A
PROJECT REPORT
ON
E-COMMERCE RECOMMENDATION SYSTEM
Submitted by
Fenil Patel (201250107008)
Sahil Patel (201250107016)
In fulfillment for the award of degree
Of
BACHELOR OF ENGINEERING
In
COMPUTER ENGINEERING
SHREE SWAMINARAYAN INSTITUTE OF TECHNOLOGY-,
BHAT
GUJARAT TECHONOLOGICAL UNIVERSITY,
AHMEDABAD
2023-2024
Shree Swaminarayan Institute of Technology,
Bhat, Gandhinagar-382428
DECLARATION
We hereby declare that the PPR Reports, submitted along with the Project Report for the
project entitled “E-commerce Recommendation System” submitted in partial fulfillment
for the degree of Bachelor of Engineering in Computer Engineering to Gujarat
Technological University, Ahmedabad, is a bonafide record of the project work carried out
at Maxgen Technologies Pvt Ltd under the supervision of “Mr. Vinit Tavde ” and that
no part of any of these PPR & PDE reports has been directly copied from any students’
reports or taken from any other source, without providing due reference.
Name of Students
1.
Fenil Patel
2.
Sahil Patel
Sign of Students
Shree Swaminarayan Institute of Technology
Near EDI & Sardar Patel Ring Road Circle Gandhinagar to Ahmedabad
Airport Highway, Bhat, Gandhinagar, Gujarat 382428
CERTIFICATE
This is to certify that the project reports, submitted along with the project entitled “Ecommerce Recommendation System” has been carried out by Patel Fenil Pankajbhai
(201250107008) under my guidance in fulfillment for the degree of:
Bachelor of
Engineering in Computer Engineering 8th Semester of Gujarat Technological University,
Ahmedabad during the academic year 2020-21. These students have successfully
completed project activity under my guidance.
Prof. Jyoti Gautam
Internal Guide
Computer Engineering
SSIT, Bhat, Gandhinagar
Prof. Nirajkumar Thakor
Project Coordinator
Computer Engineering
SSIT, Bhat, Gandhinagar
Prof. Darshan P. Patel
Head of Department
Computer Engineering
SSIT, Bhat, Gandhinagar
Shree Swaminarayan Institute of Technology
Near EDI & Sardar Patel Ring Road Circle Gandhinagar to Ahmedabad
Airport Highway, Bhat, Gandhinagar, Gujarat 382428
CERTIFICATE
This is to certify that the project reports, submitted along with the project entitled “Ecommerce Recommendation System” has been carried out by Patel Sahil Rasikbhai
(201250107016) under my guidance in fulfillment for the degree of:
Bachelor of
Engineering in Computer Engineering 8th Semester of Gujarat Technological University,
Ahmedabad during the academic year 2020-21. These students have successfully
completed project activity under my guidance.
Prof. Jyoti Gautam
Internal Guide
Computer Engineering
SSIT, Bhat, Gandhinagar
Prof. Nirajkumar Thakor
Project Coordinator
Computer Engineering
SSIT, Bhat, Gandhinagar
Prof. Darshan P. Patel
Head of Department
Computer Engineering
SSIT, Bhat, Gandhinagar
Industry Offer Letter
Industry Completion Letter
Industry Offer Letter
Industry Completion Letter
ACKNOWLEDGEMENT
The successful completion of my project named MAXGEN TECHNOLOGIES stands on the
constant encouragement, guidance, and support, both technical & personal of many Individuals.
However, it would not have been possible without the kind support and help of many individuals
and institutional. We would like to extend our sincere thanks to all of them. We are highly indebted
to our internal guide Prof. Jyoti Gautam for their guidance and constant supervision as well as for
providing necessary support in the project. This project work could not have been completed
without the expert guidance Prof. Niraj Thakor who shown me proper path and direction of work.
