🧠 Customer Churn Prediction

This project predicts whether a customer is likely to churn from a bank using a machine learning model built with TensorFlow and deployed via Streamlit. The model is trained on customer profile data and financial metrics.

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📊 Dataset

The dataset used is Churn_Modelling.csv and contains the following features:

• CreditScore
• Geography
• Gender
• Age
• Tenure
• Balance
• NumOfProducts
• HasCrCard
• IsActiveMember
• EstimatedSalary

The target variable is Exited, indicating whether a customer has churned.

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📁 Project Structure

.
├── app.py                        # Streamlit app for churn prediction
├── streamlit\_regression.py      # Streamlit app for salary regression (optional)
├── model.h5                     # Trained churn prediction model
├── regression\_model.h5          # Trained regression model
├── scaler.pkl                   # StandardScaler for numerical features
├── lable\_encoder\_gender.pkl     # LabelEncoder for Gender
├── one\_hot\_encoder\_geo.pkl      # OneHotEncoder for Geography
├── requirements.txt             # Python dependencies
├── runtime.txt                  # Runtime environment (for deployment)
├── hyperparameter\_tuning.ipynb  # Notebook for tuning hyperparameters
├── predictions.ipynb            # Model prediction and evaluation
├── salaryregression.ipynb       # Regression model training
├── experiments.ipynb            # Initial exploratory analysis
├── Churn\_Modelling.csv          # Dataset
├── README.md                    # Project documentation

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🚀 How to Run

1. Install dependencies:

pip install -r requirements.txt

2. Run the Streamlit app:

streamlit run app.py

The app will launch in your browser where you can input customer details and see churn predictions.

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🧩 Model Workflow

1. Preprocessing

   • Encode categorical features:

     • Gender → LabelEncoded
     • Geography → OneHotEncoded

   • Scale numerical features with StandardScaler.

2. Model

   • A deep learning classification model built using Keras.
   • Output is a churn probability (between 0 and 1).

3. Prediction

   • If the probability > 0.5 → Customer is likely to churn.
   • Else → Customer is likely to stay.

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📈 Experimentation

• Hyperparameter tuning notebooks are available for training custom models.

• You can find:

  • hyperparameter_tuning.ipynb — tuning classification model
  • salaryregression.ipynb — regression analysis
  • predictions.ipynb — model inference and evaluation

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🛠 Deployment

• requirements.txt and runtime.txt are included for deployment on platforms like Heroku or Streamlit Cloud.
• For containerized deployment, a Dockerfile can be added if needed.

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