Customer Complaint Analytics System

Customer Complaint Analytics System is a data analysis and machine learning project designed to help businesses understand customer complaints, identify repeated issues, and extract useful insights from raw complaint data.

The project focuses on transforming unorganized customer complaint records into clear, structured, and actionable information. The workflow starts with data collection and preprocessing, including cleaning missing values, removing noise, standardizing text data, and preparing the dataset for analysis.

After preprocessing, I analyzed complaint patterns to identify the most frequent complaint categories, common customer issues, negative trends, and important business indicators. The system also includes a classification workflow to help categorize complaints automatically and support faster decision-making.

To make the results easier to understand, I built an interactive dashboard that displays key insights, charts, complaint trends, category distribution, and important performance indicators. This allows users or business teams to explore the data visually instead of reviewing raw complaint records manually.

The main value of the project is helping companies detect recurring problems, understand customer pain points, improve service quality, and make data-driven decisions based on real complaint analysis.