Cider

Cider

Cider uses mobile phone data to identify areas of extreme poverty and optimize humanitarian aid. Through an evidence-based approach, this tool allows organizations to direct resources effectively, especially in emergency situations such as famine or natural disasters. By providing a fast, scalable method for targeting aid, Cider significantly improves the efficiency of assistance programs, ensuring that support reaches those who need it most and when they need it most.

Cider addresses the challenge of effectively targeting aid in situations of extreme poverty and emergencies. It uses mobile data to quickly identify needs and optimize the distribution of resources, improving the efficiency and effectiveness of aid programs, especially in critical situations that require rapid and well-targeted interventions.

**Data Anonymization**: Safeguards sensitive information, ensuring privacy and compliance with data protection regulations. **Data Storage Management**: Efficiently organizes and retrieves data, enhancing data accessibility and integrity. **Fairness Assessment**: Ensures unbiased data processing, promoting ethical AI practices. **Machine Learning Facilitation**: Streamlines the development and evaluation of machine learning models, boosting predictive accuracy and performance. **Modular Design**: Offers flexibility and ease of customization to cater to specific business needs.

Built with Python: leverages its versatility and wide adoption in data science. Utilizes object-oriented programming to ensure a structured and scalable codebase. Enhances code maintainability through a modular structure that allows easy integration of new features. Adheres to PEP 8, ensuring readability and consistency. Integrates standard Python libraries to provide robust and efficient functionality.