https://github.com/pottekkat/bulldozer-prize-predictions

Predict the auction sale price for a piece of heavy equipment to create a "blue book" for bulldozers.
https://github.com/pottekkat/bulldozer-prize-predictions

bluebook bulldozer data data-science jupyter-notebook kaggle-competition machine-learning

Last synced: about 2 months ago
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Predict the auction sale price for a piece of heavy equipment to create a "blue book" for bulldozers.

• Host: GitHub
• URL: https://github.com/pottekkat/bulldozer-prize-predictions
• Owner: pottekkat
• Created: 2020-03-05T06:35:20.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-03-05T06:47:26.000Z (almost 5 years ago)
• Last Synced: 2024-05-02T05:25:45.413Z (8 months ago)
• Topics: bluebook, bulldozer, data, data-science, jupyter-notebook, kaggle-competition, machine-learning
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/c/bluebook-for-bulldozers/
• Size: 105 KB
• Stars: 5
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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".