Kaggle-telecom-customer-churn-prediction

Telecom Customer Churn Prediction

Data source: https://www.kaggle.com/blastchar/telco-customer-churn

"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs." [IBM Sample Data Sets]

Data Content

Each row represents a customer, each column contains customer’s attributes described on the column Metadata.

The data set includes information about:

Customers who left within the last month – the column is called Churn Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges Demographic info about customers – gender, age range, and if they have partners and dependents

Achivement

• Prediction model: Optimize model by changing features using loops, compare three models (logistic, lasso, random forest) by ROC curve and precision-recall tradeoff by Python and R.
• Retention Plan Design: Calculate cost-even point and profit testing different discount offer. Based on model results, we recommend do not offer discounts before they churn to maximize net profit of $561,324.