Currently Masters Student in University of North Carolina, Charlotte. Actively looking for Summer Internship opportunities in Machine Learning and Data Science. Currently Working on developing a model for Insurance Fraud Detection using Semi-supervised Machine Learning. Experienced Senior Member Of Technical Staff with a demonstrated history of working in the Wireless industry. Skilled in Python, Java, C and Shell Scripting.
Building model for Urban Analytics involving collecting data from different sources such as weather forecasts, traffic data, social media data and collectively synthesizing this data towards predicting and making improvements towards traffic flow in the city of Charlotte.
Building a model to classify Insurance claims as fraud using Semi-supervised Machine Learning.
Implemented a general game-playing agent for two-player deterministic games, using (1) minimax with alpha-beta pruning, and (2) minimax-cutoff (i.e., with cutoff test to replace terminal test and with evaluation function to replace utility function) with alpha-beta pruning. Applied it to the planar 3*3 Tic-Tac-Toe game and extended it to the planar n*n (n > 3 and n is odd). Has a friendly graphical user interface to allow a human user to play the game agaist the algorithm
GitHub