Exploring the Iris Dataset
Perform a complete exploratory analysis on the Iris dataset.
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Project Description
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.
Project Requirements
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
Technologies to Use
Python
Pandas
Seaborn
Matplotlib
Jupyter Notebook
What You Will Learn
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.
Want to See a Solution?
If you’re looking for inspiration, check out this tutorial published on Towards Data Science: 🔗 Interpreting EDA
