This end-to-end data analytics project successfully demonstrates how Python (Pandas), Advanced Excel, and Power BI can be combined to optimize utility operations and ensure billing integrity.
- Anomaly Detection (Billing Integrity): Using Python filtering, a major data anomaly was detected for Customer ID 204, where consumption spiked unexpectedly to 5,200 units. Identifying such outliers helps prevents revenue leakage and highlights potential meter faults or power theft.
- Operational Request Trends: The utility dataset showed an equal distribution (50-50%) between Move-In and Move-Out requests, indicating a balanced operational workload across the cycle.
- Financial Insights: Grouped metrics revealed that the Electricity segment drives over 90% of the total billed revenue (66,300 INR) compared to the Gas segment (7,040 INR).
- Data-Driven Decisions: By building a dynamic Power BI dashboard, operations managers can instantly isolate pending high-value requests (like Customer ID 203) to streamline the customer onboarding process.
- Python (Pandas): Data wrangling, logical multi-condition filtering (
&), and aggregation (value_counts,groupby). - Advanced Excel: Pivot Tables for structured data summarization and reporting.
- Power BI: Data modeling, dynamic slicing, KPI card integration, and interactive data visualization.