Get Hired

A Project Report Submitted in Partial Fulfilment of

the Requirements for the Degree of

Bachelor of Technology
In
COMPUTER SCIENCE AND ENGINEERING
(ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING)
by
Mr. Aviral Mishra 2003481530004
Mr. Harsh Kumar Srivastava 2003481530008
Mr. Harsh Vardhan Rai 2003481530009
Ms. Khushi Jalan 2003481530012
Under the Supervision of
Mr. Amit Kumar Sharma
(Assistant Professor)
PSIT COLLEGE OF ENGINEERING, KANPUR
to the

Faculty of Computer Science & Engineering

Dr. A.P.J. Abdul Kalam Technical University, Lucknow
(Formerly Uttar Pradesh Technical University)
May 2024
 DECLARATION
I declare that this submission is solely my work, and to the best of my knowledge and
belief, it does not include any previously published or written material by another
person. Furthermore, it does not contain any material that has substantially
contributed to the award of any degree or diploma from a university or other institute
of higher learning, except where proper acknowledgment has been provided within
the text.

Name : Mr. Aviral Mishra

Roll No : 2003481530004
Date :
Signature :

Name : Mr. Harsh Kumar Srivastava

Roll No : 2003481530008
Date :
Signature :

Name : Mr. Harsh Vardhan Rai

Roll No : 2003481530009
Date :
Signature :

Name : Ms. Khushi Jalan

Roll No : 2003481530012
Date :
Signature :

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 ACKNOWLEDGEMENT
It gives us a great sense of pleasure to present the report of B.Tech. Project “Get
Hired” undertaken during B.Tech. Final Year. We owe special debt of gratitude to our
project guide Mr. Amit Kumar Sharma (Assistant Professor, CSE), PSIT College
of Engineering Kanpur for his constant support and guide throughout course our
work. His sincerity, thoroughness and perseverance have been a constant source of
inspiration for us. It is only his cognizant efforts that our endeavors have seen light of
the day.
We also do not like to miss the opportunity to acknowledge the contribution of all
faculty member of the department for their kind assistance and cooperation during the
development of our project. Last but not the least, we acknowledge our friends for
their contribution in the completion of the project.

Name : Mr. Aviral Mishra

Roll No : 2003481530004
Date :
Signature :

Name : Mr. Harsh Kumar Srivastava

Roll No : 2003481530008
Date :
Signature :

Name : Mr. Harsh Vardhan Rai

Roll No : 2003481530009
Date :
Signature :

Name : Ms. Khushi Jalan

Roll No : 2003481530012

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Date :
Signature :

CERTIFICATE

This is to certify that the project titled “GET HIRED” is submitted by :

 Mr. Aviral Mishra (2003481530004)

 Mr. Harsh Kumar Srivastava (2003481530008)
 Mr. Harshvardhan Rai (2003481530009)
 Ms. Khushi Jalan (2003481530012)

in partial fulfilment of the requirement for the award of the degree of Bachelor of
Technology in Computer Science and Engineering to PSIT College of Engineering,
Kanpur, affiliated to Dr. A.P.J. Abdul Kalam Technical University, Lucknow, during
the academic year 2023-24, is the record of the candidate’s own work carried out by
him/her under my supervision. The matter embodied in this report is original and has
not been submitted for the award of any other degree.

Mr. Abhay Kumar Tripathi Mr. Amit Kumar Sharma

(Asst. Professor, Dept. of CSE) (Asst. Professor, Dept. of
CSE)

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v
 Get Hired
Mr. Amit Kumar Sharma
Mr.Harshvardhan Rai Mr.Aviral Mishra Mr.Harsh Kumar Srivastava Ms.Khushi Jalan

ABSTRACT
This machine learning project seeks to estimate both the possibility of an employee's
termination from a firm as well as the student’s placement based on their project
performance and academic achievements. The project makes use of a dataset that
includes pertinent data on academic performance, project specifics and work history.
This technology offers precise forecasts by utilizing cutting-edge machine learning
algorithms and approaches, assisting both students and employees in making
knowledgeable selections. The algorithm predicts students’ placement prospects based
on their academic performance and project accomplishments. The technology creates
personalized placement suggestions for students to increase their chances of getting
placed by examining past data and looking for patterns.
The project offers a seamless alternative by referring them to an ed-tech service
platform when a student's forecast shows a lesser possibility of placement. This
platform serves as a single hub for the provision of carefully selected study resources
suited to sectors and job functions. Students can fill in any knowledge gaps, learn new
skills, and fill in any educational gaps by using these resources. With the help of this
innovative method, students can become more employable and raise their chances of
landing the jobs they want.
The study also discusses how to foresee staff layoffs inside an organization. The
machine learning algorithm properly determines the possibility that an employee will
be let go by examining a variety of variables, including performance reviews, project
success, and other pertinent data. Organizations may use this predictive capability to
proactively identify individuals who may be in danger and take the necessary
precautions to prevent any possible problems. Additionally, when an employee is
thought to be at risk of being let go, they are forwarded to the ed-tech platform, where
they may access carefully selected study materials catered to their needs in terms of
professional growth. In addition to assisting people in improving their abilities, this
proactive approach encourages a culture of ongoing learning inside organizations.
This machine learning project uses predictive analytics to improve employee retention
and student placement techniques. It enables organizations to anticipate potential
termination risks and take required steps by giving precise projections and
individualized suggestions. Due to the incorporation of the ed-tech platform, people
now have access to carefully selected study materials, supporting career advancement.
In all, the project supports the development of a more prosperous and active
professional and educational ecosystem.

Keywords: Machine Learning Project, Student Placement and Employee

Termination Prediction, Education Technology Service Platform

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 TABLE OF CONTENT
Title Page
Declaration i
Acknowledgement ii
Certificate iii
Abstract iv
Table of contents v
List of Figures vi

CHAPTER 1: INTRODUCTION 1
1.1 Introduction to the problem 1
1.2 Importance 3
1.3 Objective 6

CHAPTER 2: LITERATURE REVIEW 9

2.1 Introduction 9
2.2 Related Works 10
2.3 Limitation of Existing Work 14
2.4 Feasibility Study

CHAPTER 3: PROPOSED METHODOLOGY 16

3.1 Introduction 16
3.2 Machine Learning Model Methodology 16
3.3 Placement Prediction 18
3.4 Layoff Prediction 19
3.5 Algorithms 20

CHAPTER 4: DESIGN AND IMPLEMENTATION 22

4.1 Introduction 22
4.2 Design Methodology 22
4.3 Data Flow Diagram 24
4.4 Sequence Diagram 27
4.5 Flow Chart 28
4.6 Usecase Diagram 29
4.7 Working Of The Application 30
4.8 System Architecture 31
4.9 Coding Implementation 33

CHAPTER 5: MACHINE LEARNING 45

5.1 Introduction 45
5.2 Student Placement Prediction 48
5.3 Layoff Prediction 50

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 5.4 Algorithmic Analysis 53

CHAPTER 6: MANAGEMENT SYSTEM 59

6.1 Introduction 59
6.2 Web Scraping 60
6.3 Web Application 63
6.4 Application Archietecture 63

CHAPTER 7: RESULT AND OUTPUTS 75

7.1 Introduction 75
7.2 Model Evalation 76
7.3 Graphical Analysis 80
7.4 User-Friendly Interface 90

CHAPTER 8: CONCLUSION AND RECOMMENDATIONS 95

8.1 Conclusion 95
8.2 Recommendations 96

APPENDIX 99
Libraries and Frameworks Used 99

REFERENCES 100

PLAGIARISM REPORT 103

CONTACT DETAILS 104

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 LIST OF FIGURES

Figure Name Page No.

3.3.1 Decision System
4.3.1 DFD-level 0
4.3.2 DFD-level 1
4.3.3 DFD-level 2
4.4.1 Sequence Diagram
4.5.1 Flowchart
4.6.1 Use Case Diagram
5.4.1 Logistic Regression
5.4.2 SVM
5.4.3 Random Forest
6.4.1 Application Architecture
7.2.1 Placement Prediction Evaluation
7.2.2 Layoff Prediction Evaluation
7.3.1 – 7.3.13 Graphical Analysis Placement Prediction
7.3.14 – 7.3.18 Graphical Analysis Layoff Prediction
7.4.1-7.4.5 User Interface

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 LIST OF TABLES

Table Name Page No.

1.1 Logistic Regression Classification (Placement Prediction) 108
1.2 Support Vector Classification (Placement Prediction) 108
1.3 Random Forest Classification (Placement Prediction) 108
2.1 Logistic Regression Classification (Layoff Prediction) 109
2.2 Support Vector Classification (Layoff Prediction) 109
2.3 Random Forest Classification (Layoff Prediction) 109

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 CHAPTER 1: INTRODUCTION

1.1 INTRODUCTION TO PROBLEM

In the contemporary landscape of organizational management and education, the

ability to anticipate future trends and outcomes is paramount. In today’s intensely
competitive job scenarios, the ability to predict employee turnover and facilitate
successful student placement stands out as a critical factor of organizational success
and student’s personal career growth. Students transitioning from academia to the
professional circle encounter complex challenges and uncertainties related to their
placement prospects, often falling short of proper guidance aligned with their
strengths and aspirations.

Similarly, as organizations, big or small, endeavor to continue on their long run and
expansion, they confront the challenge of employee turnover, seeking ways to forecast
and address potential disturbances to their personnel, resulting in unanticipated
disturbances and talent loss. Addressing these challenges asks for innovative measures
that leverage the power of data analytics and machine learning to offer practical
insights and correct recommendations.

Against the backdrop of these fluctuations, successful placement prediction and layoff
anticipation emerge as essential strategies for organizational adaptability and
competitive advantage. The project is built understanding of the necessity of utilizing
machine learning and data-driven methodologies to offer insightful guidance and
assistance to students and professionals in their journeys.

The challenge of student placement lies in the delicate equilibrium between the needs
and aspirations of students, the rapidly growing demands of the job markets and the
capability of institutions to foster meaningful connections between the two. The
Training and Placement activity in the college is one of the most important activities
in the life of the student.

Therefore it is very important to make the process hassle free so that the students
would be able to get the required information as and when required. In the ever-

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evolving scenarios of professional growth, the correct and accurate placement
predictions of students into suitable career paths is vital. The path from academia to
professional roads is a pivotal transition for students shaping their future into a bright
one. However, the process of student placement is equipped with challenges as
students find opportunities that fall in line with their skills and interests while
navigating through a competitive job environment marked by evolving employment
trends. In light of the various challenges associated with student placement, the
combination of predictive analysis emerges as a transformative measure.

In response to this challenge, the project represents a significant step by aiming to

predict the likelihood of employee termination, thereby enabling organizations to
actively identify and address potential risks. Forecasting the likelihood of an
employee being let go by the company is essential for several reasons as knowing in
advance about potential risk of termination allows employees to take active measures
to improve their skills and prepare for potential transitions in their careers, thereby
improving their performance or in another case explore other alternative career
opportunities. It empowers the employees to take necessary steps to showcase their
value in the organization and potentially avoid being laid off. By predicting
termination risks in advance, organizations can intrude proactively to cope with
underlying troubles earlier than they amplify.

In the scenario that the placement predicted of a student is not good or an employee
faces termination, the project makes sure that in both cases, the individuals are
provided with valuable materials to support their personal and professional
development and enhance their employment rates. Inculcating a focused strategy,
individuals are then directed to an exclusive platform hosting an extensive array of
educational resources bordered to meet their individual requirements and career goals.

Access to such resources helps the student to possess content for upgrading his/her
skills which would be followed by him/her secure better placement results. In the case
of terminated employees, access to such resources would help the former employees
to upskill and broaden their skills to dynamically adjust with the current trends in the
rapid changing market thereby meeting with the market ask. In all, the project focuses
on the holistic development of the students and the employees for them to excel in
their careers.

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1.2 IMPORTANCE

In the domain of educational and organizational culture, accurate prediction of student

placement and employee termination plays a profound and significant role in the
effective management of educational and organizational resources. Central to this
significance is the capability to accurately foresee future developments, empowering
pro-active decision making and strategic planning. Accurate prediction of student
placement and employee termination surpasses just mere forecasting; it forms the
foundation of strategic decision-making, resource allocation and individual
empowerment and serves as a fundamental tool that shapes individuals in several
ways. Precise forecasts provide employees and students with a sense of direction and
clarity about their personal futures.

The project's focus on accurate student placement prediction and employee

termination aligns with broader organizational objectives and societal needs. This
section explores the complex significance of these predictions, emphasizing their
broad ramifications and immediate influence on individuals, companies, and
educational institutions. Below are some points which clearly justify the importance
of accurate student placement prediction and accurate employee termination.

1.2.1 Personal Empowerment and Preparedness

Accurate prediction is essential for promoting personal development in both students

and employees. Students can customize their educational experiences to develop
appropriate skills and experiences, thereby improving their employability and career
prospects, by knowing their likelihood of placement in particular professions. In a
similar vein, employees who are aware of the possibility of termination before-hand
benefit from the chance to advance both personally and professionally. It motivates
people to actively look for chances for resilience-building, skill-building, and career
progression in order to be competitive and adaptive in the ever evolving job market.
Employees and students can start on journeys of self-improvement and continual
learning by accepting these predictions in advance, realizing their full potential and
succeeding in the long run.

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1.2.2 Resource Efficiency

The effective distribution of resources based on predicted employee and student

performance is made possible by predictive analytics. While organizations can engage
in training and development programs for employees identified as at danger of
termination, educational institutions can devote professors, resources, and funding to
programs with high placement rates. By utilizing predictive insights, institutions and
organizations can optimize resource utilization, elevate student and employee
achievements, and cultivate organizational resilience amidst dynamic environments.
This optimized resource allocation not only enhances operational efficiency but also
facilitates the delivery of high-quality services and programs to students and
employees. Furthermore, the proactive recognition of potential challenges, such as
student attrition or employee turnover, empowers timely deployment of mitigation
tactics. These approaches not only contribute to talent retention and productivity
maintenance but also nurture an environment characterized by resilience and
adaptability throughout the organization.

1.2.3 Enhanced Organizational Resilience

Predicting student placement and employee termination allows institutions and

organizations to navigate through rapidly changing circumstances and pre-emptively
mitigate potential risks. By identifying and understanding the areas of concern ahead
of time, they are empowered to proactively devise strategies focused on talent
retention, bolstering productivity, and adeptly addressing challenges as they arise.
This proactive approach not only helps in retaining valuable talent but also ensures
continuity in operations, allowing institutions and organizations to navigate through
uncertainties with resilience and adaptability. Through early identification and
strategic intervention, they can mitigate the impact of potential disruptions, maintain
momentum, and also emerge stronger in the face of adversity.

1.2.4 Strategic Planning

Accurate prediction of student placement and employee termination serves as a

cornerstone for educational institutions and organizations to embark on strategic
planning initiatives. By leveraging insights into future trends and demands,

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institutions can proactively align their programs, resources, and initiatives to
effectively accomodate the evolving needs of students and the trending job market.
This foresight enables them to stay ahead of the curve, anticipate and keenly
understand the changes in industry requirements, and customize their offerings to
meet the emerging demands. Whether it's adjusting curriculum to incorporate in-
demand skills, allocating resources towards high-growth sectors, or developing
targeted recruitment and retention strategies, accurate prediction provides a roadmap
for institutions to navigate through dynamic landscapes with confidence, foresight and
toughness. By aligning their strategic initiatives with anticipated or predicted
outcomes, institutions can position themselves for long-term success and relevance in
a rapidly evolving educational and organizational ecosystem.

