Dev Log on Steam Recommender (part 2)

1. 1Collect Steam game data using web scraping or APIs
2. 2Preprocess the data by handling missing values and encoding categorical variables
3. 3Build a recommender model using collaborative filtering or content-based filtering
4. 4Test and evaluate the model using metrics such as precision and recall
5. 5Deploy the model using a web framework or API

Data scientists and machine learning engineers can benefit from this project to improve their skills in building recommender systems, while game developers can use this system to recommend games to their users

💡 Recommender systems can be built using collaborative filtering or content-based filtering techniques

Build a Steam game recommender system using data science techniques! #datascience #gamedev