Customer Churn Prediction is critical for telecommunication companies as it can have a significant impact on the business’s bottom line. When customers churn, they take their business with them, which means lost revenue. In addition, acquiring new customers can be expensive, so it's often more cost-effective to retain existing customers.
For this reason, large telecommunications corporations are seeking to develop models to predict which customers are more likely to change and take actions accordingly.
So, we build a model to predict how likely a customer will churn by analyzing its characteristics: Demographic information Account Information Services Information
The objective is to obtain a data-driven solution that will allow us to reduce churn rates and, as a consequence, to increase customer satisfaction and corporation revenue. Dataset The data set used here is available in Kaggle and contains nineteen columns (independent variables) that indicate the characteristics of the clients of a fictional telecommunications corporation. The Churn column (response variable) indicates whether the customer departed within the last month or not. The class No includes the clients that did not leave the company last month, while the class Yes contains the clients that decided to terminate their relations with the company. The objective of the analysis is to obtain the relation between the customer’s characteristics and the churn.
- Data Reading
- Exploratory Data Analysis & Data Cleaning
- Data Visualization
- Feature Importance
- Feature Engineering
- Setting a baseline
- Splitting the data into training and testing sets
- Assessing multiple algorithms
- Algorithm Fit
- Hyperparameter Tuning
- Model Performance
- Conclusions — Summary