Employee Retention using Data Science

Employee Retention using Data Science

In this article, we will implement Data Science techniques to improve the human resources department.

Hiring and retaining top talent is an extremely challenging task that requires capital, time and skills. Small business owners spend 40% of their working hours on tasks that do not generate any income such as hiring process for new employees.

In this article, we will implement Data Science techniques to improve the human resources department.

We are going to predict which employees in a company are more willing to leave the organization.

So in this case study, we’re going to learn below points.

  1. Why do employees want to leave?
  2. Why do they want to stay?
  3. How can we make them motivated?

and much more ….:)

We have got the dataset from kaggle.com which will be used for this case study.

Here is the link for the same: https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset

We know Python is a very useful tool for Data analysis. We will use it to answer above questions.

You can get the full python code on my GitHub: https://github.com/Tariqueakhtar/Machine-Learning/tree/master/HR_Department_Solution

visualization employee-attrition data-analysis data-visualization data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Exploratory Data Analysis is a significant part of Data Science

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Exploratory Data Analysis is a significant part of Data Science

You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.

Data Cleaning in R for Data Science

A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.

Analysis, Price Modeling and Prediction: AirBnB Data for Seattle.

Analysis, Price Modeling and Prediction: AirBnB Data for Seattle. A detailed overview of AirBnB’s Seattle data analysis using Data Engineering & Machine Learning techniques.