adasyn-sampling

There are 0 repository under adasyn-sampling topic.

rohitdwivedula / enzyme-classification
Predict the enzyme class of a given FASTA sequence using deep learning methods including CNNs, LSTM, BiLSTM, GRU, and attention models along with a host of other ML methods.
enzyme-classification bioinformatics proteins smote-sampling adasyn-sampling machine-learning deep-learning neural-networks
Language:Python 10
MoinDalvs / Assignment_Decision_Tree_2
adasyn-sampling data-science decision-trees imbalanced-data smote-oversampler smoteenn smotetomek
Language:Jupyter Notebook 2
MoinDalvs / Assignment_SVM_Forest_Fire_Prediction
adasyn-sampling auc-roc-curve data-science hyperparameter-tuning kernel-trick regularization smote-sampling smoteenn smotetomek svm-classifier svm-kernel
Language:Jupyter Notebook 2
sailyshah / Credit-card-fraud-detection
Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.
credit-card-fraud hyperparameter-tuning oversampling-technique undersampling-technique adasyn-sampling smote-sampling optimal-threshold roc-curve
Language:Jupyter Notebook 1
at-akshat-2107 / Credit-Card-Fraud-Detection-Capstone-Project-EPGP
In this Upgrad/IIIT-B Capstone project, we navigated the complex landscape of credit card fraud, employing advanced machine learning techniques to bolster banks against financial losses. With a focus on precision, we predicted fraudulent credit card transactions by analyzing customer-level data from Worldline and the Machine Learning Group.
adasyn adasyn-sampling credit-risk creditcardfraud creditcardfrauddetection decision-trees knn-classification logistic-regression machine-learning machine-learning-algorithms smote smote-sampling xgboost xgboost-algorithm
Language:Jupyter Notebook 0
iliodipietro / Wine_Quality_Prediction
thesis for Mathematics in Machine Learning course at @Politecnico di Torino.
logistic-regression principal-component-analysis svm-classifier kfold-cross-validation wine-quality-prediction bootstrap adasyn-sampling regression linear-regression polynomial polynomial-regression ridge-regression lasso-regression
Language:Jupyter Notebook 0
ggjay9 / Application-Flow-Identification
Classify applications using flow features with Random Forest and K-Nearest Neighbor classifiers. Explore augmentation techniques like oversampling, SMOTE, BorderlineSMOTE, and ADASYN for better handling of underrepresented classes. Measure classifier effectiveness for different sampling techniques using accuracy, precision, recall, and F1-score.
adasyn-sampling application-identification knn machine-learning network-analysis random-forest sampling-methods smote-sampling xai-evaluation
Language:Jupyter Notebook
liyongh1 / Telemarketing-Prediction-with-Resampling-and-ML
Develop Machine Learning Models to Predict the UCI Bank Telemarketing Dataset
machine-learning smote-sampling adasyn-sampling smotetomek smoteenn
Language:Jupyter Notebook