You are reading the article Ethereum Price Prediction 2025: Collateral Network Predicted To Rival Eth In Growth updated in December 2023 on the website Cancandonuts.com. 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 Ethereum Price Prediction 2025: Collateral Network Predicted To Rival Eth In Growth
Following its recent upgrade, analysts are extremely bullish on Ethereum’s (ETH) future. No project in the industry has as much influence as Ethereum, and now it’s easier than ever for investors to stake their Ether.
With this in mind, many investors believe that Ethereum could hit an all-time high by 2025. At the same time, Collateral Network (COLT) could rival Ethereum, with price predictions suggesting COLT tokens will surge by 3500% in the next six months.
Ethereum (ETH) Activity On the Rise But Its Price CrashesEthereum is used as the foundation for thousands of projects throughout the DeFi market. It’s a popular mining option, and its popularity among stakers has grown exponentially since the Shapella update. However, despite its recent success, Ethereum’s price has declined.
After a temporary rally, Ethereum has decreased in value by 5.81%, with one Ethereum (ETH) trading at $1,776.33 at the time of writing. Additionally, Ethereum mining has become significantly less profitable.
Given the rising cost of electricity worldwide, miners’ profits have decreased, and smaller miners are beginning to make a loss. As a result, Ethereum mining could become monopolized, much like Bitcoin (BTC). While this could be bad for smaller miners, large mining operations will almost definitely capitalize.
As the DeFi market continues to expand, analysts believe that Ethereum will increase in value, potentially hitting an all-time high during the next bull market. However, given its high price, some investors may be unable to profit from this growth.
Could Collateral Network (COLT) Offer Better Returns Than Ethereum?By 2025 the crypto industry will have changed significantly, with more real-world applications than ever. One project that plans to pioneer this development is Collateral Network (COLT). Collateral Network is currently in stage one of its presale and has a long-term roadmap designed to disrupt the trillion-dollar lending industry.
With industry experience in both DeFi and lending, the Collateral Network development team spotted a huge gap in the market. Most lending processes are outdated, and few institutions will let individuals take loans against their assets. This forces individuals to sell their assets, often at below-market rates. Collateral Network lets individuals unlock liquidity against their physical assets by bringing them on-chain as fractionalized NFTs.
Once minted, investors can buy NFT fractions to loan money to borrowers. As a lender, investors will earn an interest rate, which is paid out weekly until the borrower fully repays the loan. Not only is this process easier, but it prevents the need for excess paperwork and unnecessary credit checks.
Collateral Network has been doxxed and audited to give investors peace of mind. Furthermore, security features such as 2FA are implemented on the platform to prevent any risk of a scam.
In the circumstance that a borrower defaults on their loan, their asset will be sold at a private auction to Collateral Network token holders, who will be able to purchase the asset at a below-market rate. This ensures lenders always recoup their investments.
With less than 35% of stage one’s tokens still available, Collateral Network is expected to sell out in the next two weeks, after which COLT tokens will increase in price to $0.0168. Furthermore, the price of the token is expected to increase up to $0.35 before the presale ends and surge by 100x once it hits major exchanges. Therefore, the best time to acquire COLT tokens is now.
For more information on Collateral Network, visit the website, join the presale, or join the community for regular updates.
Find out more about the Collateral Network presale here:You're reading Ethereum Price Prediction 2025: Collateral Network Predicted To Rival Eth In Growth
10 Best Ethereum (Eth) Wallets In 2023
Top Pick
ZenGo
ZenGo is the most secure non-custodial wallet in Web3 by removing the private key vulnerability, making it the simplest yet most secure wallet to invest in cryptocurrency. Create an account in less than 60 seconds and be the true owner of your crypto.
Visit ZenGo
Most Secure Non-custodial Wallet
Best for beginners and intermediate crypto investors who care about their wallet security.
ZenGo is the most secure non-custodial wallet in Web3 by removing the private key vulnerability, making it the simplest yet most secure wallet to invest in cryptocurrency. Create an account in less than 60 seconds and be the true owner of your crypto.
#1 Top Pick
ZenGo
5.0
Coins Supported: ETH, BTC, DOGE, USDC, etc.
Type of wallet: Non-custodial
Price: Free
Visit ZenGo
Features:
Store, buy, trade, send and receive crypto.
Sell Bitcoin in Europe, the UK, and the US
Over 70 crypto assets are supported including Bitcoin, Ethereum, Dogecoin (DOGE).
Enjoy 24/7 live support by real agents
Choose from the 3 network fees modes (economy, regular, fast). Applicable for BTC, ETH, ERC20.
Cancel or speed up your sent transactions before they are confirmed on the blockchain with 1 tap.
👍 Pros 👎 Cons
Allows to sell or buy within the bitcoin wallet app Limited range of cryptocurrencies
Provide 3FA security
Easy to use interface
No private key vulnerability
Key Specs:Price: Free
Best for Web3 security and privacy
If you’re in the market for a high-quality self-custodial Ethereum wallet, BlockWallet is an excellent choice. Built-in swaps, bridges, focus on privacy and security and user-friendly interface make it a standout product.
#2
BlockWallet
4.9
Coins Supported: All EVM-compatible tokens
Type of Wallet: Self-custodial browser extension software wallet
Price: Free
Visit BlockWallet
Features:
Zero tracking policy
Built-in swaps and bridges.
Hardware wallet support.
Built-in gas tracker.
NFT support.
Phishing protection.
Browse across all EVM-compatible networks.
Available on all Chromium-based web browsers (Chrome, Brave, Opera, Edge, etc.)
👍 Pros 👎 Cons
Offers extremely low fees Lacks mobile support
Provides a smooth user experience with an intuitive design
Implements plenty of security systems to safeguard funds
Designed to protect digital identity
Compatible with all DApps
Key Specs:Customer Support: 24/7 support on their Telegram community
Best Wallet for Versatile Crypto Management
NOW Wallet is a fast and secure non-custodial crypto wallet mobile app developed by the team behind the ChangeNOW Crypto Exchange service. Despite being a relatively new product, NOW Wallet has quickly emerged as a top-performing wallet, surpassing many established counterparts.
#3
NOW Wallet
4.8
Coins Supported: 500+ Including BTC, ETH, DOGE, and XMR
Type of Wallet: Non-custodial hot wallet
Price: Free
Visit NOW Wallet
Features:
Built-in ChangeNOW exchanger for instant crypto-to-crypto and fiat-to-crypto exchanges within the app.
Advanced settings, including the account nonce for ETH transactions, allowing for a more personalized experience.
WalletConnect integration connects NOW Wallet to any dApp on Ethereum, BSC, Polygon, AVAX C-chain, Optimism, Fantom and Algorand simply by scanning a QR-code.
Versatile management of a wide variety of cryptocurrencies, providing users with ample trading options.
