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Explore the Linear Regression Using Python
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Course Content

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

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 5. Chapter 3
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Challenge

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 5. Chapter 3
toggle bottom row

Challenge

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Task

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

Your plan:

  1. [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.
  2. [Line #25-26] Initialize and fit the model (assign the model to the variable model2).
  3. [Line #30-31] Calculate the MAE and assign the result to the variable MAE.
  4. [Line #34-35] Calculate the R-squared and assign the result to the variable r_squared.
  5. [Line #38] Print the intercept, the MAE and the R-squared in this order and round each value to second digit.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 3
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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