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A machine-learning project with the goal of predicting the sale price of bulldozers.

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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


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.

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.

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A machine-learning project with the goal of predicting the sale price of bulldozers.

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