This is a simplistic comparison of different Supervised Learning Models on a single data set. First one made entirely by myself and the others are imported from sklearn. Due to the binary classification yet multifeature dataset, I thought it would be interesting to explore the different results given different methodologies.

Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society.

The early diagnosis of Breast Cancer can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex Brest Cancer datasets, Machine Learning is widely recognized as the methodology of choice in Breast Cancer pattern classification and forecast modelling.

Link to data set: http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29

Link to sklearn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html