ian-whitestone
commented
4 years ago PyData New York 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
Finding the ideal apartment in Toronto, let alone one you can afford, is a daunting & time consuming task. This talk will highlight how I used Python, Slack, and some machine learning techniques to find a place to live.
Abstract as a Tweet
Searching for apartments 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 machine-learning 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, like geo-spatial filtering and transit/walk score ranking. 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.
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
- Enriching the data with other features
- Geospatial data
- Walk score and transit scores, along with relative rankings for a given neighbourhood
- Price ranking with un-supervised clustering
- Including an overview of Word2Vec and how to use pre-trained image classifiers to extract information from images
- Will also discuss a quantile regression based approach
- 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
See the github repo for more details: https://github.com/ian-whitestone/toronto-apartment-finder. A note to the speaker selection comittee, the un-supervised clustering component is currently under development and will be added to the repo prior to the conference.
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/PyData Berlin 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 ClassificationAbstract
Finding the ideal apartment in Toronto, let alone one you can afford, is a daunting & time consuming task. This talk will highlight how I used Python, Slack, and some un-supervised clustering to find a place to live.
Abstract as a Tweet
Searching for apartments 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.
A rough outline of my presentation would be as follows:
See the github repo for more details: https://github.com/ian-whitestone/toronto-apartment-finder. A note to the speaker selection comittee, the un-supervised clustering component is currently under development and will be added to the repo prior to the conference.
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.