Welcome to the Breast Cancer Prediction project repository! This project focuses on predicting breast cancer diagnosis using machine learning algorithms. Leveraging the Breast Cancer Dataset from Kaggle, an analysis is conducted on 32 different metrics to create predictive models.
- Logistic Regression and Decision Tree algorithms are implemented for classification.
- An impressive 96% accuracy is achieved in predicting breast cancer diagnosis.
- Insights into feature importance and model performance evaluation are provided.
The primary objective of this project is to explore the effectiveness of machine learning techniques in diagnosing breast cancer. By analyzing various features extracted from diagnostic images, contributions are aimed to be made to the ongoing efforts in medical research and early detection of breast cancer.
Contributions and feedback are welcomed. Forking this repository, experimenting with different algorithms, or suggesting improvements to enhance the accuracy and robustness of the predictive models are encouraged.
Future iterations may include exploring additional machine learning algorithms, optimizing hyperparameters, and integrating advanced techniques such as ensemble learning for even more accurate predictions.