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Accident prediction is an important safety issue to raise alarms before accidents happen. In this paper we formulate relevant questions to anticipate the occurrence of accidents and process the available information using Hadoop. We examine the execution time on Hadoop when compared with other methods, and propose 2 algorithms to use Congestion Control Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce (TCAMP) to effectively analyze the available data and assess road accident reasons and advise authorities to take appropriate actions to increase road safety. We apply information mining to examine recorded road attributes to reduce road accidents specifically in India, and formulate a set of standards that can be utilized by the National Highway Authority of India to improve safety.

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Security protecting and information mining is an inspection zone worried about the protection driven from identifiable data when measured for information mining. This paper tends to the security issue by allowing for the protection and algorithmic necessities at the same time. The target of this paper is to execute an Association hiding calculation for safeguarding information mining which would be proficient in giving secrecy and enhance the execution when the database stores and recovers immense measure of information. One of the procedures of information mining is association lead mining. Association rules Hiding. (ARH) is one of the real issues in the information mining space. The association rules hold numerous mysteries. So before distributing, these tenets must be hidden. Sensitive data must be covered up, since uncovering the mystery or critical association data may cause issues. In our paper, Privacy protection is finished by Association rule hiding. Amid hiding of delicate association rules, false guidelines are not created and least alteration degree is accomplished and data isn't lost.