BlueBook

###Blue Book for Bulldozers Quantitative Model

<hr / > This IPython Notebook contains a quantitative pricing model created for [Fast Iron](http://www.fastiron.com/) in the Kaggle competition 'Blue Book for Bulldozers'. The model predicts the sale price of a particular piece of heavy equipment so that Fast Iron can create a 'Blue Book' to enable customers to valuate their heavy equipment fleet at auction. Here python is used as a medium to apply supervised and unsupervised machine learning techniques to explain 88.90% of the variance observed in the training set and score an RMSLE of 0.745 when predicting values on the test set. In this competition 590 data scientists created predictive models based on a 'training dataset', provided by Fast Iron, and then used those models to predict sale prices on a 'test set' to compete for a $10,000 dollar award for the team or individual with the most accurate model. The model and methods used for my entry, which scored in the upper 20%, is shown in `BlueBook.ipynb`.

This notebook and additional information is stored at github.com/agconti/Bluebook. Information about the competition is located on Kaggle's website.

Coding and analysis by Andrew Conti

##Instructions

<hr /> Download the included IPython Notebook and the dataset from the competition website located [here](http://www.kaggle.com/c/bluebook-for-bulldozers/data) to follow along interactively. Please visit [IPython.com](http://ipython.org/install.html) for instructions on how to install the IPython IDE.

To view the notebook statically in the comfort of your own browser click here.