VenkatManda/KaggleQuestions huggingface dataset:
Category: competition-setup
Question: How to create a competition️ on Kaggle?
Answer: To create a new competition, click on the “Create new competition” button at the top of the Kaggle Community landing page.

Then, enter a descriptive title, subtitle, and URL for your competition. Be as descriptive and to the point as possible. In our example above, the title “Store Sales - Time Series Forecasting” quickly outlines the type of data, the industry of the dataset, and the type of problem to be solved.

If you want to create a competition with more privacy, you can limit your competition's visibility and restrict who can join on this page.

## Visibility: 
Competitions with their visibility set to public are viewable on Kaggle and appear in Kaggle search results. Competitions with visibility set to private are hidden and only accessible via invitation URLs from the host.

## Who Can Join: 
Competitions access can be set to three levels: 
- **Anyone**: all Kagglers can join your competition.
- **Only people with a link**: restricts access to those users provided with a special URL.
- **Restricted email list**: only Kagglers with accounts that match the emails or email domains specified will be able to join. Note: if you select restricted email list, notebooks will be turned off. This provides a way to ensure that any private data that you have in a competition is not accidentally leaked through shared notebooks. You can choose to re-enable notebooks if you choose.

Review and accept our terms of service, then click “Create Competition”.

Your competition listing is now in draft mode. You can take your time to prepare the details before making the competition public.
Category: competition-setup
Question: How to prepare dataset for a Kaggle Competition?
Answer: ## Overview

You will typically need to prepare and split your chosen dataset into four CSV files with different purposes and formatting requirements:

- `train.csv` will be given to participants to train their models. It includes the inputs and the ground truth. For example, in the grocery store competition, `train.csv` contains columns of product data and the solution columns – whether or not the product sold. Typically, this is roughly 70% of the original dataset.
- `test.csv` is given to participants and includes the features of the test set so they can create a submission file with their predictions.
- `solution.csv` is always hidden from participants and used by Kaggle’s platform to score submissions. The rows should correspond with those of `test.csv` and typically comprise roughly 30% of the original dataset.
- `sample_submission.csv` is a placeholder CSV file with the correct formatting, which helps participants understand the expected submission format for the competition.

It's up to you to determine how exactly you'd like to split your dataset into train and test files, but it's typically best practice to ensure both train and test have the same type of data represented. Also, most people go with a 70/30 or 75/25 train/test split, but it's problem and dataset dependent.

**Note:** this guide provides instructions for tabular data. Other problem types like image data are possible using similar steps.

### Implement a unique ID column

Before splitting the dataset, make sure that your dataset has an `Id` column with unique values. The `Id` column is how the scoring system knows which rows of a submission correspond to which rows of the solution. Make sure that the `Id` column is the very first column of your solution file.

### Prepare the `train.csv` file

Take a large chunk of your dataset, typically 70%, and split it into its own dataset named `train.csv`. Be sure not to remove the ground truth column(s) because participants need that information to train their models. Save and set aside for upload later. For example:

```csv
train.csv
input_feature1,input_feature2,target_feature
100,52.12,1
192,203.2,1
64,-59.1,0
```
Category: competition-setup
Question: How to set up scoring / metric in a Kaggle Competition?
Answer: Navigate to the Host tab > Evaluation Metric page in the right-side navigation to set up scoring.

## Designate your scoring metric

Choose the scoring metric you’d like to use for your competition in the drop-down menu, or see below for how to write your own metric in Python.

There are many ways to determine “how accurate” a submission may be. In the grocery store competition example, you may want to reward underestimates more than overestimates, or reward predictions exponentially more the closer they get to the ground truth. If you are unfamiliar with the types of common evaluation metrics used in machine learning, we’d encourage you to take a look at the details of common evaluation metrics to find the right fit.

Kaggle provides two types of metrics: Python (tagged with the icon ) and Legacy (no icon). There are a few key differences. The source code for Legacy metrics is not publicly available and they typically have limited documentation. The setup process is also slightly different: Legacy metrics require manually mapping every column. However, Legacy metrics do offer speed advantages in some circumstances.

When a metric is selected, your competition will be tied to the latest version of that metric. If a newer version is later published, you must manually update your competition to use it.

## Upload the `solution.csv` file

Click on the upload icon to upload your `solution.csv` file.

If you've chosen a Python metric, check that your solution file's format matches that expected by the metric's documentation, or just continue to testing a submission to see if it matches.

If you've chosen a Legacy metric, then after uploading the `solution.csv` file, the column headers will auto-populate the Solution Mapping table below. Mapping allows our metric code to understand which columns to use for calculations. Choose the correct “Expected Column” values. Note, some evaluation metrics let you score multiple columns simultaneously.

## Upload the `sample_submission.csv` file and map the verification

Click on the upload icon to upload your `sample_submission.csv` file.

If you've chosen a Legacy metric, then after uploading you'll again need to complete the same process of column mapping for the submission format.

## Upload data for participants

Click on the Data tab and “Upload first version” button on the bottom of your screen to upload all data that participants can access – `test.csv`, `train.csv` files, and `sample_submission.csv` file. Note: you will have additional data files if creating an image/video/etc. competition. Kaggle will process your data and create a versioned dataset, which will also be made accessible via Kaggle notebooks.
Category: competition-setup
Question: How to test your newly created competition?
Answer: ## Sandbox Testing

Once you set up the solution and submission files you can test submissions in the submission sandbox. You will need at least one sample submission that successfully generates a score in order to launch your competition.

