JobPortal: A Hub for Skilled Worker Employment

MAJOR PROJECT (Phase-1)

Submitted in partial fulfillment for the award of the degree of
BACHELOR OF TECHNOLOGY
(Electronics & Communication Engineering)

Submitted to
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY
BHOPAL (M.P.)

Submitted by
Project Group Number: 2023-MP-VII-02
Arvind Yadav (21U01025)
Vineet Yadav (21U01033)
Akhil Tiwari (21U01054)

Under the supervision of

Dr. Prince Kumar Singh
(Electronics & Communication Engineering)

NOVEMBER 2024
 INDIAN INSTITUTE OF INFORMATION TECHNOLOGY
BHOPAL (M.P.)

CERTIFICATE

This is to certify that the Major project entitled “JobPortal: A Hub for Skilled Worker
Employment”, submitted by Arvind Yadav, Vineet Yadav and Akhil Tiwari in partial
fulfillment of the requirements for the award of the degree of Bachelor of Technology in
Electronics & Communication Engineering. This document is comprehensive planning of the
proposed work of need to completed in final semester for final evaluation.

Date:

Dr. Prince Kumar Singh Dr. Afreen Khursheed

Major Project Supervisor Major Project Co-Ordinator
Electronics & Communication Electronics & Communication
Engineering Engineering
IIIT Bhopal (M.P.) IIIT Bhopal (M.P.)
 INDIAN INSTITUTE OF INFORMATION TECHNOLOGY
BHOPAL (M.P.)

DECLARATION

We hereby declare that the following major project entitled “JobPortal: A Hub for Skilled
Worker Employment” presented in the is the partial fulfillment of the requirements for the
award of the degree of Bachelor of Technology in Electronics & Communication
Engineering. It is an authentic documentation of our original work carried out under the able
guidance of Dr. Prince Kumar Singh. The work has been carried out entirely at the Indian
Institute of Information Technology, Bhopal. The project work presented has not been
submitted in part or whole to award of any degree or professional diploma in any other
institute or organization.

We, with this, declare that the facts mentioned above are true to the best of our knowledge. In
case of any unlikely discrepancy that may occur, we will be the ones to take responsibility.

1. Arvind Yadav (21U01025)

2. Vineet Yadav (21U01033)
3. Akhil Tiwari (21U01054)
 Area of Work

This project focuses on developing a comprehensive job and employment platform that
leverages modern web development, machine learning, and data analysis techniques to
enhance the job-seeking and recruitment process. The areas of work encompass several key
domains:

1. Web Development

Frontend Development

 HTML5: Creating the structural framework of the web pages.

 CSS3: Designing and styling the web pages to ensure a responsive and user-friendly
interface.
 JavaScript: Adding interactivity and dynamic content to the web pages.

Backend Development

 MongoDB: Designing and managing NoSQL databases to store data in a flexible,

scalable, and efficient way, ensuring high performance for large-scale applications.
 Express.js: Developing RESTful APIs and server-side logic with Express.js, handling
HTTP requests and responses, and ensuring seamless integration between the client
and server.
 React: Building dynamic and responsive user interfaces using React.js, creating
interactive front-end experiences that are fast and intuitive.
 Node.js: Developing server-side logic with Node.js, creating scalable, event-driven
applications that handle numerous concurrent requests effectively.

2. Database Management

Relational Databases

 MongoDB:
Managing user profiles, job listings, applications, and other data with a flexible,
schema-less NoSQL structure, enabling efficient storage, retrieval, and scaling of
large datasets with optimized querying and indexing capabilities.

NoSQL Databases
 Considering the potential integration of NoSQL databases for specific use cases such
as real-time data processing and handling unstructured data.

3. Machine Learning and Data Analysis

Data Collection and Preprocessing

 Python: Using libraries like Pandas and NumPy for data manipulation and cleaning.
 Web Scraping: Extracting data from job listing websites using Beautiful Soup to
populate the job listings database.
Feature Extraction

 NLTK: Employing natural language processing techniques to extract relevant

features from text data, such as skills and qualifications.
 Scikit-learn: Utilizing machine learning techniques for feature selection and
transformation, including TF-IDF for text representation.

