Bulldozer Sales Price Estimator

This project goes through an example machine learning project with the goal of predicting sale price of bulldozers. This example is a featured prediction competition in Kaggle that took place in 2013.

The goal of the contest is to predict the sale price of a particular piece of heavy equiment at auction based on it's usage, equipment type, and configuaration. The data is sourced from auction result postings and includes information on usage and equipment configurations.

Fast Iron is creating a "blue book for bull dozers," for customers to value what their heavy equipment fleet is worth at auction.

Problem Definition

Predict the auction sale price for a piece of heavy equipment to create a "blue book" for bulldozers.

Data

Data is downloaded from Kaggle

The data for this competition is split into three parts:

• Train.csv is the training set, which contains data through the end of 2011.
• Valid.csv is the validation set, which contains data from January 1, 2012 - April 30, 2012 You make predictions on this set throughout the majority of the competition. Your score on this set is used to create the public leaderboard.
• Test.csv is the test set, which won't be released until the last week of the competition. It contains data from May 1, 2012 - November 2012. Your score on the test set determines your final rank for the competition.

The key fields are in train.csv are:

• SalesID: the uniue identifier of the sale
• MachineID: the unique identifier of a machine. A machine can be sold multiple times
• saleprice: what the machine sold for at auction (only provided in train.csv)
• saledate: the date of the sale

There are several fields towards the end of the file on the different options a machine can have. The descriptions all start with "machine configuration" in the data dictionary. Some product types do not have a particular option, so all the records for that option variable will be null for that product type. Also, some sources do not provide good option and/or hours data.

The machine_appendix.csv file contains the correct year manufactured for a given machine along with the make, model, and product class details. There is one machine id for every machine in all the competition datasets (training, evaluation, etc.).

The evaluation metric for this competition is the RMSLE (root mean squared log error)between the actual and predicted auction prices.

When building regression models, the goal is to minimize the error, i.e. to minimize the evaluation metric score. In this case, out aim is to reduce RMSLE as much as possible

Sample submission files can be downloaded from the data page. Submission files should be formatted as follows:

• Have a header: "SalesID,SalePrice"
• Contain two columns
  • SalesID: SalesID for the validation set in sorted order
  • SalePrice: Your predicted price of the sale

Example lines of the submission format:

SalesID,SalePrice
1222837,36205
3044012,74570
1222841,31910.50
...