Trending December 2023 # Orbeon Protocol (Orbn) Price Prediction: Apecoin (Ape) And Polygon (Matic) # Suggested January 2024 # Top 15 Popular

You are reading the article Orbeon Protocol (Orbn) Price Prediction: Apecoin (Ape) And Polygon (Matic) updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Orbeon Protocol (Orbn) Price Prediction: Apecoin (Ape) And Polygon (Matic)

Orbeon Protocol (ORBN) is one of those new projects that have taken the crypto market by storm. It has seen significant success in the ongoing third phase of its presale with Orbeon Protocol (ORBN) seeing a price increase of 805% so far and rising.

Analysts predict that Orbeon Protocol (ORBN) could potentially give its investors a return of 6000% by the end of its presale.

Polygon’s (MATIC) recent partnership with JP Morgan and Meta has given it the boost it needed to start the year on a positive note.

Let’s discuss Orbeon Protocol, ApeCoin and Polygon to see where they could be headed in 2023.

Orbeon Protocol (ORBN)

Orbeon Protocol (ORBN) is a venture capital and crowdfunding blockchain platform. It is changing the way investors can invest in promising startups unlike in the past when venture capital and crowdfunding were dominated by a few large angel investors and institutions.

Orbeon Protocol (ORBN) allows investors to back startups using fractionalized NFTs. Startups can mint these fractionalized NFTs as a form of investment. These equity-based NFTs are then issued to investors which allows them to purchase the equity of a company of their choice for as little as $1.

Security is a core focus on Orbeon Protocol (ORBN), with a “Fill or Kill” mechanism being written into the platform’s smart contracts. This feature ensures that investors receive a complete refund if a platform fails to reach its funding goals.

Holders of the Orbeon Protocol’s native token, ORBN, enjoy several benefits including governance rights, discounts on trading fees, access to investment groups, and much more.

Given the popularity of the platform and the practical utility served by the Orbeon Protocol, the token’s presale has been a major success and is expected to grow even further in 2023. Analysts have predicted that the price of ORBN will rise by 6000% by the time the presale phase concludes.

ApeCoin (APE)

ApeCoin (APE) is the native and utility token of the largest NFT platform in the world today – the ApeDAO. The company is run by Yuga Labs, who also own the Bored Ape Yacht Club (BAYC) which is the largest NFT project globally by market cap.

Holders of ApeCoin (APE) have the right to vote on decisions made by the company among other things. The price of ApeCoin has been on the rise lately thanks to the growing popularity of meme coins, and especially the success of the BAYC ecosystem.

The recent introduction of new features on the platform such as APE staking with ApeCoin has attracted more investors to APE. Investors are optimistic that with more exposure, ApeCoin’s (APE) worth will continue its bull run in 2023.

Polygon (MATIC)

The collapse of FTX created a tidal wave across the entire crypto market. Most cryptocurrencies suffered a major decline but some like Polygon (MATIC) seem to have weathered the storm fairly better than others.

As a layer-2 scaling solution, Polygon (MATIC) is built on the Ethereum blockchain. Polygon has shown a lot of resilience in the aftermath of the FTX collapse and is currently still in the green zone.

In the last 30 days, Polygon (MATIC) has increased by approximately 40%. This makes Polygon is another promising crypto project to keep an eye on in 2023 together with ApeCoin and Orbeon Protocol.

 Find Out More About The Orbeon Protocol Presale

You're reading Orbeon Protocol (Orbn) Price Prediction: Apecoin (Ape) And Polygon (Matic)

Btc Price Prediction – Is Bitcoin A Good Investment?

Bitcoin is a revolutionary new digital currency that has recently gained immense popularity. Unlike traditional currencies, which are issued and controlled by central banks, Bitcoin is embedded in a decentralized peer-to-peer network that allows users to transfer funds securely and quickly without intermediaries. The transactions on this network are verified through cryptography, ensuring that each transaction is valid and secure. These transactions are also recorded in a public ledger, which anyone on the network can access.

But even with its growing popularity, it remains as volatile as any other cryptocurrency. As such, it is pretty natural to wonder, is Bitcoin a good investment in 2023?

