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

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

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

Все було зрозуміло?

Секція 5. Розділ 3

Challenge

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Все було зрозуміло?

Секція 5. Розділ 3

Challenge

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Все було зрозуміло?

Завдання

Let’s indicate the number of nonflavanoid phenols based on the number of flavanoids, total phenols and evaluate our model.

Your plan:

[Line #24] 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 #25-26] Initialize and fit the model (assign the model to the variable model2).
[Line #30-31] Calculate the MAE and assign the result to the variable MAE.
[Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
[Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Секція 5. Розділ 3