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Gbemiabe/README.md

Banking & Fintech Consumer Complaint Analysis (2017–2023)

I built this project around a cleaned complaints dataset to understand what banking/fintech customers complain about most, how complaints change over time, and how companies respond.

What this analysis answers

  • Which products generate the most complaints?
  • What are the top issues customers report?
  • Which states show the highest complaint volume?
  • What are the most common company response types?
  • Are responses typically marked as timely?

Quick results from this dataset

  • Total complaints analyzed: 62,516
  • Date range: 2017-05-01 → 2023-08-28
  • Top product: checking or savings account
  • Top issue: managing an account
  • Top state: CA
  • Most common response: closed with explanation
  • Timely response rate: 93.8%

Files in this repo

  • consumer_complaint_analysis.ipynb — notebook (cleaning + analysis + charts)
  • data/consumer_complaints_banking_sample_20000.csv — representative sample (GitHub-friendly)
  • outputs/ — charts (PNG) and cleaned dataset export (generated when you run the notebook)

Charts (in outputs/)

  • Top products by complaint volume
  • Top issues
  • Response types
  • Top states
  • Monthly trend

How to run

Google Colab

  1. Open the notebook in Colab.
  2. Upload data/consumer_complaints_banking_sample_20000.csv (or your full file if you have it).
  3. Run all cells.
  4. Upload the charts from outputs/ back to GitHub.

Notes

  • This repo includes a 20k-row sample to keep uploads small. The full cleaned file is ~17.7MB.

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