An In-Depth Relative Study of ML Models for Predicting Stock Value

A web application that combines Streamlit, TensorFlow/Keras, and finance to make everything smooth running. Using the application, it predicts future stock prices. Fetching and analyzing historical stock values information for higher user interaction, it offers both current predictions and actual prices through interactive Plotly charts. Basically, the tool uses a pre-trained model that is specifically customized for time series forecasting; its core allows it to recognize patterns up to Users can input a stock ticker symbol, such as GOOGL, to compare side by side historical and predicted trends in stock prices. The application has functionality for possible problems, such as missing columns and incorrect formatting, and reliability while giving clear feedback throughout the user experience. Analysts, data scientists, and investors may use it to visualize stock value movement and make informed decisions by bridging the gap between "data science" and financial planning. This illustrated the power of web technologies and of ML for practical use in the banking sector; utility sets in Python were combined to deliver one powerful platform for financial forecasting.