Breast Cancer Classification - with XAI

Description

  This is a Machine Learning Project that uses various Machine Learning Algorithms to classify that whether a cell with certain features and characteristics is benign or malignant.
I have used many classification algorithm( Supervised Machine Learning Algorithms) like Logistic Regression, Logistic Regression with Cross Validation, Decision Tree, Support Vector Machine, Random Forest Classifier, Extra Tree Classifier.

Also Explainable AI (XAI) - LIME is used to explain my model(which is blackbox model). The lime tabular explainer is used to show which features are used in the giving the prediction probalities.

Best Accuracy was given by the Logistic Regression with Cross Validation

Future Plan

  I am planning to use Deep Learning to build neural network to predict cancer. Also I am planning to create a separate project for Detecing Breast cancer from UtraSound Image

Dataset

  Dataset is present in slearn

use the command

sklearn.datasets.load_breast_cancer

Any Contribution is Welcomed.

• Fork the Repo
• Clone a personal copy of this repo
• Make your necessary Changes
• Upload the branch and the changes to the forked repo
• Open a Pull Request with a description of the changes you made

I will review the PR and merge it if I find it appropriate