Predicting Consumer Shifts to Sustainable Products using Machine Learning Models

This study evaluates the extent to which consumers are willing to switch to sustainable products and identifies the strategic measures to establish sustainable brands. Consumer preferences and behaviours are analysed to identify the early adopters of sustainable products The study employs several machine learning algorithms including Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to determine the likelihood of consumers switching towards sustainable choices. SMOTE (Synthetic Minority Over-sampling Technique) was applied to address the class imbalance in the data. The models were evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicated that the Random Forest and SVM outperformed the other models in predicting consumer willingness to adopt sustainable products. This study demonstrates the potential of machine learning techniques in understanding customer behaviour, thereby supporting marketers in promoting sustainable brands.