Stock Market Prediction using Numerical and Textual Analysis

This project is a part of The Sparks Foundation GRIP internship which highlights time series analysis of historical stock prices and sentimental analysis of news headlines.
The historical stock prices dataset has been extracted from https://finance.yahoo.com/ and the news headlines data is used from https://bit.ly/36fFPI6.
I have used Auto-ARIMA model to make stock market prices predictions using the historical stock prices data. In the sentiment analysis model, I have made use of different machine learning algorithms-Random Forest Regressor, LightGBM, Adaboost and Xgboost- to make the predictions.