![\"\"](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/3316/media/bulldozer.jpg\")
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\n\r\n Team mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the merge date. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.
\r\n
There is no maximum team size.
\r\nYou may submit a maximum of 2 entries per day.
\r\nYou may select up to 1 final submissions for judging.
\r\n\r\n Because the data can be manually decoded and thus an entry can be created by hand, scoring data will be withheld until you have locked down a model.\r\n
\r\n\r\n You must upload all kernels and model files alongside documentation in a single zip file\r\n in order for your entry to be eligible. You can do this at the time of submission, or append\r\n it to your submission at a later time (before the competition end).\r\n
\r\nStart Date: 1/25/2013 6:13 PM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 4/17/2013 11:59 PM UTC
\r\n\t\r\nAdditional data sources may be used. However, the data MUST be available at time of auction sale.
\r\nFor example, economic indicators may be used from public data sources, but the economic indicator used to evaluate a specific sale MUST have been published prior to the sale.
\r\n\r\nNonexclusive License Agreement Prize Winners Must Sign
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "data-description", "content": "View and download the benchmark code from Github
\r\nFor this competition, you are predicting the sale price of bulldozers sold at auctions.
\r\nThe data for this competition is split into three parts:
\r\nThe key fields are in train.csv are:
\r\nThe machine_appendix.csv file contains the correct year manufactured for a given machine along with the make, model, and product class details. There is one machine id for every machine in all the competition datasets (training, evaluation, etc.).
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Description", "content": "
The goal of the contest is to predict the sale price of a particular piece of heavy equiment at auction based on it's usage, equipment type, and configuaration. The data is sourced from auction result postings and includes information on usage and equipment configurations.
\r\nFast Iron is creating a \"blue book for bull dozers,\" for customers to value what their heavy equipment fleet is worth at auction.
\r\nFast Iron are a content-focused business that aids customers in creating enterprise data standards, cleansing data, and maintaining clean data. Utilizing proprietary applications and an ever growing data cleansing team, Fast Iron has normalized data for more than 2.5 million machine and customer records for the heavy equipment industry.
\r\nThis competition was launched under the Kaggle Startup Program. If you're a startup with a predictive modelling challenge, please apply!
\r\nPhoto credits: Antonis Lamnatos
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation metric for this competition is the RMSLE (root mean squared log error) between the actual and predicted auction prices.
\r\nSample submission files can be downloaded from the data page. Submission files should be formatted as follows:
\r\nSalesID,SalePrice", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Timeline", "content": "
1222837,36205
3044012,74570
1222841,31910.50
...
Friday, January 25, 2013: Competition launch: training set and validation sets released
\r\nWednesday, April 3, 2013: Deadline to submit predictions on the validation set, public leaderboard frozen.
\r\nThursday, April 4, 2013: Validation set solutions released. You may retrain your models on the combined training and validation sets at this point.
\r\nWednesday, April 10, 2013: Deadline to upload final models. You only need to upload a model to be eligible for prize money (we recommend this if you are in the top 20 or think you have a shot for the prize money).
\r\nThursday, April 11, 2013: Test set released. You now have one week to make predictions on this set and submit your predictions to Kaggle. You don't need to have uploaded a model to make your test set predictions and appear on the final leaderboard.
\r\nWednesday, April 17, 2013: Deadline for submitting test set predictions.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false } ] }