Kmeans is a widely used clustering tool for analyzing and classifying data. Often times, however, I suspect, it is not fully understood what is happening under the hood. This isn’t necessarily a bad thing if you understand what the end product conveys, but learning what happens by building the algorithm from scratch can certainly lead to a deeper understanding of the reasoning behind it.
want to start out by emphasizing that the internet is an excellent place for coders and engineers. Answers and resources are widely available and merely a Google search away. To pretend I figured all of this out on my own would be silly. I readily acknowledge that there are times that it takes reading through others’ work on algorithms to understand how to approach it better. The beauty of code is that it can be written in many different ways, each emphasizing a slightly different quality. Utilize that in your learning.
Now that I’ve touched on that point, let’s dive in!
**K Means Clustering **is, in it’s simplest form, an algorithm that finds close relationships in clusters of data and puts them into groups for easier classification.
What you see here is an algorithm sorting different points of data into groups or segments based on a specific quality… proximity (or closeness) to a center point.
Most often, Scikit-Learn’s algorithm for KMeans, which looks something like this:
from sklearn.cluster import KMeans km = KMeans( n_clusters=3, init='random', n_init=10, max_iter=300, random_state=42 ) y_km = km.fit_predict(X)
You may not understand the parts super well, but it’s fairly simple in its approach. What it basically does is it says we want 3 clusters, start with 10 iterations (or run-throughs, each one refining the clusters and positions), initialization for the 3 center points is random, maximum iterations is 300, and random state just means every time we run it, it will be the same. We then run the prediction. More can be read here on the different parameters that can be used.
So, how do we go about creating this code from scratch… especially if we’re not sure what’s going on? Let’s figure it out!
#data-science #python #data-visualization #k-means-clustering #clustering
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
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python is one of the most go-for languages among the developers due to the availability of open-source libraries and frameworks. According to a survey report, Python is the top language preferred for Statistical Modelling, and an overwhelming majority of practitioners prefer Python as the language for statistical works.
Python has become a favourite language for hackers these days. The reason is the presence of pre-built tools and libraries, which makes hacking easy. In fact, the language is adequate for ethical hacking as ethical hackers need to develop smaller scripts, and Python fulfils this criterion.
Below here, we listed down the top 7 Python libraries used in hacking.
**About: **Requests is a simple HTTP library for Python that allows a user to send HTTP/1.1 requests extremely easily. This library helps in building robust HTTP applications and includes intuitive features such as automatic content decompression and decoding, connection timeouts, basic & digits authentication, among others.
Know more here.
About: Scapy is a powerful Python-based interactive packet manipulation program and library. This library is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, store or read them using pcap files, match requests, and more. It allows the construction of tools that can easily scan or attack networks. It is designed to allow fast packet prototyping by using default values that work. It can also perform tasks such as sending invalid frames, injecting your own 802.11 frames, combining techniques, such as VLAN hopping with ARP cache poisoning, VOIP decoding on WEP encrypted channel, etc., which most other tools cannot.
Know more here.
**About: **IMpacket is a library that includes a collection of Python classes for working with network protocols. It is focused on providing low-level programmatic access to network packets. It allows Python developers to craft and decode network packets in a simple and consistent manner. The library provides a set of tools as examples of what can be done within the context of this library.
Know more here.
**About: **Cryptography is a package which provides cryptographic recipes and primitives to Python developers. It includes both high-level recipes and low-level interfaces to common cryptographic algorithms such as symmetric ciphers, message digests and key derivation functions. This library is broadly divided into two levels. One is with safe cryptographic recipes that require little to no configuration choices. The other level is low-level cryptographic primitives, which are often dangerous and can be used incorrectly.
Know more here.
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Some of my most popular blogs are about Python libraries. I believe that they are so popular because Python libraries have the power to save us a lot of time and headaches. The problem is that most people focus on those most popular libraries but forget that multiple less-known Python libraries are just as good as their most famous cousins.
Finding new Python libraries can also be problematic. Sometimes we read about these great libraries, and when we try them, they don’t work as we expected. If this has ever happened to you, fear no more. I got your back!
In this blog, I will show you four Python libraries and why you should try them. Let’s get started.
#python #coding #programming #cool python libraries #python libraries #4 cool python libraries