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Maybe you’ve heard of Lycanthropy. When the full moon approaches, legends speak of half-man half-beasts roaming across the hills of Skyrim. They can run at incredible speeds and can take on a dragon in one-on-one combat. Most of these stories are stories designed to scare children… right? What if you wanted to see one of these beasts up close and see their power for yourself. What if you want to be a werewolf?

Well, you’ve come to the right place. We have already covered about vampires in skyrim, now let us talk about warewolves. In this guide, I will be laying out everything there is to know about Lycanthropy and how to become a werewolf in Skyrim.

The Companions are an order of fighters founded by the legendary hero Ysgramor. They reside in a place called Jorrvaskr next to the Skyforge in Whiterun.

To become a werewolf, you need to join what is called The Circle. To do this, first, talk to any Companion about joining their ranks of furries. They will guide you to the Harbinger, Kodlak Whitemane. 

There will be a quest involving a sword, and once you complete it, you’ll be welcomed into the ranks of the Companions.

Complete a few radiant quests for the companions until you get the quest Proving Honor, which will be the first time you will get to see a werewolf in action. 

Once Proving Honor is complete, do a few more quests until you are given the quest The Silver Hand. The quest will direct you to talk to Skjor up at the forge about your initiation at The Underforge.

Complete the initiation ritual, and you will transform into a werewolf for the very first time.

Complete the quest to gain access to the Beast Form power permanently.

Congratulations! You are now a bonafide Lycanthrope.  

Now that you have the Beast Form power, you can use it at any time once a day to turn into a werewolf. The process takes a few seconds in which you are vulnerable to attacks. 

The werewolf transformation itself will only last 2.5 minutes of real-world time. To extend this transformation time, you will need to ‘feed’ on the human or human-like corpses. To ‘feed,’ you need to find a corpse and activate it by pressing your loot button. Each feeding will restore your health and extend the duration of your Beast Form by 30 seconds.

You can only use Beast Form once a day. However, you can use the Ring of Hircine to grant you another power that lets you transform into a werewolf as much as you want. To do this, you will need to complete the quest Ill Met by Moonlight as follows:

In Falkreath, you will hear rumors of a little girl who was murdered. You can also go to the graveyard where her funeral is taking place and talk to one of the priests to get details about who the culprit is. The quest will then guide you to Falkreath Jail to talk to the murderer. This should start the quest ill Met by Moonlight.

In Falkreath Jail, you will find the person who committed the murder, Sinding. Sinding will give you an item called the Cursed Ring of Hircine, which will randomly turn you into a werewolf. After this has happened, Sinding will escape, and you will need to find a way to purify the Ring.

The quest will now direct you to kill a White Stag. Once you have killed it, Hircine himself will talk to you and direct you to Bloated Man’s Grotto. Hircine will also ask you to kill Sinding for him.

Travel to the Grotto and find Sinding fighting the group of hunters there. He will come and talk to you after the fight. It is important here that you side with Sinding. Spare him and join him instead in killing the hunters against Hircine’s wish.

Finish the remaining part of the quest and kill the hunters with Sinding. After completing the dungeon, Sinding will talk to you and thank you for the help. Exit the dungeon, and Hircine will congratulate you on your choice and give you the purified Ring of Hircine.

Note that he will do so even though you disobeyed his orders. If he gives you the Savior’s Hide instead, reload a previous save and make sure you side with Sinding. 

Equip the Ring of Hircine, and you will be granted a power with the same name, which is different from Beast Form. Use this power to gain unlimited werewolf transformations!

If you want to go back to being in human form, wait a few in-game hours or until your timer runs off, and you will be transformed back.

As a werewolf, you are granted several powers.

Your base melee damage is set to 20, and it increases every level until 45, at 80 damage.

You gain 100 extra health and stamina.

You can sprint significantly faster and for more time.

Your stamina regenerates quicker.

Your power attacks knock back opponents.

Power attacking while sprinting will stagger almost every opponent, including Giants.

Press the power button to Howl and let out a savage roar that will send enemies fleeing.

Any crime committed during this form will not incur a bounty.

This requires the Dawnguard DLC. When you consume the heart of a corpse as a werewolf, it is possible to gain points for this perk tree. You can use these points to unlock perks such as increasing the total damage inflicted, health and stamina when in werewolf form. 

Until the Savage Feeding perk is taken, only human or humanoid enemies can be fed upon. It is a good idea to take the Savage Feeding perk as soon as possible. This will increase your leveling rate significantly.

The perk tree can only be accessed when in beast form. A total of 164 ‘hearts’ must be eaten to unlock the full tree. 

The werewolf skill tree includes eight perks.

Bestial Strength: Do more damage in Beast Form.

Totem of Ice Brothers: Howl to summon Ice Wolves to your aid.

Totem of the Moon: Howl to summon more werewolves to your aid.

Totem of Terror: Howl affects higher-level enemies.

Animal Vigor:More health and stamina in Beast Form.

Gorging: Feeding restores more health.

Savage Feeding: Can now also feed on creature corpses.

All three Totem powers available will require you to complete the Totems of Hircine quest from Aela the Huntress. These radiant quests will give you special powers you can use in your Beast Form. The locations for these totems are randomized, so there’s no way of knowing beforehand where you will find them.

Totem of The Hunt: Detect Life ability.

