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Over the past four parts, we zoomed in on single features (univariate analysis), then looked at pairs (bivariate). Now it’s time for the real fun: multivariate analysis. This is where we weave three or more features together and look for…
This is the most exciting part of EDA. We’ve learned each feature individually in Part 3. Now we ask the real question: who survived, and why? The analogy — the detective reveals the suspect 🔍 Sherlock has examined every clue…
In Part 2, we got the patient’s file open. Now, it is the time for the real examination. We’re going to look at each “organ”—each feature in our dataset — one by one. This is called univariate analysis, which is…
In Part 1, we compared EDA to a doctor’s examination. Now let’s actually open the patient file. We’ll use the Titanic dataset from Kaggle. To follow along: The analogy before we start — first day at a new job Imagine…
EDA is what Sherlock does before naming a suspect. Your dataset is the crime scene. Your ML model is the theory. You don’t build the theory until you’ve studied the scene thoroughly. Most beginners skip this step. They load data,…
