NEW 3-Week Python for Beginners Course Starts July 27! View Course

Made at Metis: The Economics of Building Backyard Homes and a Local Live Music Recommender

By Emily Wilson • April 21, 2020

This post features two projects from recent graduates of our data science bootcamp. Take a look at what's possible to create in just 12 weeks.

Prospecting LA’s Backyard Houses with Machine Learning
Anupama Garla, Metis Bootcamp Graduate

For her final project during the bootcamp, recent graduate Anupama Garla looked into answering the question: Living in Los Angeles, should I build a backyard home for extra income? She began to build a tool, which homeowners could use to determine the potential income and the feasibility of such a project. From Anupama's point of view, this tool would be beneficial to both homeowners and people like her, who are in the market to rent or own in the area. Using publicly available Airbnb and LA Geo datasets, she first built a model that predicted the income of a property.

"I then built a database of properties that could feasibly accommodate a backyard house of any size," she wrote in a blog post about the project. "Then I used transfer learning to plug my backyard house database into my price prediction model to obtain each property’s predicted nightly income of their potential backyard house." 

In her post, she breaks down the project step by step and shares conclusions and ideas for future work. Read it here to get the full picture. 

A Content-Based Live Music Recommender
Gabriel Bond, Metis Bootcamp Graduate

To merge his interest in live music with data science, recent graduate Gabriel Bond chose to create a content-based live music recommender during the bootcamp.

"The result of this exploration utilizes unsupervised learning techniques, audio feature extraction with the LibROSA Python library, and both the Spotify and Songkick APIs to generate a playlist of songs by artists with upcoming shows in the user’s city based on the user’s favorite artists," he wrote in a blog post about the project. 

Read it here to find out how he completed the project and how you can replicate it yourself by accessing his GitHub. 


See more examples of Metis student projects here

Similar Posts

Made at Metis: Street Art to Fine Art; Building a Recommendation System

By Metis • May 26, 2020

This post features two projects from recent graduates of our data science bootcamp. Take a look at what's possible to create in just 12 weeks, including a project to leverage a user’s existing street art preferences to recommend visually-similar fine art and a project to develop a collaborative filtering recommendation system using sales transaction data.

With a Desire to Solve Problems, This Grad Turned to Data Science

By Emily Wilson • April 01, 2020

Once the concept of AI grabbed the attention of bootcamp graduate Alex Smith, she began a self-study regimen. “The more I learned about data science in particular, the more I became convinced not only of its power to solve difficult problems but convinced that I wanted to use this incredible toolset to solve problems that I care about,” she said. Read how she went from the humanities to a career in data science.

Course Report Features Metis Live Online Bootcamp Graduate

By Metis • May 21, 2020

In a Q&A session with Course Report, Metis Live Online Bootcamp graduate Anupama Garla shares her experience with the online classroom and learning style of the bootcamp, her advice for other career-changers, and her plans to innovate the world of architecture now as a data scientist.