1.2.5 Cost Savings

Accurate prediction serves as a strategic tool for institutions and organizations to

preemptively mitigate unnecessary costs linked with resource underutilization and
turnover. Through proactive management of student placement and employee
retention, institutions can curtail expenses related to recruitment, hiring, and training,
thereby yielding substantial cost savings over the long term.

This forward-thinking approach not only optimizes resource allocation but also
minimizes the need for reactive measures, such as last-minute hiring or extensive
training programs. By accurately forecasting student and employee outcomes,
institutions can streamline their operations, enhance efficiency, and ultimately
maximize their financial resources for further investment in core initiatives and
organizational development. This proactive approach fosters a sustainable financial
framework, positioning institutions and organizations for long-term stability and
growth.
These insights underscore the pivotal role of predictive analytics, aligning closely
with the objectives our project aims to achieve. By leveraging accurate predictions,
our project endeavors to empower institutions and organizations with the tools needed
to thrive in an ever-evolving landscape.

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1.3 OBJECTIVE

The project’s main objective aligns with the understanding of the pivotal role of
campus placements for students and the significance of employment opportunities for
employees. In an era defined by dynamic shifts in educational paradigms and
organizational landscapes, the quest for accurate predictive insights has become
paramount. With a vision to revolutionize student placement forecasts and employee
termination predictions, our project embarks on a journey of innovation and impact,
driven by the convergence of machine learning prowess and strategic foresight. At the
forefront of our endeavor lies a steadfast commitment: to empower both students and
employees through the twin pillars of predictive insight and strategic foresight. The
main objectives are as follows :

1.3.1 Develop a Machine Learning Model

Our primary aim is to craft a robust, reliable and precise machine learning model that
can anticipate a student's placement based on their academic grades and project results
and an employee’s termination based on performance reviews, attendance records,
and employee feedback.

1. The project makes use of a variety of datasets to train the model, making sure
to include data on student placements and employee terminations as well as
other pertinent features and results.
2. By harnessing large and diverse datasets and ensuring scalability, we aspire to
create a predictive tool capable of real-time prognostication.

1.3.2 Predict Student Placement

1. Leveraging the developed machine learning model, our endeavor extends to

prognosticating the likelihood of student placement in internships or jobs.
2. To provide a comprehensive evaluation of a student's employability, take into
account a number of variables, including academic performance and project
performance.

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 3. To determine the model's efficacy in foretelling student placements, evaluate
the model's performance using metrics like accuracy, precision, recall, and F1
score.

1.3.3 Predict Employee Termination

1. Expanding the model's purview, we seek to prognosticate the probability of

employee termination within organizations.
2. Augmented by pertinent elements including performance evaluations,
attendance records and employee feedback, our model endeavors to
preemptively identify organizational risks, thereby fostering proactive
mitigation strategies.
3. When evaluating the model's performance, use the right evaluation metrics to
gauge how well it can spot organizational risks of termination.

1.3.4 Redirect Terminated employees to an ed-tech website:

1. Central to our initiative is the implemention of a system to direct dismissed

workers to a curated education-tech website which consists of study materials.
2. Access to pertinent resources that can aid former employees in upskilling,
expanding their knowledge, and improving their employability should be
made available to them.
3. Assure that the project and ed-tech platform are seamlessly integrated,
allowing fired employees to navigate and access the study materials with ease.

1.3.5 Evaluate the effectiveness of the project:

1. Analyze the project's overall success in foretelling student placements and
employee terminations in detail.
2. Compare the machine learning model's performance and accuracy to other
methods or industry standards.
3. To gauge the project's usability, utility, and impact on each user's unique
journey, gather feedback from users such as students, organizations, and fired
employees.

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1.3.6 Providing actionable Insights and Recommendations:

1. Based on the results of the predictions, give students tailored advice and
insightful information.
2. Help students identify areas where they can improve, such as particular
subjects, abilities, or projects, to improve their employability and raise the
likelihood that they will land placements.
3. Give organizations information on potential risks associated with termination
and suggest ways to reduce those risks, helping to increase employee
engagement and retention.

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 CHAPTER 2: LITERATURE REVIEW

2.1 INTRODUCTION

In the realm of predictive analytics, the accurate forecasting of student placements

and employee terminations has emerged as a critical frontier for educational
institutions and organizations alike. This literature review endeavors to navigate this
landscape, delving into existing projects in the market that address the prediction of
student placements and employee terminations. Through a comprehensive analysis,
comparison, and contrast of these endeavors, we seek to unearth insights into the
methodologies, strengths, and limitations of current approaches.

Central to our exploration is the recognition of the profound impact that predictive
analytics can have on the trajectories of both students and employees. For students,
campus placements represent not only the culmination of their academic journey but
also the gateway to fulfilling careers. Conversely, for employees, job security and
career progression hinge on accurate forecasts of potential terminations, enabling
proactive measures for skill enhancement and career resilience.

Against this backdrop, our literature review embarks on a dual-pronged inquiry: first,
to dissect and evaluate existing projects that specialize in predicting student
placements and employee terminations separately, and second, to delineate the
distinctive features and advancements incorporated into our project. Our endeavor
extends beyond mere examination, aiming to unearth innovative methodologies,
identify gaps in existing approaches, and pave the way for the development of a
holistic predictive model.

Moreover, our project stands as a beacon of innovation by not only predicting student
placements and employee terminations but also offering a curated study material
platform for terminated employees. This unique integration aims to provide holistic
support to individuals transitioning between academic and professional realms,
underscoring our commitment to driving positive outcomes and fostering lifelong
learning.

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2.2 RELATED WORKS

2.2.1 Existing Projects on Student Placement Prediction:

1. In the rapidly evolving domain of predictive analytics, numerous initiatives in
the marketplace have concentrated on the crucial task of projecting student
placements. By employing machine learning algorithms and historical
placement data, these endeavors strive to uncover patterns and trends that shed
light on the career trajectories of graduating students.
2. Although academic scores undeniably wield significant influence over
placement outcomes, existing initiatives frequently fall short in their
comprehensive analysis of additional factors. One such factor warranting
deeper exploration is project performance. Serving as the amalgamation of
academic knowledge and practical application, project performance stands as a
critical indicator of a student's capability to apply theoretical concepts in real-
world scenarios.
3. The exclusion of project performance from their analyses represents a
significant oversight, as it overlooks a pivotal aspect of student employability
and placement potential. Research indicates that students who excel in project-
based learning environments demonstrate not only mastery of subject matter
but also critical thinking, problem-solving, and collaboration skills – qualities
highly prized by employers.
4. In contrast to prevailing practices, our project adopts a holistic approach to
predictive analytics, recognizing the multifaceted nature of student success
and employability. By integrating academic scores and project performance
into our predictive model, we aspire to offer a more nuanced and precise
assessment of student placement prospects.
5. Furthermore, our project's emphasis on inclusivity extends beyond academic
realms to encompass a broader spectrum of employability factors. By
considering a diverse array of variables – including internships, extracurricular
activities, and industry certifications – we aim to capture the full spectrum of
influences on student placement success.
6. In essence, while existing initiatives in the marketplace have made significant
strides in predictive analytics for student placements, there remains untapped
potential for deeper analysis and more comprehensive modeling. By

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 addressing gaps in existing approaches and embracing a holistic framework
that integrates academic scores, project performance, and a myriad of
employability factors.

2.2.2 Enhancing Student Outcomes through Technology

1. Within the domain of predictive analytics for student placements, a

noteworthy project adopts a holistic approach by considering a blend of
academic scores, project participation, and internship experiences. This
initiative aims to provide a comprehensive understanding of a student's
employability and potential for placement.
2. By incorporating various aspects of a student's academic and extracurricular
journey, this project seeks to offer a nuanced perspective on their readiness for
the job market. Academic scores serve as a foundational measure, indicating a
student's grasp of core subjects and their performance in traditional academic
settings.
3. Furthermore, the inclusion of internship experiences enriches the predictive
model by providing real-world exposure and industry-specific skills
development. This aspect enhances the accuracy of placement predictions by
capturing the practical expertise and professional readiness of students.
4. Despite its strengths, this project has room for improvement. Specifically, it
lacks integration with an educational technology platform that could redirect
students to curated study materials for skill enhancement.
5. In summary, while this project excels in providing a holistic view of a
student's profile through its consideration of academic scores, project
participation, and internship experiences, it could benefit from enhanced
support for skill development and continuous learning.

2.2.3 Existing Projects on Employee Termination Prediction:

1. Workday People Analytics, is a comprehensive analytics solution designed to

help organizations gain deep insights into their workforce. This platform
leverages advanced machine learning and artificial intelligence to analyze vast
amounts of HR data, providing predictive and prescriptive insights. Workday
People Analytics can predict employee turnover by analyzing various data

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 points such as job satisfaction, performance metrics, engagement scores, and
demographic information. This helps HR teams identify employees at risk of
leaving and take proactive measures to retain them.
2. Visier People Analytics is a comprehensive workforce analytics platform
designed to help organizations make data-driven decisions about their human
resources. It provides deep insights into various aspects of the employee
lifecycle, including recruitment, performance, retention, and workforce
planning.
3. However, despite their ability to identify potential termination risks, these
projects often fall short in providing support for terminated employees. While
they excel in identifying at-risk individuals, they lack the capability to offer
resources or assistance to help terminated employees improve their skills and
enhance their employability.
4. This represents a significant gap in existing approaches to employee
termination prediction. Terminated employees are often left without guidance
or support to navigate their career transition. Without access to resources for
skill development or opportunities for retraining, terminated employees may
struggle to find new employment opportunities and rebuild their careers.
5. To address this gap, our project aims to offer a holistic solution that not only
predicts employee terminations but also provides terminated employees with
access to resources for skill enhancement and career development. By
integrating predictive analytics with an educational technology platform, we
seek to empower terminated employees with the tools and resources they need
to improve their skills, increase their employability, and navigate their career
transitions successfully.

In summary, while existing projects in the market excel in predicting employee

terminations, they often overlook the importance of providing support for terminated
employees. By addressing this gap and offering resources for skill enhancement and
career development, our project aims to provide a comprehensive solution that
benefits both organizations and terminated employees alike.

2.2.4 Special Qualities and Enhancements of Our Project:

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1. Through meticulous examination, our project distinguishes itself by offering a
novel approach to predicting student placement and employee termination. By
meticulously considering both academic performance and project
performance, we provide a comprehensive and nuanced assessment of a
student's employability.
This holistic evaluation enables us to offer a more accurate forecast of a
student's likelihood of placement, empowering them to focus on areas for
improvement and enhance their prospects of securing desirable employment
opportunities.
2. Ed-Tech Platform Integration: In contrast to other projects, ours distinguishes
itself by seamlessly integrating an ed-tech website into the predictive
framework. This innovative platform offers terminated employees access to
meticulously curated study materials tailored to their specific needs and career
aspirations.
3. Dual Prediction Capability: In contrast to existing projects that focus solely on
either student placement or employee termination, our project offers a unique
dual prediction capability. By encompassing both aspects, we empower
educational institutions and businesses alike to take proactive measures and
make informed decisions. Through our predictive model, we provide insights
into both student placements and employee terminations, enabling
stakeholders to anticipate future outcomes and strategically address potential
challenges.
4. Personalized Recommendations: Setting itself apart from conventional
approaches, our project delivers tailored recommendations to both
organizations and students, optimizing outcomes for all parties involved.
Students benefit from individualized guidance aimed at enhancing their
placement prospects, while organizations gain insights to identify and mitigate
potential termination risks proactively.
5. Accuracy and Performance: Central to the success of our project is the
utilization of sophisticated machine learning algorithms, meticulously crafted
using an extensive array of large-scale datasets. This strategic approach
ensures unparalleled performance and unwavering accuracy in our predictive
capabilities.

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 6. In conclusion, current market projects mainly concentrate on either employee
termination prediction or student placement prediction. However, our project
combines both elements and introduces the incorporation of an educational
technology platform for fired employees. Our project provides a
comprehensive solution for students and organizations by considering
academic standing, and project performance, and offering tailored
recommendations.

2.3 LIMITATIONS ON EXISTING WORKS

2.3.1 Limited Availability of Layoff Data:

1. One notable limitation in existing literature is the challenge of accessing layoff

data due to its confidential nature within organizations. Layoff data, which
includes details about employee terminations and workforce reductions, is
often closely guarded by companies to protect sensitive information and
maintain confidentiality.
2. As a result, researchers and practitioners may encounter difficulties in
obtaining comprehensive datasets for analyzing employee termination trends
and developing predictive models. This lack of readily available layoff data
impedes the thorough examination of factors influencing employee discharges
and the development of robust forecasting techniques, highlighting a
significant gap in current research efforts.

2.3.2 Unable to Capture Crucial Qualitative Features in Layoff Prediction

Models:

1. One limitation in existing works on layoff prediction is the inability to capture

multiple critical features such as working hours, work culture and leave payoff
in data format. While these factors play a significant role in predicting
employee terminations, they are often qualitative in nature and not easily
quantifiable or represented in traditional datasets.
2. This poses a challenge for predictive models, as they may not fully account for
the nuanced dynamics and contextual factors influencing layoff decisions
within organizations. Consequently, the absence of these essential features in

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 data format restricts the comprehensiveness and accuracy of existing layoff
prediction methodologies, highlighting a notable limitation in current research
efforts.

2.3.3 Variability of Individual Characteristics in Layoff Prediction Models:

One notable limitation in layoff prediction models is the variability of individual

characteristics among employees. Each person possesses unique traits, skills,
experiences, and work behaviors that shape their performance and interactions within
the organization. However, existing predictive models may struggle to account for this
diversity adequately. While objective metrics like performance reviews and
attendance records can be incorporated, qualitative factors such as interpersonal skills,
adaptability, and cultural fit pose challenges.

2.3.4 Limited Impact of Academic Scores on Student Placement:

Despite being commonly used as a metric for evaluating student performance,

academic scores often do not significantly influence student placement outcomes.
While high academic achievement may indicate proficiency in certain subjects, it may
not necessarily correlate with practical skills, project performance, or suitability for
specific roles within organizations.

2.3.5 Overemphasis on Technical Skills in Placement Predictions

1. Existing works often prioritize technical skills over soft skills in student
placement predictions, neglecting the critical importance of both skill sets in
securing employment. While technical proficiency is undoubtedly valuable,
employers increasingly prioritize candidates who possess a blend of technical
expertise and soft skills such as communication, teamwork, adaptability, and
problem-solving.
2. Focusing solely on technical competencies overlooks the holistic nature of job
requirements and fails to accurately reflect the multifaceted demands of the
modern workplace. Consequently, predictive models that predominantly

15
 consider technical skills may inadequately assess a student's overall
employability, resulting in suboptimal placement predictions.

CHAPTER 3: PROPOSED METHODOLOGY

3.1 INTRODUCTION

Embarking on the journey to develop a sophisticated predictive analytics project

necessitates a methodical and comprehensive approach. Our methodology
encapsulates a meticulously crafted framework designed to guide every facet of the
project's evolution, from inception to implementation. At its core lies a commitment
to precision, reliability, and ethical conduct. Rooted deeply within the fabric of our
project, this commitment underscores our unwavering resolve to ensure that the
insights derived are not only impeccably accurate but also ethically robust and
morally defensible.

The methodology outlined herein encapsulates a systematic approach to the

development and implementation of our predictive analytics project. Moreover, the
integration of user feedback and rigorous documentation serves to enhance
transparency and facilitate continuous improvement throughout the project lifecycle.
By adhering to this comprehensive methodology, we aim to deliver a robust and
impactful solution that empowers stakeholders with actionable insights and fosters a
culture of lifelong learning and professional growth.

