Restaraunt Data Analysis using Power BI, Excel and Python

This project focuses on analyzing restaurant data to gain insights into various aspects of the food industry. The dataset contains information about different restaurants, including their location, cuisine, pricing, and customer ratings.

1. Restaurant Data Analysis By – Priyansh Arya

2. S.No . Particulars 1 Overview 2 Objectives 3 Level Introduction 4 Raw Data 5 Data Handling 6 Finding Outliers 7 Box Plot 8 Cuisines & Cities 9 Price Range Distribution 10 Open Power BI 11 Conclusion Index

3. Overview This project focuses on analyzing restaurant data to gain insights into various aspects of the food industry. The dataset contains information about different restaurants, including their location, cuisine, pricing, and customer ratings. Key Features- Restaurant Name : The name of the restaurant. Location : Includes the country code, city, address, and locality. Geographic Coordinates : The longitude and latitude of the restaurant’s position. Cuisines : The types of cuisines offered. Average Cost for Two : The average cost for a meal for two people. Currency : The currency used for pricing. Booking and Delivery Options : Indicates whether the restaurant accepts table bookings and offers online delivery. Current Delivery Status : Whether the restaurant is currently delivering. Menu Information : Details related to ordering from the menu. Price Range : A rating reflecting the restaurant’s affordability. Aggregate Rating : Overall customer rating based on reviews. Rating Color and Text : Descriptive representation of the rating. Votes : The total number of votes or ratings received.

4. Customer Segmentation : Explore customer preferences by segmenting them based on factors such as cuisine type, price range, and location. This can help restaurants tailor their offerings to specific customer groups. Popular Cuisines : Identify the most popular cuisines in different cities or localities. This information can guide restaurant owners in deciding which cuisines to focus on. Pricing Strategies : Analyze the relationship between average cost for two and customer ratings. Are higher-priced restaurants consistently rated better, or do budget-friendly options also perform well? Delivery Trends : Investigate the impact of online delivery services on restaurant performance. Does offering delivery lead to higher ratings or increased customer engagement? Geospatial Insights : Visualize restaurant locations on a map using longitude and latitude data. Are there clusters of restaurants in specific areas? Are certain cuisines more prevalent in certain neighborhoods? Rating Analysis : Dive deeper into the aggregate ratings. Are there specific factors (such as table booking availability or delivery options) that correlate with higher ratings? Objectives

5. Level 1: Overview and Basic Insights In this introductory level, we’ll cover essential information about the restaurant dataset: Top Cuisines : Identify the most popular cuisines across all restaurants. Which cuisines are preferred by customers? City Analysis : Explore restaurant distribution across different cities. Are there specific cities with a higher concentration of restaurants? Price Range Distribution : Visualize the distribution of average costs for two people. Are most restaurants budget-friendly or upscale? Online Delivery : Investigate the prevalence of online food delivery services. How many restaurants offer this option? Level 2: Deeper Exploration In the second level, we’ll delve into more specific aspects: Restaurant Rating : Analyze aggregate ratings and their distribution. Which restaurants receive the highest and lowest ratings? Cuisine Combination : Explore combinations of cuisines. Are there unique fusion cuisines or popular pairings? Geographic Analysis : Use geographic coordinates (longitude and latitude) to map restaurant locations. Are there clusters of restaurants in certain areas? Restaurant Chains : Identify any restaurant chains within the dataset. How many branches do they have? Level 3: Advanced Insights For the final level, we’ll dive into detailed analyses: Restaurant Reviews : Examine customer reviews. Are there common themes or specific aspects that influence ratings? Votes Analysis : Investigate the relationship between the number of votes and restaurant ratings. Do more votes lead to higher ratings? Price Range vs. Online Delivery : Compare average costs with the availability of online delivery. Is there a correlation?

6. Raw Data Data Source : Kaggle Excel File

7. Data Handling Drop the duplicates row using python library pandas Drop Null or missing value using dropna () pandas function Finding Outliers

8. Box Plot

9. Top Cuisines are : North Indian - 43.43% North Indian & Chinese Combination – 23.71% Chinese – 16.43% Fast Food – 16.43 % Top Cities are : New Delhi – 5.47K (68.87%) Gurgaon – 1.12K (14.07%) Noida – 1.08K(13.59%) Cuisines & Cities The dataset exhibits a higher density of survey data within India.

10. Range No. Price Range in Monetory Value (Rs.) 1 0-499 2 500 - 999 3 1000 – 1300 4 1300 + The price range divided into 1,2,3 and 4 which represent the price range. The distribution of restaurant price ranges shows that 46.52% of restaurants fall into price range 1, followed by 32.55% in price range 2, 14.74% in price range 3, and only 6.13% in price range 4. Price Range Distribution

11. Open Power BI

12. Conclusion Level 1 Insights Cuisine Trends : North Indian cuisines are particularly popular, especially in combination with Chinese and fast food. City Ratings : New Delhi has the highest number of restaurants with low aggregate ratings. Price Range Distribution : Most restaurants fall into price range 1, indicating that people prefer budget-friendly options. Online Delivery Impact : Only 25% of restaurants offering online delivery have better aggregate ratings. Inner City Ratings : The inner city has the best aggregate rating, reaching 4.90.

13. Level 2 Insights Rating Categories: The most common rating categories, in order, are Good, Poor, Average, and Excellent. Popular Cuisine Combinations: North Indian and Chinese cuisines frequently appear together. Food Chains: Cafe Coffee Day is the most frequently occurring food chain in the dataset. Top-Rated Cuisine: North Indian Mughlai cuisine has the highest aggregate rating. Successful Chain: Barbeque Nation stands out as the most successful chain with high ratings and votes . Level 3 Insights Review Sentiment: The keyword analysis reveals that “average” indicates negative reviews, while other terms may indicate positive views. Table Booking Availability: Price range 3 restaurants offer table booking, followed by price range 4 and then price range 2. Votes and Ratings Correlation: Restaurants with more votes tend to have higher ratings.