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Machine learning codes are usually laden with far too many complexities (hyperparameters, preprocessing and right things coded in wrong places ...). I am on a mission to untangle ML code by logically separating work flow and creating a re-usable framework that you can easily apply to your machine learning tasks involving any type of data.
Multi-label classification is one of the standard tasks in text analytics. The objective is to perform an eXtreme multi-label classification (XMLC) on two datasets( https://www.kaggle.com/hsrobo/titlebased-semantic-subject-indexing) -EconBiz( ZBW - Leibniz Information Centre for Economics from July 2017) and PubMed(5th BioASQ challenge on large-…
An ETL in a Pure Python single script, service oriented, to make a request and download, unzip, extract, parse csv, normalize dataset and retrieve data as json
The document outlines a data cleaning project for the IMDB dataset. The project includes loading the dataset, dropping unnecessary columns, identifying missing values, filling missing values, formatting and cleaning the data. The timeline for the project is 3 days, with specific tasks assigned to each day.