📊 Extracting Business Insights from Zomato’s Restaurant Data using Python

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🍴 Zomato Data Analysis Dashboard & Recommendation System

An interactive Streamlit web application for analyzing Zomato restaurant data and recommending similar restaurants using TF-IDF and Cosine Similarity. The dashboard includes EDA (Exploratory Data Analysis) visualizations, interactive maps, and restaurant recommendations with a modern, theme-adaptive UI.

🔗 Live Demo

🍴 Zomato Data Analysis Dashboard & Recommendation System — 🌐 Open Live Demo

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Project Workflow

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🎯 Objective

The goal of this project is to explore and analyze the Zomato restaurant dataset to uncover meaningful trends and patterns that can help businesses make data-driven decisions.

Specifically:

• 🧠 Understand data structure & quality
• 🍜 Identify cuisine and restaurant trends
• 🌆 Examine ratings & delivery preferences across locations
• 💸 Analyze cost patterns in different cities

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🚀 Features

🔍 1. Restaurant Recommendation System

• Select any restaurant and get top similar restaurants based on cuisine, cost, and rating.
• Uses TF-IDF vectorization and cosine similarity to find restaurant similarities.
• Customizable number of recommendations (1–10).

📊 2. Interactive EDA Dashboards

• Explore KPIs such as total restaurants, average rating, and average cost.
• Visualize Top Cuisines, Rating Distributions, and Price vs Rating.
• Generate Cuisine WordClouds for quick insights.

🗺️ 3. Map Visualization

• Interactive Plotly Mapbox map showing restaurant locations.
• Filter by rating range dynamically.
• Highlights Top 3 restaurants with gold stars.

🏆 4. Top Restaurants Leaderboard

• Displays Top 10 restaurants based on ratings.
• Includes thumbnails, cuisines, and average costs.

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💡 Tech Stack

Component	Technology
Language	Python
Data Handling	Pandas, NumPy
Visualization	Matplotlib, Seaborn, WordCloud, Plotly Express
Machine Learning	Scikit-learn (TF-IDF, Cosine Similarity)
Frontend/UI	Streamlit
Dataset	Zomato Dataset (zomato.csv)

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📁 Dataset Overview

Column	Description
Restaurant Name	Name of the restaurant
Location	City / Area
Cuisines	Cuisine types served
Average Cost for two	Cost for two people
Has Online delivery	Yes / No
Aggregate rating	Average customer rating
Votes	Number of user ratings

📌 Dataset Source: Kaggle - Zomato Dataset

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🧠 How It Works

🔹 Recommendation Logic

1. Combine cuisines, average cost, and ratings into a single text field.
2. Convert text into numerical vectors using TF-IDF.
3. Compute cosine similarity between restaurants.
4. Recommend the most similar restaurants excluding the selected one.

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🌗 UI Theme

The app supports both Light Mode and Dark Mode for enhanced viewing.
You can switch between them using the sidebar option 🌗.

🧭 Navigation

Section	Description
Recommendation System	Find similar restaurants.
EDA Dashboards	View data insights and plots.
Map Visualization	Explore restaurant locations.
Top Restaurants	See the best-rated restaurants.

📊 Sample Visuals

📈 Top 10 Cuisines Bar Chart
⭐ Rating Distribution (highlighted Top 3)
☁️ Cuisine WordCloud
🗺️ Interactive Map with Rating Filter
🏆 Top Restaurants Leaderboard

🖼️ Gallery / Screenshots

Here are some key screenshots of the Zomato Data Analysis Dashboard:

📚 Insights You Can Derive

• 🏙️ Delhi NCR has the maximum number of restaurants.
• 🍕 North Indian is the most common cuisine.
• ⭐ Ratings tend to be higher for fine dining than for cafés.
• 🛵 Online delivery is more popular in metropolitan areas.
• 💰 Average cost for two varies significantly between cities.
• 📊 Identify high-performing restaurants for recommendations.

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🖼️ Visual Workflow

flowchart LR
    %% --- Data Loading & Cleaning Stage ---
    subgraph DC[📂 Data Loading & Cleaning]
        A["📦 Import Libraries "]
        B["📄 Load Zomato Dataset "]
        C["✂️ Clean & Preprocess Data"]
    end

    %% --- Filtering & EDA Stage ---
    subgraph FE[📊 Filtering & EDA]
        D["🔎 Apply Filters"]
        E["📈 EDA Visualization"]
    end

    %% --- Recommendation System Stage ---
    subgraph RS[🤖 Recommendation System]
        F["Build TF-IDF Matrix"]
        G["Cosine Similarity"]
        H["Top Recommendations"]
    end

    %% --- Map & Leaderboard Stage ---
    subgraph ML[🗺️ Map & Leaderboard]
        I["🗺️ Interactive Map"]
        J["🏆 Top Restaurants Leaderboard"]
    end

    %% --- Deployment Stage ---
    subgraph DEP[🚀 Deployment]
        K["🌐 Streamlit Deployment\n(Interactive Web Dashboard)"]
    end

    %% --- Flow Connections ---
    A --> B --> C --> D --> E --> F --> G --> H --> I --> J --> K

    %% --- Styles ---
    style A fill:#FFD54F,stroke:#F57F17,stroke-width:2px,color:#000
    style B fill:#4FC3F7,stroke:#0277BD,stroke-width:2px,color:#fff
    style C fill:#AED581,stroke:#33691E,stroke-width:2px,color:#000
    style D fill:#BA68C8,stroke:#4A148C,stroke-width:2px,color:#fff
    style E fill:#FF8A65,stroke:#BF360C,stroke-width:2px,color:#fff
    style F fill:#90CAF9,stroke:#0D47A1,stroke-width:2px,color:#000
    style G fill:#F48FB1,stroke:#880E4F,stroke-width:2px,color:#fff
    style H fill:#A1887F,stroke:#4E342E,stroke-width:2px,color:#fff
    style I fill:#81C784,stroke:#2E7D32,stroke-width:2px,color:#000
    style J fill:#FFB74D,stroke:#E65100,stroke-width:2px,color:#000
    style K fill:#F44336,stroke:#B71C1C,stroke-width:2px,color:#fff

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🏁 Future Improvements

• ✅ Add sentiment analysis on customer reviews.
• ✅ Include filter for cuisine-specific recommendations.
• ✅ Integrate real-time Zomato API for live data.

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👨‍💻 Author

Lomada Siva Gangi Reddy
🎓 B.Tech CSE (Data Science), RGMCET (2021–2025)
🎯 Aspiring Data Analyst | Skilled in Python, SQL, Power BI, and Data Science
📍 Open to Internships & Job Offers

📬 Contact Me

• 📞 9346493592
• 💼 LinkedIn 🌐 GitHub

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