GitHub topics: under-sampling
Estaban65/transaction-fraud-detection
Machine Learning pipeline for financial transaction fraud detection. Incorporates SMOTE, ensemble models, neural networks.
Size: 1000 Bytes - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

sushant1827/Credit-Card-Fraud-Detection
Demonstrates the use of ML for Anomaly Detection for Credit Card Transactions: Identifying Fraudulent Activity using Imbalanced Data
Language: Jupyter Notebook - Size: 11.9 MB - Last synced at: 2 months ago - Pushed at: 6 months ago - Stars: 2 - Forks: 0

AK-qr/Multi-Label-Classification
Multi-label classification project
Language: Jupyter Notebook - Size: 4.62 MB - Last synced at: 2 months ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 0

shwetajoshi601/yeast-multilabel-classifier
Multi-label classification approaches on the Yeast dataset
Language: Jupyter Notebook - Size: 919 KB - Last synced at: 2 months ago - Pushed at: about 5 years ago - Stars: 11 - Forks: 5

MylieMudaliyar/Credit-Card-Fraud-Detection
Credit Fraud Detection of a highly imbalanced dataset of 280k transactions. Multiple ML algorithms(LogisticReg, ShallowNeuralNetwork, RandomForest, SVM, GradientBoosting) are compared for prediction purposes.
Language: Jupyter Notebook - Size: 305 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

sharmaroshan/Fraud-Detection-in-Online-Transactions
Detecting Frauds in Online Transactions using Anamoly Detection Techniques Such as Over Sampling and Under-Sampling as the ratio of Frauds is less than 0.00005 thus, simply applying Classification Algorithm may result in Overfitting
Language: Jupyter Notebook - Size: 300 KB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 56 - Forks: 29

hanfei1986/Undersampling-of-imbalanced-data-with-RandomUnderSampler-and-others
Imbalanced data commonly exist in real world, especially in anomaly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. RandomUnderSampler, ClusterCentroids, CondensedNearestNeighbour, and etc. are useful undersampling tools to remove data for majority classes.
Language: Jupyter Notebook - Size: 3.9 MB - Last synced at: almost 2 years ago - Pushed at: almost 2 years ago - Stars: 0 - Forks: 0

swapnita-pandey/Credit-Card-Fraud-Detection
Credit Card Fraud Detection Using Machine Learning
Language: Jupyter Notebook - Size: 14.6 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 1 - Forks: 0

ankit-kothari/Credit-Risk-Analysis
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
Language: Jupyter Notebook - Size: 768 KB - Last synced at: over 2 years ago - Pushed at: almost 5 years ago - Stars: 4 - Forks: 4

cbrito3/Credit_Risk_Analysis
Supervised Machine Learning and Credit Risk
Language: Jupyter Notebook - Size: 986 KB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

schatzederwelt/novosib-rzd
Автоматический классификатор объектов железнодорожного транспорта
Language: Jupyter Notebook - Size: 2.71 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

abhiram-ds/credit_card_fraud_detection
Credit Card Fraud detection based on anonymized data using multiple classification algorithms
Language: Jupyter Notebook - Size: 1.73 MB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 1 - Forks: 0

jabhinav/Data-Science-and-ML-for-Structured-Data-Classification
Repo contains scripts to perform data analysis on structure data. It also provides a comparison of various ML algorithms at different stages of data preparation.
Language: Jupyter Notebook - Size: 522 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 0
