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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.
|