Overview:
This project focuses on acquiring, processing, and analyzing data from the Consumer Complaint Database provided by the Consumer Financial Protection Bureau (CFPB). It utilizes data science tools, machine learning, and web development to build an interactive system that facilitates the visualization and analysis of consumer complaints in the United States.
Objective:
Develop a web application that allows users to:
- Retrieve data from the Consumer Complaint Database using various methods (CSV downloads, JSON files, or API queries).
- Process the data for cleaning, transformation, and storage in a relational database.
- Perform analyses using AI models to extract patterns, trends, and other valuable insights.
- Present the results on an interactive and user-friendly dashboard.
Key Components of the Project:
- Data Acquisition:
- Implement scripts to download data in CSV and JSON formats.
- Integrate with the CFPB’s public API for dynamic queries and automated updates.
- Data Processing:
- Data cleaning: remove duplicates, standardize dates, and handle missing values.
- Transformations to unify formats and encodings (e.g., date conversion and handling of non-English entries).
- Storage in PostgreSQL databases for structured queries.
- AI Data Analysis:
- Apply supervised and unsupervised learning techniques to categorize complaints, identify common themes, and predict trends.
- Implement natural language processing (NLP) models to analyze consumer narratives.
- Visualization and Dashboard:
- Create an interactive dashboard using modern tools like React.js and visualization libraries such as D3.js or Chart.js.
- Key sections:
- Data acquisition and processing flow.
- General statistics (number of complaints by category, state, and company).
- AI analysis results: trend charts, heat maps, and sentiment analysis.
- Deployment:
- Deploy the application in a cloud environment (Azure App Services or Dockerized App on Azure App Containers).
Project Impact:
This system will provide an efficient and automated approach to analyzing consumer complaints, offering valuable insights to companies, researchers, and government entities to improve financial services and consumer protection.
Tools and Technologies Used:
- Data Acquisition: Python (requests, pandas), PostgreSQL.
- Processing: SQL, Python (NumPy, pandas), ETL tools.
- AI and NLP: spaCy, TensorFlow, scikit-learn.
- Visualization and Dashboard: React.js, D3.js, Chart.js.
- Deployment: Docker, Azure App Services, GitHub Actions for CI/CD.
Future Enhancements:
- Real-time analysis integration through API queries.
- Expanding AI models to offer predictions and personalized recommendations.
- Adding tools for exporting visualizations and reports.