Abstract: Prediction systems and methods are provided. The system obtains a first social media data pertaining to a first set of users, filters the first social media data to obtain a filtered social media data, generates a word embedding matrix including co-occurrence words each represented as a vector having a context, aggregates vectors pertaining each social data to obtain a first set of vectors, and trains machine learning technique(s) (MLTs) using the first set of vectors and context of the first set of vectors. The system further obtains a second social media data pertaining to a second set of users, and performs filtering, word embedding matrix generation, and aggregation operations to obtain a second set of vectors, and further applies the trained MLTs on the second set of vectors and context associated with the second set of vectors to predict age and gender of the second set of users.

Abstract: System and method for analyzing driver behavior based on telematics data are disclosed. In an example, a probability of a user driving a vehicle is computed and a risk score is generated to develop at least one driver profile based on the probability. Further, routes taken by said user driving said vehicle are clustered to generate enhanced driver profile and using the clustered output to develop dynamic intelligent contexts for each said route and adding contextual intelligence messages to customize said risk score. Furthermore, the routes taken by the said user in real time are predicted. In addition, a missing route is identified through imputation of missed routes to compute annualized mileage, and a missing distance is imputed in an analysis of at least one trip of the driver in the vehicle. Also, independent trips are stitched based on at least one recommendation from an analytics engine.

Abstract: Prediction systems and methods are provided. The system obtains a first social media data pertaining to a first set of users, filters the first social media data to obtain a filtered social media data, generates a word embedding matrix including co-occurrence words each represented as a vector having a context, aggregates vectors pertaining each social data to obtain a first set of vectors, and trains machine learning technique(s) (MLTs) using the first set of vectors and context of the first set of vectors. The system further obtains a second social media data pertaining to a second set of users, and performs filtering, word embedding matrix generation, and aggregation operations to obtain a second set of vectors, and further applies the trained MLTs on the second set of vectors and context associated with the second set of vectors to predict age and gender of the second set of users.

Abstract: System and method for analyzing driver behavior based on telematics data are disclosed. In an example, a probability of a user driving a vehicle is computed and a risk score is generated to develop at least one driver profile based on the probability. Further, routes taken by said user driving said vehicle are clustered to generate enhanced driver profile and using the clustered output to develop dynamic intelligent contexts for each said route and adding contextual intelligence messages to customize said risk score. Furthermore, the routes taken by the said user in real time are predicted. In addition, a missing route is identified through imputation of missed routes to compute annualized mileage, and a missing distance is imputed in an analysis of at least one trip of the driver in the vehicle. Also, independent trips are stitched based on at least one recommendation from an analytics engine.