Introduction

This project aims to utilize the Libraries.io API along with H2O and Keras to gather insights into the usage, popularity, and trends within various software library ecosystems.

• Package/Project Adoption
• Package/Project Popularity
• Package/Project Activity

Objectives

• Utilize the Libraries.io API to gather comprehensive data on software libraries across multiple programming languages.
• Analyze and visualize library usage trends over time.
• Identify correlations between library usage and factors such as programming language popularity, project activity, and community engagement.
• Utilize H2O for building machine learning models to predict library popularity and adoption trends.

Methodology

• Data Collection
• Data Preprocessing
• Model Training and Evaluation
• H2O
• Keras
• Model Deployment and Monitoring

Expected Outcomes

• ⁠In-depth understanding of software library ecosystems across diverse programming languages
• ⁠Visualizations illustrating trends, correlations, and predictive performance of the models developed.
• ⁠Predictive models for library popularity, adoption trends leveraging Random Forest (RF), Gradient Boosting Machines (GBM), Neural Networks with Custom Architectures, and Support Vector Machines (SVM).