This project analyses customer shopping behaviours for a retail company to identify trends, spending patterns, and loyalty drivers. It follows a complete data analytics workflow — from data cleaning in Python, to SQL analysis, Power BI dashboard creation, and a final business report & presentation.
Size: 3,900 records | Columns: 18
Key Features:
- 👤 Customer Demographics: Age, Gender, Location, Subscription Status
- 🛒 Purchase Details: Item Purchased, Category, Purchase Amount, Season, Color, Size
- 💳 Behavioral Factors: Discounts, Promo Code, Previous Purchases, Review Rating, Shipping Type
Tool Purpose
- 🐍 Python (Pandas, NumPy, Matplotlib) Data loading, cleaning, and exploratory analysis
- 🗄️ SQL Server Querying and generating insights from structured data
- 📊 Power BI Building interactive dashboards and visual reports
- 🖥️ Gamma App Creating professional presentation slides
- 💾 GitHub Version control and portfolio hosting
- Loaded and explored the dataset using Pandas.
- Handled missing values and standardized column names.
- Engineered new features such as age group and customer segments.
- Exported the cleaned dataset to SQL Server for analysis.
- Conducted summary statistics and visualized spending distributions.
- Examined correlations between age, gender, spending, and discounts.
- Identified outliers and behavioural trends.
Executed SQL queries to answer 10+ business questions, such as:
- Revenue contribution by gender and age group.
- Do subscribers spend more?
- Which products have the best review ratings?
- What are the top 3 purchased products per category?
- What’s the revenue contribution by age group?
Created an interactive dashboard summarizing:
- Revenue by gender, age, and product category.
- Subscription and shipping insights.
- Customer segmentation (New, Returning, Loyal).
- Top products by sales and review rating.
- Compiled key findings and insights in a professional report (.docx).
- Designed a Gamma presentation to communicate insights effectively to stakeholders.
Insight Observation
- 💰 Top Revenue Group Young Adults generate the highest revenue (£62,143).
- 🧍 Customer Loyalty Loyal customers form the largest segment.
- 🏷️ Discount Impact Discount users spend above average, boosting sales.
- 🚚 Shipping Type Express shipping users have higher purchase values.
- 👕 Top Categories Clothing and Accessories dominate sales and revenue.
- Young, loyal, and subscribed customers are the highest-value segments.
- Discounts and express delivery effectively increase sales and customer satisfaction.
- Targeted marketing and personalized offers can enhance engagement and retention.
- Clone this repository:
- git clone https://github.com/AbdulHassan-Git/Customer-Shopping-Behaviour-Analysis.git
- Open the Jupiter Notebook
- Run all cells in Customer_Shoping_Behavior_Analysis.ipynb to clean and prepare the dataset. - Load data into SQL Server
- Execute queries from Business Questions_SQL.sql for analysis. - Open the Power BI Dashboard
- Explore interactive visuals in Customer_Behavior_Dashboard.pbix. - Review Deliverables
📘 Customer_Shopping_Behaviour_Analysis_Report.docx — Project report
🎞️ Customer_Behavior_Presentation.gamma — Presentation slides
This project demonstrates a complete data analytics pipeline — from data wrangling to business insights.
It highlights technical and analytical skills in Python, SQL, and Power BI, along with the ability to turn raw data into actionable insights for decision-making.
MIT — feel free to fork, star, and use in your portfolio.
Abdul Valiyapurakkal Hassan
📧 Abdulkhayyoom896@gmail.com | 💼 www.linkedin.com/in/abdul-khayyoom-v-h-a65865125 | 🌐 https://github.com/AbdulHassan-Git