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House Rent Prediction ​

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Overview ​

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. So, if you want to learn how to use Machine Learning to predict the rent of a house, this article is for you. In this article, I will take you through the task of House Rent Prediction with Machine Learning using Python.

The rent of a housing property depends on a lot of factors like:

  • number of bedrooms, hall, and kitchen
  • size of the property
  • the floor of the house
  • area type
  • area locality
  • city
  • furnishing status of the house

Here is a dataset of house rent prices in a particular city. Below are the features in the dataset:

  • 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.
  • Point of Contact: Whom should you contact for more information regarding the Houses/Apartments/Flats.

Objectives ​

Your task is to predict the rent of a house based on the given features using Machine Learning algorithms.