Machine Learning Model for Fraud Detection

# Import necessary libraries

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score, confusion_matrix



# Load the dataset

data = pd.read_csv('fraud_dataset.csv')



# Split the dataset into features and target variable

X = data.drop('fraud', axis=1)

y = data['fraud']



# Split the 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)



# Create a Random Forest classifier

model = RandomForestClassifier()



# Train the model

model.fit(X_train, y_train)



# Make predictions on the test set

y_pred = model.predict(X_test)



# Evaluate the model

accuracy = accuracy_score(y_test, y_pred)

confusion_mat = confusion_matrix(y_test, y_pred)



print("Accuracy:", accuracy)

print("Confusion Matrix:")

print(confusion_mat)