Cryptocurrency Price Prediction System using YFinance, FBProphet, and Streamlit.
Cryptocurrency has become an increasingly popular investment option in recent years, with more and more people looking to invest in digital currencies such as Bitcoin, Ethereum, and others. However, with the volatility of the cryptocurrency market, it can be challenging to make informed investment decisions. In this blog, we will explore how to build a cryptocurrency price prediction system using YFinance, FBProphet, and Streamlit.
I. Introduction to Cryptocurrency Price Prediction:
A. What is Cryptocurrency Price Prediction?
Cryptocurrency price prediction is the process of using historical data and machine learning algorithms to predict the future price of a cryptocurrency. By using this information, investors and traders can make informed decisions about when to buy or sell their investments.
B. Why is Cryptocurrency Price Prediction Important?
Cryptocurrency prices are highly volatile, making it difficult for investors to know when to buy or sell their investments. By using a cryptocurrency price prediction system, investors can have a better understanding of what to expect in the future and make more informed decisions about their investments.
C. Overview of the System:
In this technical blog, we will be discussing the process of building a cryptocurrency price prediction system. The system will use historical data to train a machine learning model, which will then make predictions on future prices. We will go through each step of the process in detail, including data collection, model selection, training, and deployment.
II. Data Collection with YFinance:
A. What is YFinance?
YFinance is a Python library for retrieving historical stock and cryptocurrency data from Yahoo Finance. It allows us to easily collect data on the price of a cryptocurrency over time, which is essential for building a cryptocurrency price prediction system.
B. Collecting Data with YFinance Here is an example of how you can use YFinance to collect data on the price of Bitcoin:
III. Building the Prediction Model with FBProphet:
A. What is FBProphet?
FBProphet is a Python library for time series forecasting. It is designed for forecasting at scale and works well for datasets with clear trend and seasonality.
B. Building the Model with FBProphet Here is an example of how you can use FBProphet to build a model for predicting the price of Bitcoin:
IV. Deploying the Model with Streamlit:
A. What is Streamlit?
Streamlit is an open-source framework for building machine learning and data science apps. It allows us to easily deploy our cryptocurrency price prediction system as a web app, making it accessible to anyone with an internet connection.
B. Deploying the Model with Streamlit Here is an example of how you can use Streamlit to deploy the FBProphet model we created earlier:
V. Conclusion and Future Work:
In this blog, we explored how to build a cryptocurrency price prediction system using YFinance, FBProphet, and Streamlit. By collecting data on the price of a cryptocurrency with YFinance, building a prediction model with FBProphet, and deploying the model as a web app with Streamlit, we can make it easier for investors and traders to make informed decisions about their investments.
While our system is functional, there is always room for improvement. In the future, we could explore other models, data sources, and feature engineering techniques to improve the accuracy of our predictions.
