Diabetes Prediction With Pyspark MLLIB

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In diesem angeleitetes Projekt werden Sie:

Learn to Build and Train Logistic Regression Classifier using Pyspark MLLIB

Learn to set up Pyspark on the Google Colab Environment

Learn to work with Pyspark Dataframe

Clock1.5 hours
IntermediateMittel
CloudKein Download erforderlich
VideoVideo auf geteiltem Bildschirm
Comment DotsEnglisch
LaptopNur Desktop

In this 1 hour long project-based course, you will learn to build a logistic regression model using Pyspark MLLIB to classify patients as either diabetic or non-diabetic. We will use the popular Pima Indian Diabetes data set. Our goal is to use a simple logistic regression classifier from the pyspark Machine learning library for diabetes classification. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark.You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and the model in this project, can not be used in the real-life. We are only using this data for the educational purpose. By the end of this project, you will be able to build the logistic regression classifier using Pyspark MLlib to classify between the diabetic and nondiabetic patients.You will also be able to setup and work with Pyspark on Google colab environment. Additionally, you will also be able to clean and prepare data for analysis. You should be familiar with the Python Programming language and you should have a theoretical understanding of the Logistic Regression algorithm. You will need a free Gmail account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Kompetenzen, die Sie erwerben werden

  • Data Science
  • Machine Learning
  • Python Programming
  • Google colab
  • PySpark

Schritt für Schritt lernen

In einem Video, das auf einer Hälfte Ihres Arbeitsbereichs abgespielt wird, führt Sie Ihr Dozent durch diese Schritte:

  1. Introduction & Install Dependencies

  2. Clone and Explore Dataset

  3. Data Cleaning and Preparation

  4. Correlation analysis and Feature Selection

  5. Split Dataset and Build the Logistic Regression Model

  6. Evaluate and Save the model

  7. Model Prediction on a new set of unlabelled data

Ablauf angeleiteter Projekte

Ihr Arbeitsbereich ist ein Cloud-Desktop direkt in Ihrem Browser, kein Download erforderlich

Ihr Dozent leitet Sie in einem Video mit geteiltem Bildschirm Schritt für Schritt an.

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