Data Analysis · Machine Learning
PublicTelco Customer Churn
Machine learning project analyzing telecom customer churn and building retention strategies by customer segment.
Achievement
Completed as the capstone project for the SDA course Building Intelligent Applications with AI & ML, published on Kaggle.
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Overview
A data analysis and machine learning project that explores telecom customer behavior, identifies churn drivers such as contract type and billing patterns, builds a predictive model, and translates results into segment-based retention strategies.
Problem
Telecom companies need to understand which customers are likely to churn and how to respond with practical retention actions.
Solution
Analyze customer data, train a churn prediction model, segment customers by risk and billing profile, and define tailored retention strategies for each group.
My role
Data analysis, model building, churn segmentation, retention strategy design, and Kaggle notebook publication.
Key features
- Exploratory data analysis
- Churn prediction model
- Customer segmentation
- Retention strategy by segment
- SDA AI & ML course capstone
- Published on Kaggle