If you have wondered whether Bitcoin is a good investment, you have come to the right place. In this article, we take an in-depth look at Bitcoin’s price performance over the years and also give insights on the best exchange to buy it.

Is Bitcoin a Good Buy? An Overview

To decide whether Bitcoin is a smart investment and whether to buy Bitcoin, you need to look at its price history over time. 

Bitcoin, at its core, is a peer-to-peer cryptocurrency that prides itself as the most decentralized of all cryptocurrencies in the market today. Bitcoin is also gaining traction as Bitcoin gold. This is all thanks to its immutable nature and the fact that it has a fixed supply.

In other words, governments do not have any say in how Bitcoin operates or how it can be used. This also makes Bitcoin remarkably resilient against manipulation, and fraud, since its value does not depend on any individual or organization.

Also, with a total supply of only 21 million Bitcoins to be in circulation, compared to fiat currencies which can easily be inflated at will by central banks and governments, it’s clear that Bitcoin truly represents a distinctive form of monetary freedom. It is the closest thing that society has come to digital Gold.

But even with these strong fundamentals, is Bitcoin really a smart cryptocurrency investment?

Besides its usability as a currency and the potential to become a store of value, it is noteworthy that Bitcoin is one of the few cryptocurrencies that are gaining adoption by institutional investors. This means Bitcoin is one of the top cryptocurrencies that could go truly mainstream, which means long-term value growth.

Virtual currencies are highly volatile. Your capital is at risk

Bitcoin’s performance from Inception to Today

Before you buy a cryptocurrency, it is essential to have a good idea of its past price action. This can give you an idea of where it could go in the future.

Bitcoin was conceptualized in 2008 but launched officially in 2009. It traded at zero for most of 2009 until July 2010, when it hit a value of $0.0008. By August 2010, it had risen to $0.08. Things then slowed down until April 2013, when it hit $250 for the first time ever.

However, Bitcoin only made global headlines in December 2023 when it hit an all-time high of $20,000. While a correction followed in 2023, Bitcoin regained upside momentum again in 2023 and made a record when in November 2023, it hit an all-time high of $69,000.

While Bitcoin entered another bear market in 2023, anyone who bought in the early 2010s is still in profit, to the tune of hundreds of percentages. Those who invested just $1000 in Bitcoin back in 2010 are multi-millionaires today.

Bitcoin Price

Up to this point, you have a good idea of whether Bitcoin is a promising cryptocurrency to invest in 2023. From 2009 to 2023, Bitcoin has been on an uptrend, the ups, and downs in-between notwithstanding.

For most of 2023, Bitcoin has been affected by the same issues that have affected the rest of the financial markets, including the war in Ukraine and the surging inflation that has slowed down the global economy.

As of October 2023, Bitcoin is trading at a low $19,000 and is yet to show any signs of a major pullback in the very short term.

Bitcoin highs and lows

Up to this point, we have looked at Bitcoin’s price action from when it entered the market to date.

Nonetheless, let’s summarize Bitcoin’s price history to make things clearer for you.

Bitcoin launches in 2009 – $0

July 2010 – Bitcoin gets value for the first time at $0.0008

April 2013 – Bitcoin rallies to $250 for the first time

December 2023 – Bitcoin makes history after hitting a high of $20,000

January 2023 – Bitcoin enters a bear market dropping to $3000 by the end of year

November 2023 – Bitcoin makes an all-time high of $69,000

October 2023 – After a sustained bear run throughout the year, Bitcoin drops to $19000

From Bitcoin’s price history, you should be able to decide whether Bitcoin is a good cryptocurrency to buy in 2023. However, you should know that cryptocurrencies carry risks, and as such, you should only invest what you can afford to lose.

Bitcoin Price Prediction

Before you decide whether Bitcoin is a good investment or not, you should know that all Bitcoin price predictions are speculative. That said, these predictions are educated guesses because they are based on a mix of fundamental and technical analysis.

Most cryptocurrency market analysts believe that Bitcoin could go up in 2023. The average Bitcoin price prediction for 2023 is $30,000. However, it is more long-term that analysts expect Bitcoin to do well. For instance, analysts expect Bitcoin to trade at $200k or more in 2025.

