Project shows that real-time Twitter data can be used to predict market movement of Bitcoin Price. The goal of this project is to prove whether Twitter data relating to cryptocurrencies can be utilized to develop advantageous crypto coin trading strategies. By way of supervised machine learning techniques, have outlined several machine learning pipelines with the objective of identifying cryptocurrency market movement. The prominent alternative currency ex- amined in this paper is Bitcoin (BTC). Our approach to cleaning data and applying supervised learning algorithms such as logistic regression, Decision Tree Classifier, and LDA leads to a final prediction accuracy exceeding 70%. In order to achieve this result, rigorous error analysis is employed in order to ensure that accurate inputs are utilized at each step of the model.
OVERVIEW:
This is a Flask web app which predicts the bitcoin price based on the twitter sentiments.
INSTALLATION:
The Code is written in Python 3.6.10. To install the required packages and libraries, run this command in the project directory after cloning the repository: pip install -r requirements.txt
DEPLOYMENT ON HEROKU:
Login or signup in order to create virtual app. This can be done either connect GitHub profile or download ctl to manually deploy this project.
Heroku Login Page Overview: Next step would be to follow the instructions given on Heroku Documentation (https://devcenter.heroku.com/articles/getting-started-with-python) to deploy a web app.
DIRECTORY TREE:
DEMO:
API Link: https://bitcoinpredictionapi.herokuapp.com/
TECHNOLOGIES USED:
FUTURE WORK:
• In order to further improve the accuracy of the learning algorithms, additional research can be performed in the area of model accuracy. • Creating a training set that is completely unskewed could result in lower classification error. In addition, we can formulate a set of words where each element has a high correlation with cryptocurrency (bitcoin) market movement and use this as a basis for training the learning algorithms.