Road accident is one amongst the crucial areas in India. The information to be analyzed is collected from various sources and has various aspects. It's a challenge to collect all such relevant data, detect and analyze it together to get decision trees that give information about previous accidents. One amongst the most objectives in accident data analysis is to spot the most factors of road accidents. For this matter, there's a necessity of machine learning to research the speed of accident. The results of this project are utilized to place some accident prevention efforts within the areas identified for a various variety of accidents to beat the number of accidents.
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This project is predicated on data analysis within which we try to analyze an outsized set of knowledge not able of being analyzed by typical database or data analysis software like Excel. Patterns involved in a severe accident can be detected if we develop suitable prediction models capable of automatic classification of a variety of injury severity of assorted traffic accidents. To beat this, we try to implement distributed processing using the Hadoop and acquire the required result with Apache Zeppelin to research and visualize the information set and generate a choice tree. This project is helpful for several purposes such as:
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Modules used in this project
ATTRIBUTE SELECTION AND TREE INDUCTION
VISUALIZATION WITH APACHE zeppelin
This framework has many steps to urge the finalized analysis of knowledge.
they're as follows:
Hadoop login: The Apache Hadoop may be a collection of open-source software that facilitate a framework for distributed storage and processing of enormous data sets using the MapReduce programming model.
Structured data for Pre-Processing.
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