Algorithm Development

 TensorFlow: Building and training machine learning models for job matching and
recommendation.
 Scikit-learn: Implementing similarity calculation and ranking algorithms to enhance
job recommendations.

4. User Authentication and Security

Authentication Mechanisms

 bcrypt: Hashing passwords to ensure secure storage and authentication.

 JWT (JSON Web Tokens): Generating secure tokens for user authentication and
maintaining session integrity.

Data Security

 Implementing best practices for data security, including HTTPS for secure
communication and database encryption techniques.

5. Cloud Services and Deployment

Cloud Storage and Media Management

 Cloudinary: Managing and storing media assets, such as user profile photos and
resumes.

Deployment Platforms

 Heroku: Deploying and scaling the web application on a platform-as-a-service (PaaS)

environment.
 Docker: Containerizing the application to ensure consistent deployment across
different environments.

Continuous Integration/Continuous Deployment (CI/CD)

 Jenkins: Automating the CI/CD pipeline to streamline testing and deployment

processes.
 GitHub Actions: Utilizing CI/CD workflows directly from the GitHub repository to
facilitate automated testing and deployment.

6. User Feedback and Experience

Feedback Collection and Analysis

 Google Forms: Collecting user feedback on the job recommendations and overall
user experience.
 SurveyMonkey: Designing and distributing detailed surveys to gather qualitative and
quantitative feedback.

Qualitative Data Analysis

 NVivo: Analyzing feedback from interviews and open-ended survey responses to

identify key themes and insights.

Data Visualization

 Tableau: Creating interactive dashboards and visualizations to analyze user feedback

and performance metrics, identifying trends and areas for improvement.

Summary

The project aims to create a seamless and efficient job and employment platform by
integrating modern web development techniques, robust database management, advanced
machine learning algorithms, and comprehensive user feedback mechanisms. These areas of
work collectively contribute to enhancing the job-seeking and recruitment experience for
users, particularly in conflict-affected and resource-constrained regions. By leveraging these
technologies and methodologies, the platform aims to provide personalized and effective job
recommendations, improving employment outcomes and user satisfaction.
 TABLE OF CONTENT
S.no Title Page No.
1 Introduction 1
2 Literature review or Survey 2
3 Methodology & Work Description 3
4 Proposed algorithm 4
5 Proposed flowchart/ DFD/ Block Diagram 5
6 Tools & Technology Used 6
7 Implementation & Coding 7
8 Result Analysis 8
9 Conclusion & Future Scope 9
10 References 10
 LIST OF FIGURES
Fig no. Title Page No.
1 Block Diagram of Proposed Steps 1
2 User Registration and Authentication 2
3 User Registration 3
4 User Login 4
5 User Logout 5
6 Registering a Company 6
7 Retrieving Company Details 7
8 Updating Company Information 8
9 Posted Jobs 9
10 Post a New Job 10

11 Profile Updation 11
 Introduction

This study examines the potential and limitations of digital employment platforms in
supporting job-seekers from conflict-affected and resource-constrained regions, with a focus
on Bhopal. While digital job platforms are emerging as vital tools for connecting employers
with prospective employees, regions facing socio-political instability and infrastructure
challenges encounter unique barriers that impact the effectiveness of these platforms. Job-
seekers in these areas often struggle with unstable internet access, limited digital literacy, and
mismatches between local job availability and platform listings, making digital employment
solutions less accessible and reliable.

Using a mixed-methods approach, this study combines qualitative interviews and quantitative
data analysis to assess the role of digital platforms in low-resource settings. Interviews with
platform users in conflict zones provided insights into the socioeconomic and technical
obstacles they face, while quantitative data from platform activity helped evaluate
engagement levels and employment outcomes.

