· The deliverable should contain the following components:
(1) Overall Goals/Research Hypothesis (20 %)
1-3 research questions to navigate/direct all your project.
· You may delay this section until (1) you study all previous work and (2) you do some analysis and understand the dataset/project
(2) (Previous/Related Contributions) (40 %)
As most of the selected projects use public datasets, no doubt there are different attempts/projects to analyze those datasets. 30 % of this deliverable is in your overall assessment of previous data analysis efforts. This effort should include:
· Evaluating existing source codes that they have (e.g. in Kernels and discussion sections) or any other refence. Make sure you try those codes and show their results
· In addition to the code, summarize most relevant literature or efforts to analyze the same dataset you have picked.
· For the few who picked their own datasets, you are still expecting to do your literature survey in this section on what is most relevant to your data/idea/area and summarize those most relevant contributions.
(3) A comparison study (40 %)
Compare results in your own work/project with results from previous or other contributions (data and analysis comparison not literature review)
The difference between section 3 and section 2 is that section 2 focuses on code/data analysis found in sources such as Kaggle, github, etc. while section 3 focuses on research papers that not necessary studied the same dataset, but the same focus area
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