I would like to ask you to give me any feedback about my code. I am a newbie in coding, so the first thing I want to understand the quality of coding. My style is bad and not accurate, I know that. But I don’t understand how my code looks like.
Thank you very much.
And sorry for my bad English
Thank you very much for sharing your hard work with DQ community.
First thing you have to keep in mind that Data scientists are not born they are made. The standard strategy to becoming a Data scientist is studying hard, dedication, reading books and taking online courses on all kinds of algorithms and techniques. But that is not enough to solve real issues. For that you need experience, and you can only get experience with time and work. As a newbie it is really hard to find a opportunity to work with real issues and get that experience. However, DQ does a great job to bridge this gap into some extend as these guided projects are giving you a very good opportunity to get the experience by working with real issues.
So don’t worry about mistakes and shortcomings that occur during the initial stage. However, the important thing is figuring out your mistakes and shortcomings and working on those issues.
Now lets look at your project. In general, I do not see a big issue in your project but still there are certain areas that you have to work on to improve your project. I would like to propose following improvements for your project.
Always start the project with brief but meaningful introduction about project and dataset that you are going to work with.
Maintain separate cell for importing libraries and modules for entire project. better to use the cell after the introductory section.
Simplicity and readability of the codes are very important. Python language has some programming guidelines outlined in PEP8 - for example consistent indentation, a specific line length, writing one statement per line only, and formulating pieces of code in a rather explicit than implicit way.
Better to add comments for each code block by explaining what does that specific code or code block do. This will help readers to understand your codes and logic behind the codes easily. Always make a priority of keeping the comments up-to-date when the code changes.
Avoid very long titles and subtitles
Please refer to An In-Depth Style Guide for Data Science Projects to learn more about Data Science project formats.
I hope my feedback’s useful and will help with some more immediate improvements.
Congratulations for having completed your second project on exploring hacker news. My humble suggestion is that you take into consideration all that have been mentioned by @Scylla and with persistence you put all (codes and the styles) shall be well.
Your code looks good. To make the project report better you have received almost all the good points from @Scylla. If you follow that and add a great narrative to support your code, you will have a great report.
I’d also suggest to print more self-explanatory output.
print(total_show_comments) avg_show_comments = total_show_comments/len(show_posts) print(round(avg_show_comments)) Output : 11988 10
It is better to print
print("Total Show comments : " , total_show_comments) avg_show_comments = total_show_comments/len(show_posts) print("Avg show comments : " , round(avg_show_comments)) Output : Total Show comments : 11988 Avg show comments : 10
Also in the final output, it would have been better to add the Timezone details. Since the data was collected in EST, 15:00 EST would make more sense than simply giving 15:00 is the best time to post.
I hope this helps. Keep up the good work and have a happy learning.
Great! Thank you very much. I’ve saved your recommendations
You are right, thank you.
Kept it in my mind