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As a developer who barely knows anything about ML (machine learning), I find building a recommendation engine one of the easiest projects to get started with ML. It is practical and not that difficult to understand for beginners with no machine learning background to jump right into.
Before we get started with the actual implementation, I’ll briefly go over some concepts that you might find helpful to build a recommendation engine.
There are essentially three types of algorithms that your recommendation engine could use when recommending an item to a user:
This type of filtering looks at the general trends and popularity of an item based on users with similar demographics. This means that users with similar demographics are recommended the same items and personalized recommendations are very limited.
The underlying algorithm for this type of filtering looks at the similarity of items based on their metadata. For example, for games, the metadata would be things like platforms, genres, and publisher. Therefore, if a user liked a PC action RPG game that is published by Valve, then most likely they would like another game that has similar metadata (i.e. games that are published by Valve and are action RPG PC games). This means that personalized recommendations are now involved since games that the user liked are used to determine games that the user would probably like too.
#mls #machine-learning #programming #data-science #python
1598523846
Hrsinfrastructure are one of the best, reliable and successful pre-Engineered Building Manufacturers, which is offering the affordable PEB steel structures and steel building solutions for all types of the customers. PEB are the best quality steel structures built over a designed structural concept of primary members, secondary members, wall and roof sheeting attached to each other and different other building elements. Hrsinfrastructure Having many years of experience into PEB industry, we are serving millions of the customers just by offering the best quality PEB sheets in the affordable prices. We mainly focus on the quality, durability, reliability, flexibility in the expansion, environment-friendly, and quicker installation and many others. Being a top-leading pre-engineering building manufacturer in India, we have earned vast experience and broad knowledge of completing many constructions projects using PEB steel sheets and structures. We have got higher admiration and reputation in the market just by offering the best quality PEB sheets. Our designing and engineering team is very experienced and fully knowledgeable to prepare the best PEB structures as per the residential and commercial building’s needs and demands. Our aim is to use the best quality row materials to prepare the sheets, so every customer can get the full satisfaction easily.
#pre-engineered building manufacturers #pre-engineered buildings #pre-engineered buildings manufacturer in india
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If we plan to buy any new product, we normally ask our friends, research the product features, compare the product with similar products, read the product reviews on the internet and then we make our decision. How convenient if all this process was taken care of automatically and recommend the product efficiently? A recommendation engine or recommender system is the answer to this question.
Content-based filtering and collaborative-based filtering are the two popular recommendation systems. In this blog, we will see how we can build a simple content-based recommender system using Goodreads.com data.
Content-based recommendation systems recommend items to a user by using the similarity of items. This recommender system recommends products or items based on their description or features. It identifies the similarity between the products based on their descriptions. It also considers the user’s previous history in order to recommend a similar product.
Example: If a user likes the novel “Tell Me Your Dreams” by Sidney Sheldon, then the recommender system recommends the user to read other Sidney Sheldon novels, or it recommends a novel with the genre “non-fiction”. (Sidney Sheldon novels belong to the non-fiction genre).
As I mentioned above, we are using goodreads.com data and don’t have user reading history. Hence, we have used a simple content-based recommendation system. We are going to build two recommendation systems by using a book title and book description.
We need to find similar books to a given book and then recommend those similar books to the user. How do we find whether the given book is similar or dissimilar? A similarity measure was used to find this.
There are different similarity measures are available. Cosine Similarity was used in our recommender system to recommend the books. For more details on the similarity measure, please refer to this article.
#2020 jul tutorials # overviews #python #recommendation engine #recommender systems
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Our working final product can be tested here.
Have you ever wondered what a chatbot is and how to build one?
In this three-part series, we will teach you everything you need to build and deploy your Chatbot. By “we” here, I mean my team members (Ahmed, Dennis, Pedro, and Steven), four data science students at the Minerva Schools at KGI. The series will cover the following topics:
We use a Jupyter Python 3 notebook as a collaborative coding environment for this project, and other bits of code for the web app development and deployment. All the code for this series is available in this GitHub repository.
