Research in the field of Explainable AI and Interpretable Machine Learning.

Worked in 2 fields - Finance and Healthcare.

In the Finance sector, we used the German Credit Risk dataset and trained models such as LogReg, Random Forest, XGBoost and Naive Bayes. We explained the decision making process of these models using XAI techniques such as LIME, SHAP, PDP and ELI5.

In the Healthcare sector, we used the Breast Cancer Histopathology image dataset and trained DenseNet, ResNet, Inception and VGG models on this dataset. Techniques such as LIME, GradCAM, Saliency Maps, etc were used to understand the flow of activations in the images.

Learn more about XAI and get a detailed report about this project in the link given below.