Wireless sensor network has attracted significant attention in research and development due to its tremendous applications in medical, military and defence, medical, environmental, industrial, infrastructure protection, and commercial applications to enable to interact with each other controlled remotely. A Wireless Sensor Network (WSN) has wide applications such as environmental monitoring and tracking of the target nodes for communication. The sensor nodes are equipped with wireless interfaces used for communication between the nodes and another network. Wireless Sensor Network suffers from many constraints that make security a primary challenge. When the sensor node is deployed in a communication environment unattended, the nodes are vulnerable to various attacks. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, univariate analysis, bivariate and multivariate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate in predicting the type WSN attacks. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy, precision.