Project: Amsterdam House Price Prediction
Photo by Adrien Olichon on Unsplash
Objective: Predict House Pricing and Illustrate Machine Learning Workflow
Framework:
- The problem is framed as a regression problem since the target has continuous numerical values.
- I used a supervised learning approach
- The model will be trained offline in one batch
- The primary performance metric will be r2, combined with Root Mean Square Error (RSME) and Maximum Absolute Error (MAE)
- We aim for a model with an r2 of at least 0.7.
How would we solve the problem manually:
- One can derive the price of a given house by comparing the price with other similar houses, e.g., on pararius.nl.
List assumptions coming from research questions:
- the bigger the area of a house, the higher the cost of a house
- the bigger the number of rooms, the higher the cost of a house
- a closer location to the city centre would lead to higher housing costs.