Episode #210: Analyzing Kickstarter Campaigns with Python
Published Thu, Dec 3, 2020, recorded Mon, Nov 23, 2020.
Special guest: Jay Miller
Sponsored by us! Support our work through:
- 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.
.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.
- 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.
- Gift for the tinkering pythonista
- Use it to make plenty of cool things
- Screen/speaker/Light Sensor Built-In
- 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
- sets, ordered pairs, relations, functions,
- 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.
- 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: imagekit.io
- image cdn
- started using imagekit on my own website and noticed faster load times
- allows for some responsive “fanciness”
- Add Blurs
- Smart Cropping
- Python API or URL-Schema
- The Apple M1 mac mini wait continues. :)
- Talk Python To Me, pro edition
- PSF Fundraiser for the month of December: https://pythonbytes.fm/psf2020
- Elastic Community YouTube Channel
- Just posted my lightning talk on looking at open data from the government.
- Upcoming interview on one of our newest clients - Eland which is python client to create pandas-like dataframes with elasticsearch datastores.
- My Podcast The PIT Show weekly insights from me on my developer journey and interviews with amazing folks in the tech space.
- Elastic Blog - Just posted my first Elastic Blog post Elastic Contributor Program: How to create a video tutorial
- Q: why can't SQL and NoSQL Developers date one other?
- A: because they don't agree on relationships.