We are using satisfaction_level as the target and building a Power BI dashboard to analyze or predict employee satisfaction, here are suggestions:
Dataset Link : https://github.com/codebasics/py/blob/master/ML/7_logistic_reg/Exercise/HR_comma_sep.csv
✅ Dashboard Title Suggestions:
- “Employee Satisfaction Insights”
- “Workplace Satisfaction Analysis”
- “Understanding What Drives Employee Satisfaction”
- “HR Dashboard: Factors Influencing Satisfaction Levels”
- “Satisfaction Score Predictor: An HR Analytics Dashboard”
📊 Suggested Visuals in Power BI:
1. Satisfaction Level Distribution
- Type: Histogram or column chart
- Purpose: See how satisfaction scores are distributed across the company.
2. Average Satisfaction by Department
- Type: Bar chart
- Axis: Department (X), Avg. Satisfaction (Y)
- Purpose: Identify departments with low or high satisfaction.
3. Satisfaction Level vs. Last Evaluation
- Type: Scatter plot
- Purpose: Explore correlation between satisfaction and performance evaluations.
4. Satisfaction Level by Salary Band
- Type: Box plot or bar chart
- Purpose: Understand if salary level impacts satisfaction.
5. Satisfaction by Number of Projects
- Type: Line or bar chart
- Purpose: Check how project workload affects satisfaction.
6. Heatmap: Satisfaction vs. Avg. Monthly Hours
- Type: Heatmap (or scatter with color intensity)
- Purpose: Show how overtime or underwork impacts satisfaction.
7. Satisfaction vs. Time Spent at Company
- Type: Line or bar chart
- Purpose: Reveal trends based on tenure.
8. Prediction Gauge (Optional)
- Type: Gauge or KPI card
- Purpose: Show predicted satisfaction score for selected employee (if using ML model).
Would you like get the Power BI file to get started? Comment below