https://github.com/alihassanml/customer-churn-prediction-web-app
This project is a web application for predicting customer churn using a pre-trained machine learning model. The application takes various customer features as input and predicts whether the customer is likely to churn or not.
https://github.com/alihassanml/customer-churn-prediction-web-app
Last synced: 8 months ago
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This project is a web application for predicting customer churn using a pre-trained machine learning model. The application takes various customer features as input and predicts whether the customer is likely to churn or not.
- Host: GitHub
- URL: https://github.com/alihassanml/customer-churn-prediction-web-app
- Owner: alihassanml
- License: mit
- Created: 2024-07-22T10:54:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-22T11:03:03.000Z (about 1 year ago)
- Last Synced: 2025-01-01T15:11:43.560Z (9 months ago)
- Language: Jupyter Notebook
- Size: 330 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Customer Churn Prediction Web App
This project is a web application for predicting customer churn using a pre-trained machine learning model. The application takes various customer features as input and predicts whether the customer is likely to churn or not.
## Features
- **User Input**: Users can input customer details such as credit score, geography, gender, age, tenure, balance, number of products, whether they have a credit card, if they are an active member, and estimated salary.
- **Real-time Prediction**: The app preprocesses the input data and uses a trained neural network model to predict the likelihood of customer churn.
- **User-friendly Interface**: Built with Streamlit, the app provides an easy-to-use interface for entering data and viewing predictions.
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/alihassanml/customer-churn-prediction.git
cd customer-churn-prediction
```
2. **Install the required packages**:
```bash
pip install -r requirements.txt
```
3. **Add the model and encoders files**:
Ensure you have the following files in the project directory:
- `model.h5`
- `standard_scalar.pkl`
- `label_encoder_gender.pkl`
- `one_hot_encoder.pkl`
## Running the App
Run the Streamlit app with the following command:
```bash
streamlit run app.py