3.2 MACHINE LEARNING MODEL METHODOLOGY

The methodology begins with the meticulous curation and collection of diverse
datasets. The data collection phase serves as the foundational pillar upon which the
entire project rests, encompassing a multifaceted approach to sourcing and gathering

16
diverse datasets essential for predictive analytics. With meticulous attention to detail,
our methodology emphasizes the identification and acquisition of relevant datasets,
spanning a wide spectrum of domains including student profiles, academic results,
project details, historical placement records, employee performance evaluations,
attendance records, and feedback data. Each dataset is carefully selected to ensure its
relevance and comprehensiveness, reflecting the diverse characteristics and attributes
of the target population under study.

3.2.1. Ethical Data Collection and Diversity in Predictive Analytics

The methodology has been keenly crafted with a focus on ensuring that the datasets
utilized are varied, inclusive and accurately reflective of the target population.
Crucially, ethical considerations and adherence to data privacy regulations are central
to our approach during the data collection phase. Acknowledging the sensitivity of
personal and confidential data, we uphold stringent ethical standards and follow best
practices to safeguard individuals' privacy. Through meticulous attention to diversity,
inclusivity, accuracy, and ethical considerations, we lay the groundwork for the
development of robust and reliable predictive models that empower stakeholders with
actionable insights and drive informed decision-making.

3.2.2. Data Preprocessing for Effective Predictive Modeling

During the data preprocessing stage, our focus is on enhancing the quality and
usability of the gathered datasets. Additionally, we conduct exploratory data analysis
(EDA) to gain insights into the underlying patterns and correlations within the
datasets. By summarizing the data, we identify potential trends and relationships that
may inform our predictive models. Through systematic cleaning, exploratory analysis,
and data transformation, we strive to optimize the usability and effectiveness of the
data, facilitating the creation of reliable and insightful predictive models.

3.2.3. Systematic Machine Learning Model Development for Predictive

Analytics

17
In the machine learning model development phase, we employ a systematic approach
to select, train, and evaluate predictive models tailored to the specific requirements
and characteristics of our project data

1. Firstly, we carefully evaluate the project requirements and properties of the

data to Next, we partition the datasets into training and testing sets to facilitate
robust model evaluation. By separating the testing set, we can objectively
evaluate the model's performance on unseen data, thereby assessing its
generalization capabilities and ensuring that it does not overfit the training
data. During the training phase, we employ techniques such as hyperparameter
tuning to optimize the model's performance and maximize predictive accuracy.
By fine-tuning Following model training, we utilize the testing set to validate
the model's performance using appropriate evaluation metrics.
3.2.4. User Feedback Integration and Project Reporting Structure

33Gathering user feedback is integral to our project's development and improvement

process. We engage with students, organizations, and terminated employees to assess
the project's usefulness and efficiency. Through surveys, interviews, and user testing
sessions, we gain insights into each participant's journey, understanding usability,
satisfaction, and areas for improvement . Ultimately, user feedback guides the
ongoing refinement of our project, ensuring it effectively meets the needs of its users.

3.3 PLACEMENT PREDICTION

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 Fig 3.3.1 Decision System

The placement prediction model serves as a pivotal component, offering personalized

insights into a student's likelihood of securing a job. This model operates by ingesting
user data, including academic performance, skills, and internship experiences, and
feeding it into a decision system built using a trained model from a dataset. The
primary output of this process is a determination of whether a student is likely to be
placed or not.

The process begins with the collection of relevant user data, which is then
preprocessed to ensure consistency and accuracy. This preprocessing step involves
cleaning the data, normalizing it, and encoding categorical variables. Feature selection
and engineering techniques are then applied to identify the most significant features
for prediction, enhancing the model's performance by focusing on the most impactful
variables.

3.4 LAYOFF PREDICTION:

Layoff prediction systems serve multiple purposes, driven by the need for proactive
workforce management and employee well-being. From an organizational
perspective, these systems offer insights into workforce dynamics, enabling
companies to anticipate and prepare for potential layoffs. By identifying at-risk
employees in advance, organizations can implement targeted interventions such as
training programs, skill development initiatives, or internal mobility opportunities to
mitigate the need for layoffs.

From an employee perspective, layoff prediction systems provide early warnings of

potential job loss, empowering individuals to take proactive steps to safeguard their
employment status. By receiving advance notice, employees can explore alternative
career paths, update their skills, or seek out new job opportunities before the layoff
occurs.

19
Support vector machine (SVM) algorithms are also commonly utilized in layoff
prediction, leveraging their ability to classify data into different categories based on
input features. SVM models can effectively distinguish between employees at risk of
layoff and those with stable employment status, enabling organizations to prioritize
resources and interventions accordingly.

From an employee perspective, layoff prediction systems provide transparency and

clarity regarding job security, enabling individuals to plan and prepare for potential
layoffs. Employees can take proactive steps to mitigate the impact of layoffs, such as
updating their skills, expanding their professional networks, or exploring alternative
career opportunities..

In conclusion, layoff prediction systems represent a valuable tool for organizations to

proactively manage their workforce and mitigate the impact of layoffs on employees
and the business. By leveraging advanced analytics and machine learning techniques,
these systems enable organizations to anticipate and prepare for potential workforce
changes, fostering a culture of transparency, fairness, and accountability.

3.5 ALGORITHMS :

3.5.1 Logistic Regression:

Despite its name, logistic regression is a classification algorithm commonly used for
binary classification tasks. It models the probability of a binary outcome based on one
or more independent variables. Logistic regression works by fitting a logistic curve to
the data, which represents the probability of the outcome occurring as a function of
the input variables. It's widely used for its simplicity, interpretability, and efficiency in
handling linearly separable data.

3.5.2 Support Vector Machine (SVM):

SVM is a powerful supervised learning algorithm used for both classification and
regression tasks. It works by finding the hyperplane that best separates the data points
into different classes while maximizing the margin between the classes. SVM can
handle linear and nonlinear which map the input data into a higher-dimensional

20
feature space where it becomes linearly separable. SVM is known for its effectiveness
in handling high-dimensional

3.5.3 Random Forest:

Random Forest is an ensemble learning method that operates by constructing multiple

decision trees during training and outputting the mode of the classes for classification
tasks or the mean prediction for regression tasks. Random Forest is known for its
robustness, scalability, and ability to handle high-dimensional data with complex
relationships between features.

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 CHAPTER 4: MACHINE LEARNING TECHNIQUES

4.1 INTRODUCTION

Artificial intelligence (AI) has an area called machine learning (ML) that focuses on
using data and algorithms to let AI learn and get better over time without requiring
manual instructions. Put more simply, it involves teaching computers to learn from
data in the same way that students learn from teachers, but then applying that
information to novel and unfamiliar situations.

Machine learning (ML) is a subdomain of artificial intelligence (AI) that focuses on

developing systems that learn—or improve performance—based on the data they
ingest. Artificial intelligence is a broad word that refers to systems or machines that
resemble human intelligence. Machine learning and AI are frequently discussed
together, and the terms are occasionally used interchangeably, although they do not
signify the same thing. A crucial distinction is that, while all machine learning is AI,
not all AI is machine learning.

4.1.1 Features of Machine Learning:

1. Machine learning is data driven technology. Large amount of data generated

by organizations on daily bases. So, by notable relationships in data,
organizations makes better decisions.
2. Machine can learn itself from past data and automatically improve.
3. From the given dataset it detects various patterns on data.For the big
organizations branding is important and it will become more easy to target
relatable customer base.
4. It is similar to data mining because it is also deals with the huge amount of
data.

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A typical machine learning tasks are to provide a recommendation. Recommender
systems are a common application of machine learning, and they use historical data to
provide personalized recommendations to users. In the case of Netflix, the system
uses a combination of collaborative filtering and content-based filtering to
recommend movies and TV shows to users based on their viewing history, ratings,
and other factors such as genre preferences. Personalized recommendations based on
machine learning have become increasingly popular in many industries, including e-
commerce, social edia, and online advertising, as they can provide a better user
experience and increase engagement with the platform or service.

4.1.2 Machine Learning Lifecycle :

1. Study the Problems: The first step is to study the problem. This step involves
understanding the business problem and defining the objectives of the model.
2. Data Collection: When the problem is well-defined, we can collect the
relevant data required for the model. The data could come from various
sources such as databases, APIs, or web scraping.
3. Data Preparation: When our problem-related data is collected. then it is a good
idea to check the data properly and make it in the desired format so that it can
be used by the model to find the hidden patterns.
4. Model Selection: The next step is to select the appropriate machine learning
algorithm that is suitable for our problem. This step requires knowledge of the
strengths and weaknesses of different algorithms. Sometimes we use multiple
models and compare their results and select the best model as per our
requirements.
5. Model building and Training: After selecting the algorithm, we have to build
the model
6. Model Evaluation: Once the model is trained, it can be evaluated on the test
dataset to determine its accuracy and performance using different techniques
like classification report, F1 score, precision, recall, ROC Curve, Mean Square
error, absolute error, etc.
7. Model Tuning: Based on the evaluation results, the model may need to be
tuned or optimized to improve its performance. This involves tweaking the
hyperparameters of the model.

23
 8. Deployment: Once the model is trained and tuned, it can be deployed in a
production environment to make predictions on new data. This step requires
integrating the model into an existing software system or creating a new
system for the model.
9. Monitoring and Maintenance: Finally, it is essential to monitor the model’s
performance in the production environment and perform maintenance tasks as
required. This involves monitoring for data drift, retraining the model as
needed, and updating the model as new data becomes available.

4.1.3 Types of Machine Learning :

1. Supervised Machine Learning :

Supervised learning is defined as when a model gets trained on a “Labelled
Dataset”. Labelled datasets have both input and output parameters. In
Supervised Learning algorithms learn to map points between inputs and
correct outputs. It has both training and validation datasets labelled
2. Unsupervised Machine Learning :
Unsupervised learning is a type of machine learning technique in which an
algorithm discovers patterns and relationships using unlabeled data. Unlike
supervised learning, unsupervised learning doesn’t involve providing the
algorithm with labeled target outputs.
3. Semi – Supervised Machine Learning:
Semi-Supervised learning is a machine learning algorithm that works between
the supervised and unsupervised learning so it uses both labelled and
unlabelled data. It’s particularly useful when obtaining labeled data is costly,
time-consuming, or resource-intensive.
4. Reinforcement Learning:
Reinforcement machine learning algorithm is a learning method that interacts
with the environment by producing actions and discovering errors. Trial, error,
and delay are the most relevant characteristics of reinforcement learning. In
this technique, the model keeps on increasing its performance using Reward
Feedback to learn the behavior or pattern.

Data is a crucial component in the field of Machine Learning. It refers to the set of
observations or measurements that can be used to train a machine-learning model. The

24
quality and quantity of data available for training and testing play a significant role in
determining the performance of a machine-learning model. Data can be in various
forms such as numerical, categorical, or time-series data, and can come from various
sources such as databases, spreadsheets, or APIs.

4.1.4 Data is divided into two types :

1. Labelled Data
2. Unlabelled Data

Labeled data includes a label or target variable that the model is trying to predict,
whereas unlabeled data does not include a label or target variable.

4.2 PLACEMENT PREDICTION USING MACHINE LEARNING :

4.2.1 Introduction to the Problem :

In the dynamic landscape of education and employment, the transition from academia
to the professional realm poses significant challenges for students and educational
institutions alike. The process of securing a job placement not only impacts individual
career trajectories but also reflects the effectiveness of academic programs in
preparing students for real-world challenges. In this context, the ability to forecast a
student's likelihood of being placed in a desired position assumes paramount
importance.

The focus of this report is on leveraging machine learning techniques to predict

student placement outcomes with a high degree of accuracy and reliability. We delve
into the multifaceted factors that influence student placement decisions, ranging from
academic achievements to extracurricular experiences

Through an in-depth exploration of the theoretical underpinnings, methodological

approaches, and empirical findings, this report elucidates the potential of machine
learning in revolutionizing student placement practices. By elucidating the interplay
between academic performance, technical proficiency, soft skills development,
experiential learning, and job placement outcomes, we strive to provide actionable

25
insights that drive continuous improvement in educational practices and student
outcomes.

4.2.2 Methodology Used :

In our endeavor to predict student placement with precision and reliability, we

employed a sophisticated machine learning model that integrates various predictive
features and algorithms. The model is designed to ingest structured data
encompassing key attributes such as GPA, technical skills rating, soft skills rating,
internship experience, and the number of projects completed by each student. Through
a systematic approach to feature engineering, model selection, and validation, we
endeavored to construct a predictive framework that not only captures the nuances of
student placement dynamics but also generalizes well to unseen data.

Central to the efficacy of our model is the process of feature engineering, wherein raw
data is transformed into meaningful features that encapsulate predictive information.
Leveraging domain knowledge and statistical techniques, we engineered a diverse set
of features that capture the multidimensional nature of student placement. For
instance, GPA serves as a proxy for academic prowess, while technical and soft skills
ratings quantify the proficiency of students in relevant domains. Additionally, the
binary variable indicating internship experience and the count of projects completed
offer insights into students' experiential learning and practical acumen

4.2.3 Algorithms Used To Build Model :

To accommodate the heterogeneity of our dataset and optimize predictive

performance, we evaluated a range of classification algorithms, each with its unique
strengths and weaknesses. Logistic regression, a fundamental yet robust algorithm,
provided a baseline for comparison due to its simplicity and interpretability. Decision
trees, characterized by their ability to capture complex decision boundaries, were
explored to uncover nonlinear relationships between features and outcomes. Random
forests, an ensemble of decision trees, offered improved robustness and generalization

26
by aggregating predictions across multiple trees. Support vector machines (SVM),
renowned for their efficacy in high-dimensional spaces, were also considered to
delineate optimal hyperplanes for classification.

In addition to predictive performance, we prioritize the interpretability and

explainability of our model to facilitate stakeholder understanding and trust.
Techniques such as feature importance analysis, partial dependence plots, and model-
agnostic approaches (e.g., SHAP values) were employed to elucidate the contributions
of individual features towards the prediction of student placement. By demystifying
the underlying mechanisms driving the model's decisions, we empower educators,
policymakers, and students to make informed choices based on actionable insights
gleaned from the predictive framework.

4.2.4 Model Performance Analysis :

Having selected a diverse ensemble of algorithms, we embarked on the process of

model training and evaluation to identify the optimal configuration for predicting
student placement. Utilizing a portion of the dataset for training and the remainder for
validation, we employed techniques such as cross-validation and holdout validation to
assess the performance of each model. Metrics such as accuracy, precision, recall, and
F1-score were computed to quantify the predictive efficacy of the models across
different evaluation criteria.

4.2.5 Summary :

In summary, our utilization of a sophisticated machine learning model underscores

our commitment to harnessing data-driven approaches to solve complex real-world
problems. By integrating feature engineering, model selection, and evaluation
methodologies, we have constructed a predictive framework that augments student
placement practices with precision and reliability. Moving forward, we envisage
further refinement and enhancement of our model through continuous iteration and
incorporation of emerging methodologies. As we navigate the intersection of
education and technology, our endeavor remains steadfast: to empower students with
the tools, insights, and opportunities they need to thrive in a rapidly evolving
landscape.

27
4.3 LAYOFF PREDICTION USING MACHINE LEARNING :

4.3.1 Introduction to the Problem :

In today's dynamic and competitive business landscape, organizations are constantly

faced with the challenge of optimizing their workforce while ensuring operational
efficiency and financial sustainability. One of the most distressing decisions that
organizations encounter is the need to implement layoffs, which can have profound
implications for both the affected employees and the overall organizational culture.
Layoffs not only disrupt the lives of individuals and their families but also have
broader ramifications on morale, productivity, and reputation within the industry.