Virtual currencies are highly volatile. Your capital is at risk

So Is Bitcoin a good investment?

Bitcoin being the first-ever successful cryptocurrency, gained traction faster than the rest. This has seen it gain traction, especially at institutional levels. This means it could gain value over time.

At its core, the value of bitcoin is determined by its inherent scarcity. Like Gold, it is a limited resource: only a finite supply of bitcoin can be mined and traded on the market, which means that its price will likely continue to rise as demand grows. This has made it extremely attractive to investors eager to get in on the increasingly lucrative cryptocurrency market.

Bitcoin’s unique combination of scarcity and utility will keep investors coming back for more. And as long as that demand continues to grow, so will the profits generated by trading this increasingly valuable commodity.

Below are some of its core use cases to give you a better idea of how good Bitcoin is as an investment.

Bitcoin can be used for everyday payments. This is done peer-to-peer, so the payment cannot be controlled by any government.

Thanks to its scarcity, Bitcoin could become a store of value, just like Gold.

Bitcoin’s decentralized nature makes it perfect as a store-of-value cryptocurrency.

Bitcoin’s decentralization has already seen it start getting adopted as a currency by countries led by El Salvador. This is a factor that makes the future of Bitcoin quite bright.

Virtual currencies are highly volatile. Your capital is at risk

Where to Buy Bitcoin

Even if you choose to invest in Bitcoin in 2023, buy it from a reputable cryptocurrency exchange. It would help if you also stored your Bitcoin in a safe cryptocurrency wallet to save you from cybercriminals.

eToro – Top Broker to Invest in Bitcoin in 2023

eToro is one of the largest cryptocurrency brokers in the world today. It serves more than 20 million users globally, a sign that people truly trust eToro. One thing that makes eToro a reliable cryptocurrency broker is that it is regulated. As such you can be sure that your money is always safe. eToro is regulated in the U.S, the U.K, Cyprus, and Australia.

eToro also gives users the convenience of being able to deposit and withdraw through multiple options. For instance, you have the luxury to choose between wire transfers, debit cards, and even the plethora of online payments that exist today that range from PayPal, Skrill, and Neteller, among many others.

You will also love the fact that eToro allows you to deposit pretty small amounts. With eToro, you can deposit as low as $50, and open trades for as low as $10. Essentially with only $50, you can build a portfolio of 5 cryptocurrencies.

It is also noteworthy that with eToro, you can safely store your cryptocurrency. That’s because, with the eToro wallet, you have secure storage where you can always be sure that your Bitcoin and other cryptocurrencies are safe.

Virtual currencies are highly volatile. Your capital is at risk

Should I Buy Bitcoin?

Despite its growing popularity, Bitcoin still remains a highly volatile cryptocurrency. As such, you might still wonder, is Bitcoin a good investment in 2023?

Bitcoin is scarce, so the price will likely go up as demand grows.

Bitcoin adoption is on the rise, and from highly strategic quarters such as governments and large corporations.

Bitcoin can be used to create smart contracts, and due to its high level of security, it could gain a share of the smart contracts market.

Essentially, Bitcoin is a good investment, but as its price has shown, it has a high volatility risk. The volatility is a blessing in disguise though, as it means that the potential for returns is also exponential. Besides, since eToro allows you to invest in cryptocurrencies for as low as $10, the risk is worth it.

Virtual currencies are highly volatile. Your capital is at risk


In this article, we have comprehensively answered the question, “Is Bitcoin a good cryptocurrency investment in 2023?” We have highlighted the key reasons why Bitcoin is a worthwhile investment, including its low coin supply and the fact that adoption is on the rise.

That said, if you want to invest in Bitcoin alongside other altcoins, there are a ton of them that you can buy. To increase the potential rate of return, go for low-cap altcoins with strong use cases. In bull markets, when FOMO is at its highest, such altcoins tend to outperform Bitcoin by a huge margin.

FAQs Should I buy Bitcoin in 2023?

