Predict House Prices Using DL and ML

Regression & Classification Prediction for House Price Download from Github: https://github.com/KevinChngJY/house_price_prediction

You are now following this Submission

Overview :

This is a very interesting exploration, I'm going to explore how to utilise different approaches (Deep Learning, Machine Learning, or combination of both technique) to predict house price. Here, I will train 2 types of prediction which are classification (very cheap,cheap, normal, expensive, very expensive) and regression (House Price) . The accuracy might still not optimistic due to insufficient data or information, however, it is interesting to see how to use different approaches to predict house price.
Let me know, if you have better solution, then we can enrich our solution.

Highlights :

Technique to use machine learning for classification prediction and regression prediction
Technique to use deep learning for classification prediction and regression prediction
Compare different technique, familiar with how you can play around technique in Machine Learning and Deep Learning

Product Focus :

MATLAB
Parallel Computing Toolbox
Machine Learning and Statistics Toolbox
Deep Learning Toolbox

Written at 25 September 2019

The dataset you may download from :
Download from Github: https://github.com/KevinChngJY/house_price_prediction
However, it does not have my trained deep learning model due to the size is too big.

If you want to download the entire dataset include my trained deep learning model, you may download from
https://www.dropbox.com/s/auh8hov07fwoa14/Predict%20House%20Price%20%28Script%20%26%20Dataset%29.zip?dl=0

Cite As

Kevin Chng (2026). Predict House Prices Using DL and ML (https://ch.mathworks.com/matlabcentral/fileexchange/72855-predict-house-prices-using-dl-and-ml), MATLAB Central File Exchange. Retrieved .

Categories

Find more on Deep Learning Toolbox in Help Center and MATLAB Answers

General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
Version Published Release Notes Action
1.0.2

New Changes in description

1.0.1

Change Description

1.0.0