This is a web application developed using Streamlit that helps developers identify potential anomalies in code by highlighting patterns of unusual occurrences. The application performs data preprocessing, data analysis, and data visualization on the Linux Kernel Git Revision History dataset to provide insights into the code.

The following libraries were used to develop this application:

• Streamlit
• Matplotlib
• Seaborn
• Pandas
• NumPy

To use this application, follow these steps:

• Clone this repository to your local machine.
• Install the required libraries using pip install requirements.
• Run the application using streamlit run app.py.
• The application will open in your default browser, allowing you to interact with it.

The Linux Kernel Git Revision History dataset was used to develop this application. This dataset contains information about the Linux kernel development process, including the number of commits, the number of contributors, and the time between commits. The dataset can be found at https://www.kaggle.com/datasets/philschmidt/linux-kernel-git-revision-history