EDA-Metis Project 1

WomenTechWomenYes Street Team Analysis and Recommendations

Description of project goals

Our first Metis project was focused on data cleaning and exploratory data analysis (EDA). We were tasked with optimizing street team placement at MTA subway entrances to maximize awareness and reach for WTWY fundraising efforts and summer gala.

Features and Target Variables:

• MTA Stations with high foot traffic volumes

  • Looked at both daily and weekly trends from 03/23/19-06/01/19

• Areas of high female population density

• Areas of high median incomes

• Areas where the population has advanced degrees

• A weighted algorithm was used to determine target stations that met all these criteria

• Daily foot traffic trends for all stations were used to determine best days of week for street team implementation

Data Used

• MTA Data from 03/23/19-06/01/19
• American Community Survey Data focusing on Census tracts for our target demographics
• NYC Open Data for the MTA station locations/coordinates

Tools Used

• Numpy
• Pandas
• Pickle
• Matplotlib
• Seaborn
• Geopy/Geopandas
• Contextily
• FuzzyWuzzy

Possible impacts of your project

• Impacts in the scope of the project:

  • Determination of improved street team placement -Concepts of how to future optimize street team placement

• Personal/Professional impacts:

  • Developing an understanding of different python pacakges
  • Experience working on a data science project as part of a team
  • Experience presenting a data science project to an audience

Guide to our Repo Project Slides

• Metis Project 1.pdf

MTA Data

• MTA initial cleaning, EDA, and visualization
  • Jupyter Notebook:
    • MTA_EDA.ipynb
    • MTA_Final.ipynb
  • Image files:
    • TURNSTILE_DAILY_ENTRIES.svg
    • GRD_CNTRL_DAILY.svg
    • GRD_CNTRL_DAILY_BY_WEEK.svg
    • TOP_STATIONS.svg
    • BUSIEST_DAY.svg
  • Pickle export:
    • final_mta_v1.pkl

ACS Census Data

• Education
  • Data files:
    • Education_data_with_overlays.csv
    • Education_metadata.csv
  • Jupyter Notebook:
    • Education.ipynb
  • Pickle export:
    • Df_education.pkl
• Gender_age
  • Data files:
    • gender_age_data_with_overlays.csv
    • Gender_age_metadata.csc
  • Jupyter Notebook:
    • Gender_age.ipynb
  • Pickle export:
    • Df_gender_age.pkl
• Combine gender_age and education
  • Import files:
    • df_education.pkl
    • Df_gender_age.pkl
  • Jupiter notebook:
    • Census.ipynb
  • Pickle export:
    • df_census.pkl
• Combined Census data and NYC Open Data
  • Import files:
    • df_census.pkl
    • tl_2019_36_tract.shp
  • Jupiter notebook:
    • Merge_SpatialJoins.ipynb
• Census Data Mapping for visualization
  • Jupiter notebook
    • Mapping_Census.ipynb
  • Image files:
    • Median Income - 25 years and over.svg
    • Total Female Population - 25 years and over.svg
    • Total Population - 25 years and over - Advanced Degrees.svg

Merge MTA data with NYC Open Data/Census Data

• Import files:
  • Final_mta_v1.pkl
  • nyc_open_data_subway.csv
• Jupyter notebook:
  • mta_nyc_open_match.ipynb
• Export:
  • Final_merge_v2.csv

Apply Weighted Composite Score to 4 variables

Methology: Variables Weight Entries 5 Female Population 3 Median Income 3 Advanced Degree 1

1. Normalizing all 4 variables, based on Min-Max Scale

2. Create Weighted_Rank_Score, based on the following formula:

   Weighted_Rank_Score = 5NM_Entries + 3NM_Female + 3NM_Income + 1NM_Degree

• Import files:
  • final_merge_v2.pkl
• Jupyter notebook:
  • Apply_Weighted_Score.ipynb
• Export:
  • final_weighted_data.pkl