Businesses integrate chatbots into many processes and applications. You might need to interact with one while buying an item from Sephora, booking a flight from British Airways, or even customizing your cup of coffee from Starbucks. Developers build chatbots to understand customers’ needs and assist them without needing human help, making chatbots very useful for many customer-facing businesses. So how does a chatbot work?
Generally, there are three types of chatbots:
The chatbot we settled on creating is retrieval-based. Our bot can take a diverse set of responses, which are only slightly structured and output tailored recommendations. We had two main challenges to making this work: first, to build a flexible recommendation system in Python capable of taking in written requests by users and outputting decent recommendations. Second, implementing that algorithm in a web-app that is user-friendly and easy to use.
#movie-recommendation #towards-data-science #recommendation-system #chatbots #how to build a flexible movie recommender chatbot in python #chatbot in python
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A useful tool several businesses implement for answering questions that potential customers may have is a chatbot. Many programming languages give web designers several ways on how to make a chatbot for their websites. They are capable of answering basic questions for visitors and offer innovation for businesses.
With the help of programming languages, it is possible to create a chatbot from the ground up to satisfy someone’s needs.
Before building a chatbot, it is ideal for web designers to determine how it will function on a website. Several chatbot duties center around fulfilling customers’ needs and questions or compiling and optimizing data via transactions.
Some benefits of implementing chatbots include:
Some programmers may choose to design a chatbox to function through predefined answers based on the questions customers may input or function by adapting and learning via human input.
#chatbots #latest news #the best way to build a chatbot in 2021 #build #build a chatbot #best way to build a chatbot
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Cryptocurrency is a decentralized digital currency that uses encryption techniques to regulate the generation of currency units and to verify the transfer of funds. Anonymity, decentralization, and security are among its main features. Cryptocurrency is not regulated or tracked by any centralized authority, government, or bank.
Blockchain, a decentralized peer-to-peer (P2P) network, which is comprised of data blocks, is an integral part of cryptocurrency. These blocks chronologically store information about transactions and adhere to a protocol for inter-node communication and validating new blocks. The data recorded in blocks cannot be altered without the alteration of all subsequent blocks.
In this article, we are going to explain how you can create a simple blockchain using the Python programming language.
Here is the basic blueprint of the Python class we’ll use for creating the blockchain:
class Block(object):
def __init__():
pass
#initial structure of the block class
def compute_hash():
pass
#producing the cryptographic hash of each block
class BlockChain(object):
def __init__(self):
#building the chain
def build_genesis(self):
pass
#creating the initial block
def build_block(self, proof_number, previous_hash):
pass
#builds new block and adds to the chain
@staticmethod
def confirm_validity(block, previous_block):
pass
#checks whether the blockchain is valid
def get_data(self, sender, receiver, amount):
pass
# declares data of transactions
@staticmethod
def proof_of_work(last_proof):
pass
#adds to the security of the blockchain
@property
def latest_block(self):
pass
#returns the last block in the chain
Now, let’s explain how the blockchain class works.
Here is the code for our initial block class:
import hashlib
import time
class Block(object):
def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
self.index = index
self.proof_number = proof_number
self.previous_hash = previous_hash
self.data = data
self.timestamp = timestamp or time.time()
@property
def compute_hash(self):
string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
return hashlib.sha256(string_block.encode()).hexdigest()
As you can see above, the class constructor or initiation method ( init()) above takes the following parameters:
self
— just like any other Python class, this parameter is used to refer to the class itself. Any variable associated with the class can be accessed using it.
index
— it’s used to track the position of a block within the blockchain.
previous_hash
— it used to reference the hash of the previous block within the blockchain.
data—it
gives details of the transactions done, for example, the amount bought.
timestamp—it
inserts a timestamp for all the transactions performed.
