REAL-TIME ACADEMIC PERFORMANCE ANALYTICS

AN INDUSTRY ORIENTED MINI REPORT

Submitted to

JAWAHARLAL NEHRU TECNOLOGICAL UNIVERSITY, HYDERABAD

In partial fulfillment of the requirements for the award of the degree of

BACHELOR OF TECHNOLOGY
In

COMPUTER SCIENCE AND ENGINEERING

Submitted By

THUDI SAI NITHISHA (22UK1A05D1)

Under the guidance of

Ms. T. SUSHMA
Assistant Professor

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

VAAGDEVI ENGINEERING COLLEGE
Affiliated to JNTUH, HYDERABAD

BOLLIKUNTA, WARANGAL (T.S)

– 506005

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 DEPARTMENT OF
COMPUTER SCIENCE AND ENGINEERING
VAAGDEVI ENGINEERING COLLEGE(WARANGAL)

CERTIFICATE OF COMPLETION
INDUSTRY ORIENTED MINI PROJECT

This is to certify that the MINI PROJECT entitled “ REAL-TIME ACADEMIC

PERFORMANCE ANALYTICS” is being submitted by THUDI SAI NITHISHA
(22UK1A05D1) in partial fulfillment of the requirements for the award of the
degree of Bachelor of Technology in Computer Science & Engineering to
Jawaharlal Nehru Technological University Hyderabad during the academic
year 2024-2025.

Project Guide HOD

Ms. T. SUSHMA Dr. K. SHARMILA

REDDY
(Assistant Professor) (Professor)

External

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 ACKNOWLEDGEMENT

I wish to take this opportunity to express our sincere gratitude and deep sense of respect to
our beloved Dr. SYED MUSTHAK AHMED, Principal, Vaagdevi Engineering College for
making us available all the required assistance and for his support and inspiration to carry out
this MINI PROJECT in the institute.

I extend our heartfelt thanks to Dr. K. SHARMILA REDDY, Head of the Department of
CSE, Vaagdevi Engineering College for providing us necessary infrastructure and there by
giving us freedom to carry out the MINI PROJECT.

I express heartfelt thanks to the Coordinator, Ms. T. SUSHMA, Assistant professor,

Department of CSE for her constant support and giving necessary guidance for completion of
this MINI PROJECT.

I express heartfelt thanks to the guide, Ms. T. SUSHMA, Assistant professor, Department of
CSE for her constant support and giving necessary guidance for completion of this MINI
PROJECT.

Finally, I express our sincere thanks and gratitude to my family members, friends for their
encouragement and outpouring their knowledge and experience throughout the thesis.

THUDI SAI NITHISHA (22UK1A05D1)

iii
 DECLARATION

I declare that the work reported in the project entitled “ REAL-TIME ACADEMIC
PERFORMANCE ANALYTICS” is a record of work done by us in the partial fulfillment for the
award of the degree of Bachelor of Technology in Computer Science & Engineering, VAAGDEVI
ENGINEERING COLLEGE (An Autonomous Institution & Affiliated to JNTU Hyderabad)
Accredited by NAAC with 'A+' Grade, Certified by ISO 9001:2015 Approved by AICTE, New Delhi,
Bollikunta, Warangal-506 005, Telangana, India under the guidance of Ms. T. SUSHMA, Assistant
professor, Department of CSE.
I hereby declare that this project work bears no resemblance to any other project submitted at
Vaagdevi Engineering College of or any other university/college for the award of the degree.

THUDI SAI NITHISHA (22UK1A05D1)

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 ABSTRACT

In education, accurately and efficiently tracking student performance and attendance is

essential for upholding academic standards and supporting student success. Traditionally,
institutions relied on manual systems—such as paper registers and spreadsheets—to manage
academic records. These outdated methods often led to errors, data loss, and inefficiencies in
accessing and analyzing information.

Conventional systems lacked centralized control, real-time accessibility, and transparency,

making it difficult for staff to maintain accurate records and for students to monitor their
progress. As student populations grew and the demand for immediate academic insights
increased, the transition to automated solutions became imperative.

The Real-Time Academic Performance Analytics System is a web-based application

designed to modernize the management of academic years, departments, sections, students,
and attendance.

Powered by a Django-based backend and robust data models, the system ensures secure,
scalable, and efficient academic management. Students can independently view their
attendance records, while administrators and faculty can monitor trends and generate insights
across departments and academic years.

