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Let’s take a brief introduction to what linear regression sklearn is. Regression is the statistical method used to determine the strength and the relation between the independent and dependent variables. Generally, independent variables are those variables whose values are used to obtain output, and dependent variables are those whose values depend on the independent values. When discussing regression algorithms, you must know some of the multiple linear regression algorithms commonly used in python to train the machine learning model, like simple linear regression, lasso, ridge, etc.

In the following tutorial, we will talk about the multiple linear regression model or multilinear regression and understand how simple linear differs from multiple linear regression (MLR) in python.

Learning objectives

Understand the difference between simple linear regression and multiple linear regression in Python’s Scikit-learn library.

Learn how to read datasets and handle categorical variables for multiple linear regression using Scikit-learn.

Apply Scikit-learn’s linear regression algorithm to train a model for multiple linear regression.

This article was published as a part of the Data Science Blogathon.

Table of Contents What Is Machine Learning?

If you are on the path of learning data science, then you definitely have an understanding of what machine learning is. In today’s digital world, everyone knows what Machine Learning is because it is a trending digital technology across the world. Every step towards the adaptation of the future world is led by this current technology, which in turn, is led by data scientists like you and me.

Now, for those of you who don’t know what machine learning is, here’s a brief introduction:

Machine learning is the study of the algorithms of computers that improve automatically through experience and by the use of data. Its algorithm builds a model based on the data we provide during model building. This is the simple definition of machine learning, and when we go in deeper, we find that huge numbers of algorithms are used in model building. Generally, the most commonly used machine learning algorithms are based on the type of problem, such as regression, classification, etc. But today, we will only talk about sklearn linear regression algorithms.

Simple Linear Regression vs Multiple Linear Regression

Now, before moving ahead, let’s discuss the interaction behind the simple linear regression. Later, we will compare multiple and simple linear regression based on the intuition that we are solving our machine learning problem.

What Is Simple Linear Regression?

We considered a simple linear regression in any machine learning algorithm using an example.

Now, suppose we take a scenario of house prices where our x-axis is the size of the house, and the y-axis is the price of the house. In this example, we have two features – the first one is f1, and the second one is f2, where

 f1 refers to the size of the house and

f2 refers to the price of the house.

So, if f1 becomes the independent feature and f2 becomes the dependent feature, we usually know that whenever the size of the house increases, then the price also increases. Suppose we draw scatter points randomly. Through this scatter point, we try to find the best-fit line, which is given by the equation:

                            equation:   y = A + Bx

Suppose y is the price of the house, and x is the size of the house; then this equation seems like this:

When we discuss this equation, in which

In this equation, the intercept indicates what the base price of the house would be when the price of the house is 0. Meanwhile, the slope or coef (coefficient) indicates the unit increase in the slope, with the unit increase in size.

Now, how is it different when compared to multiple linear regression?

What Is Multiple Linear Regression?

Multiple Linear Regression (MLR) is basically indicating that we will have many features Such as f1, f2, f3, f4, and our output feature f5. If we take the same example as above we discussed, suppose:

f1 is the size of the house,

f2 is bad rooms in the house,

f3 is the locality of the house,

f4 is the condition of the house, and

f5 is our output feature, which is the price of the house.

Now, you can see that multiple independent features also make a huge impact on the price of the house, meaning the price can vary from feature to feature. When we are discussing multiple linear regression, then the equation of simple linear regression y=A+Bx is converted to something like:

                            equation:  y = A+B1x1+B2x2+B3x3+B4x4

“If we have one dependent feature and multiple independent features then basically call it a multiple linear regression.”

Now, our aim in using the multiple linear regression is that we have to compute A, which is an intercept. The key parameters B1, B2,  B3, and B4 are the slopes or coefficients concerning this independent feature. This basically indicates that if we increase the value of x1 by 1 unit, then B1 will tell you how much it will affect the price of the house. The others B2, B3, and B4, also work similarly.

So, this is a small theoretical description of multiple linear regression. Now we will use the scikit learn linear regression library to solve the multiple linear regression problem.

