Multivariable service termination risk classification using machine learning

A computer-implemented method for training a random forest model for risk classification using an ensemble learning method, the computer-implemented method comprising:

• receiving a dataset comprising a plurality of data points, each data point comprising a plurality of features;
• creating, from the data, a plurality of subsets, wherein the plurality of subsets is created by random sampling of the data points and random sampling of the plurality of features;
• generating a plurality of decision trees, each decision tree of the plurality of decision trees trained using a subset of the plurality of subsets;
• and
• generating a random forest model by combining the plurality of decision trees,
• wherein each data point corresponds to a subscriber of a wireless telecommunications service,
• wherein each data point includes a label indicating if the subscriber terminated service, and
• wherein training each decision tree is performed using supervised learning.

A system comprising:

• at least one hardware processor;
• and
• at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor, cause the system to:
• receive input data comprising a plurality of features for a plurality of subscribers of a telecommunications service provider, wherein the features comprise at least one of:
• network capacity data, network throughput data, network coverage data, network outage data, or network congestion data;
• provide the input data to a risk prediction model, the risk prediction model configured to predict a termination likelihood for each subscriber of the plurality of subscribers, wherein the risk prediction model comprises a random forest model;
• identifying, based on the predicted termination likelihood for each subscriber, an at risk population comprising a subset of the plurality of subscribers having a termination likelihood above a threshold amount;
• determining, for each subscriber of the at risk population, a profile type of a plurality of profile types;
• assigning the determined profile type to each subscriber of the at risk population;
• determining a selection of a profile type;
• and
• making available a subset of the at risk population having the selected profile.