Course Content

Explore the Linear Regression Using Python

1. What is the Linear Regression?

Basic Understanding of the Linear Regression How Mathematically Does It Work?Build the Linear Regression Working with Dataset Useful Functions for Researching and Visualizing Dataset

2. Correlation

What Is Correlation? How To Find Pearson Coefficient Correlation Matrix Calculating the Pearson Coefficient Using NumPy and Pandas Challenge

3. Building and Training Model

Train-split evaluation Fitting Prediction Challenge

4. Metrics to Evaluate the Model

Residuals Mean Absolute Error (MAE)MSE and RMSE R-squared Not Always the Linear Regression?

5. Multivariate Linear Regression

Building the Regression Model Based on Several Variables Try to Evaluate Challenge

Useful Functions for Researching and Visualizing Dataset

To get information on our dataset, we can also use the function .describe(). This method shows the summary statistics of your dataset: mean, median, standard deviation, and so on. For example, we are adding this line to the previous code to show all these characteristics of wines:


              1
            
print(data.describe())

If it is inconvenient for us to work with many digits, we can use method .round(n), where n is the number of decimal places to which you are rounding.

Numbers are great, but it's still not entirely clear what they represent. For this reason, we will use visualization to see what’s going on in our dataset. It is good to see the dataset's representation before building the regression line. The well-known library matplotlib.pyplot is irreplaceable in this situation. To see all our data as a histogram, just use method .hist(). To get a particular chart we are interested in, we can define it in parentheses. Moreover, we can set the bins by using the bins = argument.

For example, here we will see the histogram of color intensity having 20 bins:


              1234567891011121314151617181920
            
# Import the libraries
import matplotlib.pyplot as plt 
import pandas as pd 
from sklearn.datasets import load_wine 

# Load dataset 
wine = load_wine() 

# Configure pandas to show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Convert data to a dataframe to view  properly 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Get all information about our dataset 
print(data.describe()) 

# Visualize the data 
data.hist(column = 'color_intensity',bins = 20) 
plt.show()

Output:

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 5

Useful Functions for Researching and Visualizing Dataset


              1
            
print(data.describe())

If it is inconvenient for us to work with many digits, we can use method .round(n), where n is the number of decimal places to which you are rounding.

For example, here we will see the histogram of color intensity having 20 bins:


              1234567891011121314151617181920
            
# Import the libraries
import matplotlib.pyplot as plt 
import pandas as pd 
from sklearn.datasets import load_wine 

# Load dataset 
wine = load_wine() 

# Configure pandas to show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Convert data to a dataframe to view  properly 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Get all information about our dataset 
print(data.describe()) 

# Visualize the data 
data.hist(column = 'color_intensity',bins = 20) 
plt.show()

Output:

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 1. Chapter 5

Useful Functions for Researching and Visualizing Dataset


              1
            
print(data.describe())

If it is inconvenient for us to work with many digits, we can use method .round(n), where n is the number of decimal places to which you are rounding.

For example, here we will see the histogram of color intensity having 20 bins:


              1234567891011121314151617181920
            
# Import the libraries
import matplotlib.pyplot as plt 
import pandas as pd 
from sklearn.datasets import load_wine 

# Load dataset 
wine = load_wine() 

# Configure pandas to show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Convert data to a dataframe to view  properly 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Get all information about our dataset 
print(data.describe()) 

# Visualize the data 
data.hist(column = 'color_intensity',bins = 20) 
plt.show()

Output:

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?


              1
            
print(data.describe())

If it is inconvenient for us to work with many digits, we can use method .round(n), where n is the number of decimal places to which you are rounding.

For example, here we will see the histogram of color intensity having 20 bins:


              1234567891011121314151617181920
            
# Import the libraries
import matplotlib.pyplot as plt 
import pandas as pd 
from sklearn.datasets import load_wine 

# Load dataset 
wine = load_wine() 

# Configure pandas to show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Convert data to a dataframe to view  properly 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

# Get all information about our dataset 
print(data.describe()) 

# Visualize the data 
data.hist(column = 'color_intensity',bins = 20) 
plt.show()

Output:

Task

Let’s see what's going on inside our set.

[Line #7] Load wine set.
[Line #16] Get and print all information about it using the function .describe().
[Line #19] Visualize data about alcohol consistency in the set, setting column = 'alcohol' and defining bins = 15.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 1. Chapter 5

Switch to desktop for real-world practiceContinue from where you are using one of the options below