3. Project Report (Due Dec. 14) (a) Introduction: Describe the data set and the problem you want to solve. (b) Methodology: Explain the methods and tools you used. (c) Result: Summarize the results.
(d) Discussion: Potential direction of the future work and limitation of your work. Please include discussions of the extent and limitation of your results and how you over- came any challenge during project. Maximum 5 pages, tables, figures, or R codes are not counted. The report must also include your R codes in appendix or submit separate .R file together with your report. Grading Data Repositories - Kaggle (https://www.kaggle.com/datasets ) - UCI Machine Learning (https://archive.ics.uci.edu/ml/index.php ) - StatLib (http://lib.stat.cmu.edu/datasets/ ) - KDnuggets (https://www.kdnuggets.com/datasets/index.html ) - You can also use your own data set of interest.
i already did the proposal and the slides powerpoint i need the project report \ Someone tried to solve this, so I sent it to you as well. I want you to add R code and CHARTS to it, and you should also write it as a report.