Big Data Analytics for Smart City Infrastructure - F23
General
- Undergraduate; 4th year
- 50 learners; teams of 4
- 75 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 1 projects wanted
- Anywhere
- Academic experience
- Any company type
- Any industries
Categories
Project timeline
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September 30, 2023Experience start
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December 19, 2023Experience end
Overview
- Details
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Student-consultants will analyze city data sets (normally available through open city portals, etc.) through state-of-the-art Machine Learning and Data Mining technologies, to: identify trends, and/or create predictive models. Their models are used to create solutions for infrastructure sectors (transportation, building, energy, urban water/drainage, etc.) which can be deployed using digitalization in smart cities.
- Learner skills
- Machine learning, Data analysis, Sustainability, Rapidminer, Infrastructure engineering
- Deliverables
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The student will deliver the following:
- A 10 - 15 page report, explaining their problem statement and objectives, the methods they followed, The model they developed, and Their results;
- A 10-15 minute presentation
- The model(s) developed (in form of RapidMiner processes), as well as the pre-processed data they used
Project Examples
Student-consultants will analyze urban data sets using data mining and machine learning technologies to improve city efficiency, sustainability and resilience.
Some past project examples include:
- Road Condition Assessment through Data Mining
- Real Estate Price Forecast through Data Mining
- Predictions for Available Parking Spots in Various North American Cities
- Analysis of Road Safety and Road Accidents
- Improving Building Thermal Comfort and Energy Performance using Machine Learning
- Analysis and Prediction of Energy Consumption Behavior at Building, District and City Level
Required questions to apply
Companies must answer the following custom questions in order to apply to this experience:
provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.
Do you agree to provide data so the students can work on them?