Iris Species Classification

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

np.random.seed(42)

n = 150
species = np.random.choice(['Setosa', 'Versicolor', 'Virginica'], n)
data = {
    'Sepal Length': np.where(species == 'Setosa', np.random.normal(5, 0.4, n),
                            np.where(species == 'Versicolor', np.random.normal(5.9, 0.5, n), np.random.normal(6.6, 0.6, n))),
    'Sepal Width': np.where(species == 'Setosa', np.random.normal(3.4, 0.4, n),
                           np.where(species == 'Versicolor', np.random.normal(2.8, 0.3, n), np.random.normal(3, 0.3, n))),
    'Petal Length': np.where(species == 'Setosa', np.random.normal(1.4, 0.2, n),
                            np.where(species == 'Versicolor', np.random.normal(4.3, 0.5, n), np.random.normal(5.5, 0.6, n))),
    'Petal Width': np.where(species == 'Setosa', np.random.normal(0.2, 0.1, n),
                           np.where(species == 'Versicolor', np.random.normal(1.3, 0.2, n), np.random.normal(2, 0.3, n))),
    'Species': species
}
df = pd.DataFrame(data)

sns.set_style("whitegrid", {
    'axes.facecolor': '#ffffff',
    'figure.facecolor': '#ffffff',
    'grid.color': '#eeeeee'
})

palette = {'Setosa': '#F5276C', 'Versicolor': '#27D3F5', 'Virginica': '#6CF527'}

g = sns.pairplot(
    df,
    hue='Species',
    palette=palette,
    height=2,
    kind='scatter',
    diag_kind='kde',
    markers=['o', 's', 'D'],
    plot_kws={'alpha': 0.75, 's': 55, 'edgecolor': 'white', 'linewidths': 0.8},
    diag_kws={'alpha': 0.5, 'linewidth': 2.5, 'fill': True}
)

g.fig.set_facecolor('#ffffff')
for ax in g.axes.flat:
    if ax:
        ax.set_facecolor('#ffffff')
        for spine in ax.spines.values():
            spine.set_color('#dddddd')
        ax.tick_params(colors='#666666')
        ax.xaxis.label.set_color('#333333')
        ax.yaxis.label.set_color('#333333')

g.fig.suptitle('Iris Morphometric Analysis', fontsize=14, fontweight='bold', color='#1a1a1a', y=1.02)

plt.tight_layout()
plt.show()