Trending February 2024 # Polygon (Matic) Price Prediction: Filecoin (Fil) Remains Bullish # Suggested March 2024 # Top 9 Popular

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Uwerx has emerged as a promising newcomer, garnering attention and making remarkable strides in the crypto world. With its dedicated team and impressive presale achievements, Uwerx sets its sights on rivaling the success of Polygon (MATIC) and Filecoin (FIL) in the near future.

Its presale rewards have incentivized investors to become part of the Uwerx community, driving up demand for the WERX token.

Filecoin (FIL) Goes Up By 1.05%

Filecoin (FIL) dropped by 1.05% in the last seven days. Although it has had some bearish sentiments, analysts are confident about its performance in the next few weeks. The network is responsible for storing, sorting, organizing, and retrieving user data. The project’s DSN system validates each data stored on the network.

Filecoin (FIL) keeps a very secure platform. The network is impossible to hack since it has no central storage unit and depends on different nodes renting out their storage spaces. No single user is in charge of a single file. The network encrypts and distributes the files into smaller pieces across its storage chain.

Polygon (MATIC) Could Rival Ethereum (ETH)

Polygon (MATIC) increased by 5.38% in the seven days. The project has been enjoying some bullish sentiments lately. Polygon’s (MATIC) Layer-2 design has increased productivity and throughput on the Ethereum (ETH) network. Through a process known as bridging, Ethereum (ETH) holders can convert their ETH tokens to MATIC tokens and explore the Polygon (MATIC) network.

The network’s native token, MATIC, powers the platform’s transactions and validates transfers between blockchains. Users can also stake their MATIC tokens to increase their voting rights on the network. The project aims to increase connectivity across all Ethereum (ETH) protocols within a fraction of a second at a very affordable rate.

Uwerx (WERX) Tipped To Surpass Upwork

Thanks to its innovative ideas, the project’s community is always active and ready to participate in decision-making activities. Before the completion of the Alpha version, the project’s community is projected to increase by almost 100%.

The Uwerx community has convinced the team of an early token lock date. The developers decided to lock their token during the presale after 82.8% of the community voted for it. The developers’ liquidity lock will involve a 25-year commitment to the project, an assurance that the team won’t bail on the project when it launches. It will also guarantee safety from preying whales and deviant rug pullers.

As confirmed by the team, Uwerx will conduct an airdrop. Although it is a test airdrop, the team will leverage it to validate all the submitted wallet addresses.

The Uwerx team has already deployed and opened the platform’s Alpha version.  The Landing, Signup Pages, Login Page, Forgot Password, Activity Page, Hiring Dashboard, Job Creation Process Feature, a Freelancer or Client Initiation Function, and the Initial step of Job Creation and Finding Features have been released and open to users and so will the rest of the platform as soon as they are released. All these features will run on the Alpha version while the team works on the Beta version.

The Beta version will involve smooth operational testing of the platform. Users can signup and explore the platform’s capabilities in the open market. The team will collect user suggestions and reviews through a dedicated email address, [email protected].

For users requiring asset management, Uwerx has introduced a Vault to manage and protect users’ data and assets.

Uwerx has also reduced founders’ token allocation to just 7% and will relinquish their smart contract rights once Uwerx lists on centralized exchanges. The recently acquired audit approvals from SolidProof and InterFi Network will prove vital in the project’s future and longevity. Uwerx has come this far thanks to its agile methodological approach.

Uwerx is currently offering WERX tokens at a discounted price of $0.047725 along with an added bonus of 15%. The price is slated to rise to $0.05245 later today (Monday, July 10th at 18:00 UTC). We strongly recommend making your purchase promptly to benefit from this deal. Uwerx has given analysts great hope, so they believe the token’s price will reach $1.785 by Q4 2023 and $2.101 by Q2 2024.

