GitHub topics: clustercentroids
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.
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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.
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sjwedlund/Credit_Risk_Analysis
Apply machine learning to solve the challenge of credit risk
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RiteshopShrivastava/Hierarchical_Clustering
Unsupervised-ML---K-Means-Clustering-Non-Hierarchical-Clustering-Univ. Use Elbow Graph to find optimum number of clusters (K value) from K values range. The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS. Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)
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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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jbalooshie/Credit_Risk_Analysis
Testing various supervised machine learning models to predict a loan applicant's credit risk.
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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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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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JennyJohnson78/Credit_Risk_Analysis
Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.
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AJMnd/Credit_Risk_Analysis
An analysis on credit risk
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Malvi1497/Credit_Risk_Analysis
To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.
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AbdullahBera/Credit_Risk_Analysis
Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
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nicoserrano/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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