Perform a complete exploratory analysis on the classic Iris dataset.
In this project, you will perform a complete exploratory analysis on the Iris dataset — one of the most used datasets in data science. It is small, clean, and well-documented, which makes it perfect for focusing on the analysis techniques rather than the data problems.
The goal is to understand how the three flower species differ from each other using statistics and charts.
Load the Iris dataset from Seaborn or Kaggle
Check the balance of the target variable (species count)
Create histograms and box plots for each numerical feature
Use a pairplot to visualize relationships between all features at once
Compute a correlation matrix and display it as a heatmap
Write a short summary of what separates the three species
Python
Pandas
Seaborn
Matplotlib
Jupyter Notebook
You will practice univariate and bivariate analysis in a clean setting. You will also learn how to read a pairplot and a heatmap, and understand what "linearly separable" means when you actually see it in a scatter plot.
If you’re looking for inspiration, check out this tutorial published on Towards Data Science: 🔗 Interpreting EDA
Join the Community
roadmap.sh is the most starred project on GitHub and is visited by hundreds of thousands of developers every month.
Roadmaps Best Practices Guides Videos FAQs YouTube
roadmap.sh by @kamrify @kamrify
Community created roadmaps, best practices, projects, articles, resources and journeys to help you choose your path and grow in your career.
The top DevOps resource for Kubernetes, cloud-native computing, and large-scale development and deployment.
Login or Signup
You must be logged in to perform this action.