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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 |