📊 Customer Churn Prediction App

End-to-end Machine Learning project for predicting customer churn.

Includes:

• Data preprocessing

• Model training (Random Forest)

• Pickle model generation

• Deployment using FastAPI, Flask, Streamlit, Gradio

• HTML + CSS frontend

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🚀 SETUP INSTRUCTIONS (STEP-BY-STEP)

1️⃣ Fork the repository (from GitHub UI)

2️⃣ Clone your fork

git clone https://github.com/YOUR-USERNAME/YOUR-REPO-NAME.git

cd YOUR-REPO-NAME

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3️⃣ Install uv (if not installed)

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Mac / Linux

curl -Ls https://astral.sh/uv/install.sh | sh

Windows (PowerShell)

irm https://astral.sh/uv/install.ps1 | iex

Verify installation

uv --version

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4️⃣ Create Virtual Environment

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uv venv

Activate virtual environment

Windows

.venv\Scripts\activate

Mac / Linux

source .venv/bin/activate

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5️⃣ Install Project Dependencies

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uv sync

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6️⃣ Train Model (Generate Pickle File)

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Make sure dataset exists:

data/churn.csv

python train_model.py

This creates:

models/churn_model.pkl

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🌐 RUN APPLICATIONS

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▶️ Run Flask App

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python deployments/flask/flask_app.py

Open in browser:

http://127.0.0.1:5000/

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▶️ Run FastAPI App

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uvicorn deployments.fastapi.fastapi_app:app --reload

Open:

http://127.0.0.1:8000/

Swagger Docs:

http://127.0.0.1:8000/docs

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▶️ Run Streamlit App

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streamlit run deployments/streamlit_app.py

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▶️ Run Gradio App

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python deployments/gradio_app.py

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📂 PROJECT STRUCTURE

.
├── data/
│   └── churn.csv
├── models/
│   └── churn_model.pkl
├── deployments/
│   ├── flask/
│   ├── fastapi/
│   ├── streamlit_app.py
│   └── gradio_app.py
├── train_model.py
├── pyproject.toml
└── README.md

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📌 IMPORTANT NOTES

• Train the model before running any app.

• Entire preprocessing + model is saved inside pickle.

• No manual encoding required in deployment.

• SMOTE (if used) is only for training, never for production API.

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🎯 DONE

You now have a fully working ML project with multiple deployment options.