Python • SQL • Power BI • Business Intelligence
In this project, I developed a complete data analytics workflow that transforms raw e-commerce data into actionable business insights through data cleaning, validation, integration, exploratory data analysis, SQL analysis, and interactive dashboard development.
Raw Data → Data Cleaning → Data Validation → Data Integration → Exploratory Data Analysis → SQL Business Analysis → Power BI Dashboard
- Python
- Pandas
- NumPy
- SQL
- Power BI
- Jupyter Notebook
E-commerce-Sales-Analytics//
├── Notebooks/
├── dashboards/
├── images/
├── data/
├── README.md
├── requirements.txt
└── LICENSE
The raw datasets used in this project were obtained from a public Kaggle dataset and include:
- Users
- Products
- Orders
- Order Items
- Reviews
- Events
The data was cleaned, validated, transformed, and integrated into analytical datasets suitable for business intelligence reporting.
- Total Revenue
- Total Orders
- Total Customers
- Products Sold
- Average Order Value
- Monthly Revenue Trend
- Revenue by Category
- Top 10 Products by Revenue
- Revenue by Brand
- Units Sold by Category
- Customer Engagement Funnel
- Customers by Gender
- Customer Growth Trend
- Key findings
- Strategic recommendations
- Potential business value
- Conclusion and limitations
- Electronics is the highest revenue-generating category.
- Automotive is among the strongest-performing categories.
- Revenue remains relatively stable throughout most of the year.
- Customer distribution across genders is balanced.
- Data Cleaning
- Data Validation
- Data Integration
- Exploratory Data Analysis
- SQL Analytics
- Business Intelligence
- Dashboard Development
- Data Visualization
Data Analyst focused on transforming raw data into clear insights using Python, SQL, Excel, and Power BI.
Feel free to explore my other projects and connect with me.
This project is licensed under the MIT License.