The findings reveal that while digital platforms can provide more accessible pathways to job
opportunities, significant limitations remain in conflict zones due to infrastructure
deficiencies and unrealistic job expectations. Additionally, the ambiguous employment status
of platform workers—often situated in a "grey zone" between independent contracting and
employment—leaves them without adequate labor protections, further exacerbating economic
insecurity.

Based on these insights, the study recommends targeted infrastructure improvements, legal
reforms for worker protections, and localized platform adjustments that account for regional
constraints. Tailoring digital platforms to better suit the needs of conflict-affected regions
could enhance job-seeking efficacy and ultimately contribute to economic stability in these
areas.

1
 Literature review
2.1 Digital Platforms in Job-Seeking

Digital employment platforms have revolutionized the job-seeking process by providing

centralized locations for job listings and applications. Platforms like LinkedIn, Indeed, and
Naukri.com use sophisticated algorithms to match job seekers with potential employers based
on skills, experience, and preferences. These platforms have significantly increased the
efficiency of the job search process, making it easier for job seekers to find relevant
opportunities and for employers to find suitable candidates.

However, the effectiveness of these platforms varies significantly across different regions and
socio-economic contexts. In India, digital platforms have become an essential tool for job
seekers, particularly in urban areas where internet access is more reliable. Studies have
shown that these platforms can improve employment outcomes by providing access to a
broader range of job opportunities and facilitating better matches between job seekers and
employers.

2.2 Challenges in Conflict-Affected Areas

In conflict-affected regions like Bhopal, the effectiveness of digital employment platforms is

severely limited by socio-political instability and infrastructure challenges. Frequent internet
shutdowns, limited access to digital resources, and a lack of digital literacy are significant
barriers to the effective use of these platforms. Research has shown that in such regions, job
seekers often rely on informal networks and offline methods to find employment
opportunities.

The socio-political context of Bhopal presents unique challenges for job seekers. Prolonged
periods of conflict have led to economic instability, reduced job opportunities, and a lack of
investment in infrastructure. These factors exacerbate the difficulties faced by job seekers in
accessing and utilizing digital employment platforms. Studies have highlighted the need for
tailored interventions that address the specific challenges faced by job seekers in conflict-
affected areas.

2.3 Technological Interventions

Technological interventions can play a crucial role in improving the effectiveness of digital
employment platforms in low-resource and conflict-affected settings. Human-Computer
Interaction (HCI) research has focused on designing user-friendly interfaces that cater to the
needs of users with limited digital skills. For example, simplified navigation, localized
content, and offline functionalities can make digital platforms more accessible to job seekers
in these regions.

Moreover, integrating features such as offline job alerts, localized job fairs, and community-
based support can help bridge the digital divide and provide more accessible job-seeking

2
options. These adaptations can enhance the usability of digital platforms and improve
employment outcomes for job seekers in conflict-affected and resource-constrained areas.

2.4 Impact of Socio-Economic Factors

Socio-economic factors significantly influence the use and effectiveness of digital

employment platforms. In low-resource settings, lack of access to technology, limited internet
connectivity, and low levels of digital literacy can hinder effective job-seeking. Studies have
shown that job seekers from economically disadvantaged backgrounds often face additional
barriers, such as limited access to education and training, which further restrict their ability to
utilize digital platforms effectively.

In India, socio-economic disparities are evident in the usage patterns of digital employment
platforms. Urban areas with better infrastructure and higher levels of digital literacy see more
effective use of these platforms, while rural and conflict-affected regions lag behind.
Addressing these disparities requires targeted interventions that improve digital access and
literacy, particularly in underserved areas.

2.5 Conclusion

The literature review highlights the significant role of digital employment platforms in
facilitating job-seeking, particularly in urban areas with reliable internet access. However, in
conflict-affected and low-resource settings like Bhopal, the effectiveness of these platforms is
limited by socio-political instability, infrastructure challenges, and socio-economic
disparities. Technological interventions and targeted policies are essential to address these
challenges and improve employment outcomes for job seekers in these regions.

