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DCSI_522_group-415
Authors: Keanna Knebel, Cari Gostic, Furqan Khan

Project Summary

This project attempts to identify character strings that predict whether or not a vanity liscence plate submitted to the New York State Department of Motor Vehicles will be rejected. The dataset includes all accepted vanity license plate applications submitted between October, 2010 and September, 2014, and all license plates that passed an initial automatic screen against a red-list, but were ultimately rejected upon inspection by clerical staff. If strong predictors are identified, these may be added to the red list to make the initial screening of applications more effective, and therefore, reduce the time the clerical staff spends on inspecting re-submissions from applicants whose initial submissions were rejected in secondary screening.

Report

The final report can be found here.

Scipt Flow chart

Usage

To replicate the analysis performed in this project, clone this GitHub repository, install the required dependencies listed below, and run the following commands in your command line/terminal from the root directory of this project:

1. 01_download_data.R

Rscript scripts/01_download_data.R --file_path="data/raw" --filename_1="accepted_plates.csv" --url1="https://raw.githubusercontent.com/datanews/license-plates/master/accepted-plates.csv" --filename_2="rejected_plates.csv" --url2="https://raw.githubusercontent.com/datanews/license-plates/master/rejected-plates.csv"

2. 02_data_processing.py

python scripts/02_data_processing.py --file_path_read="data/raw/" --file_path_write="data/processed/" --accepted_plates_csv="accepted_plates.csv" --rejected_plates_csv="rejected_plates.csv" --reduced_plates_csv="full_vanity_plate_data.csv" 

3. 03_EDA.py

python scripts/03_EDA.py --file_path_raw="data/raw/" --file_path_pro="data/processed/" --accepted_plates_csv="accepted_plates.csv" --rejected_plates_csv="rejected_plates.csv" --reduced_plate_csv="full_vanity_plate_data.csv" --X_train_csv="X_train.csv" --y_train_csv="y_train.csv" --file_path_img="results/"

4. 04_data_model.py

python scripts/04_data_model.py --file_path_read="data/processed/" --filename_x_train="X_train.csv" --filename_x_validate="X_validate.csv" --filename_x_test="X_test.csv" --filename_y_train="y_train.csv" --filename_y_validate="y_validate.csv" --filename_y_test="y_test.csv" --filename_path_write="results/"

5. 05_general_report.rmd

Rscript -e "rmarkdown::render('docs/05_generate_report.rmd')"

Running complete project

To run the entire project, run the following commands in your command line/terminal from the root directory of this project:

make all

To clear the generated outputs from the scripts, run the following commands in your command line/terminal from the root directory of this project:

make clean

Package Dependencies

Python 3.7.3 and Python packages:

• pandas --0.25.3
• numpy --1.18.1
• scikit-learn --0.21.3
• altair --3.2.0
• docopt -- 0.6.2
• imgkit -- 1.0.2
• selenium --3.141.0
• subprocess.run --0.0.8

R version 3.6.1 and R packages:

• tidyverse --1.2.1
• docopt --0.6.1
• knitr --1.27.2
• testit --0.11

Other dependencies:

• wkhtmltopdf --0.12.4