There are 0 repository under smote-oversampler topic.
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.
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.
使用比赛方提供的脱敏数据,进行客户信贷流失预测。
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.
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.
Data analysis, visualization and prediction for the prevention of heart disease using ML models
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.
Chapter 12: Data Preparation for Fraud Analytics
The goal is to create a model predicting the grade of an essay
Testing 6 different machine learning models to determine which is best at predicting credit risk.
Using the imbalanced-learn and Scikit-learn libraries to build and evaluate machine learning models.
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
Multi-class Classification - License Status Prediction
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
Predicts if a patient will show up at a scheduled appointment based on certain features.
Detect Fraudulent Credit Card transactions using different Machine Learning models
Supervised Machine Learning Project
using machine learning to assess credit risk
This repository contains the resources and codebase for a research project aimed at predicting breast cancer cases using data from the KNUST hospital.
This project predicts hotel booking cancellations using Machine Learning techniques, benefiting both travelers and hotels.
Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV
Handling Imbalanced Data Sets
Survival prediction using Four Different kind of algorithms and optimizing the dataset using PCA and SMOTE
A Deep Learning analysis to predict success of charity campaigns
Battery analysis project
Course Dropout Prediction, Datathon Spring'24
improving correct classification of class with less representation
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.
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.
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.
Here is the repository for sharing jupyter notebooks discussed in 'AI-1402' class.
Scrape and analyse customer review data to uncover findings for British Airways
Maybank - Senior Data Scientist
This project is about credit card fraud detection using Random Forest Classifier.