Shopper Buying Intention In Social Networking Using Machine Learning

Abstract

This study focuses on predicting online shopper buying intentions through the application of prominent machine learning algorithms. The algorithms considered for analysis include Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), Naive Bayes (NVC), and Logistic Regression (LR). The research follows a systematic approach involving data collection, preprocessing, feature selection/engineering, data splitting, model training, hyperparameter tuning, model evaluation, and comparison. The performance of each algorithm is assessed using appropriate evaluation metrics, such as accuracy, precision, recall, F1-score, and ROC-AUC. The selected algorithm is determined based on its ability to generalize effectively to unseen data and provide accurate predictions. The study underscores the significance of algorithm choice, feature quality, and model monitoring for real-world deployment, emphasizing a continuous improvement cycle to enhance predictive capabilities over time.