This repository contains a Customer Churn Prediction app that allows users to predict whether a customer will churn or not, based on their data and behaviors. The app utilizes machine learning models to make predictions and provides a user-friendly interface through Streamlit.
- Model Training and Prediction: The app uses a machine learning model to predict customer churn.
- Interactive UI: Powered by Streamlit, the app allows users to input customer data and view predictions in real-time.
- Visualization: The app provides visual insights into customer churn patterns using charts and graphs.
You can try the app live by visiting the deployed link:
Customer Churn Prediction GITHUB
The project leverages a dataset that contains customer information such as age, account length, services subscribed to, and other features, which are then used to predict the likelihood of customer churn. The app provides a clean and simple interface to input data and receive predictions.
- Input Data: Users can input or upload customer data into the app.
- Model Prediction: The app uses a trained machine learning model to analyze the data and predict whether the customer is likely to churn.
- Results Visualization: Results are displayed, including the probability of churn and other insights such as visualizations of churn trends across different segments of data.
If you'd like to run this app locally, follow these steps:
Make sure you have the following installed:
- Python 3.x
- Streamlit
- Jupyter Notebook (if you'd like to work with the
.ipynbfile directly)