data-imbalance

There are 0 repository under data-imbalance topic.

• carrierlxk / py-DSLT

  pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss

  data-imbalancedeep-regressionobject-trackingshrinkage-methods
  Language:Python 15

• carrierlxk / DSLT

  Deep Regression Tracking with Shrinkage Loss (ECCV 2018).

  data-imbalanceeccv-2018object-trackingregression-trackers
  Language:Jupyter Notebook 12

• cjymz886 / modeling-data-imbalance-with-different-losses

  compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance

  data-imbalancefocal-lossdice-losscross-entropy
  Language:Python 8

• rodolfomp123 / imb-mulan

  The Mulan Framework with Multi-Label Resampling Algorithms

  imbalanced-learningimbalanced-datamulti-label-classificationmultilabelmulti-labelmulan-frameworkmulanimb-mulanimbalanceness-mulanimbalancenessmultilabel-imbalancemulti-label-imbalancenessmulti-label-learningmulti-label-imbalanceimbalance-classificationdata-imbalancemulti-label-problem
  Language:HTML 5

• ywan3223 / ECG-Heartbeat-Classification

  ECG Arrhythmia Detection with ResNet and Transfer Learning

  data-imbalancedeeplearningecg-classificationgridsearchcvtransfer-learningtuningtuning-parametersrestnet18
  Language:Jupyter Notebook 4

• RimjimRazdan / HR-Analytics

  Submission for HR Analytics Hackathon - AnalysticsVidya.

  hackathonclassificationdata-imbalancedata-analysisanalytics-vidhya-competitionxgboost
  Language:Jupyter Notebook 3

• ishwarvenugopal / Imbalanced_Datasets

  The final project for the CE888: Data Science and Decision Making module (Spring Term) at the University of Essex

  data-imbalance
  Language:Jupyter Notebook 1

• TanyaChutani / Credit-Card-Fraud-Detection

  Applied undersampling and oversampling using SMOTE.

  smoteundersamplingoversamplingdata-imbalancecredit-card-fraud-detectionfraud-detectionmachine-learning
  Language:Jupyter Notebook 1

• testing-cs / vulnerability-detection

  software vulnerability detection

  data-imbalancedeep-learningsoftware-vulnerability-detection
  Language:Python 1

• Ayoubsandali / Customer-Churn-Prediction

  Customer Retention Analysis : Predict customer churn

  classificationdata-imbalanceensemble-modeling
  Language:Jupyter Notebook 0

• b1llywitant0 / NLP-Sentiment-Analysis

  A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4

  classification-modeldata-imbalancenlproc-aucdata-sciencemulticlass-classification
  Language:Jupyter Notebook 0

• bernatsort / heart_disease_prediction

  Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.

  catboost-classifierdata-imbalancedata-sciencedeep-learninghealthcare-dataheart-disease-classificationmachine-learningneural-networksrandom-forest-classifier
  Language:Jupyter Notebook 0

• mamta987 / Bank_Marketing_Campaign

  Predicting whether a client will subscribe for a term deposit after a bank marketing campaign

  classificationdata-imbalancelogistic-regressionmachine-learning
  0

• mamta987 / telecom-churn-case-study

  Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.

  adaboost-classifierchurn-predictiondata-imbalanceedalogistic-regressionrandom-forest
  Language:Jupyter Notebook 0

• rmakkar2018 / DetectAsana

  Classification of Body postures using different ML algorithms and comparing their performances.

  random-forest-classifierdecision-tree-classifierguassian-naive-bayesdata-imbalancesgd-classifierpython3
  Language:Jupyter Notebook 0

• sakusuma / TelecomChurn

  The project is based on Indian and Southeast Asian market where mostly prepaid payment model is prevelant In this project we will use the usage-based chrun definition i.e. customers who have not done any usage either incoming or outgoing in terms of calls, internet etc. over a period of time. We focus only the High Value customers, as typically 80% of the revenue comes from top 20% of the customers The dataset spans data of four consecutive months between June - September. The objective is to predict the churn in the last month using the data from the first three months. There are typically three phases of a customer lifecycle - (a) Good Phase where the customer is happy with services. We have assumed month 6 and 7 as Good Phase period here.(b) Action Phase where customer experience starts to sore. We have assumed the 3rd month i.e. month 8 here for this (c) Churn Phase where the customer is said to have churned. This is equivalent to the month 9 here.

  data-imbalancedecision-treesedalogistic-regression-algorithmpcarandom-forestsmote
  Language:Jupyter Notebook 0

• Sharanya-Hegde / Credit-EDA-Assignment

  This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.

  binningdata-imbalanceexploratory-data-analysismatplotliboutliers-detectionpythonseaborn
  Language:Jupyter Notebook 0

• arnohe / dermoscopic-attribute-detection

  Detection of dermoscopic structures for melanoma diagonsis

  data-imbalanceimage-segmentationmachine-learningmelanoma-detection
  Language:Python

• ChenghaoMou / pytorch-dice-loss

  Dice loss for data-imbalanced NLP tasks

  data-imbalancenlplossnatural-language-processing
  Language:Python