Ramanpreet Kaur

1. Explainable AI - Global and local Interpretability of ML Models e.g. Shap, gain and split per Decision trees. 2. Causality - Bayesian and simulation methods e.g. Monte carlo. 3.Model Evaluation (AUROC, CAP ,Precision Curves, K-S Plots, Reliability Plots). 4.Data audits ( Training and Holdout set) - EDA, Outliers ( Univariate and conditional). 5.To predict model deployment effect on end-to-end business KPIs.

1.Feature Engineering - Implementation and comparison of Morse wavelet transformation and other relevant features of audio signals in Matlab, and later programmed and optimized the same in Python. 2.Visualization - Built a GUI Application using QT designer in Python to visualize Real time audio signals along with extracted features. 3.Development - Developing the audio based sensor concept in guiding medical interventional devices using C++ .

Webscraping - Created Scraping Python Modules for the Cron jobs

1. Predicting Market Value of Football Players - Applied various Regression Models by interpreting and transforming Data. Best Model is deployed using RESTful web services. 2.Built backend of News Recommender System by hybrid filtering using both news articles and user similarities.