This project focuses on developing a machine learning model for the detection of breast cancer using publicly available datasets. The goal is to analyze and classify breast cancer cases based on various features, providing insights and tools to improve early detection.
- Project Description
- Dataset
- Technologies Used
- Installation
- Usage
- Model Evaluation
- Contributing
- License
Breast cancer is one of the most common types of cancer among women. Early detection can significantly improve treatment outcomes. This project employs data science techniques to build a predictive model that can assist in identifying cancerous cases based on various clinical and diagnostic features.
The primary dataset used in this project is the Wisconsin Breast Cancer Dataset (WBCD), which contains the following features:
- Radius
- Texture
- Perimeter
- Area
- Smoothness
- Compactness
- Concavity
- Symmetry
- Fractal dimension
- Class (Malignant or Benign)
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook