Challenge Data ENS

This repository contains solutions to various data challenges organized by the École Normale Supérieure (ENS) and the Collège de France. These challenges cover a wide range of topics, including finance, biology, and industrial applications.

Table of Contents

• Challenge Data ENS
  • Table of Contents
  • Challenges
  • License

Challenges

This repository contains solutions to various data challenges organized by the École Normale Supérieure (ENS) and the Collège de France. These challenges span multiple domains, including finance, meteorology, and human resources. Below is a list of the challenges along with brief explanations:

1. Comment démasquer les fraudeurs ? par BNP Paribas PF:

   • Description: This challenge, presented by BNP Paribas Personal Finance, focuses on detecting fraudulent activities within transaction data. Participants are tasked with developing models to identify fraudulent customers based on basket data from retail partners.

2. Prévision des précipitations à court terme par PlumeLabs:

   • Description: Organized by Plume Labs, this challenge involves short-term precipitation forecasting. Participants are required to develop models that can predict rainfall using meteorological data, aiding in real-time weather predictions.

3. Comment expliquer le prix de l'électricité ? par QRT:

   • Description: Hosted by Qube Research & Technologies, this challenge aims to model electricity prices based on weather, energy, and commercial data for France and Germany. The objective is to understand the factors influencing electricity price variations.

4. Prédiction de prix de l'électricité par Elmy:

   • Description: Presented by Elmy, this challenge focuses on forecasting electricity prices by comparing the SPOT market prices with Intraday market prices. Participants are tasked with developing models to predict the direction and magnitude of price differences between these markets.

5. Prédire de manière séquentielle l'évolution d'une carrière par HrFlow.ai:

   • Description: Organized by HrFlow.ai, this challenge involves sequential prediction of career evolution. Participants are required to develop models that can predict the progression of an individual's career over time, utilizing data related to job positions, skills, and experiences.

Each challenge provides a unique opportunity to apply data science and machine learning techniques to real-world problems across various industries.

License

This project is licensed under the MIT License. See the LICENSE file for details.