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A Prediction of Customer Behavior using Logistic Regression, Naivesbayes Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Ruchita Atre, Namrata Tapaswi

Ruchita Atre and Namrata Tapaswi. A Prediction of Customer Behavior using Logistic Regression, Naivesbayes Algorithm. International Journal of Computer Applications 183(50):31-35, February 2022. BibTeX

	author = {Ruchita Atre and Namrata Tapaswi},
	title = {A Prediction of Customer Behavior using Logistic Regression, Naivesbayes Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2022},
	volume = {183},
	number = {50},
	month = {Feb},
	year = {2022},
	issn = {0975-8887},
	pages = {31-35},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2022921904},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In past two decades e-commerce platform developed exponentially, and with this advent, there came several challenges due to a vast amount of information. Customers not only buy products online but also get valuable information about a product they intend to buy through an online platform. Customers share their experiences by providing feedback which creates a pool of textual information and this process continuously generates data every day. You can analyze the content in the form of comments, ratings and reviews. Consumers decide to buy a given product by looking at these reviews and reviews rating. Such content may be positive or negative reviews made by consumers who have used the product before. Our data analysis and multi-agent simulation demonstrate the feasibility of this framework. Perform behavioral analysis on data retrieved from Amazon reviews. These comments are divided into four categories: happy, up, down and rejection. When we analyze data to calculate the sense of user reviews, our goal is to use data-driven marketing tools such as data visualization, natural language processing, and machine learning models to help understand the organization's demographics. The system is developed based on classification algorithms includes Naïve Bayes, Logistic Regression. For each topic, the existing problems are analyzed, and then, current solutions to these problems are presented and discussed. The experimental results show that the proposed sentiment analysis method has higher precision, recall and F1 score. The method is proved to be effective with high accuracy on comments.


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Costumer Behavior, Logistic Regression, NaivesBayes , Customer Reviews, Data Mining, machine learning