Problem statement: Online shoppers often have difficulty finding products that match their preferences and needs, leading to a poor user experience and lost revenue for e-commerce businesses.
Conduct a literature review of existing research on personalized recommendation systems and machine learning algorithms.
Develop a dataset of user and product information to train the machine learning algorithm.
Select an appropriate machine learning algorithm, such as collaborative filtering or content-based filtering.
Implement the recommendation system using Python and a machine learning library such as scikit-learn or TensorFlow.
Evaluate the performance of the recommendation system using metrics such as precision, recall, and F1-score.
Write a report summarizing your research, methodology, and findings.
Problem statement: Online shoppers often have difficulty finding products that match their preferences and needs, leading to a poor user experience and lost revenue for e-commerce businesses.
Conduct a literature review of existing research on personalized recommendation systems and machine learning algorithms. Develop a dataset of user and product information to train the machine learning algorithm. Select an appropriate machine learning algorithm, such as collaborative filtering or content-based filtering. Implement the recommendation system using Python and a machine learning library such as scikit-learn or TensorFlow. Evaluate the performance of the recommendation system using metrics such as precision, recall, and F1-score. Write a report summarizing your research, methodology, and findings.