• Pooja Sharma
• Dr. Sachin Kumar

Now a days, consumer behavior models are typically based on machine learning, data mining of customer data, and each model is designed to answer one question at one point in time. Predicting customer behavior is an uncertain and difficult task. Thus, developing customer behavior models requires the right technique and approach. Once a prediction model has been built, it is difficult to manipulate it for the purposes of the marketer, so as to determine exactly what marketing actions to take for each customer or group of customers. Despite the complexity of this formulation, most customer models are actually relatively simple. Because of this necessity, most customer behavior models ignore so many pertinent factors that the predictions they generate are generally not very reliable. This paper discusses various research works on consumer behavior analysis using difference machine learning, data mining techniques. The accuracy, error rate, precision is the key parameters and Python software can be used for implementation.

Consumer, Machine Learning, Prediction, Accuracy, Error, Data Mining.

"Review of Machine Learning Techniques for Consumer Behavior Prediction Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 3, page no.e340-e345, March-2022, Available :http://www.jetir.org/papers/JETIR2203444.pdf

"Review of Machine Learning Techniques for Consumer Behavior Prediction Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 3, page no. ppe340-e345, March-2022, Available at : http://www.jetir.org/papers/JETIR2203444.pdf