Bitcoin is a scarce cryptocurrency, and its demand is rising, especially among institutional players. This means it has good prospects for growth in 2023. So, yes, it makes sense to invest in Bitcoin in 2023.

What will be Bitcoin’s price in 2023?

While no one can tell what Bitcoin’s price will be in the future, most analysts expect Bitcoin to test $30k in 2023.

Is Bitcoin a safe investment?

Bitcoin is one of the most decentralized cryptocurrencies in the market today. This makes it one of the low-risk cryptocurrencies to buy now that regulators are turning their focus on the cryptocurrency market.

Shiba Inu Price Prediction – Is Bitcoin And Collateral Network A Good Buy?

In the dynamic world of cryptocurrencies, the spotlight often shifts between different tokens. Today, we delve into the recent developments of Shiba Inu (SHIB) and Bitcoin (BTC), and introduce an upcoming project, Collateral Network, which is set to disrupt the lending industry with its presale of the $COLT token.

Shiba Inu: A Meme Token with Real-World Impact

The Shiba Inu token SHIB is witnessing a high burn rate lately. This could be the push it needs to make it big in the crypto world. 

Shiba Inu, often referred to as the “Dogecoin killer,” is a decentralized meme token that grew into a vibrant ecosystem. ShibaSwap, fun tokens, and an Artist Incubator are some of the exciting features of the Shiba Inu project. 

In recent months, Shiba Inu has been making headlines for various reasons. The token’s burn rate surged by 60%, while the new Twitter CEO showed interest in the meme coin. 

All these facts indicate a bullish momentum for Shiba Inu, with analysts predicting a rally to $0.00000861 in the short term. Moreover, the memecoin could trade as high as $0.0000160 in the following weeks. 

However, it’s not all smooth sailing for Shiba Inu. The community recently had to alert its members about new scam methods targeting them. Despite these challenges, Shiba Inu continues to be a token of interest in the crypto market, as suggested by its price prediction.

Bitcoin: The Pioneer in Volatile Waters

The king of kings of the crypto world, Bitcoin, is seeing price volatility recently, due to macro events that impact all assets. 

Bitcoin, the first and most well-known cryptocurrency, has been the vanguard of the crypto market. It’s a decentralized digital currency without a central bank or single administrator. Bitcoin allows peer-to-peer transactions on the network without intermediaries.

Bitcoin’s recent developments have been a rollercoaster ride. With the Consumer Price Index (CPI) volatility, Bitcoin traders are bracing themselves as the BTC price taps $26,000. This focus on CPI volatility indicates the influence of macroeconomic factors on Bitcoin’s price.

Despite the volatility, Bitcoin remains a significant player in the crypto space. Its resilience and adaptability have allowed it to weather various storms, reinforcing its position as a stable investment.

Collateral Network: A New Era of Lending

Collateral Network, an upcoming project in the crypto space, is set to disrupt the traditional lending industry. This Ethereum-based web3 peer-to-peer lending platform is designed to allow users to borrow cryptocurrencies against physical assets on the blockchain.

The Collateral Network platform addresses several problems prevalent in the current lending industry. These include difficulties in obtaining loans for non-traditional assets, outdated pawnbroking practices, limited credit options in certain countries, and the red tape associated with short-term loans. 

Collateral Network accepts a wide range of assets as collateral. These include real estate, fine art, vintage cars, gold, fine wines, watches, diamonds, and collectibles. This broad acceptance of assets on Collateral Network opens up opportunities for individuals who have traditionally found it challenging to secure loans against these types of assets.

With an initial starting price of $0.01 and a total supply of 1.4 billion COLT tokens, analysts predict a 3,500% price increase during the presale. The token will surge by 100x when it lists on major exchanges. The team behind Collateral Network is doxxed and KYC audited, and the token smart contract is fully audited, adding to the project’s credibility. 

Find out more about the Collateral Network presale here:

Insurance Charges Prediction Using Mlib

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

Introduction on MLIB

In this MLIB  article, we will be working to predict the insurance charges that will be imposed on a customer who is willing to take the health insurance, and for predicting the same PySpark’s MLIB library is the driver to execute the whole process of this Machine learning pipeline.

