👨🏻‍💻 Customer Behavior Data Analytics Project

This project is an end-to-end Customer Behavior Analytics case study designed to demonstrate practical data analysis skills across Python, SQL, and Power BI. It replicates a real-world business analytics workflow where raw customer data is transformed into meaningful insights for decision-making.

As a student pursuing a career in Data Analytics, this project helped me practice the complete process — from understanding the business problem to delivering the final analytical report and dashboard.

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📌 Project Overview

The goal of this project is to analyze customer purchasing behavior and identify the key factors that influence sales, customer loyalty, and product performance. This project highlights my ability to:

✔ 1. Understand the Business Problem

Framed a clear business problem statement focusing on customer behavior, revenue trends, discounts, and loyalty patterns.

✔ 2. Prepare & Analyze Data using Python

• Cleaned and transformed the dataset
• Resolved missing values and inconsistencies
• Performed exploratory data analysis (EDA)
• Engineered new features such as Age Groups

✔ 3. Perform In-Depth SQL Business Analysis

Executed SQL queries to analyze:

• Revenue contribution
• Product performance
• Customer segmentation
• Discount impact
• Subscription trends
• Repeat customer behavior

✔ 4. Build a Power BI Dashboard

Designed a visually interactive dashboard that presents business insights clearly for stakeholders.

✔ 5. Document Insights & Present Findings

Prepared PDF reports and a presentation that summarize insights and business recommendations.

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🖼 Project Workflow

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📂 Project Components (As per repository)

📄 Business Problem Statement.pdf
📄 Customer Shopping Behavior Analysis.pdf
📊 Customer-Shopping-Behavior-Analysis.pptx
🐍 Customer_Shopping_Behavior_Analysis.ipynb
📘 README.md
📈 customer_behavior_dashbord.pbix
🗄 customer_behavior_sql_queries.sql
📁 customer_shopping_behavior.csv

Each file plays a specific role in the project:

1. Business Problem Statement.pdf

Defines the business understanding, objectives, and key questions.

2. Customer Shopping Behavior Analysis.pdf

A detailed summary of findings and business insights.

3. PPT Presentation

A visual walkthrough of insights suitable for interviews or academic evaluations.

4. Jupyter Notebook (.ipynb)

Contains Python-based data cleaning, feature engineering, and exploratory analysis.

5. Power BI Dashboard (.pbix)

Interactive dashboard revealing patterns in customer behavior.

6. SQL Queries

All SQL scripts used to analyze the PostgreSQL database.

7. Dataset (CSV)

The original Customer Shopping Behavior dataset used in the project.

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🎯 Key Skills Demonstrated

Technical Skills

• Python: Pandas, NumPy, Data Cleaning & EDA
• SQL: Aggregations, Joins, CTEs, Window Functions
• Power BI: DAX, Power Query, Dashboard Design
• PostgreSQL Database Management

Analytical Skills

• Customer segmentation
• Revenue analysis
• Product & discount analysis
• Insight generation
• Storytelling with data

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🚀 Future Enhancements

• RFM segmentation (Recency, Frequency, Monetary)
• Predictive modeling for customer churn
• Additional DAX measures
• Automated ETL workflow

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