Comparative Study to Perform Tweets/NEWS Classification Using Big Data Analytics for Predicting Stock Market Price Movement

2022 ◽  
pp. 75-83
Author(s):  
Dhara N. Darji ◽  
Satyen M. Parikh ◽  
Hiral R. Patel
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Comparative Study ◽  
Data Analytics ◽  
Market Price ◽  
Big Data Analytics ◽  
Price Movement

scholarly journals Identifying Feature Stock Price by Considering Most Influential Parameters using Prediction Methods in Indian Stock Exchange

2019 ◽  
Vol 8 (4) ◽  
pp. 1-4
Keyword(s):  
Big Data ◽  
Stock Market ◽  
Stock Price ◽  
Data Analytics ◽  
Stock Exchange ◽  
Market Price ◽  
Big Data Analytics ◽  
Share Price ◽  
Book Value ◽  
Share Prices

Before the evaluation of big data analytics predicting the optimal share price in the stock market is very difficult, by applying the big data analytics it is easy to predict frequent patterns and feature outcomes in any domain. So in this paper we consider the financial domain to predict feature outcomes of share prices in the Indian stock exchange. We first gathered the dataset with duration 2011-2016 financial years of TCS Company, the reason to choose TCS dataset it is a trust based company and datasets are available at open access with all parameters. Market price per share is strongly effect by company’s variable like price earnings, dividend yield, dividend per share, earnings per share, book value per share, and return on equity, after observing the results we conclude that the variables price earnings, book value per share and firm size are important determinants of share prices in the Indian stock market. The regression model achieved a high R2 (0.94) for the closed price and book value per share variable and also the model achieved a high R2 (0.98) for the closed price and price earnings.

Big Data Analytics Tools and Platform in Big Data Landscape

2018 ◽  
pp. 80-89
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

Big Data Analytics Tools and Platform in Big Data Landscape

2022 ◽  
pp. 622-631
Author(s):  
Mohd Imran ◽  
Mohd Vasim Ahamad ◽  
Misbahul Haque ◽  
Mohd Shoaib
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Apache Hadoop ◽  
Pros And Cons ◽  
The Comparative Study ◽  
Analytical Tools

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

scholarly journals Effect of SBI Bank Merger on Education Loan, a Comparative Study Using Big Data Analytics

2018 ◽  
Vol 7 (2.32) ◽  
pp. 452
Author(s):  
Anjali Mathur ◽  
K Vinitha ◽  
R Shubham ◽  
K Gowtham
Keyword(s):  
Big Data ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Global Level ◽  
Analysis Techniques ◽  
Bank Merger ◽  
Huge Data ◽  
High Level ◽  
The Impact

A bank merger is a situation in which two banks or all branches of a bank join together to become one bank. The bank merger of State Bank of India was implemented on 1stApril 2017 in India. The bank merger is a good idea to centralize the customer’s data from nationwide. However, it is a difficult task for administrators and technologists. Some high level techniques are required to collect the data from the branches, of the bank present at nationwide, and merge them accordingly. For this huge data Big-Data Analysis techniques can be used to manage and access the data. The big data analytics provides algorithms to compare, classify and cluster the data at local and global level. This research paper proposes big data analytics for education loan provided by State Bank of India. The loan granting process becomes centralized after merger. It affects the processing of granting a loan, as earlier it was according to branches only. The proposed work is for comparative study of the impact of bank merger on education loan provided by State Bank of India.  

scholarly journals Traditional machine learning and big data analytics in virtual screening: a comparative study

2020 ◽  
Vol 10 (47) ◽  
pp. 72-88 ◽  
Author(s):  
Sahar K. Hussin ◽  
Yasser M. Omar ◽  
Salah M. Abdelmageid ◽  
Mahmoud I. Marie
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Virtual Screening ◽  
Comparative Study ◽  
Data Analytics ◽  
Big Data Analytics
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