We are gonna work with the real-world insurance dataset that I’ve downloaded from Kaggle. I’ll be providing the link for your reference, so without any further wait let’s get started.

Importing All the Libraries from PySpark

Our very first step is to import all the libraries from PySpark which will be required in a complete machine learning process i.e. from the data preprocessing to the model building phase.

from chúng tôi import SparkSession from chúng tôi import Correlation import pyspark.sql.functions as func

Firstly we imported the SparkSession from PySpark to set up the spark session and then the Correlation library which eventually help in finding the colinearity between the two variables finally the last imported the functions module which will allow us to use predefined statistical functions of PySpark.

Setting Up the Spark Session for the PySpark MLIB Package

In this step, the Spark object will be created through which we can access all the deliverables, functions, and libraries that Spark has to offer. The new virtual environment will be created so that we can do all the steps involved in the ML pipeline.

spark = SparkSession.builder.appName("Insuarance cost prediction").getOrCreate() spark


Reading Insurance Dataset Using Pyspark MLIB

In this section, we will be reading the dataset using the read.csv() function of PySpark before moving forward with the further process let’s know more about the dataset.

So in this particular dataset, we have all the essential high-priority features that could help us to detect the life insurance charges which are possible to embrace on the card holders.

Features are as Follows

Age: Age of the customer.

BMI: Body Mass Index of the individual.

Children: Number of children he/she has.

Smoker: Is that individual a frequent Smoker or not.

Region: Which region/part he/she lives in and,

The charges column is our target/independent feature.

data ="insurance.csv", inferSchema = True, header = True)


Code Breakdown

As discussed we have read the insurance dataset which was in the CSV format using the chúng tôi function keeping the inferSchema parameter as True which will return the fundamental data type of each column along with that we kept the header as True so that the first row would be treated as the header of the column.

At last, we have displayed the data using PySpark’s show method.

Now it’s time to statistically analyze our dataset for that we will start by extracting the total number of records that are present in it.




Inference: So from the above output we can state that there is a total of 1338 records in the dataset which we were able to get with the help of the count function.

print("Total number of columns are: {}".format(len(data.columns))) print("And those columns are: {}".format(data.columns))


Total number of columns are: 7 And those columns are: ['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges']

Inference: In the above code we tried to get as much information about the columns present for the analysis and conclusively we got to know that it has a total of 7 columns and their name as well.

Now let’s look at the Schema i.e. the structure of the dataset where we would be able to know about the type of each column.



Inference: So from the above output we can see that adjacent to each feature we can see its data type and also a flag condition where it states whether the column can have null values or not.

Note: If you closely look at the result then one can see the sex, children, smoker, and region features are in the string format but they are categorical variables hence in the coming discussion we will convert them.

Now it’s time to see the statistical inferences of the dataset so that we can get some information related to the count, mean, standard deviation, and minimum and maximum values of the corresponding features.



Inference: So from the above output we can draw multiple inferences where we can see each feature count is the same i.e. 1338 which means there are no Null values and the maximum age in the data is 64 while the minimum is 18 similarly max number of children are 5 and minimum is 0 i.e. no child. So we can put into the note that describes function can give ample information.

There is one more way to see the records and this one is similar to the ones who have previously come across pandas data processing i.e. head function.



Inference: As one can notice that it returns the row object so from here we can assume that if we want to analyze the data per record i.e. of each tuple then grabbing them using the head function could be a better approach.

Correlation in Variables

Correlation is one of a kind technique that helps us in getting more accurate predictions as it helps us know the relationship between two or more variables and return how likely they are positively or negatively related to it.

In this particular problem statement, we will be finding the correlation between all the dependent variables (continuous/integer one only) and the independent variables.

age = data.corr("age", "charges") BMI = data.corr("bmi", "charges") print("Correlation between Age of the person and charges is : {}".format(age)) print("Correlation between BMI of the person and charges is : {}".format(BMI))


Correlation between Age of the person and charges is : 0.299008193330648 Correlation between BMI of the person and charges is : 0.19834096883362903

Inference: The correlation between the Age and insurance charges is equivalent to 0.30 while BMI and insurance charges are related to each other with a 0.20 value.