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 TABLE OF CONTENTS:-

1. INTRODUCTION.........................................................................1

1.1 OVERVIEW….............................................................................1

1.2 RESEARCH MOTIVATION....................................................2

2. LITERATURE SURVEY............................................................4

3. TRADITIONAL SYSTEM….......................................................5

4. PROPOSED SYSTEM..............................................................6-11

5. UML DIAGRAMS…..............................................................12-16

6. SOFTWARE AND HARDWARD REQUIREMENT…......17-19

7. FUNCTIONAL REQUIREMENTS…...................................20-22

8. APPLICATIONS..........................................................................23

9. EXPERIMENT AND RESULT ..........................................24-28

10.CONCLUSION...........................................................................29
11.BIBILOGRAPHY …............................................................30-31
12.FUTURE SCOPE.......................................................................32
 TABLE OF DIAGRAMS :-

1. FIG 4.1 BLOCK DIAGRAM………………………………….6

2. FIG 5.1: CLASS DIAGRAM…………………………………13
3. FIG 5.2: SEQUENCE DIAGRAM…………………………...14
4. FIG 5.3: ACTIVITY DIAGRAM…………………………….14
5. FIG 5.4: DATA FLOW DIAGRAM……………………….....15
6. FIG 5.5: COMPONENT DIAGRAM………………………...15
7. FIG 5.6: USE CASE DIAGRAM………………………..........16
8. FIG 5.7: DEVELOPMENT DIAGRAM………………..........16
9. FIG 9.2.1 HOME PAGE………………………………………25
10.FIG 9.2.2 LOGIN PAGE…………………………………......26
11.FIG 9.2.3 ADMIN PAGE……………………………..............26
12.FIG 9.2.4 ADD RESULT……………………………………..27
13.FIG 9.2.5 STUDENT PAGE…………………………….........27
14.FIG 9.2.6 MY PERFORMANCE……………………….........28
 1.INTRODUCTION
1.1 OVERVIEW

Managing academic performance and student attendance has always been a crucial aspect of
institutional administration. Educational institutions are responsible for ensuring transparency,
consistency, and accessibility in handling academic records, especially attendance data. Over
time, the growth of student populations and diversification of academic structures have made
traditional manual tracking methods inefficient. Stakeholders such as administrators, faculty,
and students require a system that offers real-time, accurate, and centralized access to critical
data. The need for automation in handling academic records emerged due to the challenges
faced in data retrieval, performance analysis, and administrative monitoring. Manual registers
and spreadsheets often result in data redundancy, loss of information, and errors, making
academic management cumbersome. An organized and real-time solution helps institutions
monitor attendance trends, evaluate student performance, and implement timely interventions.
Modern academic setups expect digital systems to minimize human intervention and eliminate
inconsistencies. The integration of digital platforms ensures secure authentication, quick data
access, and the ability to analyze attendance behavior over different departments, sections,
and academic years. Accurate data management also enhances communication between
stakeholders and encourages student accountability. Real-time academic analytics
significantly contribute to the improvement of institutional efficiency, student performance
monitoring, and transparent record-keeping. Leveraging web technologies, particularly with
the Django framework, facilitates scalable, secure, and dynamic applications that meet the
demands of contemporary educational environments.
This solution operates on a robust full-stack Python (Django) architecture, offering a
structured approach for user registration, attendance marking, report generation, and
personalized dashboards. The application supports role-based access for administrators, staff,
and students, enabling smooth workflow and controlled data interactions. Each academic
entity—year, department, section, and student—is managed as a modular component within
the system. Real-time updates, attendance insights, and performance reports can be accessed
by authorized users without delays or dependencies on physical records. By adopting this
web-based approach, institutions achieve better accuracy, productivity, and decision-making
capabilities, leading to more informed and timely academic strategies.

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1.2 Research Motivation
The research is driven by the necessity to streamline academic monitoring processes within
educational institutions. As student numbers grow and course offerings diversify, there is a
clear demand for tools that provide instant access to performance and attendance metrics.
Manual systems have shown limitations in scalability, accuracy, and data accessibility,
prompting the development of centralized digital solutions. Existing approaches lacked
automation, which often delayed decision-making and introduced errors in evaluations.
This research is motivated by the desire to eliminate human error, reduce time spent on record
maintenance, and empower educational stakeholders with actionable data. Furthermore, real-
time visibility into academic and attendance performance supports early identification of
issues, allowing institutions to implement corrective measures. With the integration of secure
user roles and a structured data model, the approach also encourages transparency and
accountability in academic management.
1.3 Problem Definition
Manual academic recordkeeping introduces several issues that impact the efficiency of
institutional management. Attendance tracking using physical registers is time-consuming,
prone to human error, and lacks scalability. Paper-based systems can be damaged or lost,
making it difficult to retrieve historical records. Calculating attendance percentages manually
is a labor-intensive process and often inaccurate. There is also limited scope for analytics,
preventing educators from identifying patterns in student behavior or academic engagement.
In multi-department environments, synchronizing data across sections and academic years
without a digital backbone becomes highly complex. Administrators face delays in
consolidating records, and students have no means to independently verify their attendance or
performance status. Additionally, unauthorized access or manipulation of data is difficult to
trace in non-digital systems, posing security and integrity risks. These limitations underline
the need for a structured, reliable, and real-time system.

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1.4 Significance
Automating academic performance analytics brings significant advantages to educational
institutions. It ensures that attendance data is captured with accuracy and stored securely. The
introduction of real-time tracking improves student accountability and motivates consistent
participation. Educators can generate reports instantly, compare performance trends, and take
proactive measures to improve learning outcomes.
Institutions benefit from having a centralized system that can be accessed by different
stakeholders according to their roles. Time spent on administrative tasks is greatly reduced,
allowing staff to focus more on student development. Furthermore, automated systems
provide a digital trail for every activity, enabling audits and compliance checks with minimal
effort. With a structured digital platform, institutions achieve better decision-making,
performance management, and overall academic governance.

