Stock Price Predictions Using Machine Learning

ABSRACT The goal of this research is to create a reliable ma- chine learning model that uses past market data to predict stock prices. This calls for the primary com- puter language to be Python and the use of special- ised tools like scikit-learn and yfinance. The inten- tion is to create a useful resource for those. Study finance or make stock market investments. This tool will assist them in making wise decisions. Accurately estimating the price of stocks is crucial for wise investment decisions. It lowers risks, assists investors in selecting the optimal stock combination, and may even increase profits. Being able to predict stock prices with reliable techniques is essential for staying competitive and making wise investment de- cisions in today's lightning-fast and information-rich financial world. at the ideal time. This study expands on what we now understand about predicting stock market movements. It demonstrates how a unique computer method called Random Forest may be quite effective in predicting the movement of stock values. This is superior than the ways we often do things. The objective is to develop a tool that can provide more accurate information and take a more intelli- gent approach to the market. In conclusion, our study aims to give a workable solution for stock price pre- diction while also advancing the field of financial forecasting by shedding light on how machine learn- ing could complement conventional analytical tech- niques.