Contenido del Curso

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

Working with Dataset

First of all, we need data on which we will work on. Scikit-learn comes with a few small standard datasets that do not require downloading and are very helpful for learning new models in machine learning. In this course, we will become high-class sommeliers and determine the quality of wine using statistics and regression. Wine recognition dataset provides a large variety of characteristics of wine: Alcohol, Ash, Magnesium, Total phenols, Color intensity, and so on.


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from sklearn.datasets import load_wine 
wine = load_wine()

We also create pandas DataFrame for easier manipulation:


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# Import the libraries
import pandas as pd 

# Show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Create DataFrame 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

If you don't feel comfortable working with pandas library, check out our course on this topic.

To inspect our data, we should know the number of records and which columns it contains. In such situations, it would be better to use functions .shape and .columns. The first one returns the number of records and columns, and the second lets us know all columns' names.


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#Get the number of records and columns
print(data.shape)

# Get the names of all columns
print(data.columns)

Okay, so now we know how to load our dataset and get some information about data. But what if we want to get a certain number of records from our wine database? Pulling all information can be very inconvenient, primarily if we work with Big Data in the future where millions of records can be stored. To see the first rows of DataFrame use .head(n), where n is the number of rows to be selected. By default, it’s 5 rows.

Look at the following example. This code shows the first 5 rows of our dataset:


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# Print first 5 rows
print(data.head())

To sum up the learned functions and their usage below:

Tarea

Let’s explore our dataset. Using the functions discussed in this chapter, find out the number of records and columns that are in our set and the names of these columns. Print the first 9 rows of the wine dataset.

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 4

Working with Dataset


              12
            
from sklearn.datasets import load_wine 
wine = load_wine()

We also create pandas DataFrame for easier manipulation:


              12345678
            
# Import the libraries
import pandas as pd 

# Show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Create DataFrame 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

If you don't feel comfortable working with pandas library, check out our course on this topic.


              12345
            
#Get the number of records and columns
print(data.shape)

# Get the names of all columns
print(data.columns)

Look at the following example. This code shows the first 5 rows of our dataset:


              12
            
# Print first 5 rows
print(data.head())

To sum up the learned functions and their usage below:

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 1. Capítulo 4

Working with Dataset


              12
            
from sklearn.datasets import load_wine 
wine = load_wine()

We also create pandas DataFrame for easier manipulation:


              12345678
            
# Import the libraries
import pandas as pd 

# Show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Create DataFrame 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

If you don't feel comfortable working with pandas library, check out our course on this topic.


              12345
            
#Get the number of records and columns
print(data.shape)

# Get the names of all columns
print(data.columns)

Look at the following example. This code shows the first 5 rows of our dataset:


              12
            
# Print first 5 rows
print(data.head())

To sum up the learned functions and their usage below:

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?


              12
            
from sklearn.datasets import load_wine 
wine = load_wine()

We also create pandas DataFrame for easier manipulation:


              12345678
            
# Import the libraries
import pandas as pd 

# Show all features 
pd.set_option('display.max_rows', None, 'display.max_columns', None) 

# Create DataFrame 
data = pd.DataFrame(data = wine['data'], columns = wine['feature_names'])

If you don't feel comfortable working with pandas library, check out our course on this topic.


              12345
            
#Get the number of records and columns
print(data.shape)

# Get the names of all columns
print(data.columns)

Look at the following example. This code shows the first 5 rows of our dataset:


              12
            
# Print first 5 rows
print(data.head())

To sum up the learned functions and their usage below:

Tarea

[Lines #2-3] Import the library pandas and load our wine dataset.
[Line #12] Set DataFrame using the function pd.DataFrame() with parameters.
[Lines #15-19] Use functions .shape, .columns to get information about records. Print the first 9 rows using function head(n) where n is 9.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

Sección 1. Capítulo 4

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones