🏦 Customer Churn Prediction Web App

A clean, interactive web application that uses a Deep Learning Artificial Neural Network (ANN) to predict whether a bank customer is likely to leave (churn).

🔗 Live Demo: https://3pkxyregnaqczcurjnzgz3.streamlit.app/

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🌟 Key Features

• ANN-Powered Predictions: Uses a pre-trained Keras Deep Learning model (model.h5) under the hood.
• Production Preprocessing: Automatically handles customer feature scaling (StandardScaler) and encoding (LabelEncoder, OneHotEncoder) in real time.
• Simple Input Fields: Easily input details like credit score, geography, gender, age, balance, and activity status.
• Instant Result Panels: Color-coded feedback showing the exact churn probability and prediction.

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📁 Core Files

• app.py: Streamlit frontend and prediction routing.
• model.h5: Trained Keras ANN model.
• scaler.pkl, label_encoder_gender.pkl, onehot_encoder_geo.pkl: Serialized data preprocessing objects.
• experement.ipynb: Training notebook (exploratory analysis, scaling, network architecture, and callback configs).
• requirements.txt: Project dependencies.

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🚀 Running Locally

1. Clone the project & navigate inside:

   git clone https://github.com/YOUR_USERNAME/ann_classification-.git
   cd ann_classification-

2. Create & activate a virtual environment:

   python -m venv venv
   # Windows:
   .\venv\python.exe -m pip install -r requirements.txt
   # macOS/Linux:
   source venv/bin/activate && pip install -r requirements.txt

3. Run the Streamlit app:

   # Windows:
   .\venv\Scripts\streamlit run app.py
   # macOS/Linux:
   streamlit run app.py

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☁️ Deployment Note

When deploying this app to Streamlit Community Cloud, ensure you configure the Advanced settings to use Python 3.11 or higher. This is required because modern TensorFlow versions are not supported on older Python versions (like Python 3.9).