Totem of Brotherhood: Summon two wolf spirits.

Totem of Fear: Howling will activate Fear against enemies.

Once found, these totems will be present in The Underforge. You can activate one of these powers by praying at the corresponding totem there.

This carries over to your human form as well. As a werewolf, you are 100% immune to disease.

This requires the Dragonborn DLC. Go to Solstheim and ask Feldis Sadri at the Retching Netch for rumors. He will talk about a werewolf sighting near Frostmoon Crag, which is north of Raven Rock. Talk to the hunters there to buy four special rings that will aid your werewolf transformation significantly.

Ring of Bloodlust: Increases both damage and damage taken in Beast Form by 50%.

Ring of Instinct: Slows time for 20 seconds when transforming into Beast Form.

Ring of the Hunt: Allows for the health regen in Beast Form.

Ring of the Moon: Increases the duration of Howl by 25%.

There are a lot many ways to play Skyrim with the help of various build options, strategies and equipment. And Lycanthropy is no different too.

Many of these problems can be fixed by transforming back into a human. But, it is still a tedious process to change forms on the fly, especially if you don’t have the Ring of Hircine yet.

As a werewolf, your dreams will be haunted by nightmares caused by Hircine’s curse. You will be unable to get the Well Rested bonus, as well as the Lover’s Comfort bonus.

Your armor and items will automatically be unequipped when you transform into a werewolf. So, you will have to manually re-equip everything once your Beast Form ends.

If you’ve decided that being a werewolf is more trouble than it is worth, here are the methods to cure yourself of Hircine’s curse.

This is the conventional method to cure Lycanthropy in Skyrim. Note that curing Lycanthropy this way will lock you out of the werewolf form forever. That means you will never be able to be a werewolf through normal methods again!

Complete the Companions questline by finishing the quest Glory of the Dead. This is the final Companion quest where you go to Ysgramor’s Tomb to cure Kodlak of Lycanthropy. Finish the quest prompts that appear and make a note of the Glenmoril Witch Cave location.

If you haven’t done so already, go to the cave and collect a Glenmoril Witch Head by killing a witch and looting her corpse. You will need one head to do this. Note that the witches don’t respawn. Make sure you grab every head possible.

Go to Ysgramor’s Tomb (make sure you use the back entrance) and toss the head in the Flame of the Harbinger. Your beast spirit will come out to fight. Kill it, and you will be a werewolf no more!

For this, you will need the Dawnguard DLC. You can ask Serana or Harkon to convert you into a vampire lord. This process will automatically cure you of Lycanthropy, as both powers cannot coexist in your player character. 

To become a werewolf again, if you’ve used this method, talk to Aela. She will perform the ritual and convert you back into a werewolf again, only once. You will, however, lose your vampire form.

PC PLAYERS ONLY! Just use the console, I guess. Only do this if you encounter bugs or something. It’s less fun this way.

Press the tilde “~” key, and a console should pop up. Type “player.removespell 92C48”. Press Enter. Now type “showracemenu“ to change your race. Viola! Instant cure for lazy people. 

If you feel the need to take a break from a Legendary difficulty lycanthropy run, you might want to check out similar games like Skyrim. Either way, there’s no denying, Bethesda Softworks have made a very enjoyable game that is still playable after such a long time since its release.

We’re sad to see you go! — The Companions (Probably)

Yes, Lycanthropy does not affect marriage. However, you cannot get the Lover’s Comfort perk by sleeping with your partner.

No, not conventionally. But everything is possible if you cheat…

No. I guess they forgot to code that in. But no. They don’t.

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Everything You Need To Know About Uranium

Since the German chemist Martin Heinrich Klaproth identified uranium in 1789, atomic number 92 has become one of the most troubling substances on the planet. It’s naturally radioactive, but its isotope uranium-235 also happens to be fissile, as Nazi nuclear chemists learned in 1938, when they did the impossible and split a uranium nucleus in two. American physicists at U.C. Berkeley were soon to discover they could force uranium-238 to decay into plutonium-239; the substance has since been used in weapons and power plants around the world. Today, the element continues to stoke international tensions as Iran stockpiles uranium in defiance of an earlier treaty, and North Korea’s “Rocket Man” leader Kim Jong-un continues to resist denuclearization.

But what is uranium, exactly? And what do you need to know about it beyond the red-hot headlines? Here we answer your most pressing nuclear questions:

Where does uranium come from?

Uranium is a common metal. “It can be found in minute quantities in most rocks, soils, and waters,” geologist Dana Ulmer-Scholle writes in an explainer from the New Mexico Bureau of Geology and Mineral Resources. But finding richer deposits—the ones with concentrated uranium actually worth mining—is more difficult.

When engineers find a promising seam, they mine the uranium ore. “It’s not people with pickaxes anymore,” says Jerry Peterson, a physicist at the University of Colorado, Boulder. These days, it comes from leaching, which Peterson describes as pouring “basically PepsiCola—slightly acidic” down into the ground and pumping the liquid up from adjacent holes. As the fluid percolates through the deposit, it separates out the uranium for harvesting.

Uranium ore. Deposit Photos

What are the different types of uranium?