👍 Pros 👎 Cons
Non-custodial nature, giving users complete control over their funds and enhancing security. No native support for hardware wallets like Ledger or Trezor.
Seamless compatibility between the mobile and desktop versions of NOW Wallet. As a newer product, NOW Wallet is still in the process of gaining wider recognition.
Instant crypto-to-crypto and fiat-to-crypto exchanges directly within the app.
User-friendly interface that simplifies the crypto management experience.
Free to use, ensuring accessibility for all users.
Key Specs:Price: free.
Best Wallet with Multiple features
Binance is one of the best platforms to create an Ethereum wallet that offers a platform for trading more than 150 cryptocurrencies. It provides an API that helps you to integrate your current trading application.
#4
Binance
4.7
Coins Supported: BTC, ETH, DOT, ADA, etc.
Type of wallet: Software wallet
Price: Free
Visit Binance
Features:
This application offers a wide range of tools for trading online.
It is one of the safest Ethereum wallets that provides 24/7 support.
This ERC20 platform is compatible with Web, iOS, Android, and PC clients.
It does 1.2 Bn average daily trading volume with more than 1,400,000 transactions per second.
👍 Pros 👎 Cons
Over 1500 cryptocurrencies for trade and Over 65 cryptocurrencies for U.S-based investors Not a good customer support.
Affordable fees
Wide selection of trading options and order types
Provides comprehensive charting feature
Key Specs:Your DeFi Entry Point
The 1inch Wallet is a powerful, secure and versatile crypto wallet that enables you to store, send and swap digital assets in the most user-friendly way. The 1inch Wallet is a self-custodial wallet. This means that you retain full control over your funds, and no one else has access to them.
Features:
You can swap crypto at the best possible rate without paying for trading or gas
Several leading security firms have confirmed that this is a safe app. Secure Enclave on iOS ensure top-level security.
1inch Wallet supports all available tokens by importing custom tokens on 10 blockchains.
This app has a built-in 24/7 chat support, help center and active Discord/Telegram communities.
👍 Pros 👎 Cons
User-friendly interface The iOS version supports swaps only via Web3 browser
No trading or gas fees
An infinite amount of HD wallet derivation paths
Compatible with Ledger
One wallet for major blockchains and tokens
24/7 customer support
Key Specs:Best for trading major cryptocurrencies
Cex.io is one of the best Ethereum wallet that helps you to buy and sell cryptocurrencies. It allows you to deposit funds using MasterCard, Visa card, or PayPal Debit MasterCard. This ERC20 platform follows scalping and frequency trading strategies to secure assets and data.
#6
CEX.IO
4.6
Coins Supported: Bitcoins, Ethereum, XRP, XMR, LTC, Cardano, Neo, etc.
Type of wallet: Software wallet
Price: Free
Visit CEX.IO
Features:
Users can trade USD for Bitcoins, Ethereum, and XRP (Ripple).
It offers protection against DDOS(Distributed Denial-of-Service) attacks using full data encryption.
You can trade with more than 10x leverage without creating an extra account.
It is one of the top Ethereum exchanges that supports platforms like mobile devices and websites.
This Dapp application offers downloadable reports showing real time balance and transaction history.
👍 Pros 👎 Cons
It offers protection against DDOS (Distributed Denial-of-Service) attacks with the help of full data encryption. Does not offer many coin options.
Supports REST and FIX APIs for automated trading.
Key Specs:Best Cold Wallet for those on a Budget
Ledger Nano is a hardware wallet that supports a multitude of cryptocurrencies. This hardware wallet has an LED display for payment validation and a PIN to confirm the transaction. This handheld device is convenient and secure.
Features:
You can easily access it via USB-compatible devices.
It allows you to install up to 100 applications on your Ledger.
This ERC20 wallet allows you to secure and control all your crypto.
It offers 2-factor authentication.
👍 Pros 👎 Cons
It supports 5500+ coins and tokens. It is expensive
It offers Bluetooth connectivity Does not offer touch screen support.
There is no need for an OTG kit. It has a USB-C cable.
It has 8-hour battery life.
Key Specs:Best Hardware Wallet for those on a Budget
Trezor is a hardware wallet that helps you to store your Ether and Bitcoins. You can easily plug it into your computer or smartphone. It helps you to randomly generated a PIN that keeps the device safe and secure.
Features:
Ultra-secure offline storage.
This ERC20 supports more than 1,000 currencies.
Easy-to-use touchscreen.
Extremely simple to use.
It allows you to hide your private key.
👍 Pros 👎 Cons
It offers a color touchscreen. Quite expensive tool
Trezor T is an Open-source firmware. Needs good customer care support
Easy to set up
Provides top-notch security
Key Specs:Best Overall ETH Wallet
Coinbase is one of the best Ethereum wallets that can be utilized for purchasing, selling, transferring, and storing digital currency. It securely stores a wide range of digital assets in offline storage. This platform supports more than 100 countries.
Features:
You can buy and sell any digital currency and keep track of them in one place.
It provides an app for both iOS and Android devices.
You can schedule your currency trading on a daily, weekly, or monthly basis.
It stores your funds in a vault for safety purposes.
It is one of the largest cryptocurrency exchanges.
👍 Pros 👎 Cons
User-friendly interface High transaction fees
Multi-signature and 2-factor authentication support. Limited trading types
Provide optional cloud backups which can protect your digital keys.
Key Specs:Best Wallet with High deposit & withdrawal limits
Kraken is one of the best Ethereum wallets that offers financial stability by maintaining full reserves, relationships, and the highest legal compliance standards.
Features:
It provides a highly comprehensive security approach.
You can reach out to their support team via live chat.
Kraken automatically checks all addresses for errors.
👍 Pros 👎 Cons
Great security It demands Higher fees when not using Kraken Pro
Gives high liquidity exchange
Nice customer care service
Straightforward user interface
Key Specs:Best for beginners
Gemini is one of the best Ethereum wallet trading tools that helps you to legitimize cryptocurrencies worldwide. It is a simple, elegant, and secure way to build a crypto portfolio.
Features:
It offers discounts up to 0% for volume traders.
It offers good security measures.
Regulation equates to trust for many investors.
👍 Pros 👎 Cons
Insured against certain unauthorized withdrawals through private plans. Higher fees
Trading available on both web platforms and mobile apps. No anonymity while using cryptocurrencies
Easy to set up
Provides top-notch security
Key Specs:Best Wallet for Canadians
Coinsmart is a digital currency wallet that enables you to buy and sell crypto with no hassle. It enables you to access your Ether payment and your cash instantly. This application provides you a quick and easy way to send an invoice to your customer using SmartPay Invoicing.
Features:
Supports a wide range of cryptocurrencies like Bitcoin Cash, XRP, Ethereum, Litecoin, and more.
Provides 24/7 live support.
