Whether you’re studying machine learning or statistics with Python, you would come across linear regression. Linear regression is one of the machine learning certification course’s important part.
What is it? How do you perform linear regression with Python?
In this article, we’ll be discovering answers to these questions. After reading this article, you’d become familiar with:
- Regressions and what are they
- What is linear regression
- How to train a linear regression model
- Applications of linear regression
Let’s get started.
What is Regression?
Regression analysis refers to specific statistical processes that you use for estimating the relations between a dependent and an independent variable.
It is popular in multiple industries, such as finance and banking. By using regression analysis, you can understand the relationship between two variables in a specific environment.
Suppose you want to find the prices of houses in a particular area. For that purpose, you will need to observe the city of the area, number of residents, availability of amenities, and many other things.
The things on which the houses’ prices will depend on are called features. And the problem where the factors are related to the cost of each home is an observation. In this example, the presumption is that the location, amenities, and other factors affect the price of each home.
In simpler terms, you make a few observations regarding a particular subject in regression analysis. Your observations have a few features and some presumptions before you start forming a relationship among them.
There are two kinds of features in the regression analysis. They are:
- Dependent features, which are called dependent outputs, variables, or responses
- Independent features, which are called independent outputs, variables, or responses
Generally, a regression problem has one continuous dependent variable. The inputs vary.
You can denote the outputs with y and inputs with x. There are no hard and fast rules for it, but it’s a general practice to use y and x for denoting these output and input.
If you have multiple independent variables, you can represent as x = (x1,…,xr), where r denotes the number of inputs.
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What is a Linear Regression?
Linear regression is the most popular type of regression. It is a statistical method to model relationships between a dependent output and a group of independent outputs.
In this article, we’ll call independent outputs ‘features’ and dependent outputs ‘responses’.
If a linear regression only has one feature, it is called Univariate linear regression. Similarly, if it has multiple features, you’d call it Multiple linear regression.
It is the simplest form of regression.
If y is the predicted value, 0 is the bias term, xn and are the feature values, and you’d represent the linear regression model by the following equation:
Y = 0 + 1x1 + 2x2 +…. +nxn
Here n denotes the model parameters.
Linear Regression Python Code
To create a linear regression model, you’ll also need a data set to begin with. There are multiple ways you can use the Python code for linear regression.
We suggest studying Python and getting familiar with python libraries before you start working in this regard.
It can help you create a basic linear regression model.
Training the Regression Model
You will have to find the necessary parameters for the model, so it best fits the data. You will have to find the best fit line (or the regression line).
The regression line is the one for which the error between the observed figures and the predicted figures is the minimum. Another name for these errors is residuals.
For measuring the error, you’ll have to define the cost function:
J () = 12m i=1m(h(xi) – yi)2
Here, h(x) stands for hypothesis function, which is denoted by the equation we discussed before:
h(x) = 0 + 1x1 + 2x2 +…. +ixi
m stands for the total number of examples in our data set.
Using these equations and an optimization algorithm, you can train your linear regression model.
There are many other methods of performing Python regression analysis, which we’ve discussed below:
Performing Linear Regression with Python Packages
You can use NumPy, which is a widespread and fundamental Python package. It is used for performing high-performance operations. It is open-source and has many mathematical routines available.
You can check out the NumPy user guide for finding out more information about it. You’d need to learn about scikit-learn as well, which is a popular Python library based on NumPy. It is popularly used for machine learning and similar activities.
For developing linear regression models and implementing them, you should also learn about statsmodels. It is another powerful Python package, which is used for performing tests and estimating statistical models.
What are the Applications of Linear Regression?
Linear regression finds uses in many industries. Here are a few applications of linear regression:
1) Understanding Trends
Linear regression can help companies in understanding market trends. This way, they can plan their strategies better and avoid making mistakes. Apart from companies, traders, as well as, research organizations can also use this technique for evaluating trends.
2) Analyzing Price Changes
Price changes in commodities can have a significant impact on the profits of produce businesses. Linear regression can help companies with this task, too, as they can find relations between the price changes and the factors contributing to them.
3) Risk Assessment
Insurance companies, as well as investors, can use linear regression to find out anomalies. Investors can find their weak investments and plan out their strategies accordingly while reducing risk.
Linear Regression is one of the important AI algorithms and we hope you found this guide on linear regression with Python useful. Python regression can be quite daunting for a beginner. That’s why we recommend getting familiar with Python packages and algorithms first.
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Knowing about those two alone will benefit you greatly in implementing linear regression.
When do we use regression?
When multiple variables are present in a problem, we might want to understand the relationship between all of them. We can use matrices to find out the potential relationships between specific pairs of variables. Using methods of correlation, we can measure the linear relationship between any pair of variables. However, this method is not adequate when we want to find out complex relationships involving several variables. In such cases, regression is a more effective method of understanding complex associations between multiple variables. Regression helps us know which variables impact a specific response and how those can explain a particular result.
How many types of regression are used in machine learning?
Regression is a technique by means of which we can predict future outcomes between a target variable and one or several independent predictor variables. Regression is very commonly used in machine learning for time series modeling, forecasting, and understanding cause-effect relationships between different variables. Different types of regression used in machine learning are linear regression, logistic regression, ridge regression, polynomial regression, and lasso regression. You can come across more types of regression analysis methods employed in machine learning. However, these are the most extensively used methods among all the others.
What are the advantages of using Python?
Python is one of the most commonly employed programming languages in machine learning. It comes with several advantages. Firstly, the syntax of Python is straightforward. It is easy to learn and understand, which makes it hugely popular among both beginners as well as seasoned programmers. Next, it is open-source and free to use and comes with a massive community of active developers and researchers. The extensive library of functions built-in the core of Python offers comprehensive support to developers, so there is no need to depend on external or third-party libraries. Moreover, Python is highly flexible and system-independent, unlike some other programming languages such as C and C++.