Skin cancer is a common and dangerous form of cancer. This is a dangerous kind of cancer, and early detection is crucial for successful treatment. Malignant tumors develop when healthy, normal skin cells experience genetic alterations and begin to grow uncontrolled. An important risk factor for skin cancer is ultraviolet (UV) radiation from the sun and artificial sources like tanning beds. Skin cancer can develop in other places of the body, although it often occurs on the face, neck, arms, and handsbecause of their exposure to sunlight. Data imbalance issues are brought on by the significant discrepancy between data from several healthcare industry classifications. Deep learning models frequently train on one class more than others due to problems with data imbalance. The dataset utilized is skin cancer MNIST: HAM10000, which contains seven kinds of skin lesions. The seven forms of skin lesions are as follows: melanocytic nevi (nv), melanoma (mel), benign keratosis(bkl), basal cell carcinoma (bcc), actinic keratosis (akiec), vascular lesions (vasc), and dermatofibroma (df). These are used to categorize skin cancer based on mutations andvariations. Deep learning models (inception v3, resnet, vgg16, and mobile net) and deep learning techniques such as data augmentation, image normalization, and image standardization were used to classify skin cancer.