How to Train a Model for Multiple Linear Regression? Step 1: Reading the Dataset

Most of the datasets are in CSV file format; for reading this file, we use pandas library:

df = pd.read_csv('50_Startups.csv') df

Here you can see that there are 5 columns in the dataset where the state stores the categorical data points, and the rest are numerical features.

Now, we have to classify independent and dependent features.

Independent and Dependent Variables

There are total 5 features in the dataset, of which profit is our dependent feature, and the rest are our independent features.

Python Code:

Step 2: Handling Categorical Variables

In our dataset, there is one categorical column, State. We must handle the categorical values inside this column as part of data preprocessing. For that, we will use pandas’ get_dummies() function:

# handle categorical variable


# dropping extra column

x= x.drop(‘State’,axis=1)

# concatation of independent variables and new cateorical variable.



Step 3: Splitting the Data

Now, we have to split the data into training and test sets using the scikit-learn train_test_split() function.

# importing train_test_split from sklearn from sklearn.model_selection import train_test_split # splitting the data x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2, random_state = 42) Step 4: Applying the Model

Now, we apply the linear regression model to our training data. First of all, we have to import linear regression from the scikit-learn library. Unlike linear regression, there is no other library to implement multiple linear regression.

# importing module from sklearn.linear_model import LinearRegression # creating an object of LinearRegression class LR = LinearRegression() # fitting the training data,y_train)

finally, if we execute this, then our model will be ready. Now we have x_test data, which we will use for the prediction of profit.

y_prediction =  LR.predict(x_test) y_prediction

Now, we have to compare the y_prediction values with the original values because we have to calculate the accuracy of our model, which was implemented by a concept called r2_score. Let’s briefly discuss r2_score:


It is a function inside sklearn. metrics module, where the value of r2_score varies between 0 and 100 percent,  we can say that it is closely related to MSE.

r2 is basically calculated by the formula given below:

                            formula:  r2 = 1 – (SSres  /SSmean )

now, when I say SSres, it means it is the sum of residuals, and SSmean refers to the sum of means.


y = original values

y^ = predicted values. and,

From this equation, we infer that the sum of means is always greater than the sum of residuals. If this condition is satisfied, our model is good for predictions. Its values range between 0.0 to 1.

”The proportion of the variance in the dependent variable or target variable that is predictable from the independent variable(s) or predictor.”

The best possible score is 1.0, which can be negative because the model can be arbitrarily worse. A constant model that always predicts the expected value of y, disregarding the input features, would get an R2 score of 0.0.

# importing r2_score module

from sklearn.metrics import r2_score

from sklearn.metrics import mean_squared_error

# predicting the accuracy score


print(‘r2 socre is ‘,score)

print(‘mean_sqrd_error is==’,mean_squared_error(y_test,y_prediction))

print(‘root_mean_squared error of is==’,np.sqrt(mean_squared_error(y_test,y_prediction)))

You can see that the accuracy score is greater than 0.8, which means we can use this model to solve multiple linear regression, and also mean squared error rate is also low.


Multiple Linear Regression is a statistical method used to study the linear relationship between a dependent variable and multiple independent variables. In the article above, we learned step-by-step how to implement MLR in Python using the Scikit-learn library. We used a simple example of predicting house prices to explain how simple linear regression works and then extended the example to multiple linear regression, which involves more than one independent variable. I hope now you have a better understanding of the topic.

Key Takeaways

Multiple linear regression is an extension of simple linear regression, where multiple independent variables are used to predict the dependent variable.

Scikit-learn, a machine learning library in Python, can be used to implement multiple linear regression models and to read, preprocess, and split data.

Categorical variables can be handled in multiple linear regression using one-hot encoding or label encoding.

Frequently Asked Questions Related

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An Introductory Note On Linear Regression

This article was published as a part of the Data Science Blogathon.

In this article, I will explain linear Regression, one of the machine learning algorithms. After reading this, we will get some basic knowledge about linear Regression, its uses, its types, and so on. Let us start with the table of contents.

Table of contents

What is Linear Regression

Uses of Linear Regression

Selection Criteria

When will Linear Regression be used?