Traditional approaches to managing layoffs often rely on reactive measures and

subjective assessments, resulting in delayed responses and suboptimal outcomes.
However, with the advent of advanced data analytics and machine learning
techniques, organizations now have the opportunity to proactively identify and
mitigate the risk of layoffs through predictive modeling and data-driven insights

In the pursuit of accurately predicting employee layoffs, we employed a machine

learning approach that leverages historical data and relevant features to classify
employees as either at risk or not at risk of being laid off. Our model is designed to
analyze various factors, including years of experience, promotions, departmental
affiliation, job title, and the provision of severance packages, to generate predictions
with a high degree of accuracy and reliability.

4.3.2 Methodology Used:

The ability to predict layoffs with a high degree of accuracy offers numerous benefits
to organizations across various sectors. By leveraging historical data and relevant
predictors such as years of experience, promotions, departmental affiliation, job title,
and the provision of severance packages, machine learning models can provide
valuable insights into the likelihood of future layoffs. This proactive approach enables
organizations to implement targeted interventions, such as workforce restructuring,

28
skills development programs, or alternative employment opportunities, to minimize
the impact on employees and preserve organizational stability.

Moreover, layoff prediction models empower organizational leaders with actionable

information to make informed decisions regarding resource allocation, budget
planning, and strategic workforce management. By identifying potential risk factors
and early warning signs of layoffs, decision-makers can implement preemptive
measures to address underlying issues, enhance employee engagement, and foster a
culture of transparency and trust within the organization.

In this document, we explore the application of machine learning techniques for layoff
prediction, focusing on the development and evaluation of predictive models using
real-world data. We delve into the factors influencing layoff predictions, discuss the
methodology behind building an effective prediction model, and present the results
and insights derived from our analysis. Through this data-driven approach, we aim to
contribute to the growing body of research on workforce analytics and provide
practical recommendations for organizations seeking to enhance their workforce
management strategies.

4.3.3 Algorithms Used To Build Model:

One of the primary algorithms utilized in our model is logistic regression, which is
well-suited for binary classification problems and provides interpretable results that
facilitate understanding and decision-making. Additionally, we explored ensemble
learning techniques such as random forests and gradient boosting to capture complex
interactions among predictor variables and improve prediction performance.

The development of our layoff prediction model involved rigorous training and
evaluation procedures to assess its performance and generalization capabilities. We
partitioned the available data into training and testing sets to enable unbiased
estimation of the model's predictive accuracy.

During the training phase, the model learns patterns and relationships from the
training data through iterative optimization of model parameters. We employed cross-
validation techniques to fine-tune hyperparameters and mitigate overfitting, ensuring
robustness and reliability in real-world scenarios.

29
4.3.4 Model Performance Analysis:

For evaluation, we utilized a range of performance metrics including accuracy,

precision, recall, and F1-score to quantify the model's predictive performance across
different evaluation criteria. Through comprehensive evaluation, we gained insights
into the model's strengths and limitations, enabling informed decision-making and
model refinement.

4.3.5 Summary:

In summary, our layoff prediction model represents a sophisticated yet practical

approach to addressing the complex challenges associated with workforce
management and organizational decision-making. By harnessing the power of
machine learning and data-driven insights, we empower organizations to proactively
identify and mitigate the risk of employee layoffs, fostering a culture of stability,
fairness, and resilience within the workplace.

4.4 ALGORITHMIC ANALYSIS:

4.4.1 Logistic Regression:

Fig 4.4.1 Graph of Logistic Regression(Sigmoid Function)

Logistic regression is a fundamental statistical method used for binary classification

tasks, where the goal is to predict the probability of an observation belonging to a
particular class (e.g., laid off or not laid off). Despite its name, logistic regression is a
classification algorithm rather than a regression algorithm. It models the relationship

30
between the independent variables (features) and the binary outcome using the
logistic function.

The logistic regression model is trained by maximizing the likelihood of observing the
given set of outcomes (labels) given the input features. This is typically achieved
through the method of maximum likelihood estimation. The objective is to find the
optimal values of the coefficients (also known as weights or parameters) that best fit
the training data and minimize the error between predicted and actual outcomes.

1. x = input value
2. y = predicted output
3. b0 = bias or intercept term
4. b1 = coefficient for input (x)

Pros :

1. Logistic regression is simple, interpretable, and computationally efficient.

2. It provides probabilistic outputs, making it easy to interpret the confidence of
predictions.
3. Logistic regression can handle both numerical and categorical features
gracefully.

Cons:

1. Logistic regression assumes a linear relationship between the features and the
log-odds of the outcome, which may not always hold true in real-world
scenarios.

31
 2. It may struggle with capturing complex non-linear relationships between
features and outcomes.

In summary, logistic regression is a powerful and versatile algorithm for binary

classification tasks, offering a balance between simplicity, interpretability, and
predictive performance. Its ability to output probabilities and provide insights into the
impact of features makes it a valuable tool in the data scientist's toolkit.

4.4.2 Support Vector Machine(SVM):

Fig 4.4.2: Support Vector Machine

Support Vector Machine (SVM) is a supervised learning algorithm used for

classification and regression tasks. In classification, SVM aims to find the optimal
hyperplane that separates the data points of different classes with the largest margin,
thereby maximizing the margin of separation.
The fundamental idea behind SVM is to transform the input feature space into a
higher-dimensional space using a kernel function, where it becomes easier to find a
separating hyperplane. The hyperplane is chosen such that it maximizes the margin,
i.e., the distance between the hyperplane and the nearest data points (support vectors)
of each class.

32
SVM can handle both linear and non-linear decision boundaries by employing
different kernel functions. Some commonly used kernel functions include:
1. Linear Kernel: Suitable for linearly separable data.
2. Polynomial Kernel: Useful for capturing non-linear relationships.
3. Radial Basis Function (RBF) Kernel: Effective in capturing complex decision
boundaries in high-dimensional space.
4. Sigmoid Kernel: Can be used for non-linear classification tasks.

Pros:

1. SVMs are effective in high-dimensional spaces and can capture complex

decision boundaries.
2. They are robust to overfitting, especially in cases where the number of
features exceeds the number of samples
3. SVMs perform well with small to medium-sized datasets and can handle both
linear and non-linear classification tasks.

Cons:

1. SVMs can be sensitive to the choice of the kernel function and its parameters.
2. They may require careful tuning of hyperparameters such as the regularization
parameter (C) and kernel parameters.
3. SVMs can be computationally expensive, especially for large datasets with
many features.

During training, SVM identifies a subset of training samples known as support

vectors, which lie closest to the decision boundary. These support vectors play a
crucial role in defining the optimal hyperplane and determining the margin.

In scenarios where the data is not perfectly separable, SVM allows for the
introduction of a soft margin, which permits misclassifications in exchange for a
wider margin. This is achieved through the use of slack variables, which penalize
misclassified points. Soft margin SVM strikes a balance between maximizing the

33
margin and minimizing the classification error, leading to better generalization
performance on unseen data.

In summary, Support Vector Machines (SVMs) are powerful and versatile algorithms
for classification tasks, capable of handling both linear and non-linear decision
boundaries. Their effectiveness, especially in high-dimensional spaces, makes them a
popular choice in the machine learning community.

4.4.3 Random Forest Algorithm:

Fig 4.4.3: Random Forest Representation

Random Forest is an ensemble learning method used for both classification and
regression tasks. It operates by constructing a multitude of decision trees during
training and outputting the mode of the classes (classification) or the average
prediction (regression) of the individual trees.
The fundamental idea behind Random Forest is to aggregate the predictions of
multiple decision trees to improve the overall predictive performance and reduce the
risk of overfitting. Each decision tree in the Random Forest is trained independently
on a random subset of the training data and a random subset of the features. This

34
randomness helps to introduce diversity among the trees and leads to more robust and
generalized predictions.
Random Forest also performs random feature selection at each split of the decision
tree. Instead of considering all features when determining the best split, each tree in
the Random Forest randomly selects a subset of features to consider. This further
enhances the diversity among the trees and prevents individual features from
dominating the decision-making process.

Pros:

1. Random Forests are robust and versatile, capable of handling both

classification and regression tasks.
2. They are less prone to overfitting compared to individual decision trees,
thanks to ensemble averaging.
3. Random Forests provide feature importance scores, allowing insights into the
relative significance of different features.

Cons:

1. Random Forests can be computationally expensive and memory-intensive,

especially for large datasets with many features.
2. They may lack interpretability compared to simpler models like logistic
regression.

Random Forests find applications in various domains such as finance (e.g., credit risk
assessment), healthcare (e.g., disease prediction), marketing (e.g., customer
segmentation), and more. Their ability to handle high-dimensional data and capture
complex relationships makes them well-suited for a wide range of classification and
regression tasks.

In summary, Random Forest is a powerful ensemble learning algorithm that combines

the predictive strength of multiple decision trees to achieve robust and accurate
predictions. Its versatility, robustness, and ability to provide insights into feature
importance make it a popular choice in the machine learning community.

35
 CHAPTER 5: MANAGEMENT SYSTEM

5.1 INTRODUCTION

5.1.1 Selenium

1. Selenium stands out as a powerful and versatile tool in the realm of web
automation, offering a comprehensive suite of tools and libraries that cater to
developers and testers across various programming languages. Its
compatibility with languages such as Java, Python, and JavaScript makes it
accessible to a wide range of professionals, regardless of their coding
preferences or expertise levels.
2. While Selenium is predominantly renowned for its role in automating web
application testing, its capabilities extend far beyond this fundamental use
case. One of Selenium's key strengths lies in its versatility, allowing it to be
applied to a myriad of tasks beyond traditional testing scenarios.
3. Additionally, Selenium is a go-to tool for browser compatibility testing,
ensuring that web applications function seamlessly across different browsers
and platforms. By automating the process of testing across multiple browsers,
Selenium helps developers identify and rectify compatibility issues early in the
development cycle, thereby enhancing the overall user experience.

36
 4. In conclusion, Selenium's broad range of applications, coupled with its user-
friendly interface and robust features, make it an indispensable tool for
developers and testers seeking to automate tasks, enhance efficiency, and
elevate the quality of web applications. Its adaptability to different
programming languages, combined with its extensive capabilities, positions
Selenium as a versatile and indispensable tool in the realm of web automation.

5.1.2 Automated Testing

1. Selenium's application in automated testing is crucial for modern software

development practices. By automating repetitive tasks such as form filling,
button clicking, and page navigation, Selenium significantly reduces the time
and effort required for testing. This allows software development teams to
release high-quality software more quickly and efficiently.
2. In addition to automated testing, Selenium's web scraping capabilities are
highly valuable for a variety of applications. For businesses, web scraping can
provide competitive insights, market research data, and real-time information
about competitors and market trends. Researchers can use web scraping to
gather data for analysis and study patterns or trends over time. By automating
the data collection process, Selenium enables users to gather large amounts of
data quickly and accurately.

5.2 WEB SCRAPING

Web scraping through Selenium is a powerful technique used to extract data from
websites. It involves using Selenium's automation capabilities to navigate through
web pages, interact with elements, and extract desired information. This technique is
widely used for various purposes, including data analysis, market research, and
monitoring changes on websites.

1. One of the key advantages of web scraping through Selenium is its ability to
handle dynamic websites. Unlike traditional web scraping tools that rely on
static HTML content, Selenium can interact with dynamic elements on a
webpage, such as JavaScript-generated content. This allows Selenium to

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 scrape data from websites that use dynamic content loading, AJAX, or
JavaScript frameworks like React or Angular.
2. Another advantage of using Selenium for web scraping is its flexibility and
programmability. Selenium supports multiple programming languages,
including Java, Python, and C#, allowing developers to write scripts to
automate the scraping process. This flexibility makes Selenium suitable for a
wide range of scraping tasks, from simple data extraction to complex web
crawling and data mining operations.
3. Web scraping through Selenium can be used for various applications. For
businesses, web scraping can provide valuable insights into market trends,
competitor activities, and customer behavior. By scraping data from
competitor websites, businesses can gather competitive intelligence and adjust
their strategies accordingly. Similarly, web scraping can be used for lead
generation, price monitoring, and sentiment analysis, among other purposes.
4. Researchers and academics also use web scraping through Selenium to gather
data for analysis and study. By scraping data from websites, researchers can
collect large datasets for statistical analysis, machine learning, and other
research purposes. This can be particularly useful in fields such as social
sciences, economics, and data science, where access to large datasets is
essential for research.
5. However, it's important to note that web scraping is subject to legal and ethical
considerations. While scraping public data from websites is generally
permissible, scraping personal data or copyrighted content may infringe on
privacy or intellectual property rights. It's essential to review the terms of
service of the website you're scraping and to ensure compliance with relevant
laws and regulations.
6. In conclusion, web scraping through Selenium is a powerful technique for
extracting data from websites. Its ability to handle dynamic content,
flexibility, and programmability make it a valuable tool for businesses,
researchers, and developers. However, it's important to use web scraping
responsibly and ethically, ensuring compliance with legal requirements and
respect for the rights of website owners.

5.2.1 Selenium Webdriver

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Selenium WebDriver serves as the cornerstone of Selenium's automation capabilities,
providing a powerful interface for automating interactions with web browsers. Its
versatility and robustness make it a preferred choice for automating browser-based
tasks and testing web applications.

One of the key aspects of Selenium WebDriver is its ability to handle a wide range of
browser interactions. This includes clicking on elements, entering text into fields,
submitting forms, navigating through pages, and handling alerts and pop-ups.
WebDriver's comprehensive API allows developers to perform these actions with
precision and control, enabling thorough testing of web applications.

1. WebDriver's support for multiple programming languages, including Java,

Python, C#, and others, makes it accessible to a broad range of developers.
This allows teams to use their preferred language and integrate Selenium
WebDriver seamlessly into their existing development workflows.
Additionally, WebDriver's compatibility with popular browsers such as
Chrome, Firefox, Safari, and Edge ensures that tests can be run across
different environments, enhancing the overall reliability of web applications.
2. Another significant feature of Selenium WebDriver is its ability to interact
with web elements based on various locators such as ID, class name, XPath,
CSS selector, and more. This flexibility allows developers to write robust and
maintainable automation scripts that can adapt to changes in the web
application's structure over time.
3. Furthermore, WebDriver's support for parallel execution enables teams to
accelerate their testing process by running tests concurrently across multiple
browsers and environments. This can significantly reduce the time required for
testing, especially in agile development environments where fast feedback is
crucial.
4. Overall, Selenium WebDriver's rich feature set, cross-browser compatibility,
and support for multiple programming languages make it an indispensable tool
for automating browser interactions and testing web applications. Its
continuous development and community support ensure that it remains at the
forefront of browser automation technologies, empowering developers to build
high-quality web applications efficiently.

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 5. For login automation, Selenium WebDriver allows developers to script the
process of entering login credentials into the respective fields on a login page.
This includes clicking the login button and verifying the success of the login
process. This functionality is crucial for testing the login functionality of web
applications under various scenarios.
6. Additionally, Selenium WebDriver allows developers to open websites and
navigate through web pages by specifying the URL of the website. It can
launch the browser and load the desired page. This feature is useful for
automating the process of accessing specific web pages as part of testing or
data scraping workflows.
7. Selenium WebDriver's headless mode facilitates web scraping without
displaying the browser window. This makes it ideal for tasks where visual
rendering of the web page is unnecessary. In headless mode, WebDriver
interacts with the browser's Document Object Model (DOM) to locate and
extract specific elements from the web page.

5.3 WEB APPLICATION

The web application developed for the Get-Hired project serves as an advanced ed-
tech service platform, offering personalized career recommendations and seamless
interaction for both students and professionals. It is constructed using the MERN
stack, a robust technology stack that includes MongoDB for the database, Express.js
for the backend, React.js for the front end, and Node.js for server-side operations.
This choice of technology ensures a scalable, responsive, and modern web
application.

