Imbalanced 2-class classification project for a predictive modeling competition (13th best ranked team out of a 104)

Codebase for "Learn Bayesian Logistic regression from imbalanced data" post.

A python class for making machine learning algorithms cost sensitive.

ML classification on loan data to predict whether loan will default or not.

Experiments with imbalanced data using undersampling and oversampling techniques.

To predict whether a given blight ticket will be paid on time

Analytics Vidhya Hackathon to predict whether the policyholder will file a claim in the next 6 months or not.

This project uses data from Olist - a Brazilian e-commerce platform to predict customer's review scores. The full Python code is presented in 3 steps: data preprocessing, EDA & modeling, followed by a Tableau Dashboard on customer ratings.

Classification models to predict patient length of stay, a multi-class categorical target with imbalanced predictor features. Optimized models include multi-class and voting ensembles.

In this project, I have take bank target marketing dataset with imbalanced classes, I have solved it through GAIN&LIFT chart as well as over-sampling method of sklearn utils.

Credit card detection fraud using fastai.

New algorithms are designed for imbalanced classification

⚖⚡ Experimental evaluation of ensemble classifiers for imbalance in Big Data.

A model that will predict whether a customer will leave the bank

Loan Binary Classification Task

Harmfulness recognition in Polish language tweets.

Task 1: Dealing with imbalanced dataset and implementing classification models | Task 2: Clustering

This project implements a machine learning model using Random Forest, XGBoost, and Support Vector Machines algorithms with oversampling and undersampling techniques to handle imbalanced classes for classification tasks in the context of predicting the rarity of monsters.