Types of Linear Regression

Understanding Linear Regression

How to find the effectiveness of the model?

R Square method

Regression analysis is a form of predictive modeling technique that investigates the relationship between X and Y, where x is the independent variable Y is the dependent variable.

Types of Regression – There are two types of Regression. One is linear Regression used with continuous variables,  and the other is logistic Regression used with categorical variables.

Linear Regression

Regression analysis is graphing a line on a set of data points that most closely fits the overall shape of the data.

In other words, Regression shows the changes in a dependent variable on the y-axis to the changes in the explanatory variable on the x-axis.

Uses of Regression

We determine the strength of predictors, for example, the relation between sales and marketing spending or the connection between age and income.

It is forecasting an effect and is used to predict the impact or impact of changes. This is used to understand how much the dependent variable changes with the evolution of the independent variable. For example, how much sales are increased with extra 1000 rupees spent on marketing?

Trend forecasting. This can be used to get the point estimates.

Selection Criteria

Classification and regression capabilities: Predicts the continuous variable (For example-Temperature of a place)

Data quality: Each missing point removes one data point that could optimize the Regression.

Computational complexity: Linear Regression is not always computationally expensive than the decision tree or the clustering algorithm.

Comprehensible and Transparent: Linear Regression is easily understandable, and a simple mathematical notation can represent transparency.

Where will Linear Regression be used?

Evaluating trends and sales estimates

Analyzing the impact of price changes

Estimation of risk in financial services and insurance domain

Types of Linear Regression

Linear Regression is of two types. One is positive Linear Regression, and the other is negative Linear Regression.

Positive Linear Regression– If the value of the dependent variable increases with the increase of the independent variable, then the slope of the graph is positive; such Regression is said to be Positive

                                                                        Source: Author

y=mx+c, where m is the slope of the line. In Positive Linear Regression, the value of m is positive.

Negative Linear Regression- If the value of the dependent variable decreases with the increase in the value of the independent variable, then such Regression is said to be negative linear Regression.

                                                                          Source: Author

Understanding Linear Regression

First of all, we need to have some data set to design the model.

Let us say the data is as below

x y

1 3

2 4

3 2

4 4

5 5

The values given are actual values.

Based on the above matters, the graph that most closely fits is as below

y=mx+c, where m is the slope of the line and c is Y-intercept.

From now on x(mean) is referred as x(m) and y(mean) as y(m).

m as per least square method=∑(x-x(m))(y-y(m))/∑(x-x(m))2

As per above data table, x(m)=3, y(m)=3.6.

x y x-x(m) y-y(m) (x-x(m))2 (y-y(m))2

1 3 -2 -0.6 4 1.2

2 4 -1 0.4 1 -0.4

3 2 0 -1.6 0 0

4 4 1 0.4 1 0.4

5 5 2 1.4 4 2.8

As per the equation of m, its value is m=4/10=0.4,c=2.4, so that the line equation would be y=0.4x+2.4.

x-x(m) is the distance of all the points x through the line y=3.

y-y(m) is the distance of all the points y through the line x=3.6.

Now we will calculate the predicted values of y based on the equation y=mx+c, where m=0.4 and c=2.4.

For x=1,y=0.4*1+2.4=2.8

For x=2,y=0.4*2+2.4=3.2

For x=3,y=0.4*3+2.4=3.6

For x=4,y=0.4*4+2.4=4.0

For x=5,y=0.4*5+2.4=4.4

Now we have actual values and predicted values of y; we need to calculate the distance between them and then reduce them, which means we need to reduce the error, and finally, the line with the minor error would be the line of Regression best fit line.

Finding the best fit line:

For different values of m, we need to calculate the line equation, where y=mx+c as the value of m changes, the equation changes. After every iteration, the predicted value changes according to the line’s equation. It needs to compare with the actual value and the importance of m for which the minimum difference gives the best fit line.

Let’s check the goodness of fit:

To test how good our model is performing, we have a method called the R Square method

R square method

This method is based on a value called the R-Squared value. It measures how close the data is to the regression line—and also known as the coefficient of determination.

