Final Project Feedback

[scott-yj-yang]

Final Project Feedback

Score (out of 12 pts)

Score = 12

Feedback

Category	Full Point	Your Score	Comment
Title & Abstract	1	1
Background	1	1
Problem Statement	1	1
Data	1	1
Proposed Solution	1.25	1.25
Metrics	1.25	1.25
Results	1.25	1.25
Interpreting the result	1.25	1.25
Limitations	1.25	1.25
Ethics & Privacy	0.75	0.75
Conclusion	1	1

Rubric

Category	Full Point	Expectation
Title & Abstract	1	Title & abstract is informative, succinct, and clear. It offers specific details about the educational issue, variables (data), context, proposed methods, and measurement of performance/success of the study.
Background	1	Use a minimum of 2 or 3 citations Include a general introduction to your topic Narrative integrates critical and logical details from the peer-reviewed theoretical and research literature. Each key research component is grounded to the literature. Attention is given to different perspectives, threats to validity, and opinion vs. evidence.
Problem Statement	1	Presents a well-defined and significant research problem Articulates clear, reasonable research questions given the purpose, design, and methods of the project. All variables and controls have been appropriately defined. Proposals are clearly supported from the research and theoretical literature. All elements are mutually supportive.
Data	1	Multiple data sources for each aspect of the project. All data sources are fully described and referenced. Data is appropriate to the question/goal and large enough data points >1k observations and >5 variable The details of the descriptions of the data also make it clear how they support the needs of the project. Details of data storage and cleaning
Proposed Solution	1.25	The elements of the process were described succinctly and with clarity about how they are connected to each other Included description how the solution will be tested.
Metrics	1.25	The metrics are described clearly and succinctly. Their appropriateness for addressing the research problem is clearly described.
Results	1.25	Does a good model/hyper-parameter selection using more than one model and hyperparameter in hyperparameter search. Include the detailed code and analysis results of the main points Performs multiple secondary analysis such as learning curves, heat maps looking at where in the parameter space things are good/bad, uses statistical testing
Interpreting the result	1.25	Think clearly about the results and obtain one main point and 2-4 secondary points (2-5 sentences per point). Highlight HOW the results support those points. Understand what they are doing in the previous “Results” section
Limitations	1.25	Has a sense of what to do next, and has good explorations of the limitations.
Ethics & Privacy	0.75	Thoughtful discussion of ethical concerns included. Ethical concerns consider the whole data science process (question asked, data collected, data being used, the bias in data, analysis, post-analysis, etc.). How your group handled bias/ethical concerns clearly described
Conclusion	1	Clearly recapitulates the results and provides context, perhaps including the literature of the field.

Comments

Seems that your result is not ideal, and part of the reason can be you didn't do the feature selection correctly, With more than 500 features it's extremely hard for the model to capture representation of the data.