Based on the World Health Organization [(WHO) report](http://www.who.int/mediacentre/factsheets/fs334/en/) in 2011, about 820,000 women and men aged 15-24 were newly infected with HIV in developing countries. Among these newly infected, more than 60% were women. Developing countries face serious reproductive health problems such as sexually transmitted infections (STIs), unintended pregnancies, and complications from childbirth. **Emphasize prevention and provision of information about STIs and other reproductive tract infections (RTIs)** was listed as one of the top priorities for policymakers, researchers, and health care providers. See [Improving Reproductive Health in Developing Countries](http://pdf.usaid.gov/pdf_docs/Pnacr613.pdf) from the U.S. National Academy of Sciences for details. To help achieve the goal of improving women's reproductive health outcomes in underdeveloped regions, this competition calls for optimized machine learning solutions so that a patient can be accurately categorized into different health risk segments and subgroups. Based on the categories that a patient falls in, healthcare providers can offer an appropriate education and training program to patients. Such customized programs have a better chance to help reduce the reproductive health risk of patients. This dataset used in this competition was collected via survey in 2015 as part of a Bill & Melinda Gates Foundation funded project exploring the wants, needs, and behaviors of women and girls with regards to their sexual and reproductive health in nine geographies. The data are made available here in accordance with the [Bill & Melinda Gates Foundation open data access policy](http://www.gatesfoundation.org/How-We-Work/General-Information/Open-Access-Policy). The data may be used and shared for non-commercial purposes.