You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
XGBoost is an open-source machine learning library that provides efficient and scalable implementations of gradient boosting algorithms. It is known for its speed, performance, and accuracy, making it one of the most popular and widely-used machine learning libraries in the data science community.
Hospital admission data was analyzed to accurately predict the patient’s Length of Stay at the time of admit so that the hospitals can optimize resources and function better.
This project is a part of research on Breast Cancer Diagnosis with Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
An XGBoost model in Python that classifies if a customer will cancel his/her hotel booking or not. I also use counterfactuals guided by prototypes from the Alibi package to explore the minimum changes needed to flip a prediction from canceled to not canceled and vice versa.
This project is a part of research on Breast Cancer Diagnosis with a Machine Learning algorithm using data-driven approaches. The final outcomes of the research were later published at an IEEE Conference and added to IEEE Xplore Digital Library.
A comparative breakdown of traditional econometric timeseries models vs. more modern ML methods for predicting a retail firm's sales over a short to medium horizon