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machine learning pipeline
January 2, 2020 Juan De Dios Santos Data Science

Extend Your Machine Learning Pipeline With Your Prediction Outcome

Close your eyes and imagine a machine learning production pipeline (come on, you can do it). What do you see? Oh yes, data sources; a data

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2020 data science predictions
December 19, 2019 Explorium Data Science Team Data Science

2020: The Year Data Dominates Data Science

Data has been a hot topic of discussion for years now. As organizations create and collect more and more data, it’s key that they use it

decision tree
December 10, 2019 Explorium Data Science Team Data Science

The Complete Guide to Decision Trees

In the world of machine learning, developers can create independent environments for projects easily. It only takes a few clicks to set and fit models in

Hit Song Classifier Part 2
November 20, 2019 Maël Fabien Data Science

Using Data Science to Predict the Next Hit Song (Part 2)

In part one of this two-part series, we explored basic models and data enrichments for our hit song classifier. In this article, we will try to

Predict customer churn
November 11, 2019 Explorium Data Science Team Data Science

Beginner’s Guide to Python Modeling Using XGBoost Package

Everyone knows that having a large number of loyal customers is the key to the success of a business. This statement is even more significant for

Data Science to Predict the Next Hit Song
November 6, 2019 Maël Fabien Data Science

Using Data Science to Predict the Next Hit Song (Part 1)

It comes as no surprise that the music industry is tough. When you decide to produce an artist or invest in a marketing campaign for a

August 11, 2019 Aviv Nutovitz Data Science

Demystifying Feature Selection: Filter vs Wrapper Methods

Feature selection algorithms are increasingly growing in significance. In this article, we will cover (and compare) two popular feature selection methodologies - Filter and Wrapper.

July 24, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 2)

The whole idea behind interpretable and explainable ML is to avoid the black box effect.

July 15, 2019 Maël Fabien Data Science

Interpretability and explainability (Part 1)

The whole idea behind interpretable and explainable ML is to avoid the black box effect.

Categories
  • All posts
  • AI Education
  • Automation
  • Business Intelligence
  • Data Enrichment
  • Data Science
  • Explainability
  • Feature Engineering
  • Feature Selection
  • General
  • Interpretability
  • Machine Learning
  • Predictive Models
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