case-study

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

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

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:

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.

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

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.