An efficient machine learning technique for prediction of consumer behaviour with high accuracy

Abstract

Customers look at ratings and feedback from other customers before deciding whether or not to buy a product. This content may take the form of favorable or negative reviews written by customers who have lately used the product in question. The Machine Learning Calculation may be able to assist us in both the visual depiction of the information as well as the factorization of the information. This investigation of customer behavior uses the Naive Bayes and Logistic Regression methods, both of which are presented in this work. The tactics based on logistic regression performed preferable than those based on other approaches. The contemporary problems are dissected, and then the contemporary solutions to those problems are presented and discussed. Afterwards, the results of the test indicate that the recommended technique has a greater accuracy as well as a higher recall and F1 score. The technique ends up being successful, with a high degree of precision on the comments. Python Spider 3.7 is the programmer that is used in order to carry out both the replication and the analysis.