AI Model Script

# ai_module.py
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Load dataset
df = pd.read_csv('dataset.csv')

# Preprocess data
X = df.drop(['target'], axis=1)
y = df['target']

# 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 random forest classifier
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

def validate_and_record(transaction):
    # Preprocess transaction data
    data = pd.DataFrame([transaction['data']], columns=['feature1', 'feature2'])

    # Make prediction
    prediction = model.predict(data)

    if prediction[0] == 1:
        # Record transaction
        return "Transaction recorded"
    else:
        return "Transaction flagged as anomaly"