Human Talent Prediction in HRM using

Machine Learning

Human Talent Prediction in HRM using Machine Learning

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Machine Learning focuses on prediction, based on known properties learned from the training data. Key technologies are:

• Artificial Neural Networks (ANN) technology is capable of learning complex relationships in data. By mimicking the functions of the brain, they can discern patterns in data and then extrapolate predictions when given new data. We have used ANN algorithms to make the best predictions on both classification problems and numeric problems.

• Support Vector Machines (SVM) performs classification by constructing an N-dimensional hyperplane that optimally separates the data into two categories.

• Random Forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction of the individual trees.

Human Talent Prediction in HRM using ANN

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ID Gender Category Degree Age Work Experience

WorkOutcomeEfficiency

Knowledge and Skill, Technical

Qualification

Knowledge and Skill, C Language Skill Level

Knowledge and Skill, Java Skill

Level

Knowledge and Skill, .Net

Skill Level

Activities and Contribution, International Conferences

Activities and Contribution,

Journals

Activities and Contribution,

Workshop

Recommendation for promotion.

Target Class (Yes or No)

2233 M P M 30.00 10.00 100.00 10.00 4.00 5.00 7.00 1.00 1.00 1.00 no2234 M P PhD 45.00 25.00 100.00 10.00 4.00 5.00 7.00 3.00 2.00 1.00 yes2235 M P B 25.00 3.00 100.00 10.00 4.00 5.00 7.00 1.00 1.00 1.00 no2236 M P D 25.00 3.00 95.00 7.00 4.00 5.00 7.00 1.00 1.00 1.00 no2237 M P C 25.00 3.00 95.00 7.00 4.00 5.00 7.00 1.00 1.00 1.00 no2238 M P M 28.00 6.00 95.00 7.00 4.00 5.00 7.00 1.00 3.00 5.00 no2239 M P M 28.00 6.00 95.00 7.00 4.00 5.00 10.00 2.00 1.00 5.00 yes2240 M P M 28.00 6.00 100.00 7.00 4.00 5.00 10.00 1.00 1.00 5.00 yes2241 M P M 28.00 6.00 100.00 7.00 4.00 9.00 10.00 1.00 1.00 5.00 yes2242 M S B 25.00 3.00 100.00 7.00 4.00 9.00 10.00 1.00 1.00 5.00 yes2243 M S B 25.00 3.00 90.00 7.00 4.00 9.00 10.00 1.00 4.00 5.00 no2244 M S B 25.00 3.00 90.00 8.00 4.00 9.00 10.00 1.00 1.00 2.00 no2245 M S B 27.00 5.00 90.00 8.00 4.00 9.00 10.00 1.00 1.00 2.00 no2246 M S B 27.00 5.00 90.00 8.00 4.00 9.00 6.00 1.00 1.00 2.00 no2247 M S B 27.00 5.00 90.00 8.00 4.00 9.00 6.00 1.00 1.00 2.00 no2248 M P M 32.00 10.00 90.00 8.00 4.00 9.00 6.00 2.00 1.00 2.00 no2249 F P M 32.00 10.00 100.00 8.00 4.00 9.00 6.00 1.00 1.00 2.00 no2250 F P PhD 40.00 18.00 100.00 8.00 4.00 9.00 6.00 2.00 1.00 2.00 yes2251 F P B 25.00 3.00 100.00 8.00 4.00 9.00 6.00 1.00 1.00 5.00 no2252 F P D 25.00 3.00 100.00 8.00 4.00 6.00 6.00 1.00 1.00 5.00 no

2265 F P B 32.00 10.00 100.00 8.00 5.00 7.00 6.00 1.00 1.00 3.00 no2266 F P M 30.00 10.00 100.00 9.00 5.00 7.00 6.00 1.00 1.00 3.00 yes2267 F P B 30.00 8.00 100.00 8.00 5.00 7.00 6.00 1.00 1.00 3.00 no2268 M P M 30.00 10.00 100.00 10.00 5.00 7.00 8.00 1.00 1.00 3.00 yes2269 M P B 30.00 8.00 100.00 8.00 5.00 7.00 6.00 1.00 1.00 3.00 no2270 M P M 30.00 8.00 100.00 8.00 5.00 7.00 6.00 1.00 1.00 3.00 no

Predictions are represented by pink color

Human Talent Prediction in HRM using ANN (cont.)

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Human Talent Prediction in HRM using ANN (cont.)

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Human Talent Prediction in HRM using ANN (cont.)

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Human Talent Prediction – Next Steps

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Additionally the following predictions for realization could be implemented:

Dismissal risk prediction Human Resources Supply Forecast Employee Tenure Prediction Employee’s Performance Prediction Prediction of employee attrition Employee Turnover prediction Predicting the Economic Value of Human Capital Investments

Human Talent Prediction in HRM using Machine Learning

Contactsvladivans@gmail.com

Vladimir Savin, Ph.D.

Vladimir_Savin@epam.com

Thank you!

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