Intro

This is the passion project that I did for the Metis bootcamp. It leverages convolutional neural network to perform artistic style transfer on videos.

Project Description

Hollywood is a complex human network. Producers, directors, writers, actors and cinematographers are working closely with each other to create movies. Their individual talents and the level of cooperation between them potentially have huge impacts on the outcome of movies.
My analysis identifies the factors that have statistical significance and quantifies their impacts on the revenue and profitability of movies.

First week at Metis was in the book. I would say that it has matched my expectation so far and I’m glad that I made the decision to join. Our cohort comes from diverse backgrounds and it was a pleasure to have the opportunity to work with some of them on our first project.
Our mornings started with pair programming exercises. The problems are not difficult but it’s inspiring that the instructor would make us think about improve the complexity of our algorithms. This is something that I did not pay much attention before coming from a non-CS background. However, I believe it’s critical to choose the blazingly fast algorithms when dealing with big data sets.
The project took a major chunk of our time. I learned tremendous amount of knowledge from my teammates, such as the BeautifulSoup, sqlite3, seaborn python packages and Carto. I’m still working on understanding HDF data format and how it’s related to the pandas panels.
The lectures are fairly light on the first week and I hope it’ll pick up pace in the coming weeks. Really looking forward to week 2. The to-do list includes:
- Clean up code for project 1.
- Wrap up the coursera algo design course.
- Understand regular expressions better.

Project Description

WomenTechWomenYes (WTWY) has an annual gala at the beginning of the summer. The management has decided to place street teams at entrances to subway stations to collect email addresses and those who sign up are sent free tickets to the gala.
Our task is use MTA turnstile data to optimize the placement of street teams so that they can collect the most signatures from people who will attend the gala and contribute to its cause.