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Machine Learning · Major Project
Crypto Price Prediction with Twitter Sentiment
Bitcoin and Dogecoin movement modelled from price history and tweet mood
3-4 weeks build
Overview
Hourly Bitcoin and Dogecoin prices are paired with tweet sentiment scored by VADER, and the combined series feeds a classifier for price direction and an LSTM for the next close. Tweets come from public Kaggle crypto-tweet datasets or the official X API. The report looks hard at volatility and noise, and the app says plainly that it is not investment advice.
What makes it stand up
- 01Hourly price and sentiment series aligned on a common clock
- 02VADER sentiment with bot and duplicate-tweet filtering
- 03Direction classifier and next-close LSTM compared on the same data
- 04Lag analysis showing whether sentiment leads or trails price
- 05Learning project only, with a not-investment-advice notice
Module breakdown
- 01
Price feed
Pulls hourly OHLC data from a public exchange API.
- 02
Tweet processing
Deduplication, spam filtering and VADER scoring.
- 03
Alignment
Resamples both series and builds lagged features.
- 04
Models
Random forest classifier and a Keras LSTM regressor.
- 05
Dashboard
Streamlit charts of sentiment against price with predictions.
After this, you will be able to
- Explain how VADER scores text and where it falls short
- Defend your choice of lag window with cross-correlation plots
- Demo a live comparison of predicted and actual movement
Technology stack
- Python
- Pandas
- NLTK
- scikit-learn
- TensorFlow
- Streamlit
- Level
- Major Project
- Domain
- Machine Learning
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support