Stock Forcast
Project Date
March, 2020
Techniques
Web Parsing, RESTful API, Data Wrangling, Data Manipulation, Feature Engineering, NLP, Statistical Analysis, Random Forest
Visit
GitHub Repository
This project created machine learning models that were able to predict the movement/direction of stock prices under normal market conditions.
The data used in this project were news, historical stock prices, earnings, and trading patterns. Accuracy of predictive models ranged from 75-85%.