Exploratory Data Analysis of the Iris Dataset

This project involves performing an exploratory data analysis (EDA) on the Iris dataset. The analysis includes estimating and plotting 1D and 2D Gaussians for the dataset features, as well as creating scatter plots.

Implementation Steps

Data Preparation

The Iris dataset was divided into training and testing sets to evaluate the performance of the models. Data normalization was applied before modeling to ensure that all features contribute equally.

1D Gaussian Estimation

• Estimated and plotted the 1D Gaussian distribution for each feature in the dataset.
• Applied the Gaussian model to both the training and testing subsets.

Scatter Plot

• Created scatter plots of the dataset to visualize the relationship between different features.

2D Gaussian Estimation

• Selected two features from the dataset.
• Modeled and plotted the resulting 2D Gaussian distribution.
• Applied the 2D Gaussian model to both the training and testing subsets.

Conclusion

The project demonstrates the process of exploratory data analysis on the Iris dataset, including the estimation of 1D and 2D Gaussians and the creation of scatter plots. This analysis helps in understanding the distribution and relationship of features within the dataset, providing insights that are valuable for further machine learning tasks.