🍽️ Restaurant Data Analysis

📌 Project Overview

This project focuses on performing Exploratory Data Analysis (EDA) on a restaurant dataset to uncover meaningful insights related to restaurant ratings, chains, cuisines, customer engagement, and geographical distribution. The analysis helps understand customer preferences and restaurant performance using data-driven techniques.

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🎯 Objectives

• To analyze restaurant ratings and customer votes
• To identify popular restaurant chains
• To study cuisine combinations and their impact on ratings
• To visualize the geographical distribution of restaurants
• To extract insights that can help understand customer behavior and trends

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🛠️ Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Jupyter Notebook

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📂 Dataset Description

The dataset contains information such as:

• Restaurant Name
• Cuisines
• Aggregate Rating
• Votes
• Price Range
• Online Delivery availability
• Latitude and Longitude

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🔍 Steps Involved

1. Data Loading
   • Imported the dataset using Pandas.
2. Data Cleaning
   • Handled missing values.
   • Removed encoding issues and standardized restaurant names.
3. Exploratory Data Analysis
   • Analyzed rating distributions using histograms.
   • Identified restaurant chains using value counts.
   • Calculated average ratings for chain restaurants.
   • Analyzed customer engagement through votes.
4. Visualization
   • Bar charts for restaurant chains and ratings.
   • Histograms for rating distribution.
   • Scatter plots for geographic restaurant locations.
5. Insight Extraction
   • Interpreted patterns and trends from visualizations and statistics.

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📊 Key Results & Insights

• The majority of restaurants fall under the lower rating range (0.0–2.5).
• A small number of well-known restaurant chains dominate the dataset.
• The average number of votes per restaurant is approximately 156, indicating moderate customer engagement.
• Certain cuisine combinations tend to receive higher average ratings.
• Restaurants are highly concentrated in urban and metropolitan areas.
• Consistency in service and food quality is reflected in higher ratings for popular chains.

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✅ Conclusion

This analysis provides valuable insights into restaurant performance, customer preferences, and market trends. The findings highlight the importance of quality consistency, strategic location, and cuisine offerings in achieving higher customer ratings and engagement.

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📌 Author

Thanesh S
Internship Project – Restaurant Data Analysis

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🚀 Future Enhancements

• Interactive maps using Folium
• Sentiment analysis on customer reviews
• Machine learning model for rating prediction