itaccounts

During the high-energy, three-day marathon-style DataDive® event that took place March 4-7, 2021, volunteers on this project set out to do the following:

More accurately forecast future waste volume collected across Sanergy’s FLT network.

Build a machine learning model to predict which of The Welcome Centre’s clients are likely to become dependent on the food bank’s food and other packs
Deploy the machine learning model within The Welcome Centre’s existing system and flag those who are mostly likely to become dependent, enabling them to be prioritised for additional support from the food bank’s support work

Understand who benefits from Llamau’s services so that the organization can better support the young homeless and vulnerable populations they serve.
Identify projects and locations that have the best outcomes, as well as those that need improving, and investigate contributing causes and factors.
Evaluate the outcomes from each of Llamau’s services to gain insights to help improve the services they provide to individuals.

Partner with New York, Seattle, New Orleans, and Microsoft to explore how data science can help the Vision Zero movement, which aims to reduce traffic-related deaths and severe injuries to zero
Help New York City’s Department of Transportation improve traffic safety on its streets by understanding what existing safety interventions are working and where there is potential for improvement so the city can better allocate resources
Inform Seattle’s Department of Transportation’s Bicycle and Pedestrian Safety Analysis to provide policy makers and engineers with actionable information to best allocate funding for future safety interventions and to find out what factors may contribute to crashes–such as traffic volume, street characteristics and environmental variables
Help the City of New Orleans’ Office of Performance Accountability understand how effective street treatments, like bike lanes, traffic signage and other interventions, are at preventing traffic injuries and fatalities to inform future efforts

Using Microcred’s loan application data and internal loan status information, build statistical models to help predict customer default and better inform decision making about lending to make it more efficient and inclusive.
Identify whether loan application data and factors such as past repayment behavior or late payments during the early stages of a loan cycle could predict default.

Gain basic insights about what newly released data shows about UK companies
Uncover flaws in the data to improve future data collection efforts
See whether the data points to any promising leads for further investigation

Develop and provide Thresholds with a data warehouse to house, scale and process raw data from various sources including Thresholds internal systems, the Illinois Department of Healthcare and Family Services, the Cook County Jail and other potential future data sources.
Automate the process of data flow from a central database and create tools to help make managing patient data, generating reports and dashboards easier and more efficient for Thresholds staff, allowing them to better serve their patients.
Explore the possibilities of using predictive analytics to help Thresholds better tailor care to people suffering from mental illness.

Build a predictive model using John Jay’s data to identify students who are at risk of dropping out, to aid John Jay’s efforts to provide better and more timely support to these students and reduce dropout rates.