Note: We have only used two variables though logically we have multiple options they are in the string format so we can’t put them in this analysis, for now, you guys can repeat the same process for them after the conversion step.

String Indexer

In this section of the article, we will be converting the string type features to valid categorical variables so that our machine learning model should understand those features as we know that the ML model only works when we have all the dependent variables in the numerical format.

Hence, it is a very important step to proceed for this StringIndexer function will come to the rescue.

Note: First we will be converting all the required fields to categorical variables and then look at the line-by-line explanation.

Changing the string type “sex” column to a categorical variable using String Indexer.

from import StringIndexer category = StringIndexer(inputCol = "sex", outputCol = "gender_categorical") categorised =


Changing the string type “smoker” column to a categorical variable using String Indexer

category = StringIndexer(inputCol = "smoker", outputCol = "smoker_categorical") categorised = # Note that here I have used categorised in place of data as our updated data is in the new DataFrame i.e. "categorised"


Changing the string type “region” column to a categorical variable using String Indexer.

category = StringIndexer(inputCol = "region", outputCol = "region_categorical") categorised =


Code Breakdown

Firstly we imported the StringIndexer function from the ml. feature package of Pyspark.

Then for converting the sex column to a relevant categorical feature we took up the imported object and in the input parameter original feature was passed while in the output column feature we passed the name of the converted feature.

Similarly, we did the same for all the columns that were required to be converted to categorical variables.

Vector Assembler

As we already know that PySpark needs the combined level of features i.e. all the features should be piled up in a single column and they all will be treated as one single entity in the form of a list.

from import Vector from import VectorAssembler

Inference: So we have imported the Vector from the ML package and VectorAssembler from the feature package of ML so that we can pile up all the dependent fields.



['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges', 'gender_categorical', 'smoker_categorical', 'region_categorical']

Inference: The columns which one can see in the above output are the total columns but we don’t need the string ones instead the ones which were changed to the integer type.

concatenating = VectorAssembler(inputCols=["age","bmi", "children", "gender_categorical", "smoker_categorical", "region_categorical"], outputCol="features") results = concatenating.transform(categorised)

Inference: While combining all the features we pass in all the relevant column names in the form of a list as the input column parameter and then to see the changes as well we need to transform our original dataset as well.

for_model ="features", "charges")


Inference: Now we are creating a new DataFrame which we will send it across to our model for model creation.

Note: In the show function we have used truncate=False which means now all the features in the list will show up.

Train Test Split on MLIB

So by far, we have done each step that is required in the model building phase hence now it’s time to split out the dataset into training and testing forms so that one will be used for training the model and the other one will be to test the same.

train_data, test_data = for_model.randomSplit([0.7,0.3])

Inference: In the above random split() function we can see that the training data is 70% (0.7) and the testing data is 30% (0.3).





Inference: Describe function is used on top of both the split dataset and we can see multiple information about them like the total count of the training set is 951 while the other one has 387.

Model Building

In this phase of the article, we will be building our model using the Linear Regression algorithm as we are dealing with a continuous group of features so this is the best and go-to choice for us in the current possible problem statement.

from import LinearRegression lr_model = LinearRegression(featuresCol= "features", labelCol="charges") lr_model


Firstly we are embedding a Linear Regression object by passing in the features column that we have already separated and the label column is our target feature i.e. “charges“.

training_model = training_model


LinearRegressionModel: uid=LinearRegression_d7bb227324ac, numFeatures=6

Then the LR object which we have created fits with the training data that we got from the random split() method, in the output one can see it returned the valid information about the model i.e. the number of features it holds – 6.

Model Evaluation on MLIB

So by far, we are now done with the model development phase so it’s time to evaluate the model and get the inference of the same about whether it is a worthy model or not.

output = training_model.evaluate(train_data)

Evaluate function is to call all the metrics that are involved in the model evaluation phase so that we can decide the accuracy of the model.

print(output.r2) print(output.meanSquaredError) print(output.meanAbsoluteError)


0.7477904623279588 38900867.48971935 4324.864277057295

Here are the results of all the valid evaluation metrics available:

R-squared: This metric explains how much variance of the data is explained by the model.

