Abstract: Disclosed is a method of classifying non-visual data. The method may include a stage of receiving each of a plurality of non-visual data and a plurality of classifications. Further, the method may include a stage of transforming the plurality of non-visual data into a plurality of visual images. Additionally, the method may include a stage of generating an image classifier based on the plurality of visual images and the plurality of classifications. Further, the method may include a stage of receiving an un-classified non-visual data. Furthermore, the method may include a stage of transforming the un-classified non-visual data into an un-classified visual image. Additionally, the method may include a stage of assigning a classification to the un-classified non-visual data based on classifying the un-classified visual image using the image classifier.

Abstract: Embodiments of the present disclosure may provide a platform configured to forecast customer churn in a telecommunication network. The platform may be configured to receive customer activity data. The platform may then compute features associated with the customer activity data. These features are then inputted into a machine learning model used for predicting customer churn. Finally, the platform may then provide a report indicating customer churn predictions. The platform may be trained in a training phase prior to entering a prediction phase. The platform may employ an ensemble of statistical machine learning classifiers. An ensemble of classifiers may comprise a set of classifiers whose individual decisions are combined to generate a final decision. An ensemble consistent with embodiments of the present disclosure may be composed by several supervised classification algorithms, including, but not limited to: random forest, neural networks, support vector machines, and logistic regression.