Back to Machine Learning with PySpark: Customer Churn Analysis
Learner Reviews & Feedback for Machine Learning with PySpark: Customer Churn Analysis by Coursera Project Network
14 ratings
About the Course
This 90-minute guided-project, "Pyspark for Data Science: Customer Churn Prediction," is a comprehensive guided-project that teaches you how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company. This guided-project covers a range of essential tasks, including data loading, exploratory data analysis, data preprocessing, feature preparation, model training, evaluation, and deployment, all using Pyspark. We are going to use our machine learning model to identify the factors that contribute to customer churn, providing actionable insights to the company to reduce churn and increase customer retention. Throughout the guided-project, you'll gain hands-on experience with different steps required to create a machine learning model in Pyspark, giving you the tools to deliver an AI-driven solution for customer churn. Prerequisites for this guided-project include basic knowledge of Machine Learning and Decision Trees, as well as familiarity with Python programming concepts such as loops, if statements, and lists.
Top reviews
AB
Feb 6, 2025
Excellent course covering basic ML methods and using intermediate techniques to solve ML related problems with PySpark
JS
Jun 29, 2023
Explanation is very clear and easy to understand. Well structure.Many thanks.
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1 - 6 of 6 Reviews for Machine Learning with PySpark: Customer Churn Analysis
By Adib B
•Feb 6, 2025
Excellent course covering basic ML methods and using intermediate techniques to solve ML related problems with PySpark
By Judy S
•Jun 29, 2023
Explanation is very clear and easy to understand. Well structure.Many thanks.
By Satyajeet
•Jan 26, 2025
Good overview to understand basic codes for pyspark queries.
By Aman P
•Dec 2, 2024
Helpful in clearing concepts
By Mohamed a T
•Jan 14, 2025
thanks
By Nitin B
•Mar 15, 2024
very simple and easy to understand approach