Topic: balanced-random-forest Goto Github
Some thing interesting about balanced-random-forest
Some thing interesting about balanced-random-forest
balanced-random-forest,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.
User: abidor13
balanced-random-forest,Supervised Machine Learning and Credit Risk
User: akotovets1
balanced-random-forest,Supervised Machine Learning Project
User: annette-blackburn
balanced-random-forest,using machine learning to assess credit risk
User: baileerice
balanced-random-forest,Supervised Machine Learning and Credit Risk
User: baylex
balanced-random-forest,Supervised Machine Learning
User: biancataisepommerening
balanced-random-forest,Supervised Machine Learning and Credit Risk
User: cbrito3
balanced-random-forest,Build and evaluate several machine learning algorithms to predict credit risk.
User: cedoula
balanced-random-forest,
User: cmmgw
balanced-random-forest,Banking-Dataset-Marketing-Targets
User: daiphuongngo
balanced-random-forest,Evaluate the performance of multiple machine learning models using sampling and ensemble techniques and making a recommendation on whether they should be used to predict credit risk.
User: deving789
balanced-random-forest,Build and evaluate several machine learning algorithms to predict credit risk.
User: dsupps
balanced-random-forest,Build and evaluate several machine learning algorithms to predict credit risk.
User: dw251414
balanced-random-forest,Extract data provided by lending club, and transform it to be useable by predictive models.
User: ed12rivera
balanced-random-forest,Built and evaluated several machine learning algorithms to predict credit risk.
User: enj657
balanced-random-forest,About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics
User: eric-blankinshp
balanced-random-forest,Use scikit-learn and imbalanced-learn machine learning libraries to assess credit card risk.
User: inregards2pluto
balanced-random-forest,Predicting Credit Risk by Using Several Machine Learning algorithms
User: jamesmoonusa
balanced-random-forest,Testing various supervised machine learning models to predict a loan applicant's credit risk.
User: jbalooshie
balanced-random-forest,Testing 6 different machine learning models to determine which is best at predicting credit risk.
User: kenner82
balanced-random-forest,This repo contains code that looks into LendingClub's membership data and employs ML to see if the model can predict a user's "credit risk" based on lending.
User: lawrencegoodwyn
balanced-random-forest,Analysis of different machine learning models' performance on predicting credit default
User: ljd0
balanced-random-forest,Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results.
User: mishkanian
balanced-random-forest,Build and evaluate several machine learning algorithms to predict credit risk
User: nedaaj
balanced-random-forest,Built and evaluated variety of supervised machine learning algorithms to predict credit risk.
User: nhafer88
balanced-random-forest,Insurance claim fraud detection using machine learning algorithms.
User: nirab25
balanced-random-forest,Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
User: nusratnimme
balanced-random-forest,Using Supervised Machine Learning algorithms to identify credit risks
User: peteresis
balanced-random-forest,Determine supervised machine learning model that can accurately predict credit risk using python's sklearn library. Python, Pandas, imbalanced-learn, skikit-learn
User: ramya-ramamur
balanced-random-forest,Uses several machine learning models to predict credit risk.
User: sarahm44
balanced-random-forest,This is a very Important part of Data Science Case Study because Detecting Frauds and Analyzing their Behaviours and finding reasons behind them is one of the prime responsibilities of a Data Scientist. This is the Branch which comes under Anamoly Detection.
User: sharmaroshan
balanced-random-forest,Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.
User: shayanafzal
balanced-random-forest,Predicts credit risk of individuals based on information within their application utilizing supervised machine learning models
User: showkatewang
balanced-random-forest,Resampling exercise to predict accuracy, precision, and sensitivity in credit-loan risk
User: tracari
balanced-random-forest,Train and test multiple Machine Learning models to predict risk based on consumer credit profiles.
User: zerodarkhardy
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