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 2.LITERATURE SURVEY

[1] John Williams: Explored the limitations of traditional attendance tracking systems and
emphasized the need for digital transformation in educational institutions. His work
demonstrated how web-based solutions could overcome issues of scalability and accuracy in
large classrooms. [2] Ayesha Khan: Investigated academic performance tracking using basic
management tools and proposed integrating relational databases with web frameworks. Her
research laid the foundation for using Django-like technologies for managing educational data
securely.
[3] Michael Zhou: Focused on the impact of real-time analytics in education, showing that
immediate feedback on attendance can significantly influence student discipline. His
implementation of dynamic reporting techniques aligns closely with the goals of real-time
performance systems. [4] Priya Desai: Conducted a comparative analysis of manual and
automated systems in schools and colleges. She concluded that web applications vastly
improve operational efficiency, user satisfaction, and institutional data governance.
[5] Rakesh Sharma: Researched on centralized academic portals using Python frameworks.
His contribution included building scalable models for student and department records,
proving the effectiveness of modular data handling.
[6] Fatima Noor: Highlighted security concerns in academic data management and
recommended role-based authentication as implemented in Django. Her work ensures that
only authorized users access sensitive information, which aligns with structured access
control.
[7] Rahul Singh: Worked on performance evaluation through attendance correlation. His
models demonstrated the direct connection between attendance consistency and academic
results, supporting the need for detailed, real-time tracking.
[8] Sarah Lee: Studied user experience in academic applications and emphasized the
importance of intuitive interfaces. Her findings reinforce the use of HTML and CSS for
responsive and user-friendly dashboards.

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 3.TRADITIONAL SYSTEM
3.1 Manual Attendance Registers
Institutions have relied on manual attendance books where faculty mark student presence
daily. These registers are paper-based, susceptible to physical damage, and challenging to
maintain over extended periods. Calculating total attendance and generating summaries
requires manual counting, increasing chances of error. Storing such data for a large student
base across departments becomes impractical and lacks quick accessibility.
3.2 Spreadsheet-Based Tracking
Some institutions adopted spreadsheets like Microsoft Excel or Google Sheets to move away
from paper. While spreadsheets allow digital storage, they are not centralized and often
managed by individual faculty. The system does not support real-time updates, user-level
access control, or analytical visualization. Multiple versions of files lead to confusion, and
there is a high dependency on manual data entry and formula accuracy.
3.3 SMS or Email Attendance Reports
A few colleges used SMS/email systems where teachers manually update and send attendance
reports. Although faster than registers, this method lacks systematic storage, secure
authentication, and automated reporting. There is no unified platform for accessing data, and
retrieving old records requires digging through archives.
3.4 Biometric Devices without Central Integration
Some institutions use biometric attendance, but the data is not integrated with academic
records. These systems are standalone and do not provide departmental or student-level
analytics. Lack of a unified interface for admin or students restricts utility, and system faults
or power issues affect attendance logging.
Overall Drawbacks
1. High chances of data loss due to physical damage or human error.

2. Difficulty in accessing historical records or generating reports instantly.

3. Time-consuming process for both educators and administrators.

4. No centralized system for department-wise student management.

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 5. Inability to track attendance trends or patterns over time.

6. No transparency or real-time access for students to verify records.

7. Increased workload in large institutions with multiple section.

4.PROPOSED SYSTEM
4.1 Overview
A web-based academic analytics system built using Django framework addresses the
complete academic performance cycle with structured data modeling and real-time access.
The solution enables institutions to maintain academic years, departments, sections, students,
and attendance in an integrated environment. It ensures secure user login for different roles
including admin, staff, and students, providing customized access and control. Attendance
marking and report generation are automated, significantly reducing manual tasks. With real-
time data storage and analysis, it ensures every stakeholder is updated with current
information. The system empowers students to independently track their attendance
percentage and helps educators identify patterns quickly. Each academic entity is interlinked
and manageable from a centralized dashboard. By using full-stack Python architecture, it
ensures clean backend logic, seamless data flow, and intuitive frontend design using HTML
and CSS.

Fig 4.1 Block Diagram

4.2 Key Features and Functionalities
1. Role-based user login and authentication

2. Creation and management of academic years, departments, and sections

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 3. Student registration and detailed profile management

4. Real-time attendance marking with date-wise tracking

5. Automated attendance percentage calculation and reporting

6. Student portal for viewing individual attendance history

7. Admin control panel for centralized data management

8. Secure storage and retrieval of academic data

4.3 Technical Implementation (MVT)

The Model-View-Template (MVT) structure in Django separates data, business logic, and
presentation. Models handle data representation, Views manage processing logic, and
Templates control user interface rendering. This ensures clean and maintainable code.

Models (M) – Data Structure

1. AcademicYear
○ Stores academic years like "2024-2025".
2. Department
○ Represents academic departments (e.g., CSE, ECE).
3. Section
○ Denotes student sections (e.g., A, B).
4. Year
○ Indicates academic year level (e.g., 1st Year).
5. Semester
○ Represents semesters (e.g., Semester 1, Semester 2).
6. Subject
○ Contains subject details along with foreign keys to Department, Year, and
Semester.
7. Student
○ Links to Django User and includes academic year, department, section, roll
number, and academic year level.
8. Result
○ Stores marks of students for a specific subject and semester.

