Generate a facial recognition classifier model with high performance and accuracy using Python and scikit-learn.
Not applicable
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import classification_report, confusion_matrix

# load data
data = pd.read_csv('<PATH TO DATA>')

# Split data into features and labels
X = data.drop(columns=['label'])
y = data['label']

# split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)

# Scale the data
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# Create and train the model
model = SVC(C=1.0, kernel='poly', degree=5, gamma='auto')
model.fit(X_train, y_train)

# Make predictions and evaluate
y_pred = model.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))