HR Employee Attrition - MeriSKILL

HR Analytics Here's a glimpse into the tasks I performed and the insights I uncovered during this project:

Data Cleaning:

I started by giving the raw data a makeover, ensuring it was in prime condition for analysis. My tasks included:

• Deleting redundant columns: To eliminate unnecessary clutter and focus on the essentials.
• Renaming the columns: Making data more intuitive and user-friendly.
• Dropping duplicates: Ensuring data integrity by removing any repetitive information.
• Cleaning individual columns: Addressing data quality issues within specific columns.
• Removing NaN values from the dataset: Enhancing the reliability of the data.
• Performing additional transformations: Going the extra mile to fine-tune the dataset for analysis.

Data Visualization:

The heart of my project lay in turning this refined data into meaningful visual insights. Here's what I delved into:

• Plotting a correlation map for all numeric variables: Unveiling the relationships within the data.
• Analyzing key factors such as Overtime, Marital Status, Job Role, Gender, Education Field, Department, and Business Travel.
• Investigating the relation between Overtime and Age: Uncovering the impact of working hours on different age groups.
• Exploring the connection between Total Working Years, Education Level, Number of Companies Worked, and Distance from Home.

This project allowed me to combine my passion for data analysis and programming while working towards my goal of becoming a proficient Data Analyst. I thoroughly enjoyed the process of cleaning and transforming data, followed by the exhilarating task of creating insightful visualizations that can be used to inform and make strategic HR decisions.