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https://github.com/md-emon-hasan/ml-projects-telcom-customer-churn-prediction

📱 Customers are likely to leave a telecom service, enabling companies to take measures for retention and create accurate churn prediction models.
https://github.com/md-emon-hasan/ml-projects-telcom-customer-churn-prediction

boostrap5 customer-churn customer-segmentation data-engineering data-science data-visualization logestic-regression machine-learning telco-customer-churn-prediction telcom-churn

Last synced: 4 days ago
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📱 Customers are likely to leave a telecom service, enabling companies to take measures for retention and create accurate churn prediction models.

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README 

        
# 📊 **Telcom Customer Churn Prediction Project**



Welcome to the **Telcom Customer Churn Prediction** repository! This project aims to predict customer churn (when a customer decides to stop using a company's service) in the telecom industry. By leveraging machine learning, this system helps telecom companies identify at-risk customers, allowing them to take proactive measures to retain customers.



![Capture](https://github.com/user-attachments/assets/d60dee57-2b7c-4165-8b35-6132de5468e1)



## 📋 Contents



- [Introduction](#introduction)

- [Topics Covered](#topics-covered)

- [Getting Started](#getting-started)

- [Machine Learning Model](#machine-learning-model)

- [Data](#data)

- [Best Practices](#best-practices)

- [FAQ](#faq)

- [Troubleshooting](#troubleshooting)

- [Contributing](#contributing)

- [Additional Resources](#additional-resources)

- [Challenges Faced](#challenges-faced)

- [Lessons Learned](#lessons-learned)

- [Why I Created This Repository](#why-i-created-this-repository)

- [License](#license)

- [Contact](#contact)



---



## 📖 Introduction



This repository showcases a **Telcom Customer Churn Prediction** system that uses machine learning to predict whether a customer will churn based on their characteristics and usage patterns. By utilizing this system, telecom companies can minimize churn rates and improve customer retention strategies.



---



## 🔍 Topics Covered



- **Churn Prediction Models:** Building machine learning models like Logistic Regression, Random Forest, and XGBoost for churn prediction.

- **Data Preprocessing:** Handling missing values, encoding categorical variables, and balancing the dataset.

- **Model Evaluation:** Using performance metrics such as accuracy, recall, precision, ROC-AUC, and F1-score.

- **Model Deployment:** Exploring deployment options for practical usage, whether through APIs or dashboard applications.



---



## 🚀 Getting Started



To get started with this project, follow these steps:



1. **Clone the repository:**



   ```bash

   git clone https://github.com/Md-Emon-Hasan/ML-Projects-Telcom-Customer-Churn-Prediction.git

   ```



2. **Navigate to the project directory:**



   ```bash

   cd ML-Projects-Telcom-Customer-Churn-Prediction

   ```



3. **Create and activate a virtual environment:**



   ```bash

   python -m venv venv

   source venv/bin/activate  # For Windows: `venv\Scripts\activate`

   ```



4. **Install the dependencies:**



   ```bash

   pip install -r requirements.txt

   ```



5. **Run the application (if there is a UI component):**



   ```bash

   python app.py

   ```



6. **Access the application at:**



   ```

   http://127.0.0.1:5000/

   ```



---



## 🧠 Machine Learning Model



The project utilizes machine learning models to predict customer churn, enabling telecom companies to identify at-risk customers.



### Key Components:



- **Data Exploration:** Analyzing the customer features, such as contract types, payment methods, and usage statistics, to detect patterns that correlate with churn.

- **Model Training:** Implementing algorithms such as Logistic Regression, Decision Trees, and Random Forest to predict churn.

- **Model Evaluation:** Using evaluation metrics like accuracy, precision, recall, ROC-AUC, and F1-score to assess the model's performance.



---



## 📊 Data



- The dataset used for this project is publicly available and can be found [here](https://www.kaggle.com/datasets/blastchar/telco-customer-churn).

- It includes features like customer demographic data, service details, account status, and usage statistics.

- Preprocessing steps involve handling missing values, encoding categorical variables, and feature scaling.



---



## 🌟 Best Practices



Suggestions for maintaining and improving this project:



- **Feature Engineering:** Continue to refine feature selection and extraction to improve model accuracy.

- **Model Tuning:** Experiment with hyperparameter tuning and other advanced techniques like GridSearchCV for better results.

- **Model Retraining:** Retrain the model periodically with new data to ensure it remains accurate over time.

- **Data Privacy:** Handle customer data securely, ensuring privacy and compliance with data protection regulations.



---



## ❓ FAQ



**Q: What is the purpose of this project?**  

A: This project predicts customer churn in telecom companies, providing valuable insights that can help reduce churn rates and improve retention strategies.



**Q: How can I contribute to this repository?**  

A: See the [Contributing](#contributing) section for more details.



**Q: Can I deploy the model in production?**  

A: Yes, the model can be deployed in production using APIs, dashboards, or integrated into customer management systems.



---



## 🛠️ Troubleshooting



Common issues and solutions:



- **Issue: Low Model Performance**  

  *Solution:* Experiment with different models, hyperparameter tuning, or feature engineering to improve performance.



- **Issue: Dependencies Not Installed**  

  *Solution:* Ensure the virtual environment is activated and install all dependencies with `pip install -r requirements.txt`.



---



## 🤝 Contributing



Contributions are welcome! Here’s how to get started:



1. **Fork the repository.**

2. **Create a branch:**



   ```bash

   git checkout -b feature/new-feature

   ```



3. **Make changes.**

4. **Commit:**



   ```bash

   git commit -m 'Add feature or fix issue'

   ```



5. **Push and create a pull request.**



---



## 📚 Additional Resources



For further reading on churn prediction and machine learning:



- **Churn Prediction Dataset:** [Kaggle](https://www.kaggle.com/datasets/blastchar/telco-customer-churn)

- **Machine Learning Course:** [Coursera](https://www.coursera.org/learn/machine-learning)

- **Flask Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)



---



## 💪 Challenges Faced



Key challenges during development:



- Handling class imbalance (more non-churning customers than churning customers).

- Dealing with missing values and incomplete data.



---



## 📚 Lessons Learned



Valuable insights gained:



- The importance of feature engineering and selecting the right features for better model performance.

- The role of model evaluation metrics in choosing the best model for predicting churn.



---



## 🌟 Why I Created This Repository



This repository was created to explore how machine learning can be used to solve business challenges in the telecom industry by predicting customer churn, which helps improve retention strategies and overall business performance.



---



## 📝 License



This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for details.



---



## 📬 Contact



- **Email:** [[email protected]](mailto:[email protected])

- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)

- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)

- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)

- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)



---