🛍️ Customer Behaviour Analysis – Shopping Data

📌 Project Overview

This project focuses on analyzing customer shopping behavior using a real-world dataset. The goal is to extract meaningful insights that help businesses understand customer preferences, improve marketing strategies, and increase sales.

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📂 Dataset

• Source: Shopping Customer Behavior Dataset

• Includes:

  • Customer demographics (Age, Gender)
  • Purchase details
  • Product categories
  • Review ratings
  • Shopping frequency

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🛠️ Tools & Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• SQL (Data querying & analysis)
• Power BI (Dashboard visualization)
• Jupyter Notebook

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🔍 Project Workflow

1. Data Collection & Understanding
2. Data Cleaning & Preprocessing
3. Exploratory Data Analysis (EDA)
4. SQL-based Analysis
5. Data Visualization
6. Dashboard Creation in Power BI
7. Insight Generation

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📊 Key Analysis Performed

• Average product ratings by category
• Customer purchase patterns
• Gender-wise and age-wise analysis
• High-value customers identification
• Most frequently purchased items
• Sales trends and behavior insights

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📈 Dashboard Features (Power BI)

• Interactive filters (Age, Gender, Category)
• Sales and purchase trends
• Customer segmentation visuals
• Product performance metrics

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💡 Key Insights

• Certain product categories receive consistently higher ratings
• Younger customers tend to shop more frequently
• Repeat customers contribute significantly to revenue
• Seasonal trends influence purchasing behavior

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🚀 Conclusion

This project demonstrates how data analysis can help businesses make data-driven decisions by understanding customer behavior patterns.

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📎 Files Included

• Dataset (.csv)
• SQL Queries (.sql)
• Jupyter Notebook (.ipynb)
• Power BI Dashboard (.pbix)

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🙌 Author

JERRY A

Aspiring Data Analyst | Skilled in SQL, Python & Power BI