                                                              Source: Author

To check our model’s good, we need to compare the distance between the actual value and mean versus the distance between the predicted value and mean; here comes the R formula.


If the value of R2 is nearer to 1, then the model is more effective

If the value of R2 is far away from 1, then the model is least effective

x y y-y(m) (y-y(m))2 yp (yp-y(m))2

1 3 -0.6 0.36 2.8 -0.8

2 4 0.4 0.16 3.2 -0.4

3 2 -1.6 2.56 3.6 0

4 4 0.4 0.16 4.0 0.4

5 5 1.4 1.96 4.4 0.8


This means that the data points are far away from the regression line.

If the value of R is 1, then the actual data points would be on the regression line.


We have covered all the topics related to Linear Regression. And we also found the effectiveness of the model using the R square method. For example, R-value might come close to 1 if the data is regarding a company’s sales. R-value might be too low if the information is from a doctor in psychology since different persons have different characters. So the conclusion is if the R-value is closer to one, the more accurate is the predicted value.

Thanks for reading this article. Learn more here.

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Search In Vim: A Comprehensive Guide

As a text editor, Vim has many features that make it a powerful tool for editing and manipulating text. One of the most important features of Vim is its search functionality. Vim’s search feature allows you to find text within a file and navigate to it quickly. In this article, we will explore the search functionality of Vim, including how to search for text, how to navigate search results, and some related concepts that will help you become more proficient with Vim.

What is Vim Search?

Vim search is a feature that allows you to search for text within a file. You can search for text forwards or backwards, and Vim will highlight all occurrences of the text within the file. Vim search is case-sensitive by default, but you can also perform case-insensitive searches. You can also search for regular expressions, which allows you to perform complex searches.

How to Search in Vim

In Vim, you can start a search by entering the command mode by pressing the : key. Once in command mode, you can enter the search command followed by the text you want to search for. For example, to search for the word “hello”, you would enter the following command:


This will search for the word “hello” from the cursor position and move the cursor to the first occurrence of that word. If there are multiple occurrences of the word, Vim will highlight each occurrence and you can navigate between them using the n and N keys.

If you want to search for the word “hello” backwards, you can use the ? command instead of the / command. For example, to search for the word “hello” backwards, you would enter the following command:


This will search for the word “hello” backwards from the cursor position and move the cursor to the first occurrence of that word.

By default, Vim search is case-sensitive. If you want to perform a case-insensitive search, you can use the c or C modifier. The c modifier makes the search case-insensitive, while the C modifier makes the search case-sensitive. For example, to search for the word “hello” in a case-insensitive manner, you would enter the following command:


This will search for the word “hello” in a case-insensitive manner.

Navigating Search Results

Once you have performed a search in Vim, you can navigate the search results using the n and N keys. The n key moves the cursor to the next occurrence of the search term, while the N key moves the cursor to the previous occurrence of the search term.

You can also use the * and # keys to search for the word under the cursor forwards and backwards, respectively. For example, if the cursor is on the word “hello”, you can search for the next occurrence of the word “hello” by pressing the * key.

Advanced Search Techniques

Vim search also supports regular expressions, which allows you to perform complex searches. Regular expressions are patterns that describe sets of strings. For example, the regular expression ^hello matches any string that starts with the word “hello”.

To perform a regular expression search in Vim, you can use the v modifier. For example, to search for the word “hello” followed by one or more digits, you would enter the following command:


This will search for the word “hello” followed by one or more digits.


Technical Seo For Podcasts: A Comprehensive Guide

Do you feel like the conventional strategies you’ve tried to improve your website’s search engine rankings have fallen short?

Do you feel it’s getting harder to find backlinks through guest blogging? Want to have more one-on-one conversations with possible clients?

You can find what you’re looking for in podcasts. You can reach people who are seriously interested in what you say by starting a podcast or appearing as a guest on others’. And you can get the attention of the search engines you need to make a difference with backlinks from associated online profiles.

As podcasters ourselves, we can attest to the medium’s potency. A marketer’s dream in a world where marketing messages are becoming increasingly watered down: the audience believes what we say.

Can SEO Benefit From Using Podcasts?

