Building a House Price Recommendation System Using Machine Learning

Local Chapter Berlin, Germany Local Chapter

Coordinated by ,

Project background.

Houses play an integral role in our day-to-day lives, serving as the hub for activities such as eating, sleeping, unwinding, and nurturing our families. This pivotal element in our lifestyle, however, comes with a considerable price tag. Over the past seven years, the cost of housing in the EU has surged by approximately 50%, significantly outpacing the average wage growth of 11%.

Moreover, the housing sector is a key component of our economic structure. It constitutes a substantial portion of household wealth, often used as collateral for loans and contributes considerably to the construction industry.

In light of the rapid adoption of remote working arrangements following the Covid-19 pandemic, the real estate market is expected to experience prolonged volatility.

Project plan.

  • Week 1

    – DATA GATHERING: choose a real estate website and scrape it
    – MODELLING: get familiar with the neural network model we will be using. Reading the article describing it and finding a dataset to start playing
    – DEPLOYMENT: get familiar with Docker, GCP, FastAPI, and a frontend solution

  • Week 2

    – DATA GATHERING: improve on the scraping
    – MODELLING: prepare inputs for the neural network, first results
    – DEPLOYMENT: deploy mock frontend

  • Week 3

    – DATA GATHERING: finalize scraping
    – MODELLING: tune the model
    – DEPLOYMENT: deploy the current version of the model

  • Week 4

    – MODELLING: finalize the model
    – DEPLOYMENT: deploy the last version of the model

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