This project analyzes customer support tickets to identify common issues, trends, and areas for process improvement. It is part of a Data Science internship task that involves applying NLP, data cleaning, and visualization techniques to draw actionable insights from real-world customer service data.
- Identify frequently reported customer issues.
- Analyze resolution delays and satisfaction ratings.
- Suggest actionable process improvements using data insights.
- Create clean visualizations to support the analysis.
- Python 🐍 (Pandas, NumPy, NLTK)
- Seaborn & Matplotlib 📊
- WordCloud ☁️
- KaggleHub (for loading dataset)
- Jupyter Notebook / Google Colab
- Source: Kaggle - Customer Support Ticket Dataset
- Rows: 8,469
- Columns: 17
- Fields include: Ticket subject, description, status, channel, priority, product, timestamps, and customer demographics.
- Ticket trends over time
- Top issues by category
- Resolution time distribution
- Customer satisfaction rating distribution
- Channel & priority breakdown
- WordCloud of issue keywords
- Product preferences by gender and age
- Automate Common Issues: Setup, compatibility, and installation make up a majority of the support queries.
- Fix Resolution Time Errors: Some tickets have negative resolution times due to incorrect timestamps. Add backend validation.
- Reduce Social Media Delays: Tickets from social media channels show longer resolution times.
- Reclassify "Other" Issues: A large chunk of tickets are poorly categorized. Apply better NLP classification or UI-based tagging.
- Prioritize by Volume: Allocate more resources to high-priority categories that take longer to resolve.
- Complete analysis in a Jupyter/Colab notebook.
- Summary report with insights and process improvement ideas.
- Optional: Exportable PDF version of analysis.
- For queries or suggestions, feel free to connect via LinkedIn or raise an issue in this repo.