Mean Squared Error: This returns the residual values of a regression fit line and magnifies the large errors

Mean Absolute Error: Does the same thing but this one focuses on minimizing the small errors which MSE might ignore.

It’s time to make predictions!!

features_unlabelled_data ="features") final_pred = training_model.transform(features_unlabelled_data)


Code breakdown: This is the final step where we will be doing the predictions based on the model we have built and comparing the actual result with the predicted one.

Created a DataFrame for the unlabelled data i.e. our features column

Then we transformed the unlabelled data using the same function we used before.

At the last, we show the prediction results which one can see in the above output.

Conclusion on MLIB

Finally, we can predict the insurance charges with the help of Pyspark’s MLIB library we have performed every step from the ground level i.e. from reading the dataset to making the predictions from the evaluated model.

Let’s discuss in a descriptive way whatever we have learned so far!

First of all, we read the insurance dataset which was a real-world dataset from Kaggle

Then we performed the data preprocessing steps where we got to know about the dataset’s columns, statistics, and changing the string type columns to categorical variables.

Then comes the Model building phase where we build the Linear Regression model using Pyspark’s MLIB library.

At the last, we evaluated the model and made the predictions from the same.

 The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


50% Bonus With Dogetti. Will Dogetti Thrive Over Dogecoin And Polygon?

Each cryptocurrency is vying for a piece of the market in the digital currency space, where competition is fierce. Comparable to a race, every coin is lining up to be the first to cross the finish line. Apart from price and market capitalization, the ability to cater to the needs of the user and deal with real-world tackles are paramount in achieving the goalposts. In this article, we’ll take a closer look at some of the most well-known altcoins, including Dogetti, Dogecoin, and Polygon, to see how they compare to one another in the ongoing struggle for supremacy in the crypto sphere.

Dogetti’s Upward Rally

Crypto enthusiasts are paying attention to Dogetti (DETI), a brand-new meme coin project that is currently gaining traction on presale as they try to maximize the potential earnings it offers. Presale for Dogetti (DETI) is one of the most exciting opportunities to invest in a cryptocurrency asset early. It opens up possibilities to investors who have the competence to recognize and support it. Early purchasers can own the token before launch and benefit from the meme coin’s potential to soar. Dogetti’s success in the presale demonstrates that, despite the volatility, presales are one of the safest ways to purchase cryptocurrencies because starting price is lower than the market price, and presales protect early buyers from market volatility. Early investors can survive with only minor losses, even in the worst-case scenario.

Will Dogetti Beat In Gaining Market Supremacy?

There are currently over 12,000 altcoins, each with distinct benefits and features.

Considering the three popular coins on the market, all three have a chance to win out in the breakdown of global banks.

Runs contrary, Polygon provides efficiency and scalability, making it the ideal choice for those looking to invest in a layer-2 scaling solution that aims to maximize the usability of Ethereum.

Similarly, Dogecoin has a quicker block time and comparatively low transaction fees, indicating that transactions can be processed more quickly. In everyday transactions like the purchase of goods and services, Dogecoin is now more effective.

Dogecoin Being the Dog of Meme

As a humorous and entertaining Bitcoin substitute, software developers Billy Markus and Jackson Palmer created Dogecoin in 2013. There is a large and vibrant community of Dogecoin supporters who have embraced the coin’s lighthearted and humorous image. This community has contributed to building a strong and uplifting brand image for Dogecoin, which has boosted its adoption and popularity.

Despite being intended as a joke at first, Dogecoin quickly became one of the most well-known altcoins. Its market value has surpassed billions of dollars. Dogecoin may not have the same level of technical development or sophistication as some other alternative coins, but its acceptance and meme-inspired culture have made it a popular meme coin.

