This project uses Supervised Learning to predict customer retention by classifying customer behaviour.
Understanding and predicting customer behaviour is crucial for business analysis and contributes to growth.
Scikit-Learn • Python • Data Preprocessing • Model Evaluation • Model Optimization • Feature Selection • Classification • Supervised Learning • Tableau
Customer Value: Customers that stay longer tend to pay more monthly.
Significance: Businesses should prioritize brand loyalty by rewarding long-term customers with incentives like points, special offers, as they tend to have higher monthly spending.
Customer Retention: New customers are the most likely to leave.
Significance: Businesses should focus retention efforts on new customers.
Model Evaluation: Random Forest algorithm is used in a “grey-box” approach, achieving more explainability and control in contrast to the “black-box” nature of some commercial tools while having slightly better performance.
Data Source: Real-world dataset from IBM, representing telecommunications customer data, which still needed to be preprocessed
Exploring the dataset to discover trends and correlation between features; namely monthly charges, and tenure.
