Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2.3 million new cases in 2020, making it a significant health problem in the present day. The key challenge in breast cancer detection is to classify tumors as malignant or benign. Malignant refers to cancer cells that can invade and kill nearby tissue and spread to other parts of your body. Unlike cancerous tumors (malignant), Benign does not spread to other parts of the body and is safe somehow.

Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu Also can be found on UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/Breast The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features.

There are 3 kinds of machine learning approaches- Supervised, Unsupervised, and Reinforcement Learning techniques. Supervised learning as we know is where data and labels are present. Unsupervised Learning is where only data and no labels are present. Reinforcement learning is where the agents learn from the actions taken to generate rewards. Imagine a situation where for training there is less number of labelled data and more unlabelled data.