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Polygon (Matic) & Terra (Luna) Price Drop, Bitgert Surge To Bullish

The crypto market looks a bit bearish today, with most cryptocurrencies already posting price drops in the past 24 hours. But a few cryptocurrencies are going against the grain to post impressive gains. Bitgert is one of the cryptocurrencies that have been bullish when the likes of Polygon (MATIC) and Terra (LUNA) prices have been dropping. Read more about the Bitgert price surge below:  

Bitgert

The

Centcex

The

Polygon (MATIC)

Polygon (MATIC) is among the large cryptocurrencies that are posting a drop in price today. Though the market is generally bearish, Matic is among the cryptocurrencies recording a significant drop. But the Polygon price drop could also be influenced by the increasing competition from other projects like Bitgert. The coming of the Bitgert chain may be one of the reasons Polygon (MATIC) is performing poorly. With Bitgert offering a faster speed and lower fee for gas than Polygon, it might start experiencing a reduced adoption rate. That’s why the price could be dropping faster.  

Terra (LUNA)

The Terra (LUNA) price performance has been one of the best in the crypto market. This is one of the cryptocurrencies that did very well in most of the 4th week of February. With an over 50% increase over the past 7 days, Terra has been increasing at a very fast rate. But the last few days have shown price drop when Bitgert is skyrocketing.

The crypto market looks a bit bearish today, with most cryptocurrencies already posting price drops in the past 24 hours. But a few cryptocurrencies are going against the grain to post impressive gains. Bitgert is one of the cryptocurrencies that have been bullish when the likes of Polygon (MATIC) and Terra (LUNA) prices have been dropping. Read more about the Bitgert price surge below:The Bitgert price has been booming even when the market is bearish. For most of the last two weeks of February, Bitgert has been skyrocketing even when the cryptocurrency market was crashing. This is because of the incredible speed of delivering products that Bitgert is moving at. Mid-Feb, the Bitgert team launched a game-changing BRC20 blockchain that has taken the crypto industry by storm. The Bitgert BRC20 blockchain is a zero-gas fee chain that charges $0.0000000000001 to transact on the chain. It is the cheapest gas in the crypto industry right now, which is why the crypto community is excited about it. The Bitgert chain has a 100k TPS, making it the fastest chain after overtaking Solana 65k. With so many products launching onto the chain, including Brise CEX, more investors are buying $BRISE. That’s why Bitgert price is skyrocketing during a bearish chúng tôi Centcex team is delivering the roadmap faster and within the set timelines. The team has done a lot in developing products for the network. In fact, the products development has already started, which shows how the team is committed to this project. Among the recent news that the team released is the completion of key components of the Centcex exchange. But there are more products, in fact, an unlimited number, that the Centcex team is building. The products will also include dApps, staking programs and many others. With all these products, the Centcex staking reward is a 100% APY. These are the major reasons why Centcex’s prices is very stable.Polygon (MATIC) is among the large cryptocurrencies that are posting a drop in price today. Though the market is generally bearish, Matic is among the cryptocurrencies recording a significant drop. But the Polygon price drop could also be influenced by the increasing competition from other projects like Bitgert. The coming of the Bitgert chain may be one of the reasons Polygon (MATIC) is performing poorly. With Bitgert offering a faster speed and lower fee for gas than Polygon, it might start experiencing a reduced adoption rate. That’s why the price could be dropping chúng tôi Terra (LUNA) price performance has been one of the best in the crypto market. This is one of the cryptocurrencies that did very well in most of the 4week of February. With an over 50% increase over the past 7 days, Terra has been increasing at a very fast rate. But the last few days have shown price drop when Bitgert is skyrocketing. The Terra (LUNA) has not launched a lot of products like Bitgert as well as game-changing utilities like BRC20 blockchain. In fact, the Terra team is yet to confirm the building of its own blockchain. Therefore, Bitgert might continue outperforming Terra.

Safemoon Price Prediction, Will Sfm’s Price Hit $0.00032?

SFM should be around $0.00025.

SafeMoon price prediction 19 Jul 2023: SafeMoon’s price for 19 Jul 2023 according to our analysis should range between $0.00024 to $0.00028 and the average price of SFM should be around $0.00026.

SafeMoon price prediction 20 Jul 2023: SafeMoon’s price for 20 Jul 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00024.

SafeMoon price prediction 21 Jul 2023: SafeMoon’s price for 21 Jul 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00024.

