Popularity_predictor

We have created a web app using Streamlit and deployed a custom machine learning model that can predict whether any GitHub repository is safe to consume or not just by passing the URL. Here, we generate a score based on the data we scrapped from some famous and random repositories on GitHub.

• Folder Notebooks contains data and script to extract data, analysis of data or the model creation code.
• We have used github api and Kaggle to collect the github data stored in the file github_api.csv and kaggle_data.csv respectively which has columns repo_name, star, fork, watch, issue, tags, most_used_lang, discription, contributors, license, and repo_url.
• data_extraction.ipynb file contains script to extract the information from repositories, analysis.ipynb file contains cleaning and visualization operations on the dataset. model.ipynb building a machine learning model that can predict which repositories will gain how much stars in the future. 😃