Analysis and Learn from HR Attrition-Rate Dataset

The purpose of this project is to master the exploratory data analysis (EDA) in HR Dataset with attrition rate analysis with Pandas framework.

Download Dataset file : https://docs.google.com/spreadsheets/d/1h6FRlAS2lgCISGfKFYrBBg0cHqQCP6_S/edit?usp=sharing&ouid=102809472116957999830&rtpof=true&sd=true

Dashboard link : https://public.tableau.com/app/profile/fajar.fatahillah/viz/HRAnalyticsDashboard_17231981849450/HRANALYTICSDASHBOARD?publish=yes

Goals of the Project:

1. Explore a HR Data with attrition rate dataset with Pandas framework.
2. Build pivot tables.
3. Visualize the dataset with various plot types.

Outline

1. Materials and methods
2. General Part : Data Preprocessing, Data Cleaning, Data Visualization
3. Tasks

Materials and Methods

How To Learn with Cross-Industry Standard Process for Data Mining (CRISP-DM)

1. Business Understanding : The Business Understanding phase focuses on understanding the objectives and requirements of the project. In this project, we should know about what's happening, why it's happening and who is involved in HR Dataset with attrition rate.

2. Data Understanding : Next is the Data Understanding phase. Adding to the foundation of Business Understanding, it drives the focus to identify, collect, and analyze the data sets that can help you accomplish the project goals. The data is consist of Attrition Count, Age, Job Role, Education, Education Field,Department, Job Satisfaction and so on.

3. Data Preparation : This phase, which is often referred to as “data munging”, prepares the final data set(s) for modeling. In this methods , we cleaning the data from anomali, null, not correlation data and any others missed from the data source. And Also transform data type from object to int with Label Encoder. Last but not least, replace unnecessary sign, space, and etc with no space or no sign and then change data type.

4. Modeling : What is widely regarded as data science’s most exciting work is also often the shortest phase of the project. Here you’ll likely build and assess various models based on several different modeling techniques.

5. Evaluation : Whereas the Assess Model task of the Modeling phase focuses on technical model assessment, the Evaluation phase looks more broadly at which model best meets the business and what to do next

6. Deployment : A model is not particularly useful unless the customer can access its results. The complexity of this phase varies widely.

Conclution

In this section, we do several action to cleaning the data and consuming and analyzing clearly. Some of the task are :

1. Check the Info off all data type.
2. Check missing or null value.
3. Check sign, space, and any other unnecessary format.
4. Change, Replace and Edit all unnecesary format.
5. Describe all summary data like mean, median, modus, std dev, and so on
6. Export from data frame to excel file.
7. Change format from object to int with LabelEncoder for matrix correlation chart.
8. Create simple visualization for furhter analysis

For good and detail result of the analysis , you can used data visualization tools like Tableau, Power BI or Looker Studio. In this project, I used Tableau for deeper analysis. From This Dashboard we can conclude that The Attrition rate for the company is 16.12% from 1470 Count of Employee (About 237 Employee). Average age of it is 37 Years old. 56,12% of that are from R&D Department. see the detail from Dashoard Picture.