Intro Machine Learning

DataFrame

holds the type of data you might think of as a table

count

how many rows have non-missing values

selecting features

provide a list of column names inside brackets Ex. melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']

columns property

shows a list of all columns in the dataset

.head()

shows the top few rows

min, 255, 50%, 75%, and max

sorting each column from lowest to highest value

std

standard deviation, measures how numerically spread out the values are

mean

the average

features

the columns inputted into our model ex. y = melbourne_data.Price

selecting prediction target

use dot notation to select the column we want to predict

data tree

using data to break options into multiple groups