For students, the platform provides a user-friendly interface to explore various career
options based on their skills, interests, and educational background. It offers
personalized career guidance and recommendations, helping students make informed
decisions about their future.

Professionals can also benefit from the platform by using it to assess their layoff
chances and explore new job opportunities. The platform provides a platform for
professionals to network with industry experts, attend webinars and workshops, and

40
access career advancement resources. Overall, the web application aims to streamline
the career planning and job search process for both students and professionals,
making it more efficient, personalized, and effective.

5.4 SYSTEM ARCHITECTURE

The architecture of the Get-Hired ed-tech service platform is designed around three
core components: the front end, the back end, and the database. This architecture
follows a client-server model, where the front end acts as the client, while the back
end and the database serve as the server components.

5.4.1 Core Components

The front end of the platform is responsible for the user interface and user
interactions. It is built using React.js, a popular JavaScript library for building
interactive user interfaces. The front end communicates with the back end through a
set of APIs to fetch and display data to the users.

On the other hand, the back end of the platform is responsible for processing user
requests, handling business logic, and interacting with the database. It is built using
Node.js and Express.js, which are JavaScript frameworks for building server-side
applications. The back end exposes a set of RESTful APIs that the front end uses to
communicate with it.

Lastly, the database component of the platform stores all the data related to users,
careers, job listings, and other relevant information. MongoDB, a NoSQL database, is
used for its flexibility and scalability, making it suitable for storing and managing
large amounts of data. Overall, the architecture of the Get-Hired platform is designed
to be scalable, modular, and efficient, allowing for seamless interactions between the
front end, back end, and database components.

5.4.2 Frontend

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The front end of the Get-Hired platform is constructed using ReactJS, a widely-used
JavaScript library known for its ability to create dynamic and responsive user
interfaces. ReactJS plays a crucial role in ensuring that the platform delivers an
engaging and seamless experience for both students and employees. Through ReactJS,
the front end can efficiently handle user interactions and provide real-time updates,
enhancing the overall usability of the platform.

To facilitate communication between the front end and the back end, the front end
utilizes RESTful API calls. These API calls enable the front end to request and
receive data from the back end, ensuring that the user interface remains up-to-date and
responsive.

The front end serves as the user's primary point of interaction with the platform,
acting as the "face" of Get-Hired. It is responsible for presenting the platform's
features and functionalities in a user-friendly manner, guiding users through their
journey on the platform.

1. FOR STUDENTS

For students, the Get-Hired platform offers a range of features designed to

enhance their learning and career prospects. The homepage serves as a
welcoming introduction to the platform, providing a brief overview of its
purpose and functionality. From here, students can easily access key features
such as the Placement-prediction Model, which provides insights into their
potential placement opportunities based on their profile and skills.

The course list is a comprehensive directory of all available courses on the

platform, complete with detailed descriptions to help students make informed
decisions about their learning journey. Each course listing includes essential
information such as course duration, prerequisites, and learning objectives.

User details pages provide students with a personalized view of their account
information, including their name, email address, and other relevant details.
This page serves as a central hub for students to manage their account settings

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 and preferences, ensuring a seamless and personalized experience on the
platform.

Overall, these features are designed to empower students with the tools and
resources they need to succeed in their academic and professional endeavors.
From accessing course information to managing their account settings, Get-
Hired provides students with a comprehensive platform to support their
learning and career goals.

2. FOR INSTRUCTORS

The dashboard feature provides instructors with a quick snapshot of their co-
urses' performance, highlighting key metrics such as enrollment numbers,
completion rates, and student feedback. This overview allows instructors to
quickly assess the overall success of their courses and identify any areas that
may require attention. In addition to course metrics, the dashboard may also
display notifications about upcoming deadlines, student inquiries, or other
important information.

The insights page offers instructors a deeper dive into their course analytics,
providing detailed data on student engagement, course completion rates, and
student demographics. Instructors can use this information to tailor their
courses to better meet the needs of their students and improve overall course
effectiveness. For example, if a particular lesson has a low completion rate,
the instructor may choose to revise the content or delivery method to better
engage students.

Course management pages are a central hub for instructors to create, update,
and manage their courses. Instructors can use these pages to upload course
materials, create assignments and quizzes, and communicate with students.
Additionally, instructors can use these pages to set course pricing, manage
enrollment, and track student progress. This level of control allows instructors
to customize their courses to meet the needs of their students and adapt to
changing circumstances.

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 The platform also offers instructors the ability to view and edit their profile
details, ensuring that their information is always up-to-date and accurate.
Instructors can use this feature to add new credentials, update their bio, or
change their contact information. This ensures that students have access to the
most current information about their instructors, helping to build trust and
credibility in the platform.

Overall, these features work together to provide instructors with the tools and
insights they need to create successful and engaging courses. By offering a
comprehensive suite of features, the Get-Hired platform empowers instructors
to deliver high-quality courses and build a strong and loyal student base.

5.4.3 Backend

Get-Hired's back-end architecture is structured as a monolith, a design paradigm that

consolidates all application components into a single, cohesive program with a unified
codebase. This approach enhances control, security, and performance. The back end is
primarily developed using Node.js, a runtime environment that enables the execution
of JavaScript code outside the browser, and Express.js, a web application framework
for Node.js that streamlines web application development.

Node.js and Express.js are chosen for their efficiency and scalability in handling
HTTP requests and managing application logic. Node.js provides a non-blocking,
event-driven architecture, allowing for asynchronous operations and optimal resource
utilization. Express.js, built on top of Node.js, simplifies the creation of robust APIs
and web applications with its minimalist and flexible approach.

MongoDB serves as the primary database, offering a flexible NoSQL storage solution
suitable for the diverse data needs of Get-Hired. MongoDB's schema-less design
allows for easy adaptation to evolving data structures, crucial for an application like
Get-Hired that deals with complex data relationships and user interactions. The use of
MongoDB Atlas, the fully managed cloud database service, ensures scalability, high
availability, and security of the database.

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Together, Node.js, Express.js, and MongoDB form a robust back-end foundation for
Get-Hired, enabling seamless communication between the front end and database,
efficient data processing, and scalable architecture to support the platform's growth
and functionality. The modular nature of the monolithic architecture allows for easy
integration of additional features and services as the platform evolves.

5.4.4 User Authentication and Authorization

User authentication and authorization are critical components of the platform's

security infrastructure, ensuring that only legitimate users gain access to its features
and resources. The login system is designed with robustness in mind, incorporating
multiple layers of security measures.

At the forefront is a secure authentication mechanism that verifies user identity

through a combination of credentials, typically a username and password. These
credentials are encrypted before being transmitted over the network to prevent
interception by malicious actors.

1. USER ROLES

Students can register and log in using their institutional email address and a
password of their choice. Once logged in, students gain access to features
tailored to their academic needs. This includes the ability to view course
materials, submit assignments, and access their grades and academic progress.
These features are essential for students to engage with their coursework and
track their performance within the platform.

Employees, on the other hand, register and log in using their work email
address and a designated password. Employees have access to a broader array
of features, reflecting their administrative roles within the platform. These
functionalities may include managing student data, creating new courses,
monitoring student progress and performance, and overseeing the overall
operation of the platform.

2. AUTHENTICATION

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 The "Email and Password" authentication method is a fundamental yet
effective approach to user authentication. It involves users creating an account
by providing their email address and setting a strong, secure password. This
method is widely used across various online platforms due to its simplicity and
reliability in establishing a secure login process.

When a user registers with their email and password, the platform securely
stores this information in its database. During the login process, users are
required to enter their registered email address and password. The platform
then compares these credentials with the stored information to authenticate the
user.

The "OTP Verification" method enhances security by adding an extra layer of

authentication. When users opt for this method, they receive a one-time
password (OTP) on their registered email address or phone number. This OTP
is typically valid for a short period and can only be used once, making it
highly secure.

This link is valid for a limited time and allows the user to set a new password.
This process ensures that only the legitimate account owner can reset the
password, as access to the registered email address is required.

The "Forgot Password" functionality adds convenience for users who may
have trouble remembering their passwords. It also contributes to the platform's
security by providing a secure method for password recovery.

3. CLOUD BASED USER MANAGEMENT

Cloudinary is a versatile cloud-based media management service that plays a

pivotal role in the Get-Hired platform. It serves as a centralized repository for
storing and managing all types of media content, including images, videos,
and documents. By leveraging Cloudinary, Get-Hired ensures that media files
are stored securely and are easily accessible whenever needed.

One of the key advantages of using Cloudinary is its scalability. As Get-Hired

grows and the volume of media content increases, Cloudinary can seamlessly

46
 handle the increased load, ensuring that the platform remains responsive and
efficient. Additionally, Cloudinary provides a range of tools and
functionalities for manipulating and optimizing media files, allowing Get-
Hired to deliver high-quality content to users while minimizing load times.

Another benefit of using Cloudinary is its integration capabilities. It can be

easily integrated with other services and platforms, allowing Get-Hired to
incorporate media content from various sources and formats. This integration
flexibility enables Get-Hired to provide a rich and engaging user experience,
with multimedia content that is dynamic, interactive, and visually appealing.

4. CENTRALIZED STORAGE

Centralized storage in the context of Get-Hired's media assets means that all
images, videos, and documents are stored in a single, secure location. This
centralized approach offers several advantages. Firstly, it eliminates the need
for scattered storage solutions, where files might be saved on different servers
or devices.

Secondly, centralized storage simplifies access for authorized users. With all
media assets stored in one place, users can easily find and retrieve the files
they need without having to search through multiple locations. This improves
efficiency and productivity, especially in a platform like Get-Hired, where
users may need quick access to a variety of media assets for learning or
teaching purposes.

Additionally, centralized storage allows for better management and

organization of media assets. Administrators can set access permissions and
control who can view, edit, or delete files, ensuring that sensitive information
is protected. Overall, centralized storage enhances the security, accessibility,
and organization of media assets in Get-Hired.

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5. STREAMLINED WORKFLOWS

Streamlined workflows in the context of Get-Hired refer to the optimization of

processes related to uploading, organizing, tagging, and managing media
assets. By streamlining these workflows, Get-Hired can save valuable time
and resources, leading to increased efficiency and productivity.

One key aspect of streamlined workflows is the ease of uploading media

assets. With a centralized storage system, users can upload files quickly and
easily, without the need for complex procedures. This is especially beneficial
for instructors who may need to upload course materials regularly.

Organizing and tagging media assets is also simplified with streamlined

workflows. Get-Hired can implement intuitive categorization systems and
tagging mechanisms, making it easy for users to search for and find specific
media assets. This not only saves time but also improves the overall user
experience.

Furthermore, streamlined workflows enable efficient management of media

assets. Administrators can easily monitor and track the usage of media files,
ensuring that resources are allocated effectively. This optimization of
workflows helps Get-Hired operate more smoothly and ensures that resources
are used efficiently, ultimately benefiting both the platform and its users.

6. SCALIBILITY

Scalability is a critical feature of Cloudinary for Get-Hired, as it ensures that

the platform can accommodate its growing media library without encountering
storage limitations or requiring extensive IT management. With Cloudinary's
scalable infrastructure, Get-Hired can expand its media storage capacity
seamlessly as the platform's needs evolve, without experiencing downtime or
disruptions.

One of the key benefits of Cloudinary's scalability is its ability to handle a

large volume of media assets efficiently. As Get-Hired's media library grows,
Cloudinary can automatically adjust its resources to accommodate the

48
 increased storage requirements, ensuring that the platform remains responsive
and reliable for users.

Another advantage of Cloudinary's scalability is its ease of implementation.

Get-Hired can scale its media storage capacity without the need for complex
hardware upgrades or configuration changes. Cloudinary's cloud-based
infrastructure allows for quick and seamless scaling, minimizing the need for
extensive IT involvement and reducing the risk of downtime.

Overall, Cloudinary's scalability ensures that Get-Hired can continue to grow

its media library and provide a seamless user experience without being limited
by storage constraints or IT complexities.

5.5 DATABASE

The data models and database schema in the back end of Get-Hired are crucial
components that enable the platform to manage user data and course information
efficiently.

The Student schema, for instance, not only stores basic information like name, email,
and password but can also include additional details such as academic history, career
interests, and preferred job locations. This comprehensive approach allows Get-Hired
to provide personalized career recommendations tailored to each student's profile.

Similarly, the Employee schema can store a wide range of information, including job
history, skills, and performance reviews. This data is valuable for employees seeking
new job opportunities or evaluating their layoff chances, as it helps them understand
their strengths and areas for improvement.

The Resource schema, on the other hand, plays a critical role in managing course
materials. In addition to storing basic information like course name and description, it
can also include details about course modules, assignments, and assessments. By
organizing course content in this structured manner, Get-Hired ensures that students

49
and employees can easily access the information they need to enhance their skills and
advance their careers.

Overall, the data models and database schema in the back end of Get-Hired are
designed to provide a comprehensive and user-friendly experience. Through careful
planning and implementation, these components help the platform deliver valuable
insights and recommendations to users, empowering them to make informed decisions
about their careers.

Fig 5.5.1: System Architecture

5.6 API DESIGN

The API design of the Get-Hired platform plays a crucial role in enabling seamless
communication between the front end and back end. Following the REST
architectural style ensures that the API is intuitive, easy to understand, and follows
best practices in web development.

Node.js and Express.js are used to implement the API, leveraging their capabilities to
handle HTTP requests efficiently. These technologies are well-suited for building
APIs, providing a robust foundation for the platform's backend functionality.

JSON (JavaScript Object Notation) is chosen as the data exchange format due to its
lightweight nature and ease of parsing. JSON allows for the structured representation

50
of data, making it ideal for transmitting complex data structures between the client
and server.

The API endpoints are designed to align with standard HTTP request methods. GET
requests are used for retrieving data, POST requests for creating new data, PUT
requests for updating existing data, and DELETE requests for removing data. This
adherence to HTTP standards ensures that the API is consistent and predictable,
simplifying development and integration with other systems.

5.6.1 API endpoints

1. /api/auth/signup (POST) - This endpoint is used to create a new user account.

Users can sign up as either students or instructors by providing the necessary
information such as name, email, and password. Upon successful account
creation, the user will be able to access the platform's features based on their
role
2. /api/auth/login (POST) - This endpoint is used for user authentication. Users can
log in using their email and password. Upon successful authentication, the
server generates a JSON Web Token (JWT) that can be used for subsequent
requests to authenticate the user.
3. /api/auth/verify-otp (POST) - In case of using OTP verification for additional
security, this endpoint is used to verify the OTP sent to the user's registered
email or phone number. Once the OTP is verified, the user can proceed with
their intended action, such as logging in or resetting their password.

5.6.2 Sample APIs

1. DELETE /api/courses/:id - This request is used to delete a course by its ID.

Request: DELETE /api/courses/123

Response: { "message": "Course deleted successfully"

2. GET /api/v1/dashboard/my-profile - This request is used to retrieve all profile

details of the currently logged-in user.

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 Request: GET /api/v1/dashboard/my-profile

Response: { "name": "John Doe", "email": “john.doe@example.com”, “role” : “Student” }

CHAPTER 6: DESIGN AND IMPLEMENTATION

6.1 INTRODUCTION

The primary objective of our project is to design and implement a user-friendly

platform that integrates three key components: placement prediction, layoff
prediction, and job management. We aim to provide users with accurate predictions of
job placement opportunities and layoff risks, as well as real-time access to relevant
job listings from the Indeed website.

The Placement and Layoff Prediction System with Job Management holds significant
importance in today's job market landscape. For freshers, accurate placement
predictions offer invaluable guidance in navigating the job market, helping them make
informed decisions about their career paths. Similarly, employees facing layoff risks
benefit from early detection and proactive measures to safeguard their employment
status.

