Cervical Cancer Risk Prediction Using Machine Learning

In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records

• Understand the theory and intuition behind XGBoost Algorithm
• Perform exploratory data analysis
• Develop, train and evaluate XGBoost classifier model using Scikit-Learn

• Basic familiarity with the software or workflow used in the project
• Ability to follow step-by-step instructions in English