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vaitybharati / P36.-Supervised-ML---Decision-Tree---C5.0-Entropy-Iris-Flower-

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Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model

python entropy numpy sklearn matplotlib decision-trees decision-tree-classifier classification-report model-validation model-building sklearn-dataset model-testing train-test-split sklearn-tree label-encoder
  • Updated Nov 9, 2021
  • Jupyter Notebook

abhipatel35 / ML-Regression-Lifecycle

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Explore the complete lifecycle of a machine learning project focused on regression. This repository covers data acquisition, preprocessing, and training with Linear Regression, Decision Tree Regression, and Random Forest Regression models. Evaluate and compare models using R2 score. Ideal for learning and implementing regression use cases.

machine-learning linear-regression ml regression pandas decision-tree-regression random-forest-regression sklearn-library r2-score sklearn-tree machine-learning-lifecycle sklearn-models ml-life sklearn-ensemble
  • Updated Jan 18, 2024
  • Python

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