Analyzing Consumer Behavior Predictions: A Review of Machine Learning Techniques

These days, most models of consumer behaviour are built using machine learning and data mining techniques applied to actual customer information, and every model is tailored to relate to a specific question at certain duration. Customer behaviour forecasting is a challenging and uncertain endeavour. So, the correct method and strategy are necessary for creating models of client behaviour. It is challenging for a marketer to manipulate a prediction model for their own objectives, so that they can decide the best course of marketing activity for each individual customer or subset of customers. While this formulation may seem complicated, most customer models are far more straightforward. As a result of this requirement, most consumer behaviour models tend to disregard a large number of relevant elements, leading to less-than-reliable forecasts. This study reviews the available literature on the topic of analysing consumer behaviour by means of various machine learning and data mining approaches. Implementation in Python is feasible due to the software's ease of use and the importance of accuracy, error rate, and precision.