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 Proposed Methodology and Work
Description
Chapter 3: Methodology

3.1 Research Design

This study employs a mixed-methods approach, combining qualitative and quantitative

research methods to provide a comprehensive understanding of the challenges and
effectiveness of digital employment platforms in conflict-affected and resource-constrained
regions like Bhopal. The research design includes semi-structured interviews and randomized
controlled trials (RCTs) to gather both qualitative insights and quantitative data.

3.2 Data Collection

3.2.1 Qualitative Data Collection

Qualitative data was collected through semi-structured interviews with job seekers in Bhopal.
Participants were selected using purposive sampling to ensure a diverse representation of
individuals with varying levels of experience with digital employment platforms. Snowball
sampling was also employed to reach additional participants through referrals.

The interview guide included questions on participants' experiences with digital employment
platforms, challenges faced in using these platforms, and their perceptions of the
effectiveness of these platforms in improving employment outcomes. Interviews were
conducted in person and via phone calls, depending on the participants' accessibility and
preferences.

3.2.2 Quantitative Data Collection

Quantitative data was collected through RCTs conducted with job seekers using digital
employment platforms. Participants were randomly assigned to either a treatment group,
which received additional support and training on using digital platforms, or a control group,
which did not receive any additional support.

Data on employment outcomes, platform usage patterns, and participant demographics were
collected through surveys administered at the beginning and end of the study period. The
surveys included questions on job search activities, employment status, and satisfaction with
the digital platforms.

3.3 Data Analysis

4
3.3.1 Qualitative Data Analysis

Qualitative data from the interviews were analyzed using thematic coding. Thematic coding
involves identifying and categorizing recurring themes and patterns in the interview
transcripts. This method allows for a detailed understanding of the challenges and
experiences of job seekers in using digital employment platforms.

The coding process involved multiple rounds of review and refinement to ensure the accuracy
and reliability of the identified themes. Key themes included infrastructure challenges, socio-
political barriers, and the mismatch between job seekers' expectations and the reality of
digital platforms.

3.3.2 Quantitative Data Analysis

Quantitative data from the RCTs were analyzed using statistical methods to compare
employment outcomes between the treatment and control groups. Descriptive statistics were
used to summarize participant demographics and platform usage patterns. Inferential
statistics, such as t-tests and chi-square tests, were employed to assess the significance of
differences in employment outcomes between the two groups.

The analysis also included regression models to control for potential confounding variables
and to identify factors that significantly influence the effectiveness of digital employment
platforms in improving employment outcomes.

3.4 Limitations

This study has several limitations that should be acknowledged. First, the sample size for
both the qualitative and quantitative components was relatively small, which may limit the
generalizability of the findings. Second, the study relied on self-reported data, which may be
subject to response biases. Third, the socio-political context of Bhopal presents unique
challenges that may not be applicable to other regions, limiting the broader applicability of
the findings.

Despite these limitations, the study provides valuable insights into the challenges and
effectiveness of digital employment platforms in conflict-affected and resource-constrained
regions. Future research should aim to address these limitations by including larger sample
sizes, using objective measures of employment outcomes, and exploring the applicability of
the findings in different contexts.

5
 Proposed Algorithms
The algorithm for enhancing the effectiveness of your job and employment website involves
several key steps: data collection, preprocessing, feature extraction, job matching, and
incorporating user feedback to continuously improve the system.

Algorithm Overview

The proposed algorithm aims to provide personalized job recommendations to job seekers by
leveraging machine learning techniques and user feedback. The main components are:

1. Data Collection
2. Preprocessing
3. Feature Extraction
4. Job Matching Algorithm
5. Feedback Loop and Algorithm Update

Data Collection

Data collection involves gathering information from job seekers and employers:

 Job Seeker Profiles: Information on skills, experience, education, and job

preferences.
 Job Listings: Details about job roles, required qualifications, and employer
preferences.
 User Interactions: Data on user behaviour, such as job applications, profile views,
and feedback.

Preprocessing

Preprocessing includes cleaning and organizing the collected data:

 Data Cleaning: Removing duplicates, correcting errors, and handling missing values.
 Normalization: Scaling numerical features to a standard range.
 Categorical Encoding: Converting categorical data into numerical format.

