We are an active, friendly community of data scientists based in Toronto. Trying to utilize Machine Learning techniques to solve real-world problems.

Project Jam Weekend

On our project jam weekends, you are welcome to drop by and:

• pitch machine learning/data mining project you are working on: find collaboration/teaming opportunity
• short-form present your project at the end of the session
• decide the capstone/presentation date for the project and a broader community of audiences/potential business partners will be invited to attend

or join

Existing Projects

1. build toronto election ward demo database (background data for people) + integrate activity/behavior data (automatic streaming of tweets and 311 calls (city of toronto open data)

• Integrated Application Goals to achieve for beneficiaries (real estate developers, small business owners, home finders/renters, city urban planning, city social service providers, MPs-election)

  (1) identify business development opportunities to benefit real estate developers, small business owners to open business, home finders, home renters (factors include: rent increase rate, house price rate, building types)

  (2) identify the face/and changing face/service request needs of the election-ward and link it to election results to benefit MPs

  • demographics, populatioin densities (demo factor): status quo and changing trend overtime
  • economic factors: unemployment rate: status quo and changing trend overtime
  • livelihood: 311 social service requests + tweets: status quo and chaning trend overtime
  • crime rate: status quo and changing trend overtime

  (3) identify urban change: for urban planners , city officials

• data sources:

  (1) background data

  - statscan census
      
  - (ward profiles toronto city)[(https://www1.toronto.ca/wps/portal/contentonly?vgnextoid=2394fe17e5648410VgnVCM10000071d60f89RCRD]
  

• steps/stages:

  (1) build mongodb db hosted on the cloud for the background data (), rent prices, house prices

      
  **achieving:** Identify where different immigrant communities settle in GTA: (a) the status quo before the latest cencus (2) reflect the change over time (3) link it to change of election results if any + change in rent + change in house prices + change to social services request 
      
  - BeautifulSoup to scrape Stats Canada web site.
  - integrate data for mapping: ggmaps
  - integrated interactive map hosted on the cloud: explore bokeh or others
      
  Reference: http://benrifkind.github.io/Mapping-Stats-Canada-Data-with-R-part-1-of-3/
      
  

  (2) integrate (automatic streaming from 311 calls json format from website) + tweets (json format) into mongodb

  (3) make user interface on the mongodb for data visualization (python/d3.js)

  (4) predictive model for election result and others/sentiment analysis/etc.

• project lead:

  
  Harriet,  
  
  Patrick (DATA SCIENCE AD ANAYSIS) : step 2 - automatic streaming of 311 json from city of toronto website
  
  Anna : step 2 for tweets, 3, 4 
  
  Martin: Step 2 for tweets 
  
  Holly: Step 4
  
  Ayazhan Zhakhan: step 4
  
  Ashraf Ghonaim
  
  
  

• capstone date:

  Step 1 + 2 : Capstone date - tentative Sep 30th

• code base / project page:

• To-do Issues:

  (1) find a free cloud hosted mondb db

  (2) get a list of issues/topics for sraped tweets:

  • use help of news report website

  • generate keywords for filter tweets

Jam Days

August 19, 2017:

projects pitched:
 1. build toronto election ward demo database (background data for people) + integrate activity/behavior data (automatic streaming of tweets and 311 calls (city of toronto open data)
 2. driveless cars
 3. Kaggle image processing

August 25, 2017:

project pitched:

1. predictive model to identify terrorist groups responsible for an terrorist event

* data source
* GitHub repo
* slack channel
* dropbox
* project leads: Ayazhan Zhakhan, Ashraf Ghonaim, Raul Samayoa
* accomplishments: 

2. build toronto election ward demo database (background data for people) + integrate activity/behavior data (automatic streaming of tweets and 311 calls (city of toronto open data)

* slack channel: https://torontods.slack.com/messages/G6S24FRT9/details/
* GitHub repo
* project team: Harriet, Anna, Ayazhan Zhakhan, Ashraf Ghonaim
* accompolishments:
  
  (a) scrape statscan census data
  
  Things to be done:
  (a) inflow statscan census data into local mongodb
  
  ref: install mongodb on local windows: https://docs.mongodb.com/getting-started/shell/tutorial/install-mongodb-on-windows/
  
  (b) migrate it into a cloud-hosted mongodb

More Information

Our list of past and future events can be found in our meetup site.

Email to ask to join or speak.

Contact us

datascientistswithoutbordersto@gmail.com

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