Stock Price Prediction

This repository contains a project aimed at predicting stock prices using various deep learning models, including LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and RNN (Recurrent Neural Networks). The goal is to analyze historical stock price data and provide insights for future price trends.

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Table of Contents

• Project Overview
• Features
• Getting Started
• Data Description
• Modeling
  • LSTM
  • GRU
  • RNN
• Results
• Dependencies
• Usage
• Contributing
• License

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Project Overview

This project implements machine learning techniques to predict stock prices. The models utilized in this project leverage sequential data structures and are trained on historical stock price data to provide robust and accurate predictions.

The project is divided into multiple stages:

1. Data preprocessing.
2. Model building and training.
3. Evaluation and visualization of results.

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Features

• Implementation of deep learning architectures for stock price prediction.
• Data preprocessing pipeline for handling missing values, scaling, and feature engineering.
• Comparative performance analysis between LSTM, GRU, and RNN models.
• Visualization of predicted vs. actual stock prices.

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Getting Started

Prerequisites

To run this project, ensure you have Python (>=3.8) installed along with the required libraries.

Installation

1. Clone the repository:

   git clone https://github.com/your_username/stock-price-prediction.git
   cd stock-price-prediction

2. Install dependencies:

   pip install -r requirements.txt

Running the Project

1. Download and prepare the stock price dataset (see Data Description).
2. Run the preprocessing script to prepare data for training:

   python preprocess_data.py

3. Train the models:

   python train_model.py --model [lstm/gru/rnn]

4. Evaluate the models and visualize predictions:

   python evaluate_model.py

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Data Description

The dataset used in this project consists of historical stock prices, including features such as:

• Date
• Open price
• High price
• Low price
• Close price
• Volume

Ensure that your dataset is in a .csv format with the appropriate columns. You can use publicly available stock price data from sources like Yahoo Finance or Kaggle.

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Modeling

LSTM

LSTM networks are used for modeling sequential data with long-term dependencies, making them ideal for time-series prediction tasks.

GRU

GRUs are similar to LSTMs but are computationally more efficient. They perform well in scenarios where LSTM models might be overkill.

RNN

RNNs are the simplest type of recurrent networks but may suffer from vanishing gradients. They are included here for comparison purposes.

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Results

The results of the project include:

• Comparative metrics such as RMSE, MAE, and R-squared values for all three models.
• Graphs illustrating the predicted vs. actual stock prices for a selected test dataset.

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Dependencies

The project requires the following libraries:

• Python (>=3.8)
• NumPy
• Pandas
• Scikit-learn
• Matplotlib
• TensorFlow/Keras

To install all dependencies, use:

pip install -r requirements.txt

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Usage

To experiment with the models, you can modify the configuration files or command-line arguments:

• Adjust hyperparameters like learning rate, batch size, or the number of epochs.
• Use a different dataset by placing it in the data/ folder and updating the paths in the scripts.

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Happy Predicting!