FeaturedUdemy
Marketing Analytics: Predicting Customer Churn in Python
4.9(176)18k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate course outlines how to build customer churn models in Python using pandas and scikit-learn. It covers the full data science workflow from exploratory analysis to model training, evaluation, and stakeholder reporting.
Who It's For
Data analysts and marketers working in SaaS, telecom, or subscription businesses who want to identify churn risks and boost retention.
Key Takeaways
- Exploratory Data Analysis: Visualize customer behavior metrics and uncover key churn signals with pandas.
- Predictive Modeling: Prepare datasets and train supervised machine learning models with scikit-learn.
- Actionable Insights: Evaluate model predictions and translate metrics into proactive retention recommendations for stakeholders.
About this course
Churn is when a customer stops doing business or ends a relationship with a company. It’s a common problem across a variety of industries, from telecommunications to cable TV to SaaS, and a company that can predict churn can take proactive action to retain valuable customers and get ahead of the competition. This course will provide you with a roadmap to create your own customer churn models. You’ll learn how to explore and visualize your data, prepare it for modeling, make predictions using machine learning, and communicate important, actionable insights to stakeholders. By the end of the course, you’ll become comfortable using the pandas library for data analysis and the scikit-learn library for machine learning.
Price
Free
This course is free to enrol.
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