Forecasts based on machine learning can make your business applications smarter. However, this is still difficult in practice and many data scientist teams experience similar difficulties:
Identify vulnerable neighborhoods, monitor real-time, and improve liveability.
Get insight into the sustainability of your neighborhood.
Accelerate the flow chain and match tenants more efficiently.
Detect different types of fraud and improve accessibility.
Start with the acceleration of the flow in the housing market. Get the chain optimisation going and improve the flow- through rate. Identify empty nesters and presorted, and see how the relocation chain can be optimised. Compare the current situation with the new situation on housing costs, financial data, housing characteristics and facilities in the neighbourhood and gain insight into the mutation rate.
and many more...
Using real-time insights and predictive analyses, neighborhoods can be designed more efficiently, and recommendations can be made on how to improve the quality of life in vulnerable neighbourhoods. Real-time monitoring allows you to perform multiple analyses in the areas of maintenance, safety, integration, mutation complaints, and livability. Impact analysis gives you a better understanding of key variables and different forecasts to ensure that your daily operations are continuously improved.
Clappform B.V.
Overschiestraat 61
1062 XD Amsterdam
info@clappform.com