import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.metrics import accuracy_score
from sklearn.metrics import accuracy_score, classification_report
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv")
df.head()
X = df.drop(columns=["Outcome"])
y = df["Outcome"]


pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=42)
kernels = ["linear", "poly", "rbf", "sigmoid"]

for kernel in kernels:
    model = SVC(kernel=kernel, random_state=42)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f"Kernel: {kernel}, Accuracy: {accuracy:.2f}")

    # Plotting decision boundary
    ax = DecisionBoundaryDisplay.from_estimator(
        model,
        X_train,
        response_method="predict",
    )