Machine Learning

Machine Learning Project

Within this project, my group and I analysed data from Airbnb using unsupervised machine learning to gain a deeper insight into the tourist industry and the factors affecting pricing, namely in the United States on P2P hosting platforms. After cleaning and normalising our chosen data, we trained our machine learning program of k-means clustering due to its versatility, scaleability and ease of interpretability.

We found limited insights due to the limitations of the data and the external factors that we could not analyse, but it could be very beneficial for companies like Airbnb to implement machine learning as it could be continuously updated and provide more accurate insights. Read the full report below to gain a more comprehensive understanding of the capabilities and limitations of unsupervised machine learning within the industry.

Click here to view the full report:

  • Unsupervised machine-learning

    • CLick here to download the python code for this prject:

  • Data cleaning code
  • Feature Engineering code
  • Machine Learning (k-means) code