It processes all Fiat withdrawal in few days.
Enables you to place customized orders seamlessly.
This platform can be accessed from mobile and desktop.
👍 Pros 👎 Cons
Easy to set up with quick account verification. Available in very limited countries
Fast and same-day deposits. It currently only offers 16 coins.
It offers a referral program
Key Specs:Best Wallet with Zero Fee
Features:
Advanced security for your cryptocurrency.
Your asset stays securely offline on Trezor hardware.
It allows you to monitor market movements on the go.
It allows you to send, receive and exchange cryptocurrency with your desktop, mobile, and hardware wallets.
👍 Pros 👎 Cons
Compatible with various devices like Trezor Model T, Trezor One, Linux, iOS, Android The lack of native 2-factor authentication may bother some users.
It supports a wide variety of cryptocurrencies Does not provide multi-signature support.
Easy- to use interface
Built-in portfolio and trade functions
Gives you total control over your crypto assets.
Great customer support
Key Specs:Best for beginners
Atomic Wallet is a cryptocurrency wallet that helps you manage Bitcoin, Ethereum, Litecoin, XLM, and other 300 other coins and tokens. It is one of the best crypto wallets which helps you to instant exchange with cashback.
Features:
It helps you to manage, exchange, stock, and buy all your favorite assets in one secure interface.
Allows you to buy Bitcoin, Ethereum, Ripple, Bitcoin Cash, Litecoin, and other top cryptocurrencies.
You can fully control your funds.
It offers quick and efficient responses via live chat or email.
👍 Pros 👎 Cons
Easy to use No hardware wallet support
Buy crypto directly through the wallet
Supports more than 300 cryptocurrencies
Key Specs:Best browser-based crypto wallet app
MetaMask is a web-based free wallet that works as an extension for different browsers such as Chrome, Opera, Brave, and Firefox. It allows users to access an Ethereum wallet through a browser extension or mobile app, which can then interact with Dapp (decentralized) applications.
Features:
MetaMask is a crypto wallet platform that offers a key vault, secure login, token wallet, and token exchange.
It helps you to find the best price every time.
Supported platforms like Firefox, Brave, and Chrome extensions.
👍 Pros 👎 Cons
Open-source Only supports Ethereum and ERC-20 tokens
Easy to use
Integrations with Trezor and Ledger devices
Key Specs: How do I get an Ethereum wallet?Here are the steps to get an Ethereum Wallet:
Step 3) Then, you will be asked to add a bank statement or a debit/credit card. You can select your choice. Here, we selected the Credit/Debit Card method. You can select Bank if you want to do a bank transfer.
Step 6) Then upload your photo. After the verification is done, enter the billing address details.
Step 7) Enter the billing address for your debit/credit card.
Step 8) Enter your card details.
Step 10) Now, you just need to enter the amount of Ethereum that you want to buy. In this case, we have purchased $1750 worth of ETH.
Within a few minutes, your funds will be shown in your account.
FAQ:Ethereum wallet is an application that lets you manage your Ethereum account. It allows you to check your Ethereum balance, perform transactions, and connect to other applications. You can also manage multiple Ethereum accounts from a single application.
Following are the best Ethereum wallets / Ethereum (ETH) Tokens Wallet:
👍 ZenGo – Most Secure Non-custodial Wallet
👍 BlockWallet – Best for Web3 security and privacy
👍 NOW Wallet – Best Wallet for Versatile Crypto Management
👍 Binance – Best Wallet with Multiple features
1inch Wallet – Best for An infinite amount of HD wallet derivation paths
Cex.io – Best Wallet for Canadians
Ledger Nano – Best Cold Wallet for those on a Budget
Trezor – Best Hardware Wallet for those on a Budget
Coinbase – Best Overall ETH Wallet
Kraken – Best Wallet with High deposit & withdrawal limits
Gemini – Best for beginners
Coinsmart – Best for beginners who have never tried cryptos
There are five types of Ethereum Wallets:
Type Pro Cons
Online It is free, easy to set up, and easily accessible. It offers limited options and does not offer access to the private key.
Software Free and easy to use and also allows private key access. It is less secure and also offers only manual updates.
Mobile Free, easy to set up, and access. It is less secure and offers limited options.
Full Mode Free, easy to set up, and access. It requires more disk space.
Hardware It is the safest option. It is expensive and less accessible.
To fund your Ether wallet, you should buy Ethereum from an exchange. Once you buy, you can easily withdraw the Ether to your wallet.
Ether payments are fast but less secure than Bitcoin payments. You can get a confirmation in maybe 10 seconds, although you need to wait a bit longer than that for more confirmations.
Cold storage is an offline wallet used to store cryptocurrencies. Cold storage helps you store your digital wallet on a platform that is not connected to the Internet. It means your digital wallet will be safe from unauthorized access, cyberattacks, and other vulnerabilities.
The common consensus is that either paper or hardware wallets will be the best Ethereum (ERC20) wallet options for most Ether holders.
Yes, most definitely. No matter what sort of wallet you are using, it can be hacked at any point in time. Some wallets are difficult to hack than other hardware, paper, and software wallets that help you protect your cryptocurrency.
To get free Ether (ETH), you need to sign up for an account on Idle-Empire and answer some paid surveys. You can also watch videos or complete tasks to get offers to get Ether points.
When you download or buy an Ethereum wallet, you are given an address in the wallet software.
ERC20 tokens wallet is a wallet that stores Ether currency and ECR20 tokens. These tokens are created with the help of blockchain technology and stored on the Ethereum address. A user can use it to pay their transaction fees.
Here are some important things you should look for in your Ethereum wallet:
Security: Security should be your number one priority when choosing an Ethereum wallet.
Smart contract support: Best ETH wallet should support smart contracts.
Private keys: Look for a wallet that allows you to retain control of your private key.
Ease of use: Cryptocurrency wallets can be sometimes confusing and complicated, so look for a wallet that is easy to use.
Support for multiple currencies: Your selected Ethereum wallet should provide support for multiple currencies.
Also check: Best Ethereum Mining Software
Here are some important tips for securely storing your Ethereum:
Store your private keys somewhere private and also ensure that it is encrypted.
You should back up your wallet regularly to protect against unexpected accidents or mishaps.
Select a wallet that suits your needs, as some wallets are focused on security and can be time-consuming to use.
Set up two-factor authentication.
Here are some Ethereum wallets that are most secure:
Physical hardware wallets: allow you to keep your crypto offline.
Mobile applications: make your funds accessible from anywhere.
Web wallets: help you to interact with your account via a web browser.
Desktop applications: helps you manage your funds via different platforms like Windows, macOS, Linux.
Ethereum wallets often include following security features:
End-to-end encryption
Passphrases
Two-factor authentication (2FA)
Protection against cyberattacks
Yes, most of the Ethereum wallet app are free to use. However, hardware Ethereum wallets are paid and expensive, which offer additional security features to their users.
