Breast Cancer Classification Project

Breast Cancer Classification using Logistic Regression & Naive Bayes This project focuses on early breast cancer detection using two classic machine learning models: Logistic Regression and Naive Bayes. We begin by preprocessing and cleaning a real-world medical dataset to ensure quality inputs. Data features are then standardized using StandardScaler, followed by an efficient Train/Test split strategy. Each model is trained separately and evaluated through: Accuracy scores Confusion matrices Classification reports To ensure a clear comparison, results are presented both numerically and visually, allowing for transparent performance insights. Objectives: Develop interpretable AI models for medical diagnosis Analyze and visualize key trends in cancer-related data Compare model performance to improve detection accuracy