SafeMoon price prediction 26 Jul 2023: SafeMoon’s price for 26 Jul 2023 according to our analysis should range between $0.00021 to $0.00025 and the average price of SFM should be around $0.00023.

SafeMoon price prediction 31 Jul 2023: SafeMoon’s price for 31 Jul 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.

SafeMoon price prediction 10 Aug 2023: SafeMoon’s price for 10 Aug 2023 according to our analysis should range between $0.00021 to $0.00024 and the average price of SFM should be around $0.00023.

SafeMoon price prediction September 2023: SafeMoon’s price for September 2023 according to our analysis should range between $0.00022 to $0.00025 and the average price of SFM should be around $0.00024.

SafeMoon price prediction October 2023: SafeMoon’s price for October 2023 according to our analysis should range between $0.00023 to $0.00026 and the average price of SFM should be around $0.00025.

SafeMoon price prediction November 2023: SafeMoon’s price for November 2023 according to our analysis should range between $0.00024 to $0.00027 and the average price of SFM should be around $0.00025.

SafeMoon price prediction December 2023: SafeMoon’s price for December 2023 according to our analysis should range between $0.00024 to $0.00028 and the average price of SFM should be around $0.00026.

SafeMoon price prediction 2024: SafeMoon’s price for 2024 according to our analysis should range between $0.00032 to $0.00049 and the average price of SFM should be around $0.0004.

SafeMoon price prediction 2025: SafeMoon’s price for 2025 according to our analysis should range between $0.00043 to $0.00065 and the average price of SFM should be around $0.00054.

SafeMoon price prediction 2026: SafeMoon’s price for 2026 according to our analysis should range between $0.00057 to $0.00086 and the average price of SFM should be around $0.00072.

SafeMoon price prediction 2027: SafeMoon’s price for 2027 according to our analysis should range between $0.00077 to $0.0011 and the average price of SFM should be around $0.00096.

SafeMoon price prediction 2028: SafeMoon’s price for 2028 according to our analysis should range between $0.001 to $0.0015 and the average price of SFM should be around $0.0012.

SafeMoon price prediction 2029: SafeMoon’s price for 2029 according to our analysis should range between $0.0013 to $0.002 and the average price of SFM should be around $0.0017.

SafeMoon price prediction 2030: SafeMoon’s price for 2030 according to our analysis should range between $0.0018 to $0.0027 and the average price of SFM should be around $0.0022.

SafeMoon price prediction 2031: SafeMoon’s price for 2031 according to our analysis should range between $0.0024 to $0.0036 and the average price of SFM should be around $0.003.

SafeMoon price prediction 2032: SafeMoon’s price for 2032 according to our analysis should range between $0.0032 to $0.0048 and the average price of SFM should be around $0.004.

SafeMoon price prediction 2033: SafeMoon’s price for 2033 according to our analysis should range between $0.0042 to $0.0063 and the average price of SFM should be around $0.0053.

SafeMoon price prediction 2034: SafeMoon’s price for 2034 according to our analysis should range between $0.0056 to $0.0085 and the average price of

Terra Luna Classic Price Prediction 2025

Disclaimer: The datasets shared in the following article have been compiled from a set of online resources and do not reflect AMBCrypto’s own research on the subject. 

In the last few days, the Terra Classic community has passed multiple proposals which included raising the Terra Luna Classic [LUNC] burn tax to 0.5%, rewarding people for staking, whitelisting Dapp’s contracts, and increasing community pool funding.

The value of LUNC stands at the same price level as it was a week earlier. The coin has become a playground depicting extremely volatile moves in both directions.

The project’s founder Do Kwon is currently under arrest after being caught in Montenegro. As per a latest report by Forkast, a South Korean court has approved local prosecutors’ request to freeze assets and properties worth $176 million belonging to Do Kwon. 

Besides, a Terra Classic network-based project named Cremation Coin has set the tone by burning over 9 million LUNC tokens as part of its weekly campaign. Cremation Coin’s cumulative burn now exceeds 390 million LUNC tokens, bringing some reassurance to the crypto world amid a falling LUNC burn pace.

🐳 #LUNCBURN ALERT 🐳

9,447,631 $LUNC was just incinerated. Thanks to @Cremation_Coin for the contribution!

