Update README.md

README.md

@@ -35,5 +35,23 @@ To use the model to predict house rents, run the predict.py script. You will be
 
 To run the script: python test.py
 
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+The model you'll be interacting with is a machine-learning model specifically designed to predict house rent prices based on various property features. 
+It's been trained on a dataset of housing information and uses what it learned to make predictions for new, unseen houses.
+Rent: The existing rent of the house.
+Property Type: The type of property, such as apartment, house, etc.
+Area: The area where the house is located.
+Suburb: The suburb within the area where the house is located.
+Bedrooms: The number of bedrooms in the house.
+Bathrooms: The number of bathrooms in the house.
+Garages: The number of garages the house has.
+nGparking: The number of non-garage parking spaces the house has.
+Floor Size: The size of the house in square feet or meters.
+Pool: Whether the house has a pool (1 if yes, 0 if no).
+Garden: Whether the house has a garden (1 if yes, 0 if no).
+Study: Whether the house has a study or office room (1 if yes, 0 if no).
+Pets: Whether pets are allowed in the house (1 if yes, 0 if no).
+Furnished: Whether the house is furnished (1 if yes, 0 if no).
+Fibre: Whether the house has fibre internet connection (1 if yes, 0 if no).
+Based on the information you provide for a house, the model will give an estimate of what it thinks the house's rent would be.
+Please note that while the model tries its best to make accurate predictions, there is error in its estimates.