This is the first official PROD release of PyRapidML
Installation:
pip install PyRapidML
Upgrade when a new version is out:
pip install PyRapidML --upgrade
Introduction:
Are you tired of writing hefty lines of code for your data science problem?
Are you having difficulty figuring out what algorithm performs the best?
Is it hard for you to compare multiple algorithms and see which one has the best accuracy?
Do you face issues in Hyperparameter tuning?
Do you want easy model deployments?
Do you a dream of auto-ml?
Are you facing problems in Exploratory data analysis?
Do you want a library that can automatically perform all steps of data science lifecycle?
Do you want a library that can do Exploratory data analysis, Feature Engineering, Feature Selection, Compare multiple Machine Learning Algos, Hyperparamter tuning, model deployments?
If the answer is Yes to the above questions then PyRapidML is the library for you.
PyRapidML is an open source Python machine learning library.
PyRapidML is essentially a Python wrapper around several machine learning libraries and frameworks such as Pycaret, scikit-learn, XGBoost, LightGBM, CatBoost, spaCy, Optuna, Hyperopt, Ray, and many more.
PyRapidML is an open source Python library which not only helps in automating Machine Learning Workflows but also helps in building end to end ML algorithms .
PyRapidML is a low code library which means writing basic and less lines of code, one can achieve high accuracy in their machine learning models.
There's no need to write hefty lines of code as PyRapidML would compare all possible machine learning algorithms to solve your problem in just a single line of code.
Once PyRapidML gives you the best algorithm. You can further tune the model (in just a single line of code) to further tune the model.
Initial idea of PyRapidML was inspired by PyCaret library in Python.