Housing Price Prediction

About

This is a project to scrape the data of the houses (title, description, region, price, year of construction, area, number of rooms) from https://divar.ir and save them in database (sqlite) after preprocessing.

By using machine learning (Decision Tree), a model is trained to predict house prices.

Getting Started

1. Clone the Repository

   git clone https://github.com/masoudgheisari92/house_price_prediction.git

2. Make a .env file in house_price_prediction directory containing all the following environment variables:

   DJANGO_SECRET_KEY=""
   DJANGO_DEBUG=True
   DJANGO_ALLOWED_HOSTS=127.0.0.1,localhost

3. Create virtual environment and install required packages:

   # create virtual environment
   python -m venv venv
   # activate virtual environment
   venv/Scripts/activate
   # install required packages
   pip install -r project/requirements.txt

4. Run the migrations

   cd project
   python manage.py migrate

5. Create superuser (admin)

   python manage.py createsuperuser

6. Run server

   python manage.py runserver

APIs

• /scrape scrape the data of the houses of a selected city.
• /predict predict the price of a house based of its city, region, year of construction, area and the number of rooms.

Contact

Masoud Gheisari

• linkedin: https://linkedin.com/in/masoud-gheisari
• email: masoud.gh20@gmail.com