Building a House Price Recommendation System Using Machine Learning

This Omdena Local Chapter Challenge runs for 4 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Berlin, Germany Local Chapter.

The problem

So how much is the fair price for a dwelling? How much should you pay for your home or ask for when you sell it?

The amount of pricing information online is enormous, so much so that it’s hard for any one person to keep an overview. Using machine learning to boil the available information down to a certain price would be useful for both buyers and sellers.

The goals

The project goals are divided into 3 areas: data gathering, modeling, and deployment. 

• In the data gathering section, we will scrape real estate websites for structured data (price, location, number of rooms, type of dwelling, etc.) and unstructured data (house descriptions, location descriptions, etc.). 
• The modeling section will be devoted to leveraging these inputs to forecast a price. It is a non-trivial neural network application that will deal with structured and unstructured inputs. 
• Finally, in the deployment, we should allow users to input the data for a dwelling and receive a suggested price.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work