Inspiration

COVID-19 pandemic is affecting economies in every continent. Unemployment rates are spiking every single day with the United States reporting around 26 million people applying for unemployment benefits, which is the highest recorded in its long history, millions have been furloughed in the United Kingdom, and thousands have been laid off around the world.

These desperate times provides a perfect opportunity for online scammers to take advantage of the desperation of thousands and millions of people looking out for jobs. We see a steep rise in these fake job postings where the posting seems genuine, often these companies will have a website as well followed by a recruitment process that is similar to other companies in the industry.

What it does

Our mission is to create awareness among the job seekers regarding the seriousness of this issue and how, through a machine learning model integrated in our designed app "Job", we can predict whether a job posting is fraudulent or not as well as job seekers can also apply for the particular listed job.

How I built it

We explored the data and provided insights into which industries are more affected and what are the critical red flags which can give away these fake postings. Then we applied machine learning models to predict how we can detect these counterfeit postings.

In further detail:
Data collection: We used an open source dataset that contained 17,880 job post details with 900 fraudulent ones.
Data visualisation: We visualised the data to understand if there were any key differences between real and fake job postings, such as if the number of words in fraud job postings was any lesser than real ones.
Data split: We then split the data into training and test sets.
Model Training: We trained various models such as Logistic regression, KNN, Random Forest etc. to see which model worked best for our data.
Model Evaluation: Using various classification parameters, we evaluated how well our models performed. For example, our Random Forest model had a roc_auc score of 0.76. We also evaluated how each model did in comparison to the others.

Immediate Impact

Police departments don’t have the resources to investigate these incidents, and it has to be a multi-million-dollar swindle before federal authorities get involved, so the scammers just keep getting away with it. Hence our solution saves millions of dollars and hours of investigation, whilst protecting the workers from getting scammed into fake jobs and misused information.

Revenue generated

Our Revenue model is based on:
1) Premium subscription availability to job seekers to apply for jobs
2) Revenue from the advertisements
3) Commission from the employers to post the jobs

Funding Split

1) Testing and Development: $ 10,000
2) Team Hire Costs: $ 2000
3) Patent Application Costs: $ 125
4) Further Licensing conversations: $ 225
TOTAL: $ 12,350

Future Goals

We would hope to partner up with LinkedIn or other job portals in a license agreement, to be able to integrate our machine learning model as a feature on their portal.

Built With

• adobexd
• machine-learning
• python