📊 Zomato Data Analysis Using Python

Welcome to my data analysis project focused on Zomato, one of India's leading food delivery platforms. This project uses Python to explore customer behavior, restaurant ratings, and ordering patterns based on a real-world dataset. The goal is to extract actionable insights through Exploratory Data Analysis (EDA) and visualization.

---

🧠 Project Objective

Zomato has over 17.5 million monthly transacting customers and a growing base of restaurant partners (from 208,000 to 226,000). As a data professional at Zomato, I was tasked with analyzing customer and restaurant data to answer key business questions and support strategic decisions.

---

📁 Dataset Overview

The dataset includes:

• Customer orders
• Restaurant types
• Ratings and votes
• Order modes (online/offline)
• Spending patterns

---

🔍 Questions Answered

1️⃣ What type of restaurant do the majority of customers order from?

• Insight: The majority of customers prefer Quick Bites and Casual Dining restaurants. These categories dominate due to affordability and convenience.

2️⃣ How many votes has each type of restaurant received from customers?

• Insight: Casual Dining restaurants received the highest number of votes, followed by Cafes and Fine Dining. This reflects customer engagement and satisfaction levels.

3️⃣ What are the ratings that the majority of restaurants have received?

• Insight: Most restaurants have ratings between 3.5 and 4.5, indicating generally positive customer experiences. Very few restaurants fall below 3.0.

4️⃣ Zomato has observed that most couples order most of their food online. What is their average spending on each order?

• Insight: The average spending per order by couples is approximately ₹450–₹600, depending on the restaurant type and location. This segment shows consistent ordering behavior.

5️⃣ Which mode (online or offline) has received the maximum rating?

• Insight: Online orders have received higher ratings on average, likely due to ease of access, delivery speed, and app experience.

6️⃣ Which type of restaurant received more offline orders, so that Zomato can provide those customers with some good offers?

• Insight: Casual Dining and Buffet-style restaurants received more offline orders. These are ideal candidates for targeted offline promotions and loyalty programs.

---

📈 Tools & Techniques Used

• Python Libraries: Pandas, NumPy, Matplotlib, Seaborn
• EDA Techniques: Data cleaning, grouping, aggregation
• Visualizations: Bar charts, pie charts, histograms, box plots, line grsphs
• Statistical Analysis: Mean, median, mode, correlation

---

🎓 What I Learned

• How to perform real-world EDA using Python
• How to extract business insights from raw data
• Importance of data visualization in storytelling
• How customer behavior varies across restaurant types and order modes
• How to use data to support marketing and operational decisions

---

🚀 Future Improvements

• Apply machine learning models to predict customer ratings
• Use clustering to segment customer types
• Integrate geolocation data for regional insights

---

📬 Contact

If you have any feedback or suggestions, feel free to reach out or fork the repo!

---

Thanks for checking out my project!