Comparison of Logistic Regression and Random Forest performances on OpenML Datasets

A Comparison of a support vector machine and a random forest classifier on the same dataset: the BNG_Heart_Statlog data from OpenML

Salary prediction using machine learning regression models. The project uses a public UCI Adult dataset loaded via OpenML and compares Linear Regression, Decision Tree, and Random Forest for predicting annual salary.

R implementation of an enhanced Random Forest with adaptive sampling for imbalanced classification.

Trained the classic Iris dataset from OpenML using a Random Forest Classifier

Implemented and optimized multiple machine learning classification models (KNN, Decision Tree, SVM Linear, SVM Polynomial, Random Forest) to predict dress choices from a random OpenML dataset. Applied data preprocessing techniques to improve accuracy as part of an Introduction to AI course.