Microsoft Malware Prediction

️ Microsoft Malware Prediction

This project focuses on building machine learning models to predict whether a Windows machine will be infected by malware, using the Microsoft Malware Prediction dataset (Kaggle).

Key Features

Dataset: Large-scale telemetry data from Windows Defender with millions of records.

Target: Binary classification (HasDetections = 0/1).

Preprocessing:

Handling missing values.

Encoding categorical features (Label Encoding / Frequency Encoding).

Feature selection & dimensionality reduction (PCA/Variance Threshold).

Balancing data distribution.

Models:

Logistic Regression & Random Forest (baseline).

Gradient Boosting (LightGBM, XGBoost, CatBoost) for high performance.

Evaluation Metrics: AUC-ROC, accuracy, precision, recall, F1-score.

Optimization: Hyperparameter tuning (GridSearch/Optuna).

Workflow

Exploratory Data Analysis (EDA): Understanding feature distributions, correlations, and missing values.

Feature Engineering: Encoding categorical features, handling imbalances.

Model Training: Comparing baseline ML models with advanced boosting algorithms.

Evaluation: Measuring model performance with cross-validation and ROC curves.

Deployment (optional): Flask/Streamlit web app for real-time malware prediction.

Applications

Cybersecurity & malware detection.

Real-time threat prevention in Windows OS.

Demonstrating scalable ML on large, imbalanced datasets.