Problem Statement

This project aims to create a classifier that will have the capability to identify fake and real jobs. The final result will be evaluated based on two different models. Since the data provided has both numeric and text features one model will be used on the text data and the other on numeric data. The final output will be a combination of the two. The final model will take in any relevant job posting data and produce a final result determining whether the job is real or not.

This project uses data from Kaggle.

Metrics

The models will be evaluated based on two metrics:

1. Accuracy:

2. F1-Score: F1 score is a measure of a model’s accuracy on a dataset.

F1-score is used because in this scenario both false negatives and false positives are crucial. This model needs to identify both categories with the highest possible score since both have high costs associated to it.

Analysis

Data Exploration

The data for this project is available at Kaggle - https://www.kaggle.com/shivamb/real-or-fake-fake-jobposting-prediction. The dataset consists of 17,880 observations and 18 features.

The data is combination of integer, binary and textual datatypes. A brief definition of the variables is given below:

#	Variable	Datatype	Description
1	job_id	int	Identification number given to each job posting
2	title	text	A name that describes the position or job
3	location	text	Information about where the job is located
4	department	text	Information about the department this job is offered by
5	salary_range	text	Expected salary range
6	company_profile	text	Information about the company
7	description	text	A brief description about the position offered
8	requirements	text	Pre-requisites to qualify for the job
9	benefits	text	Benefits provided by the job
10	telecommuting	boolean	Is work from home or remote work allowed
11	has_company_logo	boolean	Does the job posting have a company logo
12	has_questions	boolean	Does the job posting have any questions
13	employment_type	text	5 categories – Full-time, part-time, contract, temporary and other
14	required_experience	text	Can be – Internship, Entry Level, Associate, Mid-senior level, Director, Executive or Not Applicable
15	required_education	text	Can be – Bachelor’s degree, high school degree, unspecified, associate degree, master’s degree, certification, some college coursework, professional, some high school coursework, vocational
16	Industry	text	The industry the job posting is relevant to
17	Function	text	The umbrella term to determining a job’s functionality
18	Fraudulent	boolean	The target variable  0: Real, 1: Fake

Since most of the datatypes are either Booleans or text a summary statistic is not needed here. The only integer is job_id which is not relevant for this analysis. The dataset is further explored to identify null values.

Variables such as department and salary_range have a lot of missing values. These columns are dropped from further analysis.

After initial assessment of the dataset, it could be seen that since these job postings have been extracted from several countries the postings were in different languages. To simplify the process this project uses data from US based locations that account for nearly 60% of the dataset. This was done to ensure all the data is in English for easy interpretability. Also, the location is split into state and city for further analysis. The final dataset has 10593 observations and 20 features.

The dataset is highly unbalanced with 9868 (93% of the jobs) being real and only 725 or 7% of the jobs being fraudulent. A countplot of the same can show the disparity very clearly.

Exploratory Analysis

The first step to visualize the dataset in this project is to create a correlation matrix to study the relationship between the numeric data.