Podcast search engine optimization (SEO) is a branch of audio SEO that has seen rapid growth in recent years. It can be used to significantly effect to improve search engine rankings via the use of audio SEO. Podcasts, webinars, and other audio programs have become one of the most successful types of audio SEO.

The number of people who listen to podcasts has increased by 29.5% over the past three years. Due to the wide range of podcast listeners, there is always a market for specialized podcasts.

Most podcast listeners are college-educated millennials, making them a prime target audience for brands and producers.

In response to this trend, Google introduced its own platform for podcast SEO, favouring podcasts that adhere to Google’s SEO guidelines. Therefore, podcasts can unquestionably aid in SEO efforts, provided best SEO practices are adhered to.

Tips For Improving Your Podcasts Using Technical SEO

You should implement some basic technical SEO techniques to increase your podcast’s visibility and searchability on search engines like Google.

Here are some suggestions for improving the podcast’s technical SEO −

The title of your podcast should include searchable terms so that it can be found in the results. Doing so will improve your podcast’s visibility in search engines.

Improve the podcast’s description by including pertinent keywords and phrases in a detailed description of your podcast. This will increase your podcast’s visibility in search results by giving search engines a better grasp of the content.

Metadata, such as tags and categories, can aid search engines in correctly categorizing and indexing your podcast content. This will also improve your podcast’s discoverability when users look for content on similar subjects.

Rely on transcripts − Make your podcast episodes accessible to readers online by transcribing them. Your podcast will rank higher in search results if search engines can better decipher its content.

Use schema markup to give search engines more information about your podcast. The title, synopsis, and episode air date can all be included.

Ensure the mobile-friendliness of your website Ensure the mobile-friendliness and speed of your podcast’s website are top-notch.

Benefits Of Technical SEO For Podcasts

In this part, we’ll go over why technical SEO is so important for podcasts.

Better Placement In Search Results

You can boost your podcast’s visibility in search results (SERPs) by optimizing your website and content for search engines. Most people won’t bother looking past the first page of search results, so this is crucial. Ranking highly in search engine results pages (SERPs) is a great way to get your podcast in front of new listeners.

Enhanced Natural Search Volume

Metadata, sitemaps, and schema markup are just a few of the technical aspects of SEO for podcasts that can be improved. These components boost your podcast’s search engine results by providing search engines with a better knowledge of its content and structure. As a consequence, as more people discover your podcast through search engines, you may experience an increase in organic traffic to your website.

User Satisfaction Increased Enhanced Recognition Of The Brand

Your podcast’s online reputation will improve if people can easily find it through search engines like Google and Bing. Potential sponsorship deals, joint ventures, and promotional alliances with complementary businesses could expand.

Gains In The Long Run Submitting Your Podcast To Google Podcasts Will Increase Its Exposure

Submitting your podcast to Google Podcasts can help your search engine optimization (SEO). The fastest way to increase your online visibility is to optimize your Google podcasts for search engines so that you can profit from their audience. You can incorporate them into your RSS feed to have podcast excerpts appear in search results.

Podcast SEO is here to stay for the long haul, especially now that Google is paying more attention to them. When making your podcast, ensure the content and sound quality come first. This is the key to establishing rapport with your target demographic. Instead of written content, podcasts allow you to humanize your brand and foster connections with your audience.


Technical SEO is necessary if you want more people to listen to and find your podcast. You can improve your podcast’s visibility online, raise brand awareness, and delight your audience by making your site and episodes more search-engine friendly. Your podcast’s visibility and search engine rankings can be boosted by submitting it to Google Podcasts.

Technical search engine optimization for podcasts is a crucial facet of podcasting that should not be disregarded. You can grow your podcast into a successful and sustainable brand by devoting time and energy to optimizing it for search engines like Google and Bing. Henceforth, you should adopt technical SEO for Podcasts.

Understanding The Internal Components Of A Router: A Comprehensive Guide


Welcome to the world of routers! If you’ve ever wondered what’s going on inside these essential networking devices, you’re in for a treat. In this comprehensive guide, we’ll dive deep into understanding the internal components of a router and unravel their mysteries.