Uranium has several important isotopes—different flavors of the same substance that vary only in their neutron count (also called atomic mass). The most common is uranium-238, which accounts for 99 percent of the element’s presence on Earth. The least common isotope is uranium-234, which forms as uranium-238 decays. Neither of these products are fissile, meaning their atoms don’t easily split, so they can’t sustain a nuclear chain reaction.

That’s what makes the isotope uranium-235 so special—it’s fissile, so with a bit of finessing, it can support a nuclear chain reaction, making it ideal for nuclear power plants and weapons manufacturing. But more on that later.

There’s also uranium-233. It’s another fissile product, but its origins are totally different. It’s a product of thorium, a metallic chemical much more abundant than uranium. If nuclear physicists expose thorium-232 to neutrons, the thorium is liable to absorb a neutron, causing the material to decay into uranium-233.

Just as you can turn thorium into uranium, you can turn uranium into plutonium. Even the process is similar: Expose abundant uranium-238 to neutrons, and it will absorb one, eventually causing it to decay to plutonium-239, another fissile substance that’s been used to create nuclear energy and weapons. Whereas uranium is abundant in nature, plutonium is really only seen in the lab, though it can occur naturally alongside uranium.

How do you go from a rock to a nuclear fuel source?

People don’t exactly lay out step-by-step guides to refining nuclear materials. But Peterson got pretty close. After you’ve extracted uranium from the earth, he says chemical engineers separate the uranium-rich liquid from other minerals in the sample. When the resulting uranium oxide dries, it’s the color of semolina flour, hence the nickname “yellowcake” for this intermediate product.

From there, a plant can purchase a pound of yellowcake for $20 or $30. They mix the powder with hydrofluoric acid. The resulting gas is spun in a centrifuge to separate from uranium-238 and uranium-235. This process is called “enrichment.” Instead of the natural concentration of 0.7 percent, nuclear power plants want a product that’s enriched to between 3 and 5 percent uranium-235. For a weapon, you need much more: These days, upwards of 90 percent is the goal.

Once that uranium is enriched, power plant operators pair it with a moderator, like water, that slows down the neutrons in the uranium. This increases the probability of a consistent chain reaction. When your reaction is finally underway, each individual neutron will transform into 2.4 neutrons, and so on, creating energy all the while.

Uranium glass dinnerware. Deposit Photos

Any fun facts I should take with me to my next dinner party?

Try this: In PopSci‘s “Danger” issue earlier this year, David Meier, a research scientist at Pacific Northwest National Lab, talked about his work to create a database of plutonium sources. Turns out, every plutonium product has a visible origin story, because “there’s not one way of processing it,” Meier says. The United States had two plutonium production sites. While the intermediate product from Hanford, Washington (the Manhattan Project site from which PNNL grew) was brown and yellow, the Savannah River site in Akon, South Carolina, produced “a nice blue material,” Meier says. Law enforcement officials hope these subtle differences—which may also correspond to changes in the chemical signature, particle size, or shape of the material—will one day help them track down illicit nuclear development.

Or, dazzle your guests with a short history of radioactive dinnerware. The manufacture of uranium glass, also called canary glass or Vaseline glass began in the 1830s. Before William Henry Perkin created the first synthetic color in 1856, dyes were terribly expensive and even then they didn’t last. Uranium became a popular way to give plates, vases, and glasses a deep yellow or minty green tinge. But put these household objects under a UV light and they all fluoresce a shocking neon chartreuse. Fortunately for the avid collectors who actively trade in uranium glass, most of these objects aren’t so radioactive as to pose a risk to human health.

Last one: In 2002, the medical journal The Lancet published an article on the concerning potential for depleted uranium—the waste leftover after uranium-235 extraction—to end up on the battlefield. The concern is that its high density would make it an incredible projectile, capable of piercing even the most well-enforced battle tank. Worse yet, it could then contaminate the surrounding landscape and anyone it.

Everything You Should Know About Data Structures In Python

Overview

Data structures in Python are a key concept to learn before we dive into the nuances of data science and model building

Learn about the different data structures Python offers, including lists, tuples and much more

Introduction

A Data Structure sounds like a very straightforward topic – and yet a lot of data science and analytics newcomers have no idea what it is. When I quiz these folks about the different data structures in Python and how they work, I’m met with a blank stare. Not good!

Python is an easy programming language to learn but we need to get our basics clear first before we dive into the attractive machine learning coding bits. That’s because behind every data exploration task we perform, even analytics step we take, there is a basic element of storage and organization of the data.

And this is a no-brainer – it’s so much easier for us to extract information when we store our data efficiently. We save ourselves a ton of time thanks to our code running faster – who wouldn’t want that?

And that’s why I implore you to learn about data structures in Python.

In this article, we will explore the basic in-built data structures in Python that will come in handy when you are dealing with data in the real world. So whether you’re a data scientist or an analyst, this article is equally relevant for you.

Make sure you go through our comprehensive FREE Python course if you’re new to this awesome programming language.

Table of Contents

What are Data Structures in Python?

Data Structure #1: Lists in Python

Creating Lists

Accessing List elements

Appending values in Lists

Removing elements from Lists

Sorting Lists

Concatenating Lists

List comprehensions

Stacks & Queues using Lists

Data Structure #2: Tuples in Python

Creating Tuples in Python

Immutability of Tuples

Tuple assignment

Changing Tuples values

Data Structure #3: Dictionary in Python

Generating Dictionary

Accessing keys and values

Data Structure #4: Sets in Python

Add and Remove elements from Sets

Sets Operations

What are Data Structures?

