Case Study

Categorizing Medications using Support Vector Machine (SVM) Classifiers

The case study focuses on the most popular technique of Supervised Machine Learning algorithm, SVM its basics of Support Vector Machines (SVM) and how it can be used to Predict drug type.

Applying Support Vector Machine (SVM) Classifier To Predict The Drug Type
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    Difficulty: Advanced

    Designed for those with a technical background or industry experience

  • Asset 1
    Duration: Approximately 3 hours

Case Overview

  • This case provides a deep understanding on how to build a support vector machine model to suitably categorize the drug based on  the condition of the person.
  • The case helps the learner to implement data visualization techniques to understand several hidden patterns and analyze the relationship that exists between variables.
  • Working understanding of feature engineering techniques such as one hot encoding to convert categorical data to numerical data, as most algorithms do not support categorical input.
  • The case helps the learner to train and apply support vector machine model, to predict the class of drug effectively for future scenarios.

 

What’s included

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Access this case study for life once completed

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Desktop Only

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Skills You Will Learn

User interface

SVM Model Building and Evaluation

Predictive Analysis

Data Visualization

Data Mining and Data Management

Data Preprocessing

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