6.2 DESIGN METHODOLOGY:

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The design methodology adopted for the functioning of our project encompasses the
following sequential steps:

6.2.1 User Interaction:

Our system is meticulously crafted to ensure a high degree of user-friendliness,

aiming for seamless interaction among all stakeholders involved. This meticulous
approach underscores our commitment to creating an environment where users can
navigate effortlessly and access the information they need with ease.

6.2.2 Data Submission and Processing:

Upon input submission, the system triggers a series of actions to process the provided
data. For placement prediction, users may input details such as educational
qualifications, skills, and career preferences, while for layoff prediction, employee-
related information such as performance ratings, tenure, and departmental changes is
collected. This data is then forwarded to the backend server for processing.

6.2.3 Prediction Generation and Output Display:

The processed data is fed into the prediction models deployed on the server. Utilizing
machine learning algorithms, the system generates predictions regarding job
placement probabilities or layoff risks. Once predictions are generated, the system
fetches relevant information such as disease names, management strategies, and job
listings from the underlying database. This information is then displayed to the user
via the GUI, providing actionable insights and recommendations.

6.2.4 Enhanced Accessibility:

To cater to diverse user needs and preferences, our system incorporates features to
enhance accessibility. For instance, users have the option to access information
through alternative mediums such as audio output. By integrating a text-to-speech
conversion engine, users can opt to have the system read out the displayed
information aloud, ensuring accessibility for users with varying levels of literacy or
visual impairments.

In essence, our design approach prioritizes user experience and accessibility,

facilitating seamless interaction and providing valuable insights to users navigating

53
the complexities of the job market or anticipating potential layoff risks. Through a
combination of intuitive design, advanced algorithms, and accessibility features, our
system aims to empower users with actionable information and support their career
decision-making process.

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6.3 DATA FLOW DIAGRAM:

Data flow diagrams describes various levels of refinement of data flow i.e. flow of
data through various processes of application.

The DFD is also called as bubble chart. It is a simple graphical formalism that can be
used to represent a system in terms of input data to the system, various processing
carried out on this data, and the output data is generated by this system

The data flow diagram (DFD) is one of the most important modeling tools. It is used
to model the system components. These components are the system process, the data
used by the process, an external entity that interacts with the system and the
information flows in the system.

The various levels of refinement for our project are as follows:

Fig 6.3.1: Level 0 DFD

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Fig 6.3.2: Level 1 DFD

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Fig 6.3.3: Level 2 DFD

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6.4 SEQUENCE DIAGRAM:

Sequence diagrams in UML shows how object interact with each other and the order
those interactions occur. It’s important to note that they show the interactions for a
particular scenario. We can also use the terms event diagrams or event scenarios to
refer to a sequence diagram. Sequence diagrams describe how and in what order the
objects in a system function.

These diagrams are widely used by businessmen and software developers to

document and understand requirements for new and existing systems. The processes
are represented vertically and interactions are show as arrows.

Fig 6.4.1: Sequence Diagram for the System

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6.5 FLOW CHART:

A flowchart is a type of diagram that represents a workflow or process. A flowchart

can also be defined as a diagrammatic representation of an algorithm, a step-by-step
approach to solving a task.

The flowchart shows the steps as boxes of various kinds, and their order by
connecting the boxes with arrows. This diagrammatic representation illustrates a
solution model to a given problem. Flowcharts are used in analyzing, designing,
documenting or managing a process or program in various fields.

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 Fig 6.5.1 Flow Chart

6.6 USE CASE DIAGRAM:

A use case diagram in UML illustrates the system's functional requirements from the
user's viewpoint, showing interactions between external actors and the system. Actors,
representing users or systems, interact with use cases depicting specific
functionalities. The system boundary delineates its scope, while relationships
demonstrate interactions.

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61
6.7 WORKING OF THE APPLICATION:

6.7.1 Accessing the Portal:

John, a recent graduate in search of job opportunities, opens his web browser and
navigates to the Career Insight Portal. Upon reaching the portal's homepage, he finds
a user-friendly interface designed to simplify the job search process.

6.7.2 Inputting Personal Data:

Intrigued by the portal's promise of predictive insights, John decides to explore

further. He clicks on the "Get Started" button and is prompted to input his relevant
details, including his educational background, skills, and desired job location.

6.7.3 Triggering Prediction Generation:

After completing the input fields, John eagerly clicks the "Discover Opportunities"
button. This action triggers the portal's predictive algorithms, which analyze John's
profile to generate personalized job predictions tailored to his qualifications and
preferences.

6.7.4 Exploring Job Recommendations:

Within moments, the Career Insight Portal presents John with a curated list of job
recommendations based on his input data. He browses through the listings, each
accompanied by detailed information such as job titles, company names, and required
qualifications.

6.7.5 Integrating Real-time Job Listings:

In addition to the predicted job opportunities, John notices a section featuring real-
time job listings sourced from external platforms like Indeed. He appreciates the
portal's ability to provide up-to-date information, enabling him to stay informed about
the latest job openings in his field.

6.7.6 Utilizing Accessibility Features:

As John explores the portal's features, he discovers the option to enable text-to-speech
functionality. Intrigued, he clicks the "Listen" button, and the portal converts the

62
displayed text into audible output, ensuring accessibility for users with varying needs
and preferences.

6.7.7 Empowering Career Decisions:

Impressed by the portal's user-friendly interface and predictive capabilities, John feels
empowered to make informed decisions about his career. He bookmarks several
promising job listings and plans to explore them further, confident that the Career
Insight Portal will support him in his job search journey.

6.8 SYSTEM ARCHITECTURE:

The envisioned system architecture for our project, aimed at providing predictive
insights and job management solutions, is designed to offer farmers a user-friendly
and effective tool to navigate the complexities of the job market and mitigate potential
layoff risks. The system architecture comprises three core components: the web-based
application, the server, and the prediction models.

6.8.1 Web-Based Application:

The web-based application serves as the front-end interface, enabling users to interact
with the system seamlessly. Users, whether freshers or employees, access the
application through their web browsers. The application provides intuitive features for
data input, prediction generation, and job management. Users can input their relevant
details, such as educational background, work experience, and career preferences, and
receive predictive insights and job recommendations tailored to their profile.

Server:

Acting as the intermediary layer, the server processes user inputs and executes the
prediction algorithms deployed in the system. Upon receiving user data from the web-
based application, the server triggers the prediction models to generate predictions
regarding job placement probabilities or layoff risks. Subsequently, the server
retrieves and aggregates relevant job listings from external sources, ensuring real-time
access to job opportunities for users.

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6.8.2 Prediction Models:

The backbone of the system, the prediction models, are responsible for generating
accurate and reliable predictions based on the input data provided by users.
Leveraging machine learning algorithms such as logistic regression, support vector
machine (SVM), and random forest, the models analyze user profiles and historical
job data to predict job placement probabilities or identify potential layoff risks. These
models are trained on comprehensive datasets to ensure robust performance and
accuracy in prediction generation.

Overall, the proposed system architecture offers a comprehensive and scalable

solution to empower users with predictive insights and job management capabilities.
By integrating user-friendly interfaces, efficient server processing, and advanced
prediction models, the system aims to support users in making informed career
decisions and navigating the challenges of the job market effectively.

64
6.9 CODING IMPLEMENTATION:

6.9.1 PLACEMENT PREDICTION MODEL:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('placement.csv')
df.head()
df.drop(['Student ID', 'Name'], axis = 1, inplace = True)
df.info()
df.isnull().sum()
df['Internship Experience (Y/N)'] = df['Internship Experience (Y/N)'].map({'Y': 1,
'N' : 0})
df['Placed (Y/N)'] = df['Placed (Y/N)'].map({'N' : 0, 'Y' : 1})
sns.boxplot(x=df['Technical Skills (Rating 1-5)'])
plt.title('Box plot of Technical skills')
plt.show()
sns.boxplot(x=df['Soft Skills (Rating 1-5)'])
plt.title('Box plot of Soft skills')
plt.show()
sns.boxplot(x=df['Previous Projects (Number)'])
plt.title('Box plot of Previous Projects')
plt.show()
df['Previous Projects (Number)'].unique()
projects_done_counts = df['Previous Projects (Number)'].value_counts()
unique_values = projects_done_counts.index
counts = projects_done_counts.values
plt.bar(unique_values, counts)
plt.title('Number of Projects Done')
plt.xlabel('Projects')
plt.ylabel('Count')
df['Placed (Y/N)'].value_counts()

65
from sklearn.utils import resample
majority_class = df[df['Placed (Y/N)'] == 0]
minority_class = df[df['Placed (Y/N)'] == 1]
minority_upsampled=resample(minority_class,replace=True,n_samples=len(majority
_class), random_state=42)
df_upsampled = pd.concat([majority_class, minority_upsampled])
df_upsampled=df_upsampled.sample(frac=1,random_state=42).reset_index(drop=Tru
e)
df = df_upsampled
df['Placed (Y/N)'].value_counts()
sns.pairplot(df)
plt.show()
grouped_data = df.groupby('Technical Skills (Rating 1-5)')['Placed (Y/N)'].mean()
grouped_data.plot(kind='bar')
plt.xlabel('Technical Skills')
plt.ylabel('Proportion of students placed')
plt.title('Effect of Technical skills on placement')
plt.show()
grouped_data = df.groupby('Soft Skills (Rating 1-5)')['Placed (Y/N)'].mean()
grouped_data.plot(kind='bar')
plt.xlabel('Soft Skills')
plt.ylabel('Proportion of students placed')
plt.title('Effect of Soft skills on placement')
plt.show()
sns.histplot(data = df[df['Placed (Y/N)'] == 1], x = df['Soft Skills (Rating 1-5)'], hue =
df['Technical Skills (Rating 1-5)'], bins = 30, kde = True)
sns.histplot(data = df[df['Placed (Y/N)'] == 1], x = df['GPA'])
sns.distplot(df['GPA'])
sns.distplot(df['Soft Skills (Rating 1-5)'])
df['transformed_GPA'] = np.log(df['GPA'])
sns.histplot(data=df,x='transformed_GPA',kde=True)plt.title('Transformed
Distribution of Column')
plt.xlabel('Values')
plt.ylabel('Frequency')
66
plt.show()
sns.histplot(data = df[df['Placed (Y/N)'] == 1], x = df['transformed_GPA'], bins = 30,
kde = True)
df.head()
counts_column1 = df['Internship Experience (Y/N)'].value_counts()
counts_column2 = df['Placed (Y/N)'].value_counts()
unique_values = counts_column1.index.tolist()
bar_width = 0.35
plt.bar(unique_values, counts_column1.values, bar_width, label='Internship')
plt.bar([x + bar_width for x in unique_values], counts_column2.values, bar_width,
label='Placed')
plt.xlabel('Unique Values')
plt.ylabel('Counts')
plt.title('Counts of Unique Values in Internship and Placed')
plt.xticks([x + bar_width / 2 for x in unique_values], unique_values)
plt.legend()
plt.show()
df['Major'].unique()
df['Major'].value_counts()
grouped_data = df.groupby('Major')['Placed (Y/N)'].mean()
grouped_data.plot(kind='bar')
plt.xlabel('Major')
plt.ylabel('Proportion of students placed')
plt.title('Effect of Major Subject on placement')
plt.show()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
one_hot_encoded = encoder.fit_transform(df[['Major']])
one_hot_df=pd.DataFrame(one_hot_encoded,columns=encoder.get_feature_names_o
ut(['Major']))
df = pd.concat([df, one_hot_df], axis=1)
df.head()
df.drop(['Major', 'GPA'], axis = 1, inplace = True)
df.corr()
67
plt.figure(figsize = (10, 6))
sns.heatmap(df.corr(), annot = True)
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
df.columns = df.columns.astype(str)
X = df.drop(columns=['Placed (Y/N)'
y = df['Placed (Y/N)']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
models = [
('Random Forest', RandomForestClassifier(random_state=42)),
('Logistic Regression', LogisticRegression(random_state=42)),
('Support Vector Machine', SVC(random_state=42))
]
best_model = None
best_accuracy = 0
for model_name, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model: {model_name}, Accuracy: {accuracy}")
if accuracy > best_accuracy:
best_model = model
best_accuracy = accuracy
if best_model is not None:
print("\nBest Model:")
print(best_model)
print("Best Accuracy:", best_accuracy)
print("Classification Report:")
y_pred = best_model.predict(X_test)
68
 print(classification_report(y_test, y_pred))
else:
print("No best model found.")
import pickle
with open('placement_model.pkl', 'wb') as f:
pickle.dump(best_model, f)

69
6.9.2 LAYOFF PREDICTION MODEL:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv('data.csv')
df.head()
df.info()
df.isnull().sum()
try:
lst = []
for i in df['Years of Experience']:
lst.append(int(i))
except ValueError:
pass
try:
df['Years of Experience'] = df['Years of Experience'].astype(int)
except ValueError:
df['Years of Experience'] = pd.to_numeric(df['Years of Experience'], errors='coerce')
df.isnull().sum()
df['Years of Experience'] = df['Years of Experience'].fillna(df['Years of
Experience'].mode()[0])
df['Years of Experience'] = df['Years of Experience'].astype(int)
df.info()
df.head()
df['Age Range'].isnull().sum()
temp = []
for i in df['Age Range']:
try:
lst = i.split('-')
temp.append((int(lst[1]) + int(lst[0]) + 1) // 2)
except:
temp.append(np.nan)

