Predicting the Sale Price of Bulldozers using Machine Learning

This is an end to end SalePrice prediction of Buldozers, based on Blue Book for Bulldozers dataset on Kaggle

In this notebook, we're going to go through an example machine learning project with the goal of predicting the sale price of bulldozers.

1. Problem definition

How well we can predict the future sale price of a bulldozer, given its characteristics and previous examples of how much similar bulldozers have been sold for?

2. Data

The data is downloaded from Kaggle Bluebook for Bulldozers competition! https://www.kaggle.com/competitions/bluebook-for-bulldozers/data

Data 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.

3. Evaluation

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

https://www.kaggle.com/competitions/bluebook-for-bulldozers/overview/evaluation

Note: The goal for most regression evalution metrics is to minimize the error. For example, our goal for this project will be to build a machine learning model which minimizes RMSLE.

4. Features

The features can be seen below alog with their description:

Steps to run:

• Create a conda environment with scikit-learn, numpy, matplotlib, jupyter, pandas, seaborn
• Clone this repo
• Run the notebook

License

This notebook is created by Abhishek Gautam and is licenced under MIT.