Stock Price Prediction using Machine Learning

This project presents a machine learning based system for stock price prediction using historical data collected through the yfinance library. The dataset contains daily stock data from five major banking stocks: State Bank of India, HDFC Bank, Axis Bank, ICICI Bank, and IndusInd Bank, covering the period from 2020 to 2026. The system applies deep learning and statistical models including LSTM, Stacked LSTM, GRU, and ARIMA to analyze historical closing prices and generate future price predictions based on the number of days provided by the user . A web-based application is developed using Flask for the backend and HTML, CSS, and JavaScript for the frontend. The system includes modules such as Home, Register, Login, Predict Price, and Logout. Users can create an account, log in securely, and input the number of days to obtain predicted stock prices. The project focuses on comparing multiple algorithms to evaluate prediction performance and accuracy. The developed system provides a structured and user-friendly interface for stock price forecasting using machine learning techniques.

Keywords: Stock Price Prediction, Machine Learning, LSTM, Stacked LSTM, GRU, ARIMA, Deep Learning, Time Series Analysis, Flask, yfinance