Winning 2nd place🥈at NUS CS5228 in-class Kaggle competition 2018!

Capstone project #2 for the Harvard University Professional Certificate in Data Science

Handwritten digit recognition with MNIST & Keras

Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated

This project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account di…

Predicts the qualified employee for promotion using Classification

This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.

Test and comparison of ensemble method with naive bayes classifier on 5 different data sets.

Official Implementation of Track2Vec: Fairness Music Recommendation with a GPU-Free Customizable-Driven Framework EvalRS-CIKM-2022

My solutions to the data analysis and forecasting case study held by Bella & Bona

This project presents a ML based solution using Ensemble methods to predict which visa applications will be approved and thus recommend a suitable profile for applicants whose visa have a high chance of approval

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built with the latest machine learning and data science techniques, AI-CryptoTrader provides a powerful toolset and advanced trading stratgies for maximizing your cryptocurrency profits.

Identification of Lung Cancer in Smoker Person Using Ensemble Methods Based on Gene Expression Data. Presented in IC2IE and published to IEEE.

Build a classification model to predict clients who are likely to default on their loans. Give recommendations to the bank on important features to consider while approving a loan. Concepts Used: Logistic Regression, Decision Trees, Random Forests, and Ensemble Methods

Diabetes prediction using bagging (ensemble methods)

Course project for Stanford's STATS 315B (Modern Applied Statistics: Learning II).

Predict sale prices via regression models, using PCA, k-means clustering, ensemble models, pipelines, etc.

The goal of this report was to identify which variable best predicts divorce using decision trees and other ensemble methods. In the data set, Class is the response variable, with 0 = still married and 1 = divorced.

Using deep learning to predict whether students can correctly answer diagnostic questions

Instructional materials (course files) for the BBT4206 course (Business Intelligence II) using R. Topic: Ensemble Methods.