Data structures are a way of storing and organizing data efficiently. This will allow you to easily access and perform operations on the data.

There is no one-size-fits-all kind of model when it comes to data structures. You will want to store data in different ways to cater to the need of the hour. Maybe you want to store all types of data together, or you want something for faster searching of data, or maybe something that stores only distinct data items.

Luckily, Python has a host of in-built data structures that help us to easily organize our data. Therefore, it becomes imperative to get acquainted with these first so that when we are dealing with data, we know exactly which data structure will solve our purpose effectively.

Lists in Python are the most versatile data structure. They are used to store heterogeneous data items, from integers to strings or even another list! They are also mutable, which means that their elements can be changed even after the list is created.

Creating Lists

Lists are created by enclosing elements within [square] brackets and each item is separated by a comma:

Python Code:



Since each element in a list has its own distinct position, having duplicate values in a list is not a problem:

Accessing List elements

To access elements of a list, we use Indexing. Each element in a list has an index related to it depending on its position in the list. The first element of the list has the index 0, the next element has index 1, and so on. The last element of the list has an index of one less than the length of the list.

But indexes don’t always have to be positive, they can be negative too. What do you think negative indexes indicate?

While positive indexes return elements from the start of the list, negative indexes return values from the end of the list. This saves us from the trivial calculation which we would have to otherwise perform if we wanted to return the nth element from the end of the list. So instead of trying to return List_name[len(List_name)-1] element, we can simply write List_name[-1].

Using negative indexes, we can return the nth element from the end of the list easily. If we wanted to return the first element from the end, or the last index, the associated index is -1. Similarly, the index for the second last element will be -2, and so on. Remember, the 0th index will still refer to the very first element in the list.

But what if we wanted to return a range of elements between two positions in the lists? This is called Slicing. All we have to do is specify the start and end index within which we want to return all the elements – List_name[start : end].

One important thing to remember here is that the element at the end index is never included. Only elements from start index till index equaling end-1 will be returned.

Appending values in Lists

We can add new elements to an existing list using the append() or insert() methods:

append() – Adds an element to the end of the list

insert() – Adds an element to a specific position in the list which needs to be specified along with the value

Removing elements from Lists

Removing elements from a list is as easy as adding them and can be done using the remove() or pop() methods:

remove() – Removes the first occurrence from the list that matches the given value

pop() – This is used when we want to remove an element at a specified index from the list. However, if we don’t provide an index value, the last element will be removed from the list

Sorting Lists

Most of the time, you will be using a list to sort elements. So it is very important to know about the sort() method. It lets you sort list elements in-place in either ascending or descending order:

But where things get a bit tricky is when you want to sort a list containing string elements. How do you compare two strings? Well, string values are sorted using ASCII values of the characters in the string. Each character in the string has an integer value associated with it. We use these values to sort the strings.

On comparing two strings, we just compare the integer values of each character from the beginning. If we encounter the same characters in both the strings, we just compare the next character until we find two differing characters. It is, of course, done internally so you don’t have to worry about it!

Concatenating Lists

We can even concatenate two or more lists by simply using the + symbol. This will return a new list containing elements from both the lists:

List comprehensions

A very interesting application of Lists is List comprehension which provides a neat way of creating new lists. These new lists are created by applying an operation on each element of an existing list. It will be easy to see their impact if we first check out how it can be done using the good old for-loops:

Now, we will see how we can concisely perform this operation using list comprehensions:

See the difference? List comprehensions are a useful asset for any data scientist because you have to write concise and readable code on a daily basis!

A list is an in-built data structure in Python. But we can use it to create user-defined data structures. Two very popular user-defined data structures built using lists are Stacks and Queues.

Stacks are a list of elements in which the addition or deletion of elements is done from the end of the list. Think of it as a stack of books. Whenever you need to add or remove a book from the stack, you do it from the top. It uses the simple concept of Last-In-First-Out.

Queues, on the other hand, are a list of elements in which the addition of elements takes place at the end of the list, but the deletion of elements takes place from the front of the list. You can think of it as a queue in the real-world. The queue becomes shorter when people from the front exit the queue. The queue becomes longer when someone new adds to the queue from the end. It uses the concept of First-In-First-Out.

Now, as a data scientist or an analyst, you might not be employing this concept every day, but knowing it will surely help you when you have to build your own algorithm!

Tuples are another very popular in-built data structure in Python. These are quite similar to Lists except for one difference – they are immutable. This means that once a tuple is generated, no value can be added, deleted, or edited.

We will explore this further, but let’s first see how you can create a Tuple in Python!

Tuples can be generated by writing values within (parentheses) and each element is separated by a comma. But even if you write a bunch of values without any parenthesis and assign them to a variable, you will still end up with a tuple! Have a look for yourself:

Ok, now that we know how to create tuples, let’s talk about immutability.

Immutability of Tuples

Anything that cannot be modified after creation is immutable in Python. Python language can be broken down into mutable and immutable objects.

Lists, dictionaries, sets (we will be exploring these in the further sections) are mutable objects, meaning they can be modified after creation. On the other hand integers, floating values, boolean values, strings, and even tuples are immutable objects. But what makes them immutable?