From routing algorithms to network configuration

it’s all here in one handy place. So let’s gear up and get ready for an exciting journey as we explore the heart and soul of routers. Read on to uncover how each component plays its part in keeping our digital lives connected smoothly and efficiently!

Router and its functioning

A router is a kind of device that connects many networks and facilitates communication by directing data packets to their intended destinations using routing algorithms and internal routing tables.

Routing algorithms and Internal Routing Tables

Routing algorithms are crucial for the efficient functioning of routers, as they determine the optimal path for data packet delivery from source to destination. They take into account factors like network congestion, node distance, and link costs to enhance data transmission performance. Routers also use internal routing tables, which act as digital traffic maps, to store vital information about network paths. These tables, along with routing algorithms, provide essential details on network topology and available paths to optimize data transfer. For instance, Cisco routers have extensive internal routing tables that help minimize latency during high-traffic periods. This collaboration between routing algorithms and routing tables ensures faster internet connectivity and reduced downtime, leading to an overall improved user experience.

Key Components of a Router

The main components of a router include the central processing unit (CPU), random access memory (RAM), read-only memory (ROM), network interface cards (NIC), and power supply.

Central Processing Unit (CPU)

The CPU is the router’s brain, responsible for managing functions and processing data. A powerful CPU ensures faster network operation, while overloading it can result in slowdowns or crashes.

Random Access Memory (RAM)

RAM is crucial for router efficiency as it temporarily stores data for quick access and faster processing. Ensure that your router has sufficient RAM capacity tailored towards meeting specific network demands for optimal performance.

Read-Only Memory (ROM)

ROM is vital for the proper functioning of a router as it holds critical startup information for the device’s operating system. It ensures that routers have fully-operational software immediately after turning them on.

Network Interface Cards (NIC)

Understanding the role of NICs is crucial when setting up and managing your router or network infrastructure. Configuring the correct settings for your NIC can optimize your network’s performance while avoiding potential security vulnerabilities.

Power Supply

The power supply is an essential component of a router that provides energy for the entire system to function. Having a reliable power supply ensures uninterrupted network connectivity, and checking functionality/power capacity may be necessary before purchasing a suitable replacement.

Understanding the function of Router components

The CPU is responsible for managing router functions and processing data, the RAM temporarily stores data for faster processing, the ROM stores firmware and startup configuration, the NIC connects the router to the network and transfers data, while the power supply provides energy for efficient operation.

CPU manages Router functions and processes data

The Central Processing Unit (CPU) is the router’s brain, managing functions and processing data. Choosing a robust and fast CPU is essential for high-performance networks.

RAM temporarily stores data for faster processing

Random Access Memory (RAM) is a key component of a router, acting as temporary storage for data processing. Sufficient, high-quality RAM is crucial for efficient router function in a fast-paced digital world.

ROM stores firmware and startup configuration

Read-Only Memory (ROM) stores the router’s firmware and startup configuration, which are essential for smooth operation. Backing up firmware and startup configurations regularly helps prevent data loss due to system failure or crashes.

NIC connects the Router to the network and transfers data

The Network Interface Card (NIC), which links a router to the network and transmits data, is an additional important router component. In essence, it serves as a link between your computer or other device and your router. It enables communication across many devices connected to the same network, enabling them to share resources like data, printers, and printer ink. The purpose of the NIC, whether it is built-in or external, is to transport data from one device to another.

If you want a fast connection, it’s critical to invest in a high-quality card because the NIC’s quality can impact how quickly data is chúng tôi example of where NICs are crucial is online gaming. A high-quality NIC can be the difference between victory and defeat when playing games that require quick reflexes and low latency connections. So whether you’re an online gamer or just looking for faster internet speeds overall, understanding how the NIC works is essential for optimizing your network performance.

Power supply provides energy for the Router

The power supply for the Router is a crucial component of any router, providing the energy needed to keep everything running smoothly. Without it, the router wouldn’t be able to function properly and the network would go down. The power supply converts AC voltage from an outlet into DC voltage that the router can use for its various components.

