- Overview
- Problem Statement
- Data Sources
- Tools & Technologies
- Methodology
- Results & Key Insights
- Conclusion & Recommendations
- Challenges & Limitations
- Future Work
- Appendices
- Contact
- Acknowledgments
The Business Dashboard is an interactive data analytics platform built with Python and Streamlit. It transforms raw business data into actionable insights through advanced visualizations, time-series forecasting, and anomaly detection. Designed for small to medium businesses, this project demonstrates expertise in data processing, exploratory analysis, and predictive modeling.
Businesses often struggle to make data-driven decisions due to a lack of accessible tools for analyzing sales, profitability, and trends. This project addresses the need for an easy-to-use platform that provides key performance metrics, forecasts future trends, and identifies anomalies in revenue data.
- Superstore Dataset: A publicly available dataset containing sales, profit, and order details. Download here.
- Exchange Rate API: Used for real-time currency conversion. API Documentation.
- Programming Languages: Python
- Libraries: Streamlit, Pandas, Prophet, Plotly, Seaborn, NumPy
- APIs: Exchange Rate API
- Visualization Tools: Plotly, Seaborn
- Version Control: Git
- Deployment: Streamlit Cloud
- Handled missing values using configurable imputation methods.
- Automated column mapping with fuzzy matching for consistent schema alignment.
- Standardized currency values using real-time exchange rates.
- Generated descriptive statistics for sales, profit, and order data.
- Visualized trends in revenue, profit margins, and category performance.
- Identified outliers and anomalies in sales data.
- Extracted time-based features (e.g., month, year) for trend analysis.
- Created profitability metrics for product and category comparisons.
- Implemented Facebook Prophet for time-series forecasting of sales and profit.
- Configured forecast periods (1-12 months) with confidence intervals.
- Evaluated forecast accuracy using historical data.
- Visualized residuals to assess model performance.
- Total Revenue: $2.3M
- Profit Margin: 18.5%
- Top Performing Category: Technology
- Lowest Performing Region: South
- Revenue Growth: Consistent growth in Q3 and Q4, driven by the Technology category.
- Profitability: Office Supplies have the lowest profit margins and should be re-evaluated.
- Anomalies: Detected revenue spikes in December due to seasonal promotions.
- Focus marketing efforts on the Technology category to maximize revenue.
- Investigate and optimize pricing strategies for Office Supplies.
- Expand operations in the West region, which shows the highest growth potential.
- Data Gaps: Missing values in the Discount and Region columns required imputation.
- Forecasting Accuracy: Prophet's performance may vary with limited historical data.
- Currency Conversion: Exchange rate API downtime could impact multi-currency analysis.
- Integrate additional datasets for customer segmentation and behavior analysis.
- Implement advanced anomaly detection algorithms for real-time monitoring.
- Add support for SQL-based data ingestion to handle larger datasets.
| Column Name | Description |
|---|---|
| Order Date | Date of the order |
| Sales | Total sales amount |
| Profit | Profit earned from the sale |
| Category | Product category |
| Region | Geographic region of the sale |
For inquiries or collaboration opportunities, please reach out via:
- LinkedIn: Prince Uwagboe
- Email: [email protected]
This project builds upon the foundation of the Data Analysis Web App with enhancements to analytical capabilities and user interface.