This project aims to predict the auction sale price of heavy equipment, specifically bulldozers, using machine learning techniques. The goal is to create a "blue book" for bulldozers, providing an accurate estimate of their market value based on various features.
The dataset used for this project is obtained from source, e.g., Kaggle's Blue Book for Bulldozers. It includes information such as:
Equipment type
Auctioneer ID
Machine hours
Usage and configuration
Year made
Sale date
Equipment specifics like model and make
The features used in this project include:
Date features: Year, month, and day of the auction.
Categorical features: Equipment type, model, and make.
Numerical features: Machine hours, age of equipment, etc.
Derived features: Features engineered from the existing dataset to enhance model performance.