This project explores the relationship between keystroke patterns and the quality of a user's writing. We seek to understand whether keystroke data can be used to predict the quality of a user's writing. This work is the final project for CSCI 567: Machine Learning at the University of Southern California.
The data used for this project comes from the Linking Writing Processes to Writing Quality Kaggle Competition.
/Dataset: Includes the dataset used in the project./Docs: Documentation files, including the project poster and any additional explanatory materials./Models: Jupyter notebooks used for exploratory data analysis and model experimentation.
- Clone the repository:
git clone https://github.com/anuranjanpandey/Key-To-Quality.git - Navigate to the project directory:
cd keystroke-analysis - Install all packages
conda install --file requirements.txt - After downloading the dataset from Kaggle, run the notebooks in
DatasetFolder to get the preprocessed dataframe and time series data to train the LSTM and Transformer models. - Use these data frames to train models in the
ModelsFolder.
Happy coding!
This project was created by the following people: