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smoteenn

Here are 81 public repositories matching this topic...

fischlerben / Machine-Learning-Credit-Risk

Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample th…

python random-forest modeling pandas logistic-regression confusion-matrix resampling classification-report smote ensemble-machine-learning cluster-centroids accuracy-score smoteenn

Updated Jan 24, 2021
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cedoula / Credit_Risk_Analysis

Build and evaluate several machine learning algorithms to predict credit risk.

machine-learning machine-learning-algorithms logistic-regression ensemble-model smote oversampling undersampling cluster-centroids randomoversampler balanced-random-forest smoteenn

Updated Oct 12, 2022
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DougUOT / Credit_Risk_Analysis

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
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Robertfnicholson / Credit_Risk_Analysis

Developed Machine Learning Models to Predict Credit Risk

python machine-learning random-forest clustering smote randomoversampler smoteenn clustercentroids easyensembleclassifier balancedrandomforestclassifier pythonlibrarires

Updated Mar 27, 2022
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stephperillo / Credit_Risk_Analysis

Using machine learning to determine which model is best at predicting credit risk amongst random oversampling, SMOTE, ClusterCentroids, SMOTEENN, Balanced Random Forest, or Easy Ensemble Classifier (AdaBoost).

machine-learning scikit-learn smote imbalanced-learning smoteenn clustercentroids easyensembleclassifier balancedrandomforestclassifier

Updated Jun 16, 2022
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MoinDalvs / Assignment_SVM_Forest_Fire_Prediction

data-science regularization hyperparameter-tuning svm-classifier kernel-trick auc-roc-curve svm-kernel smotetomek smoteenn smote-sampling adasyn-sampling

Updated Jun 14, 2022
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MoinDalvs / Assignment_Decision_Tree_2

data-science decision-trees imbalanced-data smotetomek smoteenn smote-oversampler adasyn-sampling

Updated May 31, 2022
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annakthrnlee / Credit_Risk_Analysis

Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques to train and evaluate models with unbalanced classes.

machine-learning clustering jupyter-notebook ensemble logistic-regression oversampling undersampling smoteenn

Updated Sep 7, 2022
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aadhamashraf / Fraud-Detection-Interpretability-and-Explainability

Developing fraud detection systems using a variety of machine learning and deep learning models. Emphasis is placed on model explainability to ensure transparency in predictions, an essential aspect in financial applications.

machine-learning genetic-algorithm naive-bayes-classifier ensemble-learning logistic-regression ann fraud-detection isolation-forest random-forest-classifier xgboost-classifier smoteenn comparitive-study

Updated May 12, 2025
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AnveshaM / Predicting_Customer_Churn_Neural_Network

A Comparative Study in Customer Churn Prediction through Multilayer Perceptrons and Support Vector Machines

python pytorch support-vector-machine roc-curve voting-classifier multilayer-perceptron sklearn-library smoteenn

Updated Jul 6, 2022
HTML

rtippana1 / Credit_Risk_Analysis

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

machine-learning random-forest oversampling supervised-machine-learning undersampling smoteenn smote-sampling

Updated Jul 11, 2021
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Baylex / Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

machine-learning scikit-learn imbalanced-learning balanced-random-forest smoteenn random-over-sampling smote-algorithm easy-ensemble-classifier cluster-centroids-algorithm

Updated Sep 25, 2021
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Angienoelhaverly / Credit_Risk_Analysis

Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.

machine-learning random-forest oversampling supervised-machine-learning undersampling smoteenn smote-sampling easy-ensemble-classifier

Updated Apr 4, 2021
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SMJajoo / Big-data-Credit-card-fraud-detection

In this project, three prospective approaches are demonstrated for pre-processing large data sets in practical time-frames, that can attempt to address the class imbalance by improving the running time of the relevant SMOTE+ENN oversampling techniques, with the aim of improving or enabling classifier performance. The focus of our study was to im…

cluster bigdata mapreduce smote smoteenn

Updated Sep 30, 2024
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hasanzeynal / Resampling-Techniques-For-Imbalance-Problems

The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.

cnn smote enn adasyn smotetomek smoteenn random-over-sampling tomek-link

Updated Apr 7, 2023
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anans9 / Hospital-Readmission-Model

This is my project for predicting hospital readmissions using machine learning. The focus is on creating a binary classification model that is both effective and user-friendly.

python linear-regression lightgbm sckiit-learn smoteenn

Updated Dec 19, 2024
Python

AJMnd / Credit_Risk_Analysis

An analysis on credit risk

python scikit-learn logistic-regression smote supervised-machine-learning imbalanced-learning imblearn smoteenn random-over-sampling clustercentroids easyensembleclassifier balancedrandomforestclassifier get-dummies

Updated Jun 19, 2022
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npantfoerder / credit-risk-analysis

Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk

python random-forest scikit-learn jupyter-notebook smote oversampling supervised-machine-learning ensemble-classifier undersampling credit-risk imblearn ml-models smoteenn cluster-centroids-algorithm

Updated Nov 9, 2020
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karindaamelia / Predictive-Analytics-for-Customer-Churn-A-Telecommunications-Industry-SMOTEENN-and-Hyperparameter

Developed a machine learning model to predict customer churn in the telecom industry. The project involved EDA, handling class imbalance with SMOTEENN, and applying 10 classification algorithms, such as Random Forest, SVM, Logistic Regression, KNN, and Decision Tree. Model performance was optimized using GridSearchCV and RandomizedSearchCV.

random-forest exploratory-data-analysis xgboost logistic-regression adaboost predictive-analytics knn decision-tree quadratic-discriminant-analysis linear-discriminant-analysis gradient-boosting gridsearchcv randomizedsearchcv smoteenn

Updated Jul 28, 2025
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devinaa1604 / Credit_Risk_Analysis_Using_Classification

This project aims to predict credit risk using various ensemble machine learning techniques. I have also tried to handle imbalance by using various sampling methods.

ensemble-learning logistic-regression imbalanced-learning cluster-centroids imbalance-classification smoteenn smote-sampling balanced-accuracy-scores

Updated Mar 28, 2021
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