smote-oversampler

edaaydinea/OP2-Prediction-of-the-Different-Progressive-Levels-of-Alzheimer-s-Disease-with-MRI-data

This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.

DougUOT/Credit_Risk_Analysis

We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

MsTao-68/Debt-Churn-Data-Analysis

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

FaezehAbedi2023/Statistical-Analysis-in-Sensor-Data-Processing-with-Machine-Learning-Models

This project develops an activity recognition model for a mobile fitness app using statistical analysis and machine learning. By processing smartphone sensor data, it extracts features to train models that accurately recognize user activities.

Kaushikjas10/Liquefaction-XGBoost-SHAP-Jas-Dodagoudar

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

karl0706/Essay-quality-prediction

The goal is to create a model predicting the grade of an essay

MoinDalvs/Assignment_Decision_Tree_2

AloRay/CREDIT-CARD-FRAUD

This project focuses on building a fraud detection model for credit card transactions using a dataset containing transactions made by European cardholders in September 2013. We are working with a highly unbalanced dataset and the challenge lies in effectively detecting fraudulent transactions while minimizing false positives.

ChriStingo/HeartDisease-Analysis-and-Prediction

Data analysis, visualization and prediction for the prevention of heart disease using ML models

Hands-On-Fraud-Analytics/Chapter-12-Data-Preparation-for-Fraud-Analytics

Chapter 12: Data Preparation for Fraud Analytics

Kenner82/Credit_Risk_Analysis

Testing 6 different machine learning models to determine which is best at predicting credit risk.

mmsaki/credit-risks-ml

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

Sar-Go/Adult-Income-Prediction-AzureMLStudio

Future Ready Talent Project Submission.Using Azure ML Studio to predict the income of individuals, based on their age, race, education, residence city, etc. Used the adult census dataset

Sidessh/Multi-Class-Classification---License-Status-Prediction

Multi-class Classification - License Status Prediction

TrungLuong1194/breast-cancer-classification

utsavchaudharygithub/Credit_Risk_Analysis

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, we needed to employ different techniques to train and evaluate models with unbalanced classes. Jill asks us to use imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling

Zarich12/No_Show_Appointment_ML-Logistic-Regression-

Predicts if a patient will show up at a scheduled appointment based on certain features.

DavidNart90/KNUST-BreastCancer-Prediction

This repository contains the resources and codebase for a research project aimed at predicting breast cancer cases using data from the KNUST hospital.

DivyaSudagoni/Predicting-hotel-booking-cancellation

This project predicts hotel booking cancellations using Machine Learning techniques, benefiting both travelers and hotels.

FarhanaTeli/Kyphosis_Disease_Prediction_with_FCNN_XGBoost

Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV

Hands-On-Fraud-Analytics/Chapter-11-Handling-Imbalanced-Data-Sets

Handling Imbalanced Data Sets

Harsha2k3/predictive_maintenance_ML_Project

This project utilizes advanced data analysis and machine learning techniques to predict equipment failures before they occur. The goal is to detect anomalies and possible defects in equipment and processes to enable preemptive maintenance, thereby reducing downtime and costs.

imtej/CC_Fraud-Detection-Project-for-AFAME-TECHNOLOGIES

Credit Card Fraud Detection: An ML project on credit card fraud detection using various ML techniques to classify transactions as fraudulent or legitimate. This project involves data analysis, preparation, and use of models like Logistic regression, KNN, Decision Trees, Random Forest, XGBoost, and SVM, along with various oversampling technique.

Leohrithik/Titanic-Survival-Prediction

Survival prediction using Four Different kind of algorithms and optimizing the dataset using PCA and SMOTE

melodiw82/Rayan_AI

Vault of variety of topics taught for Rayan Contest

MinaShirinchi/AI-class-1402

Here is the repository for sharing jupyter notebooks discussed in 'AI-1402' class.

Mrudula1205/Chargeback-Fraud-Prediction

Chargeback Fraud Prediction with Machine Learning

namansnghl/Stay-or-Stray

Course Dropout Prediction, Datathon Spring'24

PreethiAngelStephen01/BA_Customer-analysis

Scrape and analyse customer review data to uncover findings for British Airways

rohanarora03/Credit-Card-Defaulters-Classification

This project involves predictive analysis of credit card default payments in Taiwan, utilizing demographic and financial variables from the UCI Machine Learning Repository. The objective is to build an accurate classification model to identify potential defaulters.

rud-ninja/Imbalanced_binary_classification

improving correct classification of class with less representation

Samthesimpsons/Project-Bank-Customer-Classification

Maybank - Senior Data Scientist

sid966/credit_card_fraud_detection

This project is about credit card fraud detection using Random Forest Classifier.

SrivathsanP23/EnsembleMethod_SmartDiseasePredictionSystem

An Ensemble Method to predict the disease

Bahey1200022/Credit-Card-Fraud-Detection

solution https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud. Xgboost is an efficient method of gradient boosting that makes a random initial prediction then calculates similarity scores and gain to build the trees and decrease the gap between the actual value and the predicted value.Gridsearch was used to get the best parameters tuning.