They opened for me the offer of knowledge from repository of their experience. They have been
encouraging me throughout my project work which increased my confidence and gave me the
strength of working hard to achieve my target in time. This has impelled me to express my sincere
gratitude to my internal project guide and project coordinator Prof. Niraj Thakor.
At the same time, I would like to thank my Head of the Computer Engineering Department Prof.
Darshan Patel for his kind cooperation and support. I would also appreciate his keen interest in
helping me and his regular guidance throughout the project. I would also like to thank all the staff
members of my department for their cooperation and help me when asked for. I am also grateful to
all my colleagues for helping me with all their resources and knowledge they could, as when
required.
.
ABSTRACT
ABSTRACT
This project aims to develop an e-commerce recommendation system utilizing the MERN
(MongoDB, Express.js, React.js, Node.js) stack. The system will utilize collaborative
filtering techniques to provide personalized product recommendations to users based on
their past behaviors, preferences, and similarities with other users.
The system will consist of several key components, including a MongoDB database to store
user and product data, an Express.js server to handle HTTP requests and interact with the
database, a React.js front-end for the user interface, and Node.js for server-side JavaScript
execution.
The project will also focus on implementing features for user interaction, such as user
registration and authentication, product browsing, search functionality, and a personalized
recommendation section. The system's performance and effectiveness will be evaluated
through metrics like recommendation accuracy, user engagement, and system response
time.
Overall, this e-commerce recommendation system built on the MERN stack aims to enhance
the user shopping experience by personalized product recommendations tailored to
individual preferences and behaviors.
INDEX
1
2
3
4
5
INTRODUCTION .................................................................................................................... 1
1.1
Introduction of Company ............................................................................................... 2
1.2
Problem Summary .......................................................................................................... 2
1.3
Project Purpose ............................................................................................................... 3
1.4
Objectives ....................................................................................................................... 3
1.5
Project Future Scope ....................................................................................................... 4
1.6
Problem Specification ..................................................................................................... 4
1.7
Technology ..................................................................................................................... 5
LITERATURE REVIEW ......................................................................................................... 6
2.1
Literature Review ........................................................................................................... 7
2.2
Proposed System ............................................................................................................ 8
SOFTWARE REQUIREMENT SPECIFICATION ................................................................. 9
3.1
Software Requirements: ................................................................................................ 10
3.2
Hardware Requirements: .............................................................................................. 10
3.3
Application Environment .............................................................................................. 10
3.4
Specific Requirements .................................................................................................. 11
PROJECT MANAGEMENT ................................................................................................. 12
4.1
Project Development Approach .................................................................................... 13
4.2
Project Planning: ........................................................................................................... 13
4.3
Project Scheduling ........................................................................................................ 13
4.4
Risk Management .........................................................................................................14
4.4.1
Risk Identification ..................................................................................................... 14
4.4.2
Risk Analysis ............................................................................................................ 15
4.4.3
Risk Planning ............................................................................................................ 15
SYSTEM ANALYSIS ........................................................................................................... 16
5.1
Requirement of new system .......................................................................................... 17
5.2
Feature of New System................................................................................................. 17
5.3
Data flow and Modeling ............................................................................................. 18
5.3.1
Login Process Flow Chart ......................................................................................... 18
5.3.2
Add to Cart Flow Chart............................................................................................. 19
5.3.3
Order Process Flow Chart ......................................................................................... 20
5.3.4
Use case Diagram ...................................................................................................... 21
6
DATA DICTIONARY ............................................................................................................ 22
7
IMPLEMENTATION ............................................................................................................ 27
8
9
7.1
Pages Layout................................................................................................................. 28
7.2
Admin Page .................................................................................................................. 30
7.3
Frontend Interface......................................................................................................... 34
SYSTEM TESTING ............................................................................................................... 40
8.1
Test plan:....................................................................................................................... 41
8.2
Test Cases ..................................................................................................................... 43
8.2.1
Test Case 1 ................................................................................................................ 43
8.2.2
Test Case 2 ................................................................................................................ 44
8.2.3
Test Case 3 ................................................................................................................ 45
8.2.4
Test Case 4 ................................................................................................................ 46
LIMITATIONS AND FUTURE ENHANCEMENT .............................................................. 47
9.1
Limitations .................................................................................................................... 48
9.2
Future Scope ................................................................................................................. 48
Team ID: 445820
INTRODUCTION
CHAPTER 1
INTRODUCTION
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INTRODUCTION
1. INTRODUCTION
1.1 Introduction of Company
•
At Maxgen Technologies, we are more than just a software development company; we are your
dedicated partners in turning your ideas into reality. Recognizing the indispensable role of new
technologies in today's business landscape, we strive to facilitate seamless connections between
businesses and innovative software solutions, technological developments, and services, all
delivered with unprecedented speed, simplicity, and excellence.
