What data science programming algorithm is like Naive Bayes for continuous variables?
For Naive Bayes you can discretize your continuous numerical properties.
For example, for "% Owner occupied housing" you split all 100% scale into ten partitions(0-10%, 10-20%, ..., 90-100%) and get the frequency table.
For some properties you can move to binary values: Unemployment rate < 30% - yes/no.
Good luck in learning Machine Learning :)
I am trying to build and train a machine learning data science algorithm that correctly predicts what presidential won in what county. I have the following information for training data.
Total population    Median age  % BachelorsDeg or higher    Unemployment rate   Per capita income   Total households    Average household size  % Owner occupied housing    % Renter occupied housing   % Vacant housing    Median home value   Population growth   House hold growth   Per capita income growth    Winner
I am new to data science. I do know Naive Bayes is a good classifier for algorithms trying to predict with multiple properties. However, I read the first step for a naive bayes classifier requires a frequency table. My problem is all of the above properties are continuous numerical properties and don't fall into "Yes" or "No" categories. Do I not use a Naive Bayes classifier then?
I also considered using a k nearest neighbor algorithm, but that doesn't look like it will be the most accurate and weight the properties correctly for me...I am looking for a supervised algorithm because I have training data. Can anyone give me any recommendations as to what algorithm to use? In addition, being new to the field, how can I figure out what algorithm to use on my own in the future.