Everything in Python is an object. So we can use the in-built id() method which gives us the ability to check the memory location of an object. This is known as the identity of the object. Let’s create a list and determine the location of the list and its elements:

As you can see, both the list and its element have different locations in memory. Since we know lists are mutable, we can alter the value of its elements. Let’s do that and see how it affects the location values:

The location of the list did not change but that of the element did. This means that a new object was created for the element and saved in the list. This is what is meant by mutable. A mutable object is able to change its state, or contents, after creation but an immutable object is not able to do that.

But we can call tuples pseudo-immutable because even though they are immutable, they can contain mutable objects whose values can be modified!

As you can see from the example above, we were able to change the values of an immutable object, list, contained within a tuple.

Tuple assignment

Tuple packing and unpacking are some useful operations that you can perform to assign values to a tuple of elements from another tuple in a single line.

We already saw tuple packing when we made our planet tuple. Tuple unpacking is just the opposite-assigning values to variables from a tuple:

It is very useful for swapping values in a single line. Honestly, this was one of the first things that got me excited about Python, being able to do so much with such little coding!

Changing Tuple values

Although I said that tuple values cannot be changed, you can actually make changes to it by converting it to a list using list(). And when you are done making the changes, you can again convert it back to a tuple using tuple().

This change, however, is expensive as it involves making a copy of the tuple. But tuples come in handy when you don’t want others to change the content of the data structure.

Data Structure #3: Dictionary in Python

Dictionary is another Python data structure to store heterogeneous objects that are immutable but unordered. This means that when you try to access the elements, they might not be in exactly the order as the one you inserted them in.

But what sets dictionaries apart from lists is the way elements are stored in it. Elements in a dictionary are accessed via their key values instead of their index, as we did in a list. So dictionaries contain key-value pairs instead of just single elements.

Generating Dictionary

Dictionaries are generated by writing keys and values within a { curly } bracket separated by a semi-colon. And each key-value pair is separated by a comma:

Using the key of the item, we can easily extract the associated value of the item:

These keys are unique. But even if you have a dictionary with multiple items with the same key, the item value will be the one associated with the last key:

Dictionaries are very useful to access items quickly because, unlike lists and tuples, a dictionary does not have to iterate over all the items finding a value. Dictionary uses the item key to quickly find the item value. This concept is called hashing.

Accessing keys and values

You can access the keys from a dictionary using the keys() method and the values using the values() method. These we can view using a for-loop or turn them into a list using list():

We can even access these values simultaneously using the items() method which returns the respective key and value pair for each element of the dictionary.

Sometimes you don’t want multiple occurrences of the same element in your list or tuple. It is here that you can use a set data structure. Set is an unordered, but mutable, collection of elements that contains only unique values.

You will see that the values are not in the same order as they were entered in the set. This is because sets are unordered.

Add and Remove elements from a Set

To add values to a set, use the add() method. It lets you add any value except mutable objects:

To remove values from a set, you have two options to choose from:

The first is the remove() method which gives an error if the element is not present in the Set

The second is the discard() method which removes elements but gives no error when the element is not present in the Set

If the value does not exist, remove() will give an error but discard() won’t.

Set operations

Using Python Sets, you can perform operations like union, intersection and difference between two sets, just like you would in mathematics.

Union of two sets gives values from both the sets. But the values are unique. So if both the sets contain the same value, only one copy will be returned:

Intersection of two sets returns only those values that are common to both the sets:

Difference of a set from another gives only those values that are not present in the first set:

End Notes

Isn’t Python a beautiful language? It provides you with so many options to handle your data more efficiently. And learning about data structures in Python is a key aspect of your own learning journey.

This article should serve as a good introduction to the in-built data structures in Python. And if it got you interested in Python, and you are itching to know more about it in detail and how to use it in your everyday data science or analytics work, I recommend going through the following articles and courses:

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What Is Magacoin? Everything To Know About It

Welcome a new cryptocurrency to the market, MAGACOIN.

How would you feel when I say there is a

What is MAGACOIN?

MAGACOIN was made with an end goal to apparently communicate strong support for the previous President, Donald Trump, in the wake of his loss in the November 2023 political race. The total number of coins made till now is an affirmation of the 75 million who decided in favor of Trump in the elections. MAGACOIN thus carries on its shoulder the super weight of a major expectation since it is on the side of the competitors related with the Make America Great Again applicants. The returns from the new cryptocurrency will be diverted towards funding their future campaigns and races. This coin is different from other

Should You Invest in MAGACOIN?

Despite its goal and objectives, in any case, MAGAcoin isn’t yet listed among famous and trusted platforms including that of CoinMarketCap, Binance, and others. Further, according to RawStory’s report, despite the popularity and massive sign-ups of MAGACOIN, its users risk something and it is a data breach on their end. It was found that the MAGACOIN site uncovers user data including login credentials among others.

