Comparative Analysis of Clustering Techniques for Laptop Recommendation

User's preferences are the major focus of the recommender system.Its primary goal is to suggest products to users who are likely to like or need them based on their previous purchases.With the use of machine learning (ML) and Data Mining techniques, all the prediction can be more accurately done without having to be explicitly instructed to do so.To forecast new output values, machine learning algorithms use historical data as input.This paper presents a laptop recommendation system that will aid the vendors to know the user demand and also suggest the best laptops for the users as their need, the manufacturer can know the behavior of the customer which will help them to build new models with proper evaluation, the consumers who do not have much knowledge can earn the knowledge about the laptops and can buy laptops with their limited budget and the using purpose.Two data mining techniques have been used which are K-means Clustering, Hierarchical Clustering and one from Neural Networks which is Self-organizing map (SOM).The system was implemented using Python and surveyed using the Laravel application.Results show that K-means clustering outperforms other techniques.