venmo money, venmo problems

Design:

Motivation: What is venmo used for? Apply NLP techniques to analyze venmo transactions.

Some specific motivating questions: Do the type of transactions change over time? For example, during football season, there could be betting/fantasy football pools that have transacations for football. Post-covid, Has there been a change in the social outings, rent/utilities payments? Can I detect tips and professional jobs on Venmo? Can I identify superusers? What transactions are the superusers doing?
while this project does not get around to answer all these questions, those that aren't remain potential for Future Work

Presentation:

Slide Deck

Approach:

Data

01_Data_Collection

• Using venmo-api, data was collected from venmo on 8/9.
• API capabilities:
  • Search for users based on name
  • Pull transactions for each user (limit of 50 latest transactions, though there may be another way around this)
• Because the API does not allow pulling from a feed of the most recent transactions, to get a list of transactions, the following process was adopted:
  1. Find the most popular American names, based on information from the Social Security Administration
  2. Using searches for those names, generate a list of users (with more users for the more popular names)
  3. Pull transactions for each of the users
  • Note that this process results in data that is intended to reflect the broad population of U.S. Citizens and their usage of Venmo.

Note: For privacy considerations, individual transaction data is not be available in the public repo. This includes raw data, processed data, and outputs within notebooks.

02_Data_Processing\

• NLP to clean the data
• Handles Emojis
• Custom processing to allow for word embeddings from Google's file to be used (e.g. french_fries instead of "french fries")

03_EDA

• Initial EDA performed on the data

Modeling

Initial Modeling

• Explored dimensionality reduction techniques (LSA, NMF, LDA). However, these were not that interpretable. 04A_Modeling_spaced_notes - adding spaces
  04B_Modeling_defined
  04C_Modeling_single_defined
  04D_Modeling_demoji_notes
  04E_Modeling_single_demoji_notes

Ultimate Modeling

• This was the final approach to developing themes for each note
• 05_Word_Embeddings_download
  • obtain word embeddings for the word2vec google-news-200 dataset
• 06_Word_Vector_Matrix\
  • Convert each transaction note into word count vectorizer
  • Apply word embeddings
  • Result in vector for each note
• 07_Modeling_Clustering #This is the Champion Model
  • Use Kmeans cluster to arrive at themes for each note
  • Export dataset with themes

Exploratory Data Analysis

• Performed additional data analysis using the final dataset in tableau
• 08_venmo_tableau_workbook

Tools:

• Word Embeddings:
  • Google-news-300
• Python libraries
  • venmo-api
  • emoji
  • pandas
  • sklearn
  • seaborn
  • matplotlib
  • gensim
  • pyLDA
  • re
• Tableau
  • visualization

Data source:

Venmo Social Security Administration (for common names)

Future Work:

• File cleanup:
  • Can add more discussion to the 04 models, to better illustrate why those were not used