Malware Classification Using Machine Learning

Developed a Machine Learning classification system to identify whether a process is Malware or Benign based on system and process-related features.

The project included data cleaning, preprocessing, feature selection, feature scaling using StandardScaler, model training, and performance evaluation. Data leakage was also addressed during the model development process to achieve a more reliable evaluation.

The trained model was deployed through a simple Streamlit web application, allowing users to enter process-related features and receive a prediction.

**Tools:** Python, Pandas, NumPy, Scikit-learn, StandardScaler, Streamlit, Joblib.