customer_churn_prediction

📉 Customer Churn Prediction Using Machine Learning This project focuses on predicting customer churn — identifying which customers are likely to leave a service — using various machine learning techniques. The goal is to help businesses take proactive actions to retain at-risk customers. 🔍 Problem Statement Customer churn significantly impacts revenue and growth. By analyzing patterns in customer behavior, we can build a model to predict churn and improve retention strategies.

📁 Dataset The dataset includes customer demographics, service details, and churn labels (Yes/No). It is cleaned and preprocessed for building predictive models.

🛠️ Tech Stack

• Python 🐍
• Pandas, NumPy
• Matplotlib, Seaborn (EDA)
• Scikit-learn (ML Models)
• imbalanced-learn (SMOTE)
• XGBoost

📊 Workflow

1. Data Cleaning & Preprocessing
   • Handling missing values, encoding, scaling
2. Exploratory Data Analysis (EDA)
   • Visualizing patterns and correlations
3. Class Imbalance Handling
   • Applying SMOTE to balance target classes
4. Model Building
   • Logistic Regression, Decision Tree, Random Forest, XGBoost
5. Model Evaluation
   • Accuracy, Precision, Recall, F1-score, ROC-AUC
6. Model Interpretation
   • Feature importance analysis

📈 Results

Achieved high performance on test data, with improved recall and AUC after balancing and tuning. The model can successfully flag potential churners for retention campaigns.

✅ Future Improvements

• Add hyperparameter tuning with GridSearchCV
• Integrate SHAP for model explainability
• Build a Streamlit web app for predictions

📌 Project Status

✅ Completed basic version
🚧 Advanced improvements in progress

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📬 Contributions and feedback are welcome!

This is a test change