untitled10.ipynb

In this project, we used Jupyter Notebook in Anaconda to analyze network activity data and detect Neptune attacks. We processed the data by encoding categorical variables and scaling numerical features, then trained a Random Forest classifier. After validating the model's performance, we predicted the test dataset's activities and created a submission file, Sample_Submission.csv, with these predictions, enhancing network security by identifying potential attacks.