Learn more about: How to Mine Ethereum
Best Ethereum Wallet Apps
Dogecoin (Doge) Price Prediction For 2023
Here is the Dogecoin price prediction in 2023
Dogecoin started as a joke in 2013. Soon after launch, it developed other use cases, including charities and online tipping. Over the years, the coin performance was inconsequential. However, it started exhibiting massive growth potential in 2023. Within one year, it grew to become the biggest meme coin and effectively ranked among the top ten cryptocurrencies by market capitalizations.
DOGE Growth2023 was, without doubt, the best year for dogecoin thus far. It was trading at US$0.007 in January and went on to hit an all-time high of US$0.7381 in May. However, it has lost about 70% of its value and was trading at US$0.1715 at the end of 2023. That notwithstanding, the meme coin has gained massively since the beginning of the year. Dogecoin followed Bitcoin’s bullish run. External factors, especially key opinion leaders (KOL) and influencers contributed significantly to the proliferation of value. For instance, tweets from Elon Musk and Snoop Dog, two of the biggest KOL, impacted the growth of the Doge community. Currently, the crypto coin boasts a massive following from Twitter and Reddit. So, what is in store for dogecoin? In 2023, it is likely to experience more growth, with institutional adoption and acceptance by retailers. Tesla has mooted accepting DOGE as a payment alternative. Investors like Mark Cuban have invested heavily in Dogecoin, the investor owns Dallas Maverick, which accepts dogecoin for tickets and merchandise. Some of the community members suggest using the meme coins for hot tubs. With increasing user cases, the price of dogecoin is set to skyrocket.
Dogecoin Price Forecasts for 2023 Q1 and Q2 PredictionsAccording to
Q3 and 4 Price PredictionsCryptoPredictions’ algorithm predicts that Dogecoin will dip a little bit in July, with the expected maximum price falling to US$0.291856. Similarly, the average price will go down to US$0.232411 in August. Finally, the maximum price will drop to US$0.286380 in September. The price is expected to dip further in the fourth quarter. The site’s forecast shows that Dogecoin will trade at a maximum price of US$0.286380 in October and close the year at a maximum price of US$0.279832.However, another site providing
Fundamental AnalysisDogecoin’s satirical nature has made it one of the most popular meme coins. Endorsement from Elon Musk and Mark Cuban has only fueled its wide popularity. The coin is used as a means of payment with low transaction costs. Currently, over 1900 merchants accept dogecoin as a means of payment as per Cryptwerk data. Its tipping service is quite a unique use case, making it even more endearing to the Dogecoin community. The service allows users to give quick gifts to other users on Reddit and Twitter. Despite the above-mentioned use cases, Dogecoin has relatively less utility. Its growth will significantly depend on whether the influential figures will continue to support the meme coin. A tweet from Elon Musk in the new year could spring up the dogecoin price at the beginning of 2023. Other notable supporters like Gene Simmmons will have huge impacts on how the dog-themed coin will behave in 2023. The dogecoin community will also influence the price of DOGE in 2023. The digital coin currently boasts over 4.5 million total followers on both Reddit and Twitter. In addition, its listing on Etoro had significantly boosted its reach to users. Like other meme coins, dogecoin growth will largely depend on media attention and hype because they generally lack a strong foundation. The good news is that Dogecoin Foundation has unveiled a trail map detailing the 2023 project that will enhance its utility. At the top of this plan is to make DOGE useful and boost its adoption through utility. The foundation gives examples of paying rent and buying coffee. Essentially, they plan to make DOGE a universal coin by focusing on payments. Another development is the Gigawallet project. The API solution will enable developers to add doge transactions on their platforms. Also, DOGE is planning to establish community staking, which will help migrate the coin ecosystem from Proof of Work (POW) to proof of stake (POS). Here is an interesting development. Elon Musk is set to launch a
Technical AnalysisSince touching an all-time high in May, bears have taken control of the market. Currently, the market is oscillating in the zone of recent support. It has also failed to break above a resistance area. In fact, the chart below shows DOGE ranges within this level. The price needs to break above the US$0.1900 resistance level, which was previously a support level. If it surpasses this zone and probably retests the area, it will be set for a bullish run.
In this chart, Dogecoin shows Fibonacci retests retracement levels. The price touches 31.8% retracement levels and retraces back. If the bullish move resumes, the next stop is $0.24400 at 50% retracement level or US$0.2600 at 61.8% level.
The Dogecoin price has followed a descending channel in the bearish run. However, it has broken above the channel and is retesting the upper channel line, which will act as support. If the price fails to break below this support line, it will resume the bullish run. According to this technical analysis, the DOGE is likely set for an upward trend. The wait is now on news events that will trigger the bullish run, perhaps a tweet from Elon Musk.
