import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

def fraud_pred(t_type, amount):
    data = pd.read_csv('dataset.csv')

    lab_encoder = LabelEncoder().fit(data["type"])
    data["type"] = lab_encoder.transform(data["type"])

    X, y = data[["type", "amount"]], data["isFraud"]
    algorithm = RandomForestClassifier(n_estimators=100, max_depth=3)
    algorithm.fit(X, y)
    prediction = algorithm.predict([[lab_encoder.transform([t_type])[0], amount]])[0]
    print(prediction)
    return "Fraud" if prediction == 1 else "Not fraud"

fraud_pred("TRANSFER", 50000000.00)