Predicting heart disease using machine learning This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. We're going to take the following approach: ##Problem definition ##Data Evaluation, ##Features Modelling Experimentation, 1. Problem Definition: Given clinical parameters about a patient, can we predict whether or not they have heart disease? 2. Data: The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+Disease\n", 3. Evaluation: If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project. 4. Features: This is where you'll get different information about each of the features in your data. You can do this via doing your own research (such as looking at the links above) or by talking to a subject matter expert (someone who knows about the dataset).