Kursinhalt

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

Challenge

Let's do one more task as practice!

Aufgabe

Swipe to start coding

You have the dataset about Abbisian cats where the number of calories the cat eats every day of a certain weight is indicated (array x - weight, y - number of calories).

[Line #19] Find the correlation coefficient between x and y (in any convenient way), the slope, and the intercept of a straight line which shows the trend.
[Lines #22-24] Print these values in the order mentioned above.
[Line #27] Build line using function line() and save in built_line variable using list(map()).
[Lines #30-31] Build the line using matplotlib.pyplot functions.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 4

Challenge

Let's do one more task as practice!

Aufgabe

Swipe to start coding

You have the dataset about Abbisian cats where the number of calories the cat eats every day of a certain weight is indicated (array x - weight, y - number of calories).

[Line #19] Find the correlation coefficient between x and y (in any convenient way), the slope, and the intercept of a straight line which shows the trend.
[Lines #22-24] Print these values in the order mentioned above.
[Line #27] Build line using function line() and save in built_line variable using list(map()).
[Lines #30-31] Build the line using matplotlib.pyplot functions.

Lösung

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden

War alles klar?

Danke für Ihr Feedback!

Abschnitt 2. Kapitel 4

Wechseln Sie zum Desktop, um in der realen Welt zu übenFahren Sie dort fort, wo Sie sind, indem Sie eine der folgenden Optionen verwenden