How technocolabs100/attritionforecast-analysis-and-prediction works
This repository contains an employee-attrition CSV and a README describing an intended analysis and prediction project; it has no implementation source files. The graph therefore shows the documented, aspirational workflow and capabilities, not a running system. Relationships are based on the README, and the CSV-to-analysis connection is left unverified.
Main entry points: README.md BI dashboards · WA_Fn-UseC_-HR-Employee-Attrition.csv Employee attrition data
Components 14
Data asset
- Employee attrition data
Attrition Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv
Analysis and prediction
- Data exploration
README.md - Descriptive analytics
README.md - Data preprocessing
README.md - Feature engineering
README.md - Model training
README.md - Model monitoring
README.md - Turnover predictions
README.md - BI dashboards
README.md
HR action
- HR alerts
README.md - Retention interventions
README.md - Workforce planning
README.md - HR feedback
README.md
Other components
- HR decision-makers
Connections 14
- Employee attrition data → Data exploration: analyzed in
- HR decision-makers → BI dashboards: reviews
README.md - HR decision-makers → Retention interventions: acts on
README.md - HR decision-makers → Workforce planning: uses insights
README.md - Data preprocessing → Feature engineering: prepares for
README.md - Feature engineering → Model training: informs
README.md - Model training → Model monitoring: is monitored
README.md - Model monitoring → Turnover predictions: supports
README.md - Turnover predictions → BI dashboards: embedded in
README.md - BI dashboards → HR alerts: flags risk
README.md - HR alerts → Retention interventions: prompts
README.md - Descriptive analytics → BI dashboards: informs
README.md - HR decision-makers → HR feedback: provides
README.md - HR feedback → Model training: improves
README.md