We will build a Support Vector Machine classifier to predict the income bracket of a given person based on 14 attributes. Our goal is to see where the income is higher or lower than $50,000 per year. Hence this is a binary classification problem. We will be using the census income dataset available at
. One thing to note in this dataset is that each
datapoint is a mixture of words and numbers. We cannot use the data in its raw format, because the algorithms don't know how to deal with words. We cannot convert everything using label encoder because numerical data is valuable. Hence we need to use a combination of label encoders and raw numerical ...