import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import joblib

# Load dataset
df = pd.read_csv("creditcard.csv")

# Features and target
X = df.drop("Class", axis=1)
y = df["Class"]

# Train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

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

# Save model
joblib.dump(model, "fraud_model.pkl")