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

Prediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).

This value is the same as if we had substituted the line b + kx, where b is the estimated intersection with the model, and k is the slope. Please note that we multiply by 1 since the number of total phenols is 1 (x = 1).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Завдання

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[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

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Секція 3. Розділ 3

Prediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Завдання

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[Line #32] Print the predicted amount of flavanoids.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 3. Розділ 3

Prediction

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Завдання

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[Line #32] Print the predicted amount of flavanoids.

Все було зрозуміло?

Дякуємо за ваш відгук!

Once we've trained our module, it's time to think about test data evaluation and future predictions. We can make predictions using the method predict().

Let's look at an example. Prediction for flavanoids when the number of total phenols is 1:


              12
            
new_total_phenols = np.array([1]).reshape(-1,1)
print(model.predict(new_total_phenols))

Method .reshape() gives a new shape to an array without changing its data. Input must be a 2-dimension (DataFrame or 2-dimension array will work here).


              1
            
prediction = model.intercept_ + model.coef_*1

We can also put our testing data to get predictions for all amounts of flavanoids:


              1
            
y_test_predicted = model.predict(X_test)

Завдання

Predict with the previous split-train data the amount of flavanoids if the total phenols is 2.

[Line #6] Import the numpy library.
[Line #26] Initialize the linear regression model.
[Line #30] Assign np.array() and number of total phenols as the parameter (2) to the variable new_total_phenols (don’t forget to use the function .reshape(-1,1)).
[Line #31] Predict amount of flavanoids
[Line #32] Print the predicted amount of flavanoids.

Секція 3. Розділ 3