Trending December 2023 # Fortune Releases ‘All About Steve’ # Suggested January 2024 # Top 15 Popular

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Fortune just released a new Kindle eBook entitled All about Steve: The Story of Steve Jobs and Apple from the Pages of Fortune…

Steve Jobs’ legacy is clear: The most innovative business leader of our time, the man FORTUNE named CEO of the Decade in 2009. Now from the pages of FORTUNE comes an anthology of 17 classic stories spanning the years 1983 to 2011 about the cultural icon who revolutionized computing, telephones, movies, music, retailing, and product design. The stories lay out in unparalleled detail the career of a man with relentless drive and a single underlying passion—to carry out his vision of how all of us would use technology. Writes managing editor Andy Serwer in the book’s foreward: “In the end he was proved right a billion times over, and his company Apple became one of the most successful enterprises on the planet.” All these stories are the product of deep reporting. In many cases FORTUNE’s writers spent hours interviewing Jobs and delving into his mind. The result is a singular journalistic collection, which will leave you with a comprehensive picture of Steve Jobs and Apple, a picture that is complex in the making yet simple in its triumph.

The report includes Adam Lashinsky’s recent investigative piece, Inside Apple, which gives a behind-the scenes look at how the company really works. Lashinsky is also writing a standalone book on Apple due later.

Full Press Release and blown up ‘book cover’ follows:

Exclusively in the Kindle Store

All About Steve includes stories from veteran journalists Brent Schlender, Peter Elkind and Lashinsky, who have spoken to Apple employees and insiders at length. The eBook also contains an exclusive interview with Jobs from 2008, as well as Bill Gates and Jobs’ sit-down with FORTUNE from 1991 on the future of computing. Business leaders such as Andy Grove, Andrea Jung, Larry Ellison, Marc Andreessen, and Bill Campbell weigh in on how Jobs changed both the tech industry and society.

In addition, All About Steve includes many more stories chronicling the history of Apple from its emergence as a market leader in PCs to the creation of the iPod, iTunes, the iPhone, and the Apple Store. The epilogue includes pieces from FORTUNE’s Miguel Helft and Stanley Bing.

In his foreword to All About Steve, FORTUNE Managing Editor Andy Serwer writes, “The stories contained in this new book from the editors of Fortune lay out in remarkable and unparalleled detail that this was no straight shot to success. Jobs, a man both loved and loathed, encountered all manner of intrigue, brick walls, and pitfalls before his ultimate vindication…. All these stories are the product of deep reporting. In many cases our writers spent hours interviewing Steve and delving into his mind…. The result is a singular journalistic collection, which will leave you with a comprehensive picture of Steve Jobs and Apple, a picture that is complex in the making yet simple in its triumph.”

All About Steve Is available for download worldwide in English on chúng tôi It is published by the FORTUNE Books, an imprint of Time Home Entertainment, Inc.

Kindle books are “Buy Once, Read Everywhere”– customers can read them on their Kindle, on the web with Kindle Cloud Reader and on free Kindle reading apps for Android, iPad, iPhone, iPod touch, Blackberry, Windows Phone, PC and Mac. With Kindle Worry-Free Archive, books purchased from the Kindle Store are automatically backed up online in customers’ Kindle libraries on Amazon, where they can be re-downloaded wirelessly for free, anytime.



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Elden Ring Colosseum: Learn All About The Pvp Experience

After much waiting, Elden Ring finally received its first DLC content called Colosseum. The new update not only opened the gates for the large derelict structures inaccessible on the map but also brought dedicated PvP matchmaking options. However, to access the structures and the game mode, there are some necessary steps that you need to fulfill. This article includes all the essentials you should know before opening the grand doors to the Colosseum in Elden Ring.

What is the Colosseum in Elden Ring?

During the launch of Elden Ring, the Colosseums were these large, majestic amphitheaters in three locations on the maps – The Limgrave, Leyndell, and Caelid. It was implied in the game that the inhabitants used the Colosseums to perform combat rituals in the world of Elden Ring, much like the real-world gladiatorial fights from ancient Rome.

