https://github.com/hatoonguls/predicting-an-essay-s-score-based-on-keyboard-events

In this project I fitted the data to multiple models such as linear regression, Light GBM, Catboost and Neural Network to find the model which performs best on RMSE. Catboost and Light GBM had the best performance.
https://github.com/hatoonguls/predicting-an-essay-s-score-based-on-keyboard-events

Last synced: about 2 months ago
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In this project I fitted the data to multiple models such as linear regression, Light GBM, Catboost and Neural Network to find the model which performs best on RMSE. Catboost and Light GBM had the best performance.

• Host: GitHub
• URL: https://github.com/hatoonguls/predicting-an-essay-s-score-based-on-keyboard-events
• Owner: hatoonguls
• Created: 2024-07-20T19:10:44.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-20T19:35:42.000Z (6 months ago)
• Last Synced: 2024-07-21T20:09:05.724Z (6 months ago)
• Language: Python
• Homepage:
• Size: 1.2 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting-an-essay-s-score-based-on-keyboard-events

This project aims to predict the overall quality of writing based on typing behavior, using a dataset of keystroke logs that capture features of the writing process. Traditional writing assessments primarily focus on the final written product. This dataset emphasizes the process of writing, investigating how various writing behaviors, like pausing patterns, additions or deletions, and revisions, impact the quality of writing. More importantly, the scale of this dataset allows us to use data-driven approaches to investigate the relationship between writing behaviors and writing quality, which could lead to more robust and generalizable findings.