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Data Cleaning Process (Excel Power Query)

Raw dataset: 5,050 rows × 17 columns → Cleaned: 5,000 rows × 17 columns

  • Imputed missing values (Gender, Job_Level, Age_Group, Employment_Type, Educational_Level → mode; Salary, Performance_Score, Absenteeism → median) 0 rows dropped
  • Removed duplicate Employee IDs 50 removed
  • Standardized data types across all columns 0 format errors
  • Trimmed & title-cased text fields Uniform formatting
  • Formatted salary as ₦ currency (2dp)
  • Sorted Department/Employment_Type for aggregation

Quality check

0 duplicates, 0 nulls, 0% format errors on final dataset.

Key Insight

Turnover is not evenly distributed across salary tiers — it's concentrated in one department. Sales has only 53.6% active staff (vs 65–73% elsewhere), a 32.1% resignation rate (nearly double every other department), the lowest average performance score (3.09 vs 3.5–3.6 company-wide), and the lowest average salary (₦306K vs ₦318K–₦425K elsewhere). Contract employees also resign at a notably higher rate (31.8%) than full-time staff (20.5%).

Recommendation

Prioritize a Sales-specific retention review (compensation benchmarking + manager support) rather than a blanket salary adjustment, since pay alone doesn't explain turnover in other departments.

Tool Used

  • Excel Power query

About

HR analytics project analyzing 5,000 employee records to uncover turnover drivers. Cleaned raw data in Excel Power Query, built pivot tables and an interactive dashboard, and identified that turnover is concentrated in the Sales department (32% resignation rate) rather than driven by salary alone.

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