https://github.com/rohitinu6/telecom-customer-churn-prediction
This project aims to predict customer churn in the telecom industry using machine learning techniques.
https://github.com/rohitinu6/telecom-customer-churn-prediction
algorithms analytics churn-analysis churn-prediction customer data-science eda machine-learning machine-learning-algorithms project python visualization
Last synced: 8 months ago
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This project aims to predict customer churn in the telecom industry using machine learning techniques.
- Host: GitHub
- URL: https://github.com/rohitinu6/telecom-customer-churn-prediction
- Owner: rohitinu6
- Created: 2025-01-23T09:38:04.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-02-06T03:03:56.000Z (8 months ago)
- Last Synced: 2025-02-06T04:21:47.917Z (8 months ago)
- Topics: algorithms, analytics, churn-analysis, churn-prediction, customer, data-science, eda, machine-learning, machine-learning-algorithms, project, python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 2.13 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Telecom Customer Churn Prediction
## 📌 Project Overview
This project aims to predict customer churn in the telecom industry using machine learning techniques. By analyzing customer behavior and service usage patterns, the model helps identify customers who are likely to leave, enabling businesses to take proactive retention measures.
## 🚀 Features
- Data cleaning and preprocessing
- Exploratory data analysis (EDA) to uncover key insights
- Machine learning models for churn prediction
- Model evaluation and performance metrics
- Business insights for customer retention strategies
## 🛠 Tech Stack
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
## 📂 Dataset
The dataset contains customer information, including:
- **Contract Type**
- **Monthly Charges**
- **Tenure**
- **Payment Method**
- **Internet & Call Services Used**
- **Churn Status**
## 📊 Machine Learning Models Used
- Logistic Regression
- Random Forest Classifier
- Support Vector Machine (SVM)
- XGBoost
## 🔥 Results
The models are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC score. The best-performing model helps identify high-risk churn customers effectively.
## 📁 Repository Structure
```
📂 Telecom-Customer-Churn-Prediction
📂 data (Dataset & processed data)
📂 notebooks (Jupyter Notebooks)
📂 models (Trained models)
📂 images (Code and Results Screenshots)
📄 README.md (Project documentation)
```
## 🎨 Code and Results
Include images of code and results in the `images` folder. Example:
## 💜 How to Run the Project
1. Clone the repository:
```bash
git clone https://github.com/rohitinu6/Telecom-Customer-Churn-Prediction.git
```
2. Navigate to the project folder:
```bash
cd Telecom-Customer-Churn-Prediction
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Run the Jupyter Notebook or Python scripts to train and test models.
## 📡 Links
- **GitHub Repository:** [Telecom Customer Churn Prediction](https://github.com/rohitinu6/Telecom-Customer-Churn-Prediction.git)
- **Portfolio:** [Rohit Dubey](https://tinyurl.com/dubeyrohit)
- **GitHub Profile:** [rohitinu6](https://github.com/rohitinu6)
- **LinkedIn:** [Rohit Dubey](https://www.linkedin.com/in/rohit-dubey-d/)
- **Twitter/X:** [@rohitdubey003](https://x.com/rohitdubey003)
## 📌 Tags
`Machine Learning` `Customer Churn` `Telecom Industry` `Data Science` `Python` `EDA`
## 📝 License
This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).
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💡 **For any queries or collaboration opportunities, feel free to connect!** 🚀