Stock-Price-Predictor
A cutting-edge Stock Price Predictor leveraging AI/ML algorithms to forecast future stock prices with accuracy and precision.
Application of Stock Price Predictor
This robust machine learning model will use the historical stock market data to accurately forecast future stock prices. Leveraged advanced algorithms and statistical techniques to identify patterns and trends, providing valuable insights for informed investment decisions. Implemented the project using Python and popular libraries like Pandas, NumPy, and scikit-learn. The predictor model demonstrated high accuracy (97%) and reliability, enabling traders and investors to optimize portfolio strategies, mitigate risks, and capitalize on market opportunities. Seeking opportunities to apply this predictive model to enhance financial forecasting and drive profitability in the finance industry.
LSTM
- LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture commonly used for time series analysis and prediction tasks, making it suitable for stock price prediction.
- The LSTM model consists of multiple memory cells that can retain information over long sequences, allowing it to capture and learn patterns from historical stock price data.
- The model takes in input features such as previous stock prices, volume, and technical indicators, and uses the LSTM cells to process and analyze this sequential data.
- LSTM models are known for their ability to handle long-term dependencies and capture complex relationships in the data, making them effective for capturing patterns and trends in stock price movements.
- The LSTM model is trained using historical stock price data and corresponding target values (future stock prices). The model learns to minimize the prediction error through an optimization algorithm, such as gradient descent, and is then used to make predictions on unseen data.