This project explores the Iris dataset, a classic dataset in machine learning, to uncover insights and patterns among Iris species. The analysis includes:
- Data Import : Loading and inspecting the dataset.
- Sanity Checks : Verifying the dataset's structure and handling missing values.
- Exploratory Data Analysis (EDA): Descriptive statistics and feature distribution analysis.
- Visualization : Scatter plots, bar plots, pair plots, and heatmaps to visualize relationships and species differentiation.
Key Findings:
- Petal length and petal width are the most significant features for distinguishing Iris species.
- Visualizations reveal clear separations among species.
This analysis provides a foundation for further classification tasks and data-driven research.