Analyze survival data from the Titanic disaster using Python and Seaborn. Learn how passenger class influenced survival rates through clear visualizations and code examples.
Key Insights
- The dataset analysis revealed 342 passengers survived while 549 perished on the Titanic.
- Visualization using Seaborn's count plot clearly demonstrates the significant difference in survival numbers, with notably more passengers perishing than surviving.
- The next analytical step involves examining passenger class data (first, second, third) to understand how class distribution correlates with survival rates.
Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.
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.