• Poonam Sonwane
• Prof. Monika Raghuwanshi

Consumers choose to purchase a given item by looking at these evaluations and surveys. Such substance can be positive or negative audits made by consumers who have recently utilized the item. The Machine Learning Calculation can help us to visual portrayal of the information and vectorize the information. This paper presents the Naïve Bayes and Logistic Regression method to investigate the consumer behavior. The logistic regression strategies improved preferred performance over others. The current issues are examined, and afterward, current answers for these issues are introduced and talked about. The trial results show that the proposed strategy has higher precision, recall and F1 score. The strategy is end up being effective with high accuracy on remarks. The reproduction and analysis is finished utilizing the python spyder 3.7 software.

Naïve Bayes, Logistic Regression, Precision, Recall, F1 score, spyder

"An Efficient Machine Learning Technique for Prediction of Consumer Behaviour with High Accuracy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 8, page no.a204-a207, August-2021, Available :http://www.jetir.org/papers/JETIR2108028.pdf

"An Efficient Machine Learning Technique for Prediction of Consumer Behaviour with High Accuracy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 8, page no. ppa204-a207, August-2021, Available at : http://www.jetir.org/papers/JETIR2108028.pdf