70
len(temp)
df['Age Range'] = pd.Series(temp)
df['Age Range']
df.head()
df['Laid Off'].unique()
df.loc[df['Laid Off'] == 'True', 'Laid Off'] = 'Yes'
df.loc[df['Laid Off'] == 'False', 'Laid Off'] = 'No'
df.loc[df['Laid Off'] == 'Laid Off', 'Laid Off'] = 'Yes'
df['Laid Off'].value_counts()
df['Laid Off'] = df['Laid Off'].fillna(df['Laid Off'].mode()[0])
df['Laid Off'] = df['Laid Off'].map({'Yes' : 1, 'No' : 0})
df.head()
df['Layoff Date'].unique()
plt.subplots(1, 1, figsize = (25, 6))
plt.subplot(1, 1, 1)
sns.histplot(data = df[df['Laid Off'] == 1], x = df['Layoff Date'], bins = 30, kde =
True)
df.drop('Layoff Date', axis=1, inplace=True)
df.head()
df['Location'].unique()
df['Location'] = df['Location'].fillna(df['Location'].mode()[0])
df['Location'].value_counts()
df.loc[df['Location'] == 'Location', 'Location'] = 'Remote'
df.loc[df['Location'] == 'London', 'Location'] = 'Mumbai'
df.loc[df['Location'] == 'New York City', 'Location'] = 'Hyderabad'
df.loc[df['Location'] == 'San Francisco Bay Area', 'Location'] = 'Bangalore'
df.head()
cross_tab = pd.crosstab(df['Location'], df['Laid Off'])
cross_tab.plot(kind='bar', stacked=True)
plt.xlabel('Location')
plt.ylabel('Count')
plt.title('Laid Off Status by Location')
plt.legend(title='Laid Off')
plt.show()
71
df['Severance Package'].unique()
df.loc[df['Severance Package'] == 'True', 'Severance Package'] = 'Yes'
df.loc[df['Severance Package'] == 'False', 'Severance Package'] = 'No'
df.loc[df['Severance Package'] == 'Severance Package', 'Severance Package'] = 'Yes'
df.loc[df['Severance Package'] == 'None', 'Severance Package'] = 'No'
df['Severance Package'].value_counts()
df['Severance Package'] = df['Severance Package'].fillna(df['Severance
Package'].mode()[0])
df['Laid Off'].value_counts()
cross_tab = pd.crosstab(df['Severance Package'], df['Laid Off'])
cross_tab.plot(kind='bar', stacked=True)
plt.xlabel('Severance Package')
plt.ylabel('Count')
plt.title('Laid Off Status by Severance Package')
plt.legend(title='Laid Off')
plt.show()
df.head()
df['Promotion'].unique()
df.loc[df['Promotion'] == 'Promotion', 'Promotion'] = 'True'
df['Promotion'] = df['Promotion'].fillna(df[df['Promotion'] != np.nan]
['Promotion'].mode()[0])
df['Promotion'].value_counts()
df.drop('Transfer', axis = 1, inplace = True)
df.head()
df['Severance Package'] = df['Severance Package'].map({'Yes' : 1, 'No' : 0})
df['Promotion'] = df['Promotion'].map({'False' : 0, 'True' : 1})
df.head()
df.drop('Layoff Reason', axis=1, inplace = True)
df.head()
df['Department'].unique()
df['Department'].value_counts()
df.loc[df['Department'] == 'Department', 'Department'] = 'Sales'
df['Department'].value_counts()
df[(df['Department'] == 'Engineering') & (df['Job Title'] == 'Software Enginee')]
72
df[df['Department'] == 'Sales']['Job Title'].unique()
list(df[df['Department'] == 'Engineering']['Job Title'].unique())
grouped = df.groupby('Department')['Job Title']
df['Job Title'].value_counts()
len(df['Job Title'].unique())
job_title_laid_off_mean = df.groupby('Job Title')['Laid Off'].mean()
job_title_laid_off_mean.plot(kind='bar')
plt.title('Effect of Job Title on Laid Off')
plt.xlabel('Job Title')
plt.ylabel('Proportion of Laid Off Employees')
plt.xticks(rotation=45)
plt.show()
from sklearn.feature_extraction import FeatureHasher
hasher = FeatureHasher(n_features=10, input_type='string')
job_titles = [[job_title] for job_title in df['Job Title'].astype(str)]
hashed_features = hasher.transform(job_titles)
hashed_df = pd.DataFrame(hashed_features.toarray())
df = pd.concat([df, hashed_df], axis=1)
df.head()
df.isnull().sum()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
one_hot_encoded = encoder.fit_transform(df[['Department']])
one_hot_df=pd.DataFrame(one_hot_encoded,
columns=encoder.get_feature_names_out(['Department']))
df = pd.concat([df, one_hot_df], axis=1)
df.head()
df.head()
df['Age Range'] = df['Age Range'].fillna(df[df['Age Range'] != np.nan]['Age
Range'].mode()[0])
df.isnull().sum()
df.drop(['Department', 'Job Title'], axis = 1, inplace = True)
location_laid_off_mean = df.groupby('Location')['Laid Off'].mean()
location_laid_off_mean.plot(kind='bar', stacked=True)
73
plt.title('Effect of Location on Laid Off')
plt.xlabel('Location')
plt.ylabel('Proportion of Laid Off Employees')
plt.xticks(rotation=45)
plt.show()
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(sparse=False)
one_hot_encoded = encoder.fit_transform(df[['Location']])
one_hot_df=pd.DataFrame(one_hot_encoded,
columns=encoder.get_feature_names_out(['Location']))
df = pd.concat([df, one_hot_df], axis=1)
df.head()
df.drop(['Employee ID', 'Location'], axis = 1, inplace = True)
df.head()
location_laid_off_mean = df.groupby('Promotion')['Laid Off'].mean()
location_laid_off_mean.plot(kind='bar', stacked=True)
plt.title('Effect of Promotion on Laid Off')
plt.xlabel('Promotion')
plt.ylabel('Proportion of Laid Off Employees')
plt.xticks(rotation=45)
plt.show()
df['Laid Off'].value_counts()
from sklearn.utils import resample
majority_class = df[df['Laid Off'] == 0]
minority_class = df[df['Laid Off'] == 1]
minority_upsampled=resample(minority_class,replace=True,n_samples=len(majority
_class), random_state=42)
df_upsampled = pd.concat([majority_class, minority_upsampled])
df_upsampled=df_upsampled.sample(frac=1,random_state=42).reset_index(drop=Tru
e)
df = df_upsampled
df['Laid Off'].value_counts()
df.isnull().sum()
df.info()
74
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, classification_report
df.columns = df.columns.astype(str)
'Laid_Off'
X = df.drop(columns=['Laid Off']) # Features
y = df['Laid Off'] # Target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
models = [
('Random Forest', RandomForestClassifier(random_state=42)),
('Logistic Regression', LogisticRegression(random_state=42)),
('Support Vector Machine', SVC(random_state=42))
]

best_model = None
best_accuracy = 0
for model_name, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Model: {model_name}, Accuracy: {accuracy}")
if accuracy > best_accuracy:
best_model = model
best_accuracy = accuracy
if best_model is not None:
print("\nBest Model:")
print(best_model)
print("Best Accuracy:", best_accuracy)
print("Classification Report:")
y_pred = best_model.predict(X_test)
75
 print(classification_report(y_test, y_pred))

else:
print("No best model found.")
import pickle
with open('best_model.pkl', 'wb') as f:
pickle.dump(best_model, f)

76
6.9.3 REAL TIME JOB SCRAPING

from selenium import webdriver

from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By
# from selenium.webdriver.support.expected_conditions import EC
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.keys import Keys
import json
import time

def open_chrome():
options = webdriver.ChromeOptions()
# options.add_argument('--headless') # Consider headless mode if needed
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))
return driver

def login_to_indeed(driver, username, password):

# Implement login logic using appropriate selectors and handling potential errors
# (consider using WebDriverWait for waiting on elements)
sign_in_btn = driver.find_element(By.LINK_TEXT, 'Sign in')
time.sleep(3)
sign_in_btn.click()

def google_login(driver, username, password):

continue_with_google_btn = driver.find_element(By.ID, 'login-google-button')
time.sleep(2)
continue_with_google_btn.click()
time.sleep(3)

77
 # * Parent window
main_window_handle = driver.window_handles[0]
# * Child window
pop_up_window_handle = driver.window_handles[1]
# * switching to pop-up window
driver.switch_to.window(pop_up_window_handle)

email_input = driver.find_element(By.ID, 'identifierId')

time.sleep(2)
email_input.send_keys(username)
time.sleep(2)
email_input.send_keys(Keys.RETURN)
time.sleep(2)
password_input=driver.find_element(By.XPATH,
'//*[@id="password"]/div[1]/div/div[1]/input')
time.sleep(2)

password_input.send_keys(password)
time.sleep(2)

password_input.send_keys(Keys.RETURN)

# * switching back to main window

try:
main_window = driver.window_handles[0]
driver.switch_to.window(main_window)
except:
pass
time.sleep(7)

def search_for_jobs(driver, job_title, location):

78
 # Implement job search logic using appropriate selectors and handling potential
errors
# (use explicit waits and validate user input)
job_input = driver.find_element(By.XPATH, '//*[@id="text-input-what"]')
job_input.send_keys(job_title)
time.sleep(4)

try:
location_input = driver.find_element(By.CSS_SELECTOR, '#text-input-where')
print("location found", location_input)
time.sleep(4)
location_input.send_keys(location)
time.sleep(2)
location_input.send_keys(Keys.RETURN)
except:
print("location not found")

# location_input.send_keys(location)
# time.sleep(2)
# location_input.send_keys(Keys.RETURN)
time.sleep(5)

def extract_job_data(driver):
job_data = []
# Implement scraping logic using appropriate selectors and handling potential
errors
# (consider using WebDriverWait and exception handling)
# time.sleep(10)
jobs = driver.find_elements(By.CLASS_NAME, 'css-5lfssm eu4oa1w0')

print("jobs: ",len(jobs))
for job in jobs:
try:
driver.find_element
79
 role = job.find_element(By.TAG_NAME, 'jcs-JobTitle css-jspxzf eu4oa1w0')
company_name = job.find_element(By.CLASS_NAME, 'css-92r8pb
eu4oa1w0')
location = job.find_element(By.CLASS_NAME, 'css-1p0sjhy eu4oa1w0')
print()
print('Job Role: ', role.text)
print('Company Name: ', company_name.text)
print('Job Location: ', location.text)
print()
job_data.append({
'role': role.text,
'company_name': company_name.text,
'location': location.text
})
except Exception as e:
print(f"Error extracting job data: {e}")

return job_data

def save_to_database(job_data, database_name):

try:
con = sq.connect(database_name)
cur = con.cursor()
# Implement database operations with error handling
con.commit()
except Exception as e:
print(f"Error saving data to database: {e}")
finally:
con.close()

def save_to_json(job_data, json_filename):

# Implement JSON serialization and file writing
pass

80
def main():
job_title = input("Enter the job title you want to work in: ")
location = input("Is there any preferred location: ")

username = "devtestm52@gmail.com"
password = "Aviral$123"

driver = open_chrome()
driver.get('https://www.indeed.com')

login_to_indeed(driver, username, password) # Replace with actual login logic

google_login(driver, username, password)

search_for_jobs(driver, job_title, location)

job_data = extract_job_data(driver)
print("job_data: ",job_data)

if __name__ == "__main__":
main()

81
6.9.4 BACKEND

1. Database Connectivity

const mongoose = require('mongoose');

require('dotenv').config();

exports.connect = () => {
mongoose.connect(process.env.MONGODB_URL)

.then(() => console.log('dataBase Successfully Connected!!'))

.catch((error) => {
console.log("DB connection failed!)");
console.error(error);
process.exit(1);
})
};

2. Profile Schema

const mongoose = require("mongoose");

const profileSchema = new mongoose.Schema({
gender:{
type: String
},
dateOfBirth:{
type: String,
},
about:{
type: String,
trim: true

82
 },
contactNumber:{
type: Number,
trim: true
}
});
module.exports = mongoose.model("Profile", profileSchema)

3. User Schema

const mongoose = require('mongoose');

const userSchema = new mongoose.Schema({
firstName:{
type: String,
required: true,
trim: true,
},
lastName:{
type: String,
required: true,
trim: true,
},
email:{
type: String,
required: true,
trim: true,
},
password:{
type: String,
required: true,
},
additionalDetails:{
type: mongoose.Schema.Types.ObjectId,
required: true,

83
 ref: "Profile",
},
image:{
type: String,
},
token:{
type: String,
},
resetPasswordExpires:{
type: Date,
},
accountType:{
type: String,
enum: ["Admin", "Student", "Professional "],
required: true,
},
});
module.exports = mongoose.model("User", userSchema)

4. OTP verification

const mongoose = require("mongoose");

const mailSender = require("../utils/mailSender");
const emailTemplate = require("../mail/templates/emailVerificationTemplate");

const OTPSchema = new mongoose.Schema({

email:{
type:String,
required: true,
},
otp: {
type:String,
required:true,
},

84
 createdAt: {
type:Date,
default:Date.now(),
expires: 5*60,
}
});

//a function -> to send emails

async function sendVerificationEmail(email, otp) {
try{
const mailResponse = await mailSender(email, "Verification Email from Get-
Hired", emailTemplate(otp));
console.log("Email sent Successfully: ", mailResponse);
}
catch(error) {
console.log("error occured while sending mails: ", error);
throw error;
}
}

OTPSchema.pre("save", async function(next) {

await sendVerificationEmail(this.email, this.otp);
next();
})

module.exports = mongoose.model("OTP", OTPSchema);

85
 CHAPTER 7: RESULT AND OUTPUT

7.1 INTRODUCTION:

This section provides a comprehensive overview of the results and output obtained
from our project on placement and layoff prediction using machine learning
algorithms. The analysis encompasses the performance evaluation of three key
classes: placement prediction, layoff prediction, and job management. Through a
series of experiments and performance measures, the efficacy of the employed
machine learning models, including logistic regression, support vector machine
(SVM), and random forest, is thoroughly assessed.

The section begins by presenting sample output images illustrating the predictions for
four distinct scenarios: successful job placements, layoff predictions, job listings
retrieved from external sources, and management information for employees at risk of
layoff. These sample images offer a visual representation of the predictive capabilities
of our system, showcasing its ability to accurately identify potential job opportunities
and anticipate layoff risks.

Following the presentation of sample images, detailed discussions on the CNN model
training experiments and performance measures are provided. Each class, including
placement prediction, layoff prediction, and job management, undergoes rigorous
evaluation to assess the accuracy, precision, recall, and F1-score of the predictions.
Through comprehensive performance analysis, insights into the strengths and
limitations of each machine learning algorithm are gained, facilitating informed
decision-making and model refinement.

Additionally, graphical representations of the predicted class images are presented for
all samples, offering a visual summary of the classification results. These graphical

86
representations aid in the interpretation and understanding of the model's predictions,
enabling stakeholders to identify trends, patterns, and anomalies in the data.

Overall, this section serves as a vital component of our project, providing valuable
insights into the performance and output of our placement and layoff prediction
system. Through meticulous analysis and interpretation of the results, stakeholders
can gain a deeper understanding of the system's predictive capabilities and its
potential impact on workforce management and decision-making processes.

7.2 MODEL EVALUATION:

7.2.1 Features for placement prediction model’s evaluation

1. Major Subjects:

The major subject pursued by candidates serves as a foundational parameter in

placement prediction. Candidates' academic disciplines, such as Computer
Science, Engineering, Business Administration, or Humanities, provide
valuable context regarding their area of expertise and potential job
opportunities.

2. Technical Skills Rating (1-5):

Technical proficiency is a key determinant of candidates' suitability for

specific job roles. Candidates' proficiency in technical domains, such as
programming languages, database management, software development, and
data analysis, is rated on a scale of 1 to 5, reflecting their level of expertise and
competence.

3. Soft Skills Rating (1-5):

Soft skills play a pivotal role in candidates' professional success and

organizational fit. Attributes such as communication, teamwork, problem-
solving, adaptability, and leadership are assessed based on candidates'
demonstrated proficiency, rated on a scale of 1 to 5.

4. Number of Projects Completed:

87
 Candidates' involvement in projects provides tangible evidence of their
practical skills and application of theoretical knowledge. The number of
projects completed by candidates, whether academic or extracurricular, serves
as a measure of their hands-on experience and initiative.

5. Internships Experience:

Internship experience offers invaluable exposure to real-world work

environments and industry practices. Candidates' participation in internships,
whether within academic programs or external organizations, indicates their
readiness for professional roles and their ability to apply classroom learning in
practical settings.

By analysing these parameters collectively, the placement prediction system generates

insights into candidates' qualifications, skills, and experiences, enabling accurate
assessments of their suitability for various job opportunities. Leveraging data-driven
approaches and predictive analytics, the system facilitates informed decision-making
in talent acquisition and workforce management, ultimately enhancing organizational
performance and success.

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Fig 7.2.1: Result of Evaluation of Placement prediction model by different algorithm

7.2.2 Features for Layoff prediction model’s evaluation

1. Department:

The department in which an employee is employed serves as a critical

determinant of layoff risk. Variations in departmental performance, budget
allocations, and strategic priorities may influence the likelihood of layoffs
within specific organizational units.

2. Job Title:

Employees' job titles provide insights into their roles, responsibilities, and
hierarchical positions within the organization. Job titles may signify varying
levels of job security, with certain roles more susceptible to layoffs due to
restructuring, automation, or outsourcing.

3. Years of Experience:

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 Employees' tenure within the organization is a key factor in layoff prediction.
Longer-serving employees may be perceived as more valuable due to their
institutional knowledge and experience, while newer hires may face greater
uncertainty regarding their job stability.

