Goal: Develop a machine learning model to predict car prices based on features 📈
Models used:
K-Nearest Neighbor 🧮 Decision Tree 🌳 Catboost Classifier 🐱 Light Gradient Boosting Classifier 🌟 Classification: Categorize cars into price ranges (low, medium, high) 📉📊📈
Dataset: Large dataset of cars with prices and features 📚
Evaluation: Metrics including accuracy, precision, recall, and F1-score ⚖️
Application: Assist buyers/sellers in making informed decisions 🛒💰