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---
title: DSA Project
emoji: 📈
colorFrom: pink
colorTo: purple
sdk: gradio
sdk_version: 5.25.2
app_file: app_gradio.py
pinned: false
short_description: Customer Churn Analysis and Prediction
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
# Customer Churn Prediction Application
This application predicts customer churn based on various customer attributes using a machine learning model.
## Overview
Customer churn prediction is a critical task for businesses to identify customers who are likely to discontinue using their products or services. This application uses a Random Forest model trained on historical customer data to predict churn likelihood.
## Features
- Interactive web interface for making churn predictions
- Input validation and error handling
- Visualization of model performance through ROC curve
- Probability-based risk assessment
- Easy-to-use sliders and dropdown menus for data input
## Installation
### Prerequisites
- Python 3.8 or higher
- Required packages (see requirements.txt)
### Setup
To run this application on your local machine:
1. Clone this Space
2. Install the required packages:
```
pip install -r requirements.txt
```
3. Run the application:
```
streamlit run app.py
```
## Usage
1. Adjust the sliders and select options to input customer information:
- Age
- Gender
- Tenure (months)
- Usage Frequency
- Support Calls
- Payment Delay
- Last Interaction (days ago)
- Total Spend
- Subscription Type
- Contract Length
2. Click "Predict Churn" to see the prediction results.
3. The application will display:
- Churn prediction (Yes/No)
- Churn probability (0.00-1.00)
- Risk level (Low/Medium/High)
- ROC curve visualization showing model performance
## Model Information
The prediction model (`best_model.pkl`) is a trained Random Forest classifier that has been optimized for churn prediction. The model was trained on historical customer data with features including demographic information, usage patterns, and financial metrics.
## Deployment
This application can be deployed on Hugging Face Spaces:
1. Create a new Space on [Hugging Face](https://huggingface.co/spaces)
2. Select Streamlit or Gradio as the SDK
3. Upload the necessary files:
- `app.py` (or `app_gradio.py`)
- `best_model.pkl`
- `roc_curve_rf_tuned.png`
- `requirements.txt`
## Files Description
- `app.py`: Streamlit application code
- `app_gradio.py`: Gradio application code (alternative interface)
- `best_model.pkl`: Trained machine learning model
- `roc_curve_rf_tuned.png`: ROC curve visualization of model performance
- `requirements.txt`: List of Python dependencies |