This is my attempt at the introduction to machine learning Kaggle competition: "Titanic: Machine Learning from Disaster." The Jupyter Notebook walks through the data ingest, initial exploration, data wrangling, training a classifier, and visualizing results. The notebook shows examples of a random forest classifier, a linear svc classifier, and a gradient boosting classifier. The gradient boosting classifier was created utilizing TPOT, a Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. TPOT was created by Randy Olson. The repository can be found here: 