Breast Cancer Detection (Logistic Regression) A machine learning project using Logistic Regression to classify breast tumors as benign or malignant based on the Breast Cancer Wisconsin dataset. The model achieves high accuracy with essential preprocessing, EDA, and evaluation metrics like confusion matrix and F1-score. It leverages supervised learning to classify whether a tumor is benign or malignant based on features derived from medical imaging data.
Overview :
Model Used: Logistic Regression
Dataset: Breast Cancer Wisconsin (Diagnostic) Dataset from sklearn.datasets
Goal: Predict cancer type (Benign or Malignant)
Tools: Python, NumPy, Pandas, Matplotlib, Seaborn, scikit-learn
Features :
Data preprocessing and feature scaling
Exploratory data analysis (EDA) and correlation heatmap
Model training and evaluation
Accuracy, precision, recall, F1-score, and confusion matrix
Sample Results :
Accuracy: ~95% (may vary depending on split)
High precision and recall on both classes
Visualizations for better understanding