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).
DistML is a Ray extension library to support large-scale distributed ML training on heterogeneous multi-node multi-GPU clusters
mastrade MasTrade is a trading bot in baselines3,pytorch,gym idea we have for example 1 btc and we buy a crypto with it with market option to trade in
Ray provides a simple, universal API for building distributed applications. Ray is packaged with the following libraries for accelerating machine lear
A repository for collating all the resources such as articles, blogs, papers, and books related to Bayesian Statistics.
Predico Disease Prediction system based on symptoms provided by patient- using Python-Django & Machine Learning
Banpei Banpei is a Python package of the anomaly detection. Anomaly detection is a technique used to identify unusual patterns that do not conform to
A Microsoft Azure Web App project named Covid 19 Predictor using Machine learning Model (Random Forest Classifier Model ) that helps the user to identify whether someone is showing positive Covid sym
Tweets_Classification_Webpage The goal of this project is to be able to predict what rating customers on social media platforms would give to products
Azure MLOps (v2) solution accelerator Welcome to the MLOps (v2) solution accelerator repository! This project is intended to serve as the starting poi
Scikit-Garden or skgarden (pronounced as skarden) is a garden for Scikit-Learn compatible decision trees and forests.
CRAN Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)" This code doesn't exa
Stox A Module to predict the "close price" for the next day and give "technical analysis". It uses a Neural Network and the LSTM algorithm to predict
metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met
auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here
InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig
XGBoost-Ray is a distributed backend for XGBoost, built on top of distributed computing framework Ray.
The Ultimate FREE Machine Learning Study Plan
COVID-19-simulation Simulation of early COVID-19 using SIR model and variants (SEIR ...). Made by the Laboratory of Sustainable Life Assessment (GYRO)
iNNvestigate neural networks! Table of contents Introduction Installation Usage and Examples More documentation Contributing Releases Introduction In
dsnk-distributions package dsnk distribution is a Python module for data science and machine learning that was created with the goal of reducing calcu
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