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.

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.

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

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.

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

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SMOTE code ( used from imblearn) along with classification report and confusion matrix usage. Also, created our own dataset using make_classifcation() function of python.

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

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

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

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

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

Maybank - Senior Data Scientist

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

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

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

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.

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This project demonstrates how to use Logistic Regression to detect fraudulent transactions using SMOTE for an imbalanced data

Clasificación no balanceada: SMOTE y ADASYN. Tratamiento Inteligente de Datos (TID). Máster en Ingeniería Informática (UGR, 24-25)

Chapter 10: Data Preparation for Fraud Analytics

📘 This repository predicts OLA driver churn using ensemble methods—Bagging (Random Forest) and Boosting (XGBoost)—with KNN imputation and SMOTE. It reveals city-wise churn trends and key performance drivers, powering smarter, data-backed retention strategies for the ride-hailing industry.

Customer churn prediction using customer behavioral data and a fine-tuned ML model

Clasificación de transacciones de tarjetas de crédito con el fin de detectar anomilas

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.

Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries

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