rent-v-buy

OBJECTIVE

The purpose of our app is to provide an intuitive "look-ahead" tool that helps users choose the financially optimal option between renting an apartment and buying a home in the state of California.

Team 235: Tufte-Love

• Omer Ansari
• Richard Levine
• Felipe Lopez
• Skye Sheffield
• Sylvia Tran

APPLICATION ARCHITECTURE:

I. USING THIS REPOSITORY:

Demo Video Link

These instructions assume the user has a Linux or Mac OS.
For Windows, different instructions are required.
If you're not inclined to build the app, CLICK HERE : https://rentvbuy.herokuapp.com/

IA. Setting up a VirtualEnv & Flask

1. Download the repository to your local machine.
2. Change directories into the repository project folder
3. Set up virtual environment:

   [rent-v-buy]$
   [rent-v-buy]$ python3.7 -m venv venv
   

4. Activate virtual environment:

   [rent-v-buy]$ . venv/bin/activate
   (venv) [rent-v-buy]$
   

5. Install all requirements:

   (venv) [rent-v-buy]$ pip install -r requirements.txt
   

Note: we intentionally did not upload the virtual env directory to git. You are required to create a fresh venv/ locally.

IB. How to run (in dev environment)

(venv) [rent-v-buy]$ export FLASK_APP=rentvbuy.py
(venv) [rent-v-buy]$ flask run
 * Serving Flask app "dbtest.py"
 * Environment: production
   WARNING: Do not use the development server in a production environment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

For tips set up and run this in Pycharm, please watch this video.

Bibliography

• https://blog.miguelgrinberg.com/post/setting-up-a-flask-application-in-pycharm
• https://devcenter.heroku.com/articles/github-integration

II. Downloading Data

It is optional to run the entire data pipeline from data download to prediction generation. This is not required in order to launch the app. The outputs from the predictions generated by time series forecasting is available in the app/maps/ directory as UI_output.json.

REPOSITORY STRUCTURE:

├── README.md          <- The top-level README for developers using this project.
├── Procfile           <- needed for heroku app to run
├── financialAnalysis.py  <- Python script with formulas to calculate rent-v-buy outputs to surface to front-end
│
├── app                <- All files related to front-end app build
│   ├── static         
│   │   ├── css        <- main.css file in this directory
│   │   ├── js         <- JavaScript files for front-end build of choropleth etc.
│   │   │                 (GRADE: financialSliders.js and main.js)
│   │   ├── maps       <- topo.json files etc for choropleth build  
│   │   └── favicon.ico   
│   │
│   ├── templates      <- (GRADE).hmtl files for front-end build 
│   │
│   ├── __init__.py    
│   └── homeRoute.py   <- (GRADE) Script to generate outputs from our financial calculator for rent v. buy
│
├── data               <- Hidden folder in .gitignore / directory is created upon executing `/src/main.py`
│   ├── external       <- Data from third party sources (non-Zillow).
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump (from https://www.kaggle.com/zillow/zecon).
│
├── notebooks          <- Jupyter notebooks
│   └── Archive        <- Folder holding exploratory notebooks for methods/techniques not used & other scratch work
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials
│
├── reports            <- Project Deliverables
│   └── figures        <- Generated graphics and figures to be used in reporting
│   └── proposal       <- All proposal documents submitted on 2020-02-29
│   └── progress_rpt   <- All progress-report related documents submitted weekend of 2020-03-27
│   └── poster         <- All final report related files (due 2020-04-17)
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
└── src                <- (GRADE) Source code for entire data ETL & model pipeline (from download to model predictions).
    ├── README.md      <- Instructions on obtaining Kaggle API Key for data download to work
    ├── __init__.py    <- Makes src a Python module
    │
    ├── get_n_save_data.py     <- Script to download data from Kaggle.com & save to local machine
    ├── make_dataset.py        <- Script to do cursory cleaning in preparation for data pre-processing
    ├── california_only.py     <- Script to pare down scope of downloaded data to California
    ├── model.py               <- Script to generate time series forecast predictions for each zip code in California
    │
    ├── main.py                <- Script to EXCECUTE the entire Data Pipeline 
    └── helper.py              <- Script to aid in notebook explorations