Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Breast Cancer Wisconsin dataset is a binary classification situation where we are trying to predict one of the two possible outcomes.

INTRODUCTION: The dataset contains various measurements of breast tissue samples for cancer diagnosis. It contains measurements such as the thickness of the clump, the uniformity of cell size and shape, the marginal adhesion, and so on. Dr. William H. Wolberg of the University of Wisconsin Hospitals in Madison is the original provider of this dataset.

ANALYSIS: The baseline performance of the model achieved an average accuracy score of 97.13%. Using the same training parameters, the model processed the test dataset with an accuracy of 97.71%, which was consistent with the result from the training data.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities

Dataset Used: Breast Cancer Wisconsin (Original) Data Set

Dataset ML Model: Binary classification with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29

The HTML formatted report can be found here on GitHub.