Update README.md

README.md

@@ -1,9 +1,9 @@
 ---
 title: DSA Project
-emoji: 📊
+emoji: 📈
 colorFrom: pink
 colorTo: purple
-sdk: gradio
+sdk: streamlit
 sdk_version: 5.25.2
 app_file: app.py
 pinned: false
@@ -11,3 +11,86 @@ 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