Predicting customer behavior in online shopping using SVM classifier

Customer buying behavior is identified by people's personality and character. These personality characters vary from person to person. The character includes quality, motivation, occupation and income level, perception, psychological, personality, reference groups and demographic reasons learning, beliefs, attitude, Culture and social forces. Nowadays, Data mining normally used to investigate the customer activities on shopping by using various algorithms and methods. Data mining has gradually raised and it gains numerous industries which applies this technology. Each and every activity of a customer is stored as a byte of data in a database to collect information such as how the customer spends their valuable time, day in buying decision. Most frequents items bought and quantity of buy is also considered. These data are collected without the knowledge of the customer. In this paper, the dataset is used to analyze and categorize the customer based on their purchase behavior. The classification is performed by SVM algorithm. The inventory data set and sales data set which is available in the internet is used in this work and the performance is evaluated by using the algorithms. The experimental results are analyzed and it shows that the proposed methodology analyze a customer behavior in a better way.