📊 Customer Service Data Analysis (Python)

📌 Overview

This project analyzes customer service data using Python to identify patterns in customer interactions, service performance, and operational efficiency. The analysis focuses on cleaning raw data, performing exploratory data analysis (EDA), and generating insights that help improve customer satisfaction and business decision-making.

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🎯 Objectives

• Clean and preprocess customer service data
• Perform Exploratory Data Analysis (EDA)
• Identify trends and patterns in customer requests
• Analyze service performance using key metrics
• Visualize findings with informative charts
• Generate business recommendations

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🛠 Tools & Technologies

• Python
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib

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📂 Project Workflow

1. Import Dataset
2. Data Cleaning & Preprocessing
3. Exploratory Data Analysis (EDA)
4. Data Visualization
5. Business Insights & Recommendations

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📈 Key Insights

• Identified trends in customer service requests.
• Analyzed service performance across different categories.
• Highlighted patterns supporting operational improvements.
• Presented findings through clear visualizations.

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📁 Repository Structure

Python-Customer-Data-Analysis/
│
├── Customer_Service_Analysis.ipynb
├── customer_service.csv
├── images/
├── README.md
└── requirements.txt

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💡 Skills Demonstrated

• Data Cleaning
• Data Analysis
• Exploratory Data Analysis (EDA)
• Data Visualization
• Business Insight Generation
• Python Programming

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📊 Project Visualizations

Customer Segment Distribution

Revenue Contribution

Recency vs Monetary

Pareto Analysis

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👨‍💻 Author

Gagan Bawankule

Bachelor of Arts (French) | Aspiring Data Analyst