Linear regression algorithm predicts continous values (like price, temperature).
This is another article in the machine learning algorithms for beginners series.
It is a supervised learning algorithm, you need to collect training data for it to work.
Related course: Python Machine Learning Course
Classification output can only be discrete values. There can be ,, etcetera.
What if you want to output prices or other continous values?
Then you use a regression algorithm.
Lets say you want to predict the housing price based on features. Collecting data is the
first step. Features could be number of rooms, area in m^2, neighborhood quality and others.
Write down the feature: #area_m2.
For our example in code that looks like this.
from sklearn.linear_model import LinearRegression
Then you can create a plot based on that data (if you want to).
You see there is a correlation between the area and the price.
This is a linear relationship.
You can predict the price, with a linear regression algorithm.
First you import the linear regression algorithm from like it learn then you defined a training data X and the Y where axis the area and y is the price.
model = LinearRegression()
Linear regression algorithm because there is a linear relationship then we train the algorithm using the training data.
Now that the algorithm is trained you can make predictions using the area.
A new example, can predict the price for you.
rooms = 11
This algorithm LinearRegression only works if there is a linear relation in your data set.
If there isn’t, you need a polynomial algorithm.
Plot to verify that there is a linear relation.