from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
 
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score,confusion_matrix
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
 
# Load the Iris dataset
iris = pd.read_csv('/content/iris.csv')
X = iris.drop(columns=["Species"]) # Features
y = iris["Species"]  # Labels
 
 
 
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
# Train the SVC model with a linear kernel
model = SVC(kernel='linear', random_state=42)
model.fit(X_train, y_train)
 
# Make predictions
y_pred = model.predict(X_test)
 
# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")
 
conf_matrix = confusion_matrix(y_test, y_pred)
print('Confusion Matrix:')
print(conf_matrix)