?? Day 93 of 365: Categorical Data Visualization (Bar Plots, Count Plots) ??
Ajinkya Deokate
Data Scientist | Researcher | Author | Public Speaking Expert @PlanetSpark | Freelancer
Hey, Visualizers!
Welcome to Day 93 of our #365DaysOfDataScience journey! ??
Today, we’re switching gears and focusing on categorical data. We’ll learn how to visualize the distribution of categories and spot any imbalances in the data using bar plots and count plots.
?? What We’ll Be Exploring Today:
- Bar Plots: Great for comparing the frequency of different categories.
- Count Plots: A simple way to visualize how often each category appears.
- Understanding class imbalance and how it might impact your analysis.
?? Learning Resources:
1. Watch: [Visualizing Categorical Data](https://www.youtube.com/) (YouTube).
2. Read: Seaborn docs for [bar plots](https://seaborn.pydata.org/generated/seaborn.barplot.html) and [count plots](https://seaborn.pydata.org/generated/seaborn.countplot.html).
?? Today’s Task:
- Load a dataset with categorical variables (like the Titanic dataset).
- Use Seaborn to create bar plots and count plots to visualize the distributions.
- Analyze the class distributions—are there any imbalanced classes? How might that affect your model or analysis?
I’ll be working on visualizing these distributions with you, and together we can explore any issues that arise with class imbalance. Let’s dive into the world of categorical data and see what insights we can find! ??
Happy Learning and See you Soon!
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