From Disaster to Decision – Titanic Example Revisited

In Lesson 1, From Data to Decisions – Getting Started with TensorFlow, we have seen a minimal data analysis of the Titanic dataset. Now it's our turn to do some analytics on top of the data. Let's look at what kinds of people survived the disaster.

Since we have enough data, but how could we do the predictive modeling so that we can draw some fairly straightforward conclusions from this data? For example, being a woman, being in first class, and being a child were all factors that could boost a passengers chances of survival during this disaster.

Using the brute-force approach such as if-else statements with some sort of weighted scoring system, you could write a program to predict whether a given passenger would survive the disaster. However, writing such a program in Python does not make much sense. Naturally, it would be very tedious to write, difficult to generalize, and would require extensive fine-tuning for each variable and samples...