Malware-Detection-ML-DL

Mini-project for Machine Learning and Deep Learning

📌 Project Overview

This project aims to classify files into Malicious or Benign categories using various Machine Learning and Deep Learning techniques. The dataset contains 79 features (78 numerical and 1 string).

📂 Project Structure

• data/: Contains the original and cleaned datasets.
• notebooks/: Contains 7 Jupyter Notebooks (numbered 0-6) covering each phase of the project.
• report/: Contains the final project report in PDF.

🛠️ Methodology

1. Phase 0: Data Cleaning & Exploration.
2. Phase 1: Baseline Classification Models (with & without Standardization).
3. Phase 2: Feature Selection (Top 20 features).
4. Phase 3: Dimensionality Reduction using PCA.
5. Phase 4: Combined Approach (Feature Selection + PCA).
6. Phase 5: Deep Learning (Multi-Layer Perceptron).
7. Phase 6: Unsupervised Learning (K-Means Clustering).