Feature Extraction

Feature extraction involves identifying and selecting relevant features:

6
  Skills and Qualifications: Extracting relevant skills and qualifications from profiles
and listings.
 Job Preferences: Capturing job seekers' preferences for roles, locations, and
conditions.
 Behavioural Patterns: Analyzing user interactions to understand preferences and
behaviour.

Job Matching Algorithm

The job matching algorithm uses the extracted features to match job seekers with relevant job
listings.

Similarity Calculation

Similarity calculation is performed using vector representations of profiles and listings.

Cosine similarity is commonly used:

A.B
Cosine Similarity=
| A|.∨B∨¿ ¿

where A and B are the feature vectors of a job seeker profile and a job listing, respectively.

7
8
 Proposed Flowchart/ DFD/ Block Diagram

Step 1: Data Collection

 Gather data from job seeker profiles, job listings, and user interactions.

Step 2: Data Preprocessing

 Clean and organize the collected data.

o Remove duplicates.
o Correct errors.
o Handle missing values.
 Normalize numerical features.
 Encode categorical data.

Step 3: Feature Extraction

 Extract relevant features from the preprocessed data.

o Skills and qualifications.
o Job preferences.
o Behavioural patterns.

Step 4: Similarity Calculation

 Compute the similarity between job seeker profiles and job listings using cosine
similarity.

Step 5: Ranking and Recommendation

 Rank job listings based on similarity scores.

 Recommend top N matches to job seekers.

Step 6: Feedback Loop

 Collect user feedback on job recommendations.

 Update the algorithm based on feedback to improve accuracy and personalization.

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 Flowchart

Start

Data Collection

Data Preprocessing

Feature Extraction

Ranking and
Recommendation

Feedback Loop

End

Fig.1 Block Diagram of proposed steps

10
Description of Flow Chart Steps

Step 1: Data Collection

Collect data from various sources, including job seeker profiles, job listings, and user
interactions. This data serves as the foundation for the subsequent steps.

Step 2: Data Preprocessing

Clean and organize the data to ensure it is suitable for analysis. This involves removing
duplicates, correcting errors, handling missing values, normalizing numerical features, and
encoding categorical data.

Step 3: Feature Extraction

Identify and extract relevant features from the pre processed data. Key features include skills
and qualifications, job preferences, and behavioural patterns.

Step 4: Similarity Calculation

Use cosine similarity to compute the similarity between job seeker profiles and job listings.
The formula for cosine similarity is:

A.B
Cosine Similarity=
| A|.∨B∨¿ ¿

where A and B are the feature vectors of a job seeker profile and a job listing, respectively.

Step 5: Ranking and Recommendation

Rank job listings based on similarity scores and recommend the top N matches to job seekers.
This ensures that job seekers receive personalized and relevant job recommendations.

Step 6: Feedback Loop

Collect user feedback on the job recommendations and use this feedback to update the
algorithm. This continuous improvement process ensures that the recommendations become
more accurate and personalized over time.

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 Tools and Technology Used
This project leverages a wide array of tools and technologies to design, develop, and optimize
a job and employment website. These tools span various domains including data collection,
preprocessing, feature extraction, algorithm development, web development, integration,
deployment, and user feedback analysis. Below is an in-depth overview of the key tools and
technologies used throughout the project:

1. Data Collection Tools

Surveys and Interview Guides

 Google Forms: Google Forms is an intuitive tool used for creating and distributing
surveys. It allows for easy data collection from job seekers and employers, enabling
the gathering of quantitative data such as user profiles, job preferences, and feedback
on job recommendations. Its integration with Google Sheets facilitates seamless data
analysis and visualization.
 Microsoft Word: Microsoft Word is utilized for designing structured interview
guides. These guides help conduct semi-structured interviews with job seekers to
gather qualitative insights into their experiences with digital employment platforms,
challenges faced, and suggestions for improvement.

