Advertising Dataset – Machine Learning Model Comparison

This repository demonstrates a comparative analysis of multiple machine learning classifiers applied to an Advertising dataset.

The project emphasizes understanding model behavior, not just achieving high accuracy.

---

Models Implemented

• Logistic Regression
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• Naive Bayes
• PCA-based visualization

---

Key Learning Objectives

• Compare linear vs non-linear classifiers
• Visualize decision boundaries using PCA
• Understand bias–variance trade-offs
• Interpret confusion matrices effectively

---