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Colin Jaffe/1 min read

Visualizing Survival and Class Distribution in Titanic Data Analysis

Machine Learning Essentials

Supervised vs Unsupervised

Labeled data vs unlabeled — different problem classes.

Classification vs Regression

Predict a class label vs a continuous number.

Train/Test Split

Always evaluate on data the model never saw during training.

Hyperparameter Tuning

Grid search and cross-validation to find the best settings.

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Analyze Titanic survival rates by passenger class. Watch this tutorial to learn the key concepts and techniques.

Let's start by taking a look at our data and performing some analysis to figure out what's important here. First, how many people survived, and how many did not? Let's write some quick code to check that. Let's check the value counts for the 'survived' column.

There we go. So, 342 survived, and 549 died. Okay.

That's helpful to know. Now, let's try plotting that.

For the next part, we have the basic plot set up, but we haven't included the axis or the actual bar yet.

We don't need to set the axis to anything. We'll just create a count plot using Seaborn. Our X-axis will be 'survived' (0 or 1), and the data will be based on our Titanic dataset. We will label them as 'perished' and 'survived.'

Let's check that out. There it is.

This will visualize that many more people perished than survived.

Next, let's look at passenger class. We want to see how they did, and what kind of classes people were in. Were they in first, second, or third class? What was the overall distribution by class?

We will take a look at that next.