smoteenn

There are 0 repository under smoteenn 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 the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.
python pandas modeling logistic-regression random-forest ensemble-machine-learning accuracy-score confusion-matrix classification-report resampling smote cluster-centroids smoteenn
Language:Jupyter Notebook 11
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.
machine-learning python pandas numpy scikit-learn jupyter-notebook randomoversampler logistic-regression confusion-matrix classification-report smote-oversampler balancedrandomforestclassifier easyensembleclassifier centroids smoteenn
Language:Jupyter Notebook 7
cedoula / Credit_Risk_Analysis
Build and evaluate several machine learning algorithms to predict credit risk.
machine-learning oversampling undersampling randomoversampler smote machine-learning-algorithms cluster-centroids smoteenn balanced-random-forest ensemble-model logistic-regression
Language:Jupyter Notebook 6
Robertfnicholson / Credit_Risk_Analysis
Developed Machine Learning Models to Predict Credit Risk
balancedrandomforestclassifier clustercentroids clustering easyensembleclassifier machine-learning python pythonlibrarires random-forest randomoversampler smote smoteenn
Language:Jupyter Notebook 3
MoinDalvs / Assignment_Decision_Tree_2
adasyn-sampling data-science decision-trees imbalanced-data smote-oversampler smoteenn smotetomek
Language:Jupyter Notebook 2
MoinDalvs / Assignment_SVM_Forest_Fire_Prediction
adasyn-sampling auc-roc-curve data-science hyperparameter-tuning kernel-trick regularization smote-sampling smoteenn smotetomek svm-classifier svm-kernel
Language:Jupyter Notebook 2
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).
imbalanced-learning machine-learning scikit-learn smote balancedrandomforestclassifier clustercentroids easyensembleclassifier smoteenn
Language:Jupyter Notebook 2
AJMnd / Credit_Risk_Analysis
An analysis on credit risk
balancedrandomforestclassifier clustercentroids easyensembleclassifier get-dummies imbalanced-learning imblearn logistic-regression python random-over-sampling scikit-learn smote smoteenn supervised-machine-learning
Language:Jupyter Notebook 1
Angienoelhaverly / Credit_Risk_Analysis
Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.
machine-learning supervised-machine-learning random-forest easy-ensemble-classifier oversampling smote-sampling smoteenn undersampling
Language:Jupyter Notebook 1
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.
clustering logistic-regression machine-learning oversampling undersampling jupyter-notebook smoteenn ensemble
Language:Jupyter Notebook 1
AnveshaM / Predicting_Customer_Churn_Neural_Network
A Comparative Study in Customer Churn Prediction through Multilayer Perceptrons and Support Vector Machines
multilayer-perceptron python pytorch roc-curve sklearn-library smoteenn support-vector-machine voting-classifier
Language:HTML 1
Baylex / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
machine-learning imbalanced-learning scikit-learn random-over-sampling smote-algorithm cluster-centroids-algorithm smoteenn balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 1
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.
logistic-regression imbalanced-learning balanced-accuracy-scores imbalance-classification smote-sampling cluster-centroids smoteenn ensemble-learning
Language:Jupyter Notebook 1
jose-perth / Credit_Risk_Analysis
Employ different techniques to train and evaluate models with unbalanced classes. Evaluate the performance of these models and make recommendations on their suitability to predict credit risk.
adaboostclassifier machine-learning random-forest-classifier smote smoteenn
Language:Jupyter Notebook 1
mdabbous88 / Credit_Risk_Analysis
Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
machine-learning supervised-machine-learning accuracy precision credit-risk smoteenn smoteen random-forest-classifier oversampling-technique undersampling-technique
Language:Jupyter Notebook 1
npantfoerder / credit-risk-analysis
Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk
python jupyter-notebook scikit-learn imblearn credit-risk ml-models smote cluster-centroids-algorithm smoteenn random-forest ensemble-classifier oversampling supervised-machine-learning undersampling
Language:Jupyter Notebook 1
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
Language:Jupyter Notebook 1
shaktipanda1235 / Detection-of-Credict-Card-Default
‘Buy Now, Pay Later’ (BNPL) is a type of short-term financing used by start-ups like Slice, ZestMoney, Simpl, LazyPay, and Uni, are lowering the bars while approving applications. Building models to detect such customers beforehand.
gaussiannb hypothesis-testing lightgbm-classifier smoteenn
Language:Jupyter Notebook 1
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 implement a divide and conquer based implementation that leveraged clustering as a more intelligent division measure.
bigdata cluster smote smoteenn mapreduce
Language:Jupyter Notebook 1
trevor-leach803 / Heart_Transplant_Model
Machine learning model to predict heart transplant failure and success using XGBoost algorithm and SMOTE/ENN to balance the dataset.
machine-learning smote smoteenn xgboost heart-transplant-classification
Language:Jupyter Notebook 1
abidor13 / Credit_Risk_Analysis
Analyze of several Machine Learning techniques in order to help Jill decide on a most effective Machine Learning Model to analyze Credit Card Risk applications.
balanced-random-forest classification clustercentroids easyensembleclassifier machine-learning oversampling resampling sklearn smote smoteenn
Language:Jupyter Notebook 0
abidor13 / linear_regression_salary
There are a number of classification algorithms that can be used to determine loan elgibility. Some algorithms run better than others. We built a loan approver using different Supervised Machine Learning algorithms and compared their accuracies and performances
machine-learning ml decision-trees numpy oversampling-technique pandas sklearn smote smoteenn undersampling
Language:Jupyter Notebook 0
caseygomez / Credit_Risk_Analysis
Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
python supervised-machine-learning balancedrandomforestclassifier clustercentroids easyensembleclassifier imbalanced-learning randomoversampler scikit-learn smote smoteenn
Language:Jupyter Notebook 0
cmwardcode / Credit_Risk_Analysis
Supervised machine learning model to analyze credit risk
clustering-algorithm easy-ensemble-classifier machine-learning random-forest-classifier smoteenn
Language:Jupyter Notebook 0
DangCoop / Credit_Risk_Analysis
machine-learning random-forest-classifier randomoversampler smote smoteenn vscode
Language:Jupyter Notebook 0
diercz / Credit_Risk_Analysis
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
clustercentroids creditrisk-analysis easyensembleclassifier machine-learning random-forest-classifier randomoversampler smote-oversampler smoteenn
Language:Jupyter Notebook 0
hasanzeynal / Resampling-Techniques-For-Imbalance-Problems
The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.
adasyn cnn enn random-over-sampling smote smoteenn smotetomek tomek-link
Language:Jupyter Notebook 0
jakeryu37 / Stroke-Prediction-ML-Model
classification imbalanced-learning machine-learning smote smoteenn stroke-prediction
Language:Jupyter Notebook 0
lilyhanhub / Credit_Risk_Analysis
Supervised Machine Learning project to predict credit risk
ensemble-classifier machine-learning machine-learning-algorithms smoteenn supervised-machine-learning imbalanced-learning scikit-learn
Language:Jupyter Notebook 0
lingumd / Credit_Risk_Analysis
Machine learning models for predicting credit risk in LendingClub dataset.
balancedrandomforestclassifier cluster-centroids-undersampling easyensembleclassifier imbalanced-learn machine-learning randomoversampler smote smoteenn confusion-matrix classification-report matplotlib-pyplot numpy pandas pathlib scikit-learn google-colab get-dummies
Language:Jupyter Notebook 0
LJD0 / Credit_Risk_Analysis
Analysis of different machine learning models' performance on predicting credit default
balanced-random-forest cluster-centroid-undersampling easy-ensemble-classifier machine-learning numpy pandas python random-over-sampling smote smoteenn
Language:Jupyter Notebook 0
MiracleOny / Credit-Risk-Analysis
Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.
clustercentroids clustering-algorithm credit-risk logisticregression machine-learning matplotlib numpy oversampling-algorithms pandas randomoversampler scikit-learn seaborn smote smoteenn undersampling-algorithms
Language:Jupyter Notebook 0
sagartv / diabetes_risk_predictor
A FLASK-based web application that predicts the risk of diabetes based on the answers to a questionnaire. The app currently uses an XGBoost (Extreme Gradient Boosted Model) model trained on a Kaggle Dataset of CDC Data.
classification data-science endtoend flask-application machine-learning replit smoteenn xgboost
Language:Jupyter Notebook 0
shumph10 / Credit_Risk_Analysis
Established a supervised machine learning model trained and tested on credit risk data through a variety of methods to establish credit risk based on a number of factor
credit-risk credit-scoring easy-ensemble-classifier machine-learning naive-random-oversampler prediction-algorithm random-forest-classifier smote-sampling smoteenn
Language:Jupyter Notebook 0
tanmelissa / Credit_Risk_Analysis
Creating various machine learning models to create the most accurate model to predict credit risk
cluster-analysis easy-ensemble-classifier machine-learning random-forest smote-oversampler smoteenn
Language:Jupyter Notebook 0
tomartushar / Credit-Card-Fraud-Detection
An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.
clustercentroids credit-card-fraud decision-tree-classifier logistic-regression machine-learning random-forest-classifier recursive-feature-elimination sampling-methods smote-sampling smoteenn xgboost-classifier bivariate-analysis exploratory-data-analysis t-test univariate-analysis
Language:Jupyter Notebook 0

