To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

Last update: Jan 31, 2022

Overview

To-Be_Challenge

To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction. The challenge aims to adress the problems of medical imbalanced data classification.

I worked in a team of 3 to solve the challenge and we were able to achieve a score of 77% and rank in the top 3. We used a LGBMClassifier and a mix of two resampling techniques (RandomUnderSampler and BorderlineSMOTE).

Results

Owner

Marwan Mashra

I'm pursuing my master degree in AI at the university of Paris-Saclay / France

GitHub Repository

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To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

Related tags

Overview

To-Be_Challenge

Results

Owner

Marwan Mashra

Factorization machines in python

QML: A Python Toolkit for Quantum Machine Learning

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To-Be is a machine learning challenge on CodaLab Platform about Mortality Prediction

Related tags

Overview

To-Be_Challenge

Results

Owner

Marwan Mashra

Factorization machines in python

QML: A Python Toolkit for Quantum Machine Learning

A demo project to elaborate how Machine Learn Models are deployed on production using Flask API

Sequence learning toolkit for Python

Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

Simulation of early COVID-19 using SIR model and variants (SEIR ...).

A library of extension and helper modules for Python's data analysis and machine learning libraries.

Scikit learn library models to account for data and concept drift.

Adaptive: parallel active learning of mathematical functions

Time-series momentum for momentum investing strategy

AtsPy: Automated Time Series Models in Python (by @firmai)

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

Project to deploy a machine learning model based on Titanic dataset from Kaggle

A simple guide to MLOps through ZenML and its various integrations.

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Sleep stages are classified with the help of ML. We have used 4 different ML algorithms (SVM, KNN, RF, NN) to demonstrate them

This is the code repository for Interpretable Machine Learning with Python, published by Packt.

Extreme Learning Machine implementation in Python

Python Research Framework

DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters

AutoX是一个高效的自动化机器学习工具，它主要针对于表格类型的数据挖掘竞赛。它的特点包括: 效果出色、简单易用、通用、自动化、灵活。