Polygon Offering dApps for Crypto

Polygon is Ethereum’s biggest rival, with over 7,000 apps currently running on its network. It functions as an Ethereum Layer-2 scaling solution, enabling quicker and less expensive transactions. Its original name, Matic Network, was changed to Polygon in 2023. It offers a superior alternative to the Ethereum blockchain due to its outstanding support for all types of decentralized apps (dApps) and excellent technical architecture. It is another green altcoin that uses a more energy-efficient Proof-of-Stake (PoS) consensus algorithm. Aave, Sushiswap, and Curve Finance are a few of Polygon’s top constructions.

Dogetti Vowing Higher Yields

Dogetti is generating enormous value in the cryptocurrency market, stirring upmarket fervor, and assisting its holders in building wealth, by the example set by meme coins like Dogecoin and Shiba Inu. Using the promo code DON50 during every purchase, Dogetti embraces Dogetti Family members with incredible 50% more tokens.

Dogetti maintains its focus on creating a strong community of devoted supporters through equal distribution, involvement from the local community, and a continuous wave of innovation.

Know More About Dogetti:

Carbon Neutrality Takes Polygon’s Matic To The Green

The decentralized Ethereum scaling platform ticked off its first sustainability milestone by achieving carbon neutrality this week. By investing $400,000 in carbon credits, Polygon is now one step closer to becoming carbon negative. With credits equivalent to 104.794 tons of greenhouse gases, the network has successfully settled its CO2 debt since inception.

Triggered by this news, MATIC was quick to react. The altcoin took to trade in the green as it registered a double-digit hike on the daily chart. Rallying by over 24% in a day, MATIC was changing hands at $0.5075 at press time.

The green milestone was a result of a number of strategic steps taken by the network. According to its official blog post, the achievement came after Polygon’s ‘Green Manifesto’, launched in mid-April. Tagged as ‘a smart contract with Planet Earth’, it is a part of the network’s broader vision for sustainable development.

“Our world is facing an environmental crisis, and the blockchain industry must do far more than promise to stop adding to the problem.”

Fighting the energy consumption debate

Cryptocurrencies are very much in the kernel of energy consumption debate. According to the Cambridge Bitcoin Electricity Index, Bitcoin has consumed more electricity in a year than Sweden, Norway, or the United Arab Emirates. However, the energy consumption debate has often been challenged by the one spearheaded by the utility factor. 

In a striking point of view laid out by the World Economic forum (WEF), energy consumption becomes less a question of morality than one of basic human necessity. When something provides utility, it’s often accepted despite its high energy levels as it adds value to society. According to WEF’s report published in March this year, data centres (which give us access to popular platforms like Netflix and Playstation) in the U.S. consume 204 TWh of energy a year, while Bitcoin uses 62 TWh a year.

At present, there are an estimated 300 million users of crypto globally, and not all of them live in developed nations. A multitude in emerging economies like Kenya, Vietnam, Venezuela, and Brazil adopt digital currencies to dodge the cost of legacy financial systems, unstable monetary governance, and currency devaluation issues.

Walking the green field

The crypto-industry is quite ahead in driving the change with a plethora of environment-friendly projects. Polygon is not alone in walking the carbon neutral path as meaningful action is being taken by many in the space.

The open-source cryptocurrency Filecoin also has a role in a greener present and future. ‘Filecoin Green’ is an initiative led by the Filecoin Foundation, aimed at making its blockchain carbon-neutral and, in time, carbon negative. 

Meanwhile, Algorand celebrated this year’s Earth Day by becoming the world’s first carbon-negative blockchain. This was achieved in partnership with ClimateTrade, an organization dedicated to helping companies improve their sustainability profiles.

KlimaDAO is another climate-driven organization that helped Polygon achieve its recent feat. The decentralized autonomous organization does this through the creation of a DeFi token that is backed by real-world carbon offsets. 

A leading crypto community-driven initiative is the ‘Crypto Climate Accord’. Focused on the decarbonization of cryptocurrencies, it has more than 200 companies and individuals spanning the crypto, finance and technology field.

From Valkyrie to Filecoin and Enjin, the signatories have made a public commitment to achieve net-zero emissions from electricity consumption by 2030. 

– Authored by Diya Joseph

Update the detailed information about Orbeon Protocol (Orbn) Price Prediction: Apecoin (Ape) And Polygon (Matic) on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!