Announcing the Community Champions for this week!

Congrats Champs!! :tada: :clap:

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Thank you!
I recall when I just joined the community and saw first Community Champions’ projects, I was just astonished and thought that their level was unachieveble. But, instead of get discouraged, I got inspired by these projects (and other wonderful projects which were not recognized as Com. Champ. for whatever reason). And here I am, annouced a Community Champion :wink:

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I like your perspective. I was quite intimidated by their projects at first, too. But now that you mention it, they are more inspirational and challenging in a good way than intimidating :slight_smile:

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Here are our Community Champions for this week: :tada:

Personal Project sharers:

Most liked:

@WilfriedF and @moriturus7 had a great discussion about how accuracy can be misleading and why you should look at other metrics like Precision and Recall.

Guided Project sharers:

Guided Project reviewers:

@Elena_Kosourova and @info.victoromondi are elevating our community discussions by giving good specific feedback to the Guided Project sharers. Kudos to y’all!

Thank you for these insightful discussions, Champions! :heavy_heart_exclamation:

I am happy to present you with:

  • A 7 day extension on your current Dataquest subscription :rocket:
  • An exclusive Community Champions badge in our Community :medal_sports:


Before you go Champions, can you tell us - What’s the number 1 reason you come back to the Community?

Please share your answer by replying below! :smile:

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Thank you! I completed the functional programming module on the Data Engineer path. This was where the idea came from.

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That’s a great news @nityesh, thanks a lot! :grinning:
For me the DQ Community is a great resource to exchange ideas with other enthusiastic students, help them and get helped, find the room for improvement, which is not so easy to notice when learning alone.

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The Community has helped a lot in my learning. Also through guided projects, I get to learn new ways of coming up with informative and amazing data science projects. I also get to share my knowledge/skills to the community members to help them unblock the difficulties they are facing.

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Congraluations everyone :tada:

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Congratulation all Champions…! :trophy: :medal_sports:

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Congratulations champions! You make this community better :smiley:

Happy coding :smile:

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It is so amazing to see strangers, sitting worlds apart and having an impact on each other’s learning! That’s what our Community Champions were doing this week:

Personal Project sharers:

  • @animus.agbor shared their exciting personal project - Malaria Detection using Deep Learning. In this project, Animus tries to tackle the ambitious task of identifying whether a Red Blood Cell has been infected by Malaria parasites using neural networks.

    Also worth noting is the conversation that @animus.agbor and @WilfriedF had in that thread which allowed Animus to improve on their project!

  • @veratsien shared their attempt at coding a neural network model from scratch to build a digit classifier. As of now, this project is incomplete because of an unexpected issue with the scipy.optimize library.

    Can someone help them?

Most liked:

  • @moriturus7 shared an alternative to using Selenium for web scraping - Playwright.

    Despite its popularity, Selenium has quite a lot of disadvantages like poor scalability. Playwright is an alternative.

    That’s why I want to tell you about the library, which is quite famous among JS developers, but so far little known in the Python community.

    Max has become our community’s expert on web scraping as is evident from their detailed conversations about various web scraping solutions in various threads throughout the Community. This gives this post special credibility! Do check it out if you’re interested in web scraping.

Alternative solutions:

Guided Project sharers:

  • @adrianzchmn has shared another one of their beautiful projects with our community - Visualizing Earnings Based On College Majors. It has great analysis and beautiful visualizations. What more - they were also able to make multiple improvements to it based on @htw’s feedback!

  • @Andy shared an excellent Guided Project analyzing Hacker News posts. I love how Andy cared enough to format the outputs so they render pretty.

    This is my fourth week on Dataquest, and am having lots of fun learning to code around data! I started with absolutely no knowledge on coding, but have already learned so much in that short period of time. Dataquest is an exceptional platform with an amazing community!

    Awesome progress, Andy! :heart_eyes:

Guided Project reviewers:

This week our Guided Project sharers benefited from the excellent, actionable feedback provided by @htw and @chris_is_working! Thank you so much for your time, folks.

I’m so proud of the little community of Data Science learners that we are creating here! :heavy_heart_exclamation:

Thank you Champions for contributing to it. I appreciate your participation and would love to give y’all:

  • A 7 day extension on your current Dataquest subscription :rocket:
  • An exclusive Community Champions badge in our Community :medal_sports:


Before you go Champions, can you tell us - What’s one thing you learned last week that you didn’t know before?

Please share your answer by replying below! :smile:

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@nityesh Thank you for the shoutout. I’m a little embarrassed since it’s an unfinished project and I haven’t gotten back to it.

I’m not the first one to attempt this particular project so I was able to look up other approaches, it seems all of them are not generalized, i.e. the number of hidden layers and number of neurons are pre-determined and specified for the model. After a little digging, I think it’s because the underlying code of scipy.optimize.fmin_cg requires unpacking the weights from its vector form as the parameter. I did try to build the model with a specified hidden layer and neurons, the cost function did work but again the derivative function didn’t. I would greatly appreciate any help or input.

I am glad that I started this project though. If anything, the attempt of the project alone helped me in understanding neural network models better and I really enjoyed the math behind it.

In conclusion, I would appreciate any help or insight anyone can provide on this project. Also, it definitely makes me appreciate machine learning libraries like scikitlearn a lot more. What a time to be alive! :joy:

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Well done champions! I would like to thanks @moriturus7 for sharing the Playwright library, it’s a nice project going on. I’m quite interested in web-scraping too (I’m actually working on my personal project withBeautiful Soup at the moment).

Well done @htw and @chris_is_working! Leaving feedback is extremely useful both for you and for people receiving it, we learn much-much better this way.

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Hi, @artur.sannikov96

If you have any problems with Beautiful Soup or with the approach to the source through requests, which you probably use in conjunction with Beautiful Soup. You can write me a personal message and I will give you my recommendations for the solution.

Although I prefer to use LXML instead of Beautiful Soup

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@nityesh

Thank you very much! I am glad if my feedback helps others.

I guess participating in the discussions is partly due to the joy of helping others to solve problems and improve their coding skills, but it also helps my own progress, when I review code, think about alternative solutions and figure out why certain implementations don’t work or lead to the wrong results.

Best
htw

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I know, right. Amen to that! :joy:

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Thank you for the recognition @nityesh! Thank you @htw for helping me improve my project!
And congratulations everyone!

What’s one thing you learned last week that you didn’t know before?
The use of itertools, courtesy of @htw

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Congratulations Everyone! :tada:

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Here are our newest Community Champions: :tada:

Personal Project sharers:

Guided Project sharers:

Guided Project reviewers:

I also want to call out @WilfriedF and @jithins123 for the excellent feedback that they leave on Guided Projects shared by our community. Thank you for lifting up your peers!

And thank you everyone! :heart:

I am happy to give you:

  • A 7 day extension on your current Dataquest subscription :rocket:
  • An exclusive Community Champions badge in our Community :medal_sports:


Before you go Champions, can you tell us - what is one thing that you learned last week that you didn’t know before?

Please share your answer by replying below! :smile:

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Thank you for this amazing news @nityesh and congratulations to all the champions! :partying_face: One of the new things I learned this week was how to deal with SettingWithCopyWarning and RuntimeWarning. So now I don’t have anymore those scaring red rectangulars in my projects! :grinning:

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