Machine Learning with PySpark: Customer Churn Analysis
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.
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- Level Intermediate
- Duration 3 hours
- Course by Coursera Project Network
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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.Modules
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- Machine Learning with PySpark: Customer Churn Analysis
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Join "Machine Learning with PySpark: Customer Churn Analysis," a 90-minute professional guided project on Coursera. Dive into data science by learning to predict customer churn in telecommunications using PySpark. Gain hands-on experience in data loading, analysis, preprocessing, feature preparation, model training, evaluation, and deployment. Ideal for those with basic machine learning and Python knowledge, this course equips you with tools to enhance customer retention through actionable insights.
Ahmad Varasteh