Breast Cancer is mostly identified among women and is a major reason for increasing the rate of mortality among women. Diagnosis of breast cancer is time-consuming and due to the lesser availability of systems, it is necessary to develop a system that can automatically diagnose breast cancer in its early stages. Various Machine Learning and Deep Learning Algorithms have been used for the classification of benign and malignant tumors. The Wisconsin Breast Cancer Dataset has been used which contains 569 samples and 30 features. The paper emphasizes on various models that are implemented such as Logistic Regression, Support Vector Machine (SVM) and K Nearest Neighbour (KNN), Multi-Layer perceptron classifier, Artificial Neural Network(ANN)) etc. on the dataset taken from the repository of Kaggle. Each of these algorithms has been measured and compared with respect to accuracy and precision obtained. All the techniques are coded in python and executed in Google Colab, which is a Scientific Python Development Environment. The experiments have shown that SVM and Random Forest Classifiers are the best for predictive analysis with an accuracy of 96.5%. To increase the accuracy of prediction, deep learning algorithms such as CNN and ANN have been implemented. The maximum accuracy obtained in the case of ANN and CNN is 99.3% and 97.3% respectively. Activation functions such as Relu and sigmoid have been used to predict the outcomes in terms of probabilities.