Data Science in Cybersecurity Project

Goal: Applied machine learning techniques to analyze and classify cybersecurity threats using structured datasets.

What I Did:

Preprocessed a large dataset of network traffic or malware logs

Performed feature engineering and exploratory data analysis (EDA)

Built classification models (e.g. Random Forest, SVM) to detect anomalies or malicious activity

Evaluated model performance using accuracy, precision, recall

Visualized results with graphs and confusion matrices

Tools: Python, Pandas, scikit-learn, Matplotlib, Jupyter

Outcome: Achieved high model accuracy and demonstrated how ML can be leveraged for proactive threat detection