It’s crucial to remember that not every router uses the same kind of power source. While some require an additional power source, such as a battery backup or generator, others have built-in adapters that may be plugged straight into an outlet. However, some routers are made especially for powerful uses like streaming video or online gaming and may need more powerful or specialized power supply. In the end, knowing how your router’s power supply functions and what sort of configuration it needs will help you make sure that all of your networked devices have constant connectivity.


In conclusion, knowing a router’s internal parts is essential for anybody trying to improve network performance and resolve potential problems. It might be easier to update, customise, and implement stronger security measures if you are aware of how each component works and what part it plays in data routing.

An overview of important components such the CPU, RAM, ROM, NIC, and power supply has been given in this thorough tutorial. You’ll be better able to manage your network infrastructure by learning this information. Explore the world of router internals now to take control of your networking configuration!

Mastering 3D Lighting In Blender

Making images in Blender 3D has a lot in common with photography. In fact, if you have any photographic skills, these will transfer nicely into 3D software like Blender.

In previous articles we have discussed how you light a scene on a basic level. But how can you use all the different kinds of lamp for something approaching real cinematography?

Types of Lamp

Cinematography is all about choosing the right lights. In the virtual world of a 3D program the lights are all computed rather than real, but they perform the same function as real world lights. To get good lighting in 3D graphics images, you need to have a grasp of lighting in the real world, so a good tip is to learn how to light photographs from photography tutorials out there on the Internet.

The basic types of lamps in Blender are as follows: Point, Sun, Spot, Hemi and Area.


These lights are a tiny ball of light which are omnidirectional – that is to say scattering light in all directions like a lightbulb. Shadows fan out from the source centre in radiating lines.


Sun lights emulate the light you get from the sun; the light comes down from the source in parallel lines. Shadows cast straight down from the source and are soft.


Spotlights have a point source, but they fan out at a particular angle set in the properties, and they have a soft transition from the middle to the outer radius, the same as a real spotlight. Shadows are hard-edged and follow the angle of the beam.


These lights are like spotlights, but the difference is the source is a half sphere and the light focusses in straight lines, like a lighting brolly. Shadows are hard-edged.


Area lights are flat planes which cast light like a softbox or light reflected from a large reflective surface. Shadows are sharp when the objects are close to a surface but softer when they are distant.

Emission Surfaces

Another kind of light you can have in Blender is to turn an object into a light by selecting a surface texture of Emission. The texture emits light, meaning you can make a ball, cube or plane be a light emitter. The light is soft and the shadows smooth.

You can turn objects into lights, the benefit being that you can see the lights. The standard lights in Blender are invisible to the camera, but lights which are objects can be seen. The only light sources in this scene are the objects themselves.

Basic Setup

The basic lighting setup taught by all photography courses is to have a key, fill and rim or edge light.

The key light is either a strong, sun-like light or spotlight shining on the front of the object being lit. This casts light on the front and top of the object and shadows on the surface over any undercuts. In this example we used a sun light above and to the right of the camera. Strength is set to 700.

The fill light is positioned opposite to the key light to fill in any shadows. In this example, an Area light is positioned below the camera and to the left pointing up at the object. Strength is set to 75.

The rim or edge light is positioned behind the object pointing towards the object and the camera to highlight the edge of the object to separate it from its background. In this example, a Hemi light is positioned above, to the left and behind the skull pointing forwards towards the camera. The Strength is set to 2.

And that is how you light something perfectly.

Lighting Tips

The main tip for setting up lights and even textures in Blender is to use a rendered viewport. This makes a draft-quality rendering of the light that you can see updated in real time to allow you to position lights and shadows perfectly while seeing the effects of your light positions live on the screen.


Learn as much as you can about real world lighting for photography and transfer that knowledge to the 3D virtual world of Blender for fantastic lighting.

Image Credit: Cole Harris

Phil South

Phil South has been writing about tech subjects for over 30 years. Starting out with Your Sinclair magazine in the 80s, and then MacUser and Computer Shopper. He’s designed user interfaces for groundbreaking music software, been the technical editor on film making and visual effects books for Elsevier, and helped create the MTE YouTube Channel. He lives and works in South Wales, UK.

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