Inspiration
The inspiration for Coin Sage came from observing how sentiment plays a major role in shaping cryptocurrency market movements. Crypto markets are often swayed by the latest social media posts, news articles, and public opinions, making them highly unpredictable. Recognizing this, we wanted to explore the power of sentiment analysis and machine learning to create a model that could predict crypto trends. Using Long Short-Term Memory (LSTM) networks, which are well-suited for analyzing time-series data, we aimed to predict cryptocurrency trends based on the sentiments expressed in social media posts and news articles.
What it does
Coin Sage is a sentiment analysis tool designed to predict cryptocurrency trends. It processes data from various social media platforms and news outlets, analyzes the sentiment of posts (positive, negative, or neutral), and uses this sentiment data to forecast future price movements of cryptocurrencies. The system leverages an LSTM-based model to analyze the temporal patterns in sentiment, allowing it to predict whether the market is likely to rise or fall based on the prevailing public sentiment.
How we built it
Built using neural networks that implemented LSTM and Dense Layers. It was created in a python notebook
Challenges we ran into
Neural Networks took a long time to train. We entered the competition late, so there was less time to complete the project
Accomplishments that we're proud of
Came up with an idea that we could potentially build on in the future.
What we learned
Learned how to develop deep learning projects in NLP for the first time.
What's next for Coin Sage
We plan on building on this idea in the future and increasing the accuracy of our model along with its use case.
Built With
- kaggle
- keras
- python
- scikit-learn
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