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A Data science and Analytics project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why and which are the best and most experienced employees leaving prematurely?

License: MIT License

R 100.00%

human-resource-analytics-and-employee-churn-prediction's Introduction

Human-Resource-Analytics

A Data science project with the main aim of doing some Descriptive and Exploratory Data Analysis and then applying predictive modelling for predicting why experienced employees leaving prematurely?. Used deep multi-layer preceptron model to predict which employee will leave the company.

I have used Keras and Tensorflow in R to generate a Deep Multi-layered Perceptron classifier. Also after that I have generated an web application and deployed the keras classifer to predict whether a employee will leave the company or not using some features. The app has been deployed to the below link and it's made using Shiny .

Link to the Shiny Web app-

The App details and it's implementation can be found inside-Employee Churn Prediction App

Link to the report of Exploratory data analysis-

http://rpubs.com/anish20/humanResourceAnalytics

R Packages Used-

DplyR

TidyR

GGplot2

Keras- for predictive modelling

Predictive Modelling for predicting whether an employee will leave or not?

The plot of Epochs vs the model's metrics for predicting whether en employee will leave the company or not using a Multi layer perceptron network with 544 parameters and trained for 500 epochs.

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