"This repository contains implementations of Boosting method, popular techniques in Model Ensembles, aimed at improving predictive performance by combining multiple models. by using titanic database."

AdaBoost Analysis and Optimization [Machine Learning I UC Project]

A machine learning model to predict diamond prices based on various features using Python, scikit-learn, and pandas. It includes data preprocessing, feature engineering, model selection, and deployment options.

Comparison of XGBoost, CatBoost, and LGBM for Parkinsons Classification

Developed and evaluated machine learning and deep learning models for detecting financial fraud.

Personal projects 1 & 2

Analyze the data and come up with a predictive model to determine if a customer will leave the credit card services or not and the reason behind it

Scripts, figures and working notes for the participation in FungiCLEF-2022, part of the 13th CLEF Conference, 2022

learning python day 14

Work on combining Logit model with an information granulation method for better interpretability

Project that analyzes the performance of 5 supervised learning algorithms in ML

This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media plat…

Predictive Modeling and Clustering Insights for Kickstarter Success

Personal projects on AI and ML

Задача классификации. (предсказание открытия клиентом депозита в банке) / Classification task. Prediction of the client's deposit opening in the bank.

Tool for estimating the difficulty of phylogenetic placements

Implementation of two major ensemble learning methodologies, Bagging and Stacking, over the tasks of classification and regression. Also, compared the results of Random Forests with multiple Boosting Techniques.

Tool for estimating the Felsenstein bootstrap support of phylogenetic trees

Exploration of Boosting Algorithms in Machine Learning: This repository hosts a seminar paper examining key boosting algorithms like AdaBoost, Gradient Boosting, and XGBoost. It includes theoretical insights, practical applications, and comparative analyses, providing a comprehensive understanding of boosting techniques in ML.