Telco Customer Churn Prediction and Retention Strategy

This project focuses on predicting customer churn for a telecommunications company and developing a retention strategy to reduce churn. The goal is to predict which customers are likely to leave (churn) based on their behavior and characteristics, and implement strategies to retain them.

Project Overview

Churn prediction is essential for telecommunications companies to identify at-risk customers before they decide to leave. By using machine learning models, we can predict churn and develop retention strategies to proactively target high-risk customers. This project covers data preprocessing, model building, evaluation, and the creation of actionable insights for retention strategies.

Dataset

The dataset used was obtained from Kaggle, which included information on demographics, transaction history, customer service interactions, subscription details, and whether the customer churned. Dataset Link: Telco Customer Churn on Kaggle The dataset used for this project includes customer information such as demographics, services used, and account details. The columns in the dataset include:

• customerID: Unique identifier for each customer
• gender: Gender of the customer
• SeniorCitizen: Whether the customer is a senior citizen (1 = Yes, 0 = No)
• Partner: Whether the customer has a partner (Yes/No)
• Dependents: Whether the customer has dependents (Yes/No)
• tenure: Number of months the customer has been with the company
• PhoneService: Whether the customer has phone service (Yes/No)
• MultipleLines: Whether the customer has multiple lines (Yes/No)
• InternetService: The type of internet service (DSL/Fiber optic/No)
• OnlineSecurity: Whether the customer has online security service (Yes/No)
• OnlineBackup: Whether the customer has online backup service (Yes/No)
• DeviceProtection: Whether the customer has device protection service (Yes/No)
• TechSupport: Whether the customer has tech support service (Yes/No)
• StreamingTV: Whether the customer has streaming TV service (Yes/No)
• StreamingMovies: Whether the customer has streaming movies service (Yes/No)
• Contract: Type of contract (Month-to-month, One year, Two year)
• PaperlessBilling: Whether the customer has paperless billing (Yes/No)
• PaymentMethod: Payment method used (Electronic check, Mailed check, Bank transfer, Credit card)
• MonthlyCharges: Monthly charges for the customer
• TotalCharges: Total charges paid by the customer
• Churn: Whether the customer churned (1 = Yes, 0 = No)

Technologies Used

• Python
• Libraries:
  • pandas (for data manipulation and analysis)
  • numpy (for numerical operations)
  • matplotlib & seaborn (for data visualization)
  • scikit-learn (for machine learning models and evaluation)
  • XGBoost (for gradient boosting models)
  • shap (for model interpretability)
  • Flask or FastAPI (for deployment)

How to Run

1. Clone the repository

Clone the repository to your local machine:

git clone https://github.com/your-username/telco_churn.git
cd telco_churn

2. Install dependencies

Install the required libraries:

pip install -r requirements.txt

3. Run the notebook

Open the churn_prediction.ipynb notebook in Jupyter or Google Colab and run the cells sequentially. The notebook includes steps for:

1. Loading the dataset
2. Data preprocessing
3. Exploratory data analysis (EDA)
4. Modeling (Logistic Regression, Random Forest, XGBoost, etc.)
5. Model evaluation (accuracy, precision, recall, F1-score, ROC-AUC)
6. Retention strategy development using the insights from model predictions

4. Model Deployment (Optional)

To deploy the model as an API, use Flask or FastAPI. You can integrate the model into a CRM system to automate the retention strategy. The model file churn_model.pkl can be loaded and used for predictions.

Steps Involved

1. Data Import and Preprocessing:
   Load the dataset and handle missing values, categorical variables, and scale the numerical features.

2. Exploratory Data Analysis (EDA):
   Visualize key patterns and relationships in the data, such as customer churn distribution, tenure, and service usage.

3. Model Development:
   Train multiple machine learning models (e.g., Logistic Regression, Random Forest, XGBoost) to predict churn.

4. Model Evaluation:
   Evaluate model performance using classification metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.

5. Retention Strategy:
   Develop actionable retention strategies based on the model predictions. This may involve targeting high-risk customers with personalized offers or support.

6. Deployment:
   Optionally, deploy the model as a web API using Flask or FastAPI to integrate with customer relationship management (CRM) tools or marketing platforms for automated retention actions.

Model Evaluation Results

• Accuracy: 0.79
• Precision: 0.72
• Recall: 0.82
• F1-score: 0.77
• ROC-AUC: 0.85

Conclusion

The model accurately predicts customer churn with a relatively high recall, making it suitable for identifying customers at risk of leaving. The next step involves implementing targeted retention strategies to reduce churn and improve customer retention.

Future Improvements

• Feature Engineering: Add more features based on customer behavior, such as call duration, service usage frequency, etc.
• Model Tuning: Hyperparameter tuning using GridSearchCV or RandomizedSearchCV to improve model performance.
• Cross-validation: Use k-fold cross-validation for more robust model evaluation.
• Deployment: Deploy the model as a web service (Flask or FastAPI) to integrate with CRM or marketing platforms for real-time predictions and retention actions.