How would you feel when I say there is a new crypto coin , which is a Donald Trump-themed cryptocurrency? Funny, isn’t it? Well, then let’s welcome MAGACOIN. A lot has been happening around in the crypto world since the launch of MAGACOIN. Let’s explore more about it.MAGACOIN was made with an end goal to apparently communicate strong support for the previous President, Donald Trump, in the wake of his loss in the November 2023 political race. The total number of coins made till now is an affirmation of the 75 million who decided in favor of Trump in the elections. MAGACOIN thus carries on its shoulder the super weight of a major expectation since it is on the side of the competitors related with the Make America Great Again applicants. The returns from the new cryptocurrency will be diverted towards funding their future campaigns and races. This coin is different from other best cryptocurrencies to buy as MAGACOIN won’t hold an ICO (Initial Coin Offering), rather it will be parting with 100 coins to each new registrant to construct a powerful MAGACOIN ecosystem that will work together to get access to the crypto exchanges and backing the MAGA development. According to its website, 10 Million MAGACOINS have been given to the MAGACOIN Victory Fund, a SuperPAC made to help MAGA candidates across the nation who will battle for individual rights, religious liberty, protecting the unborn, the 2nd amendment, freedom of speech, and the entire America First Agenda. As MAGACOIN is acknowledged by crypto exchanges and the value of the SuperPAC’s coin holdings grows, these coins will be exchanged for U.S. Money and independent expenditures will be made to bolster MAGA candidates. The more prominent the value of each MAGACOIN, the more resources the MAGACOIN Victory Fund will have to support and choose MAGA applicants from one side of the country to the other. Lately, the country has been met with a ton of “far-right” movements in recent months, and this incorporates that of the Freedom Phone, which has been made for “free speech.” These far-right movements have been confronting gigantic popularity among the general population yet are blended in terms of support. The coin is additionally supposed to be available for mining, the alternative cycle of getting raw cryptocurrency.Despite its goal and objectives, in any case, MAGAcoin isn’t yet listed among famous and trusted platforms including that of CoinMarketCap, Binance, and others. Further, according to RawStory’s report, despite the popularity and massive sign-ups of MAGACOIN, its users risk something and it is a data breach on their end. It was found that the MAGACOIN site uncovers user data including login credentials among others. This is something that should be a warning for users, particularly as they can be accessed or have their cash taken from them by hackers. MAGACOIN is a genuinely new blockchain on the lookout and users should take precautions in case they are thinking of buying it. Moreover, Trump has not recognized the cryptocurrency or pronounced any apparent support. Trump hasn’t been exceptionally hopeful about crypto overall. He has even said that bitcoin appears to be a scam. And remember his extensive rant in 2023 on Twitter about crypto. One reason for his aversion toward cryptocurrency is their volatile nature and conflicting value due to which he doesn’t by and large belief them to be actual money.

Predictive Analytics Models: Everything To Know About It

Predictive analytics models are an important element of data science forecasting

Predictive analytics harness techniques, for example, machine learning and data mining to predict the future. Predictive analytics models forecast future results by utilizing data modeling. It’s one of the crucial ways a business can see its way ahead and make strategies likewise as well as an important element of data science forecasting. While not secure, this strategy will have high precision rates, which is the reason it is so widely utilized. Let’s know everything in detail about Predictive Analytics Modeling  

How do Predictive Analytics Models work?

Here the process includes taking a look at the past data and deciding the future event. Data analysts can build predictive models on holding required information. Predictive modeling techniques generally differ from data mining due to the latter one focuses on uncovering hidden relationships between these factors, while the initial relates a model to finish up a probable ending. A SaaS organization may model data on sales of past marketing expenses across each space to create a forecast model for prospect income based on marketing spend. A predictive model isn’t fixed; it is approved or reconsidered often to integrate changes in the fundamental data. At the end of the day, it is regarded as a one-and-done prediction. Predictive models make assumptions dependent on what has occurred before and what’s going on at this point. If new data shows changes in what’s going on now, the impact on the future result should be recalculated, as well. For instance, a software organization could model historical sales data against marketing expenses across various areas to make a model for future income depending on the impact of the marketing spend.  

Types of Predictive Models in Data Science

Classification Model Predictive models in machine learning, classification model alludes to a predictive modeling issue where a class label is anticipated for a given illustration of input data. From a modeling viewpoint, classification requires a training dataset with numerous instances of inputs and outputs from which to learn. A model will utilize the training dataset and will ascertain how to best guide instances of input information to explicit class labels. All things considered, the training dataset should be adequately illustrative of the issue and have numerous instances of each class label. Classification predictive modeling algorithms are assessed depending on their outcomes. Classification accuracy is a mainstream metric used to assess the performance of a model depending on the anticipated class labels.   Clustering Model Clustering is the process of separating the data sets into a specific number of clusters in such a way that the data points having a place in a cluster have similar attributes. Clusters are only the grouping of data points so that the distance data points within the clusters are insignificant. In simple terms, the clusters are regions where the density of the same data points is high. It is, by and large, utilized for the analysis of the data set, to discover valuable information among colossal data sets and draw insights from it. The clusters are found in a round shape, however, it isn’t required as the clusters can be of any shape.   Forecasting models Forecasting models are one of the numerous tools organizations use to forecast results in sales, consumer behavior,  supply and demand, and much more. These predictive analytical models are particularly valuable in the field of sales and marketing. There are numerous forecasting methods organizations utilize that give varying degrees of information. The allure of utilizing forecasting models comes from having a visual reference of anticipated results. While there are various approaches to forecast business results, there are four types of models or techniques that organizations use to anticipate operations later on – the Time series model, econometric model, judgmental forecasting model, and the Delphi technique.   Outliers model The Outliers predictive model is utilized to discover values in the data that are outside the scope of “what’s generally anticipated”. That is a subjective judgment. Some say it as values that are far away from the median, however, how far will be “far away”? Or then again, it very well may be characterized as a multiple of the standard deviation, or it could likewise be founded on interquartile ranges. Those are easy approaches to define outliers for a single variable. While many people comprehend single variable outliers, outliers can likewise exist when there are various factors. This is more normal in complex data. Like clustering models, and outliers model works best with continuous factors (for example, numeric information).   Time series models Time series models are utilized for so many reasons – anticipating future results, understanding past results, making policy recommendations, and much more. These overall objectives of time series predictive modeling don’t change altogether from modeling cross-sectional or panel information. Notwithstanding, the strategies utilized in the time series model should account for time series correlation. The Time-domain approach models future values as a component of past values and present values. The establishment of this model is the time-series regression of present values of a time series on its own past values and past values of other variables. The evaluations of these regressions are frequently utilized for predictions and this methodology is mainstream in time series econometrics.  