The second method in the class, compute_hash , is used to produce the cryptographic hash of each block based on the above values.
As you can see, we imported the SHA-256 algorithm into the cryptocurrency blockchain project to help in getting the hashes of the blocks.
Once the values have been placed inside the hashing module, the algorithm will return a 256-bit string denoting the contents of the block.
So, this is what gives the blockchain immutability. Since each block will be represented by a hash, which will be computed from the hash of the previous block, corrupting any block in the chain will make the other blocks have invalid hashes, resulting in breakage of the whole blockchain network.
The whole concept of a blockchain is based on the fact that the blocks are “chained” to each other. Now, we’ll create a blockchain class that will play the critical role of managing the entire chain.
It will keep the transactions data and include other helper methods for completing various roles, such as adding new blocks.
Let’s talk about the helper methods.
Here is the code:
class BlockChain(object):
def __init__(self):
self.chain = []
self.current_data = []
self.nodes = set()
self.build_genesis()
The init() constructor method is what instantiates the blockchain.
Here are the roles of its attributes:
self.chain — this variable stores all the blocks.
self.current_data — this variable stores information about the transactions in the block.
self.build_genesis() — this method is used to create the initial block in the chain.
The build_genesis()
method is used for creating the initial block in the chain, that is, a block without any predecessors. The genesis block is what represents the beginning of the blockchain.
To create it, we’ll call the build_block()
method and give it some default values. The parameters proof_number
and previous_hash
are both given a value of zero, though you can give them any value you desire.
Here is the code:
def build_genesis(self):
self.build_block(proof_number=0, previous_hash=0)
def build_block(self, proof_number, previous_hash):
block = Block(
index=len(self.chain),
proof_number=proof_number,
previous_hash=previous_hash,
data=self.current_data
)
self.current_data = []
self.chain.append(block)
return block
The confirm_validity
method is critical in examining the integrity of the blockchain and making sure inconsistencies are lacking.
As explained earlier, hashes are pivotal for realizing the security of the cryptocurrency blockchain, because any slight alteration in an object will result in the creation of an entirely different hash.
Thus, the confirm_validity
method utilizes a series of if statements to assess whether the hash of each block has been compromised.
Furthermore, it also compares the hash values of every two successive blocks to identify any anomalies. If the chain is working properly, it returns true; otherwise, it returns false.
Here is the code:
def confirm_validity(block, previous_block):
if previous_block.index + 1 != block.index:
return False
elif previous_block.compute_hash != block.previous_hash:
return False
elif block.timestamp <= previous_block.timestamp:
return False
return True
The get_data
method is important in declaring the data of transactions on a block. This method takes three parameters (sender’s information, receiver’s information, and amount) and adds the transaction data to the self.current_data list.
Here is the code:
def get_data(self, sender, receiver, amount):
self.current_data.append({
'sender': sender,
'receiver': receiver,
'amount': amount
})
return True
In blockchain technology, Proof of Work (PoW) refers to the complexity involved in mining or generating new blocks on the blockchain.
For example, the PoW can be implemented by identifying a number that solves a problem whenever a user completes some computing work. Anyone on the blockchain network should find the number complex to identify but easy to verify — this is the main concept of PoW.
This way, it discourages spamming and compromising the integrity of the network.
In this article, we’ll illustrate how to include a Proof of Work algorithm in a blockchain cryptocurrency project.
Finally, the latest_block() helper method is used for retrieving the last block on the network, which is actually the current block.
Here is the code:
def latest_block(self):
return self.chain[-1]
Now, this is the most exciting section!
Initially, the transactions are kept in a list of unverified transactions. Mining refers to the process of placing the unverified transactions in a block and solving the PoW problem. It can be referred to as the computing work involved in verifying the transactions.
If everything has been figured out correctly, a block is created or mined and joined together with the others in the blockchain. If users have successfully mined a block, they are often rewarded for using their computing resources to solve the PoW problem.
