/Stock-Prediction-using-textual-and-numerical-data

In this project am trying to predict the stock price of MANUTD using the historical data

Primary LanguageJupyter Notebook

Manchester United Stock Prediction using Textual and Numerical Data

In this project, I am trying to predict the stock price of MANUTD using historical data

created a hybrid model for stock price and performance prediction using numerical analysis of historical stock prices and sentimental analysis of news headlines. Stock to analyze and predict: - (MANUTD) Download historical stock prices from finance.yahoo.com Download textual (news) data from https://bit.ly/36fFPI6 Use either R or Python, or both, for separate analysis, and then combine the findings to create a hybrid model.You can download the historical data from - https://finance.yahoo.com/quote/MANU/history?p=MANU

The drawback of the textual data is that we are only able to analyze and understand the data until 2022-03-31.

Be careful while you open the news.csv file. If we change anything and save the file, the whole data will not load, which means we will not have access to news headlines until 2022.

I have chosen to implement ARIMA, SARIMAX and other machine learning algorithms, including Randomforest, XGBoost etc.

If there are any errors please do notify me.