ian-whitestone
commented
5 years ago PyCon Canada 2019 Talk Proposal
Title: Gimme Shelter: Using Python to Find an Apartment in Toronto
Duration: 30 min
Language: English
Level: Beginner
Categories: Python, Data Mining / Scraping, Word2Vec, Unsupervised ML, Image Classification
Abstract
With a continued shortage of rental units, finding the ideal apartment in Toronto, let alone one you can afford, is a daunting & time consuming task. But rest assured, Python plus a bit of webscraping can go a long way. This talk will highlight how I used Python, Slack, and some un-supervised clustering to find a place to live.
Description
In this talk, I will dive into how I used Python to continually scan apartment listings on Craigslist and Kijiji, filter them based on my preferences, and send them to slack where my girlfriend and I could discuss and upvote different options. I will talk about some unique features I built using the Google Maps API, like distance to the nearest subway station & commute time to work. We will then discuss how I utilized Word2Vec and a pre-trained image classifier for un-supervised clustering and price comparisons. I will finish off with what I learned and what I would have done differently.
Outline
A rough outline of my presentation would be as follows:
- The terrible experience of apartment searching
- Getting the data
- Including an overview of different web scraping methods in Python (requests/beautifulsoup/selenium/requests-html)
- Quick AWS architecture overview
- Enriching the data with other features
- Distance to nearest subway station
- Commute time to work
- Price ranking with un-supervised clustering
- Including an overview of Word2Vec and how to use pre-trained image classifiers to extract information from images
- Using Slack as a notifications system and a free GUI
- Including an overview of different options for interacting with slack: webhooks, bot users, and apps
- Lessons learned
- Q&A
Audience Take-Aways
My primary objective is that audience members walk away feeling motivated and inspired about how powerful Python is and all its different applications. In addition to this, they will get a deeper understanding of three specific areas: (1) web scraping, (2) interacting with slack, and (3) un-supervised clustering.
1) I'll cover some of the more difficult aspects of web-scraping, like how to extract javascript generated content. I will highlight one of the lesser known packages for doing this: html-parser.
2) When it comes to programming an integration for Slack, there are many different options to consider and it can be difficult to quickly figure out which one is best for you. I will highlight the different options for interacting with Slack via python: webhooks, bot users and apps, and talk about when you should use each option along with some starter code.
3) Un-supervised clustering is a powerful machine learning technique that is often used in data exploration, but rarely used in production systems. Audience members will learn about how this ML technique works, and see a production application of it for comparing apartment listings. They'll also learn about some powerful techniques for text and image representation.
Extra Requirements
None.
About Me
As a student of Chemical Engineering at Queen's University, Ian was pursuing a career back home in Calgary, Alberta in the Oil & Gas industry. During one summer, he was thrown into the world of data science when he started trying to make money by using Python to optimize daily fantasy sports lineups. After the oil price crashed, he realized he should probably look for work in another industry.
With a new found passion for data science, Ian started his career working for Capital One in Toronto as a data scientist. For close to three years, Ian worked on operational monitoring across the business, credit risk analysis, data infrastructure & risk models. Looking to experience work in another indsutry, Ian started working as a product data scientist for Shopify where he currently spends his days doing analysis and building data products to help make commerce better for everyone. In his spare time, Ian likes to participate in hackathons, work on side projects and play spikeball.
PyCon Canada 2018 Talk Proposal
Title: Gimme Shelter: Using Python to Find an Apartment in Toronto Duration: 10 min Language: English Level: Beginner Categories:
web scraping,slack,google maps apiAbstract
With a continued shortage of rental units, finding the ideal apartment in Toronto, let alone one you can afford, is a daunting & time consuming task. But rest assured, Python plus a bit of webscraping can go a long way. This talk will highlight how I used Python and Slack to find a place to live.
Description
In this talk, I will dive into how I used Python to continually scan apartment listings on Craigslist and Kijiji, filter them based on my preferences, and send them to slack where my girlfriend and I could discuss and upvote different options. I will talk about some of the neat features I added on top of basic "price" and "number of bedroom" filters, like how far away apartments are from various different subway stations, and how long my commute time would be to work. I will finish off with what I learned and what I would have done differently.
A rough outline of my presentation would be as follows:
Why searching for apartments sucks (1 minute)
Getting Data (3 minutes)
Enriching the Data with Other Features (2 minutes)
Using Slack as a Notifications System, and a free GUI (2 minutes)
Wrapping Up (1 minute)
Questions (1 minute) End with time for the audience to ask questions.
See the github repo for more details: https://github.com/ian-whitestone/toronto-apartment-finder
About Me
As a student of Chemical Engineering at Queen's University, Ian was pursuing a career back home in Calgary in the Oil & Gas industry. During one summer, he was thrown into the world of data science when he started trying to make money by using Python to optimize daily fantasy sports lineups. After the oil price started crashing, he realized he should probably look for work in another industry.
With a new found passion for data science, Ian started working for Capital One in Toronto as a data scientist. For the past two years, he has been working on operational monitoring across the business, credit risk analysis, data infrastructure & risk models. In his spare time, Ian likes to participate in hackathons, work on side projects (usually involving a raspberry pi), or eat burrito boyz.