girish3213/Breast-Cancer-Detection

Breast-Cancer-Detection

The dataset is of UCI, used for breast cancer detection.Eight classification models are been used to find the model of highest accuracy for the given dataset.

The features used to predict Breast Cancer here are:

1.Sample code number 2.Clump Thickness 3.Uniformity of Cell Size 4.Uniformity of Cell Shape 5.Marginal Adhesion 6.Single Epithelial 7.Cell Size 8.Bare Nuclei 9.Bland Chromatin 10.Normal Nucleoli 11.Mitoses

The eight models used are:

1. Decision Tree classification Model (Accuracy : 95.9 % )
2. K Nearest Neighbours classification Model (Accuracy : 94.7 %)
3. Kernel SVM classification Model (Accuracy: 95.3 %)
4. Logistic Regression classification Model (Accuracy: 94.7 %)
5. Naive Bayes classification Model (Accuracy: 94.1 %)
6. Random Forest classification Model (Accuracy: 93.5 %)
7. Support Vector Machine classification Model (Accuracy: 94.1 %)
8. XGBoost classification Model (Accuracy: 97.8 %)

Therefore, for the given dataset XGBoost classification Model gives the highest accuracy of 97.8 %.