Topic: randomoversampler Goto Github
Some thing interesting about randomoversampler
Some thing interesting about randomoversampler
randomoversampler,NTI-Final-Assignment Use flask(python) and shiny dashboard (R) to build simple user interface to see how choosing classification model may affect prediction accuracy, using Customer Churn Dataset.
User: abdoghareeb46
randomoversampler,Different Techniques to Handle Imbalanced Data Set
User: abhijha3011
randomoversampler,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.
User: acfthomson
randomoversampler,Python and sklearn are used to build and evaluate multiple machine learning models to predict credit risk.
User: alexgeiger1
randomoversampler,Predict Health Insurance Owners' who will be interested in Vehicle Insurance
User: anjalysam
randomoversampler,Credit_Risk_Analysis using Machine Learning
User: ashwinihegde28
randomoversampler,This repository holds the dataset and notebooks for the Amazon Books dataset 4 class Rating prediction
User: bhaskrr
randomoversampler,This repo contains the dataset and notebook for the kaggle restaurant reviews five class rating prediction
User: bhaskrr
randomoversampler,Credit Risk Classification
User: blleshi
randomoversampler,Utilizing data preparation, statistical reasoning, and supervised machine learning to solve a real-world challenge: credit card risk.
User: caseygomez
randomoversampler,Build and evaluate several machine learning algorithms to predict credit risk.
User: cedoula
randomoversampler,The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.
User: chaitanyac22
randomoversampler,
User: dangcoop
randomoversampler,Predicting customer sentiments from feedbacks for amazon. While exploring NLP and its fundamentals, I have executed many data preprocessing techniques. In this repository, I have implemented a bag of words using CountVectorizer class from sklearn. I have trained this vector using the LogisticRegression algorithm which gives approx 93% accuracy. I have found out the top 20 positive and negative feedback words from thousands how feedbacks. Also after processing this much I have automated the whole process with one function so that it can be used as generic for many machine learning algorithms. I have also tested another algorithm called DummyClassifier which gives an accuracy of around 84%. After that, I have executed the famous algorithm which is TF-IDF for NLP. I have combined TF-IDF with LogisticRegression which gives almost 93% accuracy but deep insights. Also, while working with data has solved the problem of imbalanced data through RandomOverSampler class from imblearn library.
User: dhrumil-zion
randomoversampler,Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
User: diercz
randomoversampler,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
randomoversampler,Build and evaluate several machine learning algorithms to predict credit risk.
User: dsupps
randomoversampler,Build and evaluate several machine learning algorithms to predict credit risk.
User: dw251414
randomoversampler,Train and evaluate models to determine credit card risk using a credit card dataset
User: dylansteinhauer
randomoversampler,Use different techniques to train and evaluate different machine learning models to predict credit risk with unbalanced classes
User: echoqshen
Home Page: https://echoqshen.github.io/Credit_Risk_Analysis/
randomoversampler,Built and evaluated several machine learning algorithms to predict credit risk.
User: enj657
randomoversampler,Credit Worthyness Analysis using Linear Regression
User: gsilvera24
randomoversampler,Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.
User: harmanveer-2546
randomoversampler,Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department’s prediction fails sometimes. In this project, I learn how to build a machine learning model which can predict whether there will be rainfall today or not based on some atmospheric factors.
User: harmanveer2546
randomoversampler,Using various techniques to train and evaluate a model based on loan risk. Also, using a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
User: helenaschatz
randomoversampler,Testing various supervised machine learning models to predict a loan applicant's credit risk.
User: jbalooshie
randomoversampler,Analysis using RandomOverSampler, SMOTE algorithm, ClusterCentroids algorithm, SMOTEENN algorithm, and machine learning models BalancedRandomForestClassifier and EasyEnsembleClassifier.
User: jennyjohnson78
randomoversampler,Machine learning models for predicting credit risk in LendingClub dataset.
User: lingumd
randomoversampler,To evaluate the performance of supervised machine learning models to make a written recommendation on whether they should be used to predict credit risk.
User: malvi1497
randomoversampler,Supervised Learning..Build/Evaluate Machine algorithms to predict credit risk
User: minut9
randomoversampler,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
randomoversampler,We used a dataset that included birth and personal data as well as Autism Spectrum Quotient test scores to train machine learning algorithms to predict autism. We used Logistic Regression, Neural Network Models and Keras Tuner with Random Oversampling to train one with 90% accuracy.
User: neonostrich
randomoversampler,Built and evaluated variety of supervised machine learning algorithms to predict credit risk.
User: nhafer88
randomoversampler,Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
User: nicoserrano
randomoversampler,Build and evaluate several machine learning algorithms by resampling models to predict credit risk.
User: nusratnimme
randomoversampler,Uses Logistic Regression and various machine learning techniques to train and evaluate models with imbalanced classes applied to identify the creditworthiness of borrowers.
User: paocarvajal1912
randomoversampler,Data Science Major Project Completed in IT Vedant Institute using Machine learning algorithms
User: priya2216
randomoversampler,
User: priya2216
randomoversampler,Identifying rare event.
User: priyankasett
randomoversampler,Developed Machine Learning Models to Predict Credit Risk
User: robertfnicholson
randomoversampler,Predict Health Insurance Owners who will be interested in Vehicle Insurance
User: rutujahingankar
randomoversampler,Prediction module for Tumor Teller - primary tumor prediction system
User: sanushi-salgado
randomoversampler,This project trains and avaluates machine learning model to identify creditworthiness of borrowers and classify credit risk predictions for a peer-to-peer lending services company.
User: saraparveen26
randomoversampler,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
randomoversampler,Apply machine learning to solve the challenge of credit risk
User: sjwedlund
randomoversampler,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.
User: sohrabrezaei
randomoversampler,Analyze several machine learning algorithms to predict credit risk.
User: tobi1018
randomoversampler,Logistic regression model with train_test_split data
User: twigikit
randomoversampler,Supervised Machine Learning Project: imbalanced-learn; scikit-learn; RandomOverSampler; SMOTE; ClusterCentroids; SMOTEENN; BalancedRandomForestClassifier; EasyEnsembleClassifier.
User: weihaolun
randomoversampler,Build and evaluate several machine learning algorithms to predict credit risk.
User: ybhuva
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