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Using Machine Learning for Cellular Network Data Diagnostics for Fault Detection & Prevention
A multinational telecommunications company wanted to automate the process of evaluating and predicting problem areas and their impacts on network efficiencies. Overall they wanted to improve customer satisfaction, by proactively identifying and resolving problems faced by them.
This Case Study showcases how ThirdEye built a predictive model to find and resolve problem occurrences by analyzing data from a multitude of Base receiver stations (BTS), Radio Network controllers (RNC) and user equipments. Log data from the systems were collected and analyzed to predict problems in real time significantly alleviating the problem of manual log analysis.