Regression

Now we have split the datasets and transformed the data, we will show you how to use the scikit-learn library to build up ML models. We will start with regression and show you the following examples:

• Simple linear regression
• Multiple linear regression
• Polynomial/non-linear regression

Simple linear regression

First things first, we need to prepare the dataset:

import numpy as pd
import pandas as pd
import matplotlib.pyplot as plt
dataset = pd.read_csv('Salary_Data.csv')
X = dataset.iloc[:,:-1].values
y = dataset.iloc[:, -1].values
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y , test_size = 0.2, random_state = 1)

Now we can start training our regression model. We need to import a class and feed our training data:

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

Next, we are going to predict the results...