This is the final project for UCLA ECON 425 Machine Learning I
In this project, we try to design a robust predictive framework of companies’ business success. For each company, we define the business success as an event of M&A (Merger and Acquisition) or IPO (Initial Public Offering). Many advanced techniques are applied in feature extraction, feature engineering, predictive modelling and so forth. Based on the framework we propose, we will implement different models and evaluate their performance on both validation dataset and testing dataset. Also, we will show whether different types of features add value to the predictive performance. Finally, we will train our machine learning system over smaller sets of data to explore the robustness of it.
More material will be uploaded through time. It is not surprising to see typos; please email me if you find any (anyiheng11@ucla.edu).