https://github.com/debasish-dutta/bulldozer-price-regression
Contains another of my ML model of kaggle dataset
https://github.com/debasish-dutta/bulldozer-price-regression
data-science sckit-learn
Last synced: 2 months ago
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Contains another of my ML model of kaggle dataset
- Host: GitHub
- URL: https://github.com/debasish-dutta/bulldozer-price-regression
- Owner: debasish-dutta
- Created: 2020-06-23T14:25:19.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-19T07:54:01.000Z (almost 5 years ago)
- Last Synced: 2025-01-19T07:45:20.533Z (4 months ago)
- Topics: data-science, sckit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 56.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Predicting the Sale Price of Bulldozers using Machine Learning
## 1. Problem Definetion
> The goal is to how well we can predict the future sales price of a bulldozer based on its characterstics and previous records of how much similar bulldozers are sold.
## 2. Data
The data is downloaded from 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.
## 3. Evaluation
The evaluation metric is the RMSLE (root mean squared log error) between the actual and predicted auction prices. i.e to minimize RMSLE
## 4. Features
Kaggle provided a data dictionary detailling all the features of the datasheet.