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

Fitting

Fitting the model means finding the most appropriate model based on training data. In our case, it is linear regression parameters - slope and intercept. Then we will use the appropriate model to make predictions and evaluate the predictions. As said before, for the training model you will use only the training subset and also the fit() function:


              1
            
model.fit(X_train,Y_train)

This method executes computations storing the result in the model object. So, our model has been built, and we have parameters for our straight line. Let’s take a look at the intercept and slope we have received.


              12
            
print(model.intercept_)
print(model.coef_)

All model parameters in sklearn have trailing underscores.

Having these two parameters, we can build the line to predict future values on our dataset. Let’s do the same with the wine dataset to predict the number of flavonoids on total phenols.


              1234567891011121314151617181920212223242526272829303132
            
# Import the libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import pandas as pd

from sklearn.datasets import load_wine 

# Load the dataset 
wine = load_wine() 

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

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

# Define the target 
data['flavanoids'] = wine.target 

# Define the data we will work with 
x = data[['total_phenols']] 
y = data['flavanoids'] 

# Build and fit the model 
model = LinearRegression() 
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3,random_state = 1) 
model.fit(X_train, Y_train) 

# Print parameters 
print(model.intercept_) 
print(model.coef_)

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

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

¿Todo estuvo claro?

Sección 3. Capítulo 2

Fitting


              1
            
model.fit(X_train,Y_train)


              12
            
print(model.intercept_)
print(model.coef_)

All model parameters in sklearn have trailing underscores.

Having these two parameters, we can build the line to predict future values on our dataset. Let’s do the same with the wine dataset to predict the number of flavonoids on total phenols.


              1234567891011121314151617181920212223242526272829303132
            
# Import the libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import pandas as pd

from sklearn.datasets import load_wine 

# Load the dataset 
wine = load_wine() 

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

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

# Define the target 
data['flavanoids'] = wine.target 

# Define the data we will work with 
x = data[['total_phenols']] 
y = data['flavanoids'] 

# Build and fit the model 
model = LinearRegression() 
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3,random_state = 1) 
model.fit(X_train, Y_train) 

# Print parameters 
print(model.intercept_) 
print(model.coef_)

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

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

¿Todo estuvo claro?

Sección 3. Capítulo 2

Fitting


              1
            
model.fit(X_train,Y_train)


              12
            
print(model.intercept_)
print(model.coef_)

All model parameters in sklearn have trailing underscores.

Having these two parameters, we can build the line to predict future values on our dataset. Let’s do the same with the wine dataset to predict the number of flavonoids on total phenols.


              1234567891011121314151617181920212223242526272829303132
            
# Import the libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import pandas as pd

from sklearn.datasets import load_wine 

# Load the dataset 
wine = load_wine() 

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

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

# Define the target 
data['flavanoids'] = wine.target 

# Define the data we will work with 
x = data[['total_phenols']] 
y = data['flavanoids'] 

# Build and fit the model 
model = LinearRegression() 
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3,random_state = 1) 
model.fit(X_train, Y_train) 

# Print parameters 
print(model.intercept_) 
print(model.coef_)

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

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

¿Todo estuvo claro?


              1
            
model.fit(X_train,Y_train)


              12
            
print(model.intercept_)
print(model.coef_)

All model parameters in sklearn have trailing underscores.

Having these two parameters, we can build the line to predict future values on our dataset. Let’s do the same with the wine dataset to predict the number of flavonoids on total phenols.


              1234567891011121314151617181920212223242526272829303132
            
# Import the libraries
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

import pandas as pd

from sklearn.datasets import load_wine 

# Load the dataset 
wine = load_wine() 

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

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

# Define the target 
data['flavanoids'] = wine.target 

# Define the data we will work with 
x = data[['total_phenols']] 
y = data['flavanoids'] 

# Build and fit the model 
model = LinearRegression() 
X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3,random_state = 1) 
model.fit(X_train, Y_train) 

# Print parameters 
print(model.intercept_) 
print(model.coef_)

Tarea

Let’s see the difference between our fitted models by splitting the data another way:

[Line #25] Initialize linear regression model.
[Line #26] Get train-split variables using the function train_test_split() and x, y, test_size = 0.4, random_state = 1 as parameters.
[Line #27] Fit the model inserting X_train and Y_train as parameters.
[Lines #30-31] Print the intercept and slope we have received.

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

Sección 3. Capítulo 2

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