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                <h1>Customer Churn Dataset</h1>
                <p class="lead text-muted">Comprehensive customer churn dataset with demographic, usage, and billing information</p>
            </div>

            <div class="row">
                <div class="col-md-8">
                    <h3><i class="fas fa-info-circle text-primary"></i> Description</h3>
                    <p>This dataset contains customer information including demographics, account details, usage patterns, billing history, and churn status. Perfect for building classification models to predict customer churn, analyzing retention factors, and understanding customer behavior patterns.</p>

                    <h3 class="mt-4"><i class="fas fa-star text-warning"></i> Features</h3>
                    <ul class="feature-list">
                        <li><i class="fas fa-check-circle"></i> Demographic and account information</li>
                        <li><i class="fas fa-check-circle"></i> Usage patterns and billing data</li>
                        <li><i class="fas fa-check-circle"></i> Churn labels and timestamps</li>
                        <li><i class="fas fa-check-circle"></i> Multiple feature categories</li>
                        <li><i class="fas fa-check-circle"></i> Ready for ML model training</li>
                    </ul>

                    <h3 class="mt-4"><i class="fas fa-tags text-info"></i> Technologies</h3>
                    <div>
                        <span class="tech-badge">CSV</span>
                        <span class="tech-badge">Excel</span>
                        <span class="tech-badge">Pandas</span>
                        <span class="tech-badge">NumPy</span>
                    </div>

                    <h3 class="mt-4"><i class="fas fa-signal text-success"></i> Difficulty Level</h3>
                    <span class="difficulty-badge bg-success text-white">Beginner</span>

                    <div class="mt-4">
                        <a href="customer-churn.zip" class="btn btn-download" download>
                            <i class="fas fa-download"></i> Download Dataset
                        </a>
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                </div>

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                        <div class="card-body">
                            <h5 class="card-title"><i class="fas fa-database"></i> Dataset Info</h5>
                            <p class="card-text">
                                <strong>Category:</strong> Tabular Data<br>
                                <strong>Format:</strong> CSV, Excel<br>
                                <strong>Size:</strong> ~500KB<br>
                                <strong>Records:</strong> 10,000+<br>
                                <strong>Features:</strong> 15+
                            </p>
                        </div>
                    </div>

                    <div class="card mt-3">
                        <div class="card-body">
                            <h5 class="card-title"><i class="fas fa-book"></i> Use Cases</h5>
                            <ul class="list-unstyled">
                                <li>✓ Churn prediction models</li>
                                <li>✓ Customer segmentation</li>
                                <li>✓ Retention analysis</li>
                                <li>✓ Behavior pattern analysis</li>
                                <li>✓ ML classification training</li>
                            </ul>
                        </div>
                    </div>
                </div>
            </div>

            <div class="contact-info">
                <h5><i class="fas fa-user"></i> Provided by RSK World</h5>
                <p class="mb-2">
                    <i class="fas fa-globe"></i> Website: <a href="https://rskworld.in/" target="_blank">https://rskworld.in/</a><br>
                    <i class="fas fa-envelope"></i> Email: <a href="mailto:help@rskworld.in">help@rskworld.in</a><br>
                    <i class="fas fa-phone"></i> Phone: <a href="tel:+919330539277">+91 93305 39277</a><br>
                    <i class="fas fa-link"></i> Contact: <a href="https://rskworld.in/contact.php" target="_blank">https://rskworld.in/contact.php</a>
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PROJECT_INFO.md

# Customer Churn Dataset - Project Information

## Project Details

- **ID:** 1
- **Title:** Customer Churn Dataset
- **Category:** Tabular Data
- **Description:** Comprehensive customer churn dataset with demographic, usage, and billing information for predicting customer retention and churn analysis.
- **Full Description:** This dataset contains customer information including demographics, account details, usage patterns, billing history, and churn status. Perfect for building classification models to predict customer churn, analyzing retention factors, and understanding customer behavior patterns.
- **Technologies:** CSV, Excel, Pandas, NumPy
- **Difficulty:** Beginner
- **Source Link:** ./customer-churn/customer-churn.zip
- **Demo Link:** ./customer-churn/
- **Icon:** fas fa-table
- **Icon Color:** text-warning
- **Project Image:** ./customer-churn/customer-churn.png
- **Project Image Alt:** Customer Churn Dataset - rskworld.in

## Features

- Demographic and account information
- Usage patterns and billing data
- Churn labels and timestamps
- Multiple feature categories
- Ready for ML model training

## Contact Information

**Provided by RSK World**

- 🌐 **Website:** [https://rskworld.in/](https://rskworld.in/)
- 📧 **Email:** [help@rskworld.in](mailto:help@rskworld.in)
- 📱 **Phone:** [+91 93305 39277](tel:+919330539277)
- 📞 **Contact Page:** [https://rskworld.in/contact.php](https://rskworld.in/contact.php)

## Project Structure

```
customer-churn/
├── data/
│ └── customer_churn.csv # Main dataset
├── scripts/
│ ├── __init__.py
│ ├── generate_dataset.py # Dataset generator
│ ├── data_exploration.py # Data exploration
│ ├── data_preprocessing.py # Data preprocessing
│ └── model_training.py # Model training
├── notebooks/
│ └── analysis.ipynb # Jupyter notebook for analysis
├── output/ # Generated outputs (visualizations, models)
├── index.html # Demo page
├── README.md # Main documentation
├── CONTRIBUTING.md # Contributing guidelines
├── SETUP.md # Setup instructions
├── LICENSE # License information
├── requirements.txt # Python dependencies
├── PROJECT_INFO.md # This file
├── customer-churn.zip # Compressed dataset (to be created)
└── customer-churn.png # Project image (to be created)
```

## Usage

1. Install dependencies: `pip install -r requirements.txt`
2. Generate dataset (if needed): `python scripts/generate_dataset.py`
3. Explore data: `python scripts/data_exploration.py`
4. Preprocess data: `python scripts/data_preprocessing.py`
5. Train models: `python scripts/model_training.py`
6. Use Jupyter notebook: `jupyter notebook notebooks/analysis.ipynb`

## Use Cases

- Churn prediction models
- Customer segmentation
- Retention analysis
- Behavior pattern analysis
- ML classification training

---

**All files in this project include contact information from RSK World in their headers/comments.**

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