{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

💰Crypto💰 Complete EDA + Competition Understanding

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Contents

","metadata":{}},{"cell_type":"markdown","source":"1. [Competition Overview](#competition-overview) ","metadata":{}},{"cell_type":"markdown","source":"
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If you find this notebook useful, do give me an upvote, it helps to keep up my motivation. This notebook will be updated frequently so keep checking for furthur developments.

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Competition Overview

","metadata":{}},{"cell_type":"markdown","source":"## **Description**\n\nIn this competition, you'll use your machine learning expertise to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 which you can use to build your model. Once the submission deadline has passed, your final score will be calculated over the following 3 months using live crypto data as it is collected.\n\n## **Evaluation Criteria**\n\nSubmissions are evaluated on a weighted version of the [Pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient). You can find additional details in the ['Prediction Details and Evaluation'](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition) section of this tutorial notebook.\n \nYou must submit to this competition using the provided python time-series API, which ensures that models do not peek forward in time. ","metadata":{}}]}