Increased the ROC AUC score by 2.14% of predicting the churn of users in telecommunication company using hypertuning parameter and feature engineering.
"Predict behavior to retain customers. You can analyze all relevant customer data and develop focused customer retention programs."
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