The article discusses integrating Python, R, and Julia in a single Jupyter Notebook to enhance data analysis efficiency. It outlines prerequisites for setup, including installing necessary kernels and libraries, and provides examples of how to write code in each language within the notebook. The integration allows for combining machine learning, fast data preparation, and visualization seamlessly.

We used logistic regression and random forest to predict user behaviors on a Chinese social network, Weibo.

In this project, I was trying to be a normal user in a headless browser, intended to get translated texts from DeepL’s models.

Three great materials I found on TDS(TowardDataScience.com) and the City of Statistics today.

This project uses several machine learning models to predict reservation cancellation. Among them, DART in LightGBM beats other competitors with highest testing accuracy (table) and also other metrics.

This issue is related to Sklearn, one solution is to state a eligible Solver .