Dr. S. Babbar's Projects
This is a small tutorial project that demonstrates application and evaluation methods of popular clustering algorithms namely, K-means, DBSCAN and Agglomerative.
This project addresess a medical problem of detecting cancer and understanding what causes cancer in body using machine learning.
Detection of cardiovascular disease using AWS Sagemaker PCA and Xgboost techniques
Predictive problems requires three main challenges to overcome. First, choosing the right classification algorithm. Second, building a robust building and testing environment for algorithm to learn and thirdly, picking the appropriate performance metric for evaluation. Here it is explained how these challenges can be addressed.
This project demonstrates application of PCA for improving the classification results
A small project addressing a regression problem explains implementation of multiple linear regression techniques, hyperparameter tuning, collinearity, model overfitting and complexity using LASSO, Ridge and Elastic net
Different linear and non linear regression models are demonstrated with illustration on parameter tuning using GridsearchCV in sklearn
Material for posts
This project addresses problem of early detection of Parkinson disease using Machine learning techniques
This project demonstrates machine learning pipeline to predict rainfall in Kerala state of India
This project is to identify fraudulent credit transactions using Xgboost
The objective of this project is to forecast weekly retail store sales based on historical data using XGBoost Sagemaker