Pradeep Kumar M. Kanaujia

Work place: School of Computer Engineering, KIIT University, Bhubaneswar - 751024, Orissa, India



Research Interests: Computer systems and computational processes, Data Mining, Data Compression, Data Structures and Algorithms


Pradeep Kumar M. Kanaujia has received B.Tech degree in Computer Science and Engineering from UPTU, Lucknow. He is currently an M.Tech, Computer Science and Engineering student in School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. His areas of interests are Data Mining and Data Analytics.

Author Articles
A Framework for Development of Recommender System for Financial Data Analysis

By Pradeep Kumar M. Kanaujia Manjusha Pandey Siddharth Swarup Rautaray

DOI:, Pub. Date: 8 Sep. 2017

The huge amount of data is being created every day by various organisations and users all over the world. Structured, semi-structured and unstructured data is being created at a very rapid speed from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Today no system is available for suggesting a person on how to use their money for saving, where to invest and how to manage expenditures. Few consulting systems are available which provide investment and saving tips but they are not much effective and are much complex. The presented paper proposed a collaborative filtering based recommender system for financial analysis based on Saving, Expenditure and Investment using Apache Hadoop and Apache Mahout. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommender system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Collaborative filtering and Apache Mahout is used for analysing the data and implementation of the recommender system.

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