Seaborn Tutorial with Iris Dataset

This repository demonstrates the use of the Seaborn library in Python for data visualization. In this study, I visualize the famous Iris dataset, a multivariate dataset available at the UCI Machine Learning Repository. The Iris dataset contains measurements of sepals and petals for three species of iris flowers.

In this repository, I utilize various Seaborn visualization techniques to explore and present the dataset, including relational plots, scatter plots, pair plots, faceted plots, box plots, bar graphs, and density plots.

Contents

• Iris Dataset: A brief introduction to the dataset, which includes the following features:

  • Sepal Length
  • Sepal Width
  • Petal Length
  • Petal Width
  • Species (Setosa, Versicolor, Virginica)

• Visualizations:

  • Relational Plot: Exploring relationships between different features.
  • Two-Dimensional Plot: Visualizing the data in 2D.
  • Scatter Plot: Plotting individual data points to show distribution.
  • Pair Plot: Pairwise relationships between features.
  • Faceted Plot: Visualizing data subsets using multiple subplots.
  • Box Plot: Displaying the distribution of the data, highlighting outliers.
  • Bar Graph: Visualizing categorical data such as species counts.
  • Density Plot: Showing the distribution of data in continuous variables.

Libraries Used

• Seaborn: A Python data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.
• Matplotlib: A plotting library used for creating static, animated, and interactive visualizations.
• Pandas: A data manipulation and analysis library that simplifies working with data structures like DataFrames.
• NumPy: A library for numerical operations in Python, particularly useful for handling arrays.

Getting Started

Prerequisites

• Python 3.x installed on your system.
• The following libraries are required:
  • Seaborn
  • Matplotlib
  • Pandas
  • NumPy