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Customer Churn Prediction Using ANN

This project focuses on predicting customer churn using an Artificial Neural Network (ANN). The model was developed using TensorFlow/Keras and deployed as a user-friendly web application via Streamlit. The solution leverages robust preprocessing pipelines to handle data efficiently, enabling businesses to identify and retain at-risk customers.

Features

  • Customer Churn Prediction: Predict whether a customer is likely to churn based on input features.
  • Interactive Web App: User-friendly interface to input data and get instant predictions.
  • End-to-End Preprocessing: Automated handling of missing values, normalization, and encoding of categorical variables.
  • Model Deployment: Hosted on Streamlit for easy accessibility.

Hosted Link

Access the application here: ANN Churn Prediction Web App

Technologies Used

  • TensorFlow/Keras: For building and training the ANN model.
  • Streamlit: For creating an interactive and accessible web application.
  • Pandas & NumPy: For data preprocessing and feature engineering.
  • Scikit-learn: For splitting data and preprocessing utilities.

Project Overview

  1. Data Preprocessing:
    • Handled missing values and performed feature scaling.
    • Encoded categorical variables using one-hot encoding.
  2. Model Development:
    • Designed a feedforward neural network with multiple dense layers.
    • Optimized the model using backpropagation and various hyperparameters.
    • Evaluated performance using metrics like accuracy and recall.
  3. Deployment:
    • Deployed the trained model as a Streamlit app for real-time predictions.

Installation

To run this project locally:

  1. Clone the repository: 
    git clone https://github.com/krishnaik06/ANN-CLassification-Churn.git
  2. Install the required dependencies: 
    pip install -r requirements.txt
  3. Run the Streamlit app: 
    streamlit run app.py

Usage

  • Open the hosted app or run it locally.
  • Enter customer details in the input fields.
  • Click on the "Predict" button to get the churn prediction.

Future Enhancements

  • Incorporate additional features to improve prediction accuracy.
  • Add a feature to upload bulk data for batch predictions.
  • Enhance the user interface for better user experience.

License

  • This project is licensed under the MIT License.