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mostaf22/GoBus-Data-Analytics-End-to-End

Domaine:

socioeconomic

Type de record:

project
Créateur:
mos
Hôte:
End-to-end Data Analysis project for Go Bus Egypt, from data engineering in Excel to visual storytelling in Power BI 🚌 Go Bus | End-to-End Data Analysis Project Welcome to the Go Bus Data Analysis repository. This project demonstrates a complete data workflow—from generating synthetic business data to building a strategic decision-making dashboard. 🌟 Project Context This project simulates a real-world request from Go Bus Egypt. The challenge was to create a data-driven system to monitor fleet performance, revenue trends, and passenger behavior to optimize operational efficiency. 🛠️ Phases of Development 1. Data Engineering (Microsoft Excel) Since real-world data is often confidential, I architected a synthetic dataset from scratch that mirrors Go Bus's operations: Dimensions: Routes (e.g., Cairo-Gouna, Alexandria-Dahab), Bus Classes (Elite, Business, Classic), and Timeframes. Logics: Integrated realistic occupancy rates, seasonal price fluctuations, and booking patterns. Tooling: Used Excel formulas and Power Query for initial data structuring. 2. Data Transformation (Power Query) Cleaned and reshaped the data to ensure consistency. Handled date/time formatting to allow for "Peak Hour" analysis. Created a Star Schema for efficient data modeling in Power BI. 3. Visual Storytelling (Power BI & DAX) Developed an interactive dashboard focused on Actionable Insights: Key KPIs: Total Revenue, Average Ticket Price, and Total Trips. Occupancy Analysis: Visualizing which bus classes are most profitable. Route Performance: Identifying "Gold Mines" (High demand/High revenue routes). Advanced DAX: Created measures for Month-over-Month (MoM) Growth and Capacity Utilization %. 🚀 How to Use This Repo /Data: Explore the raw Excel files to see how the data was structured. /Report: Download the .pbix file to interact with the dashboard (Requires Power BI Desktop). README.md: You are here! 💡 Key Takeaway This project isn't just about "charts"; it's about showing how data can answer: "Where should we add more buses tomorrow?" or "Which route needs a price adjustment?" Author: [Mosta …