Visualization and Prediction of Singapore's Housing Resale Prices

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Hi, check out my streamlit deployment of the random forest model I trained to predict Singapore’s housing resale prices.

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Check out the app on Streamlit Sharing here!

Aim of the app

This app can allow users to visualize housing resale prices for the past 30 years and get a prediction price for a flat that they are interested in.

  1. This app uploads the model and data, and plots an interactive user controlled map of the prices from 1990 to 2020.
  2. It also allows users to enter their interested flat address and the flat features, and it will use OneMap.sg api and the geopy package to locate nearby amenities (e.g., train station, schools, supermarkets) and the distance to the nearest one. Features will then be enginneered and passed into the model to make a prediction.
  3. A map showing the location of the user input flat and its surrounding amenities is displayed
  4. A SHAP force plot is plotted to show feature importance.
  5. Information about nearby amenities of the flat are also displayed.

The repo for the whole project is here.

If you like my work, please :star: my repo to support me. Thank you!

Help me!!

I am trying to deploy and share the app for everyone to use. So it would be really great if I can get access to Streamlit Share. If anyone can extend an invitation to me, I will really appreciate it. Thank you!

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Terrific app @teyang-lau! :clap:

I’m sure @tc1 can give you access to Sharing in no time! :slight_smile:

Charly

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This looks amazing @teyang-lau :heart: - just sent over an invite to sharing, you should get it in the next few minutes via the email you signed up with :blush:

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Nice use of the Joy Division chart type! :man_singer:

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My deployment to Streamlit Sharing is finally working!

Here is the link.

Thank you to the Streamlit community for their help and hard work, especially @andfanilo and @randyzwitch for helping me diagnose the issues I experienced!

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This is a great project and I’ll be looking at it with interest! Well done and thanks.

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Thanks for the kind words and encouragement!