Exploring Support Vector Machines (SVM) Algorithm with Breast Cancer Dataset in Python

Photo by National Cancer Institute on Unsplash

In this tutorial, we will explore the Support Vector Machine (SVM) algorithm with the Breast Cancer Dataset in Python. The Breast Cancer Dataset is a real-world dataset that contains features computed from digitized images of breast cancer biopsies. The objective of this dataset is to predict whether a breast tumor is malignant or benign based on the features of the biopsy image.

Support Vector Machine (SVM) is a popular machine-learning algorithm used for classification tasks. SVM is a binary classifier algorithm that tries to find a hyperplane in a high-dimensional space that can separate the input data points into different classes. SVM can be used for both linear and non-linear classification tasks.

We will use the scikit-learn library to implement the SVM algorithm and analyze the results.

Steps:

1. Load the dataset: First, we will load the Breast Cancer Dataset into a Pandas DataFrame. The dataset can be downloaded from the scikit-learn library.
2. Split the dataset: Next, we will split the dataset into training and testing sets using the train_test_split function from scikit-learn.
3. Preprocess the data: We will preprocess the data by scaling the features using the…