Maven Package Analysis Overview This repository contains scripts and notebooks for analyzing Maven packages. The analysis includes: Distribution of Maven packages across different ranges of star counts. Correlation matrix of popularity metrics for Maven packages. Comparison of features across the top and bottom 20% of packages, along with P-value and Cohen’s d for effect size. Hierarchical clustering to handle multi-collinearity among features, with selected metrics listed. Logistic regression analysis to generate final results. Prerequisites Python 3.8 or higher All required Python packages listed in requirements.txt Installation Clone the repository: git clone cd Install the required dependencies: pip install -r requirements.txt Usage 1. Distribution of Maven Packages by Star Count To find the distribution of Maven packages across different ranges of star counts, run the following command: python .\star_count_distribution.py 2. Correlation Matrix of Popularity Metrics To generate the correlation matrix for Maven package popularity metrics, run the following command: python .\cluster_corelation.py 3. Feature Comparison Across Top and Bottom 20% To compare features across the top and bottom 20% of packages, including P-value and Cohen’s d for effect size, run the following command: python .\minmaxmedian.py 4. Hierarchical Clustering for Feature Selection To apply hierarchical clustering and handle multi-collinearity among features: Open hierarchical_clustering.ipynb in a Jupyter Notebook environment. Run all the cells in the notebook. This step will identify the following metrics: License Commits Count Readme Exists About Info Dependencies Usages Closed Issues Percentage Release Frequency Vulnerabilities Figure 3 will also be generated during this process. 5. Logistic Regression Analysis To perform logistic regression analysis and generate the final results, run the following command: python .\Logistic_Regression.py Contact For any questions or issues, please reach out to the repository maintainer.