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Telecom Customer Churn Prediction in Apache Spark (ML)

Description

Apache Spark Began as a analysis mission on the College of California in 2009, Apache Spark is at present one of the crucial broadly used analytics engines. No surprise: it could course of information on an infinite scale, helps a number of coding languages (you should use Java, Scala, Python, R, and SQL) and runs by itself or in the cloud, in addition to on different methods (e.g., Hadoop or Kubernetes).

On this Apache Spark tutorial, I’ll introduce you to one of the crucial notable use instances of Apache Spark: machine studying. In lower than two hours, we are going to undergo each step of a machine studying mission that may present us with an correct telecom buyer churn prediction in the tip. That is going to be a totally hands-on expertise, so roll up your sleeves and put together to offer it your finest!

At the start, how does Apache Spark machine studying work?

Earlier than you study Apache Spark, you must comprehend it comes with a number of inbuilt libraries. One among them known as MLlib. To place it merely, it permits the Spark Core to carry out machine studying duties – and (as you will notice in this Apache Spark tutorial) does it in breathtaking pace. Resulting from its means to deal with vital quantities of knowledge, Apache Spark is ideal for duties associated to machine studying, as it could guarantee extra correct outcomes when coaching algorithms.

Mastering Apache Spark machine studying will also be a talent extremely wanted by employers and headhunters: increasingly firms get in making use of machine studying options for enterprise analytics, safety, or customer support. Therefore, this sensible Apache Spark tutorial can develop into your first step in the direction of a profitable profession!

Be taught Apache Spark by making a mission from A to Z your self!

I’m a agency believer that one of the best ways to study is by doing. That’s why I haven’t included any purely theoretical lectures in this Apache Spark tutorial: you’ll study every thing on the best way and have the ability to put it into observe immediately. Seeing the best way every function works will assist you study Apache Spark machine studying completely by coronary heart.

I will even be offering some supplies in ZIP archives. Ensure to obtain them at the start of the course, as you won’t be able to proceed with the mission with out it.

And that’s not all you’re getting from this course – are you able to imagine it?

Aside from Spark itself, I will even introduce you to Databricks – a platform that simplifies dealing with and organizing information for Spark. It’s been based by the identical group that originally began Spark, too. On this course, I’ll clarify create an account on Databricks and use its Pocket book function for writing and organizing your code.

After you end my Apache Spark tutorial, you’ll have a totally functioning telecom buyer churn prediction mission. Take the course now, and have a a lot stronger grasp of machine studying and information analytics in just some hours!

Spark Machine Studying Challenge (Telecom Customer Churn Prediction) for newbies utilizing Databricks Pocket book (Unofficial) (Group version Server)

On this Knowledge Science Machine Studying mission, we are going to create Telecom Customer Churn Prediction Challenge utilizing Classification Mannequin Logistic Regression, Naive Bayes and One-vs-Relaxation classifier few of the predictive fashions.

  • Discover Apache Spark and Machine Studying on the Databricks platform.

  • Launching Spark Cluster

  • Create a Knowledge Pipeline

  • Course of that information utilizing a Machine Studying mannequin (Spark ML Library)

  • Arms-on studying

  • Actual time Use Case

  • Publish the Challenge on Net to Impress your recruiter

  • Graphical  Illustration of Knowledge utilizing Databricks pocket book.

  • Remodel structured information utilizing SparkSQL and DataFrames

Telecom Customer Churn Prediction a Actual time Use Case on Apache Spark

About Databricks:

Databricks helps you to begin writing Spark ML code immediately so you may focus in your information issues.


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