Customer Shopping Behavior Analysis

Overview

This project analyzes customer shopping behavior using Python, SQL, and Power BI to uncover purchasing patterns, customer preferences, and business insights. The project follows an end-to-end data analytics workflow, from data cleaning and exploration to dashboard creation and reporting.

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Dataset

The dataset contains customer transaction records, including:

• Customer demographics
• Product categories
• Purchase amounts
• Shopping behavior
• Subscription information
• Ratings and reviews

The data was cleaned and prepared before analysis to ensure accuracy and consistency.

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Tools Used

• Python
• Pandas
• NumPy
• Jupyter Notebook
• SQL
• PostgreSQL / MySQL / SQL Server
• Power BI
• Gamma AI
• Git & GitHub

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Project Steps

1. Data Loading

• Imported the dataset into Python.
• Performed initial exploration and validation.

2. Data Cleaning

• Handled missing values.
• Standardized column names.
• Removed inconsistencies and duplicates.
• Created additional features for analysis.

3. Exploratory Data Analysis (EDA)

• Analyzed customer demographics.
• Examined spending patterns.
• Identified trends and customer segments.
• Generated visual insights using Python.

4. SQL Analysis

Loaded the cleaned dataset into a relational database and performed business-focused analysis using SQL.

Examples:

• Revenue analysis
• Customer segmentation
• Product performance analysis
• Subscription behavior analysis
• Purchase trend analysis

5. Power BI Dashboard

Built an interactive dashboard to visualize:

• Revenue metrics
• Customer insights
• Product performance
• Business trends

6. Reporting & Presentation

• Created a detailed project report.
• Developed a professional presentation using Gamma AI.

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Dashboard

The Power BI dashboard provides an interactive view of:

• Total Revenue
• Customer Segmentation
• Product Category Performance
• Purchase Trends
• Key Business Metrics

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Results

Key outcomes from the analysis include:

• Identification of customer purchasing patterns.
• Insights into customer spending behavior.
• Analysis of product performance across categories.
• Data-driven recommendations for business growth.
• Interactive dashboard for decision-making.

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How to Run

1. Clone the repository.
2. Open the Jupyter Notebook files.
3. Install required Python libraries.
4. Run the data cleaning and EDA notebooks.
5. Execute SQL scripts in PostgreSQL, MySQL, or SQL Server.
6. Open the Power BI (.pbix) file to explore the dashboard.
7. Review the project report and presentation for detailed insights.

git clone <repository-url>
cd Customer-Shopping-Behavior-Analysis

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Project Deliverables

• Python Notebooks
• SQL Scripts
• Power BI Dashboard
• Project Report
• Gamma AI Presentation

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Conclusion

This project demonstrates an end-to-end data analytics workflow involving data cleaning, exploratory data analysis, SQL-based business analysis, dashboard development, and reporting. The insights generated can help businesses better understand customer behavior and support data-driven decision-making.