Topic: cluster-centroids Goto Github
Some thing interesting about cluster-centroids
Some thing interesting about cluster-centroids
cluster-centroids,Columbia FinTech Boot Camp Homework - Programs to utilize resampling and ensemble machine learning models to predict credit risk for retail loans.
User: bwacker1
cluster-centroids,The purpose of this script is to predict credit risk by employing different techniques to train and evaluate models with unbalanced classes
User: carolinacraus
cluster-centroids,Build and evaluate several machine learning algorithms to predict credit risk.
User: cedoula
cluster-centroids,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.
User: devinaa1604
cluster-centroids,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.
User: dl777
cluster-centroids,Build and evaluate several machine learning algorithms to predict credit risk.
User: dsupps
cluster-centroids,Built and evaluated several machine learning algorithms to predict credit risk.
User: enj657
cluster-centroids,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.
User: fischlerben
cluster-centroids,Simulation of K-means Clustering algorithm using P5.JS
User: kaustubholpadkar
cluster-centroids,Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk
User: minut9
cluster-centroids,Build and evaluate several machine learning algorithms to predict credit risk
User: nedaaj
cluster-centroids,Built and evaluated variety of supervised machine learning algorithms to predict credit risk.
User: nhafer88
cluster-centroids,Uses several machine learning models to predict credit risk.
User: sarahm44
cluster-centroids,Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the performance of these models and made a recommendation on whether they should be used to predict credit risk.
User: shaunwang1350
cluster-centroids,Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.
User: shayanafzal
cluster-centroids,Analysis and preprocessing of the kdd cup 99 dataset using python and scikit-learn
User: timeamagyar
cluster-centroids,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)
User: vaitybharati
cluster-centroids,This repo is about Machine Learning and Classification
User: vmieres
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