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).
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end
Citing fastFM The library fastFM is an academic project. The time and resources spent developing fastFM are therefore justified by the number of citat
MPI for Python Overview Welcome to MPI for Python. This package provides Python bindings for the Message Passing Interface (MPI) standard. It is imple
PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification Introduction. This package includes the pyth
Predicting India’s COVID-19 Third Wave with LSTM Complete project of predicting new COVID-19 cases in the next 90 days with LSTM India is seeing a ste
A Machine Learning Course with Python Table of Contents Download Free Deep Learning Resource Guide Slack Group Introduction Motivation Machine Learnin
KNN With Spark Implementation of KNN using PySpark. The KNN was used on two separate datasets (https://archive.ics.uci.edu/ml/datasets/iris and https:
📺 ML YouTube Courses At DAIR.AI we ❤️ open education. We are excited to share some of the best and most recent machine learning courses available on
timemachines Autonomous, univariate, k-step ahead time-series forecasting functions assigned Elo ratings You can: Use some of the functionality of a s
learn python in 100 days, a simple step could be follow from beginner to master of every aspect of python programming and project also include side project which you can use as demo project for your
Our article on Towards Data Science introduces the package and provides background information. Pytorch Forecasting aims to ease state-of-the-art time
MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. Th
PyDLM Welcome to pydlm, a flexible time series modeling library for python. This library is based on the Bayesian dynamic linear model (Harrison and W
A Python library for choreographing your machine learning research.
neurodsp is a collection of approaches for applying digital signal processing to neural time series, including algorithms that have been proposed for the analysis of neural time series. It also inclu
Package: dsm Python package dsm provides an API to train the Deep Survival Machines and associated models for problems in survival analysis. The under
Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti
PosteriorStacker Combines Bayesian analyses from many datasets. Introduction Method Tutorial Output plot and files Introduction Fitting a model to a d
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