easy-ensemble-classifier

There are 0 repository under easy-ensemble-classifier topic.

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
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
Akotovets1 / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
adaboost balanced-random-forest cluster-centroids-algorithm confusion-matrix easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikitlearn-machine-learning smote-algorithm supervised-machine-learning
Language:Jupyter Notebook 0
annette-blackburn / Credit_Risk_Analysis
Supervised Machine Learning Project
balanced-random-forest easy-ensemble-classifier imbalanced-learning machine-learning naive-random-oversampler scikit-learn smote-oversampler undersampling combination-under-over-sampling
Language:Jupyter Notebook 0
ashley-green1 / Credit_Risk_Analysis
Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.
binary-encoding cluster-centroids-undersampling confusion-matrix easy-ensemble-classifier encoding feature-importance logistic-regression machine-learning random-forest random-forest-classifier random-over-sampling sklearn-library smote-oversampler smoteenn supervised-machine-learning train-test-split train-test-using-sklearn
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BaileeRice / Credit_Risk_Analysis
using machine learning to assess credit risk
machine-learning balanced-random-forest easy-ensemble-classifier naive-random-oversampler smote-oversampler undersampling
Language:Jupyter Notebook 0
BiancaTaisePommerening / Credit_Risk_Analysis-
Supervised Machine Learning
balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikit-learn smote smoteenn-combination pandas-dataframe python3
Language:Jupyter Notebook 0
cbrito3 / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
ada-boost-classifier balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier imbalance-learning machine-learning machine-learning-algorithms naive-random-oversampler over-sampling precision-recall scikit-learn scikitlearn-machine-learning smote-oversampler under-sampling
Language:Jupyter Notebook 0
cmmgw / Credit_Risk_Analysis
python numpy pandas pathlib collections scikit-learn imbalanced-learn supervised-machine-learning naive-random-oversampler smote-oversampler cluster-centroids-undersampling smoteenn-combination balanced-random-forest easy-ensemble-classifier excel jupyter-notebook
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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
ed12rivera / Credit_Risk_Analysis
Extract data provided by lending club, and transform it to be useable by predictive models.
accuracy-score balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier logistic-regression smote-oversampler smoteenn
Language:Jupyter Notebook 0
eric-blankinshp / Credit_Risk_Analysis_Supervised_ML
About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics
balanced-random-forest imbalanced-learning machine-learning python scikit-learn smote smoteen unbalanced-data easy-ensemble-classifier
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jharvey09 / solana_sentinment_analysis
For this analysis, we used computational linguistics and biometrics to systematically identify the trend using various news articles and closing prices using the "CoinGecko CSV & Crypto News API"!
balanced-random-forest easy-ensemble-classifier natural-language-processing sentiment-analysis stochastic-gradient-descent
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
Mishkanian / Credit_Risk_Analysis
Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.
machine-learning scikit-learn imbalanced-learn unbalanced-data python easy-ensemble-classifier balanced-random-forest smote smoteenn
Language:Jupyter Notebook 0
credit-risk-predictor
sarahm44 / credit-risk-predictor
Uses several machine learning models to predict credit risk.
balanced-random-forest cluster-centroids cluster-centroids-undersampling credit-risk easy-ensemble-classifier ensemble-learning fintech logistic-regression machine-learning naive-random-oversampler random-forest resampling smote smote-oversampler smoteenn-combination
Language:Jupyter Notebook 0
Credit_Risk_Analysis
shayanafzal / Credit_Risk_Analysis
Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.
cluster-centroids random-over-sampling smote balancedrandomforestclassifier balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 0
showkatewang / Credit_Risk_Analysis
Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models
balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier machine-learning naive-random-oversampler python synthetic-minority-over-sampling
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
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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
YonathanTE / Machine-Learning
Compared the effectiveness of the EasyEnsembleClassifier and LogisticRegression libraries. This was to assess the model with the best scores for balanced accuracy, recall, and geometric mean.
easy-ensemble-classifier machine-learning random-forest-classifier smoteenn
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DL777 / Risky_Business
Using machine learning (ML) models to predict credit risk using data typically analysed by peer-to-peer lending services. Resampling data with SMOTE, Cluster Centroids, SMOTEENN and applying ensemble learning classifiers: Balanced Random Forest Classifier and Easy Ensemble Classifier.
cluster-centroids credit-risk easy-ensemble-classifier ensemble-learning machine-learning random-forest-classifier smote smoteenn
Language:Jupyter Notebook
mdbinger / Credit_Risk_Analysis
Built, trained and evaluated multiple supervised machine learning algorithms to predict credit risk for loan applicants. Algorithms ran include Random Oversampler, SMOTE, Cluster Centroids, SMOTEENN, Balanced Random Forest Classifier, and Easy Ensemble Classifier.
clustering-algorithm easy-ensemble-classifier machine-learning random-forest smote smoteenn supervised-machine-learning
Language:Jupyter Notebook
tyedem / Risky_Business
Credit Risk Analysis utilizing imbalanced classification machine learning models
classification cluster-centroids-undersampling easy-ensemble-classifier ensemble-learning finance imbalanced-learn logistic-regression machine-learning random-forest resampling risk-analysis scikit-learn smote-oversampler smoteenn-combination
Language:Jupyter Notebook

easy-ensemble-classifier

Angienoelhaverly / Credit_Risk_Analysis

Baylex / Credit_Risk_Analysis

Akotovets1 / Credit_Risk_Analysis

annette-blackburn / Credit_Risk_Analysis

ashley-green1 / Credit_Risk_Analysis

BaileeRice / Credit_Risk_Analysis

BiancaTaisePommerening / Credit_Risk_Analysis-

cbrito3 / Credit_Risk_Analysis

cmmgw / Credit_Risk_Analysis

cmwardcode / Credit_Risk_Analysis

ed12rivera / Credit_Risk_Analysis

eric-blankinshp / Credit_Risk_Analysis_Supervised_ML

jharvey09 / solana_sentinment_analysis

LJD0 / Credit_Risk_Analysis

Mishkanian / Credit_Risk_Analysis

sarahm44 / credit-risk-predictor

shayanafzal / Credit_Risk_Analysis

showkatewang / Credit_Risk_Analysis

shumph10 / Credit_Risk_Analysis

tanmelissa / Credit_Risk_Analysis

YonathanTE / Machine-Learning

DL777 / Risky_Business

mdbinger / Credit_Risk_Analysis

tyedem / Risky_Business