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

Challenge

Let’s combine our knowledge!

Завдання

In this task, you build, train and fit your model and make predictions based on it. This time you will make predictions about total_phenols, based on flavanoids. It means that your target now is total_phenols.

Your plan:

[Line #18] Define the target (in this task it's total_phenols).
[Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
[Line #26] Initialize linear regression model .
[Line #27] Fit the model using your tain data.
[Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
[Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
[Line #32] Print the predicted amount of flavanoids.

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

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 4

Challenge

Let’s combine our knowledge!

Завдання

Your plan:

[Line #18] Define the target (in this task it's total_phenols).
[Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
[Line #26] Initialize linear regression model .
[Line #27] Fit the model using your tain data.
[Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
[Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
[Line #32] Print the predicted amount of flavanoids.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 4

Challenge

Let’s combine our knowledge!

Завдання

Your plan:

[Line #18] Define the target (in this task it's total_phenols).
[Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
[Line #26] Initialize linear regression model .
[Line #27] Fit the model using your tain data.
[Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
[Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
[Line #32] Print the predicted amount of flavanoids.

Все було зрозуміло?

Дякуємо за ваш відгук!

Let’s combine our knowledge!

Завдання

Your plan:

[Line #18] Define the target (in this task it's total_phenols).
[Line #25] Split the data 70-30 (70% of the data is for training and 30% is for testing) and insert 1 as a random parameter.
[Line #26] Initialize linear regression model .
[Line #27] Fit the model using your tain data.
[Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
[Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
[Line #32] Print the predicted amount of flavanoids.

Секція 3. Розділ 4