random-over-sampling

There are 0 repository under random-over-sampling topic.

shalakasaraogi / credit-card-fraud-detection
Credit card fraud detection using machine learning techniques
fraud-detection credit-card smote ros random-over-sampling random-under-sampling synthetic-minority-over-sampling
Language:R 2
AJMnd / Credit_Risk_Analysis
An analysis on credit risk
balancedrandomforestclassifier clustercentroids easyensembleclassifier get-dummies imbalanced-learning imblearn logistic-regression python random-over-sampling scikit-learn smote smoteenn supervised-machine-learning
Language:Jupyter Notebook 1
AvinandanBose / Credit-Card-Fraud-Detection-Machine-Learning-
Credit Card Fraud Detection using Python and Machine Learning.
machine-learning machine-learning-algorithms credit-card creditcard artificial-intelligence logistic-regression oversampling smote-sampling pca tomek-link-elimination support-vector-machines decision-trees random-over-sampling random-under-sampling stratified-cross-validation quantile-transformer python seaborn matplotlib matplotlib-pyplot
Language:Jupyter Notebook 1
Baylex / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
machine-learning imbalanced-learning scikit-learn random-over-sampling smote-algorithm cluster-centroids-algorithm smoteenn balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 1
sharmasapna / credit-card-fraud-detection
Code to detect credit card fraud detecton
fraud-transactions fraudulent-transactions credit-cards smote-sampling random-over-sampling random-under-sampling decision-trees logistic-regression xgboost random-forest isolation-forest k-means-clustering tsne-visualization tsne-plot outlier-detection autoencoders recall-precision accuracy transaction-numbers dnn
Language:Jupyter Notebook 1
Tek-nr / AI-Based-Fraud-Detection
A fraud detection project that processes user or credit card data using machine learning and deep learning algorithms.
adasyn deep-learning fraud-detection machine-learning random-over-sampling random-under-sampling smote
Language:Jupyter Notebook 1
Akotovets1 / Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
adaboost balanced-random-forest cluster-centroids-algorithm confusion-matrix easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikitlearn-machine-learning smote-algorithm supervised-machine-learning
Language:Jupyter Notebook 0
AnvithaChaluvadi / Credit-Risk_Module12Challenge
I will use various techniques to train and evaluate models with imbalanced classes.
credit creditrisk-analysis module-12-supervised-learning analysis logistic-regression random-over-sampling value-count
Language:Jupyter Notebook 0
ashley-green1 / Credit_Risk_Analysis
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.
machine-learning supervised-machine-learning confusion-matrix logistic-regression random-forest easy-ensemble-classifier smote-oversampler random-forest-classifier smoteenn cluster-centroids-undersampling random-over-sampling sklearn-library train-test-using-sklearn train-test-split encoding binary-encoding feature-importance
Language:Jupyter Notebook 0
BiancaTaisePommerening / Credit_Risk_Analysis-
Supervised Machine Learning
balanced-random-forest cluster-centroids-undersampling easy-ensemble-classifier imbalanced-learning machine-learning random-over-sampling scikit-learn smote smoteenn-combination pandas-dataframe python3
Language:Jupyter Notebook 0
fredec96 / Credit_Risk_Resampling
Uses several machine learning models to identify loan applicants likely to default on payments
classification machine-learning pandas python random-over-sampling sklearn
Language:Jupyter Notebook 0
hasanzeynal / Resampling-Techniques-For-Imbalance-Problems
The Repository is created to cover undersampling and oversampling methods to deal imbalance problem.
adasyn cnn enn random-over-sampling smote smoteenn smotetomek tomek-link
Language:Jupyter Notebook 0
LJD0 / Credit_Risk_Analysis
Analysis of different machine learning models' performance on predicting credit default
balanced-random-forest cluster-centroid-undersampling easy-ensemble-classifier machine-learning numpy pandas python random-over-sampling smote smoteenn
Language:Jupyter Notebook 0
NataliaVelasquez18 / credit-risk
The purpose of this study is to recommend whether PureLending should use machine learning to predict credit risk. Several machine learning models are built employing different techniques, then they are compared and analyzed to provide the recommendation.
loan-applications scikit-learn predicting-risk random-forest adaboost logistic-regression random-over-sampling cluster-centroids-undersampling supervised-machine-learning smoteen smote-sampling classification-models
Language:Jupyter Notebook 0
Credit_Risk_Analysis
shayanafzal / Credit_Risk_Analysis
Using machine learning to train and evaluate models with unbalanced classes to determine the best models to predict credit risk.
cluster-centroids random-over-sampling smote balancedrandomforestclassifier balanced-random-forest easy-ensemble-classifier
Language:Jupyter Notebook 0
Shipra-09 / Project-Vehicle-Insurance
This Github repository contains cross selling of health insurance customers on vehicle insurance product. We have to predict whether a customer would be interested in Vehicle Insurance or not by building a ML model. Exploring Insights/Inferences by performing EDA on the given project data. Finding the high accuracy
accuracy-score confusion-matrix ensemble extra-trees-classifier heatmap imblearn knn-classifier label-encoder logistic-regression matplotlib-pyplot numpy one-hot-encoding pandas random-forest-classifier random-over-sampling seaborn sklearn-library standardscaler
Language:Jupyter Notebook 0
tmard / Credit-Risk-Classification
Supervised machine learning to train and evaluate models based on loan risk.
classification-report confusion-matrix credit-risk logistic-regression pca-analysis python random-over-sampling scikit-learn supervised-machine-learning
Language:Jupyter Notebook 0
imtej / CC_Fraud-Detection-Project-for-AFAME-TECHNOLOGIES
Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.
adasyn-sampling classification-model decision-trees isolation-forest knn local-outlier-factor logistic-regression machine-learning random-forest random-over-sampling smote-oversampler svm xgboost
Language:Jupyter Notebook
shayleaschreurs / Supervised_Learning_Regression_Model
Module 12 - Using the imblearn , I'll use a logistic regression model to compare 2 versions of a dataset. First, I’ll use the original data. Next, I’ll resample the data by using RandomOverSampler. In both cases, I’ll get the count of the target classes, train a logistic regression classifier, calculate the balanced accuracy score, generate a con
balanced-accuracy-scores classification-report confusion-matrix imbalanced-learning imblearn logistic-regression numpy pandas python random-over-sampling scikit-learn sklearn train-test-split
Language:Jupyter Notebook

random-over-sampling

shalakasaraogi / credit-card-fraud-detection

AJMnd / Credit_Risk_Analysis

AvinandanBose / Credit-Card-Fraud-Detection-Machine-Learning-

Baylex / Credit_Risk_Analysis

sharmasapna / credit-card-fraud-detection

Tek-nr / AI-Based-Fraud-Detection

Akotovets1 / Credit_Risk_Analysis

AnvithaChaluvadi / Credit-Risk_Module12Challenge

ashley-green1 / Credit_Risk_Analysis

BiancaTaisePommerening / Credit_Risk_Analysis-

fredec96 / Credit_Risk_Resampling

hasanzeynal / Resampling-Techniques-For-Imbalance-Problems

LJD0 / Credit_Risk_Analysis

NataliaVelasquez18 / credit-risk

shayanafzal / Credit_Risk_Analysis

Shipra-09 / Project-Vehicle-Insurance

tmard / Credit-Risk-Classification

imtej / CC_Fraud-Detection-Project-for-AFAME-TECHNOLOGIES

shayleaschreurs / Supervised_Learning_Regression_Model