This repository presents a comprehensive exploratory data analysis (EDA) project that investigates customer shopping trends and sales performance using Python. It showcases how data-driven insights can help understand purchasing behavior, seasonal patterns, and key business questions for retail optimization.
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├── .gitattributes
├── .gitignore
├── 1_familiarization.ipynb # Jupyter notebook for dataset familiarization
├── Customer Shopping Trends EDA.py # Python script for detailed EDA on customer trends
├── sales_eda.docx # Guiding document for sales EDA tasks
├── Sales-EDA.pdf # Report version of sales analysis tasks
├── shopping_trends.csv # Primary dataset used for EDA- Age-based and gender-based purchasing patterns
- Preferred payment methods and product categories
- Purchase frequency vs. subscription/discount/promo usage
- Seasonal and regional shopping behavior
- Correlation analysis between numeric features
- Sales performance by month and city
- Product categorization (phones, accessories, etc.)
- High- and low-performing products by season
- Time-based advertising recommendations
- Price pattern evaluation and bulk order trends
- Python
- Pandas
- Seaborn
- Matplotlib
- Jupyter Notebook
consumer behavior · retail analytics · sales trends · data visualization · EDA · shopping insights · Python data analysis · customer segmentation · pandas · seaborn
The dataset used for this analysis (shopping_trends.csv) contains anonymized data of consumer purchases, including demographic features, payment preferences, seasonal buying patterns, and product details.
Clone the repo and run:
python Customer\ Shopping\ Trends\ EDA.pyor explore 1_familiarization.ipynb for step-by-step walkthroughs in Jupyter.
This project is for educational and portfolio use. Dataset rights belong to their respective owners.
This project is independently developed as a data analysis case study. All datasets used are for educational purposes only.