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multiple-regression

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srafay / Machine_Learning_A-Z

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Learning to create Machine Learning Algorithms

machine-learning-algorithms datascience naive-bayes-classifier logistic-regression support-vector-machine polynomial-regression decision-tree-classifier dataprocessing classification-algorithims random-forest-regressor support-vector-regression multiple-regression decision-tree-regression k-means-implementation-in-python k-means-clustering kernel-svm random-forest-classifier simple-linear-regression k-nearest-neighbors machine-learning-az
  • Updated Jun 15, 2021
  • Python

dominance-analysis / dominance-analysis

Star 160

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

feature-selection logistic-regression feature-engineering regression-models predictor multiple-regression feature-importance classification-model relative-importance dominance-analysis r-square shapley-value keydrivers dominance dominance-statistics predictor-importance pseudo-r-square
  • Updated Aug 17, 2023
  • Python

kamlesh0928 / machine-learning

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This repository contains machine learning algorithms implemented from scratch and using scikit-learn, covering classification, regression, and clustering. Each algorithm is well-documented, with clear code and explanations. To use K-Medoids, install sklearn_extra via pip install scikit-learn-extra. Contributions are welcome!

linear-regression naive-bayes-classifier pca logistic-regression support-vector-machine lda polynomial-regression kmedoids-clustering decision-tree-classifier kmeans-clustering hierarchical-clustering dbscan-clustering multiple-regression knn-classifier
  • Updated Feb 9, 2025
  • Python

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