🛍️ Customer Shopping Behavior Analysis

This project analyzes customer shopping behavior to identify purchasing patterns, customer segments, and key factors affecting sales. The analysis uses Python, SQL, and Power BI to transform raw transactional data into meaningful insights that support data-driven business decisions.

📌 Project Objectives

• Analyze customer purchasing patterns

• Identify high-performing product categories

• Understand customer demographics and behavior

• Evaluate the impact of discounts and promotions

• Create interactive dashboards for business insights

📂 Dataset Features

• Total Records: 3,900+

• Total Features: 18 The dataset includes customer shopping transactions with the following attributes:

• Customer ID

• Age

• Gender

• Product Category

• Item Purchased

• Purchase Amount

• Review Rating

• Discount Applied

• Promo Code Used

• Payment Method

• Purchase Frequency

• Shipping Type

Each record represents a single customer transaction.

🛠️ Tools & Technologies

• Python – Data cleaning, preprocessing, and exploratory analysis
• SQL – Querying transactional data and extracting insights
• Power BI – Interactive dashboard and data visualization
• Jupyter Notebook – Analytical workflow and documentation

• Libraries used:

• Pandas
• NumPy
• Matplotlib / Seaborn

🔎 Project Workflow

1️⃣ Data Cleaning

• Handled missing values

• Standardized dataset structure

• Prepared data for analysis

2️⃣ Exploratory Data Analysis

• Customer demographics analysis

• Category-wise purchasing trends

• Purchase frequency analysis

3️⃣ SQL Analysis

• Top-selling products

• Revenue by category

• Customer segmentation

4️⃣ Power BI Dashboard

Created an interactive dashboard showing:

• Revenue by age group

• Sales by product category

• Customer purchase frequency

• Impact of discounts and promotions

📈 Dashboard Preview

📊 Key Insights

• Certain age groups contribute the most to revenue.

• A few product categories dominate total sales.

• Discounts and promo codes increase purchase likelihood.

• Frequent customers generate higher revenue.

💡 Business Recommendations

• Focus marketing campaigns on high-revenue customer segments

• Promote top-rated products to improve conversions

• Strengthen loyalty and subscription programs

• Optimize discount strategies to increase repeat purchases

• Improve inventory planning based on popular product categories

🚀 Future Scope

• Predictive analytics using machine learning

• Customer churn prediction

• Personalized product recommendation systems

• Demand forecasting for sales trends

• Sentiment analysis of customer reviews

• Automated business insight reporting

🚀 Project Outcome

This project demonstrates how Python, SQL, and Power BI can be combined to analyze customer behavior and extract insights that help businesses improve marketing strategies and increase sales.