STOCK FORECASTING WEB APPLICATION WITH MACHINE LEARNING
DOI:
https://doi.org/10.63458/ijerst.v3i3.118Keywords:
Stock Forecasting, Web Application, Machine Learning, LSTM Algorithm, Python, Django, HTML, CSS, JavaScript, yfinance, SQLite, TensorFlow, scikit-learn, Matplotlib, Plotly, Financial APIs.Abstract
In this project we created a web application for stock forecasting that uses machine learning techniques to give users insights into stock prices in advance. The application integrates realtime stock data from financial APIs and uses Python for both machine learning and web development, making it easy to access the most recent information. The LSTM (Long ShortTerm Memory) algorithm is a kind of recurrent neural network and it is the main component of the application that analyses historical stocks data and projects future prices. Here we used Frontend technologies like HTML, CSS, and JavaScript (Angular) power the user-friendly single-page interface of the Flask or Django framework-built web interface. And here we use other model like SARIMA, Random Forest,Prophet Any appropriate database system,such as SQLite, can be used for data management. Using scikit-learn or TensorFlow libraries, machine learning functionalities are implemented, allowing the development of reliable forecasting models. The libraries Matplotlib, Plotly, or D3.js make it easier to visualise stock data, which improves user comprehension and decision-making.Alpha Vantage and other financial market APIs and yfinance library provide real-time stock data, guaranteeing the application's relevance and dependability in fluctuating market conditions. From this project users can easily understand and analyze the stock data with user-friendly interface and dynamic visualization. This project's ultimate objective is to democratize access to stock forecasting tools for all traders and they get better experience levels can confidently make an gameplay with financial market trends.
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