This was done to honor the Erdtree. However, by the time Radagon became the Elden Lord, or the player’s journey started, this practice was discarded with the remains of the giant amphitheaters lying dormant and inaccessible to everyone. This information is found in the item description for Ritual Sword Talisman, found after defeating Demi-Human Queen Gilika:

The Colosseum PvP (Image Courtesy: Bandai Namco)

Item Description of Ritual Sword Talisman

While the three Colosseums across the map stood gloriously, players were restricted from accessing them. Over the months, many data miners started poking around to check whether the developers left these structures to make them accessible. More information started coming from these, which further affirmed that these structures might open up sometime down the line, which they did. Accessing the Colosseum is pretty simple, as players need to walk up to them and open their gates by interacting with it.

Do I Require PvP Items to Join Colosseum Battles?

PvP mode in Elden Ring requires users to activate the Effigies of Martyr around the map, which opens up a particular map region for multiplayer. Then, players need to use specific PvP items, such as Furlfinger Calling Remedy, to use the multiplayer and either prepare themselves to let others come to their aid or invade them.

For the Colosseums, players do not need the multiplayer items. Instead, after entering the structure, players interact with an offering slab that opens up the matchmaking options. From here, they can further decide on the nature of PvP matches, as the three colosseums allow a different multiplayer experience.

PvP Modes in Elden Ring Colosseums

The PvP modes in the Colosseums are classified into three different kinds of experience.

1. Duel Mode

Furthermore, players cannot use Flask of Crimson Tear to heal themselves or the best Ashes of War to assist them. Flask of Cerulean Tear usage is allowed, though in a limited quantity.

A Combat Ordeal Match Between Two Players

2. United Combat

In united combat, players get divided into teams of either two, four, or six players and duke it out. The battles are limited to 300 seconds, and the team which gets the most eliminations wins. Furthermore, united combat allows respawns and limited usage of Flask of Crimson and Cerulean Tears per respawn.

3. Combat Ordeal

The third and final game mode is an all-out battle between six players fending for themselves. The combat ordeal fights continue for 300 seconds and the player with the most eliminations wins. Like united combat, combat ordeal will respawn players upon death and allows limited usage of Flask of Crimson and Cerulean Tears per respawn.

Depending on the kind of matchmaking player wants to experience, they need to venture out to that particular Colosseum by selecting the nearest site of grace, as none of the three areas provide one inside the Colosseum entrance.

All Colosseum Locations in Elden Ring 1. West Limgrave Colosseum

The Limgrave Colosseum location

Location: The first colosseum of the game is located northwest of the Warmaster’s Shack in Limgrave.

PvP Modes: Combat Ordeals and United Combat

Number of Combatants: Two, Four, and Six players

The first one is situated on the West Limgrave, the starting region of the map. Found on the east of Stormville castle. To reach it, players need to take a road northwest of the Warmaster’s Shack, which is found by taking the road behind the Stormhill Shack site of grace.

Limgrave Colosseum

Warmaster’s Shack also has a site of grace for players to activate and prepare themselves. Chronologically, the West Limgrave Colosseum in Elden Ring is the first one you will discover and has certain restrictions in place as it does not allow the use of Ashes of War during combat. The players can try united combat, a team-based battle splitting into teams of two, four, or six players, and combat ordeal.

2. Caelid Colosseum

The Caelid Colosseum Location

Location: The second Colosseum of the game is located west of the Deep Siorfa Well lift, sitting at the edge of the area.

PvP Modes: Combat Ordeal, United Combat, and Duel

Number of Combatants: Two, Four, and Six Players

The second one is located in the Caelid area of Elden Ring, near the Dragonblight region. Reaching this place requires players to venture through the Siorfa River underground area, where a lift takes them above ground.

The Caelid Colosseum

Players can then activate the Deep Siorfa Well site of grace and start following the road westward until they reach an open area where a great jar sits comfortably outside, blocking the view of the entrance. The Caelid Colosseum is a high-level area, with the road guarded by two archer golems. Hence, appropriately prepare before going to this area.

