![Installation
The most efficient way of installing PyCaret is through a virtual environment!
Here are the steps:
1. Install anaconda https://www.anaconda.com/products/distribution
2. Create a conda environment: conda create --name yourenvname python=3.8
3. Activate conda environment: conda activate yourenvname
4. Install pycaret 3.0: pip install pycaret[full]
5. Create notebook kernel:
python -m ipykernel install --user --name yourenvname --display-name "display-name](https://image.slidesharecdn.com/pycaretpedramjahangiry-240828172612-ae4e2b14/85/PyCaret_PedramJahangiryTUTORIALPYTHON-pdf-14-320.jpg){kind=icon}
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- 1. Introduction to PyCaret and installation
- 2. What is PyCaret? PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. PyCaret can be used to replace hundreds of lines of code with few lines only. You spend less time coding and more time on analysis PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, and few more.
- 3. PyCaret is ideal for: Experienced Data Scientists who want to increase productivity. Citizen Data Scientists who prefer a low code machine learning solution. Data Science Professionals who want to build rapid prototypes. Data Science and Machine Learning students and enthusiasts.
- 4. Preprocessing (setup) Data Preparation Scale and Transform Feature Engineering Feature Selection Missing values Data Types One-Hot Encoding Ordinal Encoding Cardinal Encoding Handle Unknown Levels Target Imbalance Remove outliers Normalize Feature Transform Target Transform Feature interaction Polynomial Features Group Features Bin Numeric Features Combine Rare Levels Create Clusters Feature Selection Remove Multicollinearity Principal Component Analysis Ignore Low Variance
- 5. Model training PyCaret trains multiple models simultaneously and outputs a table comparing the performance of each model by considering a few performance metrics. Creating models: create_model(dt, fold=n, ) Comparing models: compare_models(n_select = n, sort=Accuracy, ) Tuning hyperparameters: tune_model(dt, custom_grid: Optional, )
- 6. List of models (Regression)
- 7. List of models (Classification)
- 8. List of models (Clustering)
- 9. List of models (Anomaly Detection)
- 10. Analysis and interpretability My_model = create_model(Model_name) plot_model(my_model) interpret_model(model)
- 11. Finalize, Predict, Save and Deploy model My_model = create_model(Model_name) finalize_model(my_model) predict_model(my_model) save_model(my_model) deploy_model(model) Finalize: This function trains a given estimator on the entire dataset including the holdout set predict: This function makes predictions on the test data set. Save: This function saves the transformation pipeline and trained model object into the current working directory as a pickle file for later use (load_model) Deploy: This function deploys the transformation pipeline and trained model on cloud.
- 12. Workflow PyCaret offers both supervised and unsupervised workflow Classification Regression
- 13. Workflow PyCaret offers both supervised and unsupervised workflow Clustering Anomaly detection
- 14. Installation The most efficient way of installing PyCaret is through a virtual environment! Here are the steps: 1. Install anaconda https://www.anaconda.com/products/distribution 2. Create a conda environment: conda create --name yourenvname python=3.8 3. Activate conda environment: conda activate yourenvname 4. Install pycaret 3.0: pip install pycaret[full] 5. Create notebook kernel: python -m ipykernel install --user --name yourenvname --display-name "display-name
- 15. Important Links 皚 Tutorials New to PyCaret? Checkout our official notebooks! Example Notebooks Example notebooks created by community. Official Blog Tutorials and articles by contributors. Documentation The detailed API docs of PyCaret Video Tutorials Our video tutorial from various events. 鏝 Cheat sheet Cheat sheet for all functions across modules. Discussions Have questions? Engage with community and contributors. 鏝 Changelog Changes and version history. Roadmap PyCaret's software and community development plan.
- 16. PyCaret Time Series Module 皚 Time Series Quickstart Get started with Time Series Analysis Time Series Notebooks New to Time Series? Checkout our official (detailed) notebooks! Time Series Video Tutorials Our video tutorial from various events. Time Series FAQs Have questions? Queck out the FAQ's 鏝 Time Series API Interface The detailed API interface for the Time Series Module Time Series Features and Roadmap PyCaret's software and community development plan. PyCaret new time series module is now available with the main pycaret installation. Staying true to simplicity of PyCaret, it is consistent with the existing API and fully loaded with functionalities
- 17. Practical example in Python Now lets look at some practical examples in Python! https://github.com/PJalgotrader/platforms-and-tools/tree/main/PyCaret