# venkatkudipudi009/customer-churn-prediction-using-explainable-ai architecture

> Architecture diagram of the GitHub repository venkatkudipudi009/customer-churn-prediction-using-explainable-ai, 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/venkatkudipudi009/customer-churn-prediction-using-explainable-ai
- Repository: https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai

## Overview

This repository is an offline telecom churn-analysis and retention-planning project, not a deployed prediction service. The documented workflow runs in the research notebook: it processes the IBM dataset, trains and compares models, explains predictions, and produces high-risk customer outputs. The saved model artifacts and Power BI dashboard are also important deliverables. The README documents these capabilities, but the notebook internals and dashboard data connections were not sampled, so those links are left unspecified.

Main entry points: [`Notebook/customer-churn-prediction-research-ipynb.ipynb`](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb) (Research notebook), [`Dataset/IBM_telco_customer_churn.csv`](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Dataset/IBM_telco_customer_churn.csv) (Telco churn data), [`Outputs/high_risk_customers.csv`](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Outputs/high_risk_customers.csv) (High-risk customers), [`Models`](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/tree/main/Models) (Saved models)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_analysis["Analysis workflow"]
  node_notebook["Research notebook"]
  node_dataset["Telco churn data"]
  node_cleaning["Clean and explore"]
  node_features["Feature preparation"]
  node_training["Model training"]
  node_ensembles["Ensemble evaluation"]
  node_explanations["SHAP explanations"]
end

subgraph group_artifacts["Model and outputs"]
  node_models["Saved models"]
  node_predictions["Churn predictions"]
  node_highrisk["High-risk customers"]
end

subgraph group_consumption["Business consumption"]
  node_dashboard["Power BI dashboard"]
end

node_analyst(("Data analyst"))
node_customers(("Telco customers"))
node_retention(("Retention team"))

node_analyst -->|"runs"| node_notebook
node_notebook -->|"reads"| node_dataset
node_notebook -->|"performs"| node_cleaning
node_cleaning -->|"prepares"| node_features
node_features -->|"feeds"| node_training
node_training -->|"compares"| node_ensembles
node_ensembles -->|"explains"| node_explanations
node_notebook -->|"saves"| node_models
node_notebook -->|"writes"| node_predictions
node_notebook -->|"identifies"| node_highrisk
node_retention -->|"uses"| node_highrisk
node_customers -->|"represented in"| node_dataset

click node_notebook "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_dataset "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Dataset/IBM_telco_customer_churn.csv"
click node_cleaning "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_features "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_training "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_ensembles "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_explanations "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb"
click node_models "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/tree/main/Models"
click node_predictions "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Outputs/final_predictions.csv"
click node_highrisk "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Outputs/high_risk_customers.csv"
click node_dashboard "https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Dashboard/Customer_Churn_Dashboard.pbix"

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_notebook,node_dataset,node_cleaning,node_features,node_training,node_ensembles,node_explanations toneBlue
class node_models,node_predictions,node_highrisk toneAmber
class node_dashboard toneMint
class node_analyst,node_customers,node_retention toneIndigo
```

## Components

### Analysis workflow

- [Research notebook](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`
- [Telco churn data](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Dataset/IBM_telco_customer_churn.csv): `Dataset/IBM_telco_customer_churn.csv`
- [Clean and explore](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`
- [Feature preparation](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`
- [Model training](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`
- [Ensemble evaluation](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`
- [SHAP explanations](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Notebook/customer-churn-prediction-research-ipynb.ipynb): `Notebook/customer-churn-prediction-research-ipynb.ipynb`

### Model and outputs

- [Saved models](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/tree/main/Models): `Models`
- [Churn predictions](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Outputs/final_predictions.csv): `Outputs/final_predictions.csv`
- [High-risk customers](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Outputs/high_risk_customers.csv): `Outputs/high_risk_customers.csv`

### Business consumption

- [Power BI dashboard](https://github.com/venkatkudipudi009/customer-churn-prediction-using-explainable-ai/blob/main/Dashboard/Customer_Churn_Dashboard.pbix): `Dashboard/Customer_Churn_Dashboard.pbix`

### Other components

- **Data analyst**
- **Telco customers**
- **Retention team**

## Connections

- Data analyst → Research notebook: runs
- Research notebook → Telco churn data: reads
- Research notebook → Clean and explore: performs
- Clean and explore → Feature preparation: prepares
- Feature preparation → Model training: feeds
- Model training → Ensemble evaluation: compares
- Ensemble evaluation → SHAP explanations: explains
- Research notebook → Saved models: saves
- Research notebook → Churn predictions: writes
- Research notebook → High-risk customers: identifies
- Retention team → High-risk customers: uses
- Telco customers → Telco churn data: represented in