# Complex Python Script Example: Machine Learning Model with scikit-learn

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import classification_report, accuracy_score

# Load and split the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Data Preprocessing
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Model Training: Multi-layer Perceptron classifier
mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)
mlp.fit(X_train_scaled, y_train)

# Predictions and Evaluation
predictions = mlp.predict(X_test_scaled)
print("Accuracy Score:", accuracy_score(y_test, predictions))
print("Classification Report:\n", classification_report(y_test, predictions))