E-Commerce Recommendation System Using Behavioral Data Analytics

Create a recommendation engine that analyzes user browsing and purchase history to suggest personalized products. The system will improve customer engagement and sales conversion through collaborative and content-based filtering methods.

Collect user interaction and product datasets.

Clean and preprocess behavioral data.

Implement collaborative filtering algorithms.

Develop content-based recommendation models.

Compare performance using evaluation metrics.

Create recommendation dashboards.

Optimize algorithm scalability.

Conduct performance testing.

Analyze improvement in user engagement.

Prepare documentation.