We present a controlled comparison of a convolutional neural network (EfficientNet-B0) and a Vision Transformer (ViT-Base) on SpaceNet under two label-distribution regimes: a naturally imbalanced five-class split and a balanced-resampled split with 700 images per class (70:20:10 train/val/test). With matched preprocessing (224x224, ImageNet normalization), lightweight augmentations, and a 40-epoch budget on a single NVIDIA P100, we report accuracy, macro-F1, balanced accuracy, per-class recall, and deployment metrics (model size and latency). On the imbalanced split, EfficientNet-B0 reaches 93% test accuracy with strong macro-F1 and lower latency; ViT-Base is competitive at 93% with a larger parameter count and runtime. On the balanced split, both models are strong; EfficientNet-B0 reaches 99% while ViT-Base remains competitive, indicating that balancing narrows architecture gaps while CNNs retain an efficiency edge. We release manifests, logs, and per-image predictions to support reproducibility.

You've probably heard of Kaggle data science competitions, but did you know that Kaggle has many other features that can help you with your next machine learning project? For people looking for datasets for their next machine learning project, Kaggle allows you to access public datasets by others and share your own datasets. For those looking to build and train their own machine learning models, Kaggle also offers an in-browser notebook environment and some free GPU hours. You can also look at other people's public notebooks as well! Other than the website, Kaggle also has a command-line interface (CLI) which you can use within the command line to access and download datasets.

Kaggle is synonymous with competitions and hackathons in the world of data science, but it is also a great resource to learn more about the field through community-driven notebooks. In contrast to textbooks and lectures, Kaggle notebooks or kernels provide data scientists with tutorials in their language. These are essentially Jupyter notebooks that run in the browser free of charge and without even needing to set up a local environment for Jupyter. In addition, these notebooks explore and run machine learning code and discover vast public and open-sourced repositories. While there are hundreds of thousands of notebooks on Kaggle, all data enthusiasts must-read are the top eight underrated notebooks.

This article presents you the Data Science Starter Kit that will serve as a self-help guide to help you get started in your data science journey. Nor is it going to be a magical formula that will effortlessly instill you with data science knowledge and skills. This Data Science Starter Kit is going to cost you ZERO dollars (although the learning service providers mentioned herein does). What this starter kit can do for you is provide a framework that will help pinpoint you in the right direction and help you take your first steps. It's going to be tough journey.

Uber is a cab service provider for people wanting to travel from one place to another. Here, I have taken an Uber request dataset from Kaggle to try and perform analysis using the visualization libraries such as seaborn and matplotlib. At the end of this article, I have given a link to my Kaggle notebook where I have performed a detailed analysis of this Uber dataset. Let us jump right into the analysis and see what can be understood to make relevant conclusions. Before moving on to understanding the fields/observations in the data, let us import the required python libraries required for this analysis.

Kaggle, the Google-acquired data science platform, started as a virtual meeting point for machine-learning geeks to compete on predictive accuracy scores. It evolved into a Swiss Army knife for data science and analytics--one that can help data professionals, including data-driven marketers, elevate their analytics game. This is, of course, just a partial list. This post focuses on these and other marketing-friendly use cases for Kaggle. It became known as a platform for hosting machine-learning competitions.