![](\"https://storage.googleapis.com/kaggle-media/competitions/kaggle/5407/media/housesbanner.png\")
Here's a brief version of what you'll find in the data description file.
\r\nYou have some experience with R or Python and machine learning basics. This is a perfect competition for data science students who have completed an online course in machine learning and are looking to expand their skill set before trying a featured competition.
\n
Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
\nWith 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
\nThe Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.
\n\nPhoto by Tom Thain on Unsplash.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.
\nSubmissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)
\nThe file should contain a header and have the following format:
\nId,SalePrice\n
1461,169000.1
1462,187724.1233
1463,175221
etc.
You can download an example submission file (sample_submission.csv) on the Data page.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "### One account per participant\n\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n### No private sharing outside teams\n\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n### Team Mergers\n\nTeam 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 Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n### Team Limits\n\nThe maximum team size is 10.\n\n### Submission Limits\n\nYou may submit a maximum of 10 entries per day.\n\nYou may select up to 2 final submissions for judging.\n\n### Competition Timeline\nStart Date: 8/30/2016 1:08 AM UTC
\nEnd Date: This competition does not have an end date.
\n\n### COMPETITION-SPECIFIC TERMS\n\n**COMPETITION TITLE**: House Prices - Advanced Regression Techniques\n\n**COMPETITION SPONSOR**: Kaggle\n\n**COMPETITION SPONSOR ADDRESS**: 1600 Amphitheatre Pkwy, Mountain View, CA 94043\n\n**COMPETITION WEBSITE**: https://www.kaggle.com/c/house-prices-advanced-regression-techniques\n\n**DATA ACCESS AND USE**: Competition Use and Academic, Non-Commercial Use Only\n\nKaggle Learn offers hands-on courses for most data science topics. These short courses prepare you with the key ideas to build your own projects.
\nThe Machine Learning Course will give you everything you need to succeed in this competition and others like it.
\n\nGetting Started competitions were created by Kaggle data scientists for people who have little to no machine learning background. They are a great place to begin if you are new to data science or just finished a MOOC and want to get involved in Kaggle.
\n\nGetting Started competitions are a non-competitive way to get familiar with Kaggle’s platform, learn basic machine learning concepts, and start meeting people in the community. They have no cash prize and are on a rolling timeline.
\n\nThe Kaggle leaderboard has a public and private component to prevent participants from “overfitting” to the leaderboard. If your model is “overfit” to a dataset then it is not generalizable outside of the dataset you trained it on. This means that your model would have low accuracy on another sample of data taken from a similar dataset.
\n\nPublic Leaderboard\nFor all participants, the same 50% of predictions from the test set are assigned to the public leaderboard. The score you see on the public leaderboard reflects your model’s accuracy on this portion of the test set.
\n\nPrivate Leaderboard\nThe other 50% of predictions from the test set are assigned to the private leaderboard. The private leaderboard is not visible to participants until the competition has concluded. At the end of a competition, we will reveal the private leaderboard so you can see your score on the other 50% of the test data. The scores on the private leaderboard are used to determine the competition winners. Getting Started competitions are run on a rolling timeline so the private leaderboard is never revealed.
\n\nWhen you accept the competition rules, a team will be created for you. You can invite others to your team, accept a merger with another team, and update basic information like team name by going to the More < Team page.
\n\nWe've heard from many Kagglers that teaming up is the best way to learn new skills AND have fun. If you don't have a teammate already, consider asking if anyone wants to team up in the discussion forum.
\n\nKaggle Kernels is a cloud computational environment that enables reproducible and collaborative analysis. Kernels supports scripts in R and Python, Jupyter Notebooks, and RMarkdown reports. Go to the Kernels tab to view all of the publicly shared code on this competition. For more on how to use Kernels to learn data science, visit the Read more about our decision to implement a rolling leaderboard on getting started competitions here.
\n\nKaggle does not have a dedicated support team so you’ll typically find that you receive a response more quickly by asking your question in the appropriate forum. (For this competition, you’ll want to use the House Prices discussion forum).
\n\nSupport is only able to help with issues that are being experienced by all participants. Before contacting support, please check the discussion forum for information on your problem. If you can’t find it, you can post your problem in the forum so a fellow participant or a Kaggle team member can provide help. The forums are full of useful information on the data, metric, and different approaches. We encourage you to use the forums often. If you share your knowledge, you'll find that others will share a lot in turn!
\n\nIf your problem persists or it seems to be affecting all participants then please contact us.
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