# Example of using a machine learning model for predictive analytics
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Sample data
data = {
    'user_age': [25, 34, 45, 23, 35],
    'user_location': ['US', 'UK', 'US', 'CA', 'UK'],
    'purchase': [1, 0, 1, 0, 1]
}

# Convert categorical data to numerical
data['user_location'] = data['user_location'].astype('category').cat.codes

# Features and target variable
X = data[['user_age', 'user_location']]
y = data['purchase']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, predictions)
print(f'Accuracy: {accuracy}')

# Example of using collaborative filtering for personalized recommendations
from surprise import Dataset, Reader, SVD
from surprise.model_selection import train_test_split

# Load the dataset
data = Dataset.load_builtin('ml-100k')
trainset, testset = train_test_split(data, test_size=0.25)

# Use the SVD algorithm for collaborative filtering
algo = SVD()
algo.fit(trainset)

# Predict ratings for the test set
predictions = algo.test(testset)

# Example prediction for a specific user and item
user_id = '196'
item_id = '302'
predicted_rating = algo.predict(user_id, item_id).est
print(f'Predicted rating for user {user_id} and item {item_id}: {predicted_rating}')

# Example of a simple AI chatbot using NLP
from transformers import pipeline

# Load a pre-trained model for conversational AI
chatbot = pipeline('conversational')

# Example conversation
conversation = chatbot("Hello, how can I help you today?")
print(conversation)

# Example of using Bayesian optimization for A/B testing
from skopt import gp_minimize

# Define the objective function
def objective(params):
    # Simulate a conversion rate based on parameters
    conversion_rate = simulate_conversion_rate(params)
    return -conversion_rate  # Minimize the negative conversion rate

# Define the parameter space
param_space = [(0, 1), (0, 1)]  # Example parameter space

# Perform Bayesian optimization
result = gp_minimize(objective, param_space, n_calls=50, random_state=42)
print(f'Best parameters: {result.x}')
print(f'Best conversion rate: {-result.fun}')