GitHub topics: easyensembleclassifier

Repositories

sjwedlund/Credit_Risk_Analysis

Apply machine learning to solve the challenge of credit risk

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caseygomez/Credit_Risk_Analysis

Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.

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weihaolun/Credit_Risk_Analysis

Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.

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Ekanpat/Credit_Risk_Analysis

Analyzing credit card risk with machine learning models!

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ashwinihegde28/Credit_Risk_Analysis

Credit_Risk_Analysis using Machine Learning

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jbalooshie/Credit_Risk_Analysis

Testing various supervised machine learning models to predict a loan applicant's credit risk.

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lingumd/Credit_Risk_Analysis

Machine learning models for predicting credit risk in LendingClub dataset.

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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.

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Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,

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daiphuongngo/Banking-Dataset-Imbalanced-Learn-Comparison

Banking-Dataset-Marketing-Targets

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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).

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diercz/Credit_Risk_Analysis

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

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SohrabRezaei/Credit-Risk-Analysis

I am asked to resample the credit card data since it is not balanced. First, I start to split the data and perform oversampling with RandomOverSampler and SMOTE method, and I undersample with ClusterCentroids algorithm. Then, I utilize the SMOTEENN method to oversample and undersample the data. Finally, I used ensemble models.

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