[100% Off] Telecom Customer Churn Prediction In Apache Spark (Ml)
Learn Apache Spark machine learning by creating a Telecom customer churn prediction project using Databricks Notebook
Requirements
- Basic knowledge of Python or Scala programming (helpful but not mandatory).
- A general understanding of machine learning concepts (introductory level).
- No prior experience with Spark is required—everything is explained step by step.
- A computer with internet access to use Databricks (free account provided).
- Willingness to learn by doing through a hands-on real-world project.
Description
Are you ready to master Apache Spark by working on a real-world Machine Learning project? This course will take you step by step through building a Telecom Customer Churn Prediction model using Apache Spark ML.
Customer churn is one of the biggest challenges in the telecom industry. Companies invest heavily to retain their customers, and predictive analytics plays a crucial role in identifying which customers are likely to leave. In this hands-on course, you’ll learn how to use Big Data and Spark ML to solve this real-world problem with an end-to-end project.
You will begin with the basics of Spark and Machine Learning, setting up your Spark cluster in Databricks (no local installation needed), and learning how to work with DataFrames and notebooks. Then, we’ll dive deep into the churn dataset, perform data exploration, and build a machine learning pipeline for churn prediction.
By the end of this course, you will have the skills, confidence, and project experience to apply Spark ML techniques to real-world business problems—not only in telecom but across other industries like finance, e-commerce, and healthcare.
This course is designed to be practical and project-focused, ensuring that you learn by doing, with clear explanations and real examples every step of the way.
What makes this course unique?
Real-world Telecom Churn Prediction Project
Hands-on practice in Apache Spark ML with Databricks free account (no setup hassles)
Focus on both concepts and implementation
Step-by-step guidance from data exploration to model building
Applicable skills that can be reused for other ML projects
Key Skills You Will Gain
Understanding Spark basics (clusters, notebooks, and DataFrames)
Performing data preprocessing and feature engineering in Spark
Building and training machine learning models in Spark ML
Creating a prediction pipeline for customer churn
Applying ML techniques in real-world business problems
Gaining a project for your portfolio to showcase Spark ML expertise
Author(s): Bigdata Engineer
