This repository contains a comprehensive Exploratory Data Analysis (EDA) focused on customer behavior and sentiment. By analyzing demographics and service ratings, the project identifies key drivers of customer loyalty and areas for operational improvement.
- Data Cleaning: Handling missing values and preparing categorical data for analysis.
- Statistical Profiling: Distribution analysis of customer age, purchase frequency, and spending habits.
- Service Metrics: Deep dive into ratings for Product Quality, Delivery Time, and Website Ease of Use.
- Loyalty Insights: Comparison of behavior between loyalty program members and standard customers.
| Tool | Purpose |
|---|---|
| Python | Core programming and logic |
| Pandas | Data manipulation and tabular analysis |
| Matplotlib | Data visualization and plotting |
| GitHub | Version control & Documentation |
The notebook processes a customer_data.csv file featuring:
- Demographics:
Age,Gender - Behavioral:
Purchase Amount,Purchase Frequency,Return Rate - Satisfaction:
Product Quality Rating,Delivery Time Rating,Customer Service Rating
- Clone the repo:
git clone [https://github.com/abhijithshetty12/Customer-Satisfaction-Analysis.git](https://github.com/abhijithshetty12/Customer-Satisfaction-Analysis.git)
- Launch the Notebook: Open
Customer_Satisfaction_Analysis.ipynbin Google Colab or Jupyter. - Run All: Execute cells sequentially to generate visual reports.
Course Project: This repository showcases a technical mini-project developed for the Professional Skills 2 curriculum.
- Institution: Thakur College of Engineering & Technology
- Specialization: B.Tech in Artificial Intelligence & Machine Learning (AI&ML)
- Timeline: Semester 3
Developed by Abhijith Shetty
