📊 Customer Churn Prediction - Telecom Industry

🚀 Project Overview

Customer churn is a major issue in telecom businesses. This project builds a predictive model to classify customers as churn or non-churn and provides insights to reduce customer loss.

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🎯 Objective

• 🔍 Predict customer churn
• 📈 Identify key influencing factors
• 👥 Segment customers (At Risk, Dormant, Loyal)
• 💡 Provide business recommendations

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🛠️ Tech Stack

• 🐍 Python (Pandas, NumPy, Scikit-learn)
• 📊 Matplotlib, Seaborn
• 🌲 Random Forest Classifier
• 🧠 SHAP (Explainability)
• 🗄️ SQL (Concepts)

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📂 Dataset

• 📁 Telco Customer Churn Dataset
• 🎯 Target: Churn (Yes/No)

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🔄 Workflow

1. 📥 Data Collection
2. 🧹 Data Cleaning
3. ⚙️ Feature Engineering
4. 🔀 Train-Test Split
5. 🤖 Model Building
6. 📊 Evaluation
7. 🔍 Explainability
8. 👥 Customer Segmentation

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📊 Key Insights

• 📉 Month-to-month contracts have higher churn
• 💰 High monthly charges increase churn risk
• ⏳ Low tenure customers churn more
• 📊 Long-term plans reduce churn

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💡 Business Recommendations

• 🎁 Promote long-term contracts
• 📢 Improve onboarding experience
• 📦 Offer bundled services
• 🎯 Target at-risk customers

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🧾 Sample SQL Queries

SELECT Churn, COUNT(*) 
FROM customers 
GROUP BY Churn;

SELECT Contract, Churn, COUNT(*) 
FROM customers 
GROUP BY Contract, Churn;

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📈 Results

• ✅ Good prediction accuracy
• 🔍 Identified key churn drivers
• 📊 Complete ML pipeline built

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📌 Future Scope

• 🚀 Try XGBoost / Deep Learning
• 🌐 Deploy using Flask / Streamlit
• 🔄 Real-time data integration

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