An HR recruiter has interviewed a potential candidate. She thinks the interviewee could be a great fit for the organization since he has the desired experience, skills, and qualities. However, when the interviewee was asked about his salary expectations, he replied that he was expecting $ 160k per year because that was what he earned in his previous job.
She collected data for different positions and salaries of the candidate's previous company on various websites. Now, she wants to know whether the candidate was being honest or not. So, she gives you the data for you to help her solve this business problem. Additionally, she searched for him on LinkedIn and discovered that the candidate had been working as Regional Manager for two years in his last employ.
For this project I estimated four regression models.
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A Polynomial Regression.
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A Support Vector Regression.
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A Decission Tree Regression.
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A Random Forest Regression.
All the models include a brief discussion to see if this person was honest with the recruiter.
I have also included both the Python and R codes. For R, I have implemented an R-based environment on Google Colab.
If you want to know how to run R on Colab, you can check this site by Fidocia Wima Adityawarman (How to use R in Google Colab).
Note: If the Colab notebook is not being displayed, please copy the URL and paste it on nbviewer so you can see the code.
Please note that this code is intended for educational and non-commercial use only.
Contributions to this repository are welcome. If you find a bug or have suggestions for improvement, please open an issue or submit a pull request.
This project was created by Santiago Moreno Velasquez as part of an Udemy Guided Project.