Project Overview
This project provides a comprehensive analysis of restaurant data, offering valuable insights into the distribution of restaurants across different cities, the popularity of various restaurant chains, and the top-rated establishments.
Key Features:
- Data Exploration: Analyzes a dataset containing information about restaurants, including city, name, rating, and health category.
- Top Cities: Identifies the cities with the highest concentration of restaurants.
- Restaurant Ratings: Ranks restaurants based on their ratings and highlights the top-rated establishments in Indore.
- Chain Popularity: Examines the prevalence of popular restaurant chains like Domino's Pizza, KFC, McDonald's, and Subway.
Project Outputs:
- Order Analysis: Examines order volume, delivery time, and peak hours.
- Customer Behavior: Analyzes customer preferences, ratings, and repeat orders.
- Restaurant Performance: Evaluates restaurant ratings, order volume, and delivery time.
- Trend Identification: Uncovers emerging trends and patterns in the food delivery industry.
Visualizations
- Bar Charts: Displays the top 10 cities with most restaurants and the number of branches for specific restaurant chains.
- Pie Chart: Illustrates the distribution of restaurants by health category.
Benefits:
- Market Analysis: Provides valuable insights for businesses looking to expand into new cities or identify popular restaurant trends.
- Consumer Choice: Helps consumers discover highly-rated restaurants and popular chains.
- Health Awareness: Raises awareness about the availability of restaurants in different health categories.
Usage:
- Prerequisites: Install R-Studio and libraries: dplyr and ggplot2.
- Load the data: Replace the placeholder file path with the actual location of your dataset.
- Run the analysis: Execute the code to generate the visualizations and analysis.
Contributions:
Contributions are welcome! Feel free to fork the repository, make improvements, and submit a pull request.
Additional Notes:
- Data Privacy: Please ensure compliance with Swiggy's terms of service and data privacy policies when collecting and using their data.
- Customization: The analysis can be tailored to specific research questions or business objectives.
- Collaboration: We encourage collaboration and open-source contributions to enhance the project's value.
By leveraging this comprehensive analysis, stakeholders can gain valuable insights to optimize operations, improve customer satisfaction, and make data-driven decisions in the competitive food delivery market.