A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
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Updated
Jun 6, 2018 - Python
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
Cross-validation-based maximal associations
This repository contains my implementation for Energy Disaggregation of appliances from mains consumption using stacked ensemble deep learning
autoEnsemble : An AutoML Algorithm for Building Homogeneous and Heterogeneous Stacked Ensemble Models by Searching for Diverse Base-Learners
This repository contains the approach that led us to win the MLDS Republic Day Hackathon.
Utilizing Machine Learning for portfolio selection with the aim of out-performing benchmark indices
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
Predict respiratory patient mortality in ICU units using the MIMIC III database
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
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
My own stacked CNN model architecture for CIFER10 data classification
Unbalanced data classification
Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated
Project for Fundamentals of Data Science. Forked from https://github.com/luigiberducci/FDS-Project-HousePrices
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
R/cvma: Cross-validation-based maximal associations
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.
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