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Views

1. Authentication
○ register: Creates new staff accounts.
○ login_view: Authenticates users and logs them in.
○ logout_view: Logs out the user.
2. Master Data Management
○ create_academic_year: Add academic years.
○ department_list_create: Create/list departments.
○ year_list_create: Create/list academic years.
○ semester_list_create: Create/list semesters.
○ subject_list_create: Add/list subjects.
○ section_list_create: Create/list student sections.
3. Student Management
○ student_list_create: Create/list students.
○ student_view: Filter and display students based on academic year,
department, section, and year.
4. Result Management
○ add_result: Choose a student and semester to display subjects.
○ submit_marks: Save marks submitted for subjects.
○ semester_wise_result_sheet: Admin/staff view of a student's marksheet
semester-wise.
○ my_semester_wise_result_sheet: Student's own marksheet view.

Templates (T) – User Interface

1. Authentication
○ login.html: Login form for users.
○ register.html: Staff registration form.
2. Master Data Forms
○ academic_year.html: Add/view academic years.
○ department_list.html: Add/view departments.
○ year_list.html: Add/view academic year levels.
○ semester_list.html: Add/view semesters.

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 ○ subject_list.html: Add/view subjects.
○ section_list.html: Add/view sections.
3. Student
○ student_list.html: Add/view students.
○ student_view.html: Filter and view students by criteria.

4. Results
○ marks_form.html: Form to enter marks for selected student.
○ student_marksheet.html: Displays student’s semester-wise performance.

4.4 Django Framework

The system is built using the Django web framework, a high-level Python-based framework
that encourages rapid development and clean, pragmatic design. Django follows the Model-
View-Template (MVT) architectural pattern, making it suitable for scalable and secure web
applications like a Student Result Management System.

Key Features of Django Used

1. MVT Architecture
○ Model: Defines the structure of the database using Python classes.
○ View: Contains the business logic to interact with models and render
templates.
○ Template: Handles the presentation layer using HTML with Django's
templating engine.
2. Built-in Authentication System
○ Provides secure login/logout and user session management.
○ Allows different user roles (e.g., staff, student).
3. Admin Interface
○ Django’s admin dashboard is used to manage academic data like departments,
years, semesters, and students.
○ Custom models are registered and styled for easy access.
4. ORM (Object-Relational Mapping)
○ Simplifies database operations using Python classes instead of SQL.

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 ○ Enables complex queries and relationships like foreign keys between
Student, Subject, and Result.
5. Form Handling
○ Django forms are used for input validation, secure submissions, and rendering
form fields in templates.
○ Custom forms support student creation and marks entry.

6. Template System
○ Django templates support loops, conditions, and filters.
○ Dynamic pages like student lists, marks entry forms, and results are rendered
using context data passed from views.
7. Routing and URL Dispatching
○ urls.py handles clean URL mapping to views for modular navigation.
○ Supports named routes and inclusion of app-specific URL configs.
8. Messages Framework
○ Provides real-time feedback to users (e.g., “Student added successfully”,
“Login failed”).
○ Used across registration, login, and CRUD operations.
9. User Access Control
○ @login_required decorator restricts access to authenticated users.
○ Students are only allowed to view their own marksheets.
10. Security Features
○ CSRF protection is enabled for form submissions.
○ Django's built-in protections against XSS, SQL injection, and clickjacking are
utilized.

4.5 HTML and CSS

The Student Result Management System (SRMS) employs HTML (HyperText Markup
Language) and CSS (Cascading Style Sheets) as foundational technologies to construct and
style the web interface. These technologies ensure that the system is both functional and
aesthetically pleasing, providing users with an intuitive and responsive experience.

HTML: Structuring the Web Interface

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HTML serves as the backbone of the SRMS, defining the structure and layout of web pages.
Key aspects include:

● Semantic Elements: Utilization of semantic tags like <header>, <nav>, <main>,

<section>, and <footer> to organize content logically, enhancing accessibility and
SEO.
● Forms and Inputs: Implementation of forms using <form>, <input>, <select>, and
<textarea> elements to capture user data such as student information, marks, and login
credentials.
● Tables: Use of <table>, <tr>, <th>, and <td> elements to display structured data like
student results, facilitating easy reading and comparison.
● Navigation: Creation of intuitive navigation menus using <nav> and <ul> elements,
allowing users to seamlessly access different sections of the system.

CSS: Styling and Enhancing User Experience

CSS is employed to style the HTML elements, ensuring a consistent and visually appealing
interface. Key styling considerations include:

● Layout and Positioning: Use of CSS Flexbox and Grid layouts to create responsive
designs that adapt to various screen sizes, ensuring usability across devices.
● Typography and Colors: Definition of font families, sizes, and color schemes to
maintain readability and align with institutional branding.
● Interactive Elements: Styling of buttons, links, and form controls to provide visual
feedback on user interactions, enhancing usability.
● Responsive Design: Incorporation of media queries to adjust styles based on device
characteristics, ensuring optimal display on desktops, tablets, and smartphones.

