Project 2 - Supply and Demand Gap Analysis

Human Resources

Aim-The Human Resources Power BI dashboard allows users to get a clear overview of the company's employees and the most relevant top-level KPIs: Headcount, Salaries, Hires and Terminations.

Introduction-We have a data about HR Dashboard. In it we have new hire, good_hires, bad_hires and their region, age, date.

Problem statement- How many employees leave the work.

Which group(age) employees working good.

Methodology- Step 1: Data cleaning
In this step I cleaned the data with the transform data option present in Power BI because the dataset was not cleaned and included some missing values, I cleaned the data and replace the null values with 0 and then started working on it.

Step 2: Data interpretation
In this step I found out some important information about the data set.
I create a new measures-Active Emp, Bad_Hires, Good_Hires, New Hires, Total emp.

Step 3: Visualization
In visualization, I took the help of Power BI Desktop software to make graphs and charts here some relevant graphs and charts are attached.

Analysis (Data sheets pertaining to it)-In observation found some interesting points that are mentioned below

1. New hires date 530, Goods_Hires 16k, Bad_Hires 1101.

2. New Hires date by date (2008)-301(M), 93(F) and Permanent-261(49.29%), Temprary-133(25.09%).

3. New Hires date by date (2009)-487(M), 746(F) and Permanent-442(69.83%), Temprary-191(30.17%).

4. New Hires date by date (2010)-476(M), 147(F) and Permanent-427(68.54%), Temprary-196(31.46%).

5. New Hires date by date (2011)-934(M), 261(F) and Permanent-801(67.03%), Temprary-394(32.97%).

6. New Hires date by date (2012)-850(M), 272(F) and Permanent-747(66.58%), Temprary-375(33.42%).

7. New Hires date by date (2013)-848(M), 248(F) and Permanent-748(68.25%), Temprary-348(31.75%).

8. New Hires date by date (2014)-400(M), 130(F) and Permanent-351(66.23%), Temprary-179(33.77%).

9. Good Hires date by date (2008)-546(M), 167(F).
10. Good Hires date by date (2009)-1143(M), 388(F).
11. Good Hires date by date (2010)-1560(M), 436(F).
12. Good Hires date by date (2011)-1961(M), 622(F).
13. Good Hires date by date (2012)-2658(M), 815(F).
14. Good Hires date by date (2013)-2546(M), 729(F).
15. Good Hires date by date (2014)-1821(M), 520(F).
16. Bad Hires date by date (2008)-32(M), 15(F).
17. Bad Hires date by date (2009)-81(M), 19(F).
18. Bad Hires date by date (2010)-89(M), 33(F).
19. Bad Hires date by date (2011)-146(M), 40(F).
20. Bad Hires date by date (2012)-183(M), 54(F).
21. Bad Hires date by date (2013)-175(M), 55(F).
22. Bad Hires date by date (2014)-126(M), 35(F).

Insights- 25-49 year old employees working very well.

Recommendations- We can collect good datasets from kaggle dataset for analysis.

We can analyze the data of the HR Reports.

Like this dataset we can perform operations with various AgeGroup, DateTable, Employee, Gender, Jobtype.

We can work on large datasets and analyze them with the proper format of the chart.

Conclusions- In conclusion, we can take a look at the final Report for further analysis. As we can see in the Report which age group employee working good. We analyze Good hires date, Bad hires date, New hires date.

Google Drive Link-https://drive.google.com/drive/folders/1wVsHI-B6_66Gy2fmrsh1XxsZgFq04r_X?usp=drive_link