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license: apache-2.0 |
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README |
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Introduction |
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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. |
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Getting Started |
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These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. |
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Prerequisites |
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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. |
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In Ubuntu, Mint and Debian you can install Python 3 like this: sudo apt-get install python3 python3-pip |
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For other Linux flavors, macOS, and Windows, packages are available at |
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https://www.python.org/getit/ |
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Required Python Packages |
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You will also need the following Python packages: |
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pandas |
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sklearn |
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joblib |
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These can be installed using pip: pip install pandas sklearn joblib |
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Cloning the Repository |
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To clone this repository, run the following command in your terminal: |
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git clone <repository-link> |
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Running the Script |
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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. |
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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. |
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It's been trained on a dataset of housing information and uses what it learned to make predictions for new, unseen houses. |
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Rent: The existing rent of the house. |
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Property Type: The type of property, such as apartment, house, etc. |
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Area: The area where the house is located. |
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Suburb: The suburb within the area where the house is located. |
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Bedrooms: The number of bedrooms in the house. |
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Bathrooms: The number of bathrooms in the house. |
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Garages: The number of garages the house has. |
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nGparking: The number of non-garage parking spaces the house has. |
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Floor Size: The size of the house in square feet or meters. |
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Pool: Whether the house has a pool (1 if yes, 0 if no). |
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Garden: Whether the house has a garden (1 if yes, 0 if no). |
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Study: Whether the house has a study or office room (1 if yes, 0 if no). |
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Pets: Whether pets are allowed in the house (1 if yes, 0 if no). |
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Furnished: Whether the house is furnished (1 if yes, 0 if no). |
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Fibre: Whether the house has fibre internet connection (1 if yes, 0 if no). |
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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. |
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Please note that while the model tries its best to make accurate predictions, there is error in its estimates. |
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