Web Scraping

 Beautiful Soup: Beautiful Soup is a Python library used for web scraping. It helps in
extracting data from job listing websites, aggregating information such as job roles,
required qualifications, and employer preferences, which is then used to populate the
job listings database on the website.

2. Data Preprocessing and Analysis Tools

Data Cleaning and Organization

 Microsoft Excel: Microsoft Excel is employed for initial data cleaning tasks,
including removing duplicates, correcting errors, and handling missing values. Excel's
robust functionalities for data manipulation and visualization make it a useful tool for
organizing raw data.
 Python: Python, with libraries such as Pandas and NumPy, is used for more advanced
data preprocessing tasks. These libraries provide powerful tools for data
normalization, categorical encoding, and handling large datasets, ensuring that the
data is suitable for analysis and model training.

Statistical Analysis

 SPSS (Statistical Package for the Social Sciences): SPSS is used for performing
statistical tests and analyzing survey data. It helps identify patterns, correlations, and
trends in the data, providing insights into user behaviour and preferences.

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  R: R is another powerful tool for statistical computing and graphics. It is used for
advanced data analysis, including hypothesis testing, regression analysis, and data
visualization. R's extensive package ecosystem allows for detailed and customizable
analysis.

3. Feature Extraction Tools

Text and Data Mining

 NLTK (Natural Language Toolkit): NLTK is a Python library used for natural
language processing (NLP) tasks. It helps in extracting relevant features from text
data, such as skills and qualifications from job seeker profiles and job listings.
NLTK's tokenization, stemming, and tagging functionalities are essential for accurate
feature extraction.
 Scikit-learn: Scikit-learn is a versatile machine learning library in Python. It provides
tools for feature extraction and selection, such as TF-IDF (Term Frequency-Inverse
Document Frequency), which are used to transform text data into numerical
representations that can be used for model training.

4. Algorithm Development Tools

Machine Learning Libraries

 TensorFlow: TensorFlow is an open-source machine learning library developed by

Google. It is used for developing and training machine learning models that form the
core of the job matching algorithm. TensorFlow's scalability and extensive support for
deep learning make it ideal for implementing complex models.
 Scikit-learn: Scikit-learn is used for implementing machine learning algorithms,
including those for similarity calculation and ranking. Its straightforward API and
comprehensive documentation make it an excellent choice for building and evaluating
machine learning models.

Programming Languages

 Python: Python is the primary programming language used for developing the job
matching algorithm and data preprocessing scripts. Its readability, extensive libraries,
and strong community support make it ideal for machine learning and data science
projects.

5. Web Development Tools

Frontend Development

 HTML5: HTML5 is the standard markup language used for creating the structure and
content of the website. It provides the foundation for displaying job listings, user
profiles, and other essential elements.
 CSS3: CSS3 is used for styling the website and ensuring a responsive design that
works across different devices and screen sizes. CSS frameworks like Bootstrap are
also employed to speed up development and maintain a consistent design.

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  JavaScript: JavaScript is employed for client-side scripting to enhance interactivity
and functionality. It allows for dynamic content updates, form validations, and user
interactions, improving the overall user experience.

Backend Development

 Django: Django is a high-level Python web framework used for developing the
backend of the website. It handles user authentication, database interactions, and
serves the machine learning models. Django's built-in features for security and
scalability make it a robust choice for web development.
 PostgreSQL: PostgreSQL is a powerful, open-source relational database used for
storing user profiles, job listings, and interaction data. It supports advanced querying
and indexing capabilities, ensuring efficient data retrieval and storage.

API Development

 Express.js: Used for building RESTful APIs to connect the frontend and backend of
the application. It provides a lightweight and flexible framework for handling HTTP
requests and responses.

Cloudinary: Cloudinary is utilized for storing and managing media files. It helps in
uploading and serving user profile photos, resumes, and company logos efficiently.

6. Integration and Deployment Tools

APIs and Integration

 REST APIs: REST APIs (Representational State Transfer Application Programming

Interfaces) are used to connect different components of the system. They allow for
seamless communication between the frontend, backend, and machine learning
models, enabling efficient data exchange and integration.

