Predicting heart disease using Machine Learning

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Coursera Community Project Network
In this Guided Project, you will:

Proceed EDA and data pre processing;

Train a KNearestNeighbors binary classifier;

Evaluate your model using the best metrics for it.

Clock50 minutes
BeginnerBeginner
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this guided project, we will develop a predictive model capable of accurately predicting the presence or absence of heart disease from clinical and laboratory data using a K-Nearest-Neighbors Classifier. This project, which we'll run on Google Colab, was designed for those who are taking their first steps in Machine Learning algorithms, but the student should be already familiar with Python and basic ML concepts. This Guided Project was created by a Coursera community member.

Skills you will develop

Predictive ModellingMachine LearningPython ProgrammingBinary ClassifiersEDA

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Exploratory Data Analysis

  2. Training the model using Grid Search

  3. Evaluating the model

  4. Importing needed modules + uploading dataset

  5. Splitting and pre-processing

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Instructor

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Frequently Asked Questions

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