Retail Segmentation Based Marketing Campaing In R And H2o
Showcase for using H2O and R for scoring for marketing campaign in retail
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| H2o 3 | 6,618 | 62 | 33 | 3 months ago | 49 | August 09, 2023 | 2,746 | apache-2.0 | Jupyter Notebook | |
| H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc. | ||||||||||
| Benchm Ml | 1,839 | 2 years ago | 11 | mit | R | |||||
| A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.). | ||||||||||
| H2o Tutorials | 1,403 | 2 years ago | 44 | Jupyter Notebook | ||||||
| Tutorials and training material for the H2O Machine Learning Platform | ||||||||||
| Sparkling Water | 957 | 6 | 4 months ago | 195 | October 26, 2023 | 44 | apache-2.0 | Scala | ||
| Sparkling Water provides H2O functionality inside Spark cluster | ||||||||||
| Awesome Gradient Boosting Papers | 930 | 10 months ago | 1 | cc0-1.0 | Python | |||||
| A curated list of gradient boosting research papers with implementations. | ||||||||||
| Onnxmltools | 896 |  | 20 | 35 | 3 months ago | 22 | March 01, 2023 | 121 | apache-2.0 | Python |
| ONNXMLTools enables conversion of models to ONNX | ||||||||||
| Kafka Streams Machine Learning Examples | 806 | 4 months ago | 10 | apache-2.0 | Java | |||||
| This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies. | ||||||||||
| Interpretable_machine_learning_with_python | 629 | a year ago | 1 | Jupyter Notebook | ||||||
| Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. | ||||||||||
| Mli Resources | 405 | 3 years ago | 4 | Jupyter Notebook | ||||||
| H2O.ai Machine Learning Interpretability Resources | ||||||||||
| Awesome H2o | 345 | a year ago | 1 | |||||||
| A curated list of research, applications and projects built using the H2O Machine Learning platform | ||||||||||
Alternatives To Retail Segmentation Based Marketing Campaing In R And H2oSelect To Compare
Alternative Project Comparisons
Popular H2o Projects
Popular Machine Learning Projects
Popular Machine Learning Categories
Related Searches
Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
R
Machine Learning
Segmentation
Rstats
H2o