Service disruptions in network can cause huge revenue loss for telecom operators. These disruptions can be momentary glitch or total interruption of connectivity. The growing expansive network makes it even difficult for any operator to prevent such mishaps, failing customer experience to stay connected. Operators thereby need to innovate continuously to devise scalable system that can predict the scope and timing of service disruptions with high accuracy.

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Provides real time prediction with high accuracy as we store trained models.

Build stable models on large scale data sets using hyper-parameter optimisation techniques.

Helps detect reasons of network failures through decision trees

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