scholarly journals Statistical Modeling and Simulation of Online Shopping Customer Loyalty Based on Machine Learning and Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jui-Chan Huang ◽  
Po-Chang Ko ◽  
Cher-Min Fong ◽  
Sn-Man Lai ◽  
Hsin-Hung Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Credit Risk ◽  
Customer Loyalty ◽  
Online Shopping ◽  
Big Data Analysis ◽  
Web Page ◽  
The Web

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers’ perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty.

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

2020 ◽  
Vol 10 (4) ◽  
pp. 540-550
Author(s):  
Cerene Mariam Abraham ◽  
Mannathazhathu Sudheep Elayidom ◽  
Thankappan Santhanakrishnan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Work ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

2021 ◽  
Author(s):  
Bohdan Polishchuk ◽  
Andrii Berko ◽  
Lyubomyr Chyrun ◽  
Myroslava Bublyk ◽  
Vadim Schuchmann
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Technology

scholarly journals An Automated Analytics Engine for College Program Selection using Machine Learning and Big Data Analysis

2021 ◽  
Author(s):  
Jinhui Yu ◽  
Xinyu Luan ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Web Pages ◽  
Python Language ◽  
Program Selection ◽  
Front End

Because of the differences in the structure and content of each website, it is often difficult for international applicants to obtain the application information of each school in time. They need to spend a lot of time manually collecting and sorting information. Especially when the information of the school may be constantly updated, the information may become very inaccurate for international applicants. we designed a tool including three main steps to solve the problem: crawling links, processing web pages, and building my pages. In compiling languages, we mainly use Python and store the crawled data in JSON format [4]. In the process of crawling links, we mainly used beautiful soup to parse HTML and designed crawler. In this paper, we use Python language to design a system. First, we use the crawler method to fetch all the links related to the admission information on the school's official website. Then we traverse these links, and use the noise_remove [5] method to process their corresponding page contents, so as to further narrow the scope of effective information and save these processed contents in the JSON files. Finally, we use the Flask framework to integrate these contents into my front-end page conveniently and efficiently, so that it has the complete function of integrating and displaying information.

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