The development of telecommunications industry is very fast. This can see from the behavior people who use internert for communicate. This behavior case many telecommunicateions companies to increase their internet services provider which can lead to competition between provider. Customer have right to choose appropriate provider can switch from pervious provider which is the phenomenon known as Customer Churn. This phenomenon can make to reduce revenue for telecommunications companies and is therefore important to address.
In this case, participants are provided with a training dataset containing 4250 samples. Each sample consists of 19 features and one boolean target variable "churn", which indicates whether the customer will churn.
Customer churn refers to the phenomenon where customers cease conducting business with a company or terminate their subscription to a service. It is a critical metric for businesses, particularly in industries such as telecommunications, subscription services, and financial services, where long-term customer relationships are essential. Understanding and managing customer churn is vital for maintaining revenue and achieving sustainable growth.
Hence, this case focuses on predicting customer churn. It is important for companies to know this prediction so they can map out business strategies to retain customers. Accurate churn prediction is crucial for businesses to maintain customer satisfaction, retain customers, and minimize revenue loss.
Metric for this case is the optimal model with high accuracy to predict customer churn, defined as follows:
The CRISP-DM (Cross Industry Standard Process for Data Mining) methodology is a robust and comprehensive data mining process model that outlines six major phases:
- Business Understanding
- Data Understanding
- Modeling
- Evaluation
- Deployment
Python, HTML, Data Science, Machine Learning, Business Analyst
