This article was published as a part of the Data Science Blogathon. As a Machine Learning Engineer or Data Engineer, your main task is to identify and clean duplicate data and remove errors from the dataset. It is good to spend some time preparing the data and making it reliable for the machine learning models. The better the quality of the data, the higher the accuracy of your model and the better the decision-making process. Data Cleaning is not something new in machine learning.

But there are all sorts of smaller machine learning projects out there that people are building and using: pipelines, wrappers, high-level APIs, cleaners, etc. Many of the implemented functions share similarities with scikit-learn's API, but future addition functionality will not necessarily be restricted by this. Olson bills Data Cleaner as a "Python tool that automatically cleans data sets and readies them for analysis." The folder GCP-HPO contains all the code implementing the Gaussian Copula Process (GCP) and a hyperparameter optimization (HPO) technique based on it.

The popular machine learning projects, in general, are popular because they either provide a wide range of needed services or they were the first (or possibly best) to provide a particular niche service to users. These popular projects include Scikit-learn, TensorFlow, Theano, MXNet (maybe?), Weka (formerly), and so on. Depending on the particular ecosystem(s) you work in, and on your machine learning goals, the projects which you consider popular may differ slightly; however, they all share the similarity that they provide services to a large base of users. But there are all sorts of smaller machine learning projects out there that people are building and using: pipelines, wrappers, high-level APIs, cleaners, etc. They provide both niche and flexible services, usually for smaller numbers of users, for all sorts of reasons.