DATA601-Final

Overview

Explore parking violation data from Washington, DC and build a classifier

Goals

Identify the features that predict the likelihood of a parking violation
Determine if geographic factors have any impact on parking violations
Determine if day and/or time have any impact on parking violations
Create a model that scores 85% or better
Incorporate unit tests and linting through GitHub Actions in an effort to mimic a professional setting

Motivation & Background

After almost 10 years of not owning a car, in 2015 I purchased a new car for my family. Within a month I received a parking ticket. The pace continued, and I received on average 2 tickets a year. I even received tickets at parking meters that I had paid for!

Parking tickets are an expensive nuisance, an unexpected expense often totaling $100. What if one could understand the likelihood of a parking ticket at a given place and time?

Data

This effort makes use of publicly available parking violation data published by the city of DC.

Column Name	Type
DISPOSITION_CODE	Number
DISPOSITION_DATE	Date or Time
DISPOSITION_DESC	Text
DISPOSITION_TYPE	Text
FINE_AMOUNT	Number
GIS_LAST_MOD_DTTM	Date or Time
ISSUE_DATE	Date or Time
ISSUE_TIME	Text
ISSUING_AGENCY_CODE	Number
ISSUING_AGENCY_NAME	Text
ISSUING_AGENCY_SHORT	Text
LATITUDE	Number
LOCATION	Text
LONGITUDE	Number
MAR_ID	Number
MULTI_OWNER_NUMBER	Text
OBJECTID	Unique ID
PENALTY_1	Number
PENALTY_2	Number
PENALTY_3	Number
PENALTY_4	Number
PENALTY_5	Number
PLATE_STATE	Text
TOTAL_PAID	Number
VEHICLE_TYPE	Text
VIOLATION_CODE	Text
VIOLATION_PROC_DESC	Text
XCOORD	Number
YCOORD	Number

The final solution should look like this

├── LICENSE
├── README.md
├── coverage.xml
│   ├── cleaned
│   │   ├── April_2018.csv
│   │   ├── August_2018.csv
│   │   ├── December_2018.csv
│   │   ├── February_2018.csv
│   │   ├── January_2018.csv
│   │   ├── July_2018.csv
│   │   ├── June_2018.csv
│   │   ├── March_2018.csv
│   │   ├── May_2018.csv
│   │   ├── November_2018.csv
│   │   ├── October_2018.csv
│   │   └── September_2018.csv
│   ├── config
│   │   └── parking_data.json
│   ├── final
│   │   └── all_cleaned.csv
│   │   └── final_model.pkl
│   ├── incoming
│   │   ├── April_2018.csv
│   │   ├── August_2018.csv
│   │   ├── December_2018.csv
│   │   ├── February_2018.csv
│   │   ├── January_2018.csv
│   │   ├── July_2018.csv
│   │   ├── June_2018.csv
│   │   ├── March_2018.csv
│   │   ├── May_2018.csv
│   │   ├── November_2018.csv
│   │   ├── October_2018.csv
│   │   └── September_2018.csv
│   └── sample
│       └── Parking_Violations_Issued_in_August_2018.csv
├── env.sh
├── environment.yaml
├── notebooks
│   ├── Cleaning\ and\ Preparation.ipynb
│   ├── EDA.ipynb
│   ├── Getting\ Data.ipynb
│   ├── Models.ipynb
│   └── Report.ipynb
├── scripts
│   ├── __init__.py
│   ├── api.py
│   ├── cleaning.py
│   ├── main.py
│   ├── model.py
│   └── utils.py
└── tests
    ├── test_api.py
    └── test_utils.py

Getting Started

Manage Environment

Execute the shell script env.sh to build the project environment and install the required dependencies.

# make script executable
chmod +x ./env.sh
source ./env.sh

The shell script will build a data directory, create a conda environment burgwyn_data601_final and fetch and clean the data.

Project Info

Contributors: Nat Burgwyn

Packages and Software

Languages: Python

Tools: PyCharm, Jupyter Notebook

Libraries:

Library	Version
jupyterlab	2.1.5
matplotlib	3.3.2
numpy	1.19.2
pandas	1.1.3
requests	2.24.0
seaborn	0.11.0
coverage	5.3
flake8	3.8.4
python-dateutil	2.8.1
requests	2.24.0
responses	0.12.1
scikit-learn	0.23.2

burgwyn / DATA601-Final