基于LightGBM PU学习和Spark ML算法实践的灵巧家庭产品潜在客户挖掘应用
Potential customer mining application of smart home products based on LightGBM PU learning and Spark ML algorithm practice
摘要：
本文以中国电信上海公司为例，研究了基于大数据的智能产品潜在客户挖掘的案例。基于大数据表的海量数据，利用机器学习和数据分析技术，运用LightGBM算法、PySpark机器学习算法、Positive Unlabeled Learning算法，预测客户是否购买全屋产品，为客户精准营销转化为人工智能，大数据容量，推动公司智能产品的发展。
英文摘要：
This paper studies the case of big data-based intelligent product potential customer mining internal competition in China Telecom Shanghai Company. Huge amounts of data based on big data table, the use of machine Learning and data analysis technology, using the algorithm of LightGBM, PySpark machine Learning algorithms, Positive Unlabeled Learning algorithm, and predict whether customers buy whole house product, precision marketing into artificial intelligence for the customer, large data capacity, promote the development of intelligent products of the company.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
