This project aims to predict the rent of a house based on various features such as location, Furnishing Status, square footage, etc. The model has been trained on a dataset consisting of historical rental prices for houses in various Indian cities.
Related Notebook for this project: House Rent Prediction
Data: This folder contains the dataset used for training the model.src: This folder contains the source code for the project.static: This folder contains static files used by the web app.templates: This folder contains HTML templates used by the web app..gitignore: This file specifies the files and folders that should be ignored by Git.LICENSE: This file contains the license information for the project.README.md: This file contains the documentation for the project.app.py: This file contains the code for the Flask web app.requirements.txt: This file contains the list of Python dependencies required to run the project.model_trainer.py: This file should be run before app.py, as it creates the preprocessing object and trains the machine learning model.setup.py: This file contains information about the project, such as its name, version, and dependencies.
- Clone this repository to your local machine.
- Install the required dependencies by running pip install -r requirements.txt.
- Run model_trainer.py to preprocess the data and train the machine learning model.
- Run app.py to start the Flask web app.
- Open a web browser and navigate to http://localhost:5000.
- Enter the required details such as location, number of rooms, and square footage to get a predicted rent for the house
NOTE:Make sure to run model_trainer.py before running app.py to ensure that the machine learning model has been trained and is ready for use in the web app.