https://github.com/edu-rz/atm-datafest-2024
🥈 Datafest competition sponsored by BCP and ESAN. Optimization and prediction of the replenishment of BCP ATMs.
https://github.com/edu-rz/atm-datafest-2024
data-science machine-learning python webscraping
Last synced: 10 months ago
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🥈 Datafest competition sponsored by BCP and ESAN. Optimization and prediction of the replenishment of BCP ATMs.
- Host: GitHub
- URL: https://github.com/edu-rz/atm-datafest-2024
- Owner: edu-rz
- Created: 2024-12-05T14:27:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-05T14:34:24.000Z (over 1 year ago)
- Last Synced: 2025-05-30T12:21:26.527Z (about 1 year ago)
- Topics: data-science, machine-learning, python, webscraping
- Language: Jupyter Notebook
- Homepage:
- Size: 47.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DATAFEST 2024 - ESAN & BCP 🎉
[**Datafest 2024**](https://www.linkedin.com/posts/universidad-esan_as%C3%AD-arranc%C3%B3-el-datafest-2024-en-esan-activity-7245619294036725760-JMPw/?originalSubdomain=es) is the most prominent and challenging data analytics event in Peru, where 11 universities compete in an exciting contest of analyzing real-world data. This year, our goal was to develop an **innovative solution** to optimize the replenishment of BCP's ATMs by combining a predictive demand model with an optimization algorithm to maximize operational efficiency and minimize costs. 💡💰
## Challenge and Solution
The challenge was to **optimize cash management** in BCP's ATMs, ensuring cash availability for customers. Two key strategies were identified:
1. **Predicting cash demand.** 📊
2. **Optimizing cash replenishment.** ⚙️
After presenting our proposal to a jury composed of experts in management and data analytics, we are proud to have achieved **second place** in the competition! 🥈
## Lessons Learned
1. **Focus on the business problem:** Concentrating on the real problem enriched our proposals and led to effective strategy design.
2. **Deep understanding of the context:** Taking the time to understand the problem and its environment helped us formulate solutions tailored to the business reality.
3. **Data quality:** Ensuring the integrity of the data used was essential for the reliability of our predictions and recommendations. 📈
## Team
The team responsible for developing this solution included:
- [Díaz, Walter](https://www.linkedin.com/in/waltdiaz/)
- [Ramón, Eduardo](https://www.linkedin.com/in/eram%C3%B3n/)
- [Rodas, Gustavo](https://www.linkedin.com/in/gustavo-rodas/)
- [Saucedo, Renzo](https://www.linkedin.com/in/renzosaucedos/)
- [Villalva, Diana](https://www.linkedin.com/in/diana-villalva-gomez-346a93272/)