4. Age of Employee:

Age serves as a proxy for employees' career stage and potential susceptibility
to layoffs. Older employees nearing retirement age may be targeted for layoffs
as part of cost-saving measures or workforce restructuring initiatives.

5. Location:

Geographic considerations, such as the location of an employee's office or

branch, may influence their vulnerability to layoffs. Regional economic
conditions, market dynamics, and business operations may impact layoff
decisions within specific locations or regions.

6. Severance Package:

The availability and terms of severance packages offered by the organization

serve as indicators of potential layoffs. The provision of generous severance
benefits may suggest impending workforce reductions or organizational
restructuring efforts.

7. Promotion History:

Employees' history of promotions within the organization offers insights into

their performance, potential, and perceived value to the company. Employees
who have received promotions may be less likely to face layoffs due to their
demonstrated contributions and upward career trajectory.

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By analysing these features collectively, the layoff prediction model generates insights
into employees' vulnerability to job loss, enabling organizations to proactively
identify
and mitigate layoff risks. Leveraging predictive analytics and data-driven approaches,
the model facilitates informed decision-making in workforce management, succession
planning, and organizational resilience strategies, ultimately minimizing the impact of
layoffs on employees and the organization as a whole.

Fig 7.2.2 Result of Evaluation of Layoff Prediction Model by different algorithms

7.3 GRAPHICAL ANALYSIS:

7.3.1 PLACEMENT PREDICTION MODEL:

In the graphical analysis of the placement prediction model, various visualizations are
employed to illustrate the model's performance, predictive capabilities, and key
insights into job placement outcomes. Through visually compelling representations of
data and model predictions, stakeholders gain a deeper understanding of the
placement prediction process and its implications for talent acquisition and workforce
management strategies.

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92
 Fig 7.3.1: Boxplot of Technical and Soft Skills

Fig 7.3.2: Bar chart of Projects Made

Fig 7.3.3: Bar chart of Technical Skills VS Count of Placed Students

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Fig 7.3.4: Bar chart of various rating of soft skills

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Fig 7.3.5: Histogram of Soft Skills and Technical Skills Merged Together

Fig 7.3.6: Bar chart of GPA column count

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 Fig 7.3.7: Distribution Plot of GPA column

Fig 7.3.8: Distribution Plot of Soft skills column

Fig 7.3.9: Distribution Plot of Transformed GPA column

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Fig 7.3.10: Histogram of Transformed GPA column with GPA

Fig 7.3.11: Effect of Internship VS Placed Students

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Fig

7.3.12: Effect of Major Subjects on Placed Students

Fig 7.3.13: Heat Map of correlation of various features of Placement Prediction

7.3.14 LAYOFF PREDICTION:

In the graphical analysis of the layoff prediction model, various visualizations are
utilized to illustrate the model's performance, predictive accuracy, and key insights
into layoff risk assessment. Through visually compelling representations of data and
model predictions, stakeholders gain valuable insights into workforce dynamics,
organizational vulnerabilities, and proactive measures to mitigate layoff risks.

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 Fig 7.3.14: Effect of Location on Laid Off Status

Fig 7.3.15:
Effect of Severance Package on Laid Off Status

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Fig 7.3.16: Bar Representation on Count of Location VS Count of Laid Off

Fig 7.3.17: Promotion VS Laid Off Status of Employees

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Fig 7.3.18: Heatmap of correlation of various features of layoff prediction

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7.4 USER-FRIENDLY INTERFACE:

This machine learning project enhances employee retention and student placement by
accurately predicting outcomes based on academic and project data. It offers
personalized recommendations for students and identifies potential staff layoffs for
organizations, directing individuals to an educational platform for tailored
professional development. Overall, it optimizes career prospects and fosters
continuous learning in both educational and professional settings.

GRAPHICAL VIEW SCREENSHOTS

This section covers the step-wise app procedure visual appearance to help the user to
understand the work. Fig. (6.5.1) displays the front-end of the webapp.

Fig 7.4.1 Homepage of GET-HIRED

Step 1: Signing Up for a New Account:

If you don't have an account yet and need to sign up, you'll be directed to the signup
page. Here, you'll usually find a form asking for information such as your name, email
address, password. Fill in the required fields with accurate information.

Fig 7.4.1 Homepage of GET-HIRED

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Step 1: Signing Up for a New Account:

If you don't have an account yet and need to sign up, you'll be directed to the signup
page. Here, you'll usually find a form asking for information such as your name, email
address, password. Fill in the required fields with accurate information. Once all
fields are completed, review the information for accuracy and click the Create
Account button to finalize your registration.

Figure 7.4.2 Signup Page

Step 3: Verifying Your Email :

Check your email inbox for a verification message from the website. Enter the OTP
sent on the email to confirm your account.

Fig 7.4.2 Signup page

Step 2: Verifying Your E-Mail:

Check your email inbox for a verification message from the website. Enter the OTP
sent on the email to confirm your account.

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 Fig 7.4.3 Verify OTP

Step 3: Logging In to Your Account:

If you already have an account and need to log in, click on the "Login" button on the
website's homepage. You'll be taken to the login page where you'll typically find
fields for entering your email address or username and password.

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 Figure 7.4.4 Login page

Step 4: Contact Us Page:

For user support, any inquiries, or suggestions, please visit our Contact Us page.
You'll find various ways to ensure that your concerns are addressed promptly and
efficiently.

Figure 7.4.5 Contact us page

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 CHAPTER 8: CONCLUSIONS AND
RECOMMENDATIONS

8.1 CONCLUSION:

In concluding this project, we reflect on the journey that has brought us to the
forefront of talent management innovation. Our endeavour to seamlessly integrate
placement prediction, layoff prediction, and real-time job management functionalities
into a unified platform represents a transformative approach to workforce
optimization and career advancement.

At its core, our project embodies the principles of user empowerment and
technological innovation. From conception to execution, every aspect of the platform
has been meticulously crafted to prioritize user experience and meet the evolving
needs of the modern workforce. By leveraging advanced machine learning algorithms,
an intuitive graphical user interface (GUI), and real-time data integration, we have
created a dynamic ecosystem that enables individuals to take control of their careers
with confidence and clarity.

The project’s workflow is designed for efficiency and ease of use, guiding users
smoothly from start to finish. The intuitive interface makes it simple for both new
graduates seeking placements and experienced professionals dealing with layoffs to
navigate the platform. By offering personalized recommendations and real-time job
listings, we help users make informed career decisions and take advantage of growth
opportunities.

Transformative Innovations:

One of the project's key innovations lies in its ability to seamlessly transition between
placement prediction and layoff prediction functionalities based on user input. By
analysing a diverse range of features, including departmental affiliations, job titles,
experience levels, and geographical locations, our platform provides users with

106
tailored insights into their career trajectories and potential risks. This holistic
approach to talent management enables individuals to navigate the complexities of the
job market with

Moreover, the integration of a real-time job management system adds a layer of

practicality and immediacy to the platform, enabling users to access up-to-date job
listings and career resources with ease. Through web scraping technology from the
Indeed website, users can explore job opportunities relevant to their skills and
preferences in real time, facilitating proactive career management and job search
efforts. This seamless integration of predictive analytics and job market data ensures
that users are equipped with the latest information and resources to make informed
decisions about their professional futures.

As we reflect on the project's evolution and impact, it becomes evident that our
platform has the potential to revolutionize talent management and career development
practices. By embracing technological innovation and human-centred design
principles, we tend to create a dynamic ecosystem that empowers individuals to
navigate the complexities of the modern job market with confidence and resilience.
Looking ahead, we remain committed to continuous improvement and innovation,
ensuring that our platform continues to serve as a catalyst for career advancement and
organizational success in the years to come.

8.2 RECOMMENDATIONS:

8.2.1 Continuous Model Refinement: Invest in ongoing refinement of the placement

and layoff prediction models by incorporating additional features, fine-tuning
hyperparameters, and exploring alternative algorithms. Regular model updates will
ensure improved accuracy and relevance of predictions over time.

8.2.2 Dynamic User Interface: Enhance the graphical user interface (GUI) with
interactive elements, personalized dashboards, and intuitive navigation features. A
dynamic and user-friendly interface will enhance user engagement and satisfaction,
facilitating seamless interaction with the platform.

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8.2.3 Expanded Feature Set: Expand the feature set used for prediction models to
include a broader range of parameters relevant to job placement and layoff risk
assessment. Incorporating factors such as performance metrics, industry certifications,
and networking activities can enhance the predictive power of the models.

8.2.4 Cross-Validation and Validation: Conduct thorough cross-validation and

validation exercises to assess the robustness and generalizability of the prediction
models across diverse organizational contexts and demographic profiles. This will
ensure the reliability and applicability of the models in real-world scenarios.

8.2.5 Integration of Feedback Mechanisms: Implement feedback mechanisms

within the platform to gather user feedback and insights on model predictions, user
experience, and platform functionality. Leveraging user feedback will facilitate
continuous improvement and refinement of the platform.

8.2.6 Collaboration with Industry Partners: Foster collaborations with industry

partners, academic institutions, and professional organizations to validate prediction
models, gather real-world data, and gain insights into evolving workforce trends and
challenges.

8.2.7 Enhanced Data Security Measures: Strengthen data security measures to

ensure the privacy and confidentiality of user data. Implement robust encryption
protocols, access controls, and data anonymization techniques to safeguard sensitive
information.

8.2.8 Scalability and Performance Optimization: Optimize the platform's

scalability and performance to accommodate increasing user traffic and data volume.
Leveraging cloud-based infrastructure, distributed computing technologies, and
scalable database solutions will ensure smooth operation and responsiveness of the
platform.

8.2.9 Incorporation of Explainable AI Techniques: Integrate explainable artificial

intelligence (AI) techniques to enhance the interpretability and transparency of
prediction models. Providing users with insights into the factors driving model
predictions will foster trust and confidence in the platform.

108
8.2.10 User Education and Training: Develop user education and training resources
to empower users with the knowledge and skills to effectively leverage the platform's
capabilities. Offering tutorials, webinars, and documentation will enhance user
proficiency and adoption rates.

8.2.11 Diverse Job Listing Sources: Expand the sources of job listings beyond
Indeed to include a diverse range of job boards, recruitment platforms, and company
websites. This will provide users with a comprehensive view of job opportunities and
increase the likelihood of finding relevant positions.

8.2.12 Localized Job Market Insights: Provide localized job market insights and
trends to users based on their geographic location and industry preferences. Tailoring
job recommendations and career guidance to specific regions and sectors will enhance
the relevance and utility of the platform.

8.2.13 Partnerships with Career Services: Forge partnerships with career services
offices at universities, colleges, and vocational institutions to promote the platform as
a valuable resource for students and alumni. Collaborating with career counsellors and
advisors will increase awareness and adoption among the target audience.

8.2.14 Longitudinal User Engagement: Implement strategies to foster long-term

user engagement and retention, such as personalized job alerts, career development
resources, and networking opportunities. Building a vibrant and supportive
community within the platform will enhance user satisfaction and loyalty over time.

By implementing these recommendations, the project can evolve into a

comprehensive and indispensable resource for individuals navigating their careers and
organizations optimizing their workforce management strategies. Continued
innovation and collaboration will ensure the platform remains at the forefront of talent
management and career development initiatives.

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8.3 CLASSIFICATION REPORT FOR PLACEMENT
PREDICTION

Features Precision Recall F1-score Support

0 0.77 0.91 0.83 44
1 0.83 0.62 0.71 32
Accuracy 0.79 76
Macro svg 0.80 0.77 0.77 76
Weighted avg 0.80 0.79 0.78 76

Table 8.3.1: Logistic Regression Classification

Features Precision Recall F1-score Support

0 0.90 0.91 0.85 44
1 0.84 0.66 0.74 32
Accuracy 0.81 76
Macro svg 0.82 0.79 0.80 76
Weighted avg 0.81 0.81 0.80 76

Table 8.3.2: Support Vector Machine Classification

Features Precision Recall F1-score Support

0 0.83 0.93 0.88 44
1 0.89 075 0.81 32
Accuracy 0.85 76
Macro svg 0.86 0.84 0.85 0.76
Weighted avg 0.86 0.85 0.85 76

Table 8.3.3: Random Forest Classification

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8.4 CLASSIFICATION REPORT FOR LAYOFF PREDICTION

Features Precision Recall F1-score Support

0 0.77 0.91 0.83 44
1 0.83 0.62 0.71 32
Accuracy 0.79 76
Macro svg 0.80 0.77 0.77 76
Weighted avg 0.80 0.79 0.78 76

Table 8.4.1: Logistic Regression Classification

Features Precision Recall F1-score Support

0 0.90 0.91 0.85 44
1 0.84 0.66 0.74 32
Accuracy 0.81 76
Macro svg 0.82 0.79 0.80 76
Weighted avg 0.81 0.81 0.80 76

Table 8.4.2: Support Vector Machine Classification

Features Precision Recall F1-score Support

0 0.83 0.93 0.88 44
1 0.89 075 0.81 32
Accuracy 0.85 76
Macro svg 0.86 0.84 0.85 0.76
Weighted avg 0.86 0.85 0.85 76

Table 8.4.3: Random Forest Classification

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8.5 TESTING

8.5.1 PLACEMENT PREDICTION TESTING

Input Actual Output Desired Output Result

Business NO NO PASS
Administration,
3.39, 3, 1, N, 0
Marketing, 3.05, 3, NO NO PASS
2, Y, 0

Finance, 3.83, 2, YES YES PASS

1, Y, 1

8.5.2 LAYOFF PREDICTION TESTING

Input Actual Output Desired Output Result

Engineering, NO NO PASS
Software Engineer,
5, 24, Mumbai,
Promotion, Yes
Marketing, Market NO NO PASS
Manager, 10, 33,
Hyderabad, No, No

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 IT, Network YES YES PASS
Administrator, 15,
42, Remote,
Restructuring, Yes

APPENDIX

LIBRARIES/ FRAMEWORKS USED:

1. Scikit-learn: A comprehensive machine learning library in Python that provides

simple and efficient tools for data mining and data analysis. It includes various
algorithms for classification, regression, clustering, and dimensionality reduction.

2. TensorFlow: An open-source machine learning framework developed by Google

for building and training neural networks. It provides tools for creating and deploying
machine learning models across a range of platforms.

3. Pandas: A powerful data manipulation and analysis library in Python. It provides

data structures and functions for manipulating structured data and time series data.

4. NumPy: A fundamental package for scientific computing in Python. It provides

support for multidimensional arrays, mathematical functions, linear algebra
operations, and random number generation.

5. Matplotlib: A plotting library for creating static, interactive, and animated

visualizations in Python. It provides a MATLAB-like interface for generating plots
and charts.

6. Seaborn: A statistical data visualization library based on Matplotlib. It provides a

high-level interface for drawing attractive and informative statistical graphics.

113
 7. Flask: A lightweight web application framework for Python. It is designed to make
getting started with web development quick and easy, with built-in development
server and support for RESTful APIs.

REFERENCES

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Hussain & Sarita Singh
3. “A Comparative Study on Machine Learning Algorithms for Predicting the Placement
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Learning Techniques” BY Shikha N. Khera, Divya Volume 23, Issue 1 |
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Manjari, S. Rousha, D. Sumanth and J. Sirisha Devi, "Extractive Text
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CONTACT DETAILS

Name: Aviral Mishra

Roll No: 2003481530004
Phone No: 9696277390
Email: aviralm52@gmail.com

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Roll No: 2003481530008
Phone No: 7348471460
Email: harshsree677@gmail.com

Name: Harsh Vardhan Rai

Roll No: 2003481530009
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Name: Khushi Jalan

Roll No: 2003481530012
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Email: khushijalan1302@gmail.com

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