Cryptocurrency Prediction Model

This repository is my data science project focused on forecasting POSSIBLE future changes in cryptocurrency prices.

Google Colab Notebook

Key Features

1. Machine Learning Modeling:

Two main modeling approaches are utilized - a Sequential neural network model for price prediction based on specific features, and a VARMAX statistical model for time series forecasting to estimate potential future values of those features.

Sequential Model Architecture

2. Interactive Visualization:

An interactive chart is implemented to display both the observed and forecasted cryptocurrency prices, allowing users to access date and price by moving a cursor.

Prerequisites

Libraries

• re
• numpy
• pandas
• matplotlib
• bokeh
• sklearn
• keras
• statsmodels

Data

• Cryptocurrency historical data was collected from investing.com to gather a comprehensive dataset of Bitcoin exchange rates over time.
• The file includes various features such as open, high, low, close prices, trading volume, and percentage changes.
• Any historical cryptocurrency data from this site can be used in the model.

Getting Started

Obtaining the data:

• You can download data of a variety of cryptocurrencies from investing.com website.
• Bitcoin data can be obtained from the following link: https://www.investing.com/crypto/bitcoin/historical-data

Running from Source Code:

1. Clone this repository to your local machine.

   git clone https://github.com/arachnocid/Cryptocurrency-Prediction-Model.git

2. Navigate to the project directory.

   cd Cryptocurrency-Prediction-Model

3. Install the required dependencies (see requirements.txt)

   pip install -r requirements.txt

4. Run the "Cryptocurrency-Prediction-Model.py" script.

License

This project is licensed under the GPL-3.0 license.

Author

Arachnocid