Amazon Analysis

Excel | Python | SQL | Power BI

Interactive Power BI Dashboard

Why this project?

As my first end-to-end analytics project, the primary goal was not to solve a specific business problem but to build and understand a complete data analytics pipeline. The dataset was chosen because it contained realistic e-commerce transactions, sales metrics, and fulfilment information, providing an opportunity to explore the types of challenges commonly faced by analysts. The dataset, over 130,000 rows, provided enough of a challenge in regard to data cleaning and analysis. Click here to see dataset download.

Methodology

  • Dataset Aquisition: Dataset was aquired form Kaggle, a free open source data provider. This particular dataset was not a real-world dataset, but was designed to look and feel like one, simulating various formatting and data normalisation issues which are common place in real world datasets.

  • Data Cleaning in Python (VSCode): Used Pandas to import, clean, and format the raw, messy e-commerce datasets to ensure consistent dates, numeric values, and column structures.

  • Database & Modeling (PostgreSQL): Loaded the cleaned data into Postgres as a database host. Wrote basic query to explore the data and preview some buisness questions. See all python and SQL code on my GitHub here.

  • Visualization & Analysis (Power BI): Connected Power BI directly to the Postgres database and visualised key executive metrics on a well layed out summary page.

Key Insights

Buisness Insights

  • The business generated approximately £770k in revenue from over 120k orders during Q2 2022.

  • Average order value remained relatively low at around £6.22, suggesting a high-volume, low-value sales model.

  • Revenue remained broadly consistent throughout the quarter, with no significant prolonged declines in sales performance.

  • Product sales were concentrated among a small number of leading categories, with "Set" products contributing the largest share of revenue ~50%.

  • Sales performance varied significantly across Indian states, highlighting regional differences in customer demand.

  • The majority of orders were successfully fulfilled, with cancellations and delivery exceptions representing a relatively small proportion of total orders.

Project Takeaways

  • Building an end-to-end analytics pipeline requires different tools to perform different roles: Python for data preparation, PostgreSQL for data storage and querying, and Power BI for business reporting.

  • Effective dashboard design focuses on answering key business questions rather than displaying every available metric.

  • This project provided practical experience in transforming raw transactional data into a dashboard capable of supporting business decision-making.