Linear Regression & Supervised Learning in Python


Linear Regression & Supervised Learning in Python , Learn how to use Python to build linear regression models and make accurate predictions.

What you”ll learn:

  • You will be able to develop your own prediction model
  • Data Preparation, feature engineering training
  • Data visualization techniques
  • Good understanding of scikit machine learning library


The dataset for linear regression is defined as in machine learning it is an algorithm that can be categorized in supervised learning to find the target variable between the dependent variables and the independent variables; also, it can allow us to establish a relationship between those variables which are the best suit for a relationship, in machine learning it can be used to closely relate variables which are related to dependent variables and it can be used for a large amount of data when analyzing the data while constructing the model it can be used to find the anticipated value of the dependent variable.

What is Dataset for Linear Regression?

  • Linear regression is the machine learning algorithm that can be used to construct a model on the dataset for analyzing a large amount of data, and the model of dataset gives the correct anticipate values of the dependent variables, the dependent variable in the regression is the leading element when we are trying to understand the anticipated value and also a directory of the dataset which can accommodate the test data for linear regression is called as a regression.
  • The linear regression is maybe the most familiar and recognizable algorithm in statistics and in machine learning; basically, the linear regression is come out for the statistic field, but after further studies, it as a model while understanding the relationship between the input numerical variable and output numerical variable it has been taken by the machine learning algorithm, the relationship between the variables may be positive or negative in nature in which the positive relationship can happen when both the variables that are independent variables and dependent variables increases in a graphical manner and the negative relationship happens when the dependent variable decreases and independent variable increases.
  • Linear regression has two types: simple linear regression, which is necessary to give anticipate response to the values using its simple feature, and multiple linear regressions, which are used when having a large amount of data to predict the response value by using two or more features of it.

Basics of Linear Regression and Implementation

In the basics of linear regression anticipates the one variable from the second variable. The criteria variables it uses is the predicted variable when we are trying to anticipate the one variable. It is called simple regression, and when we are trying to anticipate one or more variables, it is called multiple linear regression. The dataset model have some features to make the dataset flexible and powerful when we implement a simple linear regression; we have to consider that two variables are linearly related and in the response of it gives the accurate value as per its features if we have dataset m and n with values of response for each value in n in response for values in m.

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