✅ terra1vwchc3pkrxn8kahd0g9wxd8zjr0drnduzkn4z3

Total #LunaClassic Burn is now: 55,737,474,348

— LUNC BURN TRACKER (@LUNCMetrics) May 2, 2023

Following 2023’s disasters, nearly the entire Terra blockchain development community left. And, most analysts predict a bleak future for the blockchain’s ecosystem.

On 23 March, news of Terra co-founder Do Kwon’s arrest reached the crypto-community. Terra co-founder Do Kwon, who was on the run since the Terra LUNA crisis, was finally arrested in Montenegro.   

LUNC was at the center of the collapse of the Terra ecosystem in May 2023. The coin has also been severely affected by the collapse of the crypto-exchange FTX in November last year. Its market capitalization has dropped from $1.5 billion to $735 million since then.

Transactions on the Terra 2.0 blockchain are validated through the proof-of-stake (PoS) consensus mechanism.

The leading cryptocurrency, Ethereum (ETH), has also transitioned from a proof-of-work to a proof-of-stake mechanism. This has only made the competition among PoS blockchains tougher.

The network has 130 validators working at a given point of time. As a PoS platform, it is considered being a very eco-friendly token.

Why do these projections matter?

A stablecoin is intended to safeguard coin holders against the volatility of other cryptocurrencies. It is pegged to either a fiat currency such as USD or to a supporting cryptocurrency. Terra USD (UST) was pegged to Luna Classic (LUNC- then, only LUNA).

For the initial few years, LUNC kept performing well. And, it was even among the top 10 cryptocurrencies by market value by the end of 2023.

But the Terra system collapsed in May 2023, leading to a fork. It basically launched a new version of Luna. The Terra Ecosystem Revival Plan 2 was implemented according to which both versions of the Luna token can exist.

Undoubtedly, the future of this cryptocurrency is crucial in determining if a failed crypto can make a comeback and grow.

Well, its performance after the May 2023 debacle has been, so far, less than celebratory.

But if LUNC trades well in the future, it will be a cause of celebration not only for this particular cryptocurrency, but for a lot of other cryptos.

LUNC’s price, volume, and everything in between

Since its launch in 2023, LUNC’s price kept floating around $0.2 and $1.3 until April 2023. When the crypto market boomed in mid-2024, its price increased and touched $100 by the end of the year.

Starting from 2023, it kept oscillating between $50 and $100 and reached an all-time high (ATH) of $119.18 on 5 April 2023. The next month, its price began to fall and the Terra system collapsed in mid-May.

At press time, the coin was trading at $0.00008411 with a market cap of $170 million.

Bloomberg reported in May 2023 that the market lost approximately $45 billion within a week following the Terra collapse. Terraform Labs and its co-founder Do Kwon were fined $78.4 million in corporate and income tax by the Korean National Tax Service.

On 25 May 2023, Bloomberg reported that the network launched a new version of the cryptocurrency, LUNA. The older crypto is now called Luna Classic (LUNC) and the newer one is called Luna 2.0 (LUNA).

Though LUNC, the older cryptocurrency, has not been entirely replaced, a lot of users are moving to LUNA. It should be noted here that LUNC so far has not been performing well at all.

The market capitalization of LUNC similarly reflects the market sentiment regarding crypto. Throughout 2023-20, it didn’t even reach up to $500 million, but began increasing in 2023.

Now, towards the beginning of February, it crossed the $1 billion mark. And, by the end of 2023, it was above $36 billion.

LUNC’s journey kept moving upward the next year too and in April 2023, it crossed $41 billion. But post the crash of May 2023, it oscillated between $300 million and $1.5 billion.

South Korea is now seeking to revoke Kwon’s passport, following which he might be forced to return to South Korea. A request has been passed to the nation’s Foreign Ministry to scrap the travel document, reported Bloomberg. An arrest warrant was issued against him and other members.

Now that Kwon has been arrested, we will see how the court cases are held against him. 

LUNC’s 2025 predictions

Before you read further, you should understand that predictions of different cryptocurrency platforms and analysts widely vary as different analysts rely on different sets of metrics to arrive at their conclusions.