3. Leyndell Royal Colosseum

The Leyndell Royal Colosseum Location

Location: The third Colosseum is south of the West Capital Rampart site of Grace at Leyndell, situated in the Altus Plateau

PvP Modes: Duel-mode only

Number of Combatants: Two players

The third and final Colosseum in Elden Ring is the Leyndell Royal Colosseum, located at the royal capital of Leyndell at the Altus Plateau.

Players must spawn on the West Ramparts Site of Grace to reach it. From there, follow the path south along the main route. On the way, ignore the four enemies if needed, as they won’t attack the players unless aggravated.

The Leyndell Royal Colosseum

Players will then get a winding dirt road, where two duelists patrol and guard the path to the entrance. Subdue them and keep following the road, reaching the gate of the Colosseum. The Leyndell Royal Colosseum is a duel combat-only area, allowing only 2 players to fight against each other. Like the Limgrave, players cannot use Ashes of War here.

Elden Ring Colosseum: Do You Get Any Rewards on Winning?

While Soulsborne games like Dark Souls and even Bloodborne give players an item for winning multiplayer battles, the Colosseum fight in Elden Ring does not have any rewards for winning the matches. The matches are solely for players and their friends to enjoy the PvP mode in case they do not want other players invading their story progression through the world.

Nft Vs Screenshot: All About Having The Certificate Of Ownership

NFT vs screenshot: All about having the certificate of ownership




NFTs have taken the world by storm in the last few years, with the costliest one fetching around $69 million. This gives rise to another trending topic, NFT vs screenshot.

Many believe that when you can just copy, download or screenshot an NFT, what’s actually the point in buying it.

Also, find out if you can create NFT and sell it on the web.



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Note that NFTs can be anything digital, be it artwork, an image, a video, or even an article. People usually trade these using cryptocurrency, though that’s not always the case. There are various websites that sell NFTs, OpenSea being one of the most popular.

So let’s delve into the concept and understand the whole NFT vs screenshot debate, along with how sustainable the whole idea of NFT is.

What is the difference between a fungible and non-fungible token?

When you say something is fungible, it basically means that it can be traded for another similar item. For instance, a $10 bill can be traded for another $10 bill, or a bitcoin can be traded for another since both these items have the exact same value.

In blockchain technology, cryptocurrencies are the prime example of fungible items. For non-tech readers, stocks, are a good example.

On the other hand, non-fungible tokens are those that cannot be exchanged since each have their own intrinsic value. The NFTs fall under this category. Even a baseball card or anything that’s collectible doesn’t have an identical item to be exchanged with.

But, being non-fungible in no way indicates that these cannot be sold or resold. All it means is that these are unique and there’s nothing like them out there.

Also, there can be copies of NFTs. If there are just 10 copies, it could be a rare one and fetch a good price, while those with thousand or so copies certainly won’t be as valuable. These copies that we are referring to have nothing to do with the NFT vs screenshot thing.

What is the craze around NFTs?

By now, you already know that an NFT carries a certain value since it’s unique or one of a kind. But, anyone could copy the image or video. What actually makes it unique and valuable is the proof of ownership.

Anyone who buys an NFT gets a certificate of ownership that can be stored in a digital wallet. Along with that, the exchange is recorded on the blockchain. Though make sure that your wallet supports NFTs before going ahead with the purchase.

Many believe that NFTs are a great way of purchasing digital art. Many do it to support young talents, some as a hobby, while others take it as an investment.

For instance, an NFT that goes by the name Gucci Ghost was initially listed at $200 and after passing through several owners, it was last purchased at $3600 and is currently available for $16300.

Gucci Ghost NFT (.jpeg) – Source: NiftyGateway

The costliest NFT to be sold to date remains at $69.3 million, created by an artist who goes by the name Beeple. It’s a collection or collage of no less than 5000 digital images, one created each day from 2007 to 2023.

Expert tip:

You should now have a fair understanding of how the whole NFT thing works, and the craze for it amongst people. Let’s now walk you through the main topic, NFT vs screenshot, and if the latter is as valuable as the former?

Is a screenshot as valuable as the NFT?

But, here’s the catch! While you have the screenshot or a copy of the digital art, you don’t have the certificate of ownership. And, that is what matters.

