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LinMingjie / repository
A decently featured microblogging web application written in Python and Flask that I studied as part of Flask Mega-Tutorial. http://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world
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A decently featured microblogging web application written in Python and Flask that I studied as part of Flask Mega-Tutorial. The code was in sync with current versions of Python and Flask in June 2016.
The tutorial referenced above explains how to setup a virtual environment with all the required modules.
The sqlite database must also be created before the application can run, and the db_create.py script takes care of that. See the Database tutorial for the details.
To run the application in the development web server just execute run.py with the Python interpreter from the flask virtual environment.
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me3141pi-ui /
Python script to visualise the mandelbrot set (can zoom into any part of the mandelbrot set upto a decent level and create zoom gifs / also has feature to generate the mandelbrot set in patches to be more memory inefficient).
44/100 healththemoemansour /
A modern, high-performance **mouse & keyboard automation tool** built with Python and Tkinter. Designed for precision, speed, and usability — featuring a decent UI, global hotkeys, human-like randomization, and persistent settings.
44/100 healthzrqxiangshuijiao /
I built a Bike-sharing System Analysis and Prediction system in Python based on regression models. This Bike-sharing System contains data of over 17 million bike rides in New York City during 2018. It’s my first time analysing and visualizing such large data sets. It was not smooth at the beginning. I encountered a huge problem that the score for prediction was incredibly low. It sparked my curiosity to conduct research on why it is so low and how I could improve it. After modelling on three different regression models, lag feature, ridge, and lasso regression, I finally got a decent prediction.