Built a Network Traffic Classifier with Random Forest (96.8% Accuracy)

1. 1Load the NSL-KDD dataset using Pandas
2. 2Preprocess the data using NumPy
3. 3Train a Random Forest model using Scikit-learn
4. 4Evaluate the model's performance using accuracy metrics
5. 5Deploy the model as a RESTful API using FastAPI

Cybersecurity and machine learning teams can benefit from this project to improve network traffic classification and detection of various attack categories

💡 Random Forest can be used to classify network traffic into multiple attack categories with high accuracy

🚀 Built a network traffic classifier with 96.8% accuracy using Random Forest! 🤖️