FAKE JOB DETECTION USING MACHINE LEARNING
Presented By
A. Rupasri (20NE1A0510)
Sk. Rehamunnisha (20NE1A0539)
D. Sai Supriya (20NE1A0542)
Sk. Mohammad Fahim (20NE1A0551)
Under The Esteemed Guidance Of
Mrs. J. Lakshmi M. Tech, B. Tech
Assoc. Professor
INTRODUCTION
Employment scam is one of the serious issues in recent times addressed in the domain of Online
Recruitment Frauds. In recent days, many companies prefer to post their vacancies online so that
these can be accessed easily and timely by the job-seekers. However, this intention may be one type
of scam by the fraud people because they offer employment to job-seekers in terms of taking money
from them. Fraudulent job advertisements can be posted against a reputed company for violating
their credibility. These fraudulent job post detection draws a good attention for obtaining an
automated tool for identifying fake jobs and reporting them to people for avoiding application for
such jobs. For this purpose, machine learning approach is applied which employs several
classification algorithms for recognizing fake posts. In this case, a classification tool isolates fake job
posts from a larger set of job advertisements and alerts the user. To address the problem of
identifying scams on job posting, supervised learning algorithm as classification techniques are
considered initially. A classifier maps input variable to target classes by considering training data.
Classifiers addressed in the paper for identifying fake job posts from the others are described briefly.
These classifiers based prediction may be broadly categorized into -Single Classifier based
Prediction and Ensemble Classifiers based Prediction.
ABSTRACT
As can be seen from increased number of data and privacy breaches(stolen) day-by-day it
becomes extremely difficult for one to stay safe online.
Number of victims of fake job posting is increasing drastically day by day. The companies and
fraudsters tempt the job-seekers by various methods, majority coming from digital job-providing
web sites.
Our target is to minimize the number of such frauds by using Machine Learning to predict the
chances of a job being fake so that the candidate can stay alert and take informed decisions, if
required.
The model will use NLP(Natural Language Processing) to analyze the sentiments and pattern in
the job posting. The model will be trained as a Sequential Neural Network and using very popular
Glove algorithm( Glove is an unsupervised learning algorithm for obtaining vector
representations for words).
To understand the accuracy in real world, we will use trained model to predict jobs posted. Then
we have worked on improving the model through various methods to make it robust(strong)and
realistic.
LITERATURE SURVEY
The undertaking according to the literature research, no current system is still installed or functioning
in the same way as this project is.
Serval research investigations are offered, however, there has never been a system like this before. The
literature study gives valuable insight into the field of machine learning.
People conduct research using a broad range of methodologies. There are several active studies on
machine learning approaches (Knoll, DEC, 2013).
This section's research findings show the potential benefits and popularity of machine learning. A
survey of the literature allowed us to have a better understanding of the various 19 algorithms.
The majority of the research discussed in this chapter has consistently uncovered the primary benefits
of depending on machine learning technologies.
Machine Learning
Machine learning is one of the applications of artificial intelligence (AI) that provides computers, the
ability to learn automatically and improve from experience instead of explicitly programmed.
The main aim is to allow computers to learn automatically without human intervention and adjust
actions accordingly.
Some machine learning models
Machine learning algorithms are often categorized as supervised and unsupervised.
Supervised machine learning algorithms can apply what has been learned in the past to new data
using labeled examples to predict future event.
Ex: Predicting the house prices
Unsupervised machine learning algorithms are used when the information used to train is neither
classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a
hidden structure from unlabeled data and used to search for unknown similarities.
Ex: Clustering analysis
Semi-supervised machine learning algorithms fall somewhere in between supervised and
unsupervised learning, since they use both labeled and unlabeled data for training – typically a 3 small
amount of labeled data and a large amount of unlabeled data.
Ex: Search engines like google
Applications of machine learning
Virtual Personal Assistants
Predictions while Commuting
Videos Surveillance
Social Media Services
Email Spam and Malware Filtering
Importance of machine learning in jobs
The importance of machine learning in jobs is increasing because of its ability to process huge datasets efficiently
beyond the range of human capability.
Now-a-days most of jobs are increasing drastically we can not predict that job is fake or real based on details
provided by company. So, by using this machine learning project we can predict easily whether it is fake or real and it
makes easy to understand for employees and they can get better outcome.
Identifying whether job is fake or real
Every company will have their respective job portal and in that job portal we will have complete information about
that jobs. Any company should not ask money for getting job in that company.
If a job is real then that job interviews maximum do not happen through telephone conversation, it must take virtual
interviews either online or offline.
