Boosted trees in Julia

Large Scale benchmarking of state of the art text vectorizers

Classification in TabularDataset

This project researched the credit card transaction dataset and tried various machine learning classification models on the dataset to determine the best model that would flag suspicious activity more accurately.

Lung Cancer Prediction using Machine Learning Algorithms

Predicting the Critical Temperature of Superconductors using numerous Machine Learning techniques along with a comparative analysis of their performances.

Regression Analysis - Toyota Corolla price prediction

Random Forest Classification

Implementing Catboost

This is a blog of how machine learning algorithms are used to detect if a person is prone to heart disease or not.

This project aims to detect bone fractures using machine learning and neural networks. It utilizes various machine learning models including AdaBoost, CatBoost, Logistic Regression, Random Forest, Support Vector Machine (SVM), XGBoost, Gradient Boosting, and LightGBM and and neural networks.

Predicting popularity of movies using the IMDb movies dataset with multiple regression algorithms such as XGBoost, Gradient Boosting, Regularization Regressors, and Stacking Regressor; Performed extensive data cleaning, feature engineering, and used transformation techniques such as winsorization and log-transformation

What factors influence runners

This project aims to address the challenge of predicting whether it will rain or snow in Hungary based on various meteorological variables.

Course Work on Machine Learning covering Supervised and Unsupervised Algorithms

The telecom operator Interconnect would like to forecast churn of their clients. To ensure loyalty, those who are predicted to leave will be offered promotional codes and special plans.

a project for CNCS2021