•
Our vision is simple to empower businesses of all sizes with cutting-edge technology solutions
that drive growth, efficiency, and success. We believe that by harnessing the power of
technology, we can transform industries, streamline operations, and unlock new opportunities
for our clients.
•
With a team of highly skilled developers, designers, and project managers, we have the expertise
to tackle projects of any size and complexity. Whether you are looking to develop a web
application, mobile app, enterprise software, or anything in between, we have the skills and
experience to deliver exceptional results.
1.2 Problem Summary
Handling large volumes of diverse data including user profiles, browsing history, purchase history,
product attributes, and interactions is complex. Ensuring efficient storage, retrieval, and processing
of this data within MongoDB while maintaining data consistency and integrity is crucial.
Implementing recommendation algorithms within the Node.js backend requires careful
consideration of algorithm complexity and performance. Balancing between algorithm
sophistication and computational efficiency to provide accurate and timely recommendations is
challenging.
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INTRODUCTION
Seamlessly integrating the recommendation system with the existing MERN stack architecture
while ensuring modularity, reusability, and maintainability of code requires careful design and
implementation.
Providing personalized recommendations tailored to individual user preferences and behavior
requires sophisticated algorithms and extensive user data. Implementing user profiling,
collaborative filtering, or content-based recommendation techniques within the MERN stack adds
complexity to the system.
Handling sensitive user data within the e-commerce platform, such as user profiles and purchase
history, requires robust security measures. Implementing authentication, authorization, and data
encryption within the MERN stack to protect user privacy is critical.
1.3 Project Purpose
One of the primary purposes of an e-commerce recommendation system is to enhance the user
experience by providing personalized and relevant product suggestions to customers.E-commerce
recommendation systems help users discover new products that they may not have otherwise
encountered. By leveraging techniques such as collaborative filtering, content-based filtering, or
hybrid approaches, the system can introduce users to items that align with their interests and
preferences, thereby expanding their product discovery.
The purpose of an e-commerce recommendation system project is to leverage data and algorithms
to enhance the user experience, increase engagement and sales, drive customer loyalty, and inform
strategic decision-making for the e-commerce business.
1.4 Objectives
•
Increased Sales
•
User Engagement
•
Optimized Inventory Management
•
Real-time Recommendations
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•
Customer Retention
•
Enhanced Product Discovery
•
Accessibility
•
Scalability
INTRODUCTION
1.5 Project Future Scope
The scope of this website is vast and varied, some of them are as follows:
1. Multimodal Recommendations: Incorporating multiple data modalities such as text, images,
and audio into recommendation systems can enable more diverse and engaging
recommendations. For example, visual search capabilities can allow users to find products
similar to items they've seen in images.
2. Cross-Channel Recommendations: Extending recommendation capabilities across multiple
channels and touchpoints, including websites, mobile apps, social media platforms, and email
newsletters, can create a cohesive and personalized shopping experience for users across their
entire journey.
3. AI and Machine Learning Advancements: Continued advancements in artificial intelligence
(AI) and machine learning (ML) techniques offer opportunities to enhance recommendation
algorithms further.
