DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
(DATA SCIENCE)
A
DATA VISULIZATION REPORT
ON
“Swiggy dataset”
Submitted in the partial fulfillment of the requirements in the 4th semester of
BACHELOR OF ENGINEERING IN
COMPUTER SCIENCE AND ENGINEERING (DATA SCIENCE)
BY
Utsav Awasthi
1NH23CD180
Under the guidance
of
Assistant Professor
Mr. Sankhadeep
Pujaru
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
(DATASCIENCE)
NEW HORIZON COLLEGE OF ENGINEERING
(Autonomous College Permanently Affiliated to VTU, Approved by AICTE, Accredited by
NBA & NAAC with ‘A’ Grade) Ring Road, Bellandur Post, Near Marathalli,Bangalore-
560103, INDIA.
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
(DATA SCIENCE)
CERTIFICATE
We hereby certify that, the report entitled Swiggy dataset using Tableau as a part of
Mini Project Component in partial fulfilment of the requirements during 4th
semester Bachelor of Engineering in Computer Science and Engineering (Data
Science) during the year 2024- 2025(March 2024 - August 2025) is an authentic
record of our work carried out by Utsav Awasthi (1NH23CD180), a bonafide
students of NEW HORIZON COLLEGE OF ENGINEERING.
Name & Signature of Student
(Utsav Awasthi)
Name & Signature of Guide Name & signature of HOD
( Mr. Sankhadeep Pujaru ) (Dr. Swathi B)
ACKNOWLEDGEMENT
Any achievement, be it scholastic or otherwise does not depend solely on the individual efforts
but on the guidance, encouragement and cooperation of intellectuals, elders and friends. A
number of personalities, in their own capacities have helped me in carrying out this project. I
would like to take an opportunity to thank them all.
First and foremost I thank the management, Dr. Mohan Manghnani, Chairman, New Horizon
Educational Institutions for providing necessary infrastructure and creating good environment.
I would like to thank Dr. Manjunatha, Principal, New Horizon College of Engineering,
Bengaluru, for his constant encouragement and facilities extended to us towards completing my
project work.
I extend my sincere gratitude to Dr. Swathi B, Associate Professor & Head of the Department,
Computer Science and Engineering (Data Science), New Horizon College of Engineering,
Bengaluru for her valuable suggestions and expert advice.
I deeply express my sincere gratitude to my guide, Mr.Sankhadeep Pujaru, Assistant Professor,
Computer Science and Engineering (Data Science), New Horizon College of Engineering,
Bengaluru, for his/her able guidance, regular source of encouragement and assistance throughout
this project.
I thank my Parents, and all Faculty members of Department of Computer Science and
Engineering (Data Science) for their constant support and encouragement.
Last, but not the least, I would like to thank my peers and friends who provided me with valuable
suggestions to improve my project.
Utsav Awasthi
1NH23CD180
i
ABSTRACT
A report on Swiggy DataSet
This abstract summarizes the Tableau data visualization for a Swiggy-related dataset,
swiggy_tableau_ready.csv. The primary objective was to explore and present key
insights into hotel and food establishment performance, offerings, and geographical
distribution. Utilizing Tableau's interactive visualization capabilities, the analysis
revealed the prevalent food types, customer satisfaction levels indicated by average
ratings, the cumulative rating impact of various establishments, the density of hotels
across different locations, and the distribution of offer percentages. Key findings include
the dominance of certain food categories, the identification of top-rated establishments,
areas with high hotel concentration, and locations with significant promotional activities.
These visualizations collectively provide a comprehensive overview of the dataset,
enabling stakeholders to make data-driven decisions related to service optimization,
marketing strategies, and operational planning.
ii
TABLE OF CONTENTS
Acknowledgement i
Abstract ii
Table of Contents iii
1. Introduction 6
2. Case Study 7
3. Questions and Answers 8-12
4. Conclusion 13
INTRODUCTION
Data Visualization: Transforming Data into Insight
Data Visualization is the graphical representation of information and data. By using visual elements
like charts, graphs, maps, and dashboards, data visualization tools help make complex datasets more
accessible, understandable, and usable. In today’s data-driven world, visualization is not merely about
aesthetics; it’s about communication and decision-making.
The human brain processes visuals far more efficiently than raw numbers or text. Data visualization
bridges the gap between raw data and actionable insight by allowing patterns, trends, and outliers to
emerge that might otherwise go unnoticed in spreadsheets or statistical summaries. Effective
visualizations turn abstract data into stories that can influence business strategies, public policies, or
research conclusions.
Tableau, one of the leading platforms for data visualization, empowers users to create interactive
dashboards and dynamic visuals without requiring advanced programming skills. It allows for
seamless data import, real-time filtering, and interactive user engagement. With Tableau, users can
transform raw datasets into meaningful visuals that encourage exploration and data-driven storytelling.
This project utilizes Tableau to visualize the swiggy_tableau_ready.csv dataset, offering a
comprehensive exploration of food delivery trends. It uncovers insights into popular food types,
customer satisfaction (via ratings), geographical distribution of establishments, and promotional
activities. The visualizations aim to empower data-driven decisions for optimizing services and
understanding market dynamics. .
CASE STUDY
Case Study: Swiggy Dataset
This case study uses the swiggy_tableau_ready.csv dataset to derive actionable insights for optimizing food
delivery services.
Problem: Understanding market trends, customer satisfaction, and geographical demand/offer distribution is
crucial for strategic decision-making in the food delivery sector.
Methodology: Tableau visualizations were employed to analyze key metrics:
* Food Type Distribution: Revealed the dominance of "South Indian" and "North Indian, Chinese" cuisines.
