Data Preparation - Forecasting the price of a house

What is the first step to solving the problem of forecasting the price of a house based on its characteristics?

• Feature engineering

• Data collection

• Finding correlations

To forecast the price of a house, you need to use a model of ….

• Clustering

• Classification

• Regression

What feature would be useful (and possible) to create from our dataset?

• The price per m² of the district

• Proximity to shops

• Days of sunshine in the city

Which of the following features is the most useful for predicting the price of the house?

• The number of swimming pools

• The price per m² of the district

• The living area in m²

• The year of construction

If we do not have the living area, which feature containing equivalent information can we use?

• The number of rooms

• The distance to the city center

• The number of bathrooms

If we had to choose only 3 features, which would we use to train our model?

• The surface, the selling price of the house and the year of construction

• The surface, the number of rooms and the year of construction

• The area, the average price per m² of the district and the year of construction