The world’s leading publication for data science, AI, and ML professionals.

Weekly Selection – Aug 17, 2018

The most important part of a data science project is writing a blog post

Photo by Michał Parzuchowski on Unsplash
Photo by Michał Parzuchowski on Unsplash

By William Koehrsen – 8 min read

It can be tempting to call a data science project complete after you’ve uploaded the final code to GitHub or handed in your assignment. However, if you stop there, you’re missing out on the most crucial step of the process: writing and sharing an article about your project.


Forecasting with Python and Tableau

By Greg Rafferty – 7 min read

In this post, I’ll show how I used Python code within Tableau to build an interactive dashboard implementing a time-series forecast. If you just want to play around with the dashboard first and explore the SARIMAX algorithm, download the full python-implemented dashboard here or go to this slightly dumbed-down version on Tableau Public


Better collaborative data science

By Megan Risdal – 8 min read

Last Saturday, I gave a keynote at Data Con LA 2018 (formerly Big Data Day LA), the largest data conference of its kind in Southern California. This is my talk in blog format.


Don’t make this big machine learning mistake: research vs application

By George Seif – 4 min read

These days, everyone’s getting in on Machine Learning (ML). It’s definitely a great direction to pursue for many businesses since it gives them the ability to deliver tremendous value in a fairly quick and easy way. The demand for machine learning skills is at an all time high.


Fine-tuning XGBoost in Python like a boss

By Félix Revert – 4 min read

XGBoost (or eXteme Gradient Boosting) is not to be introduced anymore, proved relevant in only too many data science competitions, is still one model that is tricky to fine-tune if you have only been starting playing with it.


A Machine Learning Approach – Building a Hotel Recommendation Engine

By Susan Li – 5 min read

All online travel agencies are scrambling to meet the AI driven personalization standard set by Amazon and Netflix. In addition, the world of online travel has become a highly competitive space where brands try to capture our attention (and wallet) with recommending, comparing, matching and sharing.


Creating custom Fortnite dances with webcam and Deep Learning

By Chintan Trivedi – 4 min read

If you know about the game Fortnite, you probably also know about the craze surrounding the in-game celebrations/emotes/dances. Gamers have spent millions of dollars purchasing dance moves with in-app purchases, making something as simple and as silly as this a big revenue generator for the game developer.


Don’t Use Dropout in Convolutional Networks

By Harrison Jansma – 4 min read

I have noticed that there is an abundance of resources for learning the what and why of deep learning. Unfortunately when it comes time to make a model, their are very few resources explaining the when and how.


What App Descriptions Tell Us: Text Data Preprocessing in Python

By Finn Qiao – 9 min read

Continuing along with the theme of data cleaning and exploration, much of effective NLP analysis is dependent on the pre-processing of textual data. I have thus decided to perform a step by step preprocessing of some textual data derived from Apple Appstore descriptions and a K-Means cluster of the resulting text.


Related Articles