Final Words on our DOGE predictions for 2023Dogecoin started as a joke in 2013. Soon after launch, it developed other use cases, including charities and online tipping. Over the years, the coin performance was inconsequential. However, it started exhibiting massive growth potential in 2023. Within one year, it grew to become the biggest meme coin and effectively ranked among the top ten cryptocurrencies by market capitalizations.2023 was, without doubt, the best year for dogecoin thus far. It was trading at US$0.007 in January and went on to hit an all-time high of US$0.7381 in May. However, it has lost about 70% of its value and was trading at US$0.1715 at the end of 2023. That notwithstanding, the meme coin has gained massively since the beginning of the year. Dogecoin followed Bitcoin’s bullish run. External factors, especially key opinion leaders (KOL) and influencers contributed significantly to the proliferation of value. For instance, tweets from Elon Musk and Snoop Dog, two of the biggest KOL, impacted the growth of the Doge community. Currently, the crypto coin boasts a massive following from Twitter and Reddit. So, what is in store for dogecoin? In 2023, it is likely to experience more growth, with institutional adoption and acceptance by retailers. Tesla has mooted accepting DOGE as a payment alternative. Investors like Mark Cuban have invested heavily in Dogecoin, the investor owns Dallas Maverick, which accepts dogecoin for tickets and merchandise. Some of the community members suggest using the meme coins for hot tubs. With increasing user cases, the price of dogecoin is set to skyrocket.According to DOGE forecasts for 2023 provided by chúng tôi DOGE will start 2023 trading at US$0.233447. During the first quarter, the forecasted minimum price is 0.198430. The price growth is expected to rise through February, averaging US$0.234112 in the month. Dogecoin is forecasted to close the first quarter at a maximum of US$0.293125. In the second quarter, the predicted average price in April is US$0.234677. Dogecoin is expected to hit a maximum price of US$0.292728 as per the site predictions. In June, the price will average US$0.234182.CryptoPredictions’ algorithm predicts that Dogecoin will dip a little bit in July, with the expected maximum price falling to US$0.291856. Similarly, the average price will go down to US$0.232411 in August. Finally, the maximum price will drop to US$0.286380 in September. The price is expected to dip further in the fourth quarter. The site’s forecast shows that Dogecoin will trade at a maximum price of US$0.286380 in October and close the year at a maximum price of US$0.279832.However, another site providing dogecoin forecasts – TradingBeasts, is a bit less optimistic and expects in Q4 an average price of US$0.221.Dogecoin’s satirical nature has made it one of the most popular meme coins. Endorsement from Elon Musk and Mark Cuban has only fueled its wide popularity. The coin is used as a means of payment with low transaction costs. Currently, over 1900 merchants accept dogecoin as a means of payment as per Cryptwerk data. Its tipping service is quite a unique use case, making it even more endearing to the Dogecoin community. The service allows users to give quick gifts to other users on Reddit and Twitter. Despite the above-mentioned use cases, Dogecoin has relatively less utility. Its growth will significantly depend on whether the influential figures will continue to support the meme coin. A tweet from Elon Musk in the new year could spring up the dogecoin price at the beginning of 2023. Other notable supporters like Gene Simmmons will have huge impacts on how the dog-themed coin will behave in 2023. The dogecoin community will also influence the price of DOGE in 2023. The digital coin currently boasts over 4.5 million total followers on both Reddit and Twitter. In addition, its listing on Etoro had significantly boosted its reach to users. Like other meme coins, dogecoin growth will largely depend on media attention and hype because they generally lack a strong foundation. The good news is that Dogecoin Foundation has unveiled a trail map detailing the 2023 project that will enhance its utility. At the top of this plan is to make DOGE useful and boost its adoption through utility. The foundation gives examples of paying rent and buying coffee. Essentially, they plan to make DOGE a universal coin by focusing on payments. Another development is the Gigawallet project. The API solution will enable developers to add doge transactions on their platforms. Also, DOGE is planning to establish community staking, which will help migrate the coin ecosystem from Proof of Work (POW) to proof of stake (POS). Here is an interesting development. Elon Musk is set to launch a dogecoin-funded space mission in the first quarter of 2023. With all these developments, Dogecoin will likely resume the uptrend. If it breaks above the current all-time high, the next stop is US$1.Since touching an all-time high in May, bears have taken control of the market. Currently, the market is oscillating in the zone of recent support. It has also failed to break above a resistance area. In fact, the chart below shows DOGE ranges within this level. The price needs to break above the US$0.1900 resistance level, which was previously a support level. If it surpasses this zone and probably retests the area, it will be set for a bullish chúng tôi this chart, Dogecoin shows Fibonacci retests retracement levels. The price touches 31.8% retracement levels and retraces back. If the bullish move resumes, the next stop is $0.24400 at 50% retracement level or US$0.2600 at 61.8% chúng tôi Dogecoin price has followed a descending channel in the bearish run. However, it has broken above the channel and is retesting the upper channel line, which will act as support. If the price fails to break below this support line, it will resume the bullish run. According to this technical analysis, the DOGE is likely set for an upward trend. The wait is now on news events that will trigger the bullish run, perhaps a tweet from Elon Musk.Dogecoin is the pioneer meme coin and boasts a massive following of the enthusiastic community. In 2023, it rose and touched new highs thanks to an endorsement from Mark Cuban, Elon Musk, and Snoop Dog. However, it has shed most of its value towards the end of 2023 and is now trading in an area of interest. It is unlikely that the coin will go any lower. Dogecoin Foundation has lined up new projects which will enhance its utility and eventually boost its value. If the bulls regain market control, we could see DOGE at a new market high that could be as high as US$1. But that will depend on the success of DOGE projects and, to some extent, whether Elon Musk tweets about Doge or not.
How To Build A Real Estate Price Prediction Model?
Introduction
Learning Objectives:
In this article, we will:
1. Understand building a price prediction model.
2. understand the process of building the model, such as data analysis, selection, prediction, interpretation, etc.
3. Learn how to make an informed decision with a given dataset.
This article was published as a part of the
Data Science Blogathon
.
Table of Contents
1.2
Data Description
2.5
Model Development
Conclusion
Understanding the Data and Problem StatementThe data we will be using is the Ames Housing Dataset, which is a dataset that contains 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa. The dataset is available on Kaggle, and you can download it here . The dataset contains 1460 rows and 81 columns; the target variable is the SalePrice column. The dataset is split into two parts: chúng tôi and chúng tôi The chúng tôi file contains the target variable, and the chúng tôi file does not. The chúng tôi file tests the model’s performance on unseen data.
Problem StatementThe problem statement is to predict the sale price of a house, given the features of the house. The features are the columns in the dataset, and the target variable is the SalePrice column. The problem is a regression problem, as the target variable is continuous.
Data DescriptionThe data description is available on Kaggle, and you can find it here. The data description contains a detailed description of each column in the dataset. The data description is very useful, as it provides a detailed description of each column in the dataset. It also provides information about the missing values in the dataset.
Data Analysis of the Price Prediction ModelThis section will find missing values and outliers and the relationship between the target variable and the features.
Python Code:
Missing Values # Making a list of columns with missing values missing_values = [col for col in data.columns if data[col].isnull().any()]# Printing the number of missing values and percentage of missing values in each column
for col in missing_values:
print(col, round(data[col].isnull().mean(), 3), ‘ % missing values’)
Output
Handling the Missing Values # Dropping the columns with more than 15% missing values data.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True) # Filling the missing values in the remaining columns with the most frequent value new_missing_values = [col for col in data.columns if data[col].isnull().any()] for col in new_missing_values: if data[col].dtype == 'O': data[col].fillna(data[col].mode()[0], inplace=True) else: data[col].fillna(data[col].median(), inplace=True) Outliers continuous_features = [col for col in data.columns if data[col].dtype != 'O'] for col in continuous_features: data_copy = data.copy() if 0 in data_copy[col].unique(): pass else: data_copy[col] = np.log(data_copy[col]) data_copy['SalePrice'] = np.log(data_copy['SalePrice']) plt.scatter(data_copy[col], data_copy['SalePrice']) plt.xlabel(col) plt.ylabel('SalePrice')Output
Exploratory Data Analysis plt.figure(figsize=(10, 15)) # Plotting the heatmap with respect to the correlation of the features with the target variable 'SalePrice' sns.heatmap(data.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False), annot=True, cmap='viridis')Output
We can see that ‘OverallQual,’ ‘GrLivArea,’ and ‘TotalBsmtSF’ strongly correlate with ‘SalePrice.’ Check the below scatter plots.
# Regression plot between the target variable and the most correlated variables who have a correlation greater than 0.5 if col == 'SalePrice': pass else: sns.regplot(x=data[col], y=data['SalePrice']) plt.show()Output
All the features have a positive correlation with the target variable.
