Conteúdo do 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

Correlation Matrix

Let’s go back to our dataset. To explore the relationships between all the columns, we can use a correlation matrix. It finds pairwise correlation coefficients of all columns(that's why the matrix is symmetric). Use the following method to build it and show correlation coefficients between all variables: dataframe.corr().

Use this code to see the matrix for our wine dataset:


              12
            
matrix= data.corr().round(2)
print(matrix)

If we want to visualize this matrix just call function sns.heatmap and import library:


              12
            
import seaborn as sns
sns.heatmap(matrix, annot=True)

If you want to improve your knowledge in Seaborn Visualization, click here!

We can see that alcohol is most positively correlated with the proline (0.64), which means that the amount of alcohol increases as the proline. The hue is most negatively correlated with the color intensity (-0.52), which means that the greater the color intensity of the wine, the lower the hue.

Tarefa

In the future, we will try to predict the characteristics of wine by the number of flavanoids in it. Flavanoids are plant pigments, and their most prominent role is to color our crops brightly.

[Lines #3-4] Import the pandas, seaborn libraries.
[Line #17] Write the code to define the correlation matrix rounding it to the second digit.
[Lines #20-24] Find with which column flavanoids have the highest positive correlation and the negative correlation. Using the previous diagram we can obviously find that that's total_phenols (0.86) and nonflavanoid_phenols(-0.54) respectively. Assign numbers above to the variables positive_cor_value and negative_cor_value respectively (positive_cor_value = 0.86 and negative_cor_value = -0.54). Assign names and numbers to the corresponding variables.

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Seção 2. Capítulo 2

Correlation Matrix

Use this code to see the matrix for our wine dataset:


              12
            
matrix= data.corr().round(2)
print(matrix)

If we want to visualize this matrix just call function sns.heatmap and import library:


              12
            
import seaborn as sns
sns.heatmap(matrix, annot=True)

If you want to improve your knowledge in Seaborn Visualization, click here!

Tarefa

In the future, we will try to predict the characteristics of wine by the number of flavanoids in it. Flavanoids are plant pigments, and their most prominent role is to color our crops brightly.

[Lines #3-4] Import the pandas, seaborn libraries.
[Line #17] Write the code to define the correlation matrix rounding it to the second digit.
[Lines #20-24] Find with which column flavanoids have the highest positive correlation and the negative correlation. Using the previous diagram we can obviously find that that's total_phenols (0.86) and nonflavanoid_phenols(-0.54) respectively. Assign numbers above to the variables positive_cor_value and negative_cor_value respectively (positive_cor_value = 0.86 and negative_cor_value = -0.54). Assign names and numbers to the corresponding variables.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Seção 2. Capítulo 2

Correlation Matrix

Use this code to see the matrix for our wine dataset:


              12
            
matrix= data.corr().round(2)
print(matrix)

If we want to visualize this matrix just call function sns.heatmap and import library:


              12
            
import seaborn as sns
sns.heatmap(matrix, annot=True)

If you want to improve your knowledge in Seaborn Visualization, click here!

Tarefa

In the future, we will try to predict the characteristics of wine by the number of flavanoids in it. Flavanoids are plant pigments, and their most prominent role is to color our crops brightly.

[Lines #3-4] Import the pandas, seaborn libraries.
[Line #17] Write the code to define the correlation matrix rounding it to the second digit.
[Lines #20-24] Find with which column flavanoids have the highest positive correlation and the negative correlation. Using the previous diagram we can obviously find that that's total_phenols (0.86) and nonflavanoid_phenols(-0.54) respectively. Assign numbers above to the variables positive_cor_value and negative_cor_value respectively (positive_cor_value = 0.86 and negative_cor_value = -0.54). Assign names and numbers to the corresponding variables.

Mude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo

Tudo estava claro?

Obrigado pelo seu feedback!

Use this code to see the matrix for our wine dataset:


              12
            
matrix= data.corr().round(2)
print(matrix)

If we want to visualize this matrix just call function sns.heatmap and import library:


              12
            
import seaborn as sns
sns.heatmap(matrix, annot=True)

If you want to improve your knowledge in Seaborn Visualization, click here!

Tarefa

In the future, we will try to predict the characteristics of wine by the number of flavanoids in it. Flavanoids are plant pigments, and their most prominent role is to color our crops brightly.

[Lines #3-4] Import the pandas, seaborn libraries.
[Line #17] Write the code to define the correlation matrix rounding it to the second digit.
[Lines #20-24] Find with which column flavanoids have the highest positive correlation and the negative correlation. Using the previous diagram we can obviously find that that's total_phenols (0.86) and nonflavanoid_phenols(-0.54) respectively. Assign numbers above to the variables positive_cor_value and negative_cor_value respectively (positive_cor_value = 0.86 and negative_cor_value = -0.54). Assign names and numbers to the corresponding variables.

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Seção 2. Capítulo 2

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