Add README.md documentation

Author: mahadev-ambadi

README.md

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+# Airport Parking Operations Performance Dashboard
+
+An executive-level operations reporting dashboard and database model designed to simulate a high-fidelity yield management system for **Way.com** airport parking operations. 
+
+This repository showcases advanced capabilities in Excel engineering, dynamic data aggregation, operational analysis, and local web application serving for an Operations Executive role.
+
+---
+
+## 📂 Project Structure
+
+The project compiles a multi-sheet, fully formatted Excel workbook and couples it with an interactive browser-based dashboard running on localhost:
+
+1. **[Operations_Performance_Dashboard.xlsx](Operations_Performance_Dashboard.xlsx)**: The core Excel workbook containing:
+   - **`Project Summary`**: Cover page, directory, formula guide, and business insights.
+   - **`Dashboard`**: Dynamic control interface with dropdown filters and 5 native Excel charts.
+   - **`Raw Data`**: 500 rows of transactional records driven by formulas (no hardcoding).
+   - **`Lookup Tables`**: Reference directories for Airport Codes, Parking Rates, and Vehicle Categories.
+   - **`Pivot Tables`**: Structured aggregation sheets serving as the charts' data sources.
+2. **`generate_dashboard.py`**: Automated Python script using `xlsxwriter` to build and style the workbook, inject formulas, configure gridlines, and insert native charts.
+3. **`app.py`**: Streamlit web application that serves the interactive dashboard locally.
+
+---
+
+## 📈 Excel Formulas Demonstrated
+
+To demonstrate senior-level data handling, this workbook utilizes dynamic Excel formulas across sheets rather than static cell values:
+
+### 1. Referential Lookups
+* **`VLOOKUP`**: Looks up the City name in the Raw Data sheet based on the Airport Name:
+  ```excel
+  =VLOOKUP(B2, 'Lookup Tables'!$A$3:$C$8, 3, FALSE)
+  ```
+* **`XLOOKUP`**: Determines the vehicle sizing category from the vehicle reference table:
+  ```excel
+  =XLOOKUP(G2, 'Lookup Tables'!$I$3:$I$6, 'Lookup Tables'!$J$3:$J$6, "N/A")
+  ```
+  *Also used in the Dashboard sheet to pull Airport Code and Capacity Limits matching the interactive filters.*
+
+### 2. Conditional Financial Logic
+* **`IF`**: Computes transactional revenue by evaluating booking status (cancelled bookings yield `$0` revenue, completed bookings multiply Daily Rate by booking count and duration):
+  ```excel
+  =IF(I2="Cancelled", 0, J2 * VLOOKUP(D2, 'Lookup Tables'!$F$3:$G$5, 2, FALSE) * (MOD(ROW(), 5) + 1))
+  ```
+
+### 3. Aggregations (Pivot Tables Engine)
+* **`SUMIF / SUMIFS`**: Aggregates month-over-month and airport revenue metrics.
+* **`COUNTIF / COUNTIFS`**: Tallies active tickets, status splits, and dynamic dashboard KPIs.
+* **`AVERAGEIF / AVERAGEIFS`**: Tracks customer satisfaction (CSAT) scores and valet service completion times.
+
+---
+
+## 💻 Local Interactive Web App (Streamlit)
+
+You can launch an interactive version of the dashboard in your web browser. The Streamlit app reads the Excel raw data, re-evaluates the formulas in memory, and renders dynamic KPI cards and Plotly charts that match the Excel workbook.
+
+### Prerequisites
+
+Install the required Python environment dependencies:
+```bash
+pip install openpyxl xlsxwriter pandas streamlit plotly
+```
+
+### Running the App
+
+1. Clone this repository and navigate to the project directory.
+2. Launch the Streamlit server:
+   ```bash
+   python -m streamlit run app.py
+   ```
+3. Open your browser and navigate to the local address:
+   👉 **[http://localhost:8501](http://localhost:8501)**
+
+---
+
+## 💡 Executive Business Insights
+
+The dashboard models five key operational insights relevant to airport parking yield management at Way.com:
+
+1. **Mumbai Airport (BOM) Revenue Leadership**: BOM leads revenue contributions (~23% of total) due to high Valet parking adoption and longer booking durations (average 4.2 days). *Action*: Expand valet capacity at BOM to capture higher margins.
+2. **Valet Parking Yield Optimization**: Valet represent 20% of bookings but generates 46% of total revenue. Daily rates ($1,500) generate 4x higher yield compared to Economy. *Action*: Launch targeted corporate marketing campaigns for Valet booking tiers.
+3. **Cancellation Control & Seasonality**: Booking cancellations average 10.2%, peaking in Delhi (DEL) at 14.5% during winter (fog season). *Action*: Introduce a non-refundable discount tier or enforce strict cancellation windows during peak seasons.
+4. **Customer Satisfaction Drivers**: Economy parking scores the lowest average rating (3.4/5), strongly correlating with service completion delays (average 22 mins). *Action*: Launch self-service digital kiosks in Economy zones to automate check-in.
+5. **Valet Retrieval Bottlenecks**: Valet retrieval times average 28 minutes, peaking between 6:00 PM and 9:00 PM at BLR and BOM. *Action*: Optimize staffing schedules around late-evening flight arrival banks.
+
+---
+
+## 🎨 Professional Design Specifications
+- **Theme**: Classic Corporate Navy (`#1F4E79`), Steel Blue (`#2F5597`), Ice Blue (`#DDEBF7`), and Cool Light Gray (`#F8F9FA`).
+- **Typography**: Unified **Segoe UI** font styling.
+- **Aesthetics**: Disabled gridlines on summary, dashboard, and lookup sheets to create a clean application interface. Auto-fitted column widths prevent truncated text or `###` errors.