Predicting the price of Bulldozers using Machine Learning

1. Problem definition

Predict the auction sale price for a piece of heavy equipment to create a "blue book" for bulldozers. https://www.kaggle.com/c/bluebook-for-bulldozers/

2. Data

The data is downloaded from the Kaggle Bluebook for Bulldozers competetion: https://www.kaggle.com/c/bluebook-for-bulldozers/data

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.

3. Evaluation

The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices. https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation

The goal is to build a ML model which minimises RMSLE.

4. Features

All the features of the dataset can be found in the data dictionary provided in "./data/bluebook-for-bulldozers".