Stock Market Analysis

#stocks,#machine learning,#algorithms

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Inspiration The demand for stocks has surged due to their growing popularity in the digital world. Predicting and analyzing stock prices can help investors make informed decisions before buying or selling. This project explores stock price prediction using Machine Learning algorithms, allowing for better insights and visualization.

What It Does We developed a stock market analysis application that focuses on leading companies such as Amazon, Tesla, Apple, and Microsoft. Our app compares their historical stock data, providing a clearer view of past market trends. By integrating yfinance, we ensure real-time stock updates. Various regression and classification algorithms were implemented to predict future stock movements, achieving an accuracy score of over 90% for each company.

How We Built It The project was built using Python and features an interactive user interface developed with Streamlit.

Challenges We Faced During development, we encountered and resolved multiple errors, gaining deeper insights into new Machine Learning algorithms. Stock analysis plays a crucial role for both short- and long-term investors, helping them make strategic decisions based on historical data, fundamental metrics, and market news. By applying these algorithms to any company's stock dataset, we can generate accurate predictions. Moreover, our system runs seamlessly on various platforms, including cloud environments.

Achievements We’re Proud Of We emerged as the winners of the Daisi Hackathon organized by HackerEarth, securing a $2500 cash prize.

What We Learned This project enhanced our understanding of stock market analysis and reinforced the significance of stock investments. We also gained hands-on experience with multiple machine learning algorithms.

Future Plans for Stock Market Analysis Our next step is to deploy the project as a fully functional application since the Daisi platform has been shut down due to internal company issues. The Python code is already complete and ready for deployment.

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