Goal: Develop a machine learning model to predict car prices based on features ๐
Models used:
K-Nearest Neighbor ๐งฎ Decision Tree ๐ณ Catboost Classifier ๐ฑ Light Gradient Boosting Classifier ๐ Classification: Categorize cars into price ranges (low, medium, high) ๐๐๐
Dataset: Large dataset of cars with prices and features ๐
Evaluation: Metrics including accuracy, precision, recall, and F1-score โ๏ธ
Application: Assist buyers/sellers in making informed decisions ๐๐ฐ