Boosted trees in Julia

Classification in TabularDataset

Lung Cancer Prediction using Machine Learning Algorithms

Large Scale benchmarking of state of the art text vectorizers

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.

Regression Analysis - Toyota Corolla price prediction

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

This is a web app where a user can signup to the website first and then login to access the website. Then, he/she can give their age, select his/her gender, bmi, number of children, select whether he/she is a smoker or not, and select his/her region. Gradient Boosting Regressor is used in this project which gives the best accuracy of 89.798.

Implementing Catboost

Random Forest Classification

A machine learning pipeline for classifying cybersecurity incidents as True Positive(TP), Benign Positive(BP), or False Positive(FP) using the Microsoft GUIDE dataset. Features advanced preprocessing, XGBoost optimization, SMOTE, SHAP analysis, and deployment-ready models. Tools: Python, scikit-learn, XGBoost, LightGBM, SHAP and imbalanced-learn

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.

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.

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