Step-by-Step Guide: Uploading Your Dataset to Kaggle Using the API

1. Set Up Kaggle API Key

• Log in to Kaggle.
• Your username is the name displayed in your Kaggle profile link, e.g., https://www.kaggle.com/xxx.
• Click on your profile avatar in the upper right corner and select "Settings".
• Scroll down to find the API section and click "Create New API Token".
• The downloaded kaggle.json file contains your API key.

2. Install and Configure the Kaggle CLI

• Install the kaggle Python package on your server:

pip install kaggle

• Verify the installation:

kaggle --version

• Set up the kaggle.json file for use:

mkdir -p ~/.kaggle
mv ./kaggle.json ~/.kaggle/kaggle.json
chmod 600 ~/.kaggle/kaggle.json

3. Prepare Data for Upload

Organize the data you want to upload into a folder. The dataset folder must contain at least the following:

• At least one data file (e.g., .csv, .xlsx, .json).
• A dataset-metadata.json file defining the metadata for the dataset.

Here, I provide an example where the ./dataset folder contains all the data to be uploaded.

An example of a dataset-metadata.json file can be found in this GitHub repository.

• title: Name of your dataset.
• id: Path to your dataset, in the format username/dataset-name.
• licenses: License type.

4. Upload Data to Kaggle

Run the following command to upload the dataset to Kaggle:

kaggle datasets create -p ./dataset

Here, the string following -p specifies the directory that contains the data files and the dataset-metadata.json file.

To update an existing dataset, use the following command:

kaggle datasets version -p ./dataset -m "update message"

5. Automated Upload Script

Run the following command and input your information into the code after completing Step 2.

python script.py

6. Check Dataset

You will receive a link to your dataset, which will default to private when opened (ensuring your data remains secure).

Questions

You are welcome to discuss any issues you encounter while running this GitHub repository.