Customer Behaviour Data Pipeline & Analytics

An end-to-end data engineering + analytics project that transforms raw customer shopping data into actionable business insights using Python, MySQL, and Power BI.

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Dashboard Preview

Interactive Power BI dashboard showing KPIs, customer segmentation, and revenue insights.

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Project Overview

This project demonstrates a complete data pipeline:

• Raw data ingestion from CSV
• Data cleaning & preprocessing using Python (Pandas)
• Data storage and querying using MySQL
• Interactive dashboard creation using Power BI

The objective is to analyze customer purchasing behavior, revenue trends, and business performance.

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Tech Stack

• Python (Pandas) → Data cleaning & transformation
• MySQL → Data storage & analytical queries
• SQLAlchemy → Database connection
• Power BI → Data visualization
• Jupyter Notebook → Development environment

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Project Structure

 Customer-Behaviour-Analytics
├── data/
│   └── cust_shopping_behavior.csv
├── notebooks/
│   └── project.ipynb
├── sql/
│   └── customer_behaviour.sql
├── dashboard/
│   └── customer_behaviour_dashboard.pbix
├── assets/
│   └── dashboard.png
├── README.md

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Data Pipeline Workflow

1. Data Cleaning (Python)

• Handled missing values (median imputation)
• Converted data types
• Ensured data consistency and quality

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2. Data Migration (MySQL)

• Created database: customer_behaviour
• Loaded cleaned data into SQL tables
• Used SQLAlchemy for seamless integration

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3. Data Analysis (SQL)

Revenue by Gender

SELECT gender, SUM(purchase_amount) AS revenue
FROM customer
GROUP BY gender;

Top Rated Products

SELECT item_purchased, ROUND(AVG(review_rating),2)
FROM customer
GROUP BY item_purchased
ORDER BY AVG(review_rating) DESC
LIMIT 5;

Discount Effectiveness

SELECT item_purchased,
ROUND(100.0 * SUM(CASE WHEN discount_applied='Yes' THEN 1 ELSE 0 END)/COUNT(*),2)
FROM customer
GROUP BY item_purchased
ORDER BY 2 DESC
LIMIT 5;

More queries available in: sql/customer_behaviour.sql

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Key Insights

• Young adults generate the highest revenue
• Clothing category dominates both revenue & sales
• Discounts significantly influence purchasing behavior
• Subscription status impacts customer spending patterns

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How to Run

1. Clone Repository

git clone https://github.com/your-username/customer-behaviour-analytics.git
cd customer-behaviour-analytics

2. Install Dependencies

pip install pandas sqlalchemy pymysql

3. Run Notebook

jupyter notebook notebooks/project.ipynb

4. Setup Database

• Create MySQL database
• Run SQL file:

source sql/customer_behaviour.sql;

5. Open Dashboard

• Open customer_behaviour_dashboard.pbix in Power BI

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Author

Aryan Srivastava