smoteenn

fischlerben / Machine-Learning-Credit-Risk

DougUOT / Credit_Risk_Analysis

cedoula / Credit_Risk_Analysis

Robertfnicholson / Credit_Risk_Analysis

MoinDalvs / Assignment_Decision_Tree_2

MoinDalvs / Assignment_SVM_Forest_Fire_Prediction

stephperillo / Credit_Risk_Analysis

AJMnd / Credit_Risk_Analysis

Angienoelhaverly / Credit_Risk_Analysis

annakthrnlee / Credit_Risk_Analysis

AnveshaM / Predicting_Customer_Churn_Neural_Network

Baylex / Credit_Risk_Analysis

devinaa1604 / Credit_Risk_Analysis_Using_Classification

jose-perth / Credit_Risk_Analysis

mdabbous88 / Credit_Risk_Analysis

npantfoerder / credit-risk-analysis

rtippana1 / Credit_Risk_Analysis

shaktipanda1235 / Detection-of-Credict-Card-Default

SMJajoo / Big-data-Credit-card-fraud-detection

trevor-leach803 / Heart_Transplant_Model

abidor13 / Credit_Risk_Analysis

abidor13 / linear_regression_salary

caseygomez / Credit_Risk_Analysis

cmwardcode / Credit_Risk_Analysis

DangCoop / Credit_Risk_Analysis

diercz / Credit_Risk_Analysis

hasanzeynal / Resampling-Techniques-For-Imbalance-Problems

jakeryu37 / Stroke-Prediction-ML-Model

lilyhanhub / Credit_Risk_Analysis

lingumd / Credit_Risk_Analysis

LJD0 / Credit_Risk_Analysis

MiracleOny / Credit-Risk-Analysis

sagartv / diabetes_risk_predictor

shumph10 / Credit_Risk_Analysis

tanmelissa / Credit_Risk_Analysis

tomartushar / Credit-Card-Fraud-Detection