credit_model.py

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pickle

# load data
data = pd.read_csv('creditcard.csv')

# separate legitimate and fraudulent transactions
legit = data[data.Class == 0]
fraud = data[data.Class == 1]

# undersample legitimate transactions to balance the classes
legit_sample = legit.sample(n=len(fraud), random_state=2)
data = pd.concat([legit_sample, fraud], axis=0)

# split data into training and testing sets
X = data.drop(columns="Class", axis=1)
y = data["Class"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=2)

# train random forest model
model = RandomForestClassifier()
model.fit(X_train, y_train)

# evaluate model performance
train_acc = accuracy_score(model.predict(X_train), y_train)
test_acc = accuracy_score(model.predict(X_test), y_test)

with open("credit_model.pickle", "wb") as f:
    pickle.dump(model, f)