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feature-engineering-ml

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MinglongCheng / tabular-ml-shap-consistency

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End-to-end tabular ML pipeline: example dataset + single script to train and evaluate all models, SHAP explainability, and cross-model consistency analysis.

python machine-learning random-forest scikit-learn xgboost lightgbm artificial-neural-networks logistic-regression support-vector-machine confusion-matrix roc-curve decision-tree model-evaluation calibration-curve shap model-interpretability decision-curve-analysis feature-engineering-ml cross-model-consistency
  • Updated Dec 4, 2025
  • Python

labex-labs / scikit-learn-for-beginners

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This comprehensive course covers the fundamental concepts and practical techniques of Scikit-learn, the essential machine learning library in Python. Learn to build, train, and evaluate machine learning models using various algorithms and preprocessing techniques.

python machine-learning programming course scikit-learn labs python-machine-learning hands-on model-evaluation-metrics beginner-ml-projects labex scikit-learn-basics data-preprocessing-sklearn model-selection-python supervised-learning-sklearn unsupervised-learning-python feature-engineering-ml basic-regression-models classification-algorithms-sklearn introductory-data-science
  • Updated Oct 23, 2025

myselfsukhendu09 / Dry-Bean-Type-Classification

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Automated classification of 7 different types of dry beans using machine learning techniques. This project leverages computer vision-extracted geometric and shape features (such as Area, Perimeter, and Shape Factors) to accurately identify bean varieties including Barbunya, Bombay, Cali, Dermason, Horoz, Seker, and Sira.

python scikit-learn classification-algorithm machine-learning-projects data-science-projects multiclass-classification-models exploratory-data-analysis-eda artificial-intelligence-and-machine-learning dry-bean-dataset feature-engineering-ml
  • Updated Jan 22, 2026
  • Jupyter Notebook

mwasifanwar / automl_framework

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Comprehensive AutoML framework that automates data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Features neural architecture search and automated data cleaning pipelines.

python data-science machine-learning scikit-learn machine-learning-algorithms hyperparameter-optimization feature-engineering automl machine-learning-models mlops data-science-projects scikit-learn-python automl-algorithms feature-engineering-algorithm mlops-workflow feature-engineering-ml
  • Updated Nov 5, 2025
  • Python

Stokaru / tabular-ml-shap-consistency

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🔍 Streamline tabular binary classification with model interpretability and SHAP consistency analysis for clear insights and robust evaluation.

python machine-learning random-forest scikit-learn xgboost lightgbm artificial-neural-networks logistic-regression support-vector-machine confusion-matrix roc-curve model-evaluation calibration-curve shap model-interpretability decision-curve-analysis feature-engineering-ml cross-model-consistency
  • Updated Jan 23, 2026
  • Python

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