Performance Evaluation of Lazy and Decision Tree Classifier: A Data Mining Approach for Global Celebrity's Death Analysis

In present world data is a valuable asset. The best utilization of this asset by means of technology gives an upper hand to an organization. Technologies like machine learning, data mining, and artificial intelligence are no exception to this either. Celebrities influence common people behaviors through biological, psychological and social processes to a great extent. Simultaneously there is vast disparity amongst their demise over the cause, region, and age. Therefore, it is a challenging and interesting endorsement to work upon. The objective of this work is to come up with the comprehensive result to understand the celebrity deaths by investigating the incidence happened over the decade. The database for training is created from the public and open access databases for years 2006–2016 comprising of 11, 200 reported deaths over the globe. Findings of the work are year by year extraction of death, the cause behind it, age, gender, and place. Lazy and decision tree classifier model of data mining is being used for the analysis based on the profession as evaluation class.