Linear regression in Python is a technique vital to the machine learning procedures and predictive analysis. By definition, linear regression is a learning algorithm that reveals the relationship between several variables.
For instance, you could put the number of customers that have bought your product on the y-axis. Then, you can set the price of the product on the x-axis. From this regression analysis in Python, you would determine how the price influences the number of customers you have. Of course, you can analyze any problem you want, and this is one of the simplest examples. However, it represents the fact that linear regression helps to solve real-life issues.
As I have already explained, Python regression analysis helps you determine how one thing influences the other. In other words, it reveals the relationship between two variables, how the tendencies shift when one changes. Therefore, you will need to use linear regression to prove that one factor influences the other.
In this course, we will be making all of the steps necessary for implementing it in Python. First of all, you will need to indicate the problem you want to solve. In other words, you can call it the problem statement. As an example, you can aim to study whether years of experience determine one’s salary.
For instance, if you take one predictor variable X, the quantitative response Y will be estimated. This technique is also referred to as the simple linear regression. In some cases, you can use multiple linear regression. It means that you choose more than one predictor variable to use for predicting the changes in the response variable.
In this course on linear regression in Python, I will start from some Python basics: arithmetic operations, strings, list tuples, and directories. I will also cover the installation processes for Python and Anaconda, opening Jupyter Notebook, and working with Numpy, Pandas, and Seaborn libraries.
Additionally, before starting the regression analysis in Python, I will go over the types of data and statistics, describing data graphically, measures of centers, and measures of dispersion. Later on, I will provide an introduction to machine learning and the way its models are built. If you are already familiar with these basics, you can skip this part and jump to the lectures about how to do linear regression in Python.
However, I recommend that you refresh your memory beforehand. I try not to be too mathematical when it comes to explaining how to do linear regression in Python. This technique is one of the simplest ways to start pursuing machine learning, but linear regression offers a decent prediction ability.
While this course covers many basic principles and procedures leading up to the Python regression analysis, you should be aware of the Python basics and its use. Additionally, one of the requirements is to have Anaconda. In case you do not have this software, please follow the instructions in one of the first lectures of this course.
Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics, and Python.
Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.