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Predicting if the best and most experienced employees leave prematurely - Kaggle Human Resource Analytics dataset using SVM and Multi Layer Perceptron with backpropagation

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Human Resource Analytics-Kaggle-Dataset

Authors - Ryan Nazareth and Hannes Draxl

A group project carried out on a dataset freely available on Kaggle https://www.kaggle.com/ludobenistant/d/ludobenistant/hr-analytics/hr-analytics

Fields in the dataset include:

  • Employee satisfaction level
  • Last evaluation
  • Number of projects
  • Average monthly hours
  • Time spent at the company
  • Whether they have had a work accident
  • Whether they have had a promotion in the last 5 years
  • Department
  • Salary
  • Whether the employee has left

Trying to predict if the best and most experienced employees leave prematurely based on features listed above, using vanilla Neural Network techniques:

  • SVM
  • Multi Layer Perceptron with Backpropagation

The original dataset is stored in the 'Original Kaggle Dataset' folder. The cleaned data and code is stored in the 'cleaned data' folder. All programming carried out in Matlab.

This work will also be ported into one of the open source deep learning frameworks - keras/tensor flow to run more sophistcated techniques not available in Matlab

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Predicting if the best and most experienced employees leave prematurely - Kaggle Human Resource Analytics dataset using SVM and Multi Layer Perceptron with backpropagation

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  • MATLAB 90.7%
  • Python 9.3%