Applying automated classification modeling to the titanic dataset

We're now going to apply automated TPOT classification modeling to a slightly more complicated dataset. You'll get your hands dirty with the Titanic dataset (https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/fa71405126017e6a37bea592440b4bee94bf7b9e/titanic.csv) – a dataset containing various attributes and descriptions of passengers who did and did not survive the Titanic accident.

The goal is to build an automated model capable of predicting whether a passenger would have survived the accident, based on various input features, such as passenger class, gender, age, cabin, number of siblings, spouses, parents, and children, among other features.

We'll start by loading the libraries and the dataset next:

1. As always, the first step is to load in the libraries and the dataset. You'll need numpy, pandas, matplotlib, and seaborn to get you started. The Matplotlib...