Machine Learning Classification Methods for Stock Market Forecasting

Making accurate stock market forecasts are of interest to both traders and computer scientists. Both historical stock data and social media analysis can be used to make predictions. The stock market's volatile nature makes it difficult to make reliable predictions based on historical data, and it's possible that certain relevant sectors were overlooked owing to their lack of significance in certain stocks or a lack of data. A difficult task in financial time series prediction is forecasting the direction of stock price indices. Traders and shareholders alike could benefit from a more informed understanding of how to forecast stock price movements. The intricacy of stock market data makes it challenging to create accurate prediction models. As the future is essentially unknown, accurate market forecasting is difficult. In order to make accurate forecasts, the data must first be cleansed. Stock price forecasts for Adani Green Energy Limited were attempted in this paper. Seven characteristics were extracted from stock prices during the past five years. The research was carried out, and around 1088 cases were considered. Five years' worth of stock price history from Yahoo Finance is obtained and used that as the basis for our dataset. The primary goal was to make reliable price predictions, therefore we focused on those metrics. Linear regression, random forest, and XGBoost Regressor were used to train the dataset. Achievable accuracy rates of 95% or more were achieved by all models, with a best-case scenario of 98.65% being achieved by the XGBoost Regression model. We used XGBoost 1.0.1 and fine-tuned its settings to maximize performance, which included a maximum depth of 7, a number of estimators in the thousands, and more. In addition, cross-validation and Repeated KFold scores were generated. The XGBoost Regressor's Mean Absolute Error (MAE) was calculated as well. The incorporation of the ChatBot into our study was the most notable improvement. This was educated with the help of natural language processing, so it could converse with the user and provide satisfactory responses to their inquiries. Furthermore, the research work proposed a number of changes that could be made to these methods in order to increase their accuracy.