You can’t really sell a screenshot since any buyer would demand the ownership proof as well. Also, how an NFT has been traded in the past can be checked on multiple websites. So anyone can easily distinguish a screenshot from the original NFT.

That should put an end to the NFT vs screenshot debate and help clear the air around the subject.

Can I create my own NFT?

Yes, you can! Anyone can create an NFT, list it on any of the specialized platforms, and sell it. But there’s a fee involved here, also referred to as gas.

Every interaction on a blockchain has to be verified and it’s done by some high-powered systems which cost you a small fee. Be it minting the NFT, purchasing, or selling it on the blockchain, each of these will require you to pay a certain fee, which is variable.

Besides, there’s no guarantee that your NFT would actually be purchased, and you might end up just spending without any returns. So, make sure you do thorough research before you get into creating, selling, or buying NFTs.

However, there are some platforms that allow you to create as well as list an NFT for free, and only charge you when it’s sold. Do check out those as well.

That’s all there is to NFT vs screenshot, and now you know why the latter has almost no value when compared with the former.

Also, if you are into cryptocurrency, check out the best security software for safer crypto-trading.

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Forget About Finding The Perfect Moment; Engage Your Audience All Year

Consistent connection matters, regardless of the season

Communicating with your customers isn’t about waiting for the right moment. It’s about reaching them during times of need, times of plenty, and everywhere in between.

Take educators, for example. Although most schools make spending decisions in March and April, our ”Digital Marketing Trends in the Education Market” report found that educators are actually most engaged during the month of July — when 97 percent of K-12 schools are out of session.

If marketers only spoke to educators during the spring purchase period, they’d miss a critical time when educators are ready to consume content. Not only do educators have less time to read and share content during the height of the spring semester, but they also often use the summer months to plan lessons for the coming fall. Without a year-round approach, marketers couldn’t truly call themselves educators’ partners.

Just like educators, other audiences have slower and busier seasons. They go in and out of purchasing cycles. Seasonal companies like ski resorts may shut down for months at a time. But great marketers don’t shut down. They know that consistent connection matters, no matter the season or point in the business cycle.

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Content for the slow season

Still, it can be tough to know how to connect with customers when there isn’t an industrywide event or purchasing period ahead. To reach your readers at all times of the year, try these four types of content:

1. Relevant content

Timely content is inherently more relevant than evergreen content. It ties into a season or time of year — like Christmas or back-to-school season — or something in the news.

Remember, even if there aren’t any breaking news stories relevant to your audience, you can always tie your message to a certain date on the calendar. Think about holidays, annual conferences, recent surveys, and other opportunities to tailor content to the moment.

In education, the school year offers dozens of opportunities for relevant campaigns, including the beginning of the year, before breaks, and college application time. But teachers still go online during the summer. Some teach summer classes. Others tutor.

If you’re marketing to them, you might offer them summer school lesson ideas or tips for professional development. Teachers enjoy the Fourth of July and Memorial Day like everyone else, so they might be interested in little-known facts or lessons involving those events.

In another WeAreTeachers campaign, we wanted to help first-year teachers hit the ground running in back-to-school season. So we threw a teacher shower for Dwayne Reed, a deserving educator who was thrilled to get some supplies in the summer to prepare for his arrival in the classroom. That event not only helped Dwayne, but it also helped us engage with other teachers at a time when they were preparing to go back to school.

2. Reactionary content

A type of user-generated content, reactionary content gives marketers a chance to start a two-way conversation with audience members. The brand asks a question, participates in a dialogue, and becomes more human to the target audience than a faceless seller of products and services.

Often, this content encourages the audience to take an action based on a question the marketer poses. That might be on social media, on the company blog, through email, or through another channel where the audience is most engaged.

People enjoy conversations year-round, so don’t limit reactionary content to big holidays or special events. Ask followers to share stories or post pictures, and then offer a prize (like an Amazon gift card or free product) to the person with the best submission.

Another fun way to get your audience engaged is to encourage people to post on social media during the summer to show the fun ways they kick back. Remember to choose and promote a hashtag to tie their voices to a broader campaign.

