Notebook

Singapore HousePrice Prediction

Conclusion

A decision tree is one of the best tools to predict real estate prices

• It accepts numerical and categorical data (for example, sq meters of the house and the name neighborhood).
• It is pretty cheap and low-resource consuming.
• It allows us to understand which features of the house drive the price higher

Unlike neuronal networks, with decision trees, we can easily track the decisions taken by the model, understanding why a house is cheaper than others.

<aside> 📕 Conclusion: The main complexity in predicting real-estate prices is not the model itself but the data; gathering information about how many schools are in the area, the criminality rate, and how many bars or clubs are nearby can be very challenging.

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⇒ In compare with Linear Regression, Lasso,… Decision Tree has the highest accuracy