Effective Compatibility and Reduction of Data for Bigdata Applications
Vikas S1, Thimmaraju S N2

1Vikas S*, VTU PG Centre Mysuru, Karnataka, India.
2Dr.Thimmaraju S N, VTU PG Centre Mysuru, Karnataka, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 3781-3784 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9821109119/2019©BEIESP | DOI: 10.35940/ijeat.A9821.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The system identifies a duplicate record from the database using the machine learning method. We must pass unstructured data. Data are prepared using any natural language processing technique such as text similarity. This prepared data is then fed into the latest machine learning method called Random Forest. After this data collection, using these files, the target file is compared to the source file. We make input and output files. This is carried out until accurate efficiency is generated.
Keywords: We Data processing fficiency enerated.