Customer_Purchase_Trend_Analysis is a data analytics project focused on understanding customer purchasing behavior and sales trends using transactional retail data. The project integrates Python-based exploratory data analysis (EDA) with SQL-driven analysis to extract actionable business insights.
- Analyze customer purchasing behavior
- Identify spending patterns and trends
- Evaluate product and category performance
- Apply SQL queries for business insights
- Present data-driven conclusions through visualization
- File Name:
customer_shopping_behavior.csv - Domain: Retail / E-commerce
- Description: The dataset contains customer-level shopping records including purchase amounts, product categories, and behavioral attributes.
- Programming Language: Python
- Libraries: Pandas, NumPy, Matplotlib, Seaborn
- Database & Query Language: SQL
- Tools: Jupyter Notebook, MySQL GitHub
├── Customer_Trend_Analysis.ipynb # Python EDA & visualization
├── Customer_Trend_Analysis.sql # SQL queries for purchase trend analysis
├── customer_shopping_behavior.csv # Dataset
├── README.md # Project documentation
- A small segment of customers contributes disproportionately to total revenue
- Certain product categories dominate purchase trends
- Purchase frequency has a strong correlation with total spending
- Behavioral insights can help improve targeted marketing strategies
- Python 3.x
- Jupyter Notebook
- SQL-supported database environment
- Clone the repository:
git clone <[https://github.com/sujata1712/Customer_Purchase_Trend_Analysis]>
- Install required libraries:
pip install pandas numpy matplotlib seaborn- Execute:
- Run
Customer_Trend_Analysis.ipynb - Execute queries from
Customer_Trend_Analysis.sql
- Customer segmentation using clustering algorithms
- Predictive modeling for purchase behavior
- Interactive dashboards using Power BI or Tableau
- Time-series sales forecasting
Sujata Sinhababu B.Tech in Computer Science & Engineering
This project is intended for educational and non-commercial use.