Data mining is a good way to find the relationship between raw data and predict the target we want which is also widely used in different field nowadays. In this project, we implement a lots of technology and method in data mining to predict the sale of an item based on its previous sale. We create a strong model to predict the sales. After evaluating this model, we Online social networks offer vast opportunities for computational social science, but effective user embedding is crucial for downstream tasks. Traditionally, researchers have used pre-defined network-based user features, such as degree, and centrality measures, and/or content-based features, such as posts and reposts. However, these measures may not capture the complex characteristics of social media users. In this study, we propose a user embedding method based on the URL domain co-occurrence network, which is simple but effective for representing social media users in competing events. We assessed the performance of this method in binary classification tasks using benchmark datasets that included Twitter users related to COVID-19 infodemic topics (QAnon, Biden, Ivermectin). Our results revealed that user embeddings generated directly from the retweet network, and those based on language, performed below expectations. In contrast, our domain-based embeddings outperformed these methods while reducing computation time. These findings suggest that the domain-based user embedding can serve as an effective tool to characterize social media users participating in competing events, such as political campaigns and public health crises.

翻译：数据挖掘是发现原始数据间关系并预测目标变量的有效手段，目前已广泛应用于多个领域。本项目采用多种数据挖掘技术与方法，基于商品历史销售数据预测其未来销量，构建了一个稳健的销售预测模型。在线社交网络为计算社会科学提供了广阔机遇，但有效的用户嵌入对于下游任务至关重要。传统方法通常使用预定义的网络特征（如节点度、中心性指标）和/或内容特征（如帖子、转发量）来描述用户。然而，这些指标无法充分捕捉社交媒体用户的复杂特征。本研究提出一种基于URL域名共现网络的用户嵌入方法，该方法简洁高效，尤其适用于表示竞争事件中的社交媒体用户。我们利用包含新冠疫情信息疫情相关话题（QAnon、拜登、伊维菌素）的推特用户基准数据集，在二分类任务中评估了该方法的性能。结果表明，直接基于转发网络生成的用户嵌入及基于语言的嵌入方法表现低于预期；而本研究提出的基于域名的嵌入方法不仅性能更优，且降低了计算耗时。这些发现表明，基于域名的用户嵌入可作为刻画参与竞争事件（如政治竞选与公共卫生危机）的社交媒体用户的有效工具。