Comparison of ensemble learning methods on diabetes disease classification with various datasets

Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling

Big Mart Sales Prediction is a data-driven project aiming to forecast product sales accurately across Big Mart outlets. Leveraging machine learning and comprehensive datasets, our project empowers retailers to optimize inventory, enhance profitability, and make informed decisions in the dynamic world of retail.

This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.

Applies Machine Learning approach to predict spam.

Predicting true low-VAF SNVs in HPV using triplicate NGS samples and machine learning

Sports Analytics in R (Gradient Boost approaches for Decision Tree in Regression problems)

Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.

This repo contains the result of my computer science course: An automated tool to classify credit card transactions. Could be used with any dataset

Kaggle challenge asking to predict how a supplier will quote a price on a given tube assembly.

Algorithms used to confirm whether a celestial body is a planet or not.

Kaggle challenge asking to predict the final price of each home based on their description/properties.

Using data to help us choice high quality wine

Credit Card Fraud Detection using Extreme Gradient Boosting

Kaggle challenge asking to predict the outcome for each animal of the shelter.

Code for the project "Predicting hospital readmission of diabetic patients using ensemble learning"

Integrative Biomechanical and Clinical Features Predict In-Hospital Trauma Mortality

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

This repository contains several machine learning projects done in Jupyter Notebooks

In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.The…