XGBoost trained model to predict the price of houses in Xindian, Taipei City, Taiwan

Machine learning project utilizing four ML algoirthms to predict the outcome of patient from the MIMIC data

Classification of different objects of the environment, viz, cars, buildings, trees, and so on. Utilizing k nearest neighbor and random forest algorithms through the tidymodel structure.

A machine learning project focused on predicting bank customer churn using logistic regression, decision trees, and random forest models. It provides a detailed introduction to bank churn, explaining the reasons behind customers' decisions to leave a bank.

A machine learning project to predict wine quality based on chemical properties. This project involved data preprocessing, eda, and the application of various predictive models, including generalized linear models, decision trees, random forests and k-nearest neighbors. The aim was to provide insights beneficial for winemakers, retailers, and consumers in the wine industry.