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Data Science:Hands-on Diabetes Prediction with Pyspark MLlib

 
Building a Machine Learning (ML) Model with PySpark ... The classification goal is to predict whether the patient has diabetes (Yes/No). ... Hands-on real-world examples, research, tutorials, and cutting-edge techniques
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What you'll learn
  • Diabetes Prediction using Spark Machine Learning (Spark MLlib)
  • Learn Pyspark fundamentals
  • Working with dataframes in Pyspark
  • Analyzing and cleaning data
  • Process data using a Machine Learning model using Spark MLlib
  • Build and train logistic regression model
  • Performance evaluation and saving model
Description

This is a Hands-on 1- hour Machine Learning Project using Pyspark. You learn by Practice.

No unnecessary lectures. No unnecessary details.

A precise, to the point and efficient course about Machine learning in Spark.

About Pyspark:

Pyspark is the collaboration of Apache Spark and Python. PySpark is a tool used in Big Data Analytics.

Apache Spark is an open-source cluster-computing framework, built around speed, ease of use, and streaming analytics whereas Python is a general-purpose, high-level programming language. It provides a wide range of libraries and is majorly used for Machine Learning and Real-Time Streaming Analytics.

In other words, it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data. We will be using Big data tools in this project.


You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures.

Learn the most important aspect of Spark Machine learning (Spark MLlib) :

  • Pyspark fundamentals and implementing spark machine learning

  • Importing and Working with Datasets

  • Process data using a Machine Learning model using spark MLlib

  • Build and train Logistic regression model

  • Test and analyze the model

We will build a model to predict diabetes. This is a 1- hour project. In this hands-on project, we will complete the following tasks:

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