3. Reliable content

Reliable content is all about predictability. Daily or weekly social posts, blog posts, and email newsletters remind audiences that content will arrive at a specific, recurring time. Use this type of content for quick tips and helpful information in a brief, easily digestible format.

Reliable posts lend themselves well to evergreen content, or content that is relevant at any time of the year. It’s also great for quick, daily snippets to keep audiences engaged without demanding too much of their time.

For example, Sanford Health, a large nonprofit healthcare system, shares tips of the day on Facebook. Those tips link back to articles on the website, covering topics like how kids can get exercise in the winter and how to plan ahead to maintain a positive mood throughout the day. Its followers look forward to these tips, which keep them engaged with Sanford outside of when they might normally think of the brand.

4. Reoccurring content

Reoccurring content is a bit like reliable content, but while reliable content focuses on short bursts of engagement, reoccurring content occurs less often and offers deeper interactions.

Monthly webinars are a popular form of reoccurring content. By offering something new at the same time every month, you can build an audience that sees your brand as the go-to source for the topics you cover. Don’t limit yourself to a single channel, though — be active on multiple platforms, wherever your audience may be.

Reoccurring and reliable content keep audiences engaged with you even when the rest of the industry is in a lull. Follow up and nurture to maintain people’s interest. Create a series of webinars, blog posts, videos, or contests, and use your social media channels to remind people when fresh content is coming.

Teachers don’t have to be in the classroom to care about their students, and marketers don’t have to wait for certain seasons to connect with their audiences. So remember your four R’s: Relevant, reactionary, reliable, and reoccurring content keeps your brand atop your audience’s mind and under your control throughout the year.

What Is Web3? What All You Need To Know About Web3 Technology?

What is web3? What do you need to know about web3 technology? Its features and layers

Web 3 or Web 3.0 has the potential to be disruptive and usher in a significant paradigm shift like Web 2.0 did. Web 3 is formed due to the fundamental ideas of decentralization, increased consumer usefulness, and openness. Web 3 technology plays the next step in the development of the internet.

Web 3 accurately translates and understands what you type through text, voice, or other media. The technology also understands what you say. In this article, we have discussed what is web3 and what all you need to know about web3 including its features of web3. Read to know about web3 technology.

What Is Web 3.0 Technology?

Web 3.0 or Web 3 is a third-generation world wide web built on top of blockchain developments and technologies in the Semantic Web. Web 3 is meant to be decentralized, and open to everyone which describes the web as a network of meaningfully linked data.

Key Features of Web3

Web3 has several distinguished features.

Decentralization: In web 2.0 computers search for the data that is kept at a fixed location mostly in a single server using HTTP in form of a web address. Information could be stored in multiple locations at the same time and become decentralized with Web 3.0 because it would be found based on its content rather than a single location. This would give people more power by destroying the massive databases that internet behemoths like Meta and Google currently maintain.

With the help of web 3, users will be able to sell their data through decentralized data networks, ensuring that they retain control of their ownership. This information will be generated by a wide range of powerful computing resources, including mobile phones, desktop computers, appliances, automobiles, and sensors.

Decentralization and open-source software-based Web 3.0 will also be trustless (i.e., participants will be able to interact directly without going through a trusted intermediary) and permissionless (each individual will be able to access without the permission of any governing body). This means that Web 3.0 applications, also known as dApps, will run on blockchains, decentralized peer-to-peer networks, or a hybrid of the two. DApps are decentralized apps.

Connectivity and ubiquity: With Web 3.0, content and information are more accessible across applications and an increasing number of commonplace internet-connected devices.

How Does Web 3 Work?

Your data is saved in web3 on your cryptocurrency notecase. On web3, you’ll interact with apps and communities via your wallet, and when you log out, your data will follow you. Because you own the data, you can theoretically decide whether to monetize it.

After we’ve established our guiding principles, we can look at how specific web3 development features are supposed to achieve these goals.

Data Ownership:

When you use a platform such as Facebook or YouTube, these companies collect, own, and recoup your data. Your information is saved in web3 on your cryptocurrency wallet. On web3, you’ll interact with apps and communities via your wallet, and when you log out, your data will follow you. Because you own the data, you can theoretically decide whether to monetize it.