Model DevelopmentNow that we have cleaned and visualized the data. The next step is to build a model to predict the sale price of a house. Several different prediction models can be used, including multiple linear regression KNN regressor, etc. We will use a series of models and pipelines to find the best model by evaluating the model’s accuracy, precision, and recall. I will also use cross-validation to ensure that the model is generalizing well.
# Encoding the categorical variables to the numeric datatype from sklearn.preprocessing import LabelEncoder for col in data.columns: if data[col].dtype == 'O': label_encoder = LabelEncoder() data[col] = label_encoder.fit_transform(data[col]) # Let's use Ridge Regression to build the model from sklearn.linear_model import Ridge from sklearn.model_selection import cross_val_score from sklearn.metrics import mean_squared_error # create X and y variables from Features and target variable X = data[['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', 'TotalBsmtSF', '1stFlrSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt', 'YearRemodAdd']] y = data['SalePrice'] # Function to perform ridge regression def ridge_regression(alpha, data): ridge = Ridge(alpha=alpha) ridge.fit(X, y) scores = cross_val_score(ridge, X, y, scoring='neg_mean_squared_error', cv=5) rmse = np.sqrt(-scores) return rmse # Finding the best value of hyper-parameter of Ridge - alpha alpha = [0.001, 0.01, 0.1, 1, 10, 100, 1000] for i in alpha: print('Alpha: ', i) print('Mean RMSE: ', ridge_regression(i, data).mean()) print('Standard Deviation: ', ridge_regression(i, data).std()) print()Output
For alpha = 100, the RMSE is the lowest, and the model performs the best. The RMSE is 38464. The model is performing the best with alpha = 100.
# I will be using the ridge regression model with alpha = 100 ridge = Ridge(alpha=100) ridge.fit(X, y) # Loading the test data test_data = pd.read_csv('test.csv') # Doing all the changes that were done in the training data test_data.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1, inplace=True) new_missing_values = [col for col in test_data.columns if test_data[col].isnull().any()] for col in new_missing_values: if test_data[col].dtype == 'O': test_data[col].fillna(test_data[col].mode()[0], inplace=True) else: test_data[col].fillna(test_data[col].median(), inplace=True) # Encoding the categorical variables of test data for col in test_data.columns: if test_data[col].dtype == 'O': label_encoder = LabelEncoder() test_data[col] = label_encoder.fit_transform(test_data[col]) # Selecting Features X_test = test_data[['OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea', 'TotalBsmtSF', '1stFlrSF', 'FullBath', 'TotRmsAbvGrd', 'YearBuilt', 'YearRemodAdd']] # Predicting the target variable y_pred = ridge.predict(X_test) # Saving the predictions in a csv file output = pd.DataFrame({'Id': test_data.Id, 'SalePrice': y_pred}) output.to_csv('my_submission.csv', index=False)In this article, we have chosen the Ames housing dataset as the price prediction model, understand the problem statement, and perform Exploratory Data Analysis. We have also performed missing value imputation and encoding of categorical variables on the train and test data sets. Then we applied Ridge regression with different alpha values and found the best value of alpha to minimize the RMSE.
Key Takeaways:
Three features, ‘OverallQual,’ ‘GrLivArea,’ and ‘TotalBsmtSF,’ were found to have strong positive correlations with the target variable ‘SalePrice.’
The model performed best with alpha = 100, resulting in amses of 38464.
The analysis showed the importance of considering multiple features in real estate price prediction models.
Regularization techniques like Ridge regression can reduce the model’s complexity and prevent overfitting.
The results of this project highlight the potential for using data science in real estate to make more informed decisions and improve predictions.
The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.
Related
Car Price Prediction – Machine Learning Vs Deep Learning
This article was published as a part of the Data Science Blogathon
1. ObjectiveIn this article, we will be predicting the prices of used cars. We will be building various Machine Learning models and Deep Learning models with different architectures. In the end, we will see how machine learning models perform in comparison to deep learning models.
2. Data UsedHere we have used the data from a hiring competition that was live on chúng tôi Use the below link to access the data and use it for your analysis.
3. Data InspectionIn this section, we will explore the data. First Let’s see what columns we have in the data and their data types along with missing values information.
We can observe that data have 19237 rows and 18 columns.
There are 5 numeric columns and 13 categorical columns. With the first look, we can see that there are no missing values in the data.
‘Price‘ column/feature is going to be the target column or dependent feature for this project.
Let’s see the distribution of the data.
4. Data PreparationHere we will clean the data and prepare it for training the model.
‘ID’ columnWe are dropping the ‘ID’ column since it does not hold any significance for car Price prediction.
df.drop('ID',axis=1,inplace=True) ‘Levy’ columnAfter analyzing the ‘Levy’ column we found out that it does contain the missing values but it was given as ‘-‘ in the data and that’s why we were not able to capture the missing values earlier in the data.
Here we will impute ‘-‘ in the ‘Levy’ column with ‘0’ assuming there was no ‘Levy’. We can also impute it with ‘mean’ or ‘median’, but that’s a choice that you have to make.
df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) ‘Mileage’ column‘Mileage’ column here means how many kilometres the car has driven. ‘km’ is written in the column after each reading. We will remove that.
#since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') ‘Engine Volume’ columnIn the ‘Engine Volumn’ column along with the Engine Volumn ‘type’ of the engine(Turbo or not Turbo) is also written. We will create a new column that shows the ‘type’ of ‘Engine’.
df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) ‘Doors’ Column df['Doors'].unique()Output:
‘Doors’ column represents the number of doors in the car. But as we can see it is not clean. Let’s clean
Handling ‘Outliers’This we will examine across numerical features.
cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); sns.boxplot(df[cols[1]]); sns.boxplot(df[cols[2]]); sns.boxplot(df[cols[3]]); sns.boxplot(df[cols[4]]);
As we can see there are outliers in ‘Levy’,’Engine volume’, ‘Mileage’, ‘Cylinders’ columns. We will remove these outliers using Inter Quantile Range(IQR) method.
def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower)Let’s examine the features after removing outliers.
plt.figure(figsize=(20,10)) df[outlier_cols].boxplot()We can observe that there are no outliers in the features now.
Creating Additional FeaturesWe see that ‘Mileage’ and ‘Engine Volume’ are continuous variables. While performing regression I have observed that binning such variables can help increase the performance of the model. So I am creating the ‘Bin’ features for these features/columns.
labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) Handling Categorical featuresI have used Ordinal Encoder to handle the categorical columns. OrdinalEncoder works similar to LabelEncoder but OrdinalEncoder can be applied to multiple features while LabelEncoder can be applied to One feature at a time. For more details please visit the below links
num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1)Checking correlation
final_all_df['price_log']=np.log(final_all_df['Price'])We can observe that features are not much correlated in the data. But there is one thing that we can notice is that after log transforming ‘Price’ column, correlation with few features got increased which is a good thing. We will be using log-transformed ‘Price’ to train the model. Please visit mentioned link below to better understand how feature transformations help improve model performance.
