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What data is most important in the predictive analysis process?

Posted: Thu Jan 23, 2025 6:05 am
by mstakh.i.mo.mi
Finance and banking : Credit risk assessment, financial fraud detection.
E-commerce : Personalization of offers, prediction of shopping trends.
Insurance : Forecasting damage costs, customer churn analysis.
Logistics : Route optimization and inventory management.
Healthcare : Early detection of diseases, forecasting the effectiveness of therapy.

The most important are:

Historical data : Serves as a basis for identifying patterns and trends.
Live data : Allows models to be updated in real time.
Data from various sources : Information from CRM, IoT, and social chinese overseas canada database media systems helps create a more complete picture.
High-quality data : It is essential to clean data to make it consistent and reliable.
Can predictive analytics work in real time?
Yes, thanks to modern technologies such as the Internet of Things (IoT) and Big Data , predictive analytics allows for the analysis of data in real time. Models are updated continuously, allowing companies to make decisions immediately after an event occurs. An example is the detection of fraud in financial transactions at the time of their execution.


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