Privacy, like data ownership, is a feature of your wallet. On web3, your wallet serves as your identification, making it difficult to link it to your actual identity. As a result, even if someone observes wallet activity, they will not be able to identify your wallet.

Some services of web 3 assist customers in connecting to cryptocurrency wallets used for illegal activity.

Although wallets improve the level of privacy for bitcoin transactions, privacy coins such as Zcash and Monero provide complete anonymity. Observers can track transactions on blockchains for privacy coins, but they cannot see the wallets involved.

Here’s All You Need To Know About Encoding Categorical Data (With Python Code)


Understand what is Categorical Data Encoding

Learn different encoding techniques and when to use them


The performance of a machine learning model not only depends on the model and the hyperparameters but also on how we process and feed different types of variables to the model. Since most machine learning models only accept numerical variables, preprocessing the categorical variables becomes a necessary step. We need to convert these categorical variables to numbers such that the model is able to understand and extract valuable information.

A typical data scientist spends 70 – 80% of his time cleaning and preparing the data. And converting categorical data is an unavoidable activity. It not only elevates the model quality but also helps in better feature engineering. Now the question is, how do we proceed? Which categorical data encoding method should we use?

In this article, I will be explaining various types of categorical data encoding methods with implementation in Python.

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What is categorical data?

Since we are going to be working on categorical variables in this article, here is a quick refresher on the same with a couple of examples. Categorical variables are usually represented as ‘strings’ or ‘categories’ and are finite in number. Here are a few examples:

The city where a person lives: Delhi, Mumbai, Ahmedabad, Bangalore, etc.

The department a person works in: Finance, Human resources, IT, Production.

The highest degree a person has: High school, Diploma, Bachelors, Masters, PhD.

The grades of a student:  A+, A, B+, B, B- etc.

In the above examples, the variables only have definite possible values. Further, we can see there are two kinds of categorical data-

Ordinal Data: The categories have an inherent order

Nominal Data: The categories do not have an inherent order

In Ordinal data, while encoding, one should retain the information regarding the order in which the category is provided. Like in the above example the highest degree a person possesses, gives vital information about his qualification. The degree is an important feature to decide whether a person is suitable for a post or not.

While encoding Nominal data, we have to consider the presence or absence of a feature. In such a case, no notion of order is present. For example, the city a person lives in. For the data, it is important to retain where a person lives. Here, We do not have any order or sequence. It is equal if a person lives in Delhi or Bangalore.

For encoding categorical data, we have a python package category_encoders. The following code helps you install easily.

pip install category_encoders Label Encoding or Ordinal Encoding

We use this categorical data encoding technique when the categorical feature is ordinal. In this case, retaining the order is important. Hence encoding should reflect the sequence.

In Label encoding, each label is converted into an integer value. We will create a variable that contains the categories representing the education qualification of a person.

Python Code:

Fit and transform train data

df_train_transformed = encoder.fit_transform(train_df) One Hot Encoding

We use this categorical data encoding technique when the features are nominal(do not have any order). In one hot encoding, for each level of a categorical feature, we create a new variable. Each category is mapped with a binary variable containing either 0 or 1. Here, 0 represents the absence, and 1 represents the presence of that category.

These newly created binary features are known as Dummy variables. The number of dummy variables depends on the levels present in the categorical variable. This might sound complicated. Let us take an example to understand this better. Suppose we have a dataset with a category animal, having different animals like Dog, Cat, Sheep, Cow, Lion. Now we have to one-hot encode this data.

After encoding, in the second table, we have dummy variables each representing a category in the feature Animal. Now for each category that is present, we have 1 in the column of that category and 0 for the others. Let’s see how to implement a one-hot encoding in python.

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':[ 'Delhi','Mumbai','Hydrabad','Chennai','Bangalore','Delhi','Hydrabad','Bangalore','Delhi' ]}) #Create object for one-hot encoding encoder=ce.OneHotEncoder(cols='City',handle_unknown='return_nan',return_df=True,use_cat_names=True) #Original Data data #Fit and transform Data data_encoded = encoder.fit_transform(data) data_encoded

Now let’s move to another very interesting and widely used encoding technique i.e Dummy encoding.

