Traditional SVM, KNN and Random Forest models Comparison on Skin Cancer Dataset

A comparative study was carried out to assess the performance of three machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF), in classifying skin cancers using a standard dataset. Classification performance is evaluated by various metrics: accuracy, precision, recall, and others, including analysis of computational efficiency based on training time, resource consumed in training, and calculation time. A holistic assessment of these proposes presents merits and demerits of each of these models. Thus, it helps in selecting the most useful algorithm for skin cancer detection. These deliverables aim to increase the comprehension of machine learning applications in distinguishing between malignant melanomas and benign cases.