Cryptocurrency Price Prediction using Machine Learning

Abstract

Cryptocurrency markets exhibit high volatility, making accurate price prediction a challenging task. This project aims to develop a cryptocurrency price prediction model using linear regression. The model is trained on historical price data, considering key features such as closing prices, trading volume, and market trends. The implementation is built with Streamlit, allowing for an interactive and user-friendly interface where users can input parameters and visualize predictions dynamically

Introduction

Summary:

Cryptocurrencies have become popular for their decentralized nature and high returns, but their price volatility makes forecasting difficult. This project develops a cryptocurrency price prediction system using linear regression, analyzing historical price, volume, and market trends. It is implemented with Streamlit to offer an interactive, user-friendly web interface for visualizing predictions and historical data.

Objectives:

• Build and evaluate a linear regression model for price prediction.

• Preprocess historical cryptocurrency data.

• Create an interactive application with Streamlit.

• Provide actionable investment insights.

Existing Systems:
Traditional prediction methods include statistical models (e.g., Moving Averages, ARIMA, GARCH), technical analysis indicators (RSI, MACD), and fundamental analysis (market sentiment, regulations). These methods face challenges with high volatility, sudden market changes, and subjective interpretations. Machine learning approaches like Linear Regression, XGBoost, and LSTM have been adopted to better capture complex patterns.

Limitations of Traditional Approaches:

• Statistical models assume trend continuation and struggle with sudden spikes.

• Technical analysis relies on historical data and subjective interpretation.

• Fundamental analysis lacks standardized data and precise predictions in crypto markets.

Related Work:
Studies show ARIMA and GARCH models work for short-term forecasts but fail with high volatility. Machine learning models such as SVM, Random Forest, and ANN improve accuracy. Hybrid models combining technical indicators and ML techniques are gaining traction.

Proposed System:
The project uses linear regression for straightforward, interpretable price prediction, supported by a Streamlit web app for visualization. It involves data preprocessing (handling missing values, feature engineering, normalization), training/testing splits, and evaluation with metrics like RMSE and R².

Implementation:
Utilizes Python libraries (Pandas, Scikit-learn, Matplotlib) and Streamlit for deployment. Real-time data can be integrated via APIs (Binance, CoinGecko). Performance is assessed through regression metrics and cross-validation.

Future Scope:

• Incorporate advanced ML models (LSTM, XGBoost) for better accuracy.

• Enable real-time data integration for dynamic predictions and responsiveness.

Conclusion

The proposed cryptocurrency price prediction system provides an efficient and user-friendly approach to forecasting price trends using linear regression and an interactive Streamlit application. By leveraging historical price data, feature engineering, and machine learning techniques, the system offers a transparent and interpretable model for traders, analysts, and investors. The integration of data preprocessing techniques, performance evaluation, and visualization tools ensures that predictions are accurate and easy to understand.

References

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Copyright

Copyright © 2025 Mr. R. Prapulla Kumar(Assistant Professor, PBR VITS, Kavali), B. Krishnaveni(PBR VITS, Kavali), R. Manvitha(PBR VITS, Kavali), T. Sruthi(PBR VITS, Kavali), T. Padma(PBR VITS, Kavali). This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.