Detecting and Analysis of Fake Job Posting using Machine Learning

Introduction

Python is one of the most promising programming language today, specially in the domain of Machine Learning and Artificial Intelligence python created revolutionary remarks by providing most powerful libraries and algorithms needed. Fake job posting is one the most common fraud today by most of private organizations and placement consultancies, After the completion of educations students will be in always in search of job using online resources in those they visit fake adds and start paying them to get their dream job but unfortunately they end up losing the money, time and effort, due the frauds and fake jobs postings online. Proposed model is based on identifying the Fake Job and Genuine Job posting using Machine learning techniques to solve this an dataset of jobs poster over internet from different parts of the glob is collected and applied with many machine learning algorithms to identify weather the post is fake or real.

Dataset

Dataset is downloaded from the kaggle.com which consists of 18 columns and 17880 ro.

Programming Environment

Python 3.8 and Jupyter Notebook is used to develop the application

Algorithms used

1. Random Forest
2. Decision Tree
3. Logistic Regression
4. Neural Network

Challenges Faced

1. Removing stop words i the challenge here some time in job post there will be a lot of stop words if you remove them it is difficult to process sentence that will be challenging here.

2. Reading data which is null values we need to fill empty text in null values cells may result in wrong prediction of that particular record.

Implementation

First of all we need to import the required libraries like

1. pandas

2. numpy.

3. matplot library

The read the dataset with the help of pandas. once the dataset is loaded jupyter shows the database as follows.