Зміст курсу

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

Build the Linear Regression

Here we will learn how to find the intercept and slope for our dataset. For this example, we will use a scientific computation library SciPy, to import stats. Using the method stats.lingress() we can get the most important linear regression parameters for the given dataset (x and y arrays). Pay attention to the first two values (slope and intercept), and other parameters will be analyzed in the following chapters. These two numbers define a straight line. The squares of the residuals of the dataset to points are minimal.


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# Import the libraries
import matplotlib.pyplot as plt
from scipy import stats 

# Initialize the data
x = [8, 10, 9.2, 8.4, 9.1, 9.6, 8, 10.2, 9.3, 9.4, 9.9, 8.7] 
y = [3.6, 5.4, 4.8, 3.9, 4.2, 5.2, 3.5, 5.5, 4.4, 4.7, 5.1, 3.7] 

# Get the linear regression parameters 
slope, intercept, r, p, std_err = stats.linregress(x, y) 

# The line shows the dependence of the height of cats on their weight 
def on_weight(x): 
   return slope * x + intercept 

# Define the line 
height_on_weight = list(map(on_weight, x)) 

# Add titles to axes 
ax = plt.gca()
ax.set_xlabel('Cat height (inches)')
ax.set_ylabel('Cat weight (kg)') 

# Visualize our data 
plt.scatter(x, y) 
plt.plot(x, height_on_weight) 
plt.show()

The output of the code execution is identical to your first task. However, now we don't work with predefined values but with a method that returns them to us knowing the dataset.

Завдання

Getting bigger, cats start to eat more. Let's see how these values are dependent. We have a dataset in which the number of calories the cat eats every day at a certain weight is indicated (array x - weight, y - number of calories).

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 1. Розділ 3

Build the Linear Regression


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# Import the libraries
import matplotlib.pyplot as plt
from scipy import stats 

# Initialize the data
x = [8, 10, 9.2, 8.4, 9.1, 9.6, 8, 10.2, 9.3, 9.4, 9.9, 8.7] 
y = [3.6, 5.4, 4.8, 3.9, 4.2, 5.2, 3.5, 5.5, 4.4, 4.7, 5.1, 3.7] 

# Get the linear regression parameters 
slope, intercept, r, p, std_err = stats.linregress(x, y) 

# The line shows the dependence of the height of cats on their weight 
def on_weight(x): 
   return slope * x + intercept 

# Define the line 
height_on_weight = list(map(on_weight, x)) 

# Add titles to axes 
ax = plt.gca()
ax.set_xlabel('Cat height (inches)')
ax.set_ylabel('Cat weight (kg)') 

# Visualize our data 
plt.scatter(x, y) 
plt.plot(x, height_on_weight) 
plt.show()

The output of the code execution is identical to your first task. However, now we don't work with predefined values but with a method that returns them to us knowing the dataset.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Все було зрозуміло?

Секція 1. Розділ 3

Build the Linear Regression


              123456789101112131415161718192021222324252627
            
# Import the libraries
import matplotlib.pyplot as plt
from scipy import stats 

# Initialize the data
x = [8, 10, 9.2, 8.4, 9.1, 9.6, 8, 10.2, 9.3, 9.4, 9.9, 8.7] 
y = [3.6, 5.4, 4.8, 3.9, 4.2, 5.2, 3.5, 5.5, 4.4, 4.7, 5.1, 3.7] 

# Get the linear regression parameters 
slope, intercept, r, p, std_err = stats.linregress(x, y) 

# The line shows the dependence of the height of cats on their weight 
def on_weight(x): 
   return slope * x + intercept 

# Define the line 
height_on_weight = list(map(on_weight, x)) 

# Add titles to axes 
ax = plt.gca()
ax.set_xlabel('Cat height (inches)')
ax.set_ylabel('Cat weight (kg)') 

# Visualize our data 
plt.scatter(x, y) 
plt.plot(x, height_on_weight) 
plt.show()

The output of the code execution is identical to your first task. However, now we don't work with predefined values but with a method that returns them to us knowing the dataset.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Все було зрозуміло?


              123456789101112131415161718192021222324252627
            
# Import the libraries
import matplotlib.pyplot as plt
from scipy import stats 

# Initialize the data
x = [8, 10, 9.2, 8.4, 9.1, 9.6, 8, 10.2, 9.3, 9.4, 9.9, 8.7] 
y = [3.6, 5.4, 4.8, 3.9, 4.2, 5.2, 3.5, 5.5, 4.4, 4.7, 5.1, 3.7] 

# Get the linear regression parameters 
slope, intercept, r, p, std_err = stats.linregress(x, y) 

# The line shows the dependence of the height of cats on their weight 
def on_weight(x): 
   return slope * x + intercept 

# Define the line 
height_on_weight = list(map(on_weight, x)) 

# Add titles to axes 
ax = plt.gca()
ax.set_xlabel('Cat height (inches)')
ax.set_ylabel('Cat weight (kg)') 

# Visualize our data 
plt.scatter(x, y) 
plt.plot(x, height_on_weight) 
plt.show()

The output of the code execution is identical to your first task. However, now we don't work with predefined values but with a method that returns them to us knowing the dataset.

Завдання

[Lines #2-3] Import the matplotlib.pyplotand also the library SciPy.
[Lines #10-17] Find the slope and the intercept using the method stats.lingress(). Add the missing parameter to the function on_weight and assign the variable feed_on_weight.
[Lines #26-27] Build line on your plot.

Секція 1. Розділ 3