Verify that the scoring is working as intended (e.g. a random submission should have a random score, a perfect submission should have a perfect score, etc.). You may have to experiment to understand what is and is not allowed in submission formats, but the system should provide clear error messages in the event something is wrong with a file.

## Benchmarking a Solution (Optional)

To create a benchmark score for your participants to meet or exceed, check the box next to the submission you’d like to use as a benchmark. You’ll then see that score listed as a benchmark on the leaderboard.
Category: competition-setup
Question: How to finalize your settings and descriptions for a Kaggle competition?

Answer: First navigate to the Host tab and complete your configuration in the Basic Details, Images, and Evaluation Metric pages.

Then click through the Overview, Data, and Rules tabs and make sure all text descriptions are polished and ready for participants.

You can also go to the Launch Checklist page which shows your remaining steps.

## Score Decimals to Display

The "Score Decimals to Display" setting on the Basic Details page controls how many decimal places are shown in the user interface. We always use full-precision scores for calculations and ranking comparisons, but it can be useful to truncate the displayed scores to make them look cleaner or to prevent leaderboard probing. For example, if participants can see full-precision scores, they could make small changes to their submission and examine the score difference to infer the ground truth of the public test set, or reverse engineer the split between public and private leaderboards.
Category: competition-setup
Question: How to launch a competiton and invite participants on Kaggle?
Answer: Go to "Host > Launch Checklist" and confirm that all the boxes are checked green. Once they are, you’re good to go! Buttons allowing you to launch the competition now or schedule launch in the future will appear – choose according to your needs.

You’ll know your competition is live when it says “Competition is active.”

You can invite participants to your competition by sharing the URL at the bottom of the Launch Checklist or Basic Details. This link respects the access settings you specified when creating the competition. If you selected anyone can join, this link will be the competition URL. If you selected only people with a link, anyone with this URL can participate in the competition, so make sure you share the link with the right audience. If you’d like a select group to participate, send the URL via email. If you’d like broad participation, use social media or encourage participants to invite their friends. If you selected restricted email access, the link will only work if the Kaggler's email address appears on the list of restricted emails you specified.
Category: dataset
Question: How to collaborate on Kaggle Datasets?
Answer: Dataset collaboration is a powerful feature. It allows multiple users to co-own and co-maintain a private or publicly shared dataset. For example, you can invite collaborators to view and edit a private dataset to work together on preparing it before changing its visibility to public.

When uploading a Dataset you may choose either yourself or any Organization you are a part of as the Owner of that Dataset. If you select yourself, that Dataset will be created with yourself as the Owner. If you select an Organization, that Organization will be the Owner of the dataset, and every other user in the Organization (including yourself) will be added as a Collaborator with editing privileges (if you are unfamiliar with Organizations, you may also want to read the section “Creating and using organizations”).

This means that Organizations are an easy way to manage access to datasets or groups of datasets.

## Inviting Collaborators

Alternatively, you may manage Collaborators directly. To do so, go to any dataset you own and navigate to Settings > Sharing. There, use the search box to find and add other users as Dataset collaborators.

If your Dataset is private, you may choose between giving Collaborators either viewing privileges (“Can view”) or editing privileges (“Can edit”). If your Dataset is public, Collaborators can only be added with editing privileges (“Can edit”), as anyone can view it already.

When you add a collaborator, they will receive a notification via email.

“Data Science for Good: Kiva Crowdfunding [Kaggle](https://www.kaggle.com/kiva/data-science-for-good-kiva-crowdfunding)” is a great example of a Collaborative Dataset.

## Using Notebooks with Dataset Collaborators

Using Notebooks, Kaggle’s interactive code editing and execution environment, is a powerful way to work with your collaborators on a Dataset. You might want to work with collaborators to write public Notebooks that help familiarize other users with your dataset. Or you may want to keep all of your code private among your collaborators as you work on privately shared projects together.

Notebooks you create are private by default, and their sharing settings are distinct from the sharing settings on your Dataset. That is, your Dataset collaborators won’t automatically see your private Notebooks. Here’s what that means and how you can productively use sharing settings on Datasets and Notebooks together:

- You can make public Notebooks on a private Dataset which will allow anyone to view your Notebook, but not the underlying private data source.

- If you want to add view or edit collaborators to a private Notebook (whether the dataset is private or public), you can do so by adding users via Options > Sharing on the Notebook.
Category: dataset
Question: What are the technical specifications of Kaggle Datasets?
Answer: Kaggle Datasets allows you to publish and share datasets privately or publicly. We provide resources for storing and processing datasets, but there are certain technical specifications:

* 100GB per dataset limit
* 100GB max private datasets (if you exceed this, either make your datasets public or delete unused datasets)
* A max of 50 top-level files (if you have more, use a directory structure and upload an archive)

When you upload a dataset we apply certain processing steps to make the dataset more usable.

* A complete archive is created so the dataset can be easily downloaded later
* Any archives (e.g., ZIP files) that you upload are uncompressed so that the files are easily accessible in Notebooks (directory structure is preserved)
* Data types for tabular data files are automatically detected (e.g., geospatial types)
* Column-level metrics are calculated for tabular data which are viewable on the data explorer on the dataset's "Data" tab

When publishing datasets, you might also want to consider the technical specifications of [Notebooks](https://www.kaggle.com/docs/notebooks#technical-specifications) if you intend to use (or encourage other Kaggle users to use) Notebooks to analyze the data.