If it is a reputed company then it is mandatory to have the details about that company in a google.
EXISTING SYSTEM
Fake or real job prediction predicts all job details. Even though, those records are not used in an
efficient manner for prediction.
To maintain the records in an efficient error free manner, the new proposed system is introduced.
Disadvantages
Doesn’t generate accurate and efficient results
Computation time is very high
Difficulty in maintenance of job details
Lack of accuracy may result in lack of efficient further treatment
PROPOSED SYSTEM
We proposed to develop a system which will help to predict Fake or real job based on some
attributes like location, salary_range, department and so on.
So, there is a need for developing a decision system which will help to predict the job condition
for employee in an easier way, which can offer prediction about the job so that further procedure
can be made effectively.
This proposed system not only accurately predicts fake jobs but also reduces time for prediction.
The machine learning algorithms like decision tree, randomforest, Naive Bayes, K Nearest
Neighbours have proven to be most accurate & reliable and hence, used in this project.
Advantages
Generates accurate and efficient results
Computation time is greatly reduced
Easy maintenance of employee details
Reduces manual work
Automated prediction
SYSTEM REQUIREMENT SPECIFICATIONS
SOFTWARE REQUIREMENTS:
Operating System : Windows 10 Home, 64-bit Operating System
Coding Language : Python
Python distribution : Anaconda
HARDWARE REQUIREMENTS:
System-type : Intel Core i3 or above
Cache memory : 4MB(Megabyte)
RAM : 8 gigabyte (GB)
Bus Speed : 5 GT/s DBI2
SYSTEM ARCHITECTURE AND DESIGN
The project is to find the phoney jobs to avoid users getting into the scams.
This makes assurance that the data they provide at the time of recruitment will not be misused.
we are working on a EMSCAD dataset to find better results using different algorithms.
The dataset for fake job post is collected and preprocessed.
The feature selection is the process of selecting some important features from the data required for
analyzing and getting a proper output.
We are applying the random Forest classifier to detect whether the job posted is a fake or a
legitimate one.
Fig: System architecture
UML DIAGRAMS
The Unified Modeling Language allows the software engineer to express an analysis model using the
modeling notation that is governed by a set of syntactic semantic and pragmatic rules. A UML system is
represented using five different views that describe the system from distinctly different perspective. Each view
is defined by a set of diagram, which is as follows.
User Model View:
• This view represents the system from the users perspective.
Structural Model view:
• In this model the data and functionality are arrived from inside the system.
Behavioral Model View:
• It represents the dynamic of behavioral as parts of the system, depicting the interactions of collection between
various structural elements described in the user model and structural model view.
Implementation Model View:
• In this the structural and behavioral as parts of the system are represented as they are to be built.
Environmental Model View:
• In this the structural and behavioral aspects of the environment in which the system is to be implemented are
represented.
Class Diagram
The class diagram is the main building block of object oriented modeling.
It is used both for general conceptual modeling of the systematic of the application, and for
detailed modeling translating the models into programming code.
Class diagrams can also be used for data modeling.
The classes in a class diagram represent both the main objects, interactions in the application
and the classes to be programmed.
A class with three sections, in the diagram, classes is represented with boxes which contain
three parts:
• The upper part holds the name of the class
• The middle part contains the attributes of the class
• The bottom part gives the methods or operations the class can take or undertake.
Use case Diagram
A Use Case Diagram at its simplest is a representation of a user's interaction with the system
and depicting the specifications of a use case.
A use case diagram can portray the different types of users of a system and the various ways
that they interact with the system.
This type of diagram is typically used in conjunction with the textual use case and will often
be accompanied by other types of diagrams as well.
Sequence Diagram
A Sequence Diagram is a kind of interaction diagram that shows how processes operate with one
another and in what order.
It is a construct of a Message Sequence Chart.
A sequence diagram shows object interactions arranged in time sequence.
It depicts the objects and classes involved in the scenario and the sequence of messages exchanged
between the objects needed to carry out the functionality of the scenario.
Sequence diagrams are typically associated with use case realizations in the Logical View of the
system under development.
Sequence diagrams are sometimes called event diagrams, event scenarios, and timing diagrams.
State Chart Diagram
A state diagram is used to represent the condition of the system or part of the system at finite
instances of time.
It’s a behavioral diagram and it represents the behavior using finite state transitions. State
diagrams are also referred to as State machines and State-chart Diagrams.
The basic purpose of a state diagram is to portray various changes in state of the class and not
the processes or commands causing the changes.
However, a flowchart on the other hand portrays the processes or commands that on execution
change the state of class or an object of the class.
THANK YOU