4.6 Integration of Django, HTML, and CSS

Views render data to templates, and templates receive and present data using HTML and CSS.
The integration ensures dynamic content rendering, seamless interaction, and responsive
design. The backend processes data, while the frontend reflects real-time changes.

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 5.UML DIAGRAMS
UML stands for Unified Modeling Language. UML is a standardized general-purpose
modeling language in the field of object-oriented software engineering. The standard is
managed, and was created by, the Object Management Group. The goal is for UML to
become a common language for creating models of object-oriented computer software. In its
current form UML is comprised of two major components: a Meta-model and a notation. In
the future, some form of method or process may also be added to; or associated with, UML.
The Unified Modeling Language is a standard language for specifying, Visualization,
Constructing and documenting the artifacts of software system, as well as for business
modeling and other non-software systems. The UML represents a collection of best
engineering practices that have proven successful in the modeling of large and complex
systems. The UML is a very important part of developing objects-oriented software and the
software development process. The UML uses mostly graphical notations to express the
design of software projects.
GOALS: The Primary goals in the design of the UML are as follows:
● Provide users a ready-to-use, expressive visual modeling Language so that they can
develop and exchange meaningful models.

● Provide extendibility and specialization mechanisms to extend the core concepts.

● Be independent of particular programming languages and development process.

● Provide a formal basis for understanding the modeling language.

● Encourage the growth of OO tools market.

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● Support higher level development concepts such as collaborations, frameworks, patterns
and components.

● Integrate best practices.

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Class diagram
The class diagram is used to refine the use case diagram and define a detailed design of the
system. The class diagram classifies the actors defined in the use case diagram into a set of
interrelated classes. The relationship or association between the classes can be either an "is-a"
or "has-a" relationship. Each class in the class diagram was capable of providing certain
functionalities. These functionalities provided by the class are termed "methods" of the class.
Apart from this, each class may have certain "attributes" that uniquely identify the class.

Fig 5.1: Class Diagram

Sequence Diagram
A sequence diagram in Unified Modeling Language (UML) is a kind of interaction diagram
that shows how processes operate with one another and in what order. It is a construct of a
Message Sequence Chart. A sequence diagram shows, as parallel vertical lines (“lifelines”),
different processes or objects that live simultaneously, and as horizontal arrows, the messages
exchanged between them, in the order in which they occur. This allows the specification of
simple runtime scenarios in a graphical manner.

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 Fig 5.2: Sequence Diagram
Activity diagram
Activity diagrams are graphical representations of Workflows of stepwise activities and
actions with support for choice, iteration, and concurrency. In the Unified Modeling
Language, activity diagrams can be used to describe the business and operational step-by-step
workflows of components in a system. An activity diagram shows the overall flow of control.

Fig 5.3: Activity Diagram

Data flow diagram
A data flow diagram (DFD) is a graphical representation of how data moves within an
information system. It is a modeling technique used in system analysis and design to illustrate
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the flow of data between various processes, data stores, data sources, and data destinations
within a system or between systems. Data flow diagrams are often used to depict the structure
and behavior of a system, emphasizing the flow of data and the transformations it undergoes
as it moves through the system.

Fig 5.4: Data Flow Diagram

Component diagram:
Component diagram describes the organization and wiring of the physical components in a
system.

Fig 5.5: Component Diagram

Use Case diagram
A use case diagram in the Unified Modeling Language (UML) is a type of behavioral
diagram defined by and created from a Use-case analysis. Its purpose is to present a graphical
overview of the functionality provided by a system in terms of actors, their goals (represented
as use cases), and any dependencies between those use cases. The main purpose of a use case
diagram is to show what system functions are performed for which actor. Roles of the actors
in the system can be depicted.

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 Fig 5.6: Use Case Diagram
Deployment Diagram
The Deployment Diagram illustrates the physical architecture of the online cooking classes
system, showing how software components are deployed on hardware. It includes the user
device, which interacts with the system through a browser, the web server hosting the Django
application, and the database server running SQLite. Interactions flow as the user's browser
sends HTTP requests to the web server, which processes them, interacts with the SQLite
database for data operations, and sends back appropriate responses to the user's browser.

Fig 5.7: Development Diagram

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 6.SOFTWARE AND HARDWARE REQUIREMENTS
6.1 Software Requirements

Python is a high-level, interpreted programming language known for its simplicity and
readability, which makes it a popular choice for beginners as well as experienced developers.
Key features of Python include its dynamic typing, automatic memory management, and a
rich standard library that supports a wide range of applications from web development to data
science and machine learning. Its object-oriented approach and support for multiple
programming paradigms allow developers to write clear, maintainable code. Python's
extensive ecosystem of third-party packages further enhances its capabilities, enabling rapid
development and prototyping across diverse fields.

