Kaggle is an online platform where data scientists and machine learners can find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

Kaggle allows users to find and publish datasets and to engage in running competitions related to machine learning. Python is heavily used on Kaggle, due to its simplicity and the wide variety of data science and machine learning libraries available, such as NumPy, pandas, Matplotlib, Seaborn, and scikit-learn, among others. Many of the posted solutions and kernels (Kaggle’s term for Jupyter notebooks) are written in Python.

Here’s a very basic workflow in Kaggle using Python:

1. Find a competition or dataset: You can browse the website to find a problem that interests you. This could be a competition or just an interesting dataset that you’d like to explore.

2. Download the data: Once you’ve found a dataset or competition that interests you, download the data. Kaggle typically provides data in CSV files, which can be easily imported into a Python environment using pandas.

3. Analyze the data: Use Python’s data analysis libraries like pandas, NumPy, and matplotlib to explore the data. You might look for patterns, correlations, or outliers in the data.

4. Build a model: Once you have an understanding of the data, you can build a machine learning model using scikit-learn or another Python machine learning library. This could be a regression model, a classification model, a clustering model, etc., depending on the problem.

5. Train your model: After creating your model, train it using the data from Kaggle. This involves feeding the data into the model and allowing the model to adjust its parameters to better predict the output.

6. Test your model: Once your model has been trained, you should test it to see how well it performs. Kaggle provides separate test data that you can use for this.

7. Submit your results: If you’re participating in a competition, you can submit your model’s predictions on the test data to Kaggle. You’ll get feedback about how well your model performed compared to others.

8. Iterate on your model: Based on your model’s performance, you might decide to adjust it. This could involve tuning its parameters, choosing a different model entirely, or changing how you preprocess the data.

Remember to leverage the power of community in Kaggle. It’s very common to see kernels shared by others, discussions about the problems, and you can learn a lot from them.