Advantages of Predictive Analytical Modeling

More or less, predictive analytics modeling brings downtime, effort, and expenses in predicting business results. Factors like environmental variables, competitive intelligence, regulation changes, and economic situations can be figured into the numerical computation to deliver more complete perspectives at generally low expenses. Instances of explicit kinds of forecasting that can profit organizations incorporate predicting demand, headcount arranging, churn analysis, fleet and IT hardware maintenance, and financial risks.  

Challenges of Predictive Modeling

It’s vital to keep predictive analytics zeroed in on delivering helpful business insights in light of the fact that not everything this innovation uncovers is valuable. Some mined data is of value just in fulfilling an inquisitive brain and has not many or no business implications. Getting diverted is an interruption few organizations can afford.

Predictive analytics harness techniques, for example, machine learning and data mining to predict the future. Predictive analytics models forecast future results by utilizing data modeling. It’s one of the crucial ways a business can see its way ahead and make strategies likewise as well as an important element of data science forecasting. While not secure, this strategy will have high precision rates, which is the reason it is so widely utilized. Let’s know everything in detail about Predictive Analytics ModelingHere the process includes taking a look at the past data and deciding the future event. Data analysts can build predictive models on holding required information. Predictive modeling techniques generally differ from data mining due to the latter one focuses on uncovering hidden relationships between these factors, while the initial relates a model to finish up a probable ending. A SaaS organization may model data on sales of past marketing expenses across each space to create a forecast model for prospect income based on marketing spend. A predictive model isn’t fixed; it is approved or reconsidered often to integrate changes in the fundamental data. At the end of the day, it is regarded as a one-and-done prediction. Predictive models make assumptions dependent on what has occurred before and what’s going on at this point. If new data shows changes in what’s going on now, the impact on the future result should be recalculated, as well. For instance, a software organization could model historical sales data against marketing expenses across various areas to make a model for future income depending on the impact of the marketing spend.Predictive models in machine learning, classification model alludes to a predictive modeling issue where a class label is anticipated for a given illustration of input data. From a modeling viewpoint, classification requires a training dataset with numerous instances of inputs and outputs from which to learn. A model will utilize the training dataset and will ascertain how to best guide instances of input information to explicit class labels. All things considered, the training dataset should be adequately illustrative of the issue and have numerous instances of each class label. Classification predictive modeling algorithms are assessed depending on their outcomes. Classification accuracy is a mainstream metric used to assess the performance of a model depending on the anticipated class labels.Clustering is the process of separating the data sets into a specific number of clusters in such a way that the data points having a place in a cluster have similar attributes. Clusters are only the grouping of data points so that the distance data points within the clusters are insignificant. In simple terms, the clusters are regions where the density of the same data points is high. It is, by and large, utilized for the analysis of the data set, to discover valuable information among colossal data sets and draw insights from it. The clusters are found in a round shape, however, it isn’t required as the clusters can be of any shape.Forecasting models are one of the numerous tools organizations use to forecast results in sales, consumer behavior, supply and demand, and much more. These predictive analytical models are particularly valuable in the field of sales and marketing. There are numerous forecasting methods organizations utilize that give varying degrees of information. The allure of utilizing forecasting models comes from having a visual reference of anticipated results. While there are various approaches to forecast business results, there are four types of models or techniques that organizations use to anticipate operations later on – the Time series model, econometric model, judgmental forecasting model, and the Delphi chúng tôi Outliers predictive model is utilized to discover values in the data that are outside the scope of “what’s generally anticipated”. That is a subjective judgment. Some say it as values that are far away from the median, however, how far will be “far away”? Or then again, it very well may be characterized as a multiple of the standard deviation, or it could likewise be founded on interquartile ranges. Those are easy approaches to define outliers for a single variable. While many people comprehend single variable outliers, outliers can likewise exist when there are various factors. This is more normal in complex data. Like clustering models, and outliers model works best with continuous factors (for example, numeric information).Time series models are utilized for so many reasons – anticipating future results, understanding past results, making policy recommendations, and much more. These overall objectives of time series predictive modeling don’t change altogether from modeling cross-sectional or panel information. Notwithstanding, the strategies utilized in the time series model should account for time series correlation. The Time-domain approach models future values as a component of past values and present values. The establishment of this model is the time-series regression of present values of a time series on its own past values and past values of other variables. The evaluations of these regressions are frequently utilized for predictions and this methodology is mainstream in time series chúng tôi or less, predictive analytics modeling brings downtime, effort, and expenses in predicting business results. Factors like environmental variables, competitive intelligence, regulation changes, and economic situations can be figured into the numerical computation to deliver more complete perspectives at generally low expenses. Instances of explicit kinds of forecasting that can profit organizations incorporate predicting demand, headcount arranging, churn analysis, fleet and IT hardware maintenance, and financial risks.It’s vital to keep predictive analytics zeroed in on delivering helpful business insights in light of the fact that not everything this innovation uncovers is valuable. Some mined data is of value just in fulfilling an inquisitive brain and has not many or no business implications. Getting diverted is an interruption few organizations can afford. Additionally, being able to utilize more data in predictive modeling is a benefit just to a point. An excessive amount of data can slant the calculation and lead to an invaluable or incorrect result. For instance, more coats are sold as the temperature drops. Yet, only to a point. We don’t purchase more covers when it’s – 20 degrees Fahrenheit outside than we do when it’s – 5 degrees underneath freezing. At one point, the cold will be adequately cold to prod the purchase of coats, and more bone-chilling temps presently don’t considerably change that pattern.

