ENS-Challenge-Data-Cryptocurrency-Clusters

Summary:

The goal of the challenge was to predict the returns vs. bitcoin of clusters of cryptoassets.

My global approach was to implement a stacked regression model, containing three base models: XGBoost, RandomForest and LightGBM regressors. For this challenge, I have achieved the 11th place out of the 51 participants.

In this repository you will find:

-The code I made for this challenge in a Jupyter Notebook (.ipynb) format.

-A PDF explaining my whole approach of the problem.

Dataset:

Unfortunately, I'm not allowed to share the dataset, but you can download it if you register on the Data Challenge website on this following link: https://challengedata.ens.fr/participants/challenges/71/

Here is the link of the leaderboard: https://challengedata.ens.fr/participants/challenges/71/ranking/public

Username: VictorHoffmann