The high volatility of Bitcoin and other cryptocurrencies encouraged us to develop a model that can forecast Bitcoin prices. It is hard for most investors and enthusiasts to make rational decisions because of the speed of price movement. We sought to develop a system based on machine learning that can help users analyze potential future price action and make better investment choices.
Our Bitcoin Price Predictor is a program that reads historical Bitcoin price data and projects the future prices based on it. It employs machine learning algorithms to discover patterns and trends from past market movement. Parameters such as timeframes can be entered, and the model generates a predicted Bitcoin price with a confidence score or trend (upward/downward). It is an analytical tool, not a financial advisor, but gives insights that can be used to make decisions.
We retrieved past Bitcoin data through APIs (such as CoinGecko or Yahoo Finance). We employed Python and libraries such as pandas for data manipulation, matplotlib and seaborn for plotting, and scikit-learn and TensorFlow for machine learning model construction and training. We tried models such as LSTM (Long Short-Term Memory) networks, which are perfect for time-series prediction. We deployed the final model via a basic web interface constructed using Flask.
Challenges we faced Data Quality: Cryptocurrency data may have missing or inconsistent entries at times, particularly in the first couple of years.
Overfitting: As the price of Bitcoin is extremely volatile, our models first overfit on training data and produce poor generalizations.
Model Selection: Selecting among various models (such as ARIMA, Prophet, LSTM) and hyperparameter tuning was challenging.
Deployment: Deploying the trained model into a smooth performing user-friendly web app was challenging.
Achievements that we're proud of We were able to create a functional Bitcoin price prediction model with decent accuracy.
The LSTM model was able to capture temporal trends well despite the volatility of Bitcoin.
We designed a clean, minimal user interface where users are easily able to see predictions.
We learned how to work with big datasets, preprocessing, and hosting ML models in an actual application.
What we learned Techniques used for time-series forecasting, particularly how various models fare on volatile data.
How data preprocessing (missing data handling, feature scaling) is crucial.
How to optimize and fine-tune machine learning models for improved outcomes.
How to deploy machine learning models in web applications with Flask.
Real-world issues of model deployment and UX design.
What's next for Bitcoin Price Predictor Improve Model Accuracy: Add more features such as trading volume, sentiment from social media, and macroeconomic indicators to enhance predictions.
Live Updates: Enable real-time fetching of data and prediction updates rather than depending on static data sets.
Expand to Other Cryptos: Implement support for other cryptocurrencies such as Ethereum, Solana, and Cardano.
Mobile App Version: Create a mobile version so users can view predictions while on the move.
Explainable AI: Add model interpretation features so users have insights on why the model is forecasting a specific trend.
Log in or sign up for Devpost to join the conversation.