ranibasna Goto Github PK
Name: Rani Basna
Type: User
Company: University of Gothenburg
Bio: Data Scientist and Mathematicians.
Name: Rani Basna
Type: User
Company: University of Gothenburg
Bio: Data Scientist and Mathematicians.
This is a repo that uses unsupervised machine learning methods to phenotype the airway disease data
This is a repo for clustering the asthma data for he WSAS cohort
Presenting an application of the Bayesian network to the interaction of socioeconomic and smoking. https://ranibasna.github.io/Bayesian-Network-Application-to-SocioEconomic-status/
Orthonormal Basis Selection using Machine Learning. https://ranibasna.github.io/ddk/
Clustering airway data using the Deep Embedding Clustering (DEC) method. https://ranibasna.github.io/Deep-learning-based-phenotyping-of-airway-diseases-in-adults-Presentation/
Phenotyping application to Allergen-specific IgE measurements data, we have applied the DEC (Deep Embedding Clustering) model to our data to derive clusters of IgE titers to foods. https://ranibasna.github.io/Deep-learning-based-phenotyping-of-food-allergy-Presentation/
This is a technical report that describes the missing data treatment for the Modification Effect of the Smoking into the SocioEconomic status on the airway disease paper
This is a reproducible Bayesian Network analysis for the modification effect of Socioeconomic status on the effect of smoking on asthma-related outcomes
https://ranibasna.github.io/MY_CV/
This package intends to convert categorical features into numerical ones. This will help in employing algorithms and methods that only accept numerical data as input. The main motivation for writing this package is to use it in clustering assignments. https://ranibasna.github.io/NumericalTransformation/
This is the repo for the statistical and machine learning analysis for the obesity surgery effects on asthma severity.
This is a presentation for the decision tree clustering approach for the airway data
A short course at the Institute of Medicine
This is only a test
This is an attempt to build an end to end machine learning project to predict Apple stock direction on weekly basis. The project use an Machine learning operation approach to duild and dploy the model on google cloud platform
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