Geospatial data refers to the time-based data related to a specific location on the Earth’s surface. It is helpful since it can reveal vital patterns and trends in the landscape. In this article, we’ll learn about an open-source tool called Kepler.gl is and how it makes visualizing and analyzing geospatial data a seamless task. I have also made the data available as a Kaggle dataset
This article initially started as a Linkedin post, but then I decided to make it more elaborate. Essentially, it talks about the
method of pandas for instantly creating a dataframe from the data copied to the clipboard.
This article is about speeding up your traditional machine learning libraries like scikit-learn by converting them to tensor-based models so that they can utilize the hardware accelerators (e.g., GPUs), thereby speeding up the model training and inference. The code is also available as a Kaggle Notebook
Heatmap is an important EDA tool you’ll encounter on many occasions. For instance, Github’s or Kaggle’s contribution plots are also a heatmap displaying time-series data. In this article, I tried to convert vanilla time series plots into professional-looking Github-style contribution plots, which was fun.