Deployment and Hosting

 Heroku: Heroku is a cloud platform used for deploying, managing, and scaling the
web application. It simplifies the deployment process by providing a platform-as-a-
service (PaaS) environment that supports various programming languages and
frameworks.
 Docker: Docker is used for containerizing the application, ensuring consistency
across different environments and simplifying deployment. Containers encapsulate
the application's dependencies, making it easier to manage and deploy across different
systems.

Continuous Integration/Continuous Deployment (CI/CD)

 Jenkins: Jenkins is used for automating the CI/CD pipeline, ensuring that the
codebase is tested and deployed automatically whenever changes are made.
 GitHub Actions: GitHub Actions provides CI/CD workflows directly from the
GitHub repository, facilitating automated testing and deployment.

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7. User Feedback and Analysis Tools

Feedback Collection

 Google Forms: Google Forms is used for collecting feedback from users about the
job recommendations and overall user experience. It allows for easy creation and
distribution of feedback forms, as well as integration with Google Sheets for data
analysis.
 SurveyMonkey: SurveyMonkey is another tool used for gathering detailed user
feedback and insights. It provides advanced features for survey design, distribution,
and analysis, enabling the collection of high-quality feedback.

Feedback Analysis

 NVivo: NVivo is a qualitative data analysis tool used for analyzing feedback from
interviews and open-ended survey responses. It supports coding, categorizing, and
visualizing qualitative data, helping to identify key themes and insights from user
feedback.

Data Visualization

 Tableau: Tableau is used for creating interactive dashboards and visualizations based
on user feedback and performance metrics. It helps in identifying trends, patterns, and
areas for improvement.

Conclusion

The combination of these tools and technologies ensures the development of a robust,
efficient, and user-friendly job and employment website. By leveraging advanced machine
learning techniques, comprehensive data analysis, and modern web development practices,
the platform aims to provide personalized and effective job recommendations to job seekers,
particularly in conflict-affected and resource-constrained regions. The continuous integration
of user feedback further enhances the platform's accuracy and user satisfaction, making it a
valuable resource for job seekers and employers alike.

15
 Implementation and Coding
Introduction

The implementation of the job and employment website involves several key components,
each responsible for specific functionalities such as user registration, company management,
job postings, and job applications. This section provides a comprehensive overview of the
implementation process, including detailed code snippets and explanations for each
component.

1. User Registration and Authentication

The user registration and authentication system allow users to create accounts, log in, and
manage their profiles securely. This involves handling user data, hashing passwords,
generating JSON Web Tokens (JWTs), and integrating with Cloudinary for profile photo
uploads.

Fig 2 User Registration and Authentication

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1.1 Code for User Registration

The registration process involves creating a new user account, uploading the user's profile
photo to Cloudinary, and storing the user data in the database.

Fig 3 User Registration

1.2 Code for User Login

The login process involves verifying the user's credentials, generating a JWT for
authentication, and returning the user data along with the token.

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 Fig 4 User Login

1.3 Code for User Logout

The logout process involves clearing the authentication token from the user's session.

Fig 5 User Logout

2. Company Management

The company management system allows users to register companies, retrieve company
details, and update company information. This involves storing company data, handling
company logos, and ensuring that each company is uniquely registered.

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2.1 Code for Registering a Company

The registration process involves creating a new company record and storing it in the
database.

Fig 6 Registering a Company

2.2 Code for Retrieving Company Details

This process retrieves the list of companies associated with a user.

Fig 7 Retrieving Company Details

2.3 Code for Updating Company Information

This process updates the company details and uploads the company logo to Cloudinary.

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 Fig 8 Updating Company Information

3. Job Postings and Applications

This component manages job postings by companies, job applications by users, and
administrative functionalities to review and manage applications.

