# nandot006/machine-health-profiling architecture

> Architecture diagram of the GitHub repository nandot006/machine-health-profiling, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/nandot006/machine-health-profiling
- Repository: https://github.com/nandot006/machine-health-profiling

## Overview

This is an academic machine-learning capstone repository: users run Jupyter notebooks to analyze concrete-strength and machine-maintenance datasets, train models, evaluate them, and export metric summaries. The README documents the regression and classification workflows and their results. Notebook source was not sampled, so internal stages and data flow below reflect README descriptions rather than verified cell-level wiring; clustering is described as a project track but not implemented in Review 1.

Main entry points: [`notebooks/02_Classification_AI4I.ipynb`](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb) (Classification Notebook), [`notebooks/01_Regression_Concrete.ipynb`](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb) (Regression Notebook), [`datasets/concrete_data.csv`](https://github.com/nandot006/machine-health-profiling/blob/main/datasets/concrete_data.csv) (Concrete Data), [`datasets/ai4i2020.csv`](https://github.com/nandot006/machine-health-profiling/blob/main/datasets/ai4i2020.csv) (AI4I Data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_regression["Regression Track"]
  node_reg_notebook["Regression Notebook"]
  node_reg_eda["EDA & Preparation"]
  node_reg_features["Feature Engineering"]
  node_reg_models["Regressor Training"]
  node_reg_validation["Tuning & Validation"]
  node_reg_metrics["Regression Metrics"]
end

subgraph group_classification["Classification Track"]
  node_class_notebook["Classification Notebook"]
  node_class_eda["Sensor Preparation"]
  node_class_features["Feature Engineering"]
  node_class_models["Classifier Training"]
  node_class_metrics["Classification Metrics"]
end

subgraph group_inputs["Input Datasets"]
  node_concrete["Concrete Data<br/>[concrete_data.csv]"]
  node_ai4i["AI4I Data<br/>[ai4i2020.csv]"]
end

subgraph group_outputs["Metric Outputs"]
  node_reg_results["Regression Results"]
  node_class_results["Classification Results"]
  node_class_excel["Classification Workbook"]
end

node_analyst(("Project Analyst"))

node_analyst -->|"runs"| node_reg_notebook
node_concrete -->|"input data"| node_reg_notebook
node_reg_notebook -->|"analyzes"| node_reg_eda
node_reg_eda -->|"prepares data"| node_reg_features
node_reg_features -->|"features"| node_reg_models
node_reg_models -->|"models"| node_reg_validation
node_reg_validation -->|"validated models"| node_reg_metrics
node_reg_notebook -->|"exports metrics"| node_reg_results
node_analyst -->|"runs"| node_class_notebook
node_ai4i -->|"input data"| node_class_notebook
node_class_notebook -->|"analyzes"| node_class_eda
node_class_eda -->|"prepares data"| node_class_features
node_class_features -->|"features"| node_class_models
node_class_models -->|"predictions"| node_class_metrics
node_class_notebook -->|"exports metrics"| node_class_results
node_class_notebook -->|"exports metrics"| node_class_excel

click node_concrete "https://github.com/nandot006/machine-health-profiling/blob/main/datasets/concrete_data.csv"
click node_ai4i "https://github.com/nandot006/machine-health-profiling/blob/main/datasets/ai4i2020.csv"
click node_reg_notebook "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_reg_eda "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_reg_features "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_reg_models "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_reg_validation "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_reg_metrics "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb"
click node_class_notebook "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb"
click node_class_eda "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb"
click node_class_features "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb"
click node_class_models "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb"
click node_class_metrics "https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb"
click node_reg_results "https://github.com/nandot006/machine-health-profiling/blob/main/results/regression_results.csv"
click node_class_results "https://github.com/nandot006/machine-health-profiling/blob/main/results/classification_results.csv"
click node_class_excel "https://github.com/nandot006/machine-health-profiling/blob/main/results/classification_results.xlsx"

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_reg_notebook,node_reg_eda,node_reg_features,node_reg_models,node_reg_validation,node_reg_metrics toneBlue
class node_class_notebook,node_class_eda,node_class_features,node_class_models,node_class_metrics toneAmber
class node_concrete,node_ai4i toneMint
class node_reg_results,node_class_results,node_class_excel toneRose
class node_analyst toneIndigo
```

## Components

### Regression Track

- [Regression Notebook](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`
- [EDA & Preparation](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`
- [Feature Engineering](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`
- [Regressor Training](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`
- [Tuning & Validation](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`
- [Regression Metrics](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/01_Regression_Concrete.ipynb): `notebooks/01_Regression_Concrete.ipynb`

### Classification Track

- [Classification Notebook](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb): `notebooks/02_Classification_AI4I.ipynb`
- [Sensor Preparation](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb): `notebooks/02_Classification_AI4I.ipynb`
- [Feature Engineering](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb): `notebooks/02_Classification_AI4I.ipynb`
- [Classifier Training](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb): `notebooks/02_Classification_AI4I.ipynb`
- [Classification Metrics](https://github.com/nandot006/machine-health-profiling/blob/main/notebooks/02_Classification_AI4I.ipynb): `notebooks/02_Classification_AI4I.ipynb`

### Input Datasets

- [Concrete Data](https://github.com/nandot006/machine-health-profiling/blob/main/datasets/concrete_data.csv): `datasets/concrete_data.csv`
- [AI4I Data](https://github.com/nandot006/machine-health-profiling/blob/main/datasets/ai4i2020.csv): `datasets/ai4i2020.csv`

### Metric Outputs

- [Regression Results](https://github.com/nandot006/machine-health-profiling/blob/main/results/regression_results.csv): `results/regression_results.csv`
- [Classification Results](https://github.com/nandot006/machine-health-profiling/blob/main/results/classification_results.csv): `results/classification_results.csv`
- [Classification Workbook](https://github.com/nandot006/machine-health-profiling/blob/main/results/classification_results.xlsx): `results/classification_results.xlsx`

### Other components

- **Project Analyst**

## Connections

- Project Analyst → Regression Notebook: runs
- Concrete Data → Regression Notebook: input data
- Regression Notebook → EDA & Preparation: analyzes
- EDA & Preparation → Feature Engineering: prepares data
- Feature Engineering → Regressor Training: features
- Regressor Training → Tuning & Validation: models
- Tuning & Validation → Regression Metrics: validated models
- Regression Notebook → Regression Results: exports metrics
- Project Analyst → Classification Notebook: runs
- AI4I Data → Classification Notebook: input data
- Classification Notebook → Sensor Preparation: analyzes
- Sensor Preparation → Feature Engineering: prepares data
- Feature Engineering → Classifier Training: features
- Classifier Training → Classification Metrics: predictions
- Classification Notebook → Classification Results: exports metrics
- Classification Notebook → Classification Workbook: exports metrics