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DHS-To-Database-dhs2CSVTables-simplified
DHS AI | Database conversion | Open Source | Contribute | Python Sep 29, 2024We are excited to announce the release of our new project: DHS-To-Database-dhs2CSVTables-simplified! This open-source tool is designed to simplify the conversion of raw Demographic and Health Surveys (DHS) data into a format suitable for database storage in CSVTables format.
What is DHS-To-Database-dhs2CSVTables-simplified?
Our project serves as a convenient wrapper around the existing DHS-To-Database tool developed by Harry Gibson. While the original tool is powerful, we recognized the need for a more accessible and user-friendly way to handle DHS data conversions. This new wrapper allows users to perform these conversions effortlessly without delving deep into the original library internals.
Key Features- User-Friendly Wrapper: Provides an easy-to-use interface for converting raw DHS data.
- Supports Python 3.8 and Above: Ensure compatibility with modern Python environments.
- Simplified Usage: Designed for seamless interaction with raw DHS data.
- CSV to SQLite Conversion (New in v0.2.0): Now supports converting relational CSV tables to a SQLite database using the
csvs-to-sqlite
command-line tool. This allows seamless migration of DHS data into a more structured relational database format.
For detailed documentation, examples, and to get started with the tool, please visit our GitHub repository: DHS-To-Database-dhs2CSVTables-simplified.
Contributing
We welcome contributions from the community! If you have suggestions or improvements, please feel free to fork the repository and submit a pull request. Your contributions can help enhance this tool for everyone.
Acknowledgments
A special thanks to Harry Gibson for his foundational work on the DHS-To-Database tool. Our project builds on his incredible contributions to the open-source community. -
MINTILO AI
DHS AI | Algorithm | Contribute | Open Source | Python
Apr 06, 2023
An open source project dedicated to utilizing Python and machine learning techniques to provide insights for policy makers in eradicating child mortality. By analyzing data from DHS surveys, MINTILO AI identifies risk factors associated with child survival, leveraging artificial intelligence to contribute to the collective efforts in improving child survival rates.
This project relies on the DHS AI WebApp platform as its data source. To access and contribute to MINTILO AI, visit the GitHub repository: GitHub - MINTILO AI.
For more information and to contribute, please feel free to reach out to us. Let us know if you would like to contribute and be part of this impactful initiative. -
WATOTO SURVIVAL
DHS AI | Algorithm | Contribute | Open Source | R
Apr 15, 2023
An open source project that utilizes classical survival analysis methods in R, including Kaplan-Meier and Cox regression techniques. Its primary objective is to enable researchers and graduate students to collaborate and identify risk factors related to child survival in children under-five years old. By leveraging statistical techniques and the rich dataset from DHS surveys, WATOTO SURVIVAL aims to provide valuable insights for policymakers and researchers in their efforts to address this critical issue.
This project relies on the DHS AI WebApp platform as its input source. You can access the project on GitHub by visiting the following link: GitHub - WATOTO SURVIVAL.
For more information and to contribute, please feel free to contact us. We welcome your participation and contributions to make a meaningful impact in this crucial area of research.