create a Pipeline that uses SVC (instead of LinearSVC), and applies GridSearchCV to tune the following hyperparameters: C: From 1 to 10 as before kernel: 'linear', 'poly', 'rbf', 'sigmoid' decision_function_shape: 'ovr', 'ovo'

Certainly! Here's an example code snippet that creates a pipeline using SVC and applies GridSearchCV to tune the specified hyperparameters:

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler

# Load the Iris dataset
data = load_iris()
X = data.data
y = data.target

# Create a pipeline with StandardScaler and SVC
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('svc', SVC())
])

# Define the hyperparameters to tune
parameters = {
    'svc__C': range(1, 11),
    'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],
    'svc__decision_function_shape': ['ovr', 'ovo']
}

# Create the GridSearchCV object
grid_search = GridSearchCV(pipeline, parameters)

# Fit the pipeline on the data using GridSearchCV
grid_search.fit(X, y)

# Print the best parameters and best score
print("Best Parameters:", grid_search.best_params_)
print("Best Score:", grid_search.best_score_)

Make sure you have the necessary packages installed (e.g., scikit-learn) and adjust the code according to your specific requirements. This code will perform a grid search over the specified hyperparameters and output the best parameters and best score found during the search.