User Behaviour Prediction in E-Commerce Using Logistic Regression

From a psychological perspective, human behaviour reflects underlying thoughts and decision-making patterns, for example, consumer behaviour may correlate with the purchase decisions. In the fast-evolving e-commerce industry, predicting user behaviour is essential for enhancing marketing strategies, improving customer experiences, and increasing sales. However, traditional heuristic (e.g. market basket analysis) approaches to analyse buyer behaviour are often rigid and fail to adapt to complex consumer interactions. This research work develops a predictive model that analyses user behaviour based on data such as historical purchasing patterns and demographic attributes. Based on a review of previous studies, Logistic Regression (LR) is utilized as the primary machine learning algorithm to estimate the likelihood of user performing specific actions including churning and conversion rate. The dataset undergoes preprocessing steps, including data cleaning, feature selection, and normalization, to enhance model accuracy. Evaluation metrics, including accuracy, confusion matrix, precision, recall and F1-Score are used to ensure the model’s performance is reliable and effective. Unlike traditional heuristic approaches, this data-driven method offers a scalable and adaptable solution for behaviour prediction. The findings of this research have the potential to revolutionize e-commerce by providing businesses with actionable insights into consumer behaviour. By leveraging predictive analytics, companies can implement targeted marketing campaigns, personalize recommendations, and improve customer retention strategies. Additionally, this study highlights the significance of behavioural modelling in detecting early signs of customer churn, allowing businesses to take proactive measures. Ultimately, this research contributes to the growing field of data-driven decision-making, offering a scalable and adaptable solution for understanding and predicting user behaviour in online shopping environments.