Customer Behavior Analysis – Retail Insights Project

🎯 Project Overview

End-to-end customer behavior analysis using Python (Pandas), PostgreSQL, and Power BI. Performed data cleaning, SQL queries, and built an interactive dashboard to uncover retail insights and drive business decisions.

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📁 Project Structure

This project follows an end-to-end data pipeline:

1. Data Cleaning & Transformation (Python - Jupyter Notebook)
2. Data Analysis & Queries (SQL - PostgreSQL)
3. Data Visualization (Power BI Dashboard)

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🛠️ Tech Stack

• Python (Pandas for data cleaning)
• PostgreSQL (Database management & SQL queries)
• Power BI (Interactive dashboard creation)
• Jupyter Notebook (For Python code execution)

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📊 Data Source

• File: customer_shopping_behavior.csv
• Rows: 3900 customer records
• Columns: 18 features including:
  • Customer demographics (Age, Gender, Location)
  • Purchase details (Item, Category, Amount)
  • Behavioral data (Discount usage, Subscription status, Review ratings)

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🔧 What I Did

1. Python Data Cleaning (Jupyter Notebook)

• Loaded and explored the dataset
• Handled missing values in review ratings
• Created new features:
  • Age groups (Young Adult, Adult, Middle-aged, Senior)
  • Purchase frequency in days
• Cleaned column names and removed duplicate columns
• Exported clean data to PostgreSQL database

2. SQL Analysis (PostgreSQL)

Created 10+ analytical queries to answer business questions like:

• Revenue comparison between male and female customers
• Top products by average review rating
• Customer segmentation (New, Returning, Loyal)
• Impact of discounts on purchase behavior
• Revenue contribution by age groups
• Subscription status vs spending patterns

3. Power BI Dashboard

Built an interactive dashboard showing:

• Customer demographics overview
• Revenue and sales metrics
• Purchase patterns by category
• Shipping type preferences
• Visual breakdowns by age group and gender

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📂 Files in this Repository

• customer_shopping_behavior.ipynb - Python data cleaning notebook
• Customer_behavior_SQL.sql - All SQL queries used for analysis
• Customer_Behavior_Dashboard.pbix - Power BI dashboard file
• customer_shopping_behavior.csv - Original dataset

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🚀 How to Run

1. Install required Python packages:

   pip install pandas sqlalchemy psycopg2-binary

2. Run the Jupyter notebook for data cleaning
3. Import the SQL file into PostgreSQL/PgAdmin
4. Open the Power BI file to view the dashboard

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📌 Key Insights

• Male customers generate higher revenue than female customers
• Subscribed customers spend more on average
• Free shipping is most popular but express shipping customers spend more
• Middle-aged customers contribute most to revenue
• Discounts significantly impact purchase decisions

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📸 Dashboard Screenshots

Customer Behavior Dashboard Overview

Interactive Power BI dashboard showing customer demographics, purchase patterns, and behavioral insights.

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🔗 Connect & Explore

• GitHub Repository: https://github.com/ThisAkshat/Customer-s_Behavior_Analysis.git
• Tools Used: Python, PostgreSQL, Power BI, Jupyter Notebook