RentRite

Overview

Terminology

These terms that appear throughout the project are defined explicitly for the reader’s convenience to clarify precisely what they are referring to.

Term	Definition
Listing	Contains all the units i.e. floor plans available to rent in a specific building.
Unit	Refers to the specific apartment in a building available to rent, synonymous with floor plan.
Amenities	Refers to premiums included with a unit or a building.
Unit amenities	Balcony, In Unit Laundry, Air Conditioning, High Ceilings, Furnished, Hardwood Floor.
Building amenities	Controlled Access, Fitness Center, Swimming Pool, Roof Deck, Storage, Residents Lounge, Outdoor Space.

Setup

This project runs on Python 3. Make sure you have a version of Python 3 installed.

Installing chromedriver

In order to run the web scrapers to extract rental listings data, you'll need to install chromedriver. Make sure homebrew is installed, then run:

brew install chromedriver 

Next get the installation path:

which chromedriver

If you see the error "Google Chrome for Testing.app is damaged and can’t be opened. You should move it to the Trash.", run the following:

xattr -cr 'Google Chrome for Testing.app'

Creating your environment

In the project directory, start by creating and activating a virtual environment:

python -m venv env # create virtual env named env
source env/bin/activate # activate it

Then install all the project requirements:

pip install -r requirements.txt

Now create a .env file in the root directory by making a copy of .env.schema. Replace the CHROMEDRIVER_PATH variable in your .env file with your chromedriver installation path.

Initialize the database

This project uses a PostgreSQL database to store the extracted rental listing data for each building and its units posted. You can recreate the setup by initializing your own PostgreSQL database (we use Neon for a serverless DB) and replacing the DATABASE_URL variable in your .env file with your database connection string.

Running the project

Running main.py in the root directory will commence the data acquisition and model training process, which executes the following steps:

1. Run the data scraper to acquire the rental listing data for the current month.
2. Push the extracted unit and building details for each listing to your PostgreSQL DB.
3. Re-train the model using the extracted data, then save the updated model as model.joblib where it will be utilized by the backend API.

python -m main

To start the backend API from the backend directory, run:

python -m uvicorn app.server:app --reload