Dummy Encoding

Dummy coding scheme is similar to one-hot encoding. This categorical data encoding method transforms the categorical variable into a set of binary variables (also known as dummy variables). In the case of one-hot encoding, for N categories in a variable, it uses N binary variables. The dummy encoding is a small improvement over one-hot-encoding. Dummy encoding uses N-1 features to represent N labels/categories.

To understand this better let’s see the image below. Here we are coding the same data using both one-hot encoding and dummy encoding techniques. While one-hot uses 3 variables to represent the data whereas dummy encoding uses 2 variables to code 3 categories.

Let us implement it in python.

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi,'Hyderabad']}) #Original Data data #encode the data data_encoded=pd.get_dummies(data=data,drop_first=True) data_encoded

Here using drop_first  argument, we are representing the first label Bangalore using 0.

Drawbacks of  One-Hot and Dummy Encoding

One hot encoder and dummy encoder are two powerful and effective encoding schemes. They are also very popular among the data scientists, But may not be as effective when-

A large number of levels are present in data. If there are multiple categories in a feature variable in such a case we need a similar number of dummy variables to encode the data. For example, a column with 30 different values will require 30 new variables for coding.

If we have multiple categorical features in the dataset similar situation will occur and again we will end to have several binary features each representing the categorical feature and their multiple categories e.g a dataset having 10 or more categorical columns.

In both the above cases, these two encoding schemes introduce sparsity in the dataset i.e several columns having 0s and a few of them having 1s. In other words, it creates multiple dummy features in the dataset without adding much information.

Also, they might lead to a Dummy variable trap. It is a phenomenon where features are highly correlated. That means using the other variables, we can easily predict the value of a variable.

Due to the massive increase in the dataset, coding slows down the learning of the model along with deteriorating the overall performance that ultimately makes the model computationally expensive. Further, while using tree-based models these encodings are not an optimum choice.

Effect Encoding:

This encoding technique is also known as Deviation Encoding or Sum Encoding. Effect encoding is almost similar to dummy encoding, with a little difference. In dummy coding, we use 0 and 1 to represent the data but in effect encoding, we use three values i.e. 1,0, and -1.

The row containing only 0s in dummy encoding is encoded as -1 in effect encoding.  In the dummy encoding example, the city Bangalore at index 4  was encoded as 0000. Whereas in effect encoding it is represented by -1-1-1-1.

Let us see how we implement it in python-

import category_encoders as ce import pandas as pd data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi,'Hyderabad']}) encoder=ce.sum_coding.SumEncoder(cols='City',verbose=False,) #Original Data data encoder.fit_transform(data) Hash Encoder

To understand Hash encoding it is necessary to know about hashing. Hashing is the transformation of arbitrary size input in the form of a fixed-size value. We use hashing algorithms to perform hashing operations i.e to generate the hash value of an input. Further, hashing is a one-way process, in other words, one can not generate original input from the hash representation.

Hashing has several applications like data retrieval, checking data corruption, and in data encryption also. We have multiple hash functions available for example Message Digest (MD, MD2, MD5), Secure Hash Function (SHA0, SHA1, SHA2), and many more.

Just like one-hot encoding, the Hash encoder represents categorical features using the new dimensions. Here, the user can fix the number of dimensions after transformation using n_component argument. Here is what I mean – A feature with 5 categories can be represented using N new features similarly, a feature with 100 categories can also be transformed using N new features. Doesn’t this sound amazing?

By default, the Hashing encoder uses the md5 hashing algorithm but a user can pass any algorithm of his choice. If you want to explore the md5 algorithm, I suggest this paper.

import category_encoders as ce import pandas as pd #Create the dataframe data=pd.DataFrame({'Month':['January','April','March','April','Februay','June','July','June','September']}) #Create object for hash encoder encoder=ce.HashingEncoder(cols='Month',n_components=6) #Fit and Transform Data encoder.fit_transform(data)

Since Hashing transforms the data in lesser dimensions, it may lead to loss of information. Another issue faced by hashing encoder is the collision. Since here, a large number of features are depicted into lesser dimensions, hence multiple values can be represented by the same hash value, this is known as a collision.

