Stock Recommendation System for Better Investment Plan

In the volatile landscape of financial markets, making well-informed decisions for intraday trading and long-term investment necessitates an integrated analysis of real-time stock data and the contextual information provided by news articles. This paper presents an innovative stock recommendation system that leverages advanced Machine Learning (ML) techniques and Natural Language Processing (NLP) to deliver timely, data-driven recommendations by assessing both risk and profit potential. Our system employs ML algorithms to process live stock price, trading volume, and technical indicators, enabling real-time assessment of a stock's performance. Developing a stock recommendation system is a challenging undertaking that frequently calls for a multidisciplinary team with experience in data science, machine learning, and finance. Simultaneously, it harnesses NLP models to extract sentiment and contextual information from news articles, providing insights into market sentiment and potential impact on stock behavior. By merging these two streams of data, our system offers a comprehensive view of stock's dynamics.