README Introduction This repository contains a Gradient Boosting Regressor model trained to predict house rents. The model was trained on a dataset that was preprocessed and cleaned to ensure the best possible predictions.
Getting Started These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
Prerequisites You need Python 3.7 or later to run the scripts. You can have multiple Python versions (2.x and 3.x) installed on the same system without problems.
In Ubuntu, Mint and Debian you can install Python 3 like this: sudo apt-get install python3 python3-pip
For other Linux flavors, macOS, and Windows, packages are available at
Required Python Packages You will also need the following Python packages:
pandas sklearn joblib These can be installed using pip: pip install pandas sklearn joblib
Cloning the Repository To clone this repository, run the following command in your terminal: git clone
Running the Script To use the model to predict house rents, run the predict.py script. You will be asked to input data for 'Area' and 'Suburb'. The script will then print the predicted rent.
To run the script: python test.py
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.
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