Project Overview
A data‑analysis project that unpacks and visualizes sales data for an e‑commerce store. Utilizes a dashboard built in Power BI to provide insights into sales trends, top‑performing products, customer behavior, and overall revenue performance.
Perform exploratory data analysis (EDA) on sales dataset: sales volume, revenue over time, product performance, and customer segmentation.
Visualize key metrics such as monthly/daily sales trends, best‑selling products, revenue distribution across categories.
Generate interactive dashboards and reports for stakeholders to make data-driven decisions (inventory management, marketing, sales strategies).
Provide business‑insight outputs: underperforming products, seasonality / demand trends, customer purchasing patterns.
/ — root directory
├── dataset/ — raw / cleaned datasets used in analysis
├── E-commerce Sales Dashboard.pbix — Power BI dashboard file
└── README.md — this file
Power BI — for data visualization and dashboard creation
Python / Pandas (optional) — for data cleaning / preprocessing
Excel / CSV — for storing and handling sales data before visualization
Clone the repository:
git clone https://github.com/lector2002/E-Commerce-Sale-Analysis.git
Open the E‑commerce Sales Dashboard.pbix file in Power BI Desktop (or Power BI service)
Load the dataset from the dataset/ folder (or re‑import if using a cleaned version)
Explore different dashboard views: sales over time, product performance, customer segment reports, etc.
(Optional) If you add data cleaning or preprocessing scripts, run them first before loading data into Power BI
Sales records: including order date, product ID, product category, quantity sold, sale price, customer metadata (if available)
Aggregated metrics: total revenue, units sold per product, monthly/quarterly/annual sales trends, product category breakdown
Derived business metrics: best‑selling products, slow‑moving inventory, revenue growth rate
Add automated data cleaning and preprocessing scripts in Python for raw data ingestion
Incorporate customer-demographics analysis (if data available): purchase frequency, retention, lifetime value
Build predictive models for sales forecasting (monthly/seasonal forecasting)
Deploy dashboards on Power BI Service and enable periodic data refresh for live analytics
This project was created to showcase practical skills in data analysis, data visualization, and business‑driven data interpretation. It demonstrates how sales data can be turned into actionable insights to support decision‑making in e‑commerce. Great for data analyst portfolios, interviews, or small‑business analytics demonstrations.
Feel free to fork, reuse, or build upon this project. For any questions or collaboration requests, contact me via GitHub or email.