Power Laws: Cold Start Energy Forecasting
Usually with buildings, bigger the historic datasets yield more accurate consumption forecasts. The goal of this challenge is to provide an accurate forecast from the very beginning of the building instrumentation life, without much consumption history.
Reboot: Box-Plots for Education
We're rebooting our first prized competition for fun and education! Tag school budgets automatically to help districts get a better grasp of their spending and how to improve the impact of their scarce resources.
United Nations Millennium Development Goals
The UN's Millennium Development Goals provide the big-picture perspective on international development. Using indicators aggregated and collected by the World Bank, try to predict progress towards select MDGs.
Warm Up: Predict Blood Donations
Can you predict whether a donor will return to donate blood given their donation history? This is the smallest, least complex dataset on DrivenData, and a great place to dive into the world of data science competitions.
Warm Up: Machine Learning with a Heart
Can you predict the presence or absence of heart disease in patients given basic medical information? This is the smallest, least complex dataset on DrivenData, and a great place to dive into the world of data science competitions.
Power Laws: Optimizing Demand-side Strategies
Storage is critical to flexible and reliable access to renewable energy sources. In this challenge, competitors combined traditional optimization methods and machine learning to build algorithms for controlling a battery charging system as efficiently as possible.
Helios1st Place Team
Power Laws: Detecting Anomalies in Usage
Commercial buildings waste an estimated 15% to 30% of energy used due to poorly maintained, degraded, and improperly controlled equipment. Competitors built quick-response algorithms to find anomalies in energy use and elevate them for human attention and intervention.
PINGANAI_1st Place Team
Power Laws: Forecasting Energy Consumption
More accurate forecasts of building energy consumption mean better planning and more efficient energy use. In this challenge, competitors used machine learning to build the most accurate predictions of the future from limited data in the past.
Pover-T Tests: Predicting Poverty
Measuring poverty is hard. Thanks to the efforts of thousands of competitors, The World Bank can now build on open source machine learning tools to help predict poverty, optimize uses of survey data, and support work to end extreme poverty ...
Ag1001st Place Team
Data scientists from more than 90 countries around the world drew on 300,000 video clips in a competition to build the best machine learning models for identifying wildlife from camera trap footage. The results are powerful and – equally important ...
N+1 fish, N+2 fish
Sustainable fishing means tracking every fish caught. New tools using automated video processing and artificial intelligence can help responsible fisheries comply with regulations, save time, and lower the safety risk and cost from an auditor on board.
Random Walk of the Penguins
Competitors built hundreds of algorithms to predict changes in Antarctic penguin populations from the most comprehensive counts available. These algorithms give researchers a greater understanding of penguin population dynamics, a leading indicator of climate change.
Keeping it Fresh: Predict Restaurant Inspections
Flag public health risks at restaurants by combining Yelp reviews with open city data on past inspections. An algorithmic approach discovers more violations with the same number of inspections.
Countable Care: Modeling Women's Health Care Decisions
Can you predict what drives women’s health care decisions in America? In an uncertain health landscape, this survey modeling challenge can help illuminate what care people receive, where they go, and how they pay.