* Average Ratings: Highlighted top-performing establishments like "2 Delicious Poha House" and "2 Pai cakes"
with high customer satisfaction. The overall rating distribution skewed heavily towards positive ratings.
* Hotel Density: Showed high concentrations of hotels in "Borivali," "Malad West," and "Kandivali West,"
indicating competitive markets.
* Offer Prevalence: Identified "Malad Kan East" and "Borivali" as areas with the highest cumulative offer
percentages, suggesting significant promotional activity.
Insights & Recommendations: The analysis indicates opportunities for targeted expansion in less saturated food
categories, benchmarking best practices from high-rated establishments, and implementing localized marketing
strategies based on hotel density and promotional activity. Ultimately, these insights can drive improved
customer satisfaction and business growth.
This case study showcases how raw survey data can be transformed into meaningful, interactive visuals that
support real-world decision-making and health awareness using Tableau’s advanced capabilities.
QUESTION AND ANSWERES:
1. Which hotel or food establishments have the highest average ratings,?
Ans:
A horizontal bar chart titled visualizing the "Avg Rating" for various "Hotel Name" entries. Each bar represents a
different hotel or food establishment, and its length corresponds to its average rating on a scale that appears to
go up to 5.0.
From the chart, we can infer that the ratings for these establishments vary significantly. For instance, "2
Delicious Poha House" and "2 Pai cakes" seem to have very high average ratings, with their bars extending close
to the 5.0 mark. Conversely, some establishments like "99 Waffles" also show a high rating. There are also
establishments with moderate ratings, and it's difficult to determine the lowest rated without specific values, but
the relative lengths of the bars provide a visual comparison. The chart effectively allows for a quick comparison
of the average ratings across different hotels or food places, highlighting those with higher customer satisfaction
based on the average rating.
Q2: What is the distribution of food types in the data set and which food types
appear to be the most prevalent ?
Ans. The chart shows various food categories, each represented by a slice of the pie, indicating their
proportion relative to the whole. While exact percentages are not visible, we can observe that "South
Indian" and "North Indian, Chinese" seem to represent larger segments, suggesting they are popular
or frequently occurring food types. Other categories like "Bakery, Desserts," "Pizzas," "Indian,"
"Chinese," "Biryani," "Snacks," "Ice Cream, Desserts," and "Beverages" also contribute to the overall
food type distribution, with some appearing to be smaller proportions compared to the larger
segments.
3.Which locations have the highest concentrations of distinct food hotels or food
establishments ?
Ans. In a treemap, the size of each rectangular segment is proportional to the
value it represents, and the colors can also indicate a dimension or measure. Here,
the varying shades of blue likely correspond to the count of distinct hotels, with
darker shades perhaps indicating a higher count.
From this chart, we can infer the distribution of hotels across different locations.
"Borivali" appears to have the largest number of distinct hotels, as its rectangle is
the largest and darkest. "Malad West" and "Kandivali West" also show a
significant number of hotels, indicated by their substantial segment sizes. Other
locations like "Malad East," "Kandivali East," "Dahisar," and "Goregaon" also have
a presence, with varying counts. The presence of smaller, lighter blue rectangles
suggests locations with fewer distinct hotels. This treemap effectively provides a
quick visual overview of which locations have a higher density of hotels, allowing
for easy identification of areas with more or fewer establishments
.
4. Which geographical locations have the highest aggregated offer
percentages,and how does the total value of offers differ across the various
locations?
Ans. The bubble chart displays the "SUM(Offer Percentage)" for various
"Locations," where the size of each bubble corresponds to the sum of the offer
percentages and the color likely represents the value of this sum (possibly a
darker blue for higher sums). We can infer that locations like "Malad Kan East"
and "Borivali" have the highest combined offer percentages, as their bubbles are
significantly larger and darker than others. "Kandivali East" also shows a
substantial sum of offer percentages. Conversely, locations such as "Dahisar" and
"Jog Gor East" appear to have lower summed offer percentages, indicated by their
smaller bubble sizes. The chart effectively provides a visual comparison of how
the total offer percentages are distributed across different geographical locations,
highlighting areas where offers are more prevalent or sum up to higher values.
5.What is the distribution of ratings within the dataset,and how does the number
of records vary across different rating?
Ans The histogram, titled "Sheet 9," illustrates the distribution of "SUM(Number of
Records)" across various "Rating (bin)" categories. It reveals a strong concentration
of records in the higher rating bins (approximately 3.8 to 4.4 and above), with the
number of records progressively increasing as ratings improve. This indicates that
the majority of data points in the dataset have favorable ratings, suggesting a
generally positive sentiment or high performance. Conversely, there are very few
records in the lower rating bins (e.g., 1.2 to 2.0).
Conclusion
Based on the analysis of the swiggy_tableau_ready.csv dataset through various Tableau
visualizations, the following conclusions can be drawn:
The dataset provides a comprehensive overview of the food delivery ecosystem, highlighting the
distribution and performance of various food establishments. Key insights include the significant
prevalence of "South Indian" and "North Indian, Chinese" cuisines, indicating their market
dominance. Customer satisfaction appears generally high, with a strong concentration of positive
ratings, and specific establishments consistently achieving top average ratings. Geographically,
certain areas like Borivali, Malad West, and Kandivali West show a high density of hotels,
suggesting competitive markets. Furthermore, locations such as Malad Kan East and Borivali
exhibit higher aggregated offer percentages, pointing to concentrated promotional activities.
Overall, the data offers valuable insights for strategic decision-making in menu development,
service quality improvement, and targeted marketing within the food delivery domain.
