论文标题
基于LightGBM PU学习和Spark ML算法实践的潜在客户采矿应用智能家居产品
Potential customer mining application of smart home products based on LightGBM PU learning and Spark ML algorithm practice
论文作者
论文摘要
本文研究了中国电信上海公司的基于大数据的智能产品潜在客户开采内部竞争的案例。使用LightGBM的算法，Pyspark机器学习算法，积极的无标记的学习算法以及客户是否购买整个房屋产品，对客户的人工智能，大量的数据，大量数据，大量数据能力，促进公司的智能产品的开发，促进公司的开发。
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.
