🛍️ Customer Shopping Behavior Analysis

📌 Project Overview

This project analyzes customer shopping behavior using transactional data (~3,900 records) across multiple product categories. The goal is to uncover patterns in customer spending, preferences, and engagement to support better business decisions.

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📊 Dataset Summary

• Total Records: 3,900
• Features: 18

Key Data Includes:

• Customer Info: Age, Gender, Location, Subscription Status

• Purchase Details: Item, Category, Purchase Amount, Season, Size, Color

• Behavior Metrics: Discount Usage, Promo Codes, Purchase Frequency, Previous Purchases, Ratings, Shipping Type

• Missing values found in the Review Rating column

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🧹 Data Cleaning & Preparation (Python)

• Imported the dataset using pandas
• Performed initial exploration using df.info() and df.describe()
• Handled missing values using median imputation
• Standardized column names into snake_case
• Created new features:
  • age_group
  • purchase_frequency_days
• Removed redundant columns
• Loaded cleaned data into PostgreSQL

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🗄️ SQL Analysis (Business Insights)

Key business questions answered:

1. Revenue by Gender
2. High-Spending Discount Users
3. Top 5 Products by Rating
4. Shipping Type Comparison
5. Subscribers vs Non-Subscribers
6. Discount-Dependent Products
7. Customer Segmentation (New, Returning, Loyal)
8. Top Products per Category
9. Repeat Buyers vs Subscription Behavior
10. Revenue by Age Group

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📈 Power BI Dashboard

An interactive dashboard was built to visualize major insights from the dataset.

Dashboard Highlights:

• Total customers and average purchase amount
• Revenue by category
• Subscription status distribution
• Sales by age group
• Revenue by age group

Add your Power BI dashboard screenshot here

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💡 Key Insights

• Loyal customers contributed the largest share of total purchases
• Customers using discounts still showed strong spending behavior
• A few product categories generated most of the revenue
• Subscription status had a visible impact on customer behavior

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🚀 Business Recommendations

• Promote subscription plans with exclusive offers
• Build loyalty programs for repeat buyers
• Review discount strategies to balance profit and sales
• Highlight top-selling and top-rated products in campaigns
• Use targeted marketing for high-value customer groups

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🛠️ Tech Stack

• Python (Pandas, NumPy)
• PostgreSQL
• Power BI

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📂 Project Structure

├── data/
├── notebooks/
├── sql/
├── dashboard/
└── README.md