Everything You Need To Know About Using “Shared With You” In Ios 15

iOS 15 has brought a new way to share images, videos, links, etc. universally: Shared with You. It works across apps like Safari, Photos, and Notes and allows users to share the files and information from those apps with others. This article takes a look at how to share photos and links using “Shared With You” share menu in iOS 15.

How “Share With You” Works in Safari

We share links to interesting web pages and articles with friends all the time. You read something like a new healthy recipe or some local news, and you want to share a link to it with someone you know.

In previous iOS versions, the recipient would have to open the Messages app, dig deep into the conversation to find the link shared, and tap on it to open it in Safari. Now, with “Shared With You”, things are much simpler.

Let’s say someone sends me a link to an interesting article. Instead of messing around in the Messages app, It will be automatically added to Safari. It doesn’t asks for any permissions; however, you can control them from settings.

The link will appear on the home page under the “Shared with You” heading. Simply tap on the link to open it in a Safari tab. It’s that easy.

The feature works in all messaging apps and not just iMessage. iOS 15 scans and collects all links and displays them in Safari.

You will also notice the name of the person who shared the link below in Safari. Tap on it to open the conversation in the Messages app. Tap and hold to send a reply in a pop-up or remove the link and declutter the space.

How do you find the link again in a sea of links in the Messages app? Simple. Long-press the link and find the option to pin it. You can now find these pinned links in the Messages search menu, Shared With You, and the conversation’s Details view.

How Does “Share With You” Work in the Photos App

Whenever someone shares a photo with you, it will appear in the Photos app under a new “Shared With You” section located under the “For You” tab in the Photos app. It could be an image you received in the Messages app or some other chat app.

You will see a download icon next to the image in the Messages app. In previous iOS versions, you would need to tap on that to download and save the image to the Photos app.

Now, you don’t need to download it initially. Open the Photos app to view all shared images under the “Shared With You” section in the “For You” tab. As seen in the below screenshot, you will see the profile picture of the sender at the top. Tapping on the same will lead you back to the conversation in the Messages app where the image was first seen.

Not shown in the image is a link at the bottom that says “Save Shared Photo,” which allows you to save the photo so that is no longer only connected to the original message. Once you save the image, the option will no longer appear.

You may want to save the photo if you are the type to often delete old messages. If you delete the message before saving the photo, the photo will be gone.

If you are receiving files in the Messages app, you can easily save it for later use.

How to Share Photos From iCloud Directly

With iOS 15, you can now share photos via an iCloud link instead of sharing the actual photo. Open the Photos app and long-press on the photo that you want to share with someone. Tap on the “Share” button, then select “Copy iCloud link.”

The generated link can be shared via any messaging app you use. If you think the photo shouldn’t be shared anymore or was shared mistakenly, you can quickly stop sharing the photo. Open the Photos app and locate the photo you shared under the “For You” tab. Tap once to open it and select the three-dot menu icon to find the “Stop Sharing” button.

Disable “Shared with You” Completely Frequently Asked Questions 1. Why can’t I see “Shared With You” in Photos?

There are many people reporting on social media sites like Reddit and Twitter about this. It is not working for some users, but that is expected to be fixed in subsequent iOS updates, hopefully.

2. When do the shared links for iCloud Photos expire?

The link is available for 30 days, after which it will expire automatically. The recipient will be greeted with a “Failed to Retrieve” message after that. A new link would have to be generated then.

Wrapping Up

This is a neat feature that would make lives easier for many people. The rollout could have been smoother though. Shared With You works in a number of apps and collects valuable data and displays it in the apps. All links are available in the Safari browser, while all photos are visible in Photos app.

It will be interesting to see where Apple takes Shared With You in future updates.

Gaurav Bidasaria

A C.A. by profession and a tech enthusiast by passion, Gaurav loves tinkering with new tech and gadgets. He dropped out of CA in the final year to follow his passion. He has over seven years of experience as a writer covering consumer tech and writes how-to guides, comparisons, listicles, and explainers for B2B and B2C apps and services. He recently started working out but mostly, you will find him either gaming or streaming.

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