train_svm.py

# train_svm.py
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
import joblib

# Load and split data
data = load_breast_cancer()
X, y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)

# Train SVM with gamma=0.01
svm_model = SVC(kernel='rbf', C=1, gamma=0.01, probability=True, random_state=42)
svm_model.fit(X_train_scaled, y_train)

# Save model and scaler
joblib.dump(svm_model, 'svm_model.pkl')
joblib.dump(scaler, 'scaler.pkl')