mirror of
https://github.com/harivansh-afk/CryptoCurrencyPredictionLSTM.git
synced 2026-04-15 06:04:40 +00:00
85 lines
3.1 KiB
Markdown
85 lines
3.1 KiB
Markdown
# 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.
|
||
|
||
---
|
||
|
||
## Dataset
|
||
The dataset includes daily closing prices of cryptocurrencies from 2013 to 2021. It can be downloaded from Kaggle:
|
||
[Every Cryptocurrency Daily Market Price](https://www.kaggle.com/).
|
||
|
||
---
|
||
|
||
## 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
|
||
|
||
---
|
||
|
||
## 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:
|
||
```bash
|
||
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.
|
||
|
||
---
|
||
|
||
## 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.
|
||
|
||
---
|
||
|
||
## Technologies Used
|
||
- **Languages:** Python
|
||
- **Frameworks:** TensorFlow, Keras
|
||
- **Libraries:** Scikit-learn, Pandas, Matplotlib
|
||
- **Platforms:** Jupyter Notebook, Google Colab
|
||
|
||
---
|
||
|
||
## 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.
|
||
|
||
---
|
||
|
||
## Acknowledgments
|
||
- **Research Team:** Lumiere Research Team
|
||
- **Mentor:** Emily Kim, Doctoral Researcher at Carnegie Mellon University Robotics.
|
||
|
||
---
|
||
|
||
## 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*.
|