?? Day 95 of 365: Visualizing Multivariate Data (Heatmaps) ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, everyone!
Welcome to Day 95 of our #365DaysOfDataScience journey! ??
We’ve made it to Day 95, and today’s all about heatmaps—a powerful way to visualize correlations and patterns in multivariate data. Heatmaps use color gradients to make relationships between variables stand out, so you can quickly spot important trends.
?? What We’ll Be Exploring Today:
- Heatmaps: Great for visualizing correlation matrices and showing how different variables relate to each other.
- Using color gradients to emphasize strong positive or negative correlations.
?? Learning Resources:
1. Watch: [Heatmaps in Seaborn](https://www.youtube.com/) (YouTube).
2. Read: Seaborn docs for [heatmaps](https://seaborn.pydata.org/generated/seaborn.heatmap.html).
?? Today’s Task:
- Choose a dataset with multiple numerical variables (like the Boston Housing dataset).
- Create a heatmap using Seaborn to visualize the correlation matrix.
- Highlight and interpret the strongest positive and negative correlations between the variables.
I’ll be building a heatmap right along with you, and we’ll discuss what those colors are telling us about our data. Feel free to share your heatmaps and findings—I’m excited to see what you uncover! ?????
Happy Learning and See you Soon!
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