This project aims to develop an Application using machine learning model that can predict stock prices based on historical data. By analyzing patterns and trends in the stock market, the model will learn to make predictions on future stock prices, providing valuable insights to investors and traders.
Using a recurrent neural network called as LSTM (Long Short-Term Memory Network)
LSTM is a type of recurrent neural network (RNN) that can effectively capture long-term dependencies and patterns in time series data. When applied to stock price prediction, LSTM models can learn from historical stock price data and make predictions on future stock prices.
Flutter is a popular framework for building cross-platform mobile applications with a single codebase which was used to built the frontend application.
Techstack :
TensorFlow, Keras, Pandas, Numpy, Python, Flutter.