Machine Learning Based Classification Models for Early Analysis and Prediction of Cervical Cancer

Cervical cancer ranks among the prevalent gynecological malignancies, with an annual global incidence of roughly 500,000 new cases and approximately 300,000 fatalities. Early detection of cervical cancer is crucial for improving patient outcomes, and Machine Learning (ML) has emerged as a promising tool for accurate and efficient detection. In this project, we focus on using ML for cervical cancer detection, leveraging the classification, regression, clustering, and survival analysis capabilities of Google Colab. Through our use of various modelling techniques, we aim to develop an effective ML model for cervical cancer detection that can help save lives. Overall, this project demonstrates the potential benefits of using ML in gynecological cancer detection, particularly in the context of cervical cancer.