1.6 Problem Specification
The recommendation system should be able to scale efficiently to handle large volumes of users
and products without sacrificing performance. It should accommodate growth in user traffic and
product catalog size while maintaining responsiveness and reliability. The system should strive to
provide accurate and relevant recommendations to users, minimizing irrelevant suggestions and
ensuring that recommended products align with user interests and needs.
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INTRODUCTION
The recommendation system should be able to personalize product recommendations for each user
based on their preferences, browsing history, purchase behavior, demographic information, and any
other relevant user data. The recommendation system should seamlessly integrate with the user
interface of the e-commerce platform, displaying recommended products in a visually appealing
and user-friendly manner that enhances the shopping experience.
1.7 Technology
1. Frontend Technologies:
•
React.js: This is the frontend library used for building user interfaces. It handles the
presentation layer of the application, allowing you to create interactive and dynamic UI
components.
•
HTML 5
•
CSS 3
2. Backend Technologies:
• Express.js: This is the backend web application framework for Node.js. It
provides a set of features for building web applications and APIs, such as
routing, middleware support, and request handling.
• Node.js: This is the backend runtime environment. It allows you to run
JavaScript code on the server-side, handling tasks such as server-side logic,
data processing, and interacting with the database.
3. Database:
• MongoDB: This is the backend database used for storing and managing the
application's data. It's a NoSQL database, which means it's highly scalable
and flexible, making it suitable for various types of data.
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LITERATURE REVIEW
CHAPTER 2
LITERATURE REVIEW
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LITERATURE REVIEW
2. LITERATURE REVIEW
2.1 Literature Review
As you embark on your literary journey, you encounter a wealth of insights and discoveries, each
contributing to your understanding of the complex ecosystem that underpins online commerce.
First, you stumble upon a study by Chen and colleagues, which serves as your guiding light into
the realm of technology stack selection. Their research illuminates the advantages of the MERN
architecture, showcasing its adaptability and scalability as key drivers for e-commerce success.
With newfound clarity, you venture deeper, uncovering a treasure trove of knowledge on
performance optimization. Imparting wisdom on caching strategies, lazy loading techniques, and
server-side rendering methods—all aimed at enhancing the speed and responsiveness of ecommerce platforms built with MERN.
Your quest for enlightenment leads you to the realm of user experience design. Their insights into
intuitive navigation, responsive layouts, and personalized recommendations resonate deeply,
underscoring the pivotal role of React in crafting captivating user interfaces that captivate and
convert.
But no journey is without its perils, and you soon find yourself navigating the treacherous waters
of security and privacy concerns. Warning of the dangers posed by cyber threats and advocating
for stringent measures to safeguard sensitive data stored in MongoDB databases.
Undeterred, you press on, guided by the beacon of scalability and reliability. Sharing tales of
horizontal scaling, load balancing, and containerization strategies essential for ensuring the
seamless expansion and robust performance of MERN powered e-commerce systems.
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LITERATURE REVIEW
2.2 Proposed System
An E-commerce recommendation system proposes personalized product suggestions to users based
on their past behavior, preferences, and demographic data. It employs algorithms like collaborative
filtering, content-based filtering, or hybrid methods to analyze user interactions and product
attributes. The system continuously learns from user feedback to improve recommendations,
enhancing user experience, increasing engagement, and driving sales for the E-commerce platform.