A good number of times, these predictions can go wildly wrong. Besides, nobody can foresee events such as the Chinese crypto ban or the Russia-Ukraine crisis. Let us now have a look at what different analysts have to say about the future of LUNC in 2025.

Telegaon predicts that the minimum and maximum prices of LUNC in 2025 will be $0.0089 and $0.028, respectively.

Other experts, after analyzing the previous performance of LUNC, predict that its average price in the said year will be $0.015.

DigitalCoinPrice predicts that the minimum and maximum prices of LUNC in 2025 are going to be $0.000268 and $0.000331. Its average price in 2025 will be $0.000321.

LUNC’s 2030 predictions

DigitalCoinPrice predicts that the minimum and maximum prices of LUNC in 2030 are going to be $0.000887 and $0.000953. Its average price in 2030 will be $0.000941.

On the other hand, Bitcoin Wisdom predicted that LUNC’s price will keep oscillating between $0.002603 and $0.002834 in 2030. Its average price in the said year will be $0.002719 as per the prediction.

Disclaimer

Now, it’s worth addressing the elephant in the room too. Pre and post-crash projections and opinions on the project have changed significantly over the last few months. This means that there is a lot of uncertainty around. For instance, back in March, Professor Carol Alexander, a member of Finder’s panel of experts, claimed,

“… as its name implies, it could actually go to the moon (for a while).”

On the contrary, there are others who believe,

“There is a lot of uncertainty around LUNA right now –⁠ the project is really ambitious and the objective an admirable one but just what the effect on the LUNA token itself will be is unclear.”

Conclusion

So far, we have provided a succinct summary of LUNA Classic (LUNC). For those of you contemplating investing in cryptocurrency, we would like to reiterate that cryptocurrency predictions cannot be relied upon entirely. And, you should conduct your own research before making an investment in LUNC.

The only thing that can save the coin is token burning, which will raise prices by reducing market oversupply. It was already put to the test in September when Binance and other significant CEXs started burning LUNC tokens, sending the price of LUNC soaring by 60% in just a few hours.

The cryptocurrency market still remains very bearish and is likely to remain volatile for the next few months.

A recent Bloomberg report says that upcoming legislation would ban algorithmic stablecoins such as TerraUSD the collapse of which led to a global crypto crash. The said bill is currently being drafted in the U.S. House. The bill would make it illegal to develop or issue new “endogenously collateralized stablecoins.”

The New York Times interviewed Ethereum co-founder Vitalik Buterin last month who claimed that the Terra Luna team attempted market manipulation in order to boost the value of the native cryptocurrency. He also recalled that many “smart people” had stated that Terra was “fundamentally bad.”

In an interview with Laura Shin on the “Unchained” podcast on 28 October, Kwon claimed that he migrated from South Korea to Singapore before the demise of the Terra environment. He also refuted reports that he is eluding law authorities.

Kwon said,

“Whatever issues existed in Terra’s design, its weakness [in responding] to the cruelty of the markets, it’s my responsibility and my responsibility alone.”

On 5 November, Terra Rebels tweeted that the first round of its lottery game had finally ended, with the winner going away with over 24 million Terra Luna Classic (LUNC). More than 10.5 million LUNC were sent to the burn wallet. As we can see, such efforts are underway in one way or another.

Please visit our website to participate in the next drawing.

— Terra Rebels (@TerraRebels) November 4, 2023

According to a recent third-party audit by JS Held, a New York-based consultancy firm, Luna Foundation Guard (LFG), the entity behind the defunct Terra ecosystem, spent $2.8 billion in crypto trying to defend the peg of algorithmic stablecoin TerraUSD (UST) in May. The audit also claims that Terraform Labs (TFL), the Terra blockchain developer, spent $613 million defending the peg.

Luna Classic has announced that it will re-enable Inter Blockchain Communication (IBC), a protocol to allow the sharing of messages and trading assets with other blockchains. A member of the Terra Classic development team confirmed this on Twitter.

— reXx™ (@CosmoSreXx) December 5, 2023

In terms of fundamentals, what may help such a break occur is progress on implementing a proposal that was recently passed by Terra Luna Classic validators. In particular, the community has adopted a plan to re-peg LUNC’s sister stablecoin, USTC.

