Veerdhaval Khot

Built Air quality data product's entire data pre-processing pipelines using AWS cloud platform and various machine learning (xgboost) techniques to interpolate data to higher spatial and temporal resolution for 52 countries. Successfully implemented Air quality forecast model using ARIMA time-series model which is deployed on top of our IoT stations for various objectives. Built weather data product using AWS cloud platform, machine learning, data-processing pipelines and (IDW, k-nearest neighbours) interpolation techniques which provide global hyperlocal weather data. Build environmental history data product pipeline on 500+ TB data using AWS glue, machine learning, interpolation (IDW) and AWS-EC2, AWS-lambda and AWS-ECS. Built and deployed different machine learnings models for different problems like xgbost, time-series model, knns, etc.

Build successfully Employee Performance prediction model using xgboost and other machine leanring techniques with good accuracy and implemented it to manage and form teams. Built Attrition prediction pipeline using machine learning classification algorithm called logistic regression and it is used to provide hiring and employee management solutions to clients. Maintained and built Auto-ML pipeline from scratch using existing machine learning and deep learning algorithms which helps to check model health and retrain on new data whenever accuracy drops below the defined threshold. Build auto-generative reports and Chat-bot using knowledge graphs and NLP techniques. I have used deep learning for auto-generative reports and google's Dialogflow for chat-bot.

Built ITSM ticket prediction system using machine learning classification algorithm known as Random forest. This model is used for predicting ticket volume, auto categorizing ticket. Built Credit card fraud detection for one of the client using xgboost and imbalanced data handling technique to improve accuracy which is a satisfied client's need successfully.