Hello, dear friend, you can consult us at any time if you have any questions, add WeChat: daixieit

With the rapid development of e-commerce, platforms such as Amazon and eBay generate massive amounts of user data every day, such as browsing records, clicks, and purchase history. However, how to use this data to accurately predict user purchasing behavior is still a major challenge facing companies. Inaccurate predictions may lead to missed personalized marketing opportunities, inefficient resource allocation, and reduced customer conversion rates. Therefore, this study aims to explore how to effectively predict user purchasing behavior on e-commerce platforms through machine learning methods to improve prediction accuracy and provide practical value to companies.

This project plans to use three machine learning algorithms - decision trees, random forests, and neural networks to build and compare their performance in predicting user purchasing behavior. Decision trees will be used as a benchmark model for understanding purchase drivers due to their simplicity and interpretability; random forests improve accuracy and reduce overfitting by integrating multiple trees; neural networks are good at capturing complex nonlinear relationships in big data. The study will use public e-commerce datasets on Kaggle (such as the "Online Retail Dataset"), which contain features such as user ID, product interactions, and purchase results. The methods include data preprocessing (such as cleaning missing values and feature standardization), feature engineering (such as extracting session duration and access frequency), model training and optimization (using scikit-learn and TensorFlow), and evaluating model performance through accuracy, precision, recall and F1 score, and finally comparing the advantages and disadvantages of the three.

The commercial value of this research is that accurate prediction can optimize advertising, inventory management and user experience, which is crucial for platforms such as Amazon and eBay. Academically, it fits the research direction of data analysis and algorithm application in the field of computer science, and deepens the understanding of machine learning methods. The current plan focuses on prediction accuracy, which can be expanded to real-time deployment or multimodal data analysis (such as review text or product images) in the future.

To ensure success, I will use the existing Python programming foundation, combined with the free GPU resources of Google Colab to run neural networks, and optimize hyperparameters through literature learning. At the same time, a detailed schedule will be made, and progress will be checked weekly to ensure that the proposal, literature review and final paper are completed on time. This research is expected to provide an efficient prediction framework for the e-commerce field, while improving my technical and research capabilities.