As the broader cryptocurrency market stabilizes ahead of a busy week of macro events, including a barrage of key US jobs data and a testimony from Fed Chair Jerome Powell before the US Congress, LUNC bulls will be hoping the cryptocurrency can continue to find support above this level.

Industry experts remain apprehensive if LUNC’s price will even reach $1 in a few years. As the latest news of Kwon’s arrests appears, the market fears that more details about the programme may sink its performance further down.   

Learn Mobile Price Prediction Through Four Classification Algorithms

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

Introduction

Mobile phones come in all sorts of prices, features, specifications and all. Price estimation and prediction is an important part of consumer strategy. Deciding on the correct price of a product is very important for the market success of a product. A new product that has to be launched, must have the correct price so that consumers find it appropriate to buy the product.

 The Problem

The data contains information regarding mobile phone features, specifications etc and their price range. The various features and information can be used to predict the price range of a mobile phone.

The data features are as follows:

Battery Power in mAh

Has BlueTooth or not

Microprocessor clock speed

The phone has dual sim support or not

Front Camera Megapixels

Has 4G support or not

Internal Memory in GigaBytes

Mobile Depth in Cm

Weight of Mobile Phone

Number of cores in the processor

Primary Camera Megapixels

Pixel Resolution height

Pixel resolution width

RAM in MB

Mobile screen height in cm

Mobile screen width in cm

Longest time after a single charge

3g or not

Has touch screen or not

Has wifi or not

Methodology

We will proceed with reading the data, and then perform data analysis. The practice of examining data using analytical or statistical methods in order to identify meaningful information is known as data analysis. After data analysis, we will find out the data distribution and data types. We will train 4 classification algorithms to predict the output. We will also compare the outputs. Let us get started with the project implementation.

First, we import the libraries.

import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import seaborn as sns import matplotlib.pylab as plt %matplotlib inline

Now, we read the data and view an overview of the data.

train_data=pd.read_csv('/kaggle/input/mobile-price-classification/train.csv') train_data.head()

Output:

Now, we will use the info function to see the type of data in the dataset.

train_data.info()

Output:

RangeIndex: 2000 entries, 0 to 1999 Data columns (total 21 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 battery_power 2000 non-null int64 1 blue 2000 non-null int64 2 clock_speed 2000 non-null float64 3 dual_sim 2000 non-null int64 4 fc 2000 non-null int64 5 four_g 2000 non-null int64 6 int_memory 2000 non-null int64 7 m_dep 2000 non-null float64 8 mobile_wt 2000 non-null int64 9 n_cores 2000 non-null int64 10 pc 2000 non-null int64 11 px_height 2000 non-null int64 12 px_width 2000 non-null int64 13 ram 2000 non-null int64 14 sc_h 2000 non-null int64 15 sc_w 2000 non-null int64 16 talk_time 2000 non-null int64 17 three_g 2000 non-null int64 18 touch_screen 2000 non-null int64 19 wifi 2000 non-null int64 20 price_range 2000 non-null int64 dtypes: float64(2), int64(19) memory usage: 328.2 KB

Now, we remove the data points with missing data.

train_data_f = train_data[train_data['sc_w'] != 0] train_data_f.shape

Output:

(1820, 21)

Let us visualize the number of elements in each class of mobile phones.

#classes sns.set() price_plot=train_data_f['price_range'].value_counts().plot(kind='bar') plt.xlabel('price_range') plt.ylabel('Count') plt.show()

Output:

So, there are mobile phones in 4 price ranges. The number of elements is almost similar.

Data Distribution

Let us analyse some data features and see their distribution.

First, we see how the battery mAh is spread.

sns.set(rc={'figure.figsize':(5,5)}) ax=sns.displot(data=train_data_f["battery_power"]) plt.show()

Output:

Now, we see the count of how many devices have Bluetooth and how many don’t.

sns.set(rc={'figure.figsize':(5,5)}) ax=sns.displot(data=train_data_f["blue"]) plt.show()

Output:

So, we can see that half the devices have Bluetooth, and half don’t.

