Application of Decision Tree Algorithm in Early Entrepreneurial Project Screening

Abstract

Venture capital firms are always faced with insufficient information and insufficient time when evaluating whether startups are worth investing in. This paper focuses on how to combine the public information of startups with the decision tree algorithm to assist investors in project screening. By extracting the public information of 1104 AI and big data companies from January 2016 to June 2017 and the financing progress in the following 18 months, this paper finds that: the six indicators of having a working background in well-known companies, being reported by well-known media, having patents and being invested by excellent institutions, working experience is highly related to this venture, and having well-known financing consultants can effectively help investors screen out projects that can obtain sustainable financing, that is, high potential projects. Considering the problem of data availability in the real world, combined with industry experience, this paper makes a more detailed variable mining for semistructured information, such as the team resume of start-ups, and selects a decision tree algorithm that is insensitive to missing values and has strong interpretability to amplify the value of fragmented information. Finally, by the improvement of the algorithm, a project screening model that can meet the needs of investment practice is designed, and the fusion mode of public information and private information is discussed, which has a more complete guiding significance for optimizing investment work.