Swiggy Delivery Analysis 🍛📦

End-to-End Data Analytics & Visualization Project

Author: K. Siddhartha 🔗 Live Demo: https://huggingface.co/spaces/Siddhartha001/swiggy-delivery-analysis

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📌 Project Overview

This project is an industry-grade data analytics application that analyzes food delivery operations inspired by Swiggy-like platforms.

It focuses on understanding delivery efficiency, late delivery probability, rider distance impact, and customer ratings using Python-based data analysis and interactive visualization.

The project is fully deployable and includes a live Gradio dashboard hosted on Hugging Face.

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🎯 Objectives

• Analyze delivery time behavior and delays
• Quantify late delivery probability
• Study the relationship between rider distance and delivery duration
• Visualize insights interactively for decision-making

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🛠 Tech Stack

• Language: Python
• Data Analysis: Pandas, NumPy, SciPy
• Visualization: Seaborn, Matplotlib, Plotly
• App Framework: Gradio
• Deployment: Hugging Face Spaces

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📊 Key Features

• Synthetic but realistic delivery dataset generation
• Statistical summaries and trend analysis
• Late-delivery modeling and probability estimation
• Interactive plots with hover insights
• End-to-end pipeline from data → analysis → deployment

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🚀 Live Application

👉 Interactive Dashboard: https://huggingface.co/spaces/Siddhartha001/swiggy-delivery-analysis

Users can explore:

• Delivery time distributions
• Distance vs delivery correlations
• City-wise performance
• Late delivery likelihood

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🧪 Run Locally

1️⃣ Install dependencies

pip install -r requirements.txt

2️⃣ Run analysis pipeline

python -m src.full_analysis

3️⃣ Launch Gradio app

python app.py