CFPB Consumer Complaints Analysis Using Hadoop

TL;DR

This paper analyzes the CFPB consumer complaints dataset using Hadoop to uncover patterns, trends, and geographic differences, providing insights for stakeholders to improve consumer protection and services.

Contribution

It introduces a scalable Hadoop-based approach to analyze large consumer complaints data, revealing key insights and patterns in consumer issues across the USA.

Findings

Most common complaint types identified

Top companies with highest complaints listed

Geographic variations in complaint patterns

Abstract

Consumer complaints are a crucial source of information for companies, policymakers, and consumers alike. They provide insight into the problems faced by consumers and help identify areas for improvement in products, services, and regulatory frameworks. This paper aims to analyze Consumer Complaints Dataset provided by Consumer Financial Protection Bureau (CFPB) and provide insights into the nature and patterns of consumer complaints in the USA. We begin by describing the dataset and its features, including the types of complaints, companies involved, and geographic distribution. We then conduct exploratory data analysis to identify trends and patterns in the data, such as the most common types of complaints, the companies with the highest number of complaints, and the states with the most complaints. We have also performed descriptive and inferential statistics to test hypotheses and draw conclusions about the data. We have investigated whether there are significant differences in the types of complaints or companies involved based on geographic location. Overall, our analysis provides valuable insights into the nature of consumer complaints in the USA and helps stakeholders make informed decisions to improve the consumer experience.