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.
-
Updated
Jan 10, 2024 - Jupyter Notebook
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.
Data analysis, visualization and prediction for the prevention of heart disease using ML models
Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.
Utilized several machine learning models to predict credit risk using Python's imbalanced-learn and scikit-learn libraries
This project predicts hotel booking cancellations using Machine Learning techniques, benefiting both travelers and hotels.
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.
This repository contains the resources and codebase for a research project aimed at predicting breast cancer cases using data from the KNUST hospital.
Kyphosis disease prediction using Fully Connected Neural Networks (FCNNs) model and XGBoost model with GridSearchCV
The goal is to create a model predicting the grade of an essay
Supervised Machine Learning and Credit Risk
Predicts if a patient will show up at a scheduled appointment based on certain features.
Course Dropout Prediction, Datathon Spring'24
In this analysis, I will demonstrate how PCA works in different tasks and how much time and resources we save in our daily 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
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 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.
using machine learning to assess credit risk
Add a description, image, and links to the smote-oversampler topic page so that developers can more easily learn about it.
To associate your repository with the smote-oversampler topic, visit your repo's landing page and select "manage topics."