E-Commerce Recommendation System: End-to-End Case Study

1. 1Build a dataset of customer interactions using historical transaction data
2. 2Run exploratory data analysis to identify key features and trends
3. 3Configure a recommendation algorithm using collaborative filtering or content-based filtering
4. 4Test the model using metrics such as precision and recall
5. 5Apply the model to a real-world e-commerce platform to measure its impact

Data scientists and product managers on a team can benefit from this case study to improve customer experience and increase sales

💡 A well-designed recommendation system can increase sales by up to 20%

💡 Build a recommendation system to boost e-commerce sales!