Data Analysis and Visualization Using Python: A Case Study of Amazon | IJCT Volume 13 – Issue 3 | IJCT-V13I3P64
International Journal of Computer Techniques
ISSN 2394-2231
Author
Sethu Madhav, Seshukumar, Dr. P. Thangavel
Abstract
The rapid growth of e-commerce has created large volumes of transactional, behavioral, and product-level data. Organizations such as Amazon depend on systematic data analysis and clear visualization to understand customer demand, product performance, pricing patterns, regional trends, and operational efficiency. This paper presents an IEEE-style case study on data analysis and visualization using Python with an Amazon sales dataset as the analytical context. The proposed workflow includes data collection, preprocessing, cleaning, exploratory data analysis, feature engineering, visualization, and insight generation. Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Scikit-learn are considered for building a complete analytical pipeline. The study demonstrates how descriptive statistics, category-wise analysis, time-series trends, rating analysis, and revenue-based visualizations can support business decision-making. The results show that Python is an effective, flexible, and scalable environment for transforming raw e-commerce data into meaningful insights.
Keywords
Amazon, data analysis, data visualization, Python, Pandas, Matplotlib, Seaborn, e-commerce analytics, exploratory data analysis.
Conclusion
This paper presented a case study on data analysis and visualization using Python with Amazon as the analytical context. The study explained the complete workflow from data loading and cleaning to EDA, visualization, interpretation, and reporting. Python libraries such as Pandas, NumPy, Matplotlib, Seaborn, Plotly, and Scikit-learn provide a powerful environment for analyzing e-commerce data.
The Amazon case study demonstrates that meaningful insights require both technical processing and business understanding. Cleaning ensures accuracy, EDA discovers patterns, visualization communicates results, and interpretation converts charts into decisions. For students and entry-level data analysts, this project is useful because it combines practical coding skills with real-world business analytics thinking.
The work can be extended into predictive analytics, dashboard development, recommendation systems, and AI-based automated analysis. Therefore, Python-based data analysis and visualization remain highly valuable for modern e-commerce intelligence and data-driven decision-making.
References
[1]W. McKinney, “Data Structures for Statistical Computing in Python,” in Proceedings of the 9th Python in Science Conference, 2010, pp. 56-61.
[2]J. D. Hunter, “Matplotlib: A 2D Graphics Environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.
[3]M. L. Waskom, “Seaborn: Statistical Data Visualization,” Journal of Open Source Software, vol. 6, no. 60, p. 3021, 2021.
[4]F. Provost and T. Fawcett, Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media, 2013.
[5]G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.
[6]T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
How to Cite This Paper
Sethu Madhav, Seshukumar, Dr. P. Thangavel (2026). Data Analysis and Visualization Using Python: A Case Study of Amazon. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
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