cfaml

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

def train_crime_prediction_model(crime_data):
    # Prepare the data
    df = pd.DataFrame(crime_data)
    X = df[['feature1', 'feature2']]  # Replace with actual features
    y = df['crime_type']
    
    # Split data into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
    
    # Train the model
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)
    
    # Evaluate the model
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f'Model Accuracy: {accuracy:.2f}')

# Example usage
crime_data = [
    {'feature1': 1, 'feature2': 2, 'crime_type': 'theft'},
    {'feature1': 3, 'feature2': 4, 'crime_type': 'assault'},
    # Add more data
]

train_crime_prediction_model(crime_data)