Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge | Building and Training Model
Explore the Linear Regression Using Python
course content

Зміст курсу

Explore the Linear Regression Using Python

Explore the Linear Regression Using Python

1. What is the Linear Regression?
Basic Understanding of the Linear RegressionHow Mathematically Does It Work?Build the Linear RegressionWorking with DatasetUseful Functions for Researching and Visualizing Dataset
2. Correlation
What Is Correlation? How To Find Pearson CoefficientCorrelation MatrixCalculating the Pearson Coefficient Using NumPy and PandasChallenge
3. Building and Training Model
Train-split evaluationFittingPredictionChallenge
4. Metrics to Evaluate the Model
ResidualsMean Absolute Error (MAE)MSE and RMSER-squaredNot Always the Linear Regression?
5. Multivariate Linear Regression
Building the Regression Model Based on Several VariablesTry to EvaluateChallenge

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [Line #32] Print the predicted amount of flavanoids.

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [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?

Секція 3. Розділ 4
toggle bottom row

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [Line #32] Print the predicted amount of flavanoids.

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [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?

Секція 3. Розділ 4
toggle bottom row

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [Line #32] Print the predicted amount of flavanoids.

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [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?

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:

  1. [Line #18] Define the target (in this task it's total_phenols).
  2. [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.
  3. [Line #26] Initialize linear regression model .
  4. [Line #27] Fit the model using your tain data.
  5. [Line #30] Assign np.array() to the variable new_flavanoids if their number is 1 (don't forget to use function .reshape(-1,1)).
  6. [Line #31] Predict and assign the amount of flavanoids to the variable predicted_value.
  7. [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
Секція 3. Розділ 4
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
We're sorry to hear that something went wrong. What happened?
some-alt