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SOFTWARE REQUIREMENT SPECIFICATION
CHAPTER 3
SOFTWARE REQUIREMENT
SPECIFICATION
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SOFTWARE REQUIREMENT SPECIFICATION
3. SOFTWARE REQUIREMENT SPECIFICATION
3.1 Software Requirements:➢ FRONT END
:- HTML5, CSS3, React.js
➢ BACK END TOOL
:- Node.js, Express.js
➢ DATABASE
:- MongoDB
➢ OPERATING SYSTEM
:- WINDOWS 7 AND ABOVE
3.2 Hardware Requirements:➢ RAM
:- Minimum 4GB – Maximum Any
➢ ROM
:- 500GB
➢ PROCESSOR
:- (i3) Intel 3th gen or more
➢ HARD DISK
:- 16 GB hard disk recommended
3.3 Application Environment:➢ Any Browser
➢ Mobile Compatible
➢ Tablet Compatible
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SOFTWARE REQUIREMENT SPECIFICATION
3.4 Specific Requirements
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PROJECT MANAGEMENT
CHAPTER 4
PROJECT MANAGEMENT
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PROJECT MANAGEMENT
4. PROJECT MANAGEMENT
4.1 Project Development Approach
The project follows an iterative development approach, allowing for continuous improvement and
flexibility during development. This approach involves planning, design, implementation, testing,
and feedback at various stages of the project.
4.2 Project Planning
Project planning involves defining the scope, setting milestones, and allocating resources to ensure
the project stays on track. Key planning elements include:
Project Scope: Define the boundaries and deliverables of the project.
Milestones: Establish key stages for project completion, such as data collection, backend
implementation, frontend implementation, and testing.
Resource Allocation: Assign tasks and responsibilities to team members based on skills and
expertise.
4.3 Project Scheduling
•Problem
Understanding
•Literature
Review
Feb
•Learning
•Designing
•Analysis
Jan
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•Implementation
•Testing
March
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PROJECT MANAGEMENT
Task No.
Task Name
Starting
Ending
1
Problem
Define and
survey
January
January
2
Analysis
February
February
3
Learning
&
Designing
March
March
4
Implementation
March
April
5
Testing
April
May
4.4 Risk Management
Risk management involves identifying, analyzing, and planning for risks that could impact the
project's success.
4.4.1 Risk Identification:
Risk identification is the process of determining potential risks that could affect the project. The
following are some common risks associated with this project:
Technical Risks: Issues related to software bugs, technology compatibility, or integration
challenges. This could include difficulties in implementing the recommendation algorithm or
interfacing with the frontend.
Project Management Risks: Risks arising from poor project planning, scheduling delays, or
inadequate resource allocation. This could impact project timelines and quality.
External Risks: Risks from outside the project, such as changes in requirements, stakeholder
expectations, or regulatory compliance issues.
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PROJECT MANAGEMENT
Security Risks: Risks related to data protection and user privacy, especially if the system involves
user-generated content or personal information.
4.4.2 Risk Analysis:
Risk analysis involves assessing the likelihood and impact of identified risks. Each risk is evaluated
on its potential to disrupt the project and its probability of occurrence. The following risk analysis
categories can be used:
High Risk: Risks with a high probability of occurrence and significant impact on the project. These
require immediate attention and contingency plans.
Medium Risk: Risks with moderate probability and impact. These require monitoring and
mitigation plans.
Low Risk: Risks with low probability or minimal impact. These are generally less critical but
should still be tracked.
4.4.3 Risk Planning:
Risk planning involves developing strategies to address identified risks. This can include mitigation
measures, contingency plans, or risk acceptance. Here are some examples:
Mitigation Strategies: Implementing measures to reduce the likelihood or impact of risks. For
example, conducting code reviews to prevent technical issues, or ensuring proper project
management practices to avoid scheduling delays.
Contingency Plans: Developing backup plans to address risks if they occur. For example, having
additional resources available in case of delays, or establishing alternative technologies if the
primary ones fail.
Risk Acceptance: Accepting certain risks as inherent to the project, with a plan to manage their
impact. This is typically done for low-risk scenarios.
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SYSTEM DESIGN
CHAPTER 5
SYSTEM DESIGN
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SYSTEM DESIGN
5. SYSTEM ANALYSIS
5.1 Requirement of new system
➢ Personalization
➢ Accuracy
➢ Scalability
➢ Real-Time Updates
➢ Diversity
➢ Management
➢ Easy to use
5.2 Feature of New System
➢ Personalized Recommendations: Provide personalized product recommendations based
on user preferences, browsing history, purchase behavior, demographics, and contextual
factors. Use machine learning algorithms to analyze user data and generate tailored
recommendations that match individual interests and needs.
