Analyzing the wisconsin breast cancer dataset using ml algorithms

Prediction of Breast Cancer with Tuned DecisionTree Model and interpreting results with SHAP

Demonstrating the use of explainable methods by interpreting the decisions of several neural network models.

In this project, the Wisconsin breast cancer dataset is visualized in two dimensions using Principal Component Analysis (PCA).

Classify breast cancer using machine learning models and analyze feature importance.

Explore the application of AdaBoost utilizing the Wisconsin breast cancer dataset.

Deep Neural Network from scratch. Wisconsin Breast Cancer dataset.

This project used machine learning models to detect cancer in biopsies with an accuracy of 98%

Machine learning model, trained to recognize if a tumor is malignant or benign