Predict House Prices Using DL and ML

Versión 1.0.2 (4,66 MB) por Kevin Chng
Regression & Classification Prediction for House Price Download from Github: https://github.com/KevinChngJY/house_price_prediction
564 Descargas
Actualizado 25 sep 2019

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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

Citar como

Kevin Chng (2024). Predict House Prices Using DL and ML (https://www.mathworks.com/matlabcentral/fileexchange/72855-predict-house-prices-using-dl-and-ml), MATLAB Central File Exchange. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2019a
Compatible con cualquier versión
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Predict House Price

Versión Publicado Notas de la versión
1.0.2

New Changes in description

1.0.1

Change Description

1.0.0