Installation

First, download the appropriate installer from the official Python website
(https://www.python.org/downloads/release/python-376/). For Windows users, run the
executable installer and ensure to check the "Add Python to PATH" option during installation;
for macOS and Linux, follow the respective package installation commands or use a package
manager like Homebrew or apt-get. After installation, verify the setup by running python --
version or python3 --version in your terminal or command prompt, which should display
"Python 3.7.6." This version-specific installation supports all major functionalities and
libraries compatible with Python 3.7.6, making it an excellent foundation for developing
robust applications in areas such as data analysis, machine learning, and GUI development.

6.1.1 Python Packages

The project requires a robust set of software libraries and tools that work together to build an
integrated system for plant disease classification. Below is an explanation of the key software
requirements and the packages used:

● Python: The project is implemented in Python, which is chosen for its extensive
ecosystem of libraries and its strong support for data analysis, machine learning, and
GUI development.

● Django is a high-level Python web framework that enables rapid development of

secure and maintainable websites. It follows the Model-View-Template (MVT)
architecture, promoting a clean separation of concerns. Django includes built-in
features like user authentication, admin interface, ORM, and URL routing. It
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 emphasizes reusability and scalability, making it suitable for both small and large-
scale applications. With strong community support and extensive documentation,
Django streamlines web development tasks efficiently.

● PyMySQL: This package provides a means to connect to a MySQL database for

handling user authentication. It facilitates operations such as user signup, login, and
data storage, ensuring secure and persistent management of user credentials.

Each of these packages plays a crucial role in ensuring that the system is robust, scalable, and
efficient—from data ingestion and preprocessing to model training, visualization, and
deployment. The combination of these tools enables the creation of an integrated, user-
friendly application for real-time plant disease classification and management.

6.2 Hardware Requirements

Python 3.7.6 can run efficiently on most modern systems with minimal hardware
requirements. However, meeting the recommended specifications ensures better performance,
especially for developers handling large-scale applications or computationally intensive tasks.
By ensuring compatibility with hardware and operating system, can leverage the full potential
of Python 3.7.6.

Processor (CPU) Requirements: Python 3.7.6 is a lightweight programming language that

can run on various processors, making it highly versatile. However, for optimal performance,
the following processor specifications are recommended:

● Minimum Requirement: 1 GHz single-core processor.

● Recommended: Dual-core or quad-core processors with a clock speed of 2 GHz or

higher. Using a multi-core processor allows Python applications, particularly those
involving multithreading or multiprocessing, to execute more efficiently.

Memory (RAM) Requirements: Python 3.7.6 does not demand excessive memory but
requires adequate RAM for smooth performance, particularly for running resource-intensive
applications such as data processing, machine learning, or web development.

● Minimum Requirement: 512 MB of RAM.

● Recommended: 4 GB or higher for general usage. For data-intensive operations, 8

GB or more is advisable.

Insufficient RAM can cause delays or crashes when handling large datasets or executing
19
computationally heavy programs.

Storage Requirements: Python 3.7.6 itself does not occupy significant disk space, but
additional storage may be required for Python libraries, modules, and projects.

● Minimum Requirement: 200 MB of free disk space for installation.

● Recommended: At least 1 GB of free disk space to accommodate libraries and

dependencies.

Developers using Python for large-scale projects or data science should allocate more storage
to manage virtual environments, datasets, and frameworks like TensorFlow or PyTorch.

Compatibility with Operating Systems: Python 3.7.6 is compatible with most operating
systems but requires hardware that supports the respective OS. Below are general
requirements for supported operating systems:

● Windows: 32-bit and 64-bit systems, Windows 7 or later.

● macOS: macOS 10.9 or later.

● Linux: Supports a wide range of distributions, including Ubuntu, CentOS, and Fedora.

The hardware specifications for the OS directly impact Python’s performance, particularly for
modern software development.

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 7.FUNCTIONAL REQUIREMENTS

1. User Authentication and Management

● Allow users (staff) to register with username, email, password, and name.
● Ensure username and email uniqueness during registration.
● Provide login functionality for users.
● Authenticate user credentials during login.
● Provide logout functionality for logged-in users.
● Display appropriate success/error messages during login and registration.

2. Academic Structure Management

● Create and list academic years (e.g., 2023–2024).

● Create and list departments (e.g., CSE, ECE).
● Create and list years (e.g., 1st Year, 2nd Year).
● Create and list semesters (e.g., Semester 1, Semester 2).
● Create and list sections (e.g., A, B, C).
● Create and list subjects with relations to:
○ Department
○ Year
○ Semester

3. Student Management

● Allow student account creation by:

○ Creating a related Django User object
○ Capturing academic year, section, department, year, and roll number
● Display a list of all students with their academic info.
● Provide student filtering based on:
○ Academic year
○ Department
○ Section
○ Year
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4. Result Management

● Allow selection of a student and semester to enter subject marks.

● Automatically fetch relevant subjects based on:
○ Student’s department
○ Year
○ Semester
● Submit marks for each subject.
● Store results in the Result model, linking student and subject.

5. Result Viewing

● For a given student, display semester-wise results:

○ Subjects taken in each semester
○ Total marks
○ Average marks per semester
● Provide a view for staff/admin to check any student’s results.
● Provide a separate view for students to check their own results using logged-in user
credentials.

6. Page Rendering and Routing

● Render base home page (Base.html).

