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Topic: "random-over-sampling"

shalakasaraogi/credit-card-fraud-detection

Credit card fraud detection using machine learning techniques

Language: R - Size: 3.29 MB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0

Tek-nr/AI-Based-Fraud-Detection

A fraud detection project that processes user or credit card data using machine learning and deep learning algorithms.

Language: Jupyter Notebook - Size: 18 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

AJMnd/Credit_Risk_Analysis

An analysis on credit risk

Language: Jupyter Notebook - Size: 192 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

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

Credit Card Fraud Detection using Python and Machine Learning.

Language: Jupyter Notebook - Size: 77.5 MB - Last synced at: about 2 months ago - Pushed at: over 3 years ago - Stars: 1 - Forks: 0

sharmasapna/credit-card-fraud-detection

Code to detect credit card fraud detecton

Language: Jupyter Notebook - Size: 3.05 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 1

AnvithaChaluvadi/Credit-Risk_Module12Challenge

I will use various techniques to train and evaluate models with imbalanced classes.

Language: Jupyter Notebook - Size: 765 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 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

Language: Jupyter Notebook - Size: 10.1 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

LJD0/Credit_Risk_Analysis

Analysis of different machine learning models' performance on predicting credit default

Language: Jupyter Notebook - Size: 18.4 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

tmard/Credit-Risk-Classification

Supervised machine learning to train and evaluate models based on loan risk.

Language: Jupyter Notebook - Size: 720 KB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

fredec96/Credit_Risk_Resampling

Uses several machine learning models to identify loan applicants likely to default on payments

Language: Jupyter Notebook - Size: 783 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

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

Language: Jupyter Notebook - Size: 914 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

Akotovets1/Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

Language: Jupyter Notebook - Size: 216 KB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

Baylex/Credit_Risk_Analysis

Supervised Machine Learning and Credit Risk

Language: Jupyter Notebook - Size: 20.8 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 11

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.

Language: Jupyter Notebook - Size: 18.5 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 0

Related Topics
logistic-regression 8 machine-learning 6 python 6 scikit-learn 5 random-under-sampling 4 imbalanced-learning 4 supervised-machine-learning 4 confusion-matrix 4 smote 4 pandas 4 smote-sampling 3 imblearn 3 numpy 3 smoteenn 3 easy-ensemble-classifier 3 balanced-random-forest 3 sklearn 2 classification-report 2 credit-card 2 matplotlib-pyplot 2 seaborn 2 fraud-detection 2 adaboost 2 decision-trees 2 cluster-centroids-algorithm 2 random-forest 2 smote-algorithm 2 tomek-link-elimination 1 credit-risk 1 heatmap 1 extra-trees-classifier 1 ensemble 1 accuracy-score 1 value-count 1 pca-analysis 1 module-12-supervised-learning 1 creditrisk-analysis 1 credit 1 analysis 1 deep-learning 1 accuracy 1 support-vector-machines 1 stratified-cross-validation 1 quantile-transformer 1 pca 1 knn-classifier 1 oversampling 1 label-encoder 1 matplotlib 1 one-hot-encoding 1 random-forest-classifier 1 machine-learning-algorithms 1 sklearn-library 1 standardscaler 1 artificial-intelligence 1 creditcard 1 autoencoders 1 credit-cards 1 dnn 1 fraud-transactions 1 fraudulent-transactions 1 isolation-forest 1 k-means-clustering 1 outlier-detection 1 recall-precision 1 transaction-numbers 1 tsne-plot 1 tsne-visualization 1 xgboost 1 classification-models 1 cluster-centroids-undersampling 1 loan-applications 1 predicting-risk 1 smoteen 1 balanced-accuracy-scores 1 train-test-split 1 balancedrandomforestclassifier 1 clustercentroids 1 easyensembleclassifier 1 get-dummies 1 cluster-centroid-undersampling 1 classification 1 ros 1 synthetic-minority-over-sampling 1 scikitlearn-machine-learning 1 adasyn 1