Implementation of Machine Learning Techniques to Solve Real-World Problems in Automotive Repair Space

CarServ is the operating system for the automotive repair industry, powered by machine learning. The objective of this project is to first identify the key areas where machine learning can be applied to CarServ datasets to solve problems facing repair facilities and then to apply machine learning solutions to solve it in a more sophisticated way. Now CarServ has gathered great historical data around the repair process and want to implement machine learning techniques to draw fruitful insights from that data and to do better improvements in existing solutions. Some of the highlighted tasks that I have undertaken are, building recommendation systems based on market price data and history repair data using supervised and unsupervised machine learning techniques, building a prediction system to predict repair service timeline based on CarServ's history of customer service data, and implementing Natural Language Processing (NLP) techniques to analyze customer reviews to improve on lacking areas based on customer feedback.