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smote-oversampler

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nive927 / Flight_Delay_Prediction

Star 15

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

machine-learning data-visualization feature-selection performance-metrics feature-extraction forecasts pipelining flights airports data-cleaning smote correlation-coefficient pre-processing auc-roc-curve imbalanced-classes two-stage feature-ranking flight-delay-prediction smote-oversampler random-under-sampling
  • Updated Jan 10, 2024
  • Jupyter Notebook

edaaydinea / OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data

Star 5

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

data-science machine-learning deep-learning tensorflow neural-networks logistic-regression keras-neural-networks chi-square-test random-forest-classifier xgboost-classifier lightgbm-classifier smote-oversampler anova-analysis catboost-classifier multi-layer-perceptron-classifier
  • Updated Nov 6, 2022
  • Jupyter Notebook

Kaushikjas10 / Liquefaction-XGBoost-SHAP-Jas-Dodagoudar

Star 3

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

python machine-learning matlab artificial-intelligence geotechnical-engineering smote boosting synthetic-dataset-generation liquefaction extreme-gradient-boosting explainable-machine-learning shapley-additive-explanations smote-oversampler kmeans-smote
  • Updated Mar 28, 2024
  • Jupyter Notebook

utsavchaudharygithub / Credit_Risk_Analysis

Star 1

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we needed to employ different techniques to train and evaluate models with unbalanced classes. Jill asks us to use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling

random-forest machine-learning-algorithms resampling-methods ensemble-machine-learning smote-sampling smote-oversampler
  • Updated Mar 8, 2022
  • Jupyter Notebook

DougUOT / Credit_Risk_Analysis

Star 1

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

python machine-learning numpy scikit-learn jupyter-notebook pandas logistic-regression confusion-matrix classification-report centroids randomoversampler smoteenn smote-oversampler easyensembleclassifier balancedrandomforestclassifier
  • Updated Mar 5, 2022
  • Jupyter Notebook

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