This project demonstrates a complete end-to-end data analytics workflow performed on a retail customer shopping behavior dataset.
The objective is to transform raw customer transaction data into meaningful business insights using Python, PostgreSQL, and Power BI, following a workflow similar to real-world analytics projects used in industry.
The project covers the complete analytics lifecycle, including:
- Data Understanding
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Database Integration
- Advanced SQL Analysis
- Business Insights
- Dashboard Development
- Project Documentation
- Business Recommendations
This repository showcases both the technical implementation and the business decision-making process involved in solving a real-world analytics problem.
A retail company wants to understand customer purchasing behavior in order to improve:
- Customer Engagement
- Product Strategy
- Marketing Campaigns
- Customer Retention
- Sales Performance
- Revenue Growth
The management team wants answers to important business questions such as:
- Which customer groups generate the highest revenue?
- Which products receive the highest customer ratings?
- How effective are promotional discounts?
- Which customers are most loyal?
- Does subscription increase spending?
- Which shipping method generates more revenue?
The goal is to convert raw transactional data into actionable business insights that help management make informed decisions.
✔ Clean and preprocess raw customer data
✔ Handle missing values using appropriate statistical techniques
✔ Perform Exploratory Data Analysis (EDA)
✔ Create meaningful features for deeper analysis
✔ Store processed data inside PostgreSQL
✔ Solve business problems using advanced SQL
✔ Build an interactive Power BI Dashboard
✔ Present business insights through visualization
✔ Prepare project documentation
✔ Publish the project on GitHub
| Technology | Purpose |
|---|---|
| Python | Data Cleaning & Analysis |
| Pandas | Data Manipulation |
| NumPy | Numerical Operations |
| PostgreSQL | Database Management |
| SQL | Business Query Analysis |
| Power BI | Dashboard Development |
| Jupyter Notebook | Development Environment |
| Git & GitHub | Version Control |
| Markdown | Documentation |
Customer-Trends-Data-Analysis
│
├── README.md
│
├── dataset
│ Customer_Shopping_Behavior.csv
│
├── notebooks
│ Customer_Analysis.ipynb
│
├── sql
│ schema.sql
│ business_queries.sql
│
├── dashboard
│ Customer_Dashboard.pbix
│ Dashboard.png
│
├── reports
│ Project_Report.pdf
│ Presentation.pdf
│
├── docs
│ Python_Documentation.md
│ SQL_Documentation.md
│ PowerBI_Documentation.md
│ Business_Insights.md
│
├── images
│ python
│ sql
│ dashboard
│
└── LICENSE
- Project Overview
- Business Problem
- Objectives
- Tech Stack
- Repository Structure
- Dataset
- Project Workflow
- Python Analysis
- PostgreSQL Integration
- SQL Analysis
- Power BI Dashboard
- Business Insights
- Business Recommendations
- Documentation
- Reports
- Future Improvements
- Author
The dataset contains customer shopping behavior collected from a retail business.
Each row represents one customer's recent purchase along with demographic information, purchasing behavior, and transactional details.
| Category | Description |
|---|---|
| Customer Information | Customer ID, Age, Gender |
| Product Details | Item Purchased, Category, Size, Color |
| Transaction Details | Purchase Amount, Shipping Type |
| Customer Behavior | Previous Purchases, Frequency |
| Marketing | Discount Applied, Subscription |
| Feedback | Review Rating |
| Payment | Payment Method |
| Seasonal Information | Season |
This dataset provides valuable insights into customer demographics, purchasing patterns, product preferences, and business performance.