Lab_ML(03/03/25)

# %%
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# %%
iris = datasets.load_iris()

# %%
X = iris.data
y = iris.target

# %%
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)

# %%
svm_classifier = SVC(kernel='linear',C=1.0,random_state=42)

# %%
svm_classifier.fit(X_train,y_train)

# %%
y_pred = svm_classifier.predict(X_test)

# %%
accuracy = accuracy_score(y_test,y_pred)
print(f"Accuracy : {accuracy * 100:.2f}%")

# %%
print("\nClassification Report: ")
print(classification_report(y_test, y_pred))

# %%
print("\nConfusion Matrix: ")
print(confusion_matrix(y_test,y_pred))

# %%
X_simplified = iris.data[:, :2]
X_train_simplified, X_test_simplified, y_train_simplified, y_test_simplified = train_test_split(X_simplified,y,test_size=0.2,random_state=42)

# %%
svm_classifier_simplified = SVC(kernel='linear',C=1.0)
svm_classifier_simplified.fit(X_train_simplified, y_train_simplified)

# %%
x_min,x_max = X_simplified[:,0].min()-1,X_simplified[:,0].max()+1
y_min,y_max = X_simplified[:,1].min()-1,X_simplified[:,1].max()+1
xx,yy=np.meshgrid(np.arange(x_min,x_max,0.02),np.arange(y_min,y_max,0.02))

# %%
Z = svm_classifier_simplified.predict(np.c_[xx.ravel(),yy.ravel()])
Z = Z.reshape(xx.shape)

# %%
plt.contour(xx,yy,Z,alpha=0.8)
plt.scatter(X_simplified[:,0], X_simplified[:,1],c=y, edgecolors='k',marker='o',s=100,cmap=plt.cm.coolwarm)
plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.title('SVM Decision Boundary (Using Only Sepal length and Sepal Width)')
plt.show()