Event Type
Research Presentation
Academic Department
Mathematics and Statistics
Start Date
25-4-2022 12:00 AM
End Date
25-4-2022 12:00 AM
Description
This paper is inspired by the extensive use of Recommendation Systems in this digital era. It draws concepts from Machine Learning and Data Science to develop a recommendation model employing Instacart’s User Dataset. It aims to utilize the concept of collaborative filtering which predicts relevant products based on the behavior patterns of similar users. K-Means Clustering is used to split customers into distinct groups depending on their attributes. The predictions are made for each cluster of users based on the cluster’s collective purchase pattern.
K-Means Clustering for Instacart Recommendations
This paper is inspired by the extensive use of Recommendation Systems in this digital era. It draws concepts from Machine Learning and Data Science to develop a recommendation model employing Instacart’s User Dataset. It aims to utilize the concept of collaborative filtering which predicts relevant products based on the behavior patterns of similar users. K-Means Clustering is used to split customers into distinct groups depending on their attributes. The predictions are made for each cluster of users based on the cluster’s collective purchase pattern.
Comments
Under the direction of Dr. Giancarlo Schrementi.