DiscoverPython Bytes#210 Analyzing Kickstarter Campaigns with Python
#210 Analyzing Kickstarter Campaigns with Python

#210 Analyzing Kickstarter Campaigns with Python

Update: 2020-12-03


The live stream recording on YouTube.

Special guest: Jay Miller

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Brian #1: Analyzing Kickstarter Campaigns with Python Data Science Tools

  • Article title: “Kickstarter Projects — Do They Succeed?”

  • Aditya Patkar

  • Using a Kaggle dataset of 378,661-ish projects up to 2018.

  • Looks at using pandas data frames to explore the data.

  • Using .describe() data frame method to learn a lot.

  • Uses matplotlib and seaborn to analyze the data further.

  • Odd statement that I’m not sure is straight faced or a really dry joke: “The data from 1970 seems to be bad or insignificant data.”

  • Examples of using heat maps, line graphs, bar charts, to look at different aspects.

  • Some results:

    • 35.64% of projects are successful (meaning goal hit)

    • tech asks for the most for goals, and has the highest average per backer.

    • Comics has the lowest pledged amount per backer average.

  • Nice that you can use the techniques to ask your own questions of the data.

Michael #2: GPU Accelerated Python for Machine Learning on Cross-Vendor Graphics Cards

  • Building machine learning algorithms using the Vulkan Kompute Python Framework

  • When you hear “CUDA”, that means Nvidia 🙂

  • Uses Vulkan Kompute framework

  • A large number of high profile (and new) machine learning frameworks such as Google’s Tensorflow, Facebook’s Pytorch, Tencent’s NCNN, Alibaba’s MNN —between others — have been adopting Vulkan as their core cross-vendor GPU computing SDK.

  • As you can imagine, the Vulkan SDK provides very low-level C / C++ access to GPUs, which allows for very specialized optimizations.

  • The main disadvantage is the verbosity involved, requiring 500–2000+ lines of C++ code to only get the base boilerplate required to even start writing the application logic.

  • The Kompute Python package is built on top of the Vulkan SDK through optimized C++ bindings, which exposes Vulkan’s core computing capabilities. Kompute is the Python GPGPU framework.

  • The main article talks through a couple of numerical computation examples.

Jay #3: Adafruit PyPortal - CircuitPython Powered Internet Display

  • Gift for the tinkering pythonista

  • CircuitPy

  • Use it to make plenty of cool things

  • Screen/speaker/Light Sensor Built-In

Brian #4: Introduction to Linear Algebra for Applied Machine Learning with Python

  • Pablo Caceres

  • Intended as a reference and not a comprehensive review.

  • Still, I very much appreciate it.

  • Includes links to both free and paid resources to thoroughly learn linear algebra

  • Covers

    • sets, ordered pairs, relations, functions,

    • vectors

    • matrices

    • linear and affine mappings

    • matrix decomposition

  • Uses numpy, pandas, and altair for examples

  • Quick (but useful) explanations of concepts, along with how to represent and do it with numpy

  • I’m really just getting into it, but I’m enjoying it and this is the right level of handholding I needed.

Michael #5: How many notebook frameworks? Many, and now +1 with Deepnote

  • Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.

  • Free for individuals, paid for teams and companies

  • Real time collaboration is a key feature

  • Built in versioning coming

  • Code review in the notebook coming

  • “View” your variables as a whole environment

  • Better — real — autocomplete

  • Dashboards coming too

Jay #6:



  • The Apple M1 mac mini wait continues. :)

  • Talk Python To Me, pro edition

  • PSF Fundraiser for the month of December:




  • Q: why can't SQL and NoSQL Developers date one other?

  • A: because they don't agree on relationships.









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#210 Analyzing Kickstarter Campaigns with Python

#210 Analyzing Kickstarter Campaigns with Python

Michael Kennedy (@mkennedy)