https://github.com/nurulashraf/telco-customer-churn-prediction-model

This repository contains a Telco Customer Churn Prediction project using machine learning. It includes data preprocessing, exploratory data analysis, feature engineering, and model development to predict customer churn. Key tools used are Python, Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn.
https://github.com/nurulashraf/telco-customer-churn-prediction-model

churn-prediction classification-model customer-churn data-visualization exploratory-data-analysis machine-learning predictive-analytics python scikit-learn

Last synced: 13 days ago
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This repository contains a Telco Customer Churn Prediction project using machine learning. It includes data preprocessing, exploratory data analysis, feature engineering, and model development to predict customer churn. Key tools used are Python, Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn.

• Host: GitHub
• URL: https://github.com/nurulashraf/telco-customer-churn-prediction-model
• Owner: nurulashraf
• License: mit
• Created: 2025-01-22T06:27:42.000Z (14 days ago)
• Default Branch: main
• Last Pushed: 2025-01-22T06:49:21.000Z (14 days ago)
• Last Synced: 2025-01-22T07:29:53.843Z (14 days ago)
• Topics: churn-prediction, classification-model, customer-churn, data-visualization, exploratory-data-analysis, machine-learning, predictive-analytics, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 539 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Telco Customer Churn Prediction Model

This repository contains a project focused on predicting customer churn in the telecommunications industry using machine learning techniques. The goal is to build a predictive model that helps identify customers likely to churn, enabling businesses to take proactive retention measures.

---

## Project Structure

- **`data/`**: Contains the dataset used for analysis and prediction.

- **`notebooks/`**: Jupyter notebooks for data analysis, feature engineering, and model building.

- **`README.md`**: Project overview and usage instructions.

---

## Features

- **Exploratory Data Analysis (EDA)**: Uncover trends and insights using data visualizations.

- **Data Preprocessing**: Handle missing values, categorical encoding, and scaling.

- **Machine Learning Models**: Train and evaluate model using Random Forest.

- **Evaluation Metrics**: Assess model performance using metrics using accuracy score.

---

## Tools and Libraries

- **Python**  

- **Pandas**  

- **NumPy**  

- **Matplotlib**  

- **Seaborn**  

- **scikit-learn**

- **Imbalanced-learn**

---

## How to Use

1. **Clone the Repository**:  

   ```bash

   git clone https://github.com/nurulashraf/telco-customer-churn-prediction-model.git

   ```

2. **Install Dependencies**:  

   ```bash

   pip install -r requirements.txt

   ```

3. **Run the Notebooks**:  

   Open and execute the notebooks in the `notebooks/` directory to explore the analysis and build models.

---

## License

This project is licensed under the [MIT License](LICENSE).

---

## Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.