A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Geolocating twitter users by the content of their tweets

Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning

Predict respiratory patient mortality in ICU units using the MIMIC III database

CoMoMo combines multiple mortality forecasts using different model combinations. See more from the paper here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3823511

This challenge organized by ENS Ulm and Collège de France was about predicting mean return of cluster's assets relatively to the bitcoin during the last hour of the day, given the last 23 hours.

autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners

This project intends to solve the house hunt problem by sending the updates of new listings as per the selection criteria of the user by filtering spam in housing listings using NLP. It uses SMTP to send emails, nltk for NLP and tkinter for creating UI

Utilizing Machine Learning for portfolio selection with the aim of out-performing benchmark indices

This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning

This project leverages advanced machine learning algorithms to detect and classify malicious emails, focusing on spam and phishing threats. As email threats grow more sophisticated, accurate detection is critical to ensuring the security and privacy of both individuals and organizations.

This repository contains the approach that led us to win the MLDS Republic Day Hackathon.

This my entry for the Titanic competition on Kaggle. May 2019: public score is 0.80382, which is a top 10% ranking on the leader board of around 11.249 participants.

In class Kaggle competition on predicting bankruptcy of a firm

Cross-validation-based maximal associations

Create an arbitrary graph of models and meta-models to form an ensemble. This can be viewed as a generalisation of stacking ensembles.

Develop Machine Learning model to predict customer loan defaults, enhancing lending risk assessment. Real-world relevance tackling financial uncertainty. #The Analytics Olympiad 2023

Sales Time Series Forecasting using Machine Learning Techniques (Random Forest, XGBoost, Stacked Ensemble Regressor)

Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.

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