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

Basic Understanding of the Linear Regression

Welcome to the course! Today we will start by investigating one of the most popular conceptions in machine learning - linear regression. We will be digging into this simple supervised learning model using some well-known libraries of Python, which you probably already know.

Let's start!

So what is regression? Before answering this question, let's look at the following scatter plot below. For example, we have done the research, got some data, then visualized it and plotted it on our graph.

We intuitively want to add a line to see what the trend is. But which one would best visualize our dependence? Blue? Green? Or maybe black?

To find the correct answer, we need to use a learning model - linear regression. Let's find out the meaning of this method. Linear regression is an approach for modelling the relationship between variables that you operate in research, finding mathematically the straight line to your data (if we are talking about two-dimensional graphs) to predict future values.

Let's express the equation of this desired line for our plot (so that in the future, we operate with numbers and not with colors on the graph):

Where b is the intercept and k is the slope of the line. x is an independent variable (input), whereas y is our output. Our goal is to find this k and b.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the 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 1

Basic Understanding of the Linear Regression

Let's start!

We intuitively want to add a line to see what the trend is. But which one would best visualize our dependence? Blue? Green? Or maybe black?

Let's express the equation of this desired line for our plot (so that in the future, we operate with numbers and not with colors on the graph):

Where b is the intercept and k is the slope of the line. x is an independent variable (input), whereas y is our output. Our goal is to find this k and b.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Все було зрозуміло?

Секція 1. Розділ 1

Basic Understanding of the Linear Regression

Let's start!

We intuitively want to add a line to see what the trend is. But which one would best visualize our dependence? Blue? Green? Or maybe black?

Let's express the equation of this desired line for our plot (so that in the future, we operate with numbers and not with colors on the graph):

Where b is the intercept and k is the slope of the line. x is an independent variable (input), whereas y is our output. Our goal is to find this k and b.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Все було зрозуміло?

Let's start!

We intuitively want to add a line to see what the trend is. But which one would best visualize our dependence? Blue? Green? Or maybe black?

Let's express the equation of this desired line for our plot (so that in the future, we operate with numbers and not with colors on the graph):

Where b is the intercept and k is the slope of the line. x is an independent variable (input), whereas y is our output. Our goal is to find this k and b.

Завдання

You have a simple dataset of the dependence of the height of Abyssinian cats on their weight (values of arrays x and y respectively).

[Line #2] Import the library matplotlib.pyplotto build the plot.
[Lines #9-10] Using the linear regression we have found the straight line which expresses dependencies of variables, the slope, and the intercept are 0.93 and -3.97 respectively. Build this line.
[Line #25] Display the plot.

Секція 1. Розділ 1