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Project 05IntermediateBusiness6-8 focused hoursPeople analytics case study
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Employee Attrition Analysis

Understand the factors associated with employee attrition and structure a predictive workflow around them.

EDAStatisticsFeature EngineeringClassification
FRAME→DATA→ANALYZE→MODEL→EVALUATE→COMMUNICATE

BUSINESS CONTEXT

Why this project matters

An HR analytics team is seeing costly employee turnover. Leaders want evidence on which workforce segments are most at risk and whether the organization can identify patterns early enough to intervene.

PROJECT OBJECTIVE

What you are expected to accomplish

Explore employee records to understand attrition drivers, develop segment-based insights, and design a prediction workflow that could support retention planning.

DATASET OVERVIEW

Expected dataset structure

Expect employee-level data containing role, department, tenure, compensation, engagement, performance, and attrition status fields.

Main entities / rows

One row per employee

Target variable

attrition (Yes/No)

employee_id

string

Unique identifier for each employee.

department

categorical

Business unit or team assignment.

job_role

categorical

Role title or standardized role family.

years_at_company

integer

Employee tenure with the organization.

monthly_income

float

Monthly compensation or salary proxy.

performance_rating

categorical

Most recent performance category.

overtime

boolean

Whether the employee regularly works overtime.

attrition

binary

Target indicating whether the employee left.

Data quality issues to expect

  • Compensation or engagement fields may be incomplete for newer employees or contractors.
  • Role and department labels may need standardization after org changes.
  • Some features may reflect events close to resignation and should be checked for timing realism.

QUESTIONS TO ANSWER

Focus the work around meaningful decisions

  1. 01

    Which departments, roles, or tenure groups have the highest attrition rates?

  2. 02

    How do overtime, compensation, and performance relate to attrition?

  3. 03

    Are there signs that specific employee segments are disengaging earlier than others?

  4. 04

    Which engineered features could improve attrition analysis or prediction?

  5. 05

    What workforce actions would be reasonable based on the patterns found?

PROJECT ROADMAP

Move through the case study in a practical sequence

  1. 01

    Step 01

    Define the attrition problem

    Clarify who counts as attrited and what intervention decision the analysis supports.

  2. 02

    Step 02

    Profile the workforce data

    Check record quality, missingness, and whether any groups are underrepresented.

  3. 03

    Step 03

    Clean organizational labels

    Standardize role, department, and compensation fields before comparing segments.

  4. 04

    Step 04

    Analyze attrition patterns

    Compare attrition across teams, tenure, compensation, and workload indicators.

  5. 05

    Step 05

    Engineer interpretable features

    Create tenure bands, pay groups, or workload indicators that sharpen the story.

  6. 06

    Step 06

    Test a predictive workflow

    If modeling, compare a baseline classifier and interpret what it captures.

  7. 07

    Step 07

    Present retention actions

    Recommend practical interventions and note where the data cannot prove causation.

TASKS / MILESTONES

Concrete work to complete

  • Measure overall attrition and segment it by department and role.
  • Inspect missing values in compensation, tenure, and engagement-related fields.
  • Compare attrition by overtime, performance rating, and salary band.
  • Create at least two grouped or engineered features that improve interpretation.
  • Check whether class imbalance affects evaluation choices if you model the target.
  • Draft retention recommendations linked to specific workforce segments.

SUGGESTED VISUALIZATIONS

Visuals worth creating

  • Attrition rate comparison by department or role
  • Tenure distribution by attrition status
  • Salary band versus attrition bar chart
  • Overtime and attrition comparison chart
  • Feature-importance view or odds-style summary if modeling

DELIVERABLES

What the learner should produce

  • Cleaned employee dataset
  • People analytics notebook or report
  • Segmented attrition visualizations
  • Feature-engineering notes
  • Retention recommendations with caveats

SUCCESS CRITERIA

What strong completion looks like

  • The project identifies clear attrition differences across meaningful workforce segments.
  • Findings separate correlation from unsupported causal claims.
  • Feature engineering improves interpretability rather than adding noise.
  • Recommendations are realistic for HR or leadership teams to act on.

GUIDANCE

Support when you need a nudge

Reveal practical hints, checkpoints, and framing help without exposing a full finished solution.

Complete this project when the work is yours

Use this only after you have worked through the roadmap, milestones, and your own analysis.

KEY LEARNING OUTCOMES

Skills reinforced by this project

  • Analyze workforce data with segment-focused EDA.
  • Use statistics and feature engineering to structure attrition insights.
  • Think carefully about causation, timing, and deployability in people analytics.
  • Communicate HR findings responsibly and clearly.

RELATED LESSONS

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