Here is the mining method in this simple cryptocurrency blockchain project:
def block_mining(self, details_miner):
self.get_data(
sender="0", #it implies that this node has created a new block
receiver=details_miner,
quantity=1, #creating a new block (or identifying the proof number) is awarded with 1
)
last_block = self.latest_block
last_proof_number = last_block.proof_number
proof_number = self.proof_of_work(last_proof_number)
last_hash = last_block.compute_hash
block = self.build_block(proof_number, last_hash)
return vars(block)
Here is the whole code for our crypto blockchain class in Python:
import hashlib
import time
class Block(object):
def __init__(self, index, proof_number, previous_hash, data, timestamp=None):
self.index = index
self.proof_number = proof_number
self.previous_hash = previous_hash
self.data = data
self.timestamp = timestamp or time.time()
@property
def compute_hash(self):
string_block = "{}{}{}{}{}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
return hashlib.sha256(string_block.encode()).hexdigest()
def __repr__(self):
return "{} - {} - {} - {} - {}".format(self.index, self.proof_number, self.previous_hash, self.data, self.timestamp)
class BlockChain(object):
def __init__(self):
self.chain = []
self.current_data = []
self.nodes = set()
self.build_genesis()
def build_genesis(self):
self.build_block(proof_number=0, previous_hash=0)
def build_block(self, proof_number, previous_hash):
block = Block(
index=len(self.chain),
proof_number=proof_number,
previous_hash=previous_hash,
data=self.current_data
)
self.current_data = []
self.chain.append(block)
return block
@staticmethod
def confirm_validity(block, previous_block):
if previous_block.index + 1 != block.index:
return False
elif previous_block.compute_hash != block.previous_hash:
return False
elif block.timestamp <= previous_block.timestamp:
return False
return True
def get_data(self, sender, receiver, amount):
self.current_data.append({
'sender': sender,
'receiver': receiver,
'amount': amount
})
return True
@staticmethod
def proof_of_work(last_proof):
pass
@property
def latest_block(self):
return self.chain[-1]
def chain_validity(self):
pass
def block_mining(self, details_miner):
self.get_data(
sender="0", #it implies that this node has created a new block
receiver=details_miner,
quantity=1, #creating a new block (or identifying the proof number) is awared with 1
)
last_block = self.latest_block
last_proof_number = last_block.proof_number
proof_number = self.proof_of_work(last_proof_number)
last_hash = last_block.compute_hash
block = self.build_block(proof_number, last_hash)
return vars(block)
def create_node(self, address):
self.nodes.add(address)
return True
@staticmethod
def get_block_object(block_data):
return Block(
block_data['index'],
block_data['proof_number'],
block_data['previous_hash'],
block_data['data'],
timestamp=block_data['timestamp']
)
blockchain = BlockChain()
print("GET READY MINING ABOUT TO START")
print(blockchain.chain)
last_block = blockchain.latest_block
last_proof_number = last_block.proof_number
proof_number = blockchain.proof_of_work(last_proof_number)
blockchain.get_data(
sender="0", #this means that this node has constructed another block
receiver="LiveEdu.tv",
amount=1, #building a new block (or figuring out the proof number) is awarded with 1
)
last_hash = last_block.compute_hash
block = blockchain.build_block(proof_number, last_hash)
print("WOW, MINING HAS BEEN SUCCESSFUL!")
print(blockchain.chain)
Now, let’s try to run our code to see if we can generate some digital coins…
Wow, it worked!
That is it!
We hope that this article has assisted you to understand the underlying technology that powers cryptocurrencies such as Bitcoin and Ethereum.
We just illustrated the basic ideas for making your feet wet in the innovative blockchain technology. The project above can still be enhanced by incorporating other features to make it more useful and robust.
Thanks for reading !
Do you have any comments or questions? Please share them below.
#python #cryptocurrency