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A project initiated to participate in DataMeka Singapore Competition to predict house prices. Multiple models are developed and compared, whereas LightGBM performs the best.

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HkFromMY/datameka-singapore-housing-price-prediction

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Background

House Hacking: Predicting Singapore’s Housing Prices 

The Singapore housing market has undergone an unprecedented rise in prices in recent years especially since COVID outbreak. Due to this price surge, it is a significant concern for the general population looking for a place of residence, as well as for the government attempting to maintain a stable real estate market. 

  1. Model Building

Build a predictive model to forecast private sector housing prices in Singapore for the next three months. Key Granularity:

  • In the train and test dataset, each row has a contract date and a property key that identifies a specific real estate property in the same building with the same size

Dataset Description

Overview

The dataset provided contains historical rental price data for various districts in Singapore, dating back to 2018. It includes variables such as property type, address, median rental price, and other relevant features. Participants are encouraged to use additional data sources, if needed, to improve their forecasting models

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Competition URL: https://datameka.com/competition/9389d2dc-5cfe-41d5-b057-ada6500ebb15?tabIndex=1

Model's Notebook: Preprocessing & Modelling - DataMeka.ipynb

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A project initiated to participate in DataMeka Singapore Competition to predict house prices. Multiple models are developed and compared, whereas LightGBM performs the best.

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