Use this script to run the model - look in Suburbs.csv and Areas.csv to find the correct format for your suburb and area

test.py

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+import pandas as pd
+from joblib import load
+
+# Load the trained model
+model = load('bestmodelyet.joblib')
+
+# Load the label encoders
+le_area = load('le_area.joblib')
+le_suburb = load('le_suburb.joblib')
+
+# Get user input
+property_type = int(input("Enter the Property type (0 for Apartment, 1 for Townhouse, 2 for House): "))
+area = input("Enter the Area: ")
+suburb = input("Enter the Suburb: ")
+bedrooms = float(input("Enter the number of Bedrooms: "))
+bathrooms = float(input("Enter the number of Bathrooms: "))
+garages = float(input("Enter the number of Garages: "))
+ngparking = int(input("Enter the number of dedicated non-garage parking spots (if none, enter 0): "))
+floor_size = int(input("Enter the floor size: "))
+pool = int(input("Enter 1 if there is a Pool, 0 otherwise: "))
+garden = int(input("Enter 1 if there is a Garden, 0 otherwise: "))
+study = int(input("Enter 1 if there is a Study, 0 otherwise: "))
+pets = int(input("Enter 1 if Pets are allowed, 0 otherwise: "))
+furnished = int(input("Enter 1 if the property is Furnished, 0 otherwise: "))
+fibre = int(input("Enter 1 if there is Fibre internet, 0 otherwise: "))
+
+# Transform the user input
+area_encoded = le_area.transform([area])
+suburb_encoded = le_suburb.transform([suburb])
+
+# Create a dataframe from the user input
+df = pd.DataFrame({
+    'Property_type': [property_type],
+    'Area': area_encoded,
+    'Suburb': suburb_encoded,
+    'Bedrooms': [bedrooms],
+    'Bathrooms': [bathrooms],
+    'Garages': [garages],
+    'nGparking': [ngparking],
+    'floor_size': [floor_size],
+    'Pool': [pool],
+    'Garden': [garden],
+    'Study': [study],
+    'Pets': [pets],
+    'Furnished': [furnished],
+    'Fibre': [fibre]
+})
+
+# Predict the rent
+predicted_rent = model.predict(df)
+
+# Print the predicted rent
+print(f"The predicted rent is: {predicted_rent[0]}")