This code repository showcases an advanced machine-learning solution designed to identify breast cancer cases through the utilization of diverse classification algorithms. By harnessing the power of Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest models, this project has accomplished an outstanding accuracy rate of 99%. The model is built upon a comprehensive dataset that encompasses several essential features related to breast cancer diagnosis.
Data description can be found here -Cancer_Prediction.ipynb
The data was taken from Kaggle.
If you wish to contribute, kindly adhere to the following instructions:
- Begin by forking the repository.
- Generate a new branch dedicated to your modifications.
- Implement the changes and commit them to your branch.
- Finally, submit a pull request to the development branch.
The license is used: "GNU General Public License v3.0".