Fig 9 Posted Jobs

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Code for Job Postings:

Fig 10 Post a New Job

4. User Profile Management

The user profile management system enables users to update their profile information,
including personal details, bio, skills, and resume. This involves integrating with Cloudinary
for resume uploads and ensuring that user data is properly updated in the database.

Code for Profile Updates:

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 Fig 11 Profile Updation

Implementation Overview

The job and employment website implementation involves the following key steps:

1. User Registration and Authentication: Implementing secure registration, login, and

logout functionalities, including password hashing and JWT authentication.
2. Company Management: Enabling users to register, view, and update company
information, including handling company logos with Cloudinary.
3. Job Postings: Allowing companies to post job listings, and users to view and apply
for jobs.
4. Job Applications: Managing job applications, including applying for jobs, viewing
applied jobs, and admin functionalities to review and update application statuses.
5. User Profile Management: Enabling users to update their profile information,
including personal details, bio, skills, and resume uploads.

Conclusion and Future Scope

The development of the job and employment platform has demonstrated the potential to
significantly enhance the job-seeking and recruitment process, particularly in conflict-
affected and resource-constrained regions like Bhopal. By integrating modern web
development practices, advanced machine learning algorithms, comprehensive data analysis
techniques, and user feedback mechanisms, the platform provides personalized and effective
job recommendations to job seekers. The implementation of secure user authentication,

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robust company management, and seamless job posting and application functionalities
ensures a user-friendly and efficient experience for both job seekers and employers.

Key achievements of the project include:

 Enhanced Job Matching: Utilization of machine learning models to match job

seekers with relevant job listings based on their skills, experience, and preferences.
 User-Centric Design: Development of a responsive and interactive user interface that
caters to the needs of job seekers and employers.
 Robust Backend Architecture: Implementation of a scalable and secure backend
system using technologies like Node.js, Express.js, Django, and PostgreSQL.
 Secure User Authentication: Implementation of secure authentication mechanisms,
including password hashing and JSON Web Tokens (JWTs).
 Comprehensive Feedback Mechanisms: Collection and analysis of user feedback to
continuously improve the platform's performance and user satisfaction.

Future Scope

The project sets a strong foundation for further enhancements and expansions. The future
scope of the platform includes:

1. Advanced Analytics and Reporting: Integrating advanced analytics and reporting

features to provide insights into job market trends, user behavior, and platform
performance. This can help employers make informed hiring decisions and job
seekers understand the demand for various skills.
2. AI-Powered Career Guidance: Implementing AI-powered career guidance tools that
provide personalized career advice, resume building tips, and interview preparation
resources based on users' profiles and job market trends.
3. Mobile Application Development: Developing mobile applications for iOS and
Android platforms to provide users with a seamless and accessible job-seeking
experience on the go.
4. Integration with External Platforms: Integrating the platform with external job
listing websites, social media platforms, and professional networking sites to expand
the reach of job postings and provide more opportunities for job seekers.
5. Enhanced Security Measures: Continuously improving security measures to protect
user data, including implementing multi-factor authentication (MFA), advanced
encryption techniques, and regular security audits.
6. Localization and Multilingual Support: Adding support for multiple languages and
localizing the platform for different regions to cater to a diverse user base and address
the specific needs of various markets.
7. Real-Time Collaboration Features: Introducing real-time collaboration features that
allow job seekers to work on their resumes and applications with mentors, career
coaches, or peers.
8. Gamification Elements: Incorporating gamification elements to engage users and
motivate them to actively participate in job-seeking activities. This could include
earning badges, completing challenges, and tracking progress through interactive
dashboards.
9. Partnerships with Educational Institutions: Collaborating with educational
institutions to provide job seekers with access to online courses, certification
programs, and skill development workshops that enhance their employability.

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 10. Research and Development: Investing in ongoing research and development to
explore new technologies, such as blockchain for secure credential verification, and
continuously improve the platform's features and capabilities.

By pursuing these future enhancements, the job and employment platform can further solidify
its position as a valuable resource for job seekers and employers, driving positive
employment outcomes and contributing to economic development in conflict-affected and
resource-constrained regions.

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