This project simulates a M.L system tha aproves or not loans to determinate bank. Working with imbalanced database some resources can be apllied for mitigate erros and prevent money loss.

Toxicity detection on imbalanced social media data. Focused on 2 main topics of toxicity detection: Class imbalance problem and detecting toxic comments.

This repository contains files on Predict probability whether a given blight ticket will be paid

Advanced Machine Learning

Predictive Modeling of Credit Risk Faced by a P2P lending platform

Cerebral stroke, a critical condition, demands vigilant analysis. Machine learning models, coupled with resampling techniques like SMOTEENN, enhance stroke prediction accuracy by addressing imbalanced datasets.

Prediction of occurrences of a bee species in the Iberian Peninsula 🐝

🔍 Evaluating Sampling Techniques for Healthcare Insurance Fraud Detection in Imbalanced Dataset.

Binary classification of lumpy skin disease (imbalanced dataset) using ML algorithms in addition to oversampling/undersampling techniques.

Building a machine learning model to check bank frauds

The aim of the project is to create a robust machine learning model that predicts the likelihood for a bank's customers to fail on their credit payments for the next month. The dataset used contains information on 24028 customers across 26 variables that includes information regarding whether customer defaulted, credit limits, bill history etc.

Statistical analysis in R of a heart disease dataset by using logistic regression and random forest.

Beginner friendly project focusing on dataset imbalances using the oversampling and under sampling techniques

In this project, I worked on a classification problem using an imbalanced dataset which predicts ecological footprints. The aim of the project was not necessarily to build a classification model but to investigate the different methods of correcting an imbalanced dataset in order not to build a biased classifier

Credit card fraud detection

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.

This project is based on supervised machine learning where you will be predicting whether a credit card transaction is original transaction or fraud transaction based on various parameters. This is a classification problem.

ML Project ,XGboost .Logistic Regression as classification,Decision Tree & balancing technique Undersampling & SMOTE.