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Impact Factor : 8.14

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Paper Title: Big Data Analytics with Machine Learning Models
Authors Name: Manish Kumar , Dr. (Prof.) Deva Prakash
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IJRTI_184962
Published Paper Id: IJRTI1906021
Published In: Volume 4 Issue 6, June-2019
DOI:
Abstract: Data that is too big, moving too quickly, or complex to process using conventional techniques is referred to as "Big Data." Formerly, the 3Vs were employed in Big Data, but today the 5Vs — volume, velocity, variety, veracity, and value — are used. While there is no doubt that these huge data have great potential. The artificial intelligence technique of information discovery for thoughtful decision-making is called machine learning. The most popular technologies for study in various analytics and computations today are Big Data analytics and machine learning. Although data preparation for Big Data is a topic in and of itself, large data may benefit from machine learning. Bigdata may facilitate the creation and fine-tuning of incremental/online/stream-oriented ML algorithms; in particular, it may be worthwhile to consider models that have already been created for drifting data. In most cases, learning is created by performing in-depth computations on pre-existing datasets to produce a learning model. Since data sizes are growing daily and a typical system cannot manage very large dataset calculations, the discovered model needs to be adjusted accordingly. Machine learning makes utilization of Big Data approaches. We are all aware that large data sets are ideal for machine learning, and here is where Big Data comes into play. Big Data is used to glean hidden knowledge or important insights from massive data sets. In a nutshell, we may assert that machine learning would be useless without large data. Big Data analytics and machine learning are crucial for classification and prediction in many businesses, including those in the health, education, agriculture, manufacturing, banking, and other industries. Data must be provided to machine learning models as input, and sometimes the more comprehensive the data, the better the model's output. To create the required result in such a scenario, huge data is provided as an input to a machine learning model. One of the input sources for the machine learning model could be Big Data. Machine learning is a technique used in artificial intelligence to find information that may be used to make wise decisions. This article discusses machine learning methodologies, key Big Data technologies, and a few machine learning applications in Big Data. It also explains machine learning algorithms in Big Data analytics, and machine learning challenges us to make decisions where there is no known "right path" for the specific problem based on previous lessons. It also enumerates some of the most widely used tools for analyzing and modeling Big Data.
Keywords: Big Data, Machine Learning, Big Data Analytics, Machine Learning Algorithms, Information Technology, Stream processing, Apache Foundation
Cite Article: "Big Data Analytics with Machine Learning Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.4, Issue 6, page no.131 - 143, June-2019, Available :http://www.ijrti.org/papers/IJRTI1906021.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI1906021
Registration ID:184962
Published In: Volume 4 Issue 6, June-2019
DOI (Digital Object Identifier):
Page No: 131 - 143
Country: -, -, India
Research Area: Mathematics
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI1906021
Published Paper PDF: https://www.ijrti.org/papers/IJRTI1906021
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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