🛍️ Customer Shopping Behavior Analysis

An end-to-end data analytics project that analyzes customer shopping behavior using Python, PostgreSQL, SQL, and Power BI. The project uncovers purchasing patterns, customer segments, product performance, and business insights to support data-driven decision making.

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📊 Project Overview

This project includes:

• Data Cleaning & Preprocessing
• Exploratory Data Analysis (EDA)
• PostgreSQL Database Integration
• Business Analysis using SQL
• Interactive Power BI Dashboard
• Business Recommendations

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📁 Dataset

• Rows: 3,900
• Columns: 18
• Customer Demographics
• Purchase Details
• Subscription Status
• Shipping Information
• Product Reviews

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🛠️ Tech Stack

Languages

• Python
• SQL (PostgreSQL)

Libraries

• Pandas
• NumPy
• Matplotlib
• Seaborn
• SQLAlchemy
• Psycopg2

Tools

• Jupyter Notebook
• PostgreSQL
• Power BI
• Git
• GitHub

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📈 Key Analysis

• Revenue Analysis
• Customer Segmentation
• Age Group Analysis
• Subscription Analysis
• Product Performance
• Discount Impact
• Shipping Analysis
• Customer Ratings
• Seasonal Trends

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💡 Key Insights

• Young Adults generated the highest revenue.
• Loyal customers formed the largest customer segment.
• Male customers contributed higher total revenue.
• Subscribers and non-subscribers had similar average spending.
• Express shipping customers spent slightly more on average.

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Power PI Dashboard

📊 Power BI Dashboard

📂 Repository Structure

Customer_Behavior_Analysis/
│── Customer-Shopping-Behavior-Analysis.pptx
│── Customer_shopping.ipynb
│── Customer_shopping_sqlqueys.sql
│── LICENSE
│── README.md
│── customer_shopping_behavior.csv

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📚 Skills Demonstrated

• Data Cleaning
• Exploratory Data Analysis (EDA)
• SQL
• PostgreSQL
• Data Visualization
• Power BI
• Business Analytics
• Data Storytelling

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👨‍💻 Author

Kunal Singh

GitHub: https://github.com/Kunalthakur01