Project Overview

AWS Consumer Complaint Intelligence Dashboard

North Carolina Credit Card Fees & Interest Analysis

GitHub Repository: https://github.com/ChieNwosu/aws-consumer-complaint-intelligence GitHub Pages: https://ChieNwosu.github.io/aws-consumer-complaint-intelligence

This is an undergraduate data analytics portfolio project that demonstrates end-to-end data analysis skills using a real-world dataset from the Consumer Financial Protection Bureau (CFPB).

What Was Built

Component Status Description
Data cleaning pipeline ✅ Implemented locally Python script that loads, validates, and exports the cleaned dataset
KPI analysis ✅ Implemented locally Python script computing all key metrics
Visualizations ✅ Implemented locally 6 PNG charts exported from real data
Athena SQL files ✅ Written, not deployed 6 SQL files covering table creation, QA, KPIs, and semantic views
AWS architecture ✅ Designed, not deployed Free Tier pipeline: S3 → Glue → Athena → QuickSight
GitHub Pages site ✅ Implemented This documentation site
Data dictionary ✅ Implemented All 18 columns documented
Stakeholder summary ✅ Implemented Non-technical findings summary
AI validation log ✅ Implemented 8 AI insights validated against real data

Dataset

  • Source: CFPB Consumer Complaint Database (public)
  • Filter: North Carolina, Credit Card, Fees or Interest
  • Records: 542 complaints
  • Period: January 2024 – April 2026
  • Columns: 18 fields including company, sub-issue, response outcome, narrative, ZIP code

Key Findings

  1. Synchrony Financial leads with 123 complaints (22.7% of total)
  2. “Problem with fees” accounts for 65.5% of all complaints
  3. 31.2% of complaints resulted in monetary relief for consumers
  4. 99.6% timely response rate across all companies
  5. “Fee” and “interest” are the most common narrative keywords
  6. March 2026 was the peak complaint month (38 complaints)

Skills Demonstrated

  • Python data analysis (pandas, matplotlib)
  • SQL query writing (Athena/Presto dialect)
  • AWS cloud architecture design (S3, Glue, Athena, QuickSight)
  • KPI definition and dashboard requirements
  • Data documentation and governance
  • AI-assisted insight validation
  • Stakeholder reporting
  • GitHub Pages documentation