A simple web application for predicting monthly apartment rent prices in Tel Aviv.
The project is based on a cleaned dataset of rental listings, and uses an Elastic Net regression model trained with scikit-learn.
Users can enter apartment details (size, rooms, address, etc.) into a web form and instantly get a predicted rent price.
- Interactive HTML form for entering apartment details
- Backend built with Flask (Python)
- Elastic Net regression model trained on Tel Aviv rental data
- Preprocessing pipeline for handling new input data
- Prediction returned instantly on the web page
| File/Folder | Description |
|---|---|
api.py |
Flask app (routes + prediction API) |
assets_data_prep.py |
Data preparation functions |
model_training.py |
Elastic Net model training script |
neigh_dist_medians.pkl |
Helper file for distances |
trained_model.pkl |
Trained Elastic Net model |
requirements.txt |
Python dependencies |
templates/index.html |
HTML UI form |
README.md |
Project documentation |
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Clone this repository:
git clone https://github.com/adirbella37/Rent-Price-Predictor-UI.git cd Rent-Price-Predictor-UI -
Create and activate a virtual environment:
python -m venv
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On Windows: venv\Scripts\activate
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On Mac/Linux: source venv/bin/activate
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Install dependencies:
pip install -r requirements.txt
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Run the Flask app:
python api.py
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Open the app in your browser:
This project is licensed under the MIT License.