ANDREWS CURVES based on Iris Flowers !

The following lines are so powerful that is going to amaze even the most talented minds:

import pandas as pd

from pandas.plotting import andrews_curves

import matplotlib.pyplot as plt

# Load the Iris dataset and specify the correct delimiter

iris = pd.read_csv('C:/Users/rober ugalde/MCST-20241030T144342Z-001/MCST/datasets/iris.data', header=None, delimiter=',')

iris.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']

# Generate the Andrews curves plot, using 'species' as the class column

andrews_curves(iris, 'species') plt.show()

The use of Andrews curves, based on three defined IRIS flower types.

The terms Setosa, Versicolor, and Virginica refer to three different species (or classes) of the Iris flower in the famous Iris dataset, not to types of data analysis. This dataset is commonly used in data science and machine learning for classification tasks because it’s relatively simple yet allows for various analyses. Here’s a breakdown:

1. Iris-setosa: One species of Iris flower.
2. Iris-versicolor: Another species of Iris flower.
3. Iris-virginica: A third species of Iris flower.

Each row in the dataset represents a sample of an Iris flower, with measurements of four features:

• Sepal length
• Sepal width
• Petal length
• Petal width

The goal of analyzing this dataset is often to classify the species of an Iris flower based on these four measurements.

Why Are These Species Useful for Data Analysis?

The Iris dataset is well-suited for exploring basic data analysis and machine learning techniques because:

• Multi-Class Classification: It provides a simple, well-defined multi-class classification problem where each class (species) is labeled.
• Pattern Recognition: There are clear patterns in the feature measurements that help distinguish between species, making it ideal for studying clustering, pattern recognition, and dimensionality reduction.
• Visualization: It’s small and easy to visualize using techniques like scatter plots, pair plots, and Andrews curves, allowing for hands-on practice in data visualization and feature analysis.

So, in summary, Setosa, Versicolor, and Virginica are not types of analysis but the classes (species) that analysts and machine learning models aim to classify based on the flower measurements.

This is a great way to learn if your data belongs or not and make analysis based on that.