---
language: [en]
license: mit
tags: [tabular-classification, customer-churn, telecom, machine-learning, scikit-learn, classification, mlops]
pipeline_tag: tabular-classification
---

# 📱 Telecom Customer Churn Prediction

An end-to-end **machine learning system for predicting customer churn in the telecommunications industry**.

The project covers the complete ML lifecycle, including **data preprocessing, exploratory data analysis, feature engineering, model training, hyperparameter optimization, evaluation, and deployment**.

## 🚀 Key Features

* 📊 Exploratory Data Analysis
* 🧹 Data preprocessing and feature engineering
* 🤖 Machine learning classification
* ⚙️ Hyperparameter optimization
* 📈 Model evaluation
* 🔮 Churn prediction
* 🌐 Interactive Streamlit application
* 🔄 End-to-end ML pipeline

## 🖼️ Project Preview

<p align="center">
  <img
    src="https://camo.githubusercontent.com/0ca2eb4b9f814c700d4949ebe233b49d7276f29cf74aa4d6421987e039287b06/68747470733a2f2f6361726565722d706c6174666f726d2d6d61792d323032362e73332e61702d736f7574682d312e616d617a6f6e6177732e636f6d2f6b726973686e61696b2e696e2f6d656469612f70726f6a6563745f62616e6e6572732f2d67656d696e695f67656e6572617465645f696d6167655f677775726334677775726334677775722d636162323536616431393638316663303637313866393863363937343232305f723866315167702e6a7067"
    alt="Telecom Customer Churn Prediction"
    width="800"
  />
</p>

## 🏗️ System Architecture

<p align="center">
  <img
    src="https://camo.githubusercontent.com/07069c1d392b7cbe0c204e0c23cb1801f3ecad4ccb07d12ae3e1b5e0cf0ea989/68747470733a2f2f6361726565722d706c6174666f726d2d6d61792d323032362e73332e61702d736f7574682d312e616d617a6f6e6177732e636f6d2f6b726973686e61696b2e696e2f6d656469612f70726f6a6563745f6172636869746563747572655f6469616772616d732f47656d696e695f47656e6572617465645f496d6167655f346869333768346869333768346869332e6a7067"
    alt="Telecom Customer Churn Prediction Architecture"
    width="850"
  />
</p>

## 🧠 ML Pipeline

```text
Customer Data
      ↓
Data Validation
      ↓
Exploratory Data Analysis
      ↓
Data Preprocessing
      ↓
Feature Engineering
      ↓
Model Training
      ↓
Hyperparameter Optimization
      ↓
Model Evaluation
      ↓
Churn Prediction
      ↓
Deployment
```

## 📋 Model Details

| Property     | Details               |
| ------------ | --------------------- |
| Task         | Binary Classification |
| Domain       | Telecommunications    |
| Target       | Customer Churn        |
| Framework    | Scikit-learn          |
| Data Type    | Tabular               |
| Optimization | Hyperparameter Tuning |
| Deployment   | Streamlit             |

## 📤 Output

The model predicts whether a customer is likely to churn:

```text
Prediction: Churn / No Churn
Probability: <VALUE>
```

## 💻 Run Locally

```bash
git clone https://github.com/mdzaheerjk/Telecom-Customer-Churn-Prediction.git

cd Telecom-Customer-Churn-Prediction

pip install -r requirements.txt

streamlit run app.py
```

## 🛠️ Tech Stack

**Python • Pandas • NumPy • Scikit-learn • Matplotlib • Seaborn • Optuna • Streamlit**

## ⚠️ Limitations

Model performance depends on the quality, representativeness, and distribution of the training data.

Predictions should be treated as **decision-support signals**, not guaranteed outcomes. Real-world performance may differ when customer behavior or telecom market conditions change.

## 🔮 Future Improvements

* Advanced ensemble models
* Real-time churn monitoring
* Explainable AI with SHAP
* Automated model retraining
* MLOps monitoring
* Customer-specific retention recommendations

## 👨‍💻 Author

**Md Zaheer JK**

AI/ML • Deep Learning • Generative AI • Computer Vision • NLP • MLOps

GitHub: https://github.com/mdzaheerjk

Hugging Face: https://huggingface.co/zaheerjk

## 📜 License

MIT License

---

### 📱 Predict Churn. Understand Customers. Improve Retention.