5. Data Splitting and ScalingWe have done an 80-20 split on the data. 80% of the data will be used for training and 20% data will be used for testing.
We will also scale the data since feature values in data do not have the same scale and having different scales can produce poor model performance.
cols_drop=['Price','price_log','Cylinders'] X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) 6. Model BuildingWe built LinearRegression, XGBoost, and RandomForest as machine learning models and two deep learning models one having a small network and another having a large network.
We built base models of LinearRegression, XGBoost, and RandomForest so there is not much to show about these models but we can see the model summary and how they converge with deep learning models that we built.
Deep Learning Model – Small Network model summary model_dl_small.summary() Deep Learning Model – Small Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() Deep Learning Model – Large Network model summary model_dl_large.summary() Deep Learning Model – Large Network _Train & Validation Loss #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid()6.1 Model Performance
We have evaluated the models using Mean_Squared_Error, Mean_Absolute_Error, Mean_Absolute_Percentage_Error, Mean_Squared_Log_Error as performance matrices, and below are the results we got.
We can observe that Deep Learning Model did not perform well in comparison with Machine Learning Models. RandomForest performed really well among Machine Learning Model.
Let’s visualize the results from Random Forest.
7. Result Visualization y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show()We can observe in the graph that the model is performing really well as seen in performance matrices as well.
8. CodeCode was done on jupyter notebook. Below is the complete code for the project.
# Loading Libraries import pandas as pd import numpy as np from sklearn.preprocessing import OrdinalEncoder from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_log_error,mean_squared_error,mean_absolute_error,mean_absolute_percentage_error import datetime from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from xgboost import XGBRegressor from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns from keras.models import Sequential from keras.layers import Dense from prettytable import PrettyTable df=pd.read_csv('../input/Participant_Data_TheMathCompany_.DSHH/train.csv') df.head() # Data Inspection df.shape df.describe().transpose() df.info() sns.pairplot(df, diag_kind='kde') # Data Preprocessing df.drop('ID',axis=1,inplace=True) df['Levy']=df['Levy'].replace('-',np.nan) df['Levy']=df['Levy'].astype(float) levy_mean=0 df['Levy'].fillna(levy_mean,inplace=True) df['Levy']=round(df['Levy'],2) milage_formats=set() def get_milage_format(x): x=x.split(' ')[1] milage_formats.add(x) df['Mileage'].apply(lambda x:get_milage_format(x)); milage_formats #since milage is in KM only we will remove 'km' from it and make it numerical df['Mileage']=df['Mileage'].apply(lambda x:x.split(' ')[0]) df['Mileage']=df['Mileage'].astype('int') df['Engine volume'].unique() df['Turbo']=df['Engine volume'].apply(lambda x:1 if 'Turbo' in str(x) else 0) df['Engine volume']=df['Engine volume'].apply(lambda x:str(x).replace('Turbo','')) df['Engine volume']=df['Engine volume'].astype(float) cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[0]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[1]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[2]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[3]]); cols=['Levy','Engine volume', 'Mileage','Cylinders','Airbags'] sns.boxplot(df[cols[4]]); def find_outliers_limit(df,col): print(col) print('-'*50) #removing outliers q25, q75 = np.percentile(df[col], 25), np.percentile(df[col], 75) iqr = q75 - q25 print('Percentiles: 25th=%.3f, 75th=%.3f, IQR=%.3f' % (q25, q75, iqr)) # calculate the outlier cutoff cut_off = iqr * 1.5 lower, upper = q25 - cut_off, q75 + cut_off print('Lower:',lower,' Upper:',upper) return lower,upper def remove_outlier(df,col,upper,lower): # identify outliers outliers = [x for x in df[col] if x upper] print('Identified outliers: %d' % len(outliers)) # remove outliers print('Non-outlier observations: %d' % len(outliers_removed)) return final outlier_cols=['Levy','Engine volume','Mileage','Cylinders'] for col in outlier_cols: lower,upper=find_outliers_limit(df,col) df[col]=remove_outlier(df,col,upper,lower) #boxplot - to see outliers plt.figure(figsize=(20,10)) df[outlier_cols].boxplot() df['Doors'].unique() df['Doors']=df['Doors'].astype(str) #Creating Additional Features labels=[0,1,2,3,4,5,6,7,8,9] df['Mileage_bin']=pd.cut(df['Mileage'],len(labels),labels=labels) df['Mileage_bin']=df['Mileage_bin'].astype(float) labels=[0,1,2,3,4] df['EV_bin']=pd.cut(df['Engine volume'],len(labels),labels=labels) df['EV_bin']=df['EV_bin'].astype(float) #Handling Categorical features num_df=df.select_dtypes(include=np.number) cat_df=df.select_dtypes(include=object) encoding=OrdinalEncoder() cat_cols=cat_df.columns.tolist() encoding.fit(cat_df[cat_cols]) cat_oe=encoding.transform(cat_df[cat_cols]) cat_oe=pd.DataFrame(cat_oe,columns=cat_cols) cat_df.reset_index(inplace=True,drop=True) cat_oe.head() num_df.reset_index(inplace=True,drop=True) cat_oe.reset_index(inplace=True,drop=True) final_all_df=pd.concat([num_df,cat_oe],axis=1) #Checking correlation final_all_df['price_log']=np.log(final_all_df['Price']) plt.figure(figsize=(20,10)) sns.heatmap(round(final_all_df.corr(),2),annot=True); cols_drop=['Price','price_log','Cylinders'] final_all_df.columns X=final_all_df.drop(cols_drop,axis=1) y=final_all_df['Price'] # Data Splitting and Scaling X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=25) scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) X_test_scaled=scaler.transform(X_test) # Model Building def train_ml_model(x,y,model_type): if model_type=='lr': model=LinearRegression() elif model_type=='xgb': model=XGBRegressor() elif model_type=='rf': model=RandomForestRegressor() model.fit(X_train_scaled,np.log(y)) return model def model_evaluate(model,x,y): predictions=model.predict(x) predictions=np.exp(predictions) mse=mean_squared_error(y,predictions) mae=mean_absolute_error(y,predictions) mape=mean_absolute_percentage_error(y,predictions) msle=mean_squared_log_error(y,predictions) mse=round(mse,2) mae=round(mae,2) mape=round(mape,2) msle=round(msle,2) return [mse,mae,mape,msle] model_lr=train_ml_model(X_train_scaled,y_train,'lr') model_xgb=train_ml_model(X_train_scaled,y_train,'xgb') model_rf=train_ml_model(X_train_scaled,y_train,'rf') ## Deep Learning ### Small Network model_dl_small=Sequential() model_dl_small.add(Dense(16,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_small.add(Dense(8,activation='relu')) model_dl_small.add(Dense(4,activation='relu')) model_dl_small.add(Dense(1,activation='linear')) model_dl_small.summary() epochs=20 batch_size=10 model_dl_small.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_small.