Food Delivery Platform Performance Analysis

📌 Project Overview

This project provides a comprehensive end-to-end analysis of a food delivery platform operating across four major Indian cities: Bangalore, Mumbai, Hyderabad, and Delhi. The objective is to evaluate operational performance, understand customer behavior, and identify key drivers for growth and profitability.

The analysis follows the full data lifecycle: Data Cleaning -> Exploratory Data Analysis (EDA) -> SQL-based Deep Dive -> Visualization -> Strategic Reporting.

📊 Key Insights & Findings

• Top Performer: Bangalore leads in revenue (INR 84,748), followed closely by Mumbai.
• Operational Bottleneck: Average delivery time is 52.52 minutes, which is a significant factor contributing to the low average customer rating of 2.98/5.0.
• Customer Loyalty: 38% of customers are repeat users, indicating a healthy but improvable retention rate.
• Preferred Trends: Fast Food is the most popular category, and UPI is the dominant payment method.

🛠️ Technologies Used

• SQL: Deep dive analysis and business question resolution.
• Python (Pandas, Matplotlib, Seaborn): Exploratory Data Analysis and data profiling.
• Power BI: Interactive dashboarding for stakeholder visualization.
• Excel: Initial data handling and cleaning.
• Markdown: Professional executive reporting.

📂 File Structure

• analysis.sql: SQL queries for business metrics.
• eda.ipynb: Jupyter notebook containing Python-based data exploration.
• dashboard.pbix: Power BI dashboard file.
• report.md: Detailed executive summary and recommendations.
• presentation.pptx: Slide deck for stakeholder presentation.
• food_delivery_dataset.xlsx: The raw dataset used for analysis.

🚀 How to Use

1. SQL Analysis: Import the dataset into your preferred SQL engine and run analysis.sql to see core metrics.
2. Python EDA: Open eda.ipynb in Jupyter Notebook or VS Code to see the data distribution and correlations.
3. Visualization: Open dashboard.pbix in Power BI Desktop to interact with the performance visuals.
4. Reporting: Read report.md for a summary of business recommendations.

💡 Strategic Recommendations

1. Reduce Delivery Lead Times: Optimize rider routing and partner with restaurants to reduce kitchen preparation time.
2. Customer Quality Guarantee: Implement initiatives for restaurants with ratings below 3.0 to improve platform sentiment.
3. Retention Programs: Introduce tiered loyalty rewards for the 62% of one-time users to convert them into repeat customers.

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Project Developed By: Dev Daxinkumar Patel