k nearest neighbors:classic, simple, easy to understand
random forest: “ensemble methods” meta classifiers built from (usually) decision trees
adaboost(boosted decision tree)
(previous algorithms:Naive Bayes, SVM, decision tree)
Process
1) do some research!
– get a general understanding
2) find sklearn documentation
3) deploy it!
4) use it to make predictions
What is a person of interest?
– indicted
– settled without admitting guilt
– testified in exchange for immunity
MORE DATA > fine-tuned algorithm
numerical – numerical values(numbers)
categorical – limited number of discrete values(category)
time series – temporal value(date, timestamp)
text – words