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() ### Large Network model_dl_large=Sequential() model_dl_large.add(Dense(64,input_dim=X_train_scaled.shape[1],activation='relu')) model_dl_large.add(Dense(32,activation='relu')) model_dl_large.add(Dense(16,activation='relu')) model_dl_large.add(Dense(1,activation='linear')) model_dl_large.summary() epochs=20 batch_size=10 model_dl_large.fit(X_train_scaled,np.log(y_train),verbose=0,validation_data=(X_test_scaled,np.log(y_test)),epochs=epochs,batch_size=batch_size) #plot the loss and validation loss of the dataset history_df = pd.DataFrame(model_dl_large.history.history) plt.figure(figsize=(20,10)) plt.plot(history_df['loss'], label='loss') plt.plot(history_df['val_loss'], label='val_loss') plt.xticks(np.arange(1,epochs+1,2)) plt.yticks(np.arange(1,max(history_df['loss']),0.5)) plt.legend() plt.grid() summary=PrettyTable(['Model','MSE','MAE','MAPE','MSLE']) summary.add_row(['LR']+model_evaluate(model_lr,X_test_scaled,y_test)) summary.add_row(['XGB']+model_evaluate(model_xgb,X_test_scaled,y_test)) summary.add_row(['RF']+model_evaluate(model_rf,X_test_scaled,y_test)) summary.add_row(['DL_SMALL']+model_evaluate(model_dl_small,X_test_scaled,y_test)) summary.add_row(['DL_LARGE']+model_evaluate(model_dl_large,X_test_scaled,y_test)) print(summary) y_pred=np.exp(model_rf.predict(X_test_scaled)) number_of_observations=20 x_ax = range(len(y_test[:number_of_observations])) plt.figure(figsize=(20,10)) plt.plot(x_ax, y_test[:number_of_observations], label="True") plt.plot(x_ax, y_pred[:number_of_observations], label="Predicted") plt.title("Car Price - True vs Predicted data") plt.xlabel('Observation Number') plt.ylabel('Price') plt.xticks(np.arange(number_of_observations)) plt.legend() plt.grid() plt.show() 9.ConclusionIn this article, we tried predicting the car price using the various parameters that were provided in the data about the car. We build machine learning and deep learning models to predict car prices and saw that machine learning-based models performed well at this data than deep learning-based models.
10. About the AuthorHi, I am Kajal Kumari. I have completed my Master’s from IIT(ISM) Dhanbad in Computer Science & Engineering. As of now, I am working as Machine Learning Engineer in Hyderabad. You can also check out few other blogs that I have written here.
The media shown in this article on LSTM for Human Activity Recognition are not owned by Analytics Vidhya and are used at the Author’s discretion.
Related
Is The Ethereum Network Too Slow For It To Surpass Bitcoin?
Is the Ethereum Network Really Slow?
One of the biggest pointers against Ethereum’s speed is its ability to handle large amounts of traffic on the network. As Ethereum gained popularity at the same time as all of the crypto space did, its network became increasingly clogged. And without the right infrastructure in place, this led to higher transaction fees and longer confirmation times slowing down processes for users. During peak periods, such as when popular applications are hammered by heavy usage or during token launches or sales, network congestion can significantly impact transaction speed. This can happen with new token sales or when a certain coin or token experiences a surge in price – either up or down. Users can keep an eye on coin prices like the Loopring Price to understand these movements and also keep an eye on gas fees and all the latest news in the crypto space to make an educated decision on how to invest or which network to use.
Some lay the blame for the drag on speed at the feet of Ethereum’s current consensus mechanism, otherwise called the Proof of Work, which requires substantial computational resources, leading to slower block validation times. If we look at Bitcoin in comparison, Bitcoin’s Proof of Work algorithm is optimized for security and has a longer average block time, making it less prone to network congestion during high transaction periods. Gas fees can be high and unpredictable, which is a significant issue for Ethereum users. Network congestion can also slow down the network and increase gas fees.
The good news however is that Ethereum is actively working on ways to improve scalability. The biggest development is clearly the introduction of Ethereum 2.0 and Layer 2 solutions like the Lightning Network. Additionally, Ethereum switched from Proof of Work to Proof of Stake, which, due to the practicalities of the practice, takes far less energy from technology or man. Warehouses full of servers were retired and as a result, the Ethereum coin is gaining speed.
These developments are still in progress though and until these solutions are fully implemented, Ethereum’s transaction speed may continue to be dragged down. Ethereum 2.0 is being released in phases and while there are still some questions on the effectiveness of the upgrade, many point to this watershed moment as the reason for Ethereum’s potential to surpass Bitcoin. The Ethereum 2.0 upgrade will change the consensus mechanism for Ethereum from a Proof-of-Work to a Proof-of-Stake consensus mechanism with the goal of reducing the amount of computational resources. Shard chains are one of the biggest changes to the 2.0 upgrade that will hopefully have the biggest impact on the network overall.
Ethereum’s Growing Ecosystem Drives InnovationOne of Ethereum’s main strengths is its fantastic ecosystem of applications and smart contracts. This highly developed ecosystem attracts developers and users, fostering innovation and driving adoption. As Ethereum continues to evolve, the demand for its network may increase, spurring the development of scalability solutions and potentially enhancing transaction speeds. With such an active development initiative, the Ethereum community has a vibrant and active developer base constantly working towards improving the network’s performance in the race to beat Bitcoin. This community of developers are dedicated to optimizing the Ethereum blockchain, creating layer 2 solutions, and exploring innovative scaling techniques. Their ongoing efforts indicate a commitment to overcoming the network’s current limitations and enhancing transaction speed.
Flexible for The FutureAlthough there are issues, the good news is that Ethereum’s design allows for easier upgrades compared to Bitcoin. This flexibility for future development provides the potential to bring on new ways of working and as the network evolves, upgrades can be implemented, which of course have the potential to improve Ethereum’s transaction speed. Align this to the dedication and passion of the development community to improve the Ethereum network and the signs are good for the future. Ethereum 2.0, when it arrives, will usher in a brave new world for crypto and blockchain networks, but will it be enough to let Ethereum take over Bitcoin? That remains to be seen but as the popularity of cryptocurrencies continues to grow it will be important for Ethereum to work on its current issues if it is to stand any chance of success.
Update the detailed information about Ethereum Price Prediction 2025: Collateral Network Predicted To Rival Eth In Growth on the Cancandonuts.com 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!