NextBuzz

Using machine learning to predict Georgia Tech bus arrival times Try the live demo, if it's still up: http://nextbuzz.ddns.net:8000

Overview

The purpose of this guide is to instruct you how do use all code that ships with NextBuzz, from exploratory analysis to modeling to running the web app. For design documentation and project motivation, check out documentation/NextBuzz Proposal.pdf and documentation/NextBuzz Final Report.pdf.

Setting up the environment

This project was built with python 2.7. Making this project compatable with python 3.6 should not be that difficult, and is on the roadmap.

Installing necessary libraries

It is recommended that you install all python libraries in a virtual environment so as not to mess with your global pip list. More information and virtualenv installation instructions can be found here: https://virtualenv.pypa.io/en/stable/

Once virtualenv is installed, you can create a new virtualenv in NextBuzz's repository doing something like virtualenv nbenv followed by source nbenv/bin/activate. Once you have entered the virtual environment, it is safe to intall all required packages. Do this with pip install -r requirements.txt. You may have to use sudo if that command fails.

Collecting Data

One of the central features of NextBuzz is its data collection script, which will query NextBus and OpenWeatherMap for updates every 20 seconds. If you do not yet have a model created, you can run python collect.py False to scrape data to a database.db file without making a NextBuzz prediction. If you do have a model, make sure model.pkl is located in the same directory as collect.py and run python collect.py.

Preparing Data

Once you have collected data, export the section of the raw data that you want to clean as a CSV. From here, you can load it into pipeline.py (looks in data/rawdata.csv by default) which will remove duplicates, turn strings into one-hot encodings, and engineer new features related to arrival detection, time, and Georgia Tech domain knowledge. The output file will be dataset.csv.

Exploratory Data Analysis

In explore.py, the dataset.csv you just created is loaded in. Assuming you have matplotlib installed, the script will then plot several features against the class to search for any correlations.

Creating a Model

In train.py, the dataset.csv you just created is loaded in. From here, define the learner you want to apply to your data using scikit-learn's API, and pass that learner in a call to either supervised.rolling_kfold for cross-validated error analysis, and then supervised.train_test_split with high training percentage to generate a production-ready model. Pickle this model to disk.

Starting the Web App

In the root directory of the NextBus, run python collect.py to begin scraping real-time data to the database. Then call cd webserver and then sh theServerIsDown.sh to start the web server. Navigate to http://0.0.0.0:8000/ in you favorite browser to see the web app.