Final Grade #1
Description
Valid Python Code:
- Grade: 4/4
- Comments: Code included runs without any errors.
Exploration of Data:
- Grade: 4/4
- Comments: Data is explored significantly, and the experimental question(s) chosen are logical and based on the data exploration. Features chosen to answer the question make sense.
Machine Learning Techniques used correctly:
- Grade: 4/4
- Comments: Algorithms used are implemented correctly and the correct conclusions are drawn from the results. Learner made good choices using linear regression vs KNN regression and KNN classification vs SVM classification. Learner understands the significance of the methods and used the pre-built functions rather than writing his own calculation (good use of libraries).
Report: Are conclusions clear and supported by data?
- Grade: 4/4
- Comments: Questions are stated clearly. The results of 2 regression algorithms and 2 classification algorithms are shown. Conclusions are clearly stated and based on evidence. The learner picked some very interesting questions to answer!
Code Formatting:
- Grade: 4/4
- Comments: Code is formatted clearly and readable
Overall
- Grade: 20/20
- Comments: User's code and analysis was on point. Learner put a good amount of effort into the presentation of the data and explanation of the results. Code written was concise but still fulfilled the project requirements beyond the expected work.