Data Structures Write-Up Grading

[parkib]

Writeup grading!

[vibha-yganji]

Grading:

Collections:
Total 3/3, Grade 0.90/1

Prakruti was able to show her understanding of sqlite databases and how she had initialized her database as well. However, she could improve upon her write-up as additional detail could be added to the descriptions of her screenshots.

Lists and Dictionaries:
Total 3/3, Grade 0.90/1

Prakruti was able to use the debugger to show the presence of both lists and dictionaries within her code and met all the requirements.

APIs and JSON
Total 5/7, Grade 0.85/1

The use of Postman was demoed well in the code but the lack of UPDATE functionality and messy presentation on the write-up made it difficult to see if she met the requirements.

Frontend
Total 7/8, Grade .90/1

Prakruti's frontend was highly organized and her write-up was able to demo that the frontend database updated after the submission of a new quote. She also had JavaScript code that formatted her response data which she was able to demo as well. However, (as stated before), the write-up aspect could be improved to indicate a better understanding.

ML Algorithm Analysis
Total 5/5, Grade 0.9/1

Prakruti used a new dataset for heart attack prediction and was able to apply Decision Tree on this dataset to create predictions. She was able to demo this process in her write-up as well which showed her understanding of the material. However, she could have better indicated her understanding of machine learning algorithms through pictures as the write-up was not enough to show her knowledge on the topic.

Regular Score: 3.55/4

Extra ML: 0.90/1

[EshikaP1]

Collections:
Total: 3/3, Grade: 0.90/1
Prakruti demonstrated proficiency in setting up sqlite databases, but her explanations could benefit from additional detail, particularly in the descriptions of her screenshots.
Lists and Dictionaries:
Total: 3/3, Grade: 0.90/1
Prakruti effectively utilized the debugger to showcase the presence of both lists and dictionaries in her code, meeting all requirements.
APIs and JSON:
Total: 5/7, Grade: 0.85/1
While Prakruti adeptly demonstrated the use of Postman in her code, the absence of UPDATE functionality and the cluttered presentation in her write-up made it challenging to assess if all requirements were met.
Frontend:
Total: 7/8, Grade: 0.90/1
Prakruti's frontend was well-organized, and her write-up showcased how the frontend database updated upon submission of a new quote. Additionally, her JavaScript code effectively formatted response data. However, there's room for improvement in the write-up to demonstrate a deeper understanding.
ML Algorithm Analysis:
Total: 5/5, Grade: 0.90/1
Prakruti employed a new dataset for heart attack prediction and applied Decision Tree for predictions. She effectively demonstrated this process in her write-up, indicating understanding. However, incorporating visuals could better illustrate her grasp of machine learning algorithms.
Regular Score: 3.6/4
Extra ML: 0.90/1

[avanthikadaita]

SQLite Databases:
Score: 3/3
Grade: 0.90/1
demonstrated proficiency in setting up SQLite databases. However, their explanations lack depth, especially regarding the descriptions of the screenshots.
Lists and Dictionaries:
Score: 3/3
Grade: 0.90/1
effectively utilized the debugger to exhibit both lists and dictionaries in their code, fulfilling all criteria.
APIs and JSON:
Score: 5/7
Grade: 0.85/1
demonstrated adept use of Postman in their code. However, the lack of UPDATE functionality and the cluttered presentation in their write-up pose challenges in assessing if all requirements were met.
Frontend:
Score: 7/8
Grade: 0.90/1
frontend was well-organized, and their write-up showcased how the frontend database updated upon submission of a new quote. Additionally, their JavaScript code effectively formatted response data. However, there's potential for improvement in the write-up to demonstrate a deeper understanding.
ML Algorithm Analysis:
Score: 5/5
Grade: 0.90/1
tilized a new dataset for heart attack prediction and employed Decision Tree for predictions. They effectively illustrated this process in their write-up, indicating understanding. However, incorporating visuals could enhance the illustration of their grasp of machine learning algorithms.
Overall Scores:

Regular Score: 3.6/4
Extra ML: 0.90/1