🤖 Machine Learning Models

📖 Overview

This repository contains my work for an Intro to AI class, where I explored, trained, and evaluated several supervised learning models on a given dataset. The primary goal was to compare different algorithms in terms of accuracy, interpretability, and computational efficiency.

The repo demonstrates the complete ML workflow:

1. Data preprocessing & feature engineering
2. Training multiple machine learning models
3. Hyperparameter tuning
4. Model evaluation & comparison
5. Reporting insights

🛠️ Models Implemented

• 🌳 Decision Tree (DT)
• 🔗 k-Nearest Neighbors (KNN)
• 📈 Logistic Regression
• 🧑‍🤖 Neural Networks
• 🔍 Heuristic Search Approaches

📦 Technologies Used

• Python – Core programming language
• Pandas / NumPy – Data manipulation and preprocessing
• Matplotlib / Seaborn – Data visualization and performance plots
• scikit-learn