This project demonstrates how to design interactive data visualizations using the classic Iris flower dataset. It explores how interactivity—like filtering, zooming, and hovering—can make visual data exploration more engaging and insightful.
- Understand the role and impact of interactivity in data visualization.
- Implement various types of interactions (filters, zoom, hover).
- Create interactive plots using Plotly and Bokeh libraries.
| Type | Description |
|---|---|
| Static Scatter Plot | Sepal Length vs Sepal Width using Matplotlib |
| Interactive Scatter Plot | Plotly-based dynamic species-wise scatter plot |
| Dropdown Filters | Filter between Setosa, Versicolor, Virginica, or view All |
| Hover Tooltips | Show species and measurement info interactively on hover |
| Range Sliders / Zoom | Plotly range sliders for interactive zoom-in/out |
| 3D Scatter Plot | Explore Sepal and Petal features in 3D |
| Parallel Coordinates | Multi-dimensional feature comparison |
| Bokeh Dropdown Filter | Live interactive filtering using Bokeh widgets |
pandasmatplotlibplotlybokeh
The Iris dataset used in this project is a classic multivariate dataset introduced by the British biologist and statistician Ronald Fisher. It’s built-in and accessed using Plotly.
- Clone the repository or upload the
.ipynbnotebook to Google Colab. - Install required libraries (if not already available):
pip install plotly bokeh