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🌊 Online machine learning in Python
| Date | Stars |
|---|---|
| 2026-07-31 | 5896 |
| 2026-08-02 | 5900 |
| 2026-08-06 | 5905 |
Today
+5 stars today
This week
— stars this week
This month
— stars this month
Momentum
35.0
growth rate 0.00%/day
<p align="center">
<img height="220px" src="https://github.com/online-ml/river/assets/8095957/64ad5fb6-383c-4bfb-af71-3d055a103a1a" alt="river_logo">
</p>
<p align="center">
<!-- Code quality -->
<a href="https://github.com/online-ml/river/actions/workflows/code-quality.yml">
<img src="https://github.com/online-ml/river/actions/workflows/code-quality.yml/badge.svg" alt="code-quality">
</a>
<!-- Documentation -->
<a href="https://riverml.xyz">
<img src="https://img.shields.io/website?label=docs&style=flat-square&url=https%3A%2F%2Friverml.xyz%2F" alt="documentation">
</a>
<!-- PyPI -->
<a href="https://pypi.org/project/river">
<img src="https://img.shields.io/pypi/v/river.svg?label=release&color=blue&style=flat-square" alt="pypi">
</a>
<!-- PePy -->
<a href="https://pepy.tech/project/river">
<img src="https://static.pepy.tech/badge/river?style=flat-square" alt="pepy">
</a>
<!-- Mypy -->
<a href="http://mypy-lang.org/">
<img src="http://www.mypy-lang.org/static/mypy_badge.svg" alt="mypy">
</a>
<!-- License -->
<a href="https://opensource.org/licenses/BSD-3-Clause">
<img src="https://img.shields.io/badge/License-BSD%203--Clause-blue.svg?style=flat-square" alt="bsd_3_license">
</a>
<!-- Discord -->
<a href="https://discord.gg/qNmrKEZMAn">
<img src="https://img.shields.io/badge/Discord-join%20chat-5865F2?style=flat-square&logo=discord&logoColor=white" alt="discord">
</a>
</p>
</br>
<p align="center">
River is a Python library for <a href="https://www.wikiwand.com/en/Online_machine_learning">online machine learning</a>. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between <a href="https://github.com/MaxHalford/creme">creme</a> and <a href="https://github.com/scikit-multiflow/scikit-multiflow">scikit-multiflow</a>.
</p>
## ⚡️ Quickstart
As a quick example, we'll train a logistic regression to classify the [website phishing dataset](http://archive.ics.uci.edu/ml/datasets/Website+Phishing). Here's a look at the first observation in the dataset.
```python
>>> from pprint import pprint
>>> from river import datasets
>>> dataset = datasets.Phishing()
>>> for x, y in dataset:
... pprint(x)
... print(y)
... break
{'age_of_domain': 1,
'anchor_from_other_domain': 0.0,
'empty_server_form_handler': 0.0,
'https': 0.0,
'ip_in_url': 1,
'is_popular': 0.5,
'long_url': 1.0,
'popup_window': 0.0,
'request_from_other_domain': 0.0}
True
```
Now let's run the model on the dataset in a streaming fashion. We sequentially interleave predictions and model updates. Meanwhile, we update a performance metric to see how well the model is doing.
```python
>>> from river import compose
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing
>>> model = compose.Pipeline(
... preprocessing.StandardScaler(),
... linear_model.LogisticRegression()
... )
>>> metric = metrics.Accuracy()
>>> for x, y in dataset:
... y_pred = model.predict_one(x) # make a prediction
... metric.update(y, y_pred) # update the metric
... model.learn_one(x, y) # make the model learn
>>> metric
Accuracy: 89.28%
```
Of course, this is just a contrived example. We welcome you to check the [introduction](https://riverml.xyz/dev/introduction/installation/) section of the documentation for a more thorough tutorial.
## 🛠 Installation
River is intended to work with **Python 3.11 and above**. Installation can be done with `pip`:
```sh
pip install river
```
There are [wheels available](https://pypi.org/project/river/#files) for Linux, MacOS, and Windows. This means you most probably won't have to build River from source.
River's core online interface (`learn_one` / `predict_one`) has no `pandas` dependency. The mini-batch interface (`learn_many`, `predict_many`, `predict_proba_many`, `transform_many`) is built on `pandas` Excerpt of 8,247 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d34b012db585d7fd, llm:Repository description and topics: 'Online machine learning in Python', topics include online-learning, streaming, incremental-learning, concept-drift, streaming-data; README states 'Python library for online machine learning' and 'machine learning on streaming data'.
matched fp:d34b012db585d7fd, llm:Repository description and topics: 'Online machine learning in Python', topics include online-learning, streaming, incremental-learning, concept-drift, streaming-data; README states 'Python library for online machine learning' and 'machine learning on streaming data'.
matched fp:d34b012db585d7fd, llm:Repository description and topics: 'Online machine learning in Python', topics include online-learning, streaming, incremental-learning, concept-drift, streaming-data; README states 'Python library for online machine learning' and 'machine learning on streaming data'.