logistic_Regr.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Load digits dataset
digits = datasets.load_digits()
X, y = digits.data, digits.target

# Split data 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)

# Standardize features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# Train SVM model
svm_model = SVC(kernel='linear')
svm_model.fit(X_train, y_train)

y_pred_svm = svm_model.predict(X_test)
svm_accuracy = accuracy_score(y_test, y_pred_svm)

# Train Logistic Regression model
log_reg_model = LogisticRegression(max_iter=10000)
log_reg_model.fit(X_train, y_train)
y_pred_log_reg = log_reg_model.predict(X_test)
log_reg_accuracy = accuracy_score(y_test, y_pred_log_reg)

# Print accuracy of both models
print(f"SVM Accuracy: {svm_accuracy:.4f}")
print(f"Logistic Regression Accuracy: {log_reg_accuracy:.4f}")