Forecasting Cryptocurrency Prices using Recurrent Neural Networks (RNNs)

Cryptocurrencies have gained significant popularity in recent years, with Bitcoin being the most well-known example. As the value of cryptocurrencies can be highly volatile, many traders and investors are interested in predicting their future prices. In this tutorial, we will explore how to use Recurrent Neural Networks (RNNs) to forecast cryptocurrency prices.

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RNNs are a type of neural network that can process sequential data, making them well-suited for time series forecasting tasks. We will use the Python programming language and the Keras library to build and train an RNN model on historical cryptocurrency price data. We will then use this model to make predictions on unseen data.

To begin, we need to gather historical cryptocurrency price data. We will use the yfinance library to download data for a specific cryptocurrency. Let's start by installing the necessary libraries:

pip install yfinance
pip install matplotlib
pip install numpy
pip install tensorflow

Downloading Historical Cryptocurrency Price Data

To download historical cryptocurrency price data, we will use the yfinance library. This library allows us to easily retrieve financial data for various assets, including…