Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
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Updated
Oct 4, 2018 - HTML
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
Unbalanced data classification
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
Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated
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
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
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.
Create an arbitrary graph of models and meta-models to form an ensemble. This can be viewed as a generalisation of stacking ensembles.
A stacked ensemble model for predicting raisin grains
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)
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
Predict respiratory patient mortality in ICU units using the MIMIC III database
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
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