README

# Telco Customer Churn Analysis

## 📊 Project Overview
This project analyzes customer churn in a telecommunications company using machine learning and data visualization techniques. The analysis is based on the [IBM Telco Customer Churn Dataset from Kaggle](https://www.kaggle.com/datasets/yeanzc/telco-customer-churn-ibm-dataset). The analysis includes customer demographics, service usage patterns, and predictive modeling to identify key factors contributing to customer churn.
This project analyzes customer churn in a telecommunications company using machine learning and data visualization techniques. The analysis includes customer demographics, service usage patterns, and predictive modeling to identify key factors contributing to customer churn.


![Demographics](plots/churn_demographics.png)
![Reasons](plots/churn_reasons.png) 
![Correlation Matrix](plots/correlation_matrix.png) 
![Feature Importance](plots/feature_importance.png) 
![Service Analysis](plots/service_analysis.png) 
![Value Analysis](plots/value_analysis.png)





## 🎯 Key Features
- Comprehensive exploratory data analysis (EDA)
- Customer demographic analysis
- Service usage pattern visualization
- Customer lifetime value (CLTV) analysis
- Churn prediction using Random Forest Classifier
- Feature importance analysis
- Detailed visualization of key metrics

## 🗃️ Dataset
The analysis uses the IBM Telco Customer Churn dataset from Kaggle, which includes:
- Customer demographics (age, gender, dependents)
- Account information (tenure, contract type, payment method)
- Services used (internet type, phone service, security)
- Charges (monthly charges, total charges)
- Churn status and reasons
- Customer Lifetime Value (CLTV)

## 📁 Project Structure
```
├── PRO_1.py           # Basic data analysis and statistics
├── PRO_2.py           # Advanced analysis and ML modeling
├── telco.csv          # Dataset (not included in repo)
└── plots/             # Generated visualization plots
    ├── churn_demographics.png
    ├── value_analysis.png
    ├── service_analysis.png
    ├── churn_reasons.png
    ├── correlation_matrix.png
    └── feature_importance.png
```

## 🛠️ Requirements
- Python 3.x
- Required packages:
  - numpy
  - pandas
  - matplotlib
  - seaborn
  - scikit-learn

Install required packages using:
```bash
pip install -r requirements.txt
```

## 🚀 Usage

0. Download the dataset:
- Download the `telco.csv` file from [Kaggle](https://www.kaggle.com/datasets/yeanzc/telco-customer-churn-ibm-dataset)
- Place it in your project root directory
1. Clone the repository:
```bash
git clone https://github.com/yourusername/telco-churn-analysis.git
```

2. Navigate to the project directory:
```bash
cd telco-churn-analysis
```

3. Run the basic analysis:
```bash
python PRO_1.py
```

4. Run the advanced analysis and modeling:
```bash
python PRO_2.py
```

## 📈 Analysis Components

### 1. Basic Analysis (PRO_1.py)
- Data overview and quality checks
- Basic customer demographics
- Initial churn analysis
- Customer value metrics
- Age group distribution
- Gender-based churn patterns

### 2. Advanced Analysis (PRO_2.py)
- Detailed visualizations
- Customer lifetime value analysis
- Service usage patterns
- Churn reason analysis
- Correlation analysis
- Predictive modeling using Random Forest
- Feature importance analysis

## 📊 Key Insights
The analysis provides several valuable insights:
- Customer demographics and their relationship with churn
- Service usage patterns and their impact on customer retention
- Key factors influencing customer churn
- Predictive model performance in identifying potential churners
- Customer lifetime value analysis

## 📉 Visualizations
The project generates several visualization plots:
1. Churn Demographics: Customer distribution and demographic analysis
2. Value Analysis: CLTV, monthly charges, and tenure analysis
3. Service Analysis: Internet type, contract type, and payment method patterns
4. Churn Reasons: Top 10 reasons for customer churn
5. Correlation Matrix: Relationships between numerical variables
6. Feature Importance: Key predictors in the churn model

## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.

## 📝 License
This project is licensed under the MIT License - see the LICENSE file for details.

## 👥 Contact
For any questions or feedback, please reach out to Dennis Carroll at denniscarrollj@gmail.com