How to perform Dimensionality Reduction using PCA
This sequence starts by loading the Iris dataset, then scales all numeric features to a common range for fair comparison. Next, it applies Principal Component Analysis (PCA) to reduce the data to fewer dimensions while keeping as much information as possible. Finally, the results are shown in a scatter plot, making it easier to spot patterns or clusters in the simplified data.