➢ Dynamic Recommendations: Offer real-time and dynamic recommendations that update
dynamically based on user interactions, changes in inventory, and market trends. Ensure
recommendations are timely, relevant, and responsive to users' evolving preferences and
behavior.
➢ Context-Aware Recommendations: Leverage contextual information such as user
location, device type, time of day, and browsing history to deliver contextually relevant
recommendations. Adapt recommendations based on situational cues to enhance user
engagement and conversion rates.
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SYSTEM DESIGN
➢ User Feedback and Ratings: Allow users to provide feedback on recommended products
through ratings, reviews, likes, or dislikes. Incorporate user feedback into the
recommendation algorithm to improve recommendation accuracy and relevance over time.
➢ Performance and Scalability: Design the recommendation system to be scalable and
performant, capable of handling large volumes of users, products, and interactions. Use
efficient algorithms, caching mechanisms, and distributed architectures to optimize
performance and ensure responsiveness even during peak traffic periods.
5.3 Data flow and Modeling
5.3.1 Login Process Flow Chart:
[Fig 5.4.1 Login Process Flow Chart]
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SYSTEM DESIGN
5.3.2 Add to Cart Flow Chart:
[Fig 5.4.2 Add to Cart Flow Chart]
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SYSTEM DESIGN
5.3.3 Order Process Flow Chart:
[Fig 5.4.3 Order Process Flow Chart]
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SYSTEM DESIGN
5.3.4 Use Case Diagram:
[Fig 5.4.4 Use case Diagram]
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DATA DICTIONARY
CHAPTER 6
DATA DICTIONARY
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DATA DICTIONARY
6. DATA DICTIONARY
Feature:Data dictionaries are integral components of structured analysis since flow diagram by themselves
don’t fully describe object of the investigation. The dictionary provides additional information about
the system
What a data dictionary is?
A data dictionary is catalogue-a repository- of the elements in a system. As the name suggest, these
elements center on data and the way they are structured to meet user requirements and organization
needs. In a data dictionary you will find a list of all the elements composing the data flowing through
a system. The major elements are data flows, data stores, and processes.
If analyst wants to know many characters are in a data item, by what other names it is referenced in
the system. They should be able to find answers in a property developed data dictionary.
Data dictionary is developed during data flow analysis and assist the analysis involved in determine
system requirements. However, its contents are used during system design as well.
Why is data dictionary important?
Analysis uses data dictionary for three important reasons:➢ To manage the detail in large systems.
➢ To communicates a common meanings for all system elements.
➢ To documents the features of the system.
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DATA DICTIONARY
[Fig 6.1]
[Fig 6.2]
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DATA DICTIONARY
[Fig 6.3]
[Fig 6.4]
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DATA DICTIONARY
[Fig 6.5]
[Fig 6.6]
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IMPLEMENTATION
CHAPTER 7
IMPLEMENTATION
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IMPLEMENTATION
7. IMPLEMENTATION
7.1 Pages Layout:-
[Fig 7.1.1 Sign-up Page]
[Fig 7.1.2]
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IMPLEMENTATION
[Fig 7.1.3 Login Page]
[Fig 7.1.4]
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IMPLEMENTATION
7.2 Admin Page:-
[Fig 7.2.1 Admin Panel]
[Fig 7.2.2]
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IMPLEMENTATION
[Fig 7.2.3 All User Details]
[Fig 7.2.4 Change User Role]
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IMPLEMENTATION
[Fig 7.2.5 All Product Details]
[Fig 7.2.6 Upload Product Panel]
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IMPLEMENTATION
[Fig 7.2.7 Edit Product Panel]
[Fig 7.2.8]
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IMPLEMENTATION
7.3 Frontend Interface:-
[Fig 7.3.1]
[Fig 7.3.2]
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IMPLEMENTATION
[Fig 7.3.3]
[Fig 7.3.4]
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IMPLEMENTATION
[Fig 7.3.5]
[Fig 7.3.6]
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IMPLEMENTATION
[Fig 7.3.7 Specific Product Page]
[Fig 7.3.8]
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IMPLEMENTATION
[Fig 7.3.9 Recommended Product]
[Fig 7.3.10 Specific Category Page]
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IMPLEMENTATION
[Fig 7.3.11 Search Product]
[Fig 7.3.12 Add To Cart Page]
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SYSTEM TESTING
CHAPTER 8
SYSTEM TESTING
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SYSTEM TESTING
8 . SYSTEM TESTING
8.1 Test plan:
•
Testing has a dual function, it is used to establish the presence of defects in a program and
it is used to judge whether or not the program is usable in practice. Thus testing is useful for
validation and verification, which ensure that software conforms to its specification and
meets the needs of the software customer.
