ML2.py

import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
plt.style.use ('ggplot') 
iris = load_iris()
X = iris.data
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

pca = PCA(n_components=0.95)

X_pca = pca.fit_transform(X_scaled)

print("Number of retained components:", pca.n_components_)

print("Variance explained by each component:", pca.explained_variance_ratio_)

print("Reduced-dimension dataset shape:", X_pca.shape)

plt.figure(figsize=(8,6))
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=iris.target, cmap='viridis', edgecolor='k')
plt.title('PCA of Iris Dataset')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.colorbar(label='Target Class')
plt.show()

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import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
plt.style.use('ggplot')

df = pd.read_csv('your_iris_dataset.csv')
X = df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]
y = df['species']


label_encoder = LabelEncoder()
y_encoded = label_encoder.fit_transform(y)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)


pca = PCA(n_components=0.95)
X_pca = pca.fit_transform(X_scaled)

print("Number of retained components:", pca.n_components_)
print("Variance explained by each component:", pca.explained_variance_ratio_)
print("Reduced-dimension dataset shape:", X_pca.shape)


plt.figure(figsize=(8, 6))
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_encoded, cmap='viridis', edgecolor='k')
plt.title('PCA of Iris Dataset')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
cbar = plt.colorbar()
plt.show()