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08 Identify Absenteeism Patterns - Cluster Analysis

Data Visualization

Identify Absenteeism Patterns - Cluster Analysis

The workflow implements the following:

  • Loading our dataset from a CSV file for our analysis.

  • Understand the importance of clustering.

  • Analyze the results of different clustering techniques.

  • Implement clustering techniques to identify groups by reasons for absenteeism.


Using the Elbow Method to find the optimal number of clusters

Implementing K-means and Hierarchical Clustering to Identify Groups by Reasons for Absenteeism

Rule Engine
EmployeeAbsenteeism.csv
CSV Reader
Hierarchical Clustering
Statistics
AbsenteeismLow/Avg/High
Rule Engine
Scorer
Python Script
Value Counter
Column Filter
Absenteeism &Continuous Columns
Python View
BMI vs Absenteeism
Density Plot
Age vs Absenteeism
Density Plot
Line Plot (Plotly)
Column Filter
Absenteeism &Categorical Columns
Python View
Scorer
Absenteeism
Auto-Binner
k-Means
Transportation Expense vs Absenteeism
Density Plot
Normalizer
Rule Engine

Nodes

Extensions

Links