This repository contains the files used for Major Project 2022

Phase 1: Classifying breast cancer in its preliminary stages is done with the help of machine learning and the concept of Transfer Learning Algorithm. Here, the classification is done by labeling the tumor as benign or malignant. The machine learning algorithms are implemented by using the scikit library in which transfer learning is also available. The algorithm completely depends upon the dataset that’s run through it and the accuracy of the same. To get the best result, the usage of a pre-trained model approach will bolster the rate of accuracy. Once the algorithm is run, the desired result would be the algorithm predicting if the tumor is benign or malignant so the patient can get the most optimal care.

Phase 2: Breast cancer is proclaimed to be the most prevalent tumor and classification of the tumor cells into Benign and Malignant ascertains to be beneficial in the preparatory stages for the affected patient to receive the optimal treatment. A contrasting comparison is drawn between two algorithms. Machine Learning makes use of the Transfer Learning technique to attain a higher learning rate and stores the knowledge gained whilst classifying the tumor cells. And the contrasting algorithm C4.5 is used in Data Mining as a Decision Tree Classifier which aids in making the most valuable decision on the given set of data and also proves its use by curtailing the conclusive overfitting rate.

Objective: To classify the Breast Cancer Cells into Benign and Malignant

Models Employed: Phase 1: Transfer Learning Phase 2: C4.5(Decision tree Classifier)

Output: Phase 1: Accuracy: 91.94% Phase 2: Accuracy: 94.16%