This project is an exploratory data analysis and classification task using the Breast Cancer Wisconsin dataset. This project aims to provide insights into breast cancer diagnosis using various statistical and machine learning techniques. The dataset includes features like tumor size, shape, and texture, aiding in distinguishing between benign and malignant tumors.
This repository contains a Jupyter notebook for exploratory data analysis (EDA) and classification of the Breast Cancer Wisconsin dataset. The project focuses on distinguishing between benign and malignant breast cancer cases using statistical and machine learning methods.
Comprehensive EDA: Includes data visualization and statistical analysis. Classification Models: Implementation of various ML models for cancer prediction. Model Evaluation: Accuracy, confusion matrix, and other metrics. Dataset The dataset features tumor characteristics like size, shape, and texture.
Python 3 Libraries: pandas, NumPy, seaborn, matplotlib, scikit-learn
Clone the repository. Install dependencies. Run the Jupyter notebook.
Contributions are welcome. Please open an issue or submit a pull request.
Distributed under the MIT License.