CryptoCurrencyPredictionLSTM/README.md

Cryptocurrency Market Prediction Using Machine Learning

Overview

This project explores the efficiency of machine learning algorithms in predicting fluctuations in the cryptocurrency market. The focus is on analyzing various models, including LSTM (Long Short-Term Memory), regression, random forest, and SVM, to understand their effectiveness in forecasting cryptocurrency prices.

Key Features:

• Utilizes a time-series dataset (2013–2021) for several cryptocurrencies.
• Implements advanced machine learning models with a focus on LSTM for sequential data analysis.
• Demonstrates the volatility and challenges of predicting cryptocurrency markets.

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Dataset

The dataset includes daily closing prices of cryptocurrencies from 2013 to 2021. It can be downloaded from Kaggle:
Every Cryptocurrency Daily Market Price.

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Prerequisites

Ensure the following tools and libraries are installed:

• Python 3.7+
• Scikit-learn
• Keras
• TensorFlow
• Jupyter Notebook or Google Colab
• Pandas, Numpy, Matplotlib, Seaborn

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How to Run

1. Download the Dataset

   • Obtain the dataset from Kaggle and unzip it locally.

2. Set Up the Environment

   • Install the required Python libraries:

     pip install scikit-learn keras tensorflow pandas matplotlib seaborn
     

3. Execute the Notebook

   • Use Google Colab or Jupyter Notebook to run the provided script:
     • Upload the CSV dataset file when prompted.
   • The script will process the data, train the models, and display results.

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Results

The project evaluates the performance of different machine learning models:

• Accuracy: 50–60%, reflecting market volatility.
• Best Model: LSTM (due to suitability for time-series data).
• Evaluation metrics include RMSE and MAPE.

Key Insights:

• Short-term price movements can be reasonably predicted using machine learning.
• LSTM models outperform others for time-series prediction.
• Potential for extending predictions to minimize trading costs.

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Technologies Used

• Languages: Python
• Frameworks: TensorFlow, Keras
• Libraries: Scikit-learn, Pandas, Matplotlib
• Platforms: Jupyter Notebook, Google Colab

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Future Work

• Improve LSTM models with techniques like dropout layers for regularization.
• Combine sentiment analysis with price prediction models.
• Extend research to include socio-economic factors influencing cryptocurrency prices.

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Acknowledgments

• Research Team: Lumiere Research Team
• Mentor: Emily Kim, Doctoral Researcher at Carnegie Mellon University Robotics.

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References

1. Sebastião, H., & Godinho, P. (2021). Forecasting and trading cryptocurrencies with machine learning under changing market conditions. Financial Innovation.
2. Alessandretti, L., et al. (2018). Anticipating cryptocurrency prices using machine learning. Complexity.
3. Akyildirim, E., et al. (2020). Prediction of cryptocurrency returns using machine learning. Annals of Operations Research.
4. Kaggle Dataset: Every Cryptocurrency Daily Market Price.