Next, we analyse the mobile depth ( in cm).

sns.set(rc={'figure.figsize':(5,5)}) ax=sns.displot(data=train_data_f["m_dep"]) plt.show()

Output:

A few mobiles are very thin and a few ones are almost a cm thick.

In a similar way, the data distribution can be analysed for all the data features. Implementing that will be very simple.

Let us see if there are any missing values or missing data.

X=train_data_f.drop(['price_range'], axis=1) y=train_data_f['price_range'] #missing values X.isna().any()

Output:

battery_power False blue False clock_speed False dual_sim False fc False four_g False int_memory False m_dep False mobile_wt False n_cores False pc False px_height False px_width False ram False sc_h False sc_w False talk_time False three_g False touch_screen False wifi False dtype: bool

Let us split the data.

#train test split of data from sklearn.model_selection import train_test_split X_train, X_valid, y_train, y_valid= train_test_split(X, y, test_size=0.2, random_state=7)

Now, we define a function for creating a confusion matrix.

#confusion matrix from sklearn.metrics import classification_report, confusion_matrix, accuracy_score def my_confusion_matrix(y_test, y_pred, plt_title): cm=confusion_matrix(y_test, y_pred) print(classification_report(y_test, y_pred)) sns.heatmap(cm, annot=True, fmt='g', cbar=False, cmap='BuPu') plt.xlabel('Predicted Values') plt.ylabel('Actual Values') plt.title(plt_title) plt.show() return cm

Now, as the function is defined, we can proceed with implementing the classification algorithms.

Random Forest Classifier

A random forest is a supervised machine learning method built from decision tree techniques. This algorithm is used to anticipate behaviour and results in a variety of sectors, including banking and e-commerce.

A random forest is a machine learning approach for solving regression and classification issues. It makes use of ensemble learning, which is a technique that combines multiple classifiers to solve complicated problems.

A random forest method is made up of a large number of decision trees. The random forest algorithm’s ‘forest’ is trained via bagging or bootstrap aggregation. Bagging is a meta-algorithm ensemble that increases the accuracy of machine learning algorithms.

The outcome is determined by the (random forest) algorithm based on the predictions of the decision trees. It forecasts by averaging or averaging the output of several trees. The precision of the outcome improves as the number of trees grows.

A random forest system is built on a variety of decision trees. Every decision tree is made up of nodes that represent decisions, leaf nodes, and a root node. The leaf node of each tree represents the decision tree’s final result. The final product is chosen using a majority-voting procedure. In this situation, the output picked by the majority of the decision trees becomes the random forest system’s ultimate output. Let us now implement the random forest algorithm.

First, we build the model.

#building the model from sklearn.ensemble import RandomForestClassifier rfc=RandomForestClassifier(bootstrap= True, max_depth= 7, max_features= 15, min_samples_leaf= 3, min_samples_split= 10, n_estimators= 200, random_state=7)

Now, we do the training and prediction.

rfc.fit(X_train, y_train) y_pred_rfc=rfc.predict(X_valid)

Let us apply the function for the accuracy metrics.

print('Random Forest Classifier Accuracy Score: ',accuracy_score(y_valid,y_pred_rfc)) cm_rfc=my_confusion_matrix(y_valid, y_pred_rfc, 'Random Forest Confusion Matrix')

Output:

Random Forest Classifier Accuracy Score: 0.9093406593406593 precision recall f1-score support 0 0.98 0.97 0.97 95 1 0.90 0.92 0.91 92 2 0.82 0.86 0.84 86 3 0.93 0.88 0.90 91 accuracy 0.91 364 macro avg 0.91 0.91 0.91 364 weighted avg 0.91 0.91 0.91 364

So, we can see that the random forest algorithm has good accuracy in prediction.

Naive Bayes

Conditional probability is the foundation of Bayes’ theorem. The conditional probability aids us in assessing the likelihood of something occurring if something else has previously occurred.

