# technocolabs100/attritionforecast-analysis-and-prediction architecture

> Architecture diagram of the GitHub repository technocolabs100/attritionforecast-analysis-and-prediction, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-10). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/technocolabs100/attritionforecast-analysis-and-prediction
- Repository: https://github.com/technocolabs100/attritionforecast-analysis-and-prediction

## Overview

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`](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md) (BI dashboards), [`Attrition Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv`](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/Attrition%20Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv) (Employee attrition data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data asset"]
  node_dataset["Employee attrition data"]
end

subgraph group_analytics["Analysis and prediction"]
  node_exploration["Data exploration<br/>[README.md]"]
  node_descriptive["Descriptive analytics<br/>[README.md]"]
  node_preprocessing["Data preprocessing<br/>[README.md]"]
  node_features["Feature engineering<br/>[README.md]"]
  node_training["Model training<br/>[README.md]"]
  node_monitoring["Model monitoring<br/>[README.md]"]
  node_predictions["Turnover predictions<br/>[README.md]"]
  node_dashboards["BI dashboards<br/>[README.md]"]
end

subgraph group_action["HR action"]
  node_alerts["HR alerts<br/>[README.md]"]
  node_retention["Retention interventions<br/>[README.md]"]
  node_planning["Workforce planning<br/>[README.md]"]
  node_feedback["HR feedback<br/>[README.md]"]
end

node_hr(("HR decision-makers"))

node_dataset -.->|"analyzed in"| node_exploration
node_hr -->|"reviews"| node_dashboards
node_hr -->|"acts on"| node_retention
node_hr -->|"uses insights"| node_planning
node_preprocessing -->|"prepares for"| node_features
node_features -->|"informs"| node_training
node_training -->|"is monitored"| node_monitoring
node_monitoring -->|"supports"| node_predictions
node_predictions -->|"embedded in"| node_dashboards
node_dashboards -->|"flags risk"| node_alerts
node_alerts -->|"prompts"| node_retention
node_descriptive -->|"informs"| node_dashboards
node_hr -->|"provides"| node_feedback
node_feedback -->|"improves"| node_training

click node_dataset "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/Attrition%20Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv"
click node_exploration "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_descriptive "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_preprocessing "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_features "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_training "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_monitoring "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_predictions "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_dashboards "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_alerts "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_retention "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_planning "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"
click node_feedback "https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_dataset toneBlue
class node_exploration,node_descriptive,node_preprocessing,node_features,node_training,node_monitoring,node_predictions,node_dashboards toneAmber
class node_alerts,node_retention,node_planning,node_feedback toneMint
class node_hr toneIndigo
```

## Components

### Data asset

- [Employee attrition data](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/Attrition%20Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv): `Attrition Dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv`

### Analysis and prediction

- [Data exploration](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Descriptive analytics](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Data preprocessing](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Feature engineering](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Model training](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Model monitoring](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Turnover predictions](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [BI dashboards](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`

### HR action

- [HR alerts](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Retention interventions](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [Workforce planning](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`
- [HR feedback](https://github.com/technocolabs100/attritionforecast-analysis-and-prediction/blob/main/README.md): `README.md`

### Other components

- **HR decision-makers**

## Connections

- Employee attrition data → Data exploration: analyzed in
- HR decision-makers → BI dashboards: reviews (evidence: `README.md`)
- HR decision-makers → Retention interventions: acts on (evidence: `README.md`)
- HR decision-makers → Workforce planning: uses insights (evidence: `README.md`)
- Data preprocessing → Feature engineering: prepares for (evidence: `README.md`)
- Feature engineering → Model training: informs (evidence: `README.md`)
- Model training → Model monitoring: is monitored (evidence: `README.md`)
- Model monitoring → Turnover predictions: supports (evidence: `README.md`)
- Turnover predictions → BI dashboards: embedded in (evidence: `README.md`)
- BI dashboards → HR alerts: flags risk (evidence: `README.md`)
- HR alerts → Retention interventions: prompts (evidence: `README.md`)
- Descriptive analytics → BI dashboards: informs (evidence: `README.md`)
- HR decision-makers → HR feedback: provides (evidence: `README.md`)
- HR feedback → Model training: improves (evidence: `README.md`)