SVM وSVR وKNN

from sklearn.svm import SVC, SVR
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.datasets import make_classification

class="tok-comment"># ── Sample data ────────────────────────────────────────────────────────
X, y = make_classification(n_samples=class="tok-num">300, n_features=class="tok-num">10, random_state=class="tok-num">42)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=class="tok-num">0.2, random_state=class="tok-num">42)

class="tok-comment"># CRITICAL: SVM requires feature scaling
svm_pipeline = Pipeline([
    (class="tok-str">'scaler', StandardScaler()),
    (class="tok-str">'svm', SVC(kernel=class="tok-str">'rbf', probability=True))
])

class="tok-comment"># Tune C and gamma
param_grid = {
    class="tok-str">'svm__C': [class="tok-num">0.01, class="tok-num">0.1, class="tok-num">1, class="tok-num">10, class="tok-num">100],
    class="tok-str">'svm__gamma': [class="tok-str">'scale', class="tok-str">'auto', class="tok-num">0.001, class="tok-num">0.01, class="tok-num">0.1]
}
grid_search = GridSearchCV(
    svm_pipeline, param_grid,
    cv=class="tok-num">5, scoring=class="tok-str">'roc_auc', n_jobs=-class="tok-num">1
)
grid_search.fit(X_train, y_train)
print(fclass="tok-str">"Best AUC: {grid_search.best_score_:.4f}")
print(fclass="tok-str">"Best params: {grid_search.best_params_}")

class="tok-comment"># SVR for regression
svr = Pipeline([
    (class="tok-str">'scaler', StandardScaler()),
    (class="tok-str">'svr', SVR(kernel=class="tok-str">'rbf', C=class="tok-num">100, epsilon=class="tok-num">0.1))
])