A BIG DATA ANALYTICAL METHODOLOGY USING LINEAR REGRESSION FOR CLASSIFICATIONS IN CRIME DATA PATTERN EVALUATION

Abstract

Big data analytics is a very familiar term in computer science world especially in data science work which has been associated with huge velocity or data and versatility in the dataset. In the dissertation a huge data set has been considered in order to get better accuracy in the result section .The Weka tool that has been used before in the existing work but We have applied Rapid Miner analytics for better pattern evaluations and meaningful result mining in huge volume of data. The tool rapid miner is having better feature and configuration to get better pattern evaluations in bulk data and provide better result in data science analytics. Our work primarily considered process cycle for result optimization. In our study we’ve implemented big data in the local domain. We have used a tool for implementing big data and for finding better outcomes. Most of the studies of big data were about the simple implementation and finding the result as mining. But in our work we transformed data for efficient result and more optimized process. For this kind of job we use various operators or filters for transforming and filtering the data.