# anhnhatcs/uma_data_mining_project architecture

> Architecture diagram of the GitHub repository anhnhatcs/uma_data_mining_project, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-30). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/anhnhatcs/uma_data_mining_project
- Repository: https://github.com/anhnhatcs/uma_data_mining_project

## Overview

This repository is a customer-churn classification project centered on a Jupyter notebook and the Telco churn CSV. Its documented workflow covers data exploration and preparation, visualization, classifier training and tuning, then error analysis and model comparison. The README describes these capabilities, but no notebook source was provided, so the graph reflects documented relationships rather than verified implementation calls.

Main entry points: [`data_mining_project_team_7.ipynb`](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb) (Explore and prepare), [`WA_Fn-UseC_-Telco-Customer-Churn.csv`](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/WA_Fn-UseC_-Telco-Customer-Churn.csv) (Telco churn data)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_data["Data analysis"]
  node_dataset["Telco churn data"]
  node_exploration["Explore and prepare"]
  node_visualization["Data visualization"]
end

subgraph group_models["Modeling"]
  node_training["Classifier training"]
  node_tuning["Hyperparameter tuning"]
end

subgraph group_evaluation["Evaluation"]
  node_error_analysis["Error analysis"]
  node_evaluation["Model evaluation"]
  node_selection["Model comparison"]
end

node_analyst(("Project user"))

node_analyst -.->|"runs analysis"| node_exploration
node_dataset -->|"loads data"| node_exploration
node_exploration -->|"examines data"| node_visualization
node_exploration -->|"splits data"| node_training
node_training -->|"tunes models"| node_tuning
node_training -->|"evaluates models"| node_evaluation
node_evaluation -->|"analyzes errors"| node_error_analysis
node_evaluation -->|"compares models"| node_selection

click node_dataset "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/WA_Fn-UseC_-Telco-Customer-Churn.csv"
click node_exploration "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_visualization "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_training "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_tuning "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_error_analysis "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_evaluation "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"
click node_selection "https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb"

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,node_exploration,node_visualization,node_analyst toneBlue
class node_training,node_tuning toneAmber
class node_error_analysis,node_evaluation,node_selection toneMint
```

## Components

### Data analysis

- [Telco churn data](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/WA_Fn-UseC_-Telco-Customer-Churn.csv): `WA_Fn-UseC_-Telco-Customer-Churn.csv`
- [Explore and prepare](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`
- [Data visualization](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`

### Modeling

- [Classifier training](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`
- [Hyperparameter tuning](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`

### Evaluation

- [Error analysis](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`
- [Model evaluation](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`
- [Model comparison](https://github.com/anhnhatcs/uma_data_mining_project/blob/main/data_mining_project_team_7.ipynb): `data_mining_project_team_7.ipynb`

### Other components

- **Project user**

## Connections

- Project user → Explore and prepare: runs analysis
- Telco churn data → Explore and prepare: loads data (evidence: `README.md`)
- Explore and prepare → Data visualization: examines data (evidence: `README.md`)
- Explore and prepare → Classifier training: splits data (evidence: `README.md`)
- Classifier training → Hyperparameter tuning: tunes models (evidence: `README.md`)
- Classifier training → Model evaluation: evaluates models (evidence: `README.md`)
- Model evaluation → Error analysis: analyzes errors (evidence: `README.md`)
- Model evaluation → Model comparison: compares models (evidence: `README.md`)