BSc IT Project Guide : Stock Prediction with LSTM
1. Introduction
The 'Stock Prediction with LSTM' project focuses on building a machine learning model to forecast stock prices using Long Short-Term Memory (LSTM) networks. LSTM, a type of Recurrent Neural Network (RNN), is particularly effective for time series data due to its ability to capture long-term dependencies. The project aims to assist investors and traders in making informed decisions by predicting future stock prices based on historical data.
2. Objectives
- To understand and preprocess historical stock market data.
- To develop and train an LSTM model for predicting future stock prices.
- To evaluate the model's performance using suitable metrics.
- To visualize actual vs. predicted stock prices.
3. Tools and Technologies Used
- Python
- TensorFlow / Keras
- Pandas, NumPy, Matplotlib
- Jupyter Notebook / Google Colab
4. System Requirements
Hardware Requirements:
· - Processor: Intel i5 or above
· - RAM: 8GB minimum
· - Storage: 100GB HDD or SSD
Software Requirements:
· - Windows/Linux/macOS
· - Python 3.x
· - Jupyter Notebook / Anaconda
5. System Design
The system involves data collection and preprocessing, model building using LSTM, training and testing, followed by predictions and result visualization.
6. Implementation Modules
1. Data Collection and Cleaning
2. Feature Scaling and Preparation
3. LSTM Model Design
4. Model Training and Validation
5. Prediction and Visualization
7. Future Scope
- Incorporate sentiment analysis from financial news and social media.
- Predict multiple stock trends in a single model.
- Build a real-time prediction dashboard.
8. Conclusion
This project demonstrates the application of deep learning models like LSTM in the domain of stock market forecasting. While no prediction can be perfectly accurate, such models offer valuable insights and can support decision-making in trading and investment strategies.