Forecast Uber stock prices using an LSTM model, leveraging historical data for accurate predictions. Embrace AI's potential in financial analysis and make informed decisions.
This GitHub repository hosts an advanced financial analysis project centered around predicting Uber's stock prices using Long Short-Term Memory (LSTM) neural networks. Leveraging historical stock data, this project demonstrates the application of deep learning techniques in the realm of finance. LSTM's ability to capture temporal dependencies makes it a potent tool for predicting stock trends.
The Long Short-Term Memory (LSTM) model is a specialized type of recurrent neural network (RNN) designed to effectively capture and learn from sequential data. It excels at understanding long-range dependencies and patterns within sequences, making it ideal for tasks like time series forecasting, natural language processing, and speech recognition.
This dataset contains historical stock market information for Uber Technologies, Inc. (UBER). It includes essential stock-related metrics such as opening and closing prices, trading volume, highs and lows, and date-time information. Analyzing this dataset enables insights into UBER's market trends and performance, aiding investors and analysts in making informed decisions.
Utilizes Python and TensorFlow for LSTM implementation. Preprocesses and transforms raw stock data into suitable input sequences. Divides data into training, validation, and test sets for robust model evaluation. Incorporates hyperparameter tuning to enhance predictive accuracy. Visualizes predictions against actual stock price trends. Offers insights into the significance and challenges of LSTM-based stock prediction.
Clone this repository: git clone https://github.com/yourusername/uber-stock-prediction.git Install required packages: pip install -r requirements.txt Explore the Jupyter Notebook for step-by-step implementation. Customize hyperparameters and architecture for experimentation. Analyze visualizations to understand model performance.
Contributions are welcome! If you find any bugs or want to enhance this project, please fork the repository and create a pull request.
This project's stock price predictions are for educational and illustrative purposes only. Actual stock market behavior is influenced by numerous complex factors, and the predictions may not reflect real-world outcomes. Make financial decisions cautiously and consult with professionals.