In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.

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

Predict if a transaction is a fraud transaction or not, also, dealing with unbalanced data and finding the pattern using correlation between the features.

Expresso Churn Prediction Challenge - dealing with imbalanced dataset

Detect potential frauds so that customers are not wrongly charged for items that they did not purchase.

Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.

Spam messages detection model

It's Technocolabs Software Data Scientist Internship Project (1st Dec 2021 - 15th Jan 2022). In this project the team was instructed to analysis big data of Spotify users and to perform Statistical and Exploratory Data Analysis and Model Development for Predicting Listener Behavior.

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

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.

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

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

Building a machine learning model to check bank frauds

Credit card fraud detection

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

Predicting Credit Risk with undersampling, oversampling, and ensemble methods across various machine learning algorithms.

Under-sampling based consensus clustering is applied on the three best clustering algorithms found after applying several Clustering Algorithms like K-means, K-modes, K-prototypes , K-means++ and fuzzy K-means on the majority class data of the IMBALANCED colon dataset to produce a BALANCED dataset.

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

The project was intended to detect fraudulent transactions from a highly imbalanced dataset.To solve the imbalance dataset problem random undersampling techniques were used.

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.