This repository contains a Jupyter notebook titled "Crypto_prediction.ipynb", focused on forecasting cryptocurrency prices. The notebook combines data analysis, machine learning, and feature engineering to create predictive models for cryptocurrency market movements.
- Crypto Forecasting: Introduction to the concept of cryptocurrency forecasting and the objectives of this notebook.
- Problem Setting: Detailed explanation of the problem being addressed, including the scope and limitations of the analysis.
- EDA (Exploratory Data Analysis): This section contains a thorough exploration of the dataset, including visualization and statistical analysis to understand trends and patterns.
- Meaning of Variables: Definitions and explanations of the various variables used in the dataset, providing clarity on the data inputs for the models.
- Feature Engineering: Techniques and strategies used to create new features from the existing data to enhance the model's predictive capabilities.
To use this notebook:
- Clone this repository to your local machine.
- Ensure you have Jupyter Notebook installed, or use an environment that supports Jupyter notebooks (like Google Colab).
- Open the
Crypto_prediction.ipynbnotebook. - Run the cells sequentially to understand the workflow and the analysis performed.
Contributions to improve the notebook or extend its capabilities are welcome. Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests.
This project is licensed under the MIT License - see the file for details.
https://www.kaggle.com/competitions/g-research-crypto-forecasting/code