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In this project we will implement a Support Vector Machine classifier in Python via a Jupyter notebook.
We will import the pima indians diabetes dataset and allocate the data to two separate arrays. After importing the dataset, we will split the data into a training and testing set using the scikit-learn function train_test_split. We will use the scikit-learn built in machine learning algorithms to predict the accuracy of training and test sets separately. Refer to the hyperlinks provided below for each algorithm for more details, such as the concept behind these classifiers and how to implement them.
We will Implement
For SVM, we will standardize attributes (features) in the dataset using StandardScaler, before training the model.
Standard scaler :
Transform both x_train and x_test to obtain the standardized versions of both.
Review the StandardScaler documentation, which provides details about standardization and how to implement it.
Train the Support Vector Machine classifier on the dataset (the link points to SVC, a particular implementation of SVM by Scikit). We will provide the accuracy on both the test and train sets.
Tune your SVM model to obtain the highest accuracy possible on the dataset. For SVM, tune the model on the standardized train dataset and evaluate the tuned model with the test dataset. Tune the hyperparameters specified below, using the GridSearchCV function in Scikit library:
Let’s practice obtaining the results of cross-validation for the SVM model. Report the rank test score and mean testing score for the best combination of hyper-parameter values that you obtained. The GridSearchCV class holds a cv_results_ dictionary that helps you report these metrics easily.
We will provide solution as a Jupyter notebook, developed by completing the provided skeleton code
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