● Render registration (register.html) and login (login.html) pages.
● Render academic and departmental setup pages:
○ academic_year.html
○ department_list.html
○ year_list.html
○ semester_list.html
○ section_list.html
○ subject_list.html
● Render student creation page (student_list.html) and filtered list (studentslist.html).
● Render marks entry form (marks_form.html).
● Render student result sheet (student_marksheet.html).
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7. Access Control

● Certain views (e.g., student_view, my_semester_wise_result_sheet) require login via

the @login_required decorator to ensure only authenticated users access them.

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 8.APPLICATIONS

1. Monitoring and managing student attendance across departments and academic years.

2. Real-time tracking of student presence and absence records.

3. Role-based access for admin, staff, and students for secure interaction.

4. Generation of automated attendance reports and analytics.

5. Centralized storage of student academic records.

6. Improved academic planning and evaluation based on reliable data.

7. Enhanced transparency and accountability in attendance management.

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 9.EXPERIMENT AND RESULT
9.1 Implementation and Description
The development of the entire web-based training course and attendance management system
was initiated through a well-organized setup using Django as the backend framework and
HTML/CSS for the frontend design. The configuration included creating a virtual
environment, installing required dependencies such as Django, setting up PostgreSQL as the
database, and structuring the project into multiple reusable apps. Static files were handled
through Django’s staticfiles module, while templates were placed in a centralized folder for
consistency and reusability. The admin panel was customized to manage academic years,
departments, students, and attendance effectively.
Models
AcademicYear
This model is designed to define academic sessions. It stores values such as start year and end
year, allowing the system to categorize students and attendance records under the correct
academic cycle.
Department
The department model stores department names (like Computer Science, Mechanical
Engineering) to classify students and manage department-specific operations like attendance
filtering and student assignments.
Section
This model defines the sections within each department. It is linked with departments and
academic years to uniquely identify student batches.
Student
This model associates each user with specific academic data including roll number, academic
year, department, and section. It forms the core of the student management module.
Attendance
This model logs student attendance status (present or absent) for a particular date. It links to
both the student and the date, helping in tracking and generating attendance reports.
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Views
The views are structured using Django’s function-based view system. Each view corresponds
to a specific URL and is responsible for rendering templates and processing user inputs. For
example, the add_student view handles both the display and saving of new student data, while
mark_attendance handles attendance entries. Views are secured with decorators like
@login_required and custom role checks to ensure proper access control.
URLs
The URL configuration connects user actions with the correct view logic. Each URL is
mapped to a corresponding view and named appropriately to simplify redirections. For
example, /students/ is connected to add_student, and /attendance/ to mark_attendance. These
patterns allow modular routing and improve maintainability. The structure also uses
namespace-based separation for better clarity in template linking and redirection logic.
9.2 RESULTS

Fig 9.2.1 Home page

The figure displays the Home page of "Your Performance," with the main heading "Your
Performance." The navigation bar at the top right includes "Home," "Staff Login," "Admin
Login," and "Student Login" options. This page serves as the central entry point for different
user types to access the performance management system.

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 Fig 9.2.2 Login Page
The figure displays a "Login" form. It includes input fields for "Username" and "Password."
A "Login" button is present at the bottom to submit the credentials. Below the login button,
there is a "Don't have an account? Register" link. This page provides a clear interface for
users to log into the system, with an option to register if they don't have an account.

Fig 9.2.3 Admin Page

The figure displays the Admin page of "Your Performance" system. The navigation bar at the
top includes "Home," "Academic years," "Departments," "More," and "Logout" options.
Under the "More" dropdown, additional options like "Sections," "Add Years," "Add
Semester," "Add Subject," "Add Staff accounts," and "Student accounts" are available. This

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page provides a comprehensive interface for administrators to manage academic structure,
subjects, and user accounts within the system.

Fig 9.2.4 Add Results

The figure displays an "Add Results" page, which is designed for staff to input student
performance data. It features dropdown menus to select "Academic Year," "Department,"
"Section," and "Year." A "Fetch Students" button is present to retrieve student lists based on
the selected criteria. The navigation bar at the top includes "Home," "Add Marks," "View
Marks," and "Logout" options. This page provides a structured interface for managing and
adding student results.

Fig 9.2.5 Student Page

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The figure displays a student's performance page with "Your Performance" as the title. The
top right navigation bar includes "Home," "My Performance," and "Logout" options. This
page is designed for students to access and view their academic performance.

Fig 9.2.6 My Performance

The figure displays a student's performance page, which breaks down results by semester. For
"Semester: Semester I," it lists "DBMS" with 20 marks, "Python" with 30 marks, a "Total" of
50 marks, and an "Average" of 25.0%. Similarly, for "Semester: Semester II," it shows
"DBMS" with 80 marks, "Python" with 60 marks, and a "Total" of 140 marks. This page
provides a clear overview of a student's academic performance across different subjects and
semesters.

