VC Investment Kits
A Better Way to Improve Your IQ (Investment Quotient)
Venture Capital (VC) and startups is the engine driving innovation and economic growth in the society. However, the way VC analyze and invest startups is ironically traditional. Our vision is to improve the investment process in venture capital industry by building up a robust database and adopting machine learning algorithm to scale up their investment targets and performance.
Data Collection
In this project, I used python to execute automatic data collection. To be more specific, I adopted application program interface (API) and web scrape to collect data from different websites such as Crunchbase, Linkedin and Glassdoor which are the real data VC utilized to know a startup team.
Database
We used SQLite in this project because it is a very common database and built into Python. Our vision is to improve VC investment process and with a robust database and an accessible user interface, we could help VC facilitate scaling up and executing meaning research on startups.
Machine Learning
We took series A funding as an index and used startups in 2011 to 2013 to make a cross validation. To be more specific, we adopted random forest method in machine learning to demonstrate our concept. We got a promising preliminary result showing that our algorithm could raise their performance over two times.