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Build and deploy a stroke prediction model using R
  • Level Professional
  • المدة 3 ساعات hours
  • الطبع بواسطة Coursera Project Network
  • Offered by

عن

In this project, you’ll help a leading healthcare organization build a model to predict the likelihood of a patient suffering a stroke. The model could help improve a patient’s outcomes. Working with a real-world dataset, you’ll use R to load, clean, process, and analyze the data and then train multiple classification models to determine the best one for making accurate predictions. Upon completion, you’ll produce a well-validated prediction model that showcases your ability to perform a complete data analysis project involving feature engineering, handling missing data, model evaluation, model selection, and model deployment. There isn’t just one right approach or solution in this scenario, which means you can create a truly unique project that helps you stand out to employers. ROLE: Data Analyst SKILLS: R, Data Analysis, Predictive Modeling PREREQUISITES: Load, clean, explore, manipulate, and visualize data in R, Use R to build a prediction model Use R documentations and vignettes to write new codes

الوحدات

Part 1: Overview

    • 3 Readings
Show info about module content

3 Readings

  • Getting Started
  • The Project Scenario
  • See Example Projects

Part 2: Build Your Project

    • 4 Readings
    • 1 Plugin
    • 1 Lab
Show info about module content

1 Labs

  • Option A: Using Coursera's RStudio Environment

4 Readings

  • Choose Your Platform
  • Option B: Working Off-Platform
  • Feeling stuck? Here are some helpful resources
  • Review Your Project Checklist

Part 3: Sharing with Employers

    • 1 Readings
Show info about module content

1 Readings

  • Learn how Coursera helps you showcase your work to employers

Auto Summary

"Build and Deploy a Stroke Prediction Model Using R" is a professional-level course focused on Data Science & AI, led by Coursera. It involves creating a stroke prediction model for a healthcare organization, leveraging R for data loading, cleaning, analysis, and predictive modeling over 120 minutes. Ideal for data analysts with R experience, the course offers practical skills in feature engineering, model evaluation, and deployment with a Starter subscription option.

Arimoro Olayinka Imisioluwa
Instructor

Arimoro Olayinka Imisioluwa