Topic: smoteenn Goto Github
Some thing interesting about smoteenn
Some thing interesting about smoteenn
smoteenn,Using Resampling and Ensemble Learning to look at data and predict default rates on loans.
User: aarongalloway
smoteenn,Built several supervised machine learning models to predict the credit risk of candidates seeking loans.
User: abdullahbera
smoteenn,An analysis on credit risk
User: ajmnd
smoteenn,Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.
User: angienoelhaverly
smoteenn,Using my skills in data preparation, statistical reasoning, and machine learning I employed different techniques to train and evaluate models with unbalanced classes.
User: annakthrnlee
smoteenn,A Comparative Study in Customer Churn Prediction through Multilayer Perceptrons and Support Vector Machines
User: anvesham
smoteenn,Analysis of a dataset using different techniques to train and evaluate models with unbalanced classes, aimed at reducing bias and predicting accurate credit risk.
User: aryanakh7
smoteenn,Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning
User: arzuisiktopbas
smoteenn,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.
User: ashley-green1
smoteenn,Machine learning for credit card default. Precision-recalls are calculated due to imbalanced data. Confusion matrices and test statistics are compared with each other based on Logit over and under-sampling methods, decision tree, SVM, ensemble learning using Random Forest, Ada Boost and Gradient Boosting. Easy Ensemble AdaBoost classifier appears to be the model of best fit for the given data.
User: ava33343
smoteenn,Supervised Machine Learning and Credit Risk
User: baylex
smoteenn,Columbia FinTech Boot Camp Homework - Programs to utilize resampling and ensemble machine learning models to predict credit risk for retail loans.
User: bwacker1
smoteenn,Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
User: caseygomez
smoteenn,Build and evaluate several machine learning algorithms to predict credit risk.
User: cedoula
smoteenn,Supervised machine learning model to analyze credit risk
User: cmwardcode
smoteenn,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
smoteenn,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
smoteenn,Continuing with telemarketing model to predict campaign subscriptions in a portuguese bank institution. For this project I have evaluated the performance of four resampling techniques and selected the best one to implement the logistic model.
User: domingosdeeulariadumba
smoteenn,We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.
User: douguot
smoteenn,Extract data provided by lending club, and transform it to be useable by predictive models.
User: ed12rivera
smoteenn,Built and evaluated several machine learning algorithms to predict credit risk.
User: enj657
smoteenn,Week 16 - Decision Trees
User: existentialplantperson
smoteenn,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
smoteenn,The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.
User: hasanzeynal
smoteenn,Build an End-to-End Data Science Project to predict customer churn for the Telecom industry and provide prescriptive countermeasures.
User: jayds22
smoteenn,Testing various supervised machine learning models to predict a loan applicant's credit risk.
User: jbalooshie
smoteenn,Employ different techniques to train and evaluate models with unbalanced classes. Evaluate the performance of these models and make recommendations on their suitability to predict credit risk.
User: jose-perth
smoteenn,Supervised Machine Learning project to predict credit risk
User: lilyhanhub
smoteenn,Develop Machine Learning Models to Predict the UCI Bank Telemarketing Dataset
User: liyongh1
smoteenn,Analysis of different machine learning models' performance on predicting credit default
User: ljd0
smoteenn,The objective of this analysis was to use machine learning models to accurately predict credit risk.
User: lsuantah
smoteenn,Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
User: mdabbous88
smoteenn,Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk
User: minut9
smoteenn,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.
User: miracleony
smoteenn,Build and evaluate several machine learning algorithms to predict credit risk
User: nedaaj
smoteenn,The purpose of this analysis was to create a supervised machine learning model that could accurately predict credit risk using machine learning.
User: nenukorah
smoteenn,Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
User: nicoserrano
smoteenn,Using Scikit-learn and Imbalanced-learn to build and evaluate ML models that predict credit risk
User: npantfoerder
smoteenn,Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
User: nusratnimme
smoteenn,Developed Machine Learning Models to Predict Credit Risk
User: robertfnicholson
smoteenn,Perform a Credit Risk Supervised Machin Learning Analysis using scikit-learn and imbalanced-learn libraries.
User: rtippana1
smoteenn,βBuy Now, Pay Laterβ (BNPL) is a type of short-term financing used by start-ups like Slice, ZestMoney, Simpl, LazyPay, and Uni, are lowering the bars while approving applications. Building models to detect such customers beforehand.
User: shaktipanda1235
smoteenn,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
User: shumph10
smoteenn,In this project, three prospective approaches are demonstrated for pre-processing large data sets in practical time-frames, that can attempt to address the class imbalance by improving the running time of the relevant SMOTE+ENN oversampling techniques, with the aim of improving or enabling classifier performance. The focus of our study was to implement a divide and conquer based implementation that leveraged clustering as a more intelligent division measure.
User: smjajoo
smoteenn,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).
User: stephperillo
smoteenn,Predicted credit risk using resampling models, SMOTEENN algorithm and Ensemble classifiers.
User: tekateka
smoteenn,Machine learning model to predict heart transplant failure and success using XGBoost algorithm and SMOTE/ENN to balance the dataset.
User: trevor-leach803
A declarative, efficient, and flexible JavaScript library for building user interfaces.
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. πππ
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google β€οΈ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.