Inspired by a group of citizen data scientists, Pycaret aims to democratize machine learning for everyone. So what exactly is it? And above all, what are its functionalities? That's what we're going to find out in this article.
What is PyCaret?
Pycaret is an open source, low-code Machine Learning library based on Python. This solution automates the end-to-end machine learning workflow. By automating tasks and managing ML models, PyCaret speeds up the experimentation cycle. As a result, data scientists are much more productive and able to develop even more powerful machine learning models.
PyCaret is more than just a Python-based ML library. And for good reason, it encompasses several machine learning libraries and frameworks. For example: scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, etc.
Best of all, it’s a ready-to-deploy Python library. In other words, every step of an ML experience can be reproduced from one environment to another.
Good to know: PyCaret also integrates with many other solutions, such as Microsoft Power BI, Tableau, Alteryx and KNIME. So you can add a layer of machine learning to all your business intelligence work. And it’s easy to do.
Why use PyCaret?
A low-code library
As the library is based on low code, PyCaret doesn’t require hundreds of lines of code, just a few. Even when it comes to performing complex machine learning tasks, PyCaret remains low code.
This means that data scientists can concentrate more on analyzing datasets. They spend less time coding, and more time generating relevant predictive analyses and training efficient machine learning models.
Data processing
Data scientists can choose from multiple data preprocessing functions to save precious time in data processing. Here are just a few examples of the functions available in the Python library:
- Data preparation: before exploring data to deploy models, it needs to be processed. PyCaret can identify missing values, data types and eliminate outliers.
- Scalability and transformation: this involves both normalizing data and modifying the shape of the distribution if necessary.
- Engineering: in particular to create links between data sets.
The modules
PyCaret works through modules, each encapsulating a specific task:
- Supervised Machine Learning models: these include classification and regression. This allows you to predict class labels and continuous variables.
- Unsupervised Machine Learning models: these involve clustering (the grouping of certain populations according to common characteristics) and anomaly detection (data that doesn’t fit the pattern).
- Time series: this involves forecasting time series to inform strategic decision-making.
- Data sets: use this module to access PyCaret’s extensive range of ML data sets.
The functions
For all these modules, PyCaret groups together coherent sets of actions capable of automating the data scientist’s workflow. Here are the main functions available on this ML library in Python:
- analytics data exploration ;
- ML model deployment ;
- model training ;
- iteration.
By automating Machine Learning tasks, these functions reduce the cycle time between hypothesis and understanding in a machine learning experience.
Thanks to its low-code solution, its data preprocessing solution, its modules and its numerous functions, PyCaret aims to democratize machine learning for everyone. Not just data scientists with solid technical expertise. But also those able to perform simple, moderately sophisticated analyses.
However, PyCaret is also very useful for experienced data scientists, enabling them to increase their productivity exponentially.
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While PyCaret aims to democratize Machine Learning for everyone, the development of machine learning models capable of solving complex problems still requires specific technical skills. And it’s these skills that companies are looking for.
It is therefore essential to train in data science. DataScientest makes it possible.
Through our training courses, you’ll learn everything you need to know about Machine Learning, from data exploration to model deployment, training and iteration. In doing so, you’ll become operational as soon as the course is over.
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