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Prediction of Road Accidents has gained importance over the years however road accidents may not be stopped but rather can be controlled. Driver feelings, for example, tragic, sad, and anger can be one purpose behind accidents. In the meantime, weather conditions, for example, climate, traffic conditions, sort of road, health of driver, and speed can likewise be the purposes behind accidents. Big data is a term utilized for vast and complex informational collections for handling as the traditional data mining techniques are incomplete for preparing them. In this paper an Enhanced Expectation-Maximization (EEM) Algorithm is utilized which works dependent on the Gaussian dissemination. In the proposed work the entire dataset is divided into different clusters based on vehicle type and again these groups are separated into sub groups dependent on parameter on each vehicle type. Strong Association Rules using Improved Association Rule Mining (IARM) algorithm are designed for every vehicle class and for each parameter. The Congestion control using Machine Framework (CCMF) and Traffic Congestion Analyzer using Map Reduce TCAMP () algorithms are used for training the machine and to apply each and every association rule on the dataset and accurate prediction set is generated.

The problem of detecting and eliminating duplicated data is one of the major problems in the broad area of data cleaning and data quality in data warehouse. Many times, the same logical real world entity may have multiple representations in the data warehouse. Duplicate elimination is hard because it is caused by several types of errors like typographical errors, and different representations of the same logical value. Also, it is important to detect and clean equivalence errors because an equivalence error may result in several duplicate tuples. Recent research efforts have focused on the issue of duplicate elimination in data warehouses. This entails trying to match inexact duplicate records, which are records that refer to the same real-world entity while not being syntactically equivalent. This paper mainly focuses on efficient detection and elimination of duplicate data. The main objective of this research work is to detect exact and inexact duplicates by using duplicate detection and elimination rules. This approach is used to improve the efficiency of the data.

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