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Data Science Process Pipeline to Solve Employee Attrition and Their Job Performance and Predicting with AI

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Introduction:

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.  Machine learning enables models to train on data sets before being deployed. Some machine- learning models are online and continuous. This iterative process of online models leads to an improvement in the types of associations made between data elements. Due to their complexity and size, these patterns and associations could have easily been overlooked by human observation. After a model has been trained, it can be used in real time to learn from data. The improvements in accuracy are a result of the training process and automation that are part of machine learning.

 

 Problem Definition: 

Now a day’s data science predictions are  used in IT  industries,  for  the  improvement  in  market  investment, employee management etc. Retention of valuable employees within an organization has become an important issue as it is  hard  to  find  out  the  reasons  that  why  employees  are leaving  an  organization  and  keep  them  satisfied  is  a  big challenge, for this a report is made to predict the retention of an  employee  in  an  organization  using  the  python programming with data science methods. The main  idea of this report is  to find  out that  which valuable employee  will leave  the  company  and  the  features  which  are  affecting him/her  to  making  this  decision  like  salary  level,  no.  of hours spending in week, promotion, no. of work accident etc. The application was developed in python programming   and    are  made  with  the  help  of  data science  and machine  learning  models. The  design  criteria and the implementation details are presented in this report.

  • Machine learning has major challenge called acquisition which is based on different algorithms through which data needs to be processed. It must be processed before providing as input to respective algorithms. Thus it has a significant impact on results to be achieved or obtained.
  • Interpretation is another backlog that is needed to determine the effectiveness of machine learning algorithms.
  • The use of machine learning algorithms is limited. It is not having any surety that its algorithms will always work in every case imaginable. In most cases machine learning fails. Thus it requires some understanding of the problem at hand to apply the right algorithm.

 

Objective of project:

We know that larger companies contain  more  than thousand employees working for them, so taking care of the  needs  and  satisfaction  of  each  employee  is  a challenging task to do, it results in valuable and talented employees leave the company without giving the proper reason. This project provides solution for the given problem as it gives a prediction model that can be used to predict which employee will leave the company and which will not leave. It also helps in finding the exact reasons which are motivating the employees for shifting companies like lower salary, less promotions or heavy work load etc. To find the result in the form of yes or no.

 

 Limitations of the project :

AI Fairness 360(AIF), a comprehensive open source toolkit of metrics to check for unwanted bias in datasets and machine learning models. This model eliminates the bias in the dataset. The model developed will be able to predict whether an employee will stay or not this will help company to know the status of an employee in advance and take necessary actions to prevent loss that will incur.  The findings of the study are subjected to bias and prejudice of the respondents time factor can be considered as main limitations the findings of the study are solely depend on the information provided by respondents. The accuracy of findings is limited by the accuracy of statistical tools used for analysis. Findings of the research may change due to area demography, age condition of economy etc.

 

System Architecture:

Data Science Process Pipeline to Solve Employee Attrition

 

 

Software and Hardware Requirements:

Hardware:

  • OS – Windows 7, 8 and 10 (32 and 64 bit)
  • RAM – 4GB

 

Software:

  • Python
  • Anaconda
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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