Good morning ! Доброе утро! 早上 好 !

analysis

a dependent variable (target variable) and one (simple regression) or more (multiple

regression) independent variables. 

we train the model, she will predict salary

to view it, preferably using headphones. There are subtitles in

Chinese here.

https://www.coursera.org/lecture/machine-learning/model-representation-db3jS

https://www.coursera.org/learn/machine-learning/home/week/1

ascending and descending order such as the height of a person.
Categorical variables are

values that can be sorted in groups or categories such as the gender of a person.
Multiple linear regression accepts not only numerical variables, but also categorical ones. To include a categorical variable in a regression model, the variable has to be encoded as a binary variable (dummy variable).

exploration that most of the columns in our data set

are strings, but the algorithms in scikit-learn understand only numeric data. Luckily, the scikit-learn library provides us with many methods for converting string data into numerical data. One such method is the LabelEncoder() method. We will use this method to convert the categorical labels in our data set like ‘won’ and ‘loss’ into numerical labels. To visualize what we are trying to to achieve with the LabelEncoder() method let’s consider the images below.

named ‘color’ and three records ‘Red’, ‘Green’ and ‘Blue’.






Since the

machine learning algorithms in scikit-learn understand only numeric inputs, we would like to convert the categorical labels like ‘Red, ‘Green’ and ‘Blue’ into numeric labels. When we are done converting the categorical labels in the original dataframe, we would get something like this