•
I resorted Alpha testing, usually comes in after the basic design of the program has been
completed. The project scientist will look over the program and make suggestion or give
ideas to us to improve or to correct the design. They also report and give ideas to help get
rid of or work around any major problems. There is bound to be a number of bugs after a
program is created and they are most likely to get known to the developers the alpha testing.
I carried out testing process in four stages i.e.
•
Unit testing,
•
Module testing,
•
Subsystem tests
•
system testing.
•
In another method called Black Box or Functional testing. I am concerned about the output
of the module and software, i.e. whether the Web Site gives proper output as per requirement
or not. In another words, these testing aims to test a given program’s behaviour.
•
Against its specification without making any reference to the internal structures of the
program or the algorithms used. Therefore the source code is not needed, and so even
purchased modules can be tested. The program just gets a certain input, and its functionality
is examined by observing the output.
•
This can be done in the following way:
•
Input
•
Interface
•
Processing
•
Output
•
Interface
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•
SYSTEM TESTING
In another method called Black Box or Functional testing. I am concerned about the output of
the module and software, i.e. whether the software gives proper output as per our requirement
or not. In another words these testing aims to test a given program’s behavior.
•
The tested program gets a certain input or the input is observed. Then the product does its job &
generates a certain output, which is collected by a second interface. This result is then compared
to the expected output, which has been determined before the test.
•
While white box testing was used as an important primary testing approach; code is inspected
to see what it does, tests are designed to exercise the code. Code is tested using code: scripts,
drivers, stubs, etc. are employed to directly interface with and drive the code.
•
Testing & debugging is done in two steps in our project. Actually the testing processes started
right from the word go in the project life span as each and every module was being worked upon.
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Team ID: 445820
SYSTEM TESTING
8.2 Test Case :
By definition, a test case is a set of data that the system will process as normal input. The philosophy
behind testing is to find errors. I devised the test cases with this purpose in mind.
8.2.1 Test Case 1
The objective of this test case is to find out whether chatbot is responding as per requirement or not.
Test case id
01
Module name
Login Form
Description
Login Form Testing
Result
Success
Remarks
May need few changes in the style
OUTPUT:
• Successful Login Implementation.
• It showed the messages and assistance in case of incorrect entering of User ID and
Passwords.
CONCLUSION:
Login form worked successfully for all Admins and leads to secure login to their respective
Dashboards.
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Team ID: 445820
SYSTEM TESTING
8.2.2 Test Case 2
The objective of this test case is to find out whether chatbot is responding as per requirement or not.
Test case id
02
Module name
Admin dashboard
Description
Functional Testing for various dashboards
Result
Success
Remarks
Shows all the required fields and buttons
OUTPUT:
• Dashboard requires no changes.
• It showed every detail and worked as per the requirement.
• All the buttons and hover features implemented as per requirement.
CONCLUSION:
After this testing we can conclude that these dashboards are ready for deployment.
