Recommender (Build intelligence to help customers discover products they may like and most likely purchase)

Problem Statement

In the e-commerce industry, providing personalized product recommendations to customers is crucial for enhancing user experience, increasing customer engagement, and boosting sales. Building an effective shopping recommender system can significantly improve customer satisfaction and drive repeat purchases. As a data scientist, your task is to develop a shopping recommender system that suggests relevant products to customers based on their preferences and browsing history, thereby increasing the likelihood of purchase and overall customer retention.

Project Focus:

The main focus of this exercise is to build a shopping recommender system that can:

• Recommend products to customers based on their historical purchase data and browsing behavior.
• Enhance the customer shopping experience by providing personalized and relevant product suggestions.
• Increase customer engagement and satisfaction by accurately predicting products of interest.
• Leverage various recommendation techniques and evaluation metrics to deliver an optimal solution.

Tasks/Activities List:

The project code should include the following activities and analyses:

1. Data Collection: Collect product data, customer profiles, purchase history, and browsing behavior from the e-commerce platform.
2. Data Preprocessing: Clean, preprocess, and transform the data to make it suitable for recommendation modeling.
3. Recommendation Techniques: Implement collaborative filtering, content-based filtering, and hybrid models for product recommendations.
4. Evaluation Metrics: Utilize metrics like precision, recall, F1-score, and mean average precision to evaluate the performance of the recommender system.

Success Metrics:

The project’s success will be evaluated based on the following metrics:

1. The recommender system should achieve a recommendation accuracy of 80% or higher on a validation dataset.
2. The system should provide personalized product recommendations to users based on their preferences and show high relevance to their interests.

Bonus Points:

To earn bonus points, you can consider the following:

1. Package your solution in a well-structured zip file with a detailed README explaining how to set up and run the shopping recommender system.
2. Demonstrate excellent documentation skills by providing clear explanations of the models and their benefits to both customers and the e-commerce organization.