Moreover, hashing encoders have been very successful in some Kaggle competitions. It is great to try if the dataset has high cardinality features.

Binary Encoding

Binary encoding is a combination of Hash encoding and one-hot encoding. In this encoding scheme, the categorical feature is first converted into numerical using an ordinal encoder. Then the numbers are transformed in the binary number. After that binary value is split into different columns.

Binary encoding works really well when there are a high number of categories. For example the cities in a country where a company supplies its products.

#Import the libraries import category_encoders as ce import pandas as pd #Create the Dataframe data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi','Hyderabad','Mumbai','Agra']}) #Create object for binary encoding encoder= ce.BinaryEncoder(cols=['city'],return_df=True) #Original Data data #Fit and Transform Data data_encoded=encoder.fit_transform(data) data_encoded

Binary encoding is a memory-efficient encoding scheme as it uses fewer features than one-hot encoding. Further, It reduces the curse of dimensionality for data with high cardinality.

Base N Encoding

Before diving into BaseN encoding let’s first try to understand what is Base here?

In the numeral system, the Base or the radix is the number of digits or a combination of digits and letters used to represent the numbers. The most common base we use in our life is 10  or decimal system as here we use 10 unique digits i.e 0 to 9 to represent all the numbers. Another widely used system is binary i.e. the base is 2. It uses 0 and 1 i.e 2 digits to express all the numbers.

For Binary encoding, the Base is 2 which means it converts the numerical values of a category into its respective Binary form. If you want to change the Base of encoding scheme you may use Base N encoder. In the case when categories are more and binary encoding is not able to handle the dimensionality then we can use a larger base such as 4 or 8.

#Import the libraries import category_encoders as ce import pandas as pd #Create the dataframe data=pd.DataFrame({'City':['Delhi','Mumbai','Hyderabad','Chennai','Bangalore','Delhi','Hyderabad','Mumbai','Agra']}) #Create an object for Base N Encoding encoder= ce.BaseNEncoder(cols=['city'],return_df=True,base=5) #Original Data data

#Fit and Transform Data data_encoded=encoder.fit_transform(data) data_encoded

In the above example, I have used base 5 also known as the Quinary system. It is similar to the example of Binary encoding. While Binary encoding represents the same data by 4 new features the BaseN encoding uses only 3 new variables.

Hence BaseN encoding technique further reduces the number of features required to efficiently represent the data and improving memory usage. The default Base for Base N is 2 which is equivalent to Binary Encoding.

Target Encoding

Target encoding is a Baysian encoding technique.

Bayesian encoders use information from dependent/target variables to encode the categorical data.

In target encoding, we calculate the mean of the target variable for each category and replace the category variable with the mean value. In the case of the categorical target variables, the posterior probability of the target replaces each category..

#import the libraries import pandas as pd import category_encoders as ce #Create the Dataframe data=pd.DataFrame({'class':['A,','B','C','B','C','A','A','A'],'Marks':[50,30,70,80,45,97,80,68]}) #Create target encoding object encoder=ce.TargetEncoder(cols='class') #Original Data Data

#Fit and Transform Train Data encoder.fit_transform(data['class'],data['Marks'])

We perform Target encoding for train data only and code the test data using results obtained from the training dataset. Although, a very efficient coding system, it has the following issues responsible for deteriorating the model performance-

It can lead to target leakage or overfitting. To address overfitting we can use different techniques.

In the leave one out encoding, the current target value is reduced from the overall mean of the target to avoid leakage.

In another method, we may introduce some Gaussian noise in the target statistics. The value of this noise is hyperparameter to the model.

The second issue, we may face is the improper distribution of categories in train and test data. In such a case, the categories may assume extreme values. Therefore the target means for the category are mixed with the marginal mean of the target.

Frequently Asked Questions

To summarize, encoding categorical data is an unavoidable part of the feature engineering. It is more important to know what coding scheme should we use. Having into consideration the dataset we are working with and the model we are going to use. In this article, we have seen various encoding techniques along with their issues and suitable use cases.

If you want to know more about dealing with categorical variables, please refer to this article-


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