📊 Customer Shopping Behavior Analysis

Overview

This project demonstrates an end-to-end data analytics workflow — from raw customer transaction data to meaningful business insights. It includes data loading and cleaning in Python, exploratory data analysis (EDA), SQL-based analysis, and interactive visualization using Power BI.

The objective is to analyze customer shopping behavior and highlight key trends in spending, subscriptions, and product performance.

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Dataset

The dataset contains customer shopping and transaction data, including:

• Customer demographics (age, gender, location)
• Purchase details (product, category, amount, season)
• Shopping behavior (discounts, reviews, frequency, subscriptions)

The dataset is used to study purchasing patterns and customer segmentation.

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Tools & Technologies

• Python – Pandas, NumPy, Matplotlib, Seaborn
• SQL – PostgreSQL / MySQL / SQL Server
• Power BI – Dashboard and data visualization
• Excel – Supporting analysis
• Jupyter Notebook / VS Code – Development environment

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Project Workflow

1. Data Loading & Cleaning (Python)

• Loaded dataset using Pandas
• Handled missing values and inconsistent data
• Renamed columns for clarity
• Performed feature engineering (age groups, purchase frequency)
• Prepared cleaned data for database storage

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2. Exploratory Data Analysis (EDA)

• Distribution of purchase amounts
• Customer segmentation analysis
• Category-wise and product-wise sales analysis
• Subscription behavior comparison
• Review rating trends

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3. SQL Analysis

The cleaned data was loaded into a SQL database and analyzed using queries such as:

• Revenue by gender
• High-spending customers
• Top-rated products
• Shipping type comparison
• Subscribers vs non-subscribers analysis
• Repeat customer behavior

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4. Power BI Dashboard

An interactive Power BI dashboard was created to visualize:

• Total customers and average purchase value
• Revenue by category and age group
• Subscription distribution
• Sales trends and customer segments

Users can filter insights by category, gender, shipping type, and subscription status.

📸 Dashboard Preview

🔷 Customer Shopping Behavior Dashboard

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Results & Insights

• Identified high-value customer segments
• Highlighted top-performing and top-rated products
• Compared subscriber and non-subscriber purchasing behavior
• Found age groups contributing most to total revenue
• Identified discount-dependent products

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Project Deliverables

• Python notebooks for EDA and data cleaning
• SQL queries for analysis
• Power BI dashboard (.pbix)
• Project report
• Presentation (PPT)

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How to Run This Project

Step 1: Clone the Repository

git clone https://github.com/your-username/customer-shopping-behavior-analysis.git
cd customer-shopping-behavior-analysis

Step 2: Install Python Dependencies

pip install pandas numpy matplotlib seaborn sqlalchemy psycopg2

Step 3: Run Exploratory Data Analysis

Open Jupyter Notebook or VS Code and run:

customer_behavior_analysis.ipynb

Step 4: Load Data into SQL

• Create a database in PostgreSQL/MySQL/SQL Server
• Run the provided SQL schema
• Use Python script to insert cleaned data

Step 5: Open Power BI Dashboard

• Open the .pbix file in Power BI Desktop
• Update database credentials if needed
• Refresh data

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