In this lesson we will built this Support Vector Machine for classification using scikit-learn and the Radial Basis Function (RBF) Kernel. Our training data set contains continuous and categorical data from the UCI Machine Learning Repository to predict whether or not a patient has heart disease.
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Support Vector Machines in Python, From Start to Finish
Instructor: Josh Starmer
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(153 reviews)
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What you'll learn
Import data into, and manipulating a pandas dataframe
Format the data for a support vector machine, including One-Hot Encoding and missing data.
Optimize parameters for the radial basis function and classification
Build, evaluate, draw and interpret a support vector machine
Skills you'll practice
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About this Guided Project
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:
Import the modules that will do all the work (4 min)
Import the data (3 min)
Missing Data Part 1: Identifying Missing Data (4 min)
Missing Data Part 2: Dealing With Missing Data (5 min)
Format Data Part 1: Split the Data into Dependent and Independent Variables (3 min)
Format the Data Part 2: One-Hot Encoding (11 min)
Format the Data Part 3: Centering and Scaling (2 min)
Build A Preliminary Support Vector Machine (2 min)
Optimize SVM with Cross Validation (2 min)
Building, Evaluating, Drawing, and Interpreting the Final Support Vector Machine (10 min)
Recommended experience
Some Python and the concepts behind Support Vector Machines, the Radial Basis Function, Regularization, Cross Validation and Confusion Matrices.
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By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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