Stock-prediction_kaggle | using market data provided by intrinio and news data

Stock-prediction_kaggle is a Jupyter Notebook library. Stock-prediction_kaggle has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

using market data provided by intrinio and news data provided by thomson reuters to predict the stock price performance in the competition hosted by two sigma. the abundance of data available today enables investors to mine over it and make better investment decisions. the challenge in this competition is cleaning the data to find which is useful and which is not rather than choosing the right algorithm to predict data because data in this set is abundant and finding the content takes a lot of effort. by going through the market data and joining it with the news data to find the correct trends in market price change, we are going to predict the stock market prices for the future. since the data is not freely available to download, we are supposed to use kaggle notebooks and develop a kernel for the project and cannot use other sources to code. a small sample of the market data and news data is given though that is free to download. we are planning to use various preprocessing techniques to extract useful information from those 4 million records to build a better stock market prediction model. in this project we predict a signed confidence value [-1,1] which is multiplied