clustercentroids
There are 22 repositories under clustercentroids topic.
Robertfnicholson/Credit_Risk_Analysis
Developed Machine Learning Models to Predict Credit Risk
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).
AJMnd/Credit_Risk_Analysis
An analysis on credit risk
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.
AbdullahBera/Credit_Risk_Analysis
Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
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.
acfthomson/Credit_Risk_Analysis
Data analysts were asked to examine credit card data from peer-to-peer lending services company LendingClub in order to determine credit risk. Supervised machine learning was employed to find out which model would perform the best against an unbalanced dataset. Data analysts trained and evaluated several models to predict credit risk.
caseygomez/Credit_Risk_Analysis
Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
diercz/Credit_Risk_Analysis
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
dw251414/Credit_Risk_Analysis
Build and evaluate several machine learning algorithms to predict credit risk.
DylanSteinhauer/Credit_Risk_Analysis
Train and evaluate models to determine credit card risk using a credit card dataset
jbalooshie/Credit_Risk_Analysis
Testing various supervised machine learning models to predict a loan applicant's credit risk.
JeffZimmerman/Credit_Risk_Analysis
This project applies supervised machine learning models to predict credit risk, and compare algorithm effectiveness in an unbalanced classification problem
JennyJohnson78/Credit_Risk_Analysis
Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.
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.
nicoserrano/Credit_Risk_Analysis
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
sjwedlund/Credit_Risk_Analysis
Apply machine learning to solve the challenge of credit risk
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.
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.
weihaolun/Credit_Risk_Analysis
Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.
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)
willenny/Credit_Risk_Analysis
Using skills in data preparation, statistical reasoning, and machine learning, real-world challenges of credit card risk are assessed and solved.