This project aims to predict rental home prices based on various features using a machine learning model. The web application allows users to input features and get predictions on rental prices.
Click here to access the Rental Price Prediction Web App
Root (Rental Home Price Prediction)
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├── app.py # Main Flask application file
├── dvc.yaml # DVC configuration file
├── poetry.lock # Poetry lock file for dependencies
├── pyproject.toml # Poetry configuration file
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├── configs # Configuration files
│ └── config.yaml
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├── data # Data directory for raw and processed datasets
│ ├── raw # Raw data
│ └── processed # Processed data
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├── models # ML models
│ └── nas_rental_prediction
│ └── best_model_new_saved.pkl
│
├── notebooks # Jupyter notebooks for EDA and modeling
│ └── EDA_and_Modeling.ipynb
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├── scripts # Utility scripts
│ └── train_model.py
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├── static # Static files for the web app
│ ├── css # CSS files
│ ├── js # JavaScript files
│ └── loading.gif # Loading animation GIF
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├── templates # Flask templates
│ ├── nas_rental_prediction.html
│ └── navbar.html
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└── tests # Test cases
└── test_app.py
Setup and Installation
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Clone the repository: git clone https://github.com/boss2256/MLOps-Web-Application.git cd yourrepository
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Install dependencies using Poetry: poetry install
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Activate the virtual environment: poetry shell
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Run the Flask application: flask run
- Predict Rental Price: Enter the required features such as amenities, availability, bathrooms, accommodates, number of reviews, maximum nights, bedrooms, beds, and guests included to predict the rental price of budget accommodations.
Contributions are welcome! Please submit a pull request or open an issue to discuss any changes.