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 10.CONCLUSION

The complete web application provides an efficient solution for managing training course
enrollments and student attendance tracking in an academic environment. Through an
intuitive interface and a role-based access system, it accommodates the different
responsibilities of administrators, staff, and students. The separation of academic years,
departments, and sections ensures scalability and adaptability for institutions with multiple
courses and batches. Django’s model-view-template (MVT) architecture played a crucial role
in organizing the codebase, streamlining data handling, and maintaining logical separation
between backend logic and frontend display.
A major achievement of the implementation is the attendance management system. It offers a
reliable mechanism to log and track student presence without duplication, thanks to model
constraints and clean validation processes. Attendance percentage calculation and reporting
features make it easier for staff and administrators to monitor student engagement. Likewise,
course management functionalities like adding, updating, and deleting training sessions
empower administrators to maintain a well-structured repository of academic content.
Security has also been well integrated, using Django’s built-in mechanisms for password
hashing, session control, and form validation. Only authenticated users can access sensitive
sections, and roles such as staff and admin are verified before allowing access to their
respective functionalities. The ability for students to independently view their attendance
record ensures transparency, while administrative privileges enable efficient data management
at scale.
The user interface was kept minimal yet responsive. Frontend elements using HTML and CSS
make the system usable across devices. Navigation has been structured logically, with clearly
defined flows for adding data, managing enrollments, and accessing reports.
This system brings together a complete academic management workflow without relying on
third-party APIs. It simplifies course registration, attendance marking, reporting, and student
management in a unified platform. Every functionality has been tested through real-world
scenarios to ensure its usability and reliability. The modular nature also allows future
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developers to extend its capabilities without overhauling the existing system.

11.BIBILOGRAPHY

1. Harikumar Pallathadka, Alex Wenda, Edwin Ramirez-Asís, Maximiliano Asís-López,

Judith Flores-Albornoz, Khongdet Phasinam, “Classification And Prediction Of
Student Perfor-mance Data Using Various Machine Learning Algorithms”, Materials
Today: Proceedings Elsevier, 2021.
2. Ihsan A. Abu Amra, Ashraf Y. A. Maghari, “Students Performance Prediction Using
KNN And Naïve Bayesian”, 8th International Conference on Information Technology
(ICIT), 2017.
3. Dr. R Senthil Kumar, Jithin Kumar.K.P, “Analysis Of Student Performance Based On
Classification And Mapreduce Approach In Bigdata”, International Journal of Pure
and Applied Mathematics, Volume 118 No. 14, 141–148, 2018.
4. Shanmugarajeshwari, R. Lawrance, “Analysis of Students’ Performance Evaluation
Using Classification Techniques”, IEEE, 2016.
5. Leila Ismail, Huned Materwala, Alain Hennebelle, “Comparative Analysis Of
Machine Learning Models For Students’ Performance Prediction”,
https://www.researchgate.net/publication/350057919, 2021.
6. Ajibola Oyedeji, Olaolu Folorunsho, Olatilewa Raphael Abolade, “Analysis And
Prediction Of Student Academic Performance Using Machine Learning”,
https://www.researchgate.net/publication/340310208, 2020.
7. Annisa Uswatun Khasanah, Harwati, “A Comparative Study to Predict Student’s
Performance Using Educational Data Mining Techniques”, IOP Conference Series:
Materials Science and Engineering, 2017.
8. Leena H. Alamri, Ranim S. Almuslim, Mona S. Alotibi, Dana K. Alkadi, Irfan Ullah
Khan, Nida Aslam, “Predicting Student Academic Performance Using Support Vector
Machine and Random Forest”, https://www.researchgate.net/publication/351653053,
2020.

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9. Ms.Tismy Devasia, Ms.Vinushree T P, Mr.Vinayak Hegde, “Prediction Of Students
Performance Using Educational Data Mining”, ResearchGate, 2020,
10. Snjeˇzana Kriˇzanic, “Educational Data Mining Using Cluster Analysis And Decision
Tree Technique: A Case Study”, International Journal of Engineering Business
Management, 2020.

11. C. A. Palacios, J. A. Reyes-Suárez, L. A. Bearzotti, V. Leiva, and C. Marchant,

“Knowledge discovery for higher education student retention based on data mining:
Machine learning algorithms and case study in chile”, Entropy, vol. 23, no. 4, pp. 1–
23, 2021.
12. S. Rovira, E. Puertas, and L. Igual, “Data-driven system to predict academic grades
and dropout”, PLoS One, vol. 12, no. 2, Feb. 2017.
13. T. Muncie, “Using Machine Learning Models to Predict Student Retention: Building
A State- Wide Early Warning System”, Morehead State University, 2020.
14. G. M. Rao and P. K. K. Kumar, “Students Performance Prediction in Online Courses
Using Machine Learning Algorithms,” United Int. J. Res. Technol., vol. 02, no. 11,
2021.
15. Hassan Zeineddine and Udo C. Braendle, “Enhancing Prediction of Student Success:
Automated Machine Learning Approch”, Article in Computers & Electrical
Engineering, 2021

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 12.FUTURE SCOPE

In future iterations, the system can be enhanced by introducing features such as graphical
dashboards for analytics, automated email alerts for low attendance, PDF export of reports,
and mobile responsiveness using frontend frameworks like Bootstrap. Additionally,
integrating face recognition or QR code-based attendance tracking would add a layer of
automation and reduce manual intervention.

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