Predicting HDB Housing Prices Using Neural Networks

Predicting HDB Housing Prices Using Neural Networks

Predicting HDB Housing Prices Using Neural Networks. I will work on a deep dive into the data visualization aspect to derive deeper insights into Singapore HDB housing market for my readers.

Ever wondered how much your HDB is worth besides asking your housing agent? Or wondered what are the main features that have a great impact on your HDB? Seeing that over 92.2% of Singaporean own HDB, I believe these are common questions that most Singaporeans have had, and it would be good to see if I can use data analytics to derive any insights into the Singapore government housing market. The sequential prediction model gives a Mean Absolute Error of $22,429(+/- 571.83)

For more information on visualization check out my tableau public profile. For more information on the code check out my GitHub. All data is obtained at data.gov.sg.

Overview

All data is available on data.gov. The following data analytics project follows a typical methodological approach (CRISP-DM).

  • Data Cleaning +Exploratory Data Analysis
  • Sequential Neural Network (3 Layers)
  • Evaluation
  • Conclusion
  • Future Work

In the EDA stages that follows, I will work on a deep dive into the data visualization aspect to derive deeper insights into Singapore HDB housing market for my readers.

Data Cleaning

The dataset comprises of data spanning from 1990 to 2020. There is a total of 677,114 rows and 10 columns. While there are no null values in the dataset there are a few abnormally (e.g. caps vs uncapped features, etc.). For one, since we are considering flats that are being sold after MOP (Minimum Occupancy Period) any flats that have more than 95 year lease(remaining lease) are removed.

However, in the data set there are large numbers of duplicates and they have been removed.

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