🚀 Quick Commerce Data Analysis

📌 Project Overview

This project focuses on analyzing Quick Commerce (Q-Commerce) business data using a synthetic yet realistic dataset containing 1 million records. The dataset is inspired by popular quick commerce platforms and is designed to simulate real-world data analytics challenges.

The project uses Python, Pandas, and data visualization techniques to clean, analyze, and extract meaningful business insights from the data.

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🎯 Project Objectives

The main objectives of this project are:

• Perform Data Cleaning
• Conduct Exploratory Data Analysis (EDA)
• Create Data Visualizations
• Perform Feature Engineering
• Solve real-world business problems
• Generate business insights
• Build a Mini KPI Dashboard
• Create a portfolio-ready data analytics project

The dataset intentionally includes missing values, outliers, and realistic distributions to simulate practical analytical challenges.

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📊 Business Questions Answered

This project answers the following business questions:

1️⃣ Revenue Analysis

Which quick commerce platform has the highest total revenue?

2️⃣ Average Order Value

Which platform has the highest Average Order Value (AOV)?

3️⃣ Customer Ratings

How do customer ratings vary across different platforms?

4️⃣ Delivery Performance

Does delivery time affect delivery partner ratings?

5️⃣ Product Category Analysis

What is the most popular product category on Swiggy Instamart among customers aged 30–40 in Mumbai?

6️⃣ Expansion Opportunities

Which cities should quick commerce companies expand into based on performance?

7️⃣ Discount Analysis

Are discounts increasing order volume or simply reducing revenue?

8️⃣ Operational Efficiency

Which company has the best operational efficiency based on delivery time and order volume?

These questions form the core business analysis performed in the project.

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🛠️ Technologies Used

• 🐍 Python
• 🐼 Pandas
• 🔢 NumPy
• 📊 Matplotlib
• 📈 Seaborn
• 📓 Jupyter Notebook

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🔄 Project Workflow

Data Collection
       ↓
Data Understanding
       ↓
Data Cleaning
       ↓
Exploratory Data Analysis
       ↓
Data Visualization
       ↓
Business Problem Solving
       ↓
Insights Generation
       ↓
Mini KPI Dashboard

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📁 Project Structure

Quick-Commerce-Data-Analysis/
│
├── data/
│   └── modified.csv
│
├── notebooks/
│   └── Quick_Commerce_Analysis.ipynb
│
├── README.md
└── requirements.txt

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🚀 Installation and Usage

1. Clone the Repository

git clone <your-repository-url>

2. Navigate to the Project Directory

cd Quick-Commerce-Data-Analysis

3. Install Required Libraries

pip install pandas numpy matplotlib seaborn jupyter

4. Run the Jupyter Notebook

jupyter notebook

Open the project notebook and run the cells to perform the analysis.

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📈 Key Analysis Areas

The project explores:

• 💰 Platform Revenue
• 🛒 Average Order Value
• ⭐ Customer Ratings
• 🚚 Delivery Time and Partner Ratings
• 📦 Product Category Preferences
• 🏙️ City Performance and Expansion Opportunities
• 🏷️ Discount Impact
• ⚙️ Operational Efficiency
• 📊 KPI Dashboard

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📊 Mini Dashboard

The project concludes with a Mini Dashboard containing key performance indicators (KPIs) to provide a quick overview of important business metrics and insights.

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🔮 Future Improvements

• Create an interactive Power BI dashboard
• Build a Streamlit web application
• Add advanced statistical analysis
• Perform predictive analytics
• Automate data processing workflows
• Add more interactive visualizations

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👨‍💻 Author

Kushagra Tewari

🎓 B.Tech CSE Student 📊 Data Science & Data Analytics Enthusiast 🤖 Machine Learning & Generative AI 🐍 Python | SQL | Power BI

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