Breast Cancer Prediction with DecisionTree is a comprehensive project aimed at predicting breast cancer outcomes using a finely tuned Decision Tree Classifier. This project leverages the Breast Cancer Wisconsin (Diagnostic) Data Set to classify cancer as either Malignant or Benign based on significant attributes. The model is enhanced through hyperparameter tuning and feature reduction using Principal Component Analysis (PCA), ensuring optimal performance.

The project employs the SHAP library for model interpretability, providing insights into how individual features influence predictions. This transparency is crucial for understanding the decision-making process of the model. The project is open source, allowing for community collaboration and further development.

Key Features:

• Utilizes a Decision Tree Classifier for prediction.

• Incorporates PCA for feature reduction.

• Employs SHAP for model interpretability.

• Open source with a MIT license.

Installation is straightforward, with options to use a Makefile or uv for setting up the environment and dependencies. The output predictions are saved in a CSV format, making it easy to analyze and utilize the results.