Bulldozer Price Prediction Using Machine Learning

• In this notebook, I have gone through a machine learning project with the goal of predicting the sale price of bulldozers (regresssion problem).

• It is a Structured Data Project (Structured data is data you'd usually find in an Excel spreadsheet, pandas DataFrame or similar tabular style file.)

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

  For more on the evaluation of this project check: https://www.kaggle.com/c/bluebook-for-bulldozers/overview/evaluation

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Data:

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

• There are 3 main datasets:

  • 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.

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The libraries used are:

• Pandas for data analysis.

• NumPy for numerical operations.

• Matplotlib/seaborn for plotting or data visualization.

• Scikit-Learn for machine learning modelling and evaluation.