stacked-ensembles

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

A Python Package for Identifying 23 Kinds of Animals in Camera Trap Videos

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

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.

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.

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

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

Unbalanced data classification

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

In class Kaggle competition on predicting bankruptcy of a firm

R/cvma: Cross-validation-based maximal associations

My own stacked CNN model architecture for CIFER10 data classification

Project for Fundamentals of Data Science. Forked from https://github.com/luigiberducci/FDS-Project-HousePrices

Python code for stacking models, includes extracting model probabilities and assessing misclassified cases