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Shree Swaminarayan Institute of Technology
Team ID: 445820
SYSTEM TESTING
8.2.3 Test Case 3
The objective of this test case is to find out whether chatbot is responding as per requirement or not.
Test case id
03
Module name
Add to Cart
Description
Functional Testing for various inputs
Result
Success
Remarks
Shows all the details of items
OUTPUT:
• User can add and delete items.
• All the buttons and hover features implemented as per requirement.
CONCLUSION:
After this testing we can conclude that these cart are ready for deployment.
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Shree Swaminarayan Institute of Technology
Team ID: 445820
SYSTEM TESTING
8.2.4 Test Case 4
The objective of this test case is to find out whether chatbot is responding as per requirement or not.
Test case id
04
Module name
Search bar
Description
Functional Searching for various products
Result
Success
Remarks
All the buttons are working properly
OUTPUT:
• Dashboard requires no changes.
• It showed every detail and worked as per the requirement.
• All the buttons and hover features implemented as per requirement.
CONCLUSION:
After this testing we can conclude that these Search bar are ready for deployment.
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Team ID: 445820
LIMITATIONS AND FUTURE ENHANCEMENT
CHAPTER 9
LIMITATIONS
AND
FUTURE ENHANCEMENT
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LIMITATIONS AND FUTURE ENHANCEMENT
9. LIMITATIONS AND FUTURE ENHANCEMENT
9.1 Limitations:
➢ Complexity of Full-Stack Development: Developing a full-fledged e-commerce system
using the MERN stack requires expertise in multiple technologies and frameworks.
Managing the interaction between these components and ensuring smooth integration can
be challenging.
➢ Scalability: E-commerce systems need to handle large volumes of traffic, especially during
peak shopping periods or promotional events. While the MERN stack offers scalability
advantages, such as horizontal scaling with Node.js and MongoDB's sharding capabilities,
scaling a complex e-commerce system requires careful planning and infrastructure
management. Developers need to design the application architecture with scalability in mind
and implement best practices for load balancing, caching, and database optimization.
➢ Performance Optimization: E-commerce websites need to provide a seamless user
experience with fast page loading times and smooth navigation.
➢ Data Sparsity: E-commerce platforms often have vast catalogs of products and a large
number of users. However, individual users may only interact with a small subset of these
products. This leads to data sparsity, where the available data for making recommendations
is limited, making it challenging to provide accurate and diverse recommendations.
9.2 Future Scope:
➢ Personalization and Context-Awareness: Future e-commerce recommendation systems
will focus on delivering highly personalized recommendations tailored to individual user
preferences, behaviors, and contexts. By leveraging advanced machine learning techniques,
such as deep learning and reinforcement learning, recommendation systems will become
more adept at understanding user intent and providing relevant suggestions across various
touchpoints and devices.
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LIMITATIONS AND FUTURE ENHANCEMENT
➢ Multimodal Recommendations: With the proliferation of multimedia content and
interaction modalities, future recommendation systems will incorporate multimodal data
sources, including text, images, audio, and video, to enhance recommendation accuracy and
diversity. These systems will be capable of understanding and recommending products
based on visual and auditory cues, enabling richer and more immersive shopping
experiences.
➢ Real-Time and Dynamic Recommendations: Future e-commerce recommendation
systems will operate in real-time, continuously adapting and updating recommendations
based on evolving user preferences, trends, and contextual factors. By leveraging streaming
data processing technologies and event-driven architectures, recommendation systems will
deliver timely and dynamic suggestions that reflect the most relevant and up-to-date
information.
➢ Ethical and Fair Recommendations: With growing concerns about algorithmic bias and
fairness, future e-commerce recommendation systems will prioritize ethical considerations
and fairness principles in recommendation algorithms. By incorporating fairness-aware
machine learning techniques and diversity metrics, recommendation systems will strive to
mitigate biases, promote diversity, and ensure equitable recommendations for all users.
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THANK YOU