The rent of a house depends on a lot of factors. With appropriate data and Machine Learning techniques, many real estate platforms find the housing options according to the customer’s budget.
To build a house rent prediction system, we need data based on the factors affecting the rent of a housing property. I found a dataset from Kaggle which includes all the features we need. You can download the dataset from here.
- BHK: Number of Bedrooms, Hall, Kitchen.
- Rent: Rent of the Houses/Apartments/Flats.
- Size: Size of the Houses/Apartments/Flats in Square Feet.
- Floor: Houses/Apartments/Flats situated in which Floor and Total Number of Floors (Example: Ground out of 2, 3 out of 5, etc.)
- Area Type: Size of the Houses/Apartments/Flats calculated on either Super Area or Carpet Area or Build Area.
- Area Locality: Locality of the Houses/Apartments/Flats.
- City: City where the Houses/Apartments/Flats are Located.
- Furnishing Status: Furnishing Status of the Houses/Apartments/Flats, either it is Furnished or Semi-Furnished or Unfurnished.
- Tenant Preferred: Type of Tenant Preferred by the Owner or Agent.
- Bathroom: Number of Bathrooms.
- Keras Sequential Model
- Dense and LSTM layers
Enter House Details to Predict Rent
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Number of BHK (1-6): 1
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Size of the House (10-8000): 1000
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Area Type (Super Area = 1, Carpet Area = 2, Built Area = 3): 3
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City Codes: Mumbai: 4000 Chennai: 6000 Bangalore: 5600 Hyderabad: 5000 Delhi: 1100 Kolkata: 7000
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Pin Code of the City: 7000
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Furnishing Status of the House (Unfurnished = 0, Semi-Furnished = 1, Furnished = 2): 0
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Tenant Type (Bachelors = 1, Bachelors/Family = 2, Only Family = 3): 2
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Number of bathrooms (1-10): 1
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Predicted House Price =
[[22519.922]]