Customer Shopping Behavior Analysis

📌 Project Overview

This project analyzes customer shopping behavior to understand purchasing patterns, customer preferences, spending behavior, and satisfaction levels.

The project follows an end-to-end data analytics workflow, starting from data exploration and cleaning in Python, followed by SQL-based analysis in PostgreSQL, and finally presenting key insights through an interactive Power BI dashboard.

🛠️ Tools & Technologies

• Python
• Jupyter Notebook
• Pandas
• NumPy
• PostgreSQL
• SQL
• Power BI
• Git & GitHub

📂 Project Files

File	Description
customer_shopping_behavior.csv	Original customer shopping behavior dataset
Customer_Satisfaction.ipynb	Data cleaning, exploration, and analysis using Python
customer_behavior_database.sql	SQL queries and database analysis performed in PostgreSQL
Customer Behavior Dashboard.pbix	Interactive Power BI dashboard

🔄 Project Workflow

1. Data Collection

The customer shopping behavior dataset was loaded into Python for analysis.

2. Data Cleaning & Preparation

Python and Pandas were used to:

• Explore the dataset
• Handle missing values
• Check data types
• Clean and transform columns
• Prepare the data for analysis

3. Exploratory Data Analysis

The dataset was analyzed to identify:

• Customer purchasing patterns
• Spending behavior
• Purchase frequency
• Customer preferences
• Product-related trends

4. PostgreSQL Analysis

The cleaned data was loaded into PostgreSQL.

SQL queries were used to perform:

• Data aggregation
• Customer segmentation
• Purchase analysis
• Category-level analysis
• Business-focused analysis

5. Power BI Dashboard

The analyzed data was used to create an interactive Power BI dashboard.

The dashboard provides insights into:

• Total customers
• Customer spending
• Purchase behavior
• Product categories
• Customer satisfaction
• Purchase frequency
• Key customer trends

📊 Dashboard

The Power BI dashboard provides an interactive view of customer shopping behavior and helps identify important business trends and customer patterns.

Power BI File: Customer Behavior Dashboard.pbix

💡 Key Objectives

• Understand customer shopping behavior
• Identify purchasing patterns
• Analyze customer spending
• Compare product categories
• Understand purchase frequency
• Analyze customer satisfaction
• Generate actionable business insights

📈 Business Insights

The analysis helps businesses understand their customers better and supports data-driven decisions related to:

• Customer engagement
• Product strategy
• Marketing
• Customer retention
• Sales performance
• Customer satisfaction

👨‍💻 Project Author

Devendra Patil

---