Image:  Illustration of how a Gaussian Naive Bayes (GNB) classifier works

Gaussian Naive Bayes is a Naive Bayes variation that allows continuous data and follows the Gaussian normal distribution. The Bayes theorem is the foundation of a family of supervised machine learning classification algorithms known as naive Bayes. It is a basic categorization approach with a lot of power. When the dimensionality of the inputs is high, they are useful. The Naive Bayes Classifier may also be used to solve complex classification issues.

Let us implement the Gaussian NB classifier.

from sklearn.naive_bayes import GaussianNB gnb = GaussianNB()

Now, we perform the training and prediction.

gnb.fit(X_train, y_train) y_pred_gnb=gnb.predict(X_valid)

Now, we can check the accuracy.

print('Gaussian NB Classifier Accuracy Score: ',accuracy_score(y_valid,y_pred_gnb)) cm_rfc=my_confusion_matrix(y_valid, y_pred_gnb, 'Gaussian NB Confusion Matrix')

Output:

Gaussian NB Classifier Accuracy Score: 0.8461538461538461 precision recall f1-score support 0 0.93 0.92 0.92 95 1 0.79 0.73 0.76 92 2 0.74 0.80 0.77 86 3 0.92 0.93 0.93 91 accuracy 0.85 364 macro avg 0.84 0.85 0.84 364 weighted avg 0.85 0.85 0.85 364

We can see that the model is performing well.

KNN Classifier

The K Nearest Neighbor method is a type of supervised learning technique that is used for classification and regression. It’s a flexible approach that may also be used to fill in missing values and resample datasets. K Nearest Neighbor examines K Nearest Neighbors (Data points) to forecast the class or continuous value for a new Datapoint, as the name indicates.

The K-NN method saves all available data and classifies a new data point based on its similarity to the existing data. This implies that fresh data may be quickly sorted into a well-defined category using the K-NN method. The K-NN algorithm is a non-parametric algorithm, which means it makes no assumptions about the underlying data. It’s also known as a lazy learner algorithm since it doesn’t learn from the training set right away; instead, it saves the dataset and performs an action on it when it comes time to classify it.

Let us perform the implementation of the classifier.

from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=3,leaf_size=25)

Now, we train the data and make our predictions.

knn.fit(X_train, y_train) y_pred_knn=knn.predict(X_valid)

Now, we check the accuracy.

print('KNN Classifier Accuracy Score: ',accuracy_score(y_valid,y_pred_knn)) cm_rfc=my_confusion_matrix(y_valid, y_pred_knn, 'KNN Confusion Matrix')

Output:

KNN Classifier Accuracy Score: 0.9340659340659341 precision recall f1-score support 0 0.99 0.98 0.98 95 1 0.93 0.97 0.95 92 2 0.87 0.88 0.88 86 3 0.94 0.90 0.92 91 accuracy 0.93 364 macro avg 0.93 0.93 0.93 364 weighted avg 0.93 0.93 0.93 364

The KNN classifier is quite adept at its task.

SVM Classifier

Support Vector Machine, or SVM, is a prominent Supervised Learning technique that is used for both classification and regression issues. However, it is mostly utilised in Machine Learning for Classification purposes.

The SVM algorithm’s purpose is to find the optimum line or decision boundary for categorising n-dimensional space so that we may simply place fresh data points in the proper category in the future. A hyperplane is the optimal choice boundary.

Check this article for more information on SVM.

Let us do the implementation of SVM.

from sklearn import svm svm_clf = svm.SVC(decision_function_shape='ovo') svm_clf.fit(X_train, y_train) y_pred_svm=svm_clf.predict(X_valid)

Now, we check the accuracy.

print('SVM Classifier Accuracy Score: ',accuracy_score(y_valid,y_pred_svm)) cm_rfc=my_confusion_matrix(y_valid, y_pred_svm, 'SVM Confusion Matrix')

Output:

SVM Classifier Accuracy Score: 0.9587912087912088 precision recall f1-score support 0 0.98 0.98 0.98 95 1 0.93 0.97 0.95 92 2 0.94 0.93 0.94 86 3 0.99 0.96 0.97 91 accuracy 0.96 364 macro avg 0.96 0.96 0.96 364 weighted avg 0.96 0.96 0.96 364

We can see that the SVM classifier is giving the best accuracy.

Conclusion

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