*Inspiration *

Since the occurrence of COVID-19, large and small businesses alike have incurred major revenue losses and are in danger. This follows a loss of jobs because companies downsizing and,due to spending all the time at home.We all face burdens when it comes to remote working. Frustrations surround the business environment as we are dealing with a dangerous virus. Companies are dealing with hard times telling employees they can not no longer be a part of their teams. It's no longer an issue as we solved these problems with a Platform for re-hiring talented employees between companies before letting them off from the company.

*What it does *

HI is a matchmaking job and platform that holds all companies together and provides companies that are going to fire their employees with analytics that tell him if the employee is going to leave and why so it's a win-win situation for both companies, the hiring and the firing and for the employee. We also offer for the company that is going to hire with analytics predicting what will be the satisfaction and productivity of employees that we recommend them for.It uses the database of employers and employees to make the best hiring deals for both based on analytics generated by AI Models which leave a better impact on both companies business and actually change employee's lives.

How we built it

We used a dataset containing employee profiles of a large company, where each record is an employee (Kaggle dataset). Webuild employee churn prediction model With a 97% classification rate we predict who will leave the company and why, precision which means employee is going to leave, that employee actually left 95% of the time and , Recall: If there is an employee who left present in the test set and our Gradient Boosting model can identify it 92% of the time.

So if an employee is chosen for the downsizing and be matchmaked based on our HR analytics if he suitable with other offers submitted by other companies

We also offer for the company that is going to hire with analytics predicting what will be the satisfaction and productivity in their new job that we recommend them for.

After doing our Exploratory Data Analysis we observed that the most important factor for any employee to stay or leave is satisfaction and performance in the company so we used k-means algorithm to cluster them into 3 groups then we built our model. We used Python in the backend and the front end is JavaScript ,CSS and HTML. As an additional feature we can add specific HR special analytics for every individual in the company.

Challenges we ran into

  • How might we help employee find the suitable job for him
  • Help companies to pass economic crisis and work force reduction without being affected
  • Help company to find suitable employees based on analytics

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