MeriSkill HR Analytics

Project Description:

In the HR Analytics project, I played a pivotal role as a Data Analyst in cleansing and visualizing human resources data to derive meaningful insights. The focus was on optimizing data quality, exploring correlations, and presenting key HR-related visualizations.

Project Objectives:

Data Cleaning:
- Delete Redundant Columns: I removed unnecessary columns to streamline the dataset.
- Rename Columns: I ensured clear and descriptive column names for better understanding.
- Drop Duplicates: I eliminated duplicate entries to maintain data accuracy.
- Clean Individual Columns: I performed specific cleaning tasks on columns as needed.
- Remove NaN Values: I addressed missing data by removing NaN values from the dataset.
- Additional Transformations: I conducted further transformations to enhance data usability.
Data Visualization:
- Correlation Map: I plotted a correlation map for all numeric variables to uncover relationships.
- Visualizations on Various Variables:
  - Overtime
  - Marital Status
  - Job Role
  - Gender
  - Education Field
  - Department
  - Business Travel
- Relations Between Variables:
  - Overtime and Age
  - Total Working Years
  - Education Level
  - Number of Companies Worked
  - Distance from Home

Project Tasks:

Data Cleaning:

Delete Redundant Columns:
- I removed redundant columns to streamline the dataset.
Rename Columns:
- I ensured clear and descriptive column names for better understanding.
Drop Duplicates:
- I eliminated duplicate entries to maintain data accuracy.
Clean Individual Columns:
- I performed specific cleaning tasks on columns as needed.
Remove NaN Values:
- I addressed missing data by removing NaN values from the dataset.
Additional Transformations:
- I conducted further transformations to enhance data usability.

Data Visualization:

Visualizations on Demographic information
- I created a dashboard displaying the demographic information.
Visualizations on Various Variables:
- I created visualizations for Overtime, Marital Status, Job Role, Gender, Education Field, Department, and Business Travel.
Relations Between Variables:
- I explored and visualized relationships between Overtime and Age, Total Working Years, Education Level, Number of Companies Worked, and Distance from Home.

My Achievements:

Data Quality Enhancement:

I successfully improved data quality by removing redundant columns, renaming columns, dropping duplicates, and handling missing values.

Insightful Visualizations:

I created visually appealing and informative plots to represent correlations and relationships within the HR dataset.

Enhanced Decision-Making:

I provided actionable insights derived from data, contributing to more informed HR-related decision-making processes.

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