Stock Prediction

Project Overview

This project aims to predict stock prices using Long Short-Term Memory (LSTM) neural networks. LSTM networks are well-suited for time-series forecasting tasks like stock price prediction due to their ability to capture long-term dependencies in sequential data.

Technologies Used

• Python: Core programming language.
• Keras/TensorFlow: For building and training the Siamese network.
• OpenCV: For face detection and preprocessing.
• NumPy/Pandas: Data manipulation and handling.
• Matplotlib: Visualization of loss curves and training progress.

Project Structure

.
├── dataset/                # CSV files for NIFTY50 dataset
│   ├── stock1.csv
│   ├── stock2.csv
│   └── ...
├── models/                 # Trained model weights
├── Model.ipynb             # Jupyter Notebook for model
├── requirements.txt        # Python dependencies
└── README.md               # Project documentation

Installation

1. Clone Repository

   git clone https://github.com/Blaze-DSP/Stock-Prediction.git
   cd Stock-Prediction
   

2. Install Dependencies

   pip install -r requirements.txt

Dataset

The dataset used for training and evaluation consists of historical stock prices of the NIFTY-50 stocks. This dataset is used for training the LSTM network with Mean-Squrared Loss for stock prediction.

Usage

Prediction & Inference: Provide instructions on how to use the trained model for making predictions.

Future Enhancements

Enhancements: List potential improvements or optimizations that could be implemented to enhance the model's performance. Extensions: Discuss possible extensions to the current project, such as integrating with real-time data sources or applying ensemble techniques.