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Churn-Predictor-ANN

Visit the web application: Churn Predictor App

Description

Churn Predictor - Web Application: Predict customer churn using an ANN model deployed in a user-friendly Streamlit app. The application leverages standard preprocessing techniques including label encoding, one-hot encoding, and scaling. Users can input customer details to predict churn probability with a dynamic and interactive interface. The model achieves an accuracy of 85%.

Features

  • Predict customer churn using an ANN model.
  • User-friendly interface built with Streamlit.
  • Standard preprocessing techniques: label encoding, one-hot encoding, and scaling.
  • Interactive input for customer details.

Video Demo

Check out the video demo below to see the application in action:

streamlit-app-2024-12-20-13-12-24.webm

Technologies Used

  • Programming Language: Python
  • Libraries: Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, TensorFlow, Keras, Pickle
  • Web Framework: Streamlit
  • Deployment: Streamlit

Installation

  1. Clone the repository: 
    git clone https://github.com/sai-manas/Churn-Predictor-ANN.git
  2. Navigate to the project directory: 
    cd Churn-Predictor-ANN
  3. Install the required dependencies: 
    pip install -r requirements.txt

Usage

Running the Streamlit Web Application

  1. Navigate to the project directory: 
    cd Churn-Predictor-ANN
  2. Run the Streamlit app: 
    streamlit run app.py

Jupyter Notebooks

Check the notebooks for model training and prediction processes here:

Deployment on Streamlit

To deploy the application on Streamlit from GitHub:

  1. Go to Streamlit.
  2. Sign in with your GitHub account.
  3. Create a new app and connect it to your GitHub repository.
  4. Select the branch and the main file (app.py) to deploy.
  5. Click "Deploy" to launch your application.