imbalanced-classes

⚡ Analisis sentimen machine learning based dan ketidakseimbangan kelas pada dataset

Company Bankruptcy Prediction with Naïve Bayes Algorithm

The main objective is to build a predictive model that predicts whether a new client will subscribe to a term deposit or not, based on data from previous marketing campaigns.

Image classification pipeline relying on Deep Learning models dealing with training over multiple datasets with unbalanced labels and classes. In addition, also classification using histogram data was done for better generalization over different datasets.

With imbalanced observed data, a search for the best model is conducted. The bank is seeing its customers leave. Wondering if there are patterns to their decision to exit, the bank wishes to anticipate for this trend. When the positive class is the minority in an imbalanced dataset, a model need to be trained for robustness.

Evaluating ensemble performance in long-tailed datasets (Neurips 2023 Heavy Tails Workshop)

Google Developer Group Ahmedabad - Machine Learning for Imbalanced Class Distributions Session code

Deployment of a classification model on a webapp using FLASK for the backend and html/CSS/JS for frontend

End-to-end machine learning pipeline for customer churn prediction on imbalanced data. Includes ADASYN oversampling, DVC for version control, and MLflow for experiment tracking and model management.

"Machine learning in banking - predicting lead conversion for a lending product" - my final project from the Data Science bootcamp organized by "Kodołamacz". Article about the project: https://www.kodolamacz.pl/blog/2024-01-04-uczenie-maszynowe-w-bankowosci

Official implementation of "PanopMamba: Vision State Space Modeling for Nuclei Panoptic Segmentation".

This is an interesting problem in flagging credit risk when all variables are dimensionally reduced

This repo contains various techniques to handle imbalanced classes

Spatio-temporal Urban Change Mapping with Time-Series SAR data

Imbalanced Data Visualization and Random Forest

Multi-View LEArning-based data Proliferator (MV-LEAP) for boosting classification using highly imbalanced classes.

Developed machine learning models that can help bank tellers to predict telemarketing campaign response from clients. Analyzed precision-recall trade off, customized for models for different business scenarios

⚖️ Imbalance-degree measure implemented in python.

A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

NLP based Classification Model that predicts a person's personality type as one of the 16 Myers Briggs personality types. Extremely challenging project dealing